AR 13 ATTACHMENT 2 — Pasadena City Council, May 5, 2025

Technical Advisory Panel Meeting #1
June 5, 2024
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Managing Consultant
ATTACHMENT 2
2  Kickoff – D avid (10 min)
 Introductions – A ll (10 min)
 Study Scope Overview – N ick (25 min)
 Questions & Comments – A ll (15 min)
Agenda
3 Steering Committee
Project Leadership Team
E3 Project Team
Nick Schlag
Partner
Project Lead
Nathan Lee
Sr. Managing Consultant
Project Manager
Michaela Levine
Managing Consultant
Project Manager
Additional Subject Matter Experts
Eric Cutter
Partner
Distribution Planning
Mike Sontag
Associate Director
DR & Flexible Loads
Arne Olson
Senior Partner
Lakshmi Alagappan
Partner
Transmission Planning
Aaron Burdick
Director
Dyami Andrews, Managing Consultant Mandy Kim, Consultant Ruoshui Li, Senior Consultant Maddie MacMillan, Consultant Brendan Mahoney, Senior Consultant Caitlin McMahon, Consultant Jack Moore, Senior Director Cameron Morelli, Consultant Jimmy Nelson, Director Emily Peterson, Senior Managing Consultant Melissa Rodas, Consultant Sam Schrieber, Managing Consultant Vignesh Venugopal, Senior Managing Consultant Parker Wild, Consultant Chen Zhang, Managing Consultant Jun Zhang, Senior Managing Consultant Angineh Zohrabian, Managing Consultant Amber Mahone
Managing Partner
4  In January 2023, Pasadena City Council
pas
sed Resolution 9977, setting ambitious
goals for the city amidst a climate
emergency
 Goal to source all electricity from carbon-
free
sources by 2030 far exceeds
requirements of California state policy
• Establishes City of Pasadena as a leader on
clim
ate change in a state with already
aggressive policy goals
• Requires a head-on c onfrontation with a series
of technical challenges well-established in
literature regarding the transition to a carbon-
free electricity system
Context for the Need for an Optimized Strategic Plan
Section 3. The City Council hereby sets a policy goal to source 100% of Pasadena's electricity from carbon free
sources by the end of 2030.

Section 4. The City Council hereby directs the City
Manager to utilize the 2023 IRP process to plan multiple approaches to transition to the goal described in
Section 3 and to optimize affordability, rate equity,
stability, and reliability of electricity while achieving this goal.

A RESOLUTION OF THE CITY COUNCIL OF THE CITY

OF PASADENA, CALIFORNIA DECLARING A CLIMATE

EMERGENCY AND SETTING A GOAL TO SOURCE
100% OF PASADENA'S ELECTRICITY FROM CARBON
FREE SOURCES BY 2030
5  City Council’s approval of IRP was
ac
companied by a directive to develop an
Optimized Strategic Plan:
 E3 began working with PWP and the City
Ma
nager’s Office to develop a proposed scope
in January 2024
 Proposed scope of work represents a cohesive
an
d comprehensive framework built on
rigorous technical analysis to inform an
optimized plan
Development of a Proposed Scope of Work for the Optimized Plan Recommended studies to support Optimized
Strategic Plan as discussed with Municipal
Services Committee, February 26, 2024“City Manager’s Office to engage 3rd party consultant with expertise in green energy to advise in development of
optimized plan. Optimized plan development to be
completed within six months and presented to the
Municipal Services Committee. ”
6  E3’s work with utilities and regulators to develop long-t erm electric system resource plans that achieve ambitious clean energy targets support four common findings:

1. Technologies available today can enable significant progress towards ambitious state and utility clean energy objectives

2. A technology-neutral approach to planning and procurement will enable utilities to meet reliability and clean energy goals most affordably

3. Decarbonization of the “last 10%” poses the greatest challenge, and may lead to significant increases in costs

4. Some form of firm capacity is needed for reliability even under a deeply decarbonized grid
 These findings are supported by a growing body of literature, including re
cent studies by the National Renewable Energy Laboratory (NREL), Princeton University, the Electric Power Research Institute (EPRI), and the Massachusetts Institute of Technology (MIT)
Deep Decarbonization Planning Studies: Common Trends
Firm Resources
Today: nuclear, natural gas,
geothermal, biogas
Future: hydrogen, long-duration
storage, nuclear SMR, CCS
Scalable Low-Cost Clean
Energy Resources
Today: wind, solar, efficiency
Future: nuclear small modular
reactors (SMR), carbon capture &
sequestration (CCS)
Balancing Resources
Today: batteries, pumped storage,
hydro, demand response
Future: advanced flexible loads, other
storage technologies
Blueprint for a Low Carbon Grid
7 Questions/Issues/Challenges Specific to Pasadena
Issue Implications/Questions
Glenarm’s importance for
local reliability
Due to import limits and potential contingencies, maintaining sufficient generating capacity within the PWP system is necessary to ensure reliability across all hours
Limited interconnection to
CAISO
Timing of planned upgrade at Goodrich receiving station has significant potential impacts on sequencing of other elements of plan Aging T&D infrastructure Replacement of undergrounded subtransmission lines staged prior to transformer upgrades at Goodrich
Limited land availability Opportunities to add new generation resources within load pocket are limited Lack of advanced metering
infrastructure
Installation of smart meters necessary for implementation of any advanced demand response/load flexibility solutions
8 The Optimized Strategic Plan is…
…a roadmap that defines the key actions and future decision points that will best position PWP to achieve the city’s goal to source all electricity from carbon-free sources by 2031 while maintaining reliability and limiting cost impacts to customers
The Optimized Strategic Plan will…
…consider how new generation resources, investments in T&D infrastructure, and customer programs can facilitate transition to Pasadena’s carbon-free goal Defining an “Optimized Strategic Plan”
What to Expect
from an
Optimized
Strategic Plan
Infrastructure Needs
• New generation resources (types, general locations, timing) • Existing generation resource retirements
• Transmission & distribution upgrades (including dependencies) Cost Metrics
• Total incremental cost relative to Reference Case (state policy) • Average system rate
• Relative customer bill impacts
Clean Energy Metrics
• Share of annual energy needs sourced from renewable energy • Share of annual energy needs sourced from carbon-fr ee energy • Share of hourly energy needs sourced from carbon-fr ee energy • Reduction in carbon emissions relative to baseline (1990) Customer Participation
• Projected future customer energy demands
• Distributed energy resources & virtual power plants (including solar, st
orage, demand response, flexible loads, vehicle charging) Reliability
• Validation of key local reliability results
• Consistency with state regulatory requirements for resource adequacy Preparatory
Studies
Distributed
Solar & Storage
Potential
Demand
Response & Flex
Load Potential
New & Emerging
Technology
Transmission
Expansion
Options
Portfolio
Development
Glenarm
Conversion &
Replacement
Analysis
Long-Term
Capacity
Expansion
Studies
Production
Simulation
Studies
Distribution
System Analysis
Optimized
Strategic Plan
Cost Impact
Assessment
Final Report
Stakeholder & Community Outreach
Overview of
Study Plan
Where relevant and useful, the study
plan will leverage findings and
information from previous planning
efforts (Integrated Resource Plan,
Power Delivery Master Plan)
Preparatory
Studies
Distributed
Solar & Storage
Potential
Demand
Response & Flex
Load Potential
New & Emerging
Technology
Transmission
Expansion
Options
Portfolio
Development
Glenarm
Conversion &
Replacement
Analysis
Long-Term
Capacity
Expansion
Studies
Production
Simulation
Studies
Distribution
System Analysis
Optimized
Strategic Plan
Cost Impact
Assessment
Final Report
Stakeholder & Community Outreach
Overview of
Study Plan
Studies to characterize technical potential for
local resource development in Pasadena service
area with focus on demand-side solutions and
distributed resources (solar, storage, DR, flex
loads, electric vehicles)
Preparatory
Studies
Distributed
Solar & Storage
Potential
Demand
Response & Flex
Load Potential
New & Emerging
Technology
Transmission
Expansion
Options
Portfolio
Development
Glenarm
Conversion &
Replacement
Analysis
Long-Term
Capacity
Expansion
Studies
Production
Simulation
Studies
Distribution
System Analysis
Optimized
Strategic Plan
Cost Impact
Assessment
Final Report
Stakeholder & Community Outreach
Overview of
Study Plan
Studies to characterize supply-side resources and
transmission solutions with a focus on options
that can support local reliability needs (e.g. H2,
long duration storage, Goodrich expansion)
Also includes external resource options
Preparatory
Studies
Distributed
Solar & Storage
Potential
Demand
Response & Flex
Load Potential
New & Emerging
Technology
Transmission
Expansion
Options
Portfolio
Development
Glenarm
Conversion &
Replacement
Analysis
Long-Term
Capacity
Expansion
Studies
Production
Simulation
Studies
Distribution
System Analysis
Optimized
Strategic Plan
Cost Impact
Assessment
Final Report
Stakeholder & Community Outreach
Overview of
Study Plan
Analysis of options to convert or
replace Glenarm while maintaining
adequate supply of local resources
& imports across all conditions –
including peak loads/extreme
weather and transmission
contingencies
Preparatory
Studies
Distributed
Solar & Storage
Potential
Demand
Response & Flex
Load Potential
New & Emerging
Technology
Transmission
Expansion
Options
Portfolio
Development
Glenarm
Conversion &
Replacement
Analysis
Long-Term
Capacity
Expansion
Studies
Production
Simulation
Studies
Distribution
System Analysis
Optimized
Strategic Plan
Cost Impact
Assessment
Final Report
Stakeholder & Community Outreach
Overview of
Study Plan
Optimization modeling to
develop multiple candidate
portfolios that minimize cost
while meeting environmental
and reliability objectives –
incorporating information
from preceding steps
Preparatory
Studies
Distributed
Solar & Storage
Potential
Demand
Response & Flex
Load Potential
New & Emerging
Technology
Transmission
Expansion
Options
Portfolio
Development
Glenarm
Conversion &
Replacement
Analysis
Long-Term
Capacity
Expansion
Studies
Production
Simulation
Studies
Distribution
System Analysis
Optimized
Strategic Plan
Cost Impact
Assessment
Final Report
Stakeholder & Community Outreach
Overview of
Study Plan
Hourly dispatch simulation of
portfolios to ensure operational
reliability and evaluate operating
costs
Geospatial analysis of future
substation loads to characterize
distribution system impacts and
identify options for targeted DERs
Preparatory
Studies
Distributed
Solar & Storage
Potential
Demand
Response & Flex
Load Potential
New & Emerging
Technology
Transmission
Expansion
Options
Portfolio
Development
Glenarm
Conversion &
Replacement
Analysis
Long-Term
Capacity
Expansion
Studies
Production
Simulation
Studies
Distribution
System Analysis
Optimized
Strategic Plan
Cost Impact
Assessment
Final Report
Stakeholder & Community Outreach
Overview of
Study Plan
Calculation of energy supply costs across
a range of portfolios to inform selection
of Optimized Strategic Plan
Synthesis of findings and description of
Optimized Strategic Plan
Technical Advisory Panel Meeting #1 - Follow up
June 27, 2024
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Managing Consultant
Angineh Zohrabian, Managing Consultant
2  Follow-up questions from previous meeting
 Local solar + storage study
• Objectives
• Technical potential methodology
• Adoption Methodology
 DR and flexible load study
 Emerging technology study
• Objectives
• Data sources
• Technologies considered
Topics Covered
3 1. Fixing the Goodrich bottleneck that limits importation of electricity is a large infrastructure investment.

On the other hand, DERs are comparatively more expensive than imported solar/storage, but provide resilience and reliability in the case there is an issue with Goodrich. Will this tradeoff be assessed in the studies and scenarios to be modeled?

2. Are there strategies PWP has considered and chosen not to pursue related to greening the energy supply? What are those and why not?
Follow-Up Questions: Study Overview
4 3. Your slides note the limited land available for DER. According to Project Sunroof1 there is approximately 389 MW DC capacity in Pasadena on rooftops alone. What tools will you use to measure the available land for solar and storage?

4. The Project Sunroof methodology says explicitly that the estimate does *not* take into account of grid constraints: Will the OSP include simulations of the Pasadena grid under different PV adoption scenarios to have a more reliable estimate of solar potential? Such a simulation system will also be useful for determining the type and cost of grid investments that will be most impactful/efficient towards achieving the 2030 goal. It will also provide guidelines for, not just planning and deployment, but also for the efficient operation of the updated/new DER infrastructure once it is put in place.

5. Will you include parking lots, commercial rooftop, and PUSD properties in the analysis of available space for DER?

6. Will publicly-owned solar and storage on private residences (e.g. via PPA) be included as an option during the solar study?

7. Will the OSP consider the use of microgrids with neighborhood storage as a way to optimize supply and storage and also decrease demand?
Follow-Up Questions: Distributed Solar + Storage Study

1. https://sunroof.withgoogle.com/data-explorer/place/ChIJUQszONzCwoARSo_RGhZBKwU/) 5 8. Is there a “safe” low hanging fruit goal for increasing rooftop solar (through a marketing campaign by the City, streamlining permitting and removing restrictions) that could be sought this year to begin increasing adoption in advance of the completion of the optimized plan?

9. Regarding question 8, what would be the cost of such a marketing campaign? Could the information be made available for outside organizations to spread for PWP? What permitting streamlining could be done? What restrictions would need to be removed?

10. In the IRP there was disagreement about how to estimate the cost of residential and community rooftop solar. The assumption was made that increases in the residential rooftop solar, beyond the historical increase, would be paid by PWP and passed on to ratepayers. How do you plan to do this?

11. What is/are PWP's current subsidy(ies) for non-utility-owned rooftop solar? If known and as applicable, please break out direct monetary and non-monetary subsidies (cash rebates, tax incentives, permitting exceptions) and indirect monetary subsidies (customer-avoided cost).

12. What is the current state of rebates/subsidies for residential rooftop solar? Commercial rooftop solar? Follow-Up Questions: Distributed Solar + Storage Study, cont’d 6 13. Has or would PWP consider lease/electric power sharing with property owners as a way to incentivize solar installation? (If a property owner allows PWP to install solar on a rooftop, that property owner gets a discount on electric costs, for example and PWP gets the power generated.)

14. Regarding question 13, does PWP have experience with such? Does PWP have experience with or will the OSP provide cost estimates for the maintenance costs of such facilities on private property? What would the insurance model look like, and what would the cost for such (ex., City self-insured? A third- party private carrier policy for all such facilities? Requiring AI certificates for the homeowners/property- owners CGL insurance policies?)
Follow-Up Questions: Distributed Solar + Storage Study, cont’d 7 15. Slide 6, under Blueprint for a Low Carbon Grid, includes Carbon Capture & Sequestration (CCS) as well as biogas, which are carbon neutral resources (as opposed to carbon free). Please verify that these will not be included in the Optimized Strategic Plan “that will best position PWP to achieve the city’s goal to source all electricity from carbon-free sources by 2031. ”

16. Is linked neighborhood geothermal an available resource in Pasadena? Follow-Up Questions: New and Emerging Tech Study
8 17. For the Demand Response/flex load study, there are different ways of reducing demand, from voluntary requests, to TOU rates, to VPP and grid optimization, to mandatory management by PWP .Will each of these be considered in the study? Do all of these use the same kind of AMI?

18. What experience has PWP had with the performance of such programs? Also, will we look to the study for examples of the performance of such programs elsewhere? Follow-Up Questions: Demand Response and Flexible Load Study 9 19. How will you discount the value of the Glenarm Plant for risks arising from plant shut downs, equipment defects, and maintenance costs? The General Electric GT-5 combined cycle turbine (which was installed in 2017) was shut down in November 2023 and remains in Texas for repair of a serious defect probably throughout the summer. The cost of the repairs has been increasing the longer the part stays in Texas. GE has been the subject of lawsuits over defective gas turbines although we don’t know if the Pasadena GT-5 is related.

20. Regarding question 19, how will you apply similar discounts for other resources (e.g., rooftop solar, battery storage, and neighborhood geothermal)?

21. Regarding question 1 and Goodrich, how much is the currently anticipated large infrastructure investment? What is the anticipated reliability figure for Goodrich with the investment (e.g., 98%? 99%?

99.9999%)? Do we have similar reliability figures for generation or storage resources?

22. What are the regulatory/ISO restrictions we need to consider? Are there workarounds?

23. What is actually planned for the entire Glenarm site? Follow-Up Questions: Glenarm Replacement Studies
10 Distributed Solar
and Storage
Emerging Tech
DR + Flex Loads
Tx Options
Glenarm
ReplacementTechnical
Potential
Customer
Adoption
Resource
Characterization
Resource
Characterization
LTCE
Reliability Metrics /
Replacement Portfolios
Resource Portfolios
DER Portfolios
Goodrich
PCM
Distribution
System Analysis
Cost Impacts
Dx System Costs
Operational Costs
Revenue
Requirement
Average Rate
Resource Options Portfolio Development Cost Analysis and Portfolio Comparison Detailed Illustration of Study Dependencies
Solar + Storage Study
12  Questions addressed in Local Solar Storage Study:

1. What is technical potential for solar and storage within PWP service territory (rooftop, parking canopy, ground mount)?

2. What are cost and performance characteristics of potential solar and storage resources?

3. What levels of customer solar and storage adoption could occur under various rate designs and incentive structures?  Questions not addressed in Local Solar Storage Study:

1. What is the value provided by solar and storage resources adopted by PWP customers?

2. What is total resource cost resulting from with different levels of customer solar adoption?

3. What bill impacts to non-participating customers will result from different levels of customer solar adoption? Scope of Local Solar & Storage Study
Addressing these questions requires a complete view of the supply- and demand-side solutions that enable PWP to achieve its goals and will be part of the Cost Impacts study 13 Local Solar & Storage Technical Potential Methodology Rooftop Solar & Storage Ground Mount SolarParking Canopy Solar Merge Google Sunroof database with PWP
customer data and apply various screens to
determine technical potential by segment.

GIS-based screening of land use to exclude
buildings, parks, roads, other impervious
surfaces, and sloped terrain
Filter parking lots identified in OpenStreet
maps for land use restrictions and building
safety code.

14 Local Solar + Storage Adoption Methodology
 The local solar + storage adoption analysis will use
a bass diffusion model framework
• Bass diffusion is an empirical market share model to
determine the long-run market equilibrium of customer
adoption.
• The relationship between economic attractiveness and
maximum market share is based on payback periods
or benefit-cost ratios.
• Logistic curves can be calibrated based on historical adoption rate and cost-effectiveness.
 Adoption will be modeled reflecting payback
periods under several tariffs including:
• Net energy metering
• Net billing
• Buy-all / Sell-all
• LCOE

15 Options to Consider for Customer Solar & Storage Compensation Retail
rate
Retail
rate
Retail
rate
Retail
rate
Retail
rate
Avoided
cost Avoided
cost
Retail
rate
Net customer demand Net customer demand
Total customer demand
Solar
generation
Option 1: Net Energy Metering
All generation (both self-consumption
& exports) credited at customer retail
rate (current design)
Time-dependent signal: no
Requires AMI: no
Option 2: Net Billing
Generation consumed by customer
credited at full retail rate; generation
exported to grid credited at utility
avoided cost
Time-dependent value: yes
Requires AMI: yes
Option 3: Buy-All/Sell-All
All generation (both self-consumption
& exports) credited at utility avoided
cost
Time-dependent value: yes
Requires AMI: yes
Consideration of broader changes to rate design (e.g. TOU rates) is beyond the scope of this study DR and Flexible Loads Study
17  LBNL produces supply curves for the achievable potential for shed and shift demand response curves characterizing the resource availability at a given cost. • Shed (“conventional”) – Loads that can be curtailed to provide capacity reductions • Shift – Loads that can be shifted between hours
 LBNL also characterized the shape DR resource in the latest phase of study. • Shape (“load-modifying”) - Reshaping customer load profiles for significant portions of the year through price responsiveness or behavioral campaigns.

LBNL Potential Study Overview
1 3 5 7 9 11 13 15 17 19 21 23
Baseline Load Post-Shift Load
Shift DR
1 3 5 7 9 11 13 15 17 19 21 23
Baseline Load Shed Load
Shed DR
1 3 5 7 9 11 13 15 17 19 21 23
18  Objective: Assess cost and potential for
demand response in PWP’s service territory.
 Outputs:
• Shift and shed DR supply curves.
• Options for managed vs unmanaged load shapes
 Methodology:
• Scale Lawrence Berkeley National Laboratory’s CA
Demand Response Potential Study results from an
SCE SubLap to PWP’s service territory.
– Leverage data from PWP on customer segmentation
and end-use load studies for calibration.
– Apply constraints to supply curve factoring in PWP’s
AMI deployment schedule and capacity to expand load
flexibility programs.
• Develop managed and unmanaged charging load
profiles recommending alternative load scenarios
for use in PWP using E3’s RESHAPE-EV model.

Objective, Outputs, Methodology, and Uses
LBNL (2024), Figure 15, https://eta-publications.lbl.gov/sites/default/files/phase_4_dr_potential_study_final_2024-05-21.pdf

19 E3’s RESHAPE-EV model can be leveraged to model custom scenarios of EV charging load shapes:
 Base or unmanaged charging load shapes are created based on drivers’ travel needs and access to different charger types  Managed charging load shapes are then developed by
optimizing load in response to price signals, such as time-of- use rates, wholesale market prices, or utilities’ avoided costs  Managed charging can be passive, in response to time varying rates, or active with participation in demand response
programs.

EV charging load shape modeling
E3’s RESHAPE-EV model
E3’s RESHAPE-EV model generates diversified EV charging load shapes considering the driving pattern of thousands of drivers and characteristics of the driver population including charger access, vehicle types, and cost to charge vehicles in various locations.

New & Emerging Technologies
Study
21  Primary purpose: identify a broad menu
of supply-side resource options to
consider in development of PWP’s
Optimized Strategic Plan
• Includes both resources commercially
available today and “emerging” technologies
not yet developed at scale
 Questions addressed in New & Emerging
Technologies Study:
• What supply-side generation technologies
may contribute to PWP’s efforts to eliminate
carbon from its power supply?
• What are the risks and challenges associated
with developing these resources over the
next decade?
• What reasonable range of cost and
performance assumptions should be used to
characterize resource options in further
studies?
New & Emerging Technologies Study Purpose

2. Conduct Initial Assessment of Technology Suitability for PWP’s Goals • Commercial readiness & expected development timelines • Land use intensity & technical potential limits, ability to develop locally • Other development risks

3. Identify/Select Subset of Technologies for Further Study in OSP

4. Collect Cost, Performance, Potential Data Necessary to Represent Resources in Long-Term Capacity Expansion & Operational Models
• Capital & operating/maintenance costs
• Operational characteristics (e.g. hourly profiles, storage duration, round drip efficiency) 22 Renewable
• Utility-scale solar PV
• Land-based wind (in-
and out-of-state)
• Floating offshore
wind
• Geothermal
(conventional)
• Geothermal
(enhanced)
Short to Mid-Duration
Storage (< 24 hrs)
• Li battery storage
• Flow battery
• Flywheel storage
• Pumped storage
• Novel Pumped Hydro
• Gravity-Based
• Adiabatic
Compressed Air
Energy Storage (A-
CAES)
• Liquid Air (LAES)
• Liquid CO2
Long Duration
Storage (> 24 hrs)
• Sensible Heat
• Latent Heat
• Thermochemical
heat
• Aqueous Electrolyte
Flow Batteries
• Metal Anode Battery
• Hybrid Flow Battery
Clean Fuels
• Renewable Natural
Gas
• Hydrogen
Combustion
• Hydrogen Fuel Cell
Proposed list of technologies to consider
 Question 4: Any particular technology to add or remove from this list? Notes:

By design and within the constraints of
Resolution 9977, all gas-fueled and nuclear
emerging technologies are excluded
Demand-side resources (local solar, storage,
demand response, and load flexibility)
considered separately in Local Solar &
Storage and DR & Flexible Loads Studies
23  Main data sources for characterizing emerging technologies:
• DOE’s 2023 and 2024 Commercialization reports
• DOE’s Technology Readiness Levels
• DOE’s Commercial Adoption Readiness Assessment Tool
• CPUC IRP’s Zero-Carbon Technology Assessment
 Main data sources for cost, potential and performance:
• CPUC IRP Resource cost estimates
• CEC/CPUC land-use screened renewable energy potential data • 2023 NREL Annual Technology Baseline (ATB) and upcoming 2024 NREL ATB • Lazard Cost data
• CEC Long Duration Storage Reports
• Prior E3 work
Main data sources
Appendix
25 How much solar and how to get it?
2031 2024 2036
Incremental Local
Solar/Storage Need (MW)
Total need for 2030
goal and reliability
Higher customer
compensation
Lower customer
compensation
Customer Adoption
Scenario Total Resource Cost Revenue
Requirement Rate Impact
Higher Compensation
Lower Compensation
Utility
Owned
Customer
Owned
How much?
How to get it?
Scenario A
Local Solar +
Storage Study
Cost Study
26 Rooftop Solar Can Be Compensated by the Utility in Many Different Ways
Tariff Self-consumption Exports Bill Savings Cost Shift Net Energy
Metering (NEM)
All generation (both self-consumption and exports) credited at the customer’s import rate
+++ +++
Net Billing
(NEB)
Self-consumption credited
at the import rate
Exports credited at a reduced export rate ++ ++
Buy-All, Sell-All
(BA, SA)
All generation (both self-consumption and exports) credited at a reduced export rate
+ +
Note: “Net Metering / NEM” is often used erroneously/ colloquially to describe all tariffs for crediting exports from customer- generators
Self-consumption
3000 kWh/yr
Imports
4500 kWh/yr
Exports
4500 kWh/yr
7500kWh total consumption
7500kWh total generation
Technical Advisory Panel Meeting #3
July 17, 2024
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Mike Sontag, Associate Director
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Managing Consultant
2 This meeting:

Ê Review of study plan and “synthesis” stage
Ê Technical studies methodology deep dives:
• Local Solar + Storage
• Emerging Technology
• Demand Response and Flexible Loads
Potential topics for next meeting (tentative/draft):

Ê Draft analysis results
• Local Solar + Storage
• Emerging Technology
• Demand Response and Flexible Loads
Ê Overview of Glenarm conversion/replacement scope
Ê Discussion of core study scenarios
Agenda
3 The Optimized Strategic Plan is…
…a roadmap that lays out the key steps and future decision points that will best position PWP to achieve its goal to source all electricity from carbon-free sources by the end of 2030 while maintaining reliability and limiting cost impacts to customers
The Optimized Strategic Plan will…
…consider how new generation resources, investments in T&D infrastructure, and customer programs can facilitate transition to Pasadena’s carbon-free goal Defining an “Optimized Strategic Plan”
4 Distributed Solar and StorageEmerging TechDemand Response + Flex LoadsTransmission Options Glenarm Conversion/ ReplacementLong-Term Capacity ExpansionProduction Cost ModelingDistribution System Analysis Cost Impacts
Resource OptionsPortfolio DevelopmentBuild multiple portfolios that meet decarbonization goals and maintain reliability under a range of scenarios Cost Analysis and Portfolio ComparisonCalculate cost metrics. Identify feasibility considerations, dependencies, and risks of each portfolio.

Optimized Strategic Plan Study Workflow
Identify all options and key uncertainties
Technical AnalysisAction PlanInformed by technical analysis, develop an action plan.

Action Plan
Pasadena Water and Power
Progress to Carbon-Free Electricity
5 Decarbonization Progress Highlights
• Metric 1
> PWP’s progress to achieving State
compliance goals
• Metric 2
> The percentage of PWP’s energy portfolio
that is comprised of carbon-free resources
• Metric 3
> The percentage of every hour that is served
by carbon-free resources
6 Balance of Carbon-Free Energy Resources based on Currently Executed Contracts
Hourly Carbon-Free Electricity vs. Hourly Load
0% 25% 50% 75% 100% 125% 150% 175% 200%
2030 Carbon-Free Electricity Supply (Executed Contracts Only) 24/7 Hourly Matching (“Metric 3”): 83% 2025 Carbon-Free Electricity Supply 24/7 Hourly Matching (“Metric 3”): 25% Jan 1 Day of Year Dec 31
12am
12pm
12am
12am
12pm
12am
Jan 1 Day of Year Dec 31
Not an OSP result – based on data underlying metrics presented by PWP to MSC July 9 7 Ê Historical operational patterns of Glenarm
Power Plant consistent with a resource whose
primary purpose is supporting reliability:
• Low capacity factor, frequently not operated
• Dispatched up to full capacity in a select number of
hours per year
Ê Conditions that currently require operations
of Glenarm:
• Peak demand conditions (above import capability)
• Transmission/distribution contingencies
• High wholesale electricity prices in California
Independent System Operator (CAISO)
• CAISO resource deficiencies
A Historical Perspective on the Role of Glenarm
2018 4.6% capacity factor
2019 3.9%
2020 3.5%
2021 1.2%
2022 3.3%
2023 2.6%
Throughout most of the year, PWP serves loads
by importing from CAISO without dispatching
Glenarm power plant
In a small number
of hours, Glenarm
operates at or
near full capacity,
playing a critical
role in meeting
local reliability
needs # Hours
A long-term reliability solution will require local
resources that can operate reliably under very specific circumstances
Local Solar + Storage
9 Ê Questions addressed in Local Solar Storage Study:

1. What is technical potential for solar and storage within PWP service territory (rooftop, parking canopy, ground mount)?

2. What are cost and performance characteristics of potential solar and storage resources?

3. What levels of customer solar and storage adoption could occur under various rate designs and incentive structures?
Ê Questions not addressed in Local Solar Storage Study:

1. What is the value provided by solar and storage resources adopted by PWP customers?

2. What is total resource cost resulting from with different levels of customer solar adoption?

3. What bill impacts to non-participating customers will result from different levels of customer solar adoption?

4. The granular analysis of the distribution system needed to identify the ability of the distribution system to absorb more solar.

Scope of Local Solar & Storage Study
Addressing these questions requires a complete view of the supply- and demand-side solutions that enable PWP to achieve its goals and will be part of the Cost Impacts study 10 Local Solar & Storage Technical Potential Methodology Rooftop Solar & Storage Ground Mount SolarParking Canopy Solar Merge Google Sunroof database with PWP
customer data and apply various screens to
determine technical potential by segment.

GIS-based screening of land use to exclude
buildings, parks, roads, other impervious
surfaces, and sloped terrain
Filter parking lots identified in OpenStreet
maps for project viability.

11 Local Solar + Storage Customer Adoption Methodology
Ê The local solar + storage adoption analysis will use
a bass diffusion model framework
• Bass diffusion is an empirical market share model to
determine the long-run market equilibrium of customer
adoption.
• Th
e relationship between economic attractiveness and
maximum market share is based on payback periods
or be nefit -cost ratios.
• Logistic curves can be calibrated based on historical adoption rate and cost-effectiveness.

Ê Adoption will be modeled reflecting payback
periods under several tariffs. For example,
• Net Energy Metering (NEM)
• Net billing
• Buy-all / Sell-all

12 Designs for Customer Solar & Storage Compensation for OSP Analysis
Retail
rate
Retail
rate
Retail
rate
Retail
rate
Retail
rate
Export
Rate Export
Rate
Retail
rate
Net customer demand Net customer demand
Total customer demand
Solar
generation
Design 1: Net Energy Metering
All generation (both self-consumption
& exports) credited at customer retail
rate (current design)
Time-dependent signal: no
Requires AMI: no
Design 2: Net Billing
Generation consumed by customer
credited at full retail rate; generation
exported to grid credited at an export
rate
Time-dependent value: yes
Requires AMI: yes
Design 3: Buy-All/Sell-All
All generation (both self-consumption
& exports) credited at an export rate
Time-dependent value: yes
Requires AMI: yes
Does not incentivize pairing storage
adoption with solar
AMI = Advanced Metering Infrastructure
Designs considered in OSP do not reflect full spectrum of options but are meant to capture a range of potential options Demand Response and
Flexible Loads
14 Ê Objective: Assess cost and potential for demand response in PWP’s service territory.

Ê Motivation: Given constraints on PWP to leverage utility-scale resources due to limited import capability and in-zone resource availability, demand response is one of the zero-carbon demand-side resources that can contribute to meeting PWP’s capacity needs for maintaining reliability.

Objective, Motivation, Research Questions
LBNL (2024), Figure 15, https://eta-publications.lbl.gov/sites/default/files/phase_4_dr_potential_study_final_2024-05-21.pdf Questions answered in this study:

1. How much demand response potential is
available from PWP customers?

2. What are the costs of demand response?

3. How can PWP leverage managed electric
vehicle (EV) charging to reduce grid impacts of
electrification?
Questions not answered in this study:

1. How should PWP design programs and tariffs to
procure demand response?
How much?
At what cost?
15 Ê LBNL produces supply curves for the achievable potential for shed and shift demand response curves characterizing the resource availability at a given cost. • Shed (“conventional”) – Loads that can be curtailed to provide capacity reductions • Shift – Loads that can be shifted between hours
Ê LBNL also characterized the shape DR resource in the latest phase of study. • Shape (“load-modifying”) - Reshaping customer load profiles for significant portions of the year through price responsiveness or behavioral campaigns.

LBNL Potential Study Overview
1 3 5 7 9 11 13 15 17 19 21 23
Baseline Load Post-Shift Load
Shift DR
1 3 5 7 9 11 13 15 17 19 21 23
Baseline Load Shed Load
Shed DR
1 3 5 7 9 11 13 15 17 19 21 23
16 Based on the Investor Owned Utilities’ customer base and flexibility needs
Ê Driven by electrification and shifting periods of
system need:
• Light-Duty Vehicles (LDVs), Medium and Heavy-Duty
Vehicles (MHDVs), and residential water heating are
em
erging as end uses with large potential.
• Space cooling DR potential is declining in the long-term (2040+).
• Refrigeration, industrial process loads, and agricultural pumping are consistent sources of DR.

Ê Dynamic price signals can capture a large portion (40- 50%) of the technical potential for demand response.

Ê DR enabling technologies will be crucial for capturing the DR resource.

Key findings LBNL’s DR Potential Study Phase 4
Source: LBNL, 2024: https://eta-publications.lbl.gov/sites/default/files/phase_4_dr_potential_study_final_2024-05-21.pdf 17 E3’s RESHAPE-EV model can be leveraged to model custom scenarios of EV charging load shapes:

Ê Base or unmanaged charging load shapes are created based on drivers’ travel needs and access to different charger types Ê Managed charging load shapes are then developed by
optimizing load in response to price signals, such as time-of- use rates, wholesale market prices, or utilities’ avoided costs Ê Managed charging can be passive, in response to time varying rates, or active with participation in demand response
programs.

New & Emerging Technologies
Study
19 Ê Primary purpose: identify a broad
menu of supply-side resource options
to consider in development of PWP’s
Optimized Strategic Plan
• Includes both resources commercially
available today and “emerging”
technologies not yet developed at scale
Ê Questions addressed in New &
Emerging Technologies Study:
• What supply-side generation technologies
may support PWP’s efforts to eliminate
ca
rbon from its power supply?
• What are the risks and challenges
associated with developing these
resources over the next decade?
• What reasonable range of cost and
performance assumptions should be used
to characterize resource options for
su
bsequent OSP studies?
New & Emerging Technologies Study Purpose

2. Conduct Initial Assessment of Technology Suitability for PWP’s Goals • Commercial readiness & expected development timelines • Land use intensity & potential siting/locational constraints • Other development risks

3. Identify/Select Subset of Technologies for Further Study in OSP

4. Collect Cost, Performance, Potential Data Necessary to Represent Resources in Long-T erm Capacity Expansion & Operational Models
• Capital & operating/maintenance costs
• Operational characteristics (e.g. hourly profiles, storage duration, round trip efficiency) 20 Ê New & Emerging Technologies study will include a comprehensive review of supply-side generating technologies that may play a role in meeting the City’s goals Ê Based on results of review, a subset of these technologies will be further considered in technical analysis
Technologies for consideration in the “New & Emerging
Technologies Study”
Renewable
• Utility-scale solar PV
• Land-based wind (in-
and out-of-state)
• Floating offshore
wind
• Geothermal
(conventional)
• Geothermal
(enhanced)
Short to Mid-Duration
Storage (< 24 hrs)
• Lithium ion battery
• Flow battery
• Pumped storage
• Gravity-based
• Compressed Air
Energy Storage
(CAES)
• Liquid Air (LAES)
Long Duration
Storage (> 24 hrs)
• Sensible heat
• Latent heat
• Thermochemical
heat
• Aqueous flow battery
• Metal anode battery
• Hybrid flow battery
Clean Firm Fuels
• Hydrogen
combustion
• Hydrogen fuel cell
• Renewable natural
gas
• Nuclear small
modular reactors
Notes:

Identify feasibility
considerations,
dependencies, and
risks of each portfolio.

Optimized Strategic Plan Study Workflow
Identify all options
and key uncertainties
T echnical Analysis Action Plan
Informed by
technical analysis,
develop an action
plan.

Action Plan
4 Status Updates: Optimized Strategic Plan Supporting Studies Local Solar & Storage
•Draft geospatial analysis of technical potential complete (ground mount, parking canopy, rooftop) •Adoption modeling beginning soon
Demand Response & Flexible Loads
•Downscaling methodology to adapt LBNL DR supply curves developed •Awaiting data inputs from LBNL
New & Emerging Technologies
•Review of emerging technology characteristics and risk factors complete •Currently developing cost and performance assumptions for portfolio modeling Transmission Options
Glenarm Conversion & Replacement
•Compiling inputs and assumptions, reviewing proposed scope of analysis with PWP Long-Term Capacity Expansion
•Reviewing data provided by PWP, compiling additional inputs and assumptions needed for modeling Production Simulation
Distribution System Impacts
Cost Impacts
Active Workstreams
On Deck
Not yet started
Progress Updates: Local Solar
& Storage Study
6  Questions addressed in Local Solar Storage Study:

1. What is technical potential for solar and storage within PWP service territory (rooftop, parking canopy, ground mount)?

2. What are cost and performance characteristics of potential solar and storage resources?

3. What levels of customer solar and storage adoption could occur under various rate designs and incentive structures?
 Questions addressed in future studies of the Optimized Strategic Plan:

1. What is the value provided by solar and storage resources adopted by PWP customers?

2. What is total resource cost resulting from with different levels of customer solar adoption?

3. What bill impacts to non-participating customers will result from different levels of customer solar adoption?

4. What is the capacity of the distribution system to absorb more solar and what is the cost to integrate more solar onto the distribution system?
Scope of Local Solar & Storage Study
Addressing these questions requires a complete view of the supply - and demand-side solutions that enable PWP to achieve its goals and will be part of the Cost Impacts study Today’s focus
7 Local Solar & Storage Technical Potential Methodology Rooftop Solar & Storage Ground Mount SolarParking Canopy Solar Merge Google Sunroof database with PWP
customer data and apply various screens to
determine technical potential by segment.

Technical Potential: The total amount of solar capacity available for development based on physical constraints such as available land or developable roof area. Does not consider economic viability or consumer willingness to adopt . 8 Additional Context for Rooftop Solar Technical Potential Definition
Not
T echnically
Feasible
Capacity in
excess of on-
site load
Not
T echnically
Feasible
Not adopted due
to economics or
other market
barriers
Theoretical Maximum Physical T echnical Potential
Capacity available assuming all viable roof areas is developed regardless of cost, grid capacity, or customer interest Load-Limited T echnical Potential
Maximum theoretical technical potential capped at
building energy usage
Naturally Occurring Customer
Adoption
Not
T echnically
Feasible
Capacity in
excess of on-
site load
Focus for
today’s
discussion
Work in
progress
Total Roof Area
Installed Capacity (MW)
9  Despite land constraints, technical
potential for local solar within
Pasadena footprint is significant:
 Key insight: the amount of local solar
included in the OSP will be primarily
determined by the needs of the grid
and economics rather than by
constraints on available space
Solar T echnical Potential within Pasadena Water and Power Rooftop, Parking Canopy, and Ground-Mounted Solar
oofto o a
a a o y o a ou d ou t d o a
*These values represent the maximum capacity available for development in a given region and do not consider the
economic or market viability, historical landmark status, or other physical conditions that may make installation cost prohibitive.

Rooftop: 222 MW*
Parking Canopy: 340 MW*
Ground-Mounted: 10 MW*
10 Rooftop Solar Technical Potential Screens
Total roof area in Pasadena: 1.5 GW
+ Aspects facing north excluded
+ Aspects with contiguous area < 13 m2 or
max mum tch > 6 ˚ xc ud d
+ Setback factor applied to perimeter of each aspect
(3 ft for residential, 6 ft for commercial)
Total physical technical potential in Pasadena: 413 MW
222 MW of load-limited technical potential+ Systems sized to 100% of annual load 968 MW
555 MW
586 MW
+ Aspects with capacity factor <75% of unshaded potential excluded 413 MW 11  Finalize storage technical potential
 Solar and Storage adoption modeling based on 3 rate scenarios (function of payback period, tech potential, logistic adoption curve)
Solar and Storage Study Next Steps
2031 2024 2036
Incremental Local
Solar/Storage Adoption
(MW)
Reta il
rate
Reta il
rate
Reta il
rate
Net Energy Metering
Reta il
rate
Reta il
rate
Export
rate
Net Billing
Export
Rate
Reta il
rate
Buy-all Sell-all Rate
Technical Potential
12  Branding, reliability, and customer testimonials build trust and credibility. 1,2,3,4,5  Community engagement, partnerships, and word of mouth increase adoption .8,9,15,16  Neighbor adoption and non -residential installations can increase residential adoption. 6, 7, 8, 9,10  Consumer motivations and predispositions are key factors that drive interest in solar adoption. 11,12  Educational content reduces barriers to solar adoption .8,9  Different forms of advertising have varying impacts based on audience and customer tolerance for frequency of messaging.13,14
 Streamlining permitting reduces bottlenecks to adoption.

Non-Economic Factors Influencing Solar Adoption
Sources:
1: Wang, Zhang, Li, 2018, "Po licy simulation for promotin g residential PV considering anecdotal informatio n exchanges based o n social network mo delling"
2: Shakeel, Yousaf, Irfan, Rajala, 2023, "Solar PV adoption at househ old level" 3: Tsantopoulos, Arabatzis, Tampakis, 2014, "Public attitudes to wards photovoltaic develo pments: Case study" 4: Abdullah, Zhou, Shah , Jebran, Ali, 2017, "Acceptance and willingness to pay for solar ho me system: Survey evidence" 5: Gardner, 2024, "Mastering Solar Marketing Strategies" 6: Shakeel, Jun tunen, Rajala, 2024, "Business models for enhanced solar photovoltaic (PV) adoptio n: Transforming customer interaction and engagement practices"
7: Shakeel, Rajala, 2022, "Transforming Energy Marketing Practices for Enhanced Solar PV Adoption" 8: Bioen ergy Consult, 2023, "Solar Marketing Strategy Fo r Solar Companies" 9: M Studio Agency, 2024, "Illuminating Success: Best Practices in Solar Energy Marketing" 10: NREL 2024, "Commercial-Scale Solar PV Increases Local Residential Solar Adoptio n" 11: Wo lske, Todd, Ro ssol, McCall, Sigrin, 2018, "Accelerating demand for residential so lar photovoltaics" 12: Wo lske, 2020, "Profiles of high-in come an d low-in come rooftop solar ado pters in the United States" 13: Kumar, Ramachandran, Kumar, 2021, "Influence of new-age technologies on marketing" 14: NREL, 2024 "Winter 2024 Solar Industry Update"
15: Yale, 2019, "An Evidence-Based Guide for Accelerating the Adoption of Residential Solar" 16: Bo llin ger, Gillingham, Lamp, Tsvetanov, 2023, "Promotion Campaign Duration an d Word-of-Mo uth in Solar Adoptio n" Literature on the impact of marketing on solar adoption is mostly qualitative in nature and based on case studies of specific programs or campaigns conducted by varying entities.

Progress Updates: New &
Emerging Technologies Study
14  Primary purpose: identify a broad menu
of supply-side resource options to
co s d d v o m t of ’s
Optimized Strategic Plan
• Includes both resources commercially
available today and “emerging” technologies
not yet developed at scale
 Questions addressed in New & Emerging
Technologies Study:
• What supply-side generation technologies
may contribute to PWP’s efforts to eliminate
carbon from its power supply?
• What are the risks and challenges associated
with developing these resources over the
next decade?
• What reasonable range of cost and
performance assumptions should be used to
characterize resource options in further
studies?
New & Emerging Technologies Study Purpose

1. Identify Broad Set of New & Emerging Resource Options Consistent with Resolution 9977
• Renewable resources
• Energy storage (short, medium, long duration)
• Clean fuels (hydrogen, renewable natural gas)
•(Demand-side resources, including solar, storage, DR, and flexible loads considered in other studies)
. o duct I t a Ass ssm t of T ch o o y u tab ty fo ’s Goals
• Commercial readiness & expected development timelines • Land use intensity & potential siting/locational constraints • Other development risks

3. Identify/Select Subset of Technologies for Further Study in OSP

4. Collect Cost, Performance, Potential Data Necessary to Represent Resources in Long-Term Capacity Expansion & Operational Models • Capital & operating/maintenance costs
• Operational characteristics (e.g. hourly profiles, storage duration, round trip efficiency)
Complete
Draft
Complete
Draft
Complete
In
Progress
15  New & Emerging Technologies study will include a comprehensive review of supply-side generating t ch o o s that may ay a o m t th ty’s oa s  Based on results of review, a subset of these technologies will be further considered in technical analysis
Technologies Reviewed in the New & Emerging Technologies Study Renewable
• Utility-scale solar PV
• Land-based wind (in-
and out-of-state)
• Floating offshore
wind
• Geothermal
(conventional)
• Geothermal
(enhanced)
• Concentrated solar
power
Short to Mid-Duration
Storage (<= 24 hrs)
• Lithium ion battery
• Flywheel
• Flow battery
• Pumped storage
• Gravity-based
• Compressed Air
Energy Storage
(CAES)
• Liquid Air (LAES)
Long Duration
Storage (> 24 hrs)
• Sensible heat
• Latent heat
• Thermochemical
heat
• Aqueous flow battery
• Metal anode battery
• Hybrid flow battery
Clean Firm Fuels
• Green hydrogen
combustion
• Green hydrogen fuel
cell
• Renewable natural
gas
• Nuclear small
modular reactors
Notes:

By design and within the constraints of
Resolution 9977, natural gas-fueled
technologies are not considered
Demand-side resources (local solar, storage,
demand response, and load flexibility)
considered separately in Local Solar &
Storage and DR & Flexible Loads Studies
17  There are a significant number of long-duration storage technologies currently in R&D stages of development
• Multiple studies indicate that technical capabilities of LDES resources are well- suited to contributing to decarbonized portfolios, particularly reliability challenges • Inflation Reduction Act has acted as an additional catalyst spurring interest in this sector
 Most LDES technologies have not yet reached full market readiness; many indications that this level of technological maturity will not occur until mid 2030s:
• Requirements for 8-hour storage in CPUC Mid-T erm Reliability decision largely satisfied by procurement of lithium-ion storage
• Multiple utilities currently pursuing pilots of LDES technologies at a small scale (~5- 10 MW)
• DOE Pathways to Commercial Liftoff establishes an optimistic roadmap to commercialize LDES technologies by 2030
• CPUC’s PD for AB 1373 (as of 7/19/2024) issued LDES need for 1 GW 12+ hr, and 1 GW multi-day storage after 2031
 Rapidly evolving technology landscape means that care should be taken not to be overly prescriptive in selecting LDES in resource planning Key Highlights from Long Duration Energy Storage (LDES) Technology Review
Key Risks & Uncertainties
Commercial readiness: most emerging
long-duration storage technologies have not
been demonstrated at grid scale; multiple
utilities are working on small pilots; lack of
commercial readiness results in both
development and operational risks
Land use: longer duration storage
technologies are typically less energy dense
and require larger footprints
Cost: lack of technological maturity
translates to greater uncertainty around
cost; early movers likely to pay higher costs
to develop first-of-a-kind projects
18  Interest in hydrogen as an energy carrier – especially in California – has grown considerably in the past several years
• Generous incentives for green hydrogen (produced via electrolysis fueled by renewable energy) established by Inflation Reduction Act • Alliance for Renewable Clean Hydrogen Energy Systems (ARCHES) currently has over $12 billion in funding – including $1.2 billion in federal funding from DOE – to catalyze development of hydrogen production, transport, and storage infrastructure  I th ct c ty s cto , hyd o s a “c a f m” sou c o t o that provides a long-duration storage service while displacing natural gas from peaking power plants
• Current generation technologies allow for blending up to 50% by volume, but most turbine manufacturers are planning for 100% capability by 2030 • Many utilities (including LADWP and GWP) have developed plans that include full conversion to hydrogen fuels by mid 2030s
 Scale and timing of fuel needs in electric sector will require development of infrastructure to transport and store hydrogen • While trucking & on-site storage may be suitable for pilot projects to demonstrate capabilities, dedicated pipelines are likely required for combustion of hydrogen in peaking power plants
• Implication: the viability of relying on hydrogen at scale will also require the presence of a broader network for hydrogen distribution within the LA Basin Key Highlights from Hydrogen Technology Review
Key Risks & Uncertainties
Commercial readiness: 100% hydrogen
combustion turbines are yet to be
demonstrated; multiple utilities include
hydrogen in their plans in mid to late 2030s
Fuel supply: Hydrogen fuel supply for
power generation may largely depends on
hydrogen expansion for other sectors of the
economy such as transportation
Cost: Hydrogen storage and transport costs
are uncertain and largely depend on the
scale and site; hydrogen production costs
are expected to go down in 2030s when
demand for hydrogen scales and IRA
incentives are still available
Initial Scoping of Portfolio
Development
20  OSP supporting studies focus on development and analysis of “ o tfo os”: unique combinations of resources to meet PWP future needs
 Goal of developing and analyzing multiple portfolios is not to select a single one as the Optimized Strategic Plan, but to synthesize learnings across all cases to inform creation of an OSP Multiple Portfolios
Developed and
Analyzed
Varying technology sets
Varying milestones
Role of Portfolio Analysis in Development of the Optimized Strategic Plan
Results of Technical
Analysis Synthesized
Common themes across
portfolios
Relative cost impacts
“Least regrets” actions
Key risks & uncertainties
Path dependencies, tools to
preserve optionality
Optimized Strategic
Plan Created
“…a roadmap that lays out the
key steps and future decision
points that will best position
PWP to achieve its goal to
source all electricity from
carbon-free sources by the end
of 2030 while maintaining
reliability and limiting cost
impacts to customers”
21 Initial Learnings and Implications from Preparatory Studies Local Solar & Storage Study
• Potential for ground-mounted solar within Pasadena city limits is severely limited by constraints on land availability • Technical potential for rooftop and parking canopy solar is significant, comparable in magnitude to levels of local solar studied in 2023 IRP
• High technical potential for rooftop/parking canopy solar means that levels of solar and storage will be based on grid needs and economics, not limited by availability of space Demand Response & Flexible Loads Study
• New sources of load flexibility, enabled by Advanced Metering Infrastructure (AMI), can allow customers to shift
consumption from peak/net peak periods to periods of higher resource availability (typically solar hours)
• Managed charging of electric vehicles can limit the need for incremental generation and distribution capacity, while increasing the utilization factor of the grid
New & Emerging Technologies Study
• Wind, solar, and battery storage are today widely available as carbon-free resource options despite upward cost pressures in the wake of COVID-19
• Emerging longer-duration storage technologies are unlikely to reach commercial maturity before early 2030s
• Deploying hydrogen for peaking capacity is technically feasible but will require technological advancements and significant new infrastructure development
Transmission Options Study
• Significant investments in PWP’s subtransmission system are necessary to replace aging infrastructure in the near term • Replacement of transformers at Goodrich will allow for increased import capability but is unlikely to occur before mid 2030s
• Opportunities to expand import capability beyond current plans still being explored
22 Strawman Portfolio Proposal Informed by Initial Learnings from Preparatory Studies

A B C D
Technology Sets State Policy
Glenarm
Limited Use
Carbon-Free
Owned &
Contracted
Resources
24x7 Carbon
Free
1 Mature Technologies Only 1A 1B 1C 1D
2 Mature Technologies +
Hydrogen 2C 2D
3 Mature Technologies + Long
Duration Storage 3C 3D
Portfolio compliant with
SB100 requirements
––
Metric 1 = 100%
Local resources added to
reduce frequency of
Glenarm operations
Glenarm
converted/replaced
––
Metric 2 = 100%
Glenarm
converted/replaced
––
Unspecified purchases
eliminated
––
Metric 3 = 100%
Increasing stringency of clean energy requirements
23 Load
Flexibility
Long Duration
Storage
Summary of Portfolio Assumptions
Solar Wind Geothermal
Short Duration
Storage TMG Expansion Glenarm
24 Detailed Portfolio Assumptions & Inheritance Tree
Portfolio Key Assumptions
1A • All statutory clean energy requirements (e.g. SB100) met or exceeded • CAISO resource adequacy requirements satisfied
1B • All requirements of Portfolio 1A
• Additional local solar, storage, and DSM resources added to reduce frequency of Glenarm operations • (Optional) Renewable natural gas purchased to support limited operations of Glenarm 1C • All requirements/assumptions of Portfolio 1A
• Additional local solar, storage, and DSM resources added fully replace Glenarm capabilities by end of 2030 1D • All requirements of Portfolio 1C
• Additional renewables & storage resources added to eliminate reliance on unspecified market purchases 2C • All requirements/assumptions of Portfolio 1A
• Glenarm Power Plant is converted to operate using hydrogen fuel by end of 2030 2D • All requirements/assumptions of Portfolio 2C
• Additional renewables & storage resources added to eliminate reliance on unspecified market purchases 3C • All requirements/assumptions of Portfolio 1A
• Glenarm Power Plant is replaced by a combination of local solar, battery storage, DSM, and emerging LDES technologies • Timing of Glenarm replacement portfolio coincides with upgrades to transformers at Goodrich • (Optional) Renewable natural gas used as a bridge fuel to allow limited operations at Glenarm until replacement is possible 3D • All requirements/assumptions of Portfolio 3C
• Additional renewables & storage resources added to eliminate reliance on unspecified market purchases Thank You
Nick Schlag, [email protected]
Mike Sontag, [email protected]
Michaela Levine, [email protected]
Technical Advisory Panel Meeting #5
September 18, 2024
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Mike Sontag, Associate Director
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Managing Consultant
3  Optimized Strategic Plan progress update
 Portfolio development scope
• Glenarm Conversion and Replacement Scope and Methodology • Long-Term Capacity Expansion Modeling Scope and Methodology  Next steps
Agenda
Primary objective for today: Build shared understanding of tools and modeling approaches used in Portfolio
Development phase to allow deeper technical
conversations in future meetings
4 Status Updates: Optimized Strategic Plan Supporting Studies Local Solar & Storage
•Draft geospatial analysis of technical potential complete (ground mount, parking canopy, rooftop) •Customer adoption modeling under multiple rate structures currently in progress Demand Response & Flexible Loads
•Electric vehicle charging profiles (managed vs. unmanaged) developed •DownscalingLawerence Berkeley National Laboratory (LBNL) Demand Response (DR) supply curves New & Emerging Technologies
•Review of emerging technology characteristics and risk factors complete •Cost and performance assumptions for portfolio modeling currently under review by PWP Glenarm Conversion & Replacement
•Preliminary modeling underway
Long-Term Capacity Expansion
•Reviewing data provided by PWP, compiling additional inputs and assumptions needed for modeling Transmission Options
Production Simulation
Distribution System Impacts
Cost Impacts
Active Workstreams
On Deck
Not yet started
5 Phase 1
Preparatory Studies
Identify and characterize all
potential resource options
Phase 2
Portfolio Development
Use detailed power system
modeling to construct multiple
portfolios that meet clean
energy goals and maintain
reliability
Phase 3
Impact Assessment
Calculate cost metrics;
identify feasibility concerns,
dependencies, and risks of
each portfolio
Result
Optimized Strategic
Plan
Informed by technical
analysis, develop an action
plan.

Distributed Solar
and Storage
(in progress)
New & Emerging
Tec h
(in progress)
Demand Response
+ Flex Loads
(in progress)
Transmission
Options
(on deck)
Glenarm
Conversion/
Replacement
(on deck)
Long-Term
Capacity
Expansion
(on deck)
Production Cost
Modeling
Distribution
System Analysis
Cost Impacts
Optimized Strategic Plan: Study Workflow
Action Plan/
Final Report
Te chni cal Analysi s Synthesis
6 Legend: Solar PV Wind Geothermal Flexible Loads Battery Transmission Expansion Long Duration Storage Summary of Portfolio Assumptions
Presented to MSC & EAC, September 10, 2024

7 1A. Evaluate infrastructure investments and options for converting Glenarm to hydrogen gas.
1B. Identify multiple portfolios of local resources
that that could replace Glenarm while providing
a similar levels of reliability.

2. Identify resources needed in addition to those
found in step 1.

Portfolios Developed Through a Two-Step Process
1 Identify resources needed to
ensure local reliability 2
Identify additional resources
needed to meet PWP system needs,
the goals of Res. 9977, and resource
adequacy requirements.

Glenarm Conversion and Replacement Long-Te r m Capa cit y Expa nsio n Each portfolio will be optimized for least cost independently. Portfolio costs will be compared in the Cost Impacts study. 8 Phase 2: Portfolio DevelopmentPhase 1:

Resource Options
Technical Analysis Plan for Portfolio Development
Distributed
Solar and
Storage
New &
Emerging Tech
Demand
Response +
Flex Loads
Transmission
Options
Long-Te rm
Capacity
Expansion
Glenarm
Conversion
and
Replacement
Stochastic simulations of
system conditions
accounting for load and
renewable variability,
generator and transmission
outages across 1000
“draws,” each representing a
plausible calendar year of
conditions
Co-optimization of new
resource investments and
system operations to
minimize costs while
meeting clean energy targets
and reliability requirements
Local solar &
storage
potential
Supply-side
resource
characteristics
Supply curves
for load
management
Options for
transmission
expansion
Phase 3: Impact
Assessments
Production Cost
Modeling
Distribution
System
Analysis
Cost Impacts
Evaluation
Portfolio 1
Portfolio 2
…
Portfolio n
Glenarm Conversion and
Replacement Scope and
Methodology
10 Step Approach
Options for
Hydrogen
Conversion
Assess infrastructure
investments needed to convert
Glenarm to H2, including pilot
project and milestones
Development of
Replacement
Portfolios
Use loss-of-load-probability
modeling to identify
alternative generation
portfolios that yield similar
levels of reliability within PWP
system
Review of
Regulatory
Considerations
Review requirements
ass c ated w th PWP’s
participation in CAISO market
 ’ W
peaking facility comprising five units fueled by
natural gas
 While operations of Glenarm are limited (<5% annual
capacity factor), the power plant plays a crucial role
in maintaining local reliability
 W ’ -free supply requires
a long-term transition plan for Glenarm that either:

1. Results in continued operations in a limited fashion using a carbon-free fuel

2. Provides for the replacement of Glenarm with a portfolio of local resources resulting in comparable levels of local reliability
 This study is not a master plan for the Glenarm site.

Rather, it focuses on a potential resource portfolios
to convert or replace the power supply provided by
Glenarm.

Scope of Glenarm Conversion & Replacement Study
11 W  Historical operational patterns of Glenarm
Power Plant consistent with a resource whose
primary purpose is supporting reliability:
• Low capacity factor, frequently not operated
• Dispatched up to full capacity in a select number of
hours per year
 Conditions that currently require operations
of Glenarm:
• Peak demand conditions (above import capability)
• Transmission/distribution contingencies
• High wholesale electricity prices in California
Independent System Operator (CAISO)
• CAISO resource deficiencies
A Historical Perspective on the Role of Glenarm
2018 4.6% capacity factor
2019 3.9%
2020 3.5%
2021 1.2%
2022 3.3%
2023 2.6%
Throughout most of the year , PWP serves loads
by importing from CAISO without dispatching
Glenarm power plant
In a small number
of hours, Glenarm
operates at or
near full capacity,
playing a critical
role in meeting
local reliability
needs
# Hours
A long-term reliability solution will require local
resources that can operate reliably under very specific circumstances
𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝐹𝑎𝑐𝑡𝑜𝑟 [%] = 𝐴𝑛𝑛𝑢𝑎𝑙 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 [𝑀𝑊ℎ/𝑦𝑟]
𝑁𝑎𝑚𝑒𝑝𝑙𝑎𝑡𝑒 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑀𝑊 ∗ 8760 ℎ𝑟𝑠/𝑦𝑒𝑎𝑟
12 Goal of Glenarm Replacement Study
Glenarm
200 MW
System Reliability Contribution
Distributed Solar
?? MW
Storage
?? MW
Flexible Loads
?? MW
The goal of the Glenarm Replacement analysis is to identify combinations of local resources that can contribute an equivalent amount to system reliability as Glenarm currently does. 13 E3’s RECAP: Loss of Load Probability Modeling
 ’
Planning (RECAP) model is a
probabilistic method to consider
system reliability across a wide
range of load and weather
conditions
 Monte Carlo simulations
consider system operations
across a range of conditions
• Broad range of loads & renewables
• Randomly simulated plant outages
• Dispatch of use-limited resources
 Primary results are probability -
weighted statistics of loss of load
frequency, duration, and
magnitude – but can also be used
to derive Planning Reserve
Margin (PRM) requirements and
ELCCs of different resources
Monte Carlo simulation of loads,
renewable profiles, and generator
outages used to simulate 1,000 years of
plausible system conditions
1 year
x1000Load
Firm Resources (with outages)
Solar
Wind
System reliability measured relat ve t “ e day te year” standard; periods of high loss of load probability identified Effective load carrying capability (ELCC) for a wide range of types of resources evaluated
Examp le RECAP result from Long-Run Resour ce Adequacy u nder Deep Decarboniz ation Path ways fo r Californi a (Calpine, 2019)
14  Distributed solar and storage, which will play large roles in Glenarm replacement portfolios, have limitations as variable and energy limited resources that need to captured in determining their ability to contribute to system reliability.
 These limitations include:
• Resource availability: RECAP accounts for the probability that periods of low solar output will coincide with high load. • Saturation effects: At increasing penetrations, variable and energy-limited resources experience declining marginal capacity value. – Variable res ources: rel ab l ty r sks sh ft t wards “ et peak” whe res urce ava lab l ty s l wer – Energy-limited resources: reliability risk extends across greater number of hours, requiring additional duration Why do we need hourly simulations and probabilistic models to construct replacement portfolios?

15 Evaluating Reliability with Different Metrics
 System reliability can be measured in different ways, such as the resource adequacy of a power system,
but generally based on characteristics of loss-of-load
events that the system may encounter, in terms of:
• Frequency
• Duration
• Magnitude
 Target reliability metrics are not standard across the industry and are often not rigorously justified • F r example, “ -day-in-10-year" Loss-of-Load Expectation (LOLE) is often used but this metric does not capture the duration or magnitude of individual events
 W ’ , ,
two commonly- “ ” -of-load events
• LOLE: Loss-of-Load Expectation (days/year) - Average number of days with loss of load • EUE: Expected Unserved Energy (MWh/year) - Average quantity of unserved energy Loss of Load Event
Resource
Capacity
Load
Resources
16  Reliability metrics measure outages in terms of frequency, magnitude, and duration. • No single metric captures all three dimensions of loss-of-load events.  Target reliability metrics are not standard across the industry • LOLE has historically been the most commonly used in setting standards, though increasing interest in alternative metrics (particularly EUE)
Standard Reliability Metric Definitions
Reliability Metric Units Definition
Loss-of-Load Expectation
(L OLE)
days/year Average number of days with loss of load (at least once during the day) due to system demand exceeding available generation capacity Expected Unserved Energy
(EUE)
MWh/year Average quantity of unserved energy (MWh) over a year due to system demand exceeding available generation capacity
Loss-of-Load Probability
(LOLP)
% Probability of system demand exceeding available generation capacity over a given time period (e.g., season, year)
Loss-of-Load Hours
(L OLH)
hours/year Average number of hours per year where system demand exceeded available generation capacity
Loss-of-Load Events
(L OLEV)
events/year Average number of events (of any duration or magnitude) during which system demand exceeded available generation capacity
17  Representation of PWP system in RECAP
generally includes three components:

1. PWP retail energy demand
– Capturing hourly demand patterns throughout the year
across a range of different weather conditions

2. Import capability from CAISO
– 280 MW under normal operating conditions
– Reduced to 140 MW in the event of a transformer
outage/maintenance at TM Goodrich

3. Glenarm availability
– Five units totaling 198 MW of capacity
– Each unit susceptible to independent forced outages
based on probabilities provided by PWP
 Simulations repeated across hundreds of
“ ,”
sample of load and outages
Representation of PWP Existing System in RECAP
In any hour that PWP retail energy
demand exceeds import capability from
CAISO plus availability of Glenarm, a
“reliability event” occurs
Glenarm
Goodrich

ILLUSTRATIVE
Loss of Load
Local Solar
Demand Response
18  To capture a wide range of different potential
weather conditions and corresponding solar
production patterns, simulated profiles are
’ System Advisor Model
and National Solar Radiation Database
• Historical period covered: 2006 -2022
• Chronological resolution: hourly
• Spatial resolution: 4km
 Incorporation of multiple weather years of
solar profiles into LOLP modeling ensures low
probability tail events – with potential impacts
to reliability – are captured with representative
probabilities
• Example: overcast days with low solar production
Representing a Wide Range of Solar Production Profiles
Average Hourly Profile
Capacity Factor: 26%
Lowest Daily Profile
(July 11, 2013 )
Capacity Factor: 6%
Pasadena Distributed Solar PV Hourly Profile, July
(% of maximum capacity)
Profiles for each day
in July, 2008 -2022
Based on profiles simulated at multiple locations and with multiple configurations P NREL’ S A v 6-2022 19  To capture uncertainties in demands, E3
models multiple years of base load profiles
that reflect a broad range of weather
conditions that Pasadena may experience.
 E3 detrends historical load profiles for growth
in BTM PV and energy efficiency to isolate the
impacts of weather variability on load.
• Existing and incremental BTM PV is modeled as a
resource.
• Future energy efficiency is accounted for in scaling
up load profiles to a future year forecast.
 Loads are scaled up to future years (e.g. 2030)
W ’ k
demand forecast.
• E3 models transportation electrification load growth
using and end -use specific load shape.

Representing a Wide Range of Load Conditions
Median weather year
peak day
Pasadena peak day in 2024 across weather year conditions (% of maximum capacity) Profiles for each
peak day in weather
year 2006 -2022
20 Process for Developing Alternative “Replacement” Portfolios 2 3
Evaluate local reliability of
current PWP system (2024)
Calculate reliability metrics (EUE
and LOLE) to establish reliability
baseline
Include simulations of hourly
load and outages at Glenarm
and Goodrich
1 Remove Glenarm and rerun
LOLP simulations in 2030
Frequency and magnitude of
unserved energy events
increases, particularly during
peak periods and outages at
Goodrich
Add portfolios of new
resources to restore
original level of reliability
Add solar, storage, and flexible
load resources until reliability is
restored
Maintain same sampling of load
and outage conditions as Step 1
3 21
 , “
”:
portfolios by iteratively identifies and adds least-cost resources until target reliability is reached
 This tool will aim to add resources cost-effectively to a base portfolio to achieve the maximized reliability benefit

1. Smart search starts with a local portfolio that is insufficiently reliable relative to the target reliability;

2. It then evaluates the incremental improvement to reliability that results from adding small, equal -cost increments of candidate resources;

3. The resource that improves reliability the most is added to the portfolio, and the process is then repeated with the same set of resources until the portfolio is sufficiently reliable
 We will run smart search with various combinations or resources that can be selected to bring reliability back to its baseline level.

Approach for Identifying a Replacement Portfolio
Illustrative Smart Search Process
22 System Load-Resource Balance, 2031
0 100
200
300
400
500
0 24 48 72 96 120 144Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 System Dispatch during high load week
Base system with Full Import Capability (280 MW)
(MW)
CAISO Imports
DG PV
Glenarm
Under normal operations, Glenarm is seldom
operated except for periods of peak load.
23 0
100
200
300
400
500
0 24 48 72 96 120 144
System Load-Resource Balance with TMG outage, 2031
System Dispatch during high load week
Base system with Derated Import Capability (140 MW)
(MW)
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
CAISO Imports
DG PV
Glenarm
When import capability is derated, Glenarm
plays a critical role in serving loads reliably.

Loss of Load
24 0
100
200
300
400
500
0 24 48 72 96 120 144
System Load-Resource Balance with Glenarm Retired, 2031 System Dispatch during high load week
System without Glenarm with Derated Import Capability (140 MW) (MW)
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
CAISO
Imports
DG PV
Loss of Load
Without Glenarm, replacement resources are essential
for maintaining PWP reliability, especially during
contingency conditions that limit imports from CAISO
25 Beyond Goodrich Contingency, Technical Challenge for Reliability Will Likely Center Around Non-Solar Hours
Hourly Carbon-Free Electricity vs. Hourly Load
0% 25% 50% 75% 100% 125% 150% 175% 200%
2030 Carbon-Free Electricity Supply, as a proxy for Difficulty of Supplying Carbon-Free Capacity 12am
12pm
12am
Jan 1 Day of Year Dec 31
Abundant solar generation during the day helps meet gross system peak, but significant levels of energy storage and load shifting will be needed to deliver that energy to other times of the day and year, and provide system capacity in all hours Midday hours can
leverage abundant solar
(both distributed and
utility scale) to fulfill
capacity requirements at
relatively low difficulty
Evenings and some
mornings in shoulder
seasons can leverage
shorter duration battery
storage
Peak summer evening/nights and
eventually winter mornings will
place the most strain on local
reliability. Contingency events (ex.

Goodrich going down), must also
be considered

DRAFT - PRE-READ DISTRIBUTION FOR TAP MEMBERS
26  Replacement portfolios are under
development for:
• Multiple technology sets (corresponding to
the portfolios)
• Multiple years (to account for changes in
load and eventual expansion of transmission
limits)
 Each replacement portfolio serves as the
basis for a local resource reliability
constraint in Long-Term Capacity
Expansion study
Next Steps: Combinations of Replacement Resources to Explore Mature Technologies
Only
Mature Technologies +
LDES
Resource Options 2030 2035 2030 2035
Distributed Solar   ** 
Battery Storage (4hr)   ** 
Mid Duration Storage (10hr) * * ** 
Long Duration Storage (100hr) * * ** 
Flexible Loads   ** 
Demand Response   ** 
Transmission Expansion *  ** 
* T echnology options not included in specified replacement analysis ** I the “ ature Tech l g es + ” p rtf l s, Gle arm s reta ed f r emerge cy conditions through 2035
Long-Term Capacity Expansion
(LTCE)Scope and Methodology
28  Long-term planning of optimized
portfolios is a complex, multifaceted
process that considers a horizon of 15 to
20 + years, for example
 Long-term capacity expansion models
use optimization to construct optimized
portfolios that:
• Minimize an objective function (such as cost)
• Meet reliability, policy, and any other
applicable constraints (such as reliability,
and/or carbon-free energy, among others)
• Implicitly evaluate tradeoffs among a wide
range of resource options to find appropriate
balance
Development of Optimized Portfolios using Long -Term Capacity Expansion Modeling
Resource options
(with unique characteristics)
Objective
Candidate Resource Options
Distributed Solar PV
Utility-Scale Solar PV
Wind (California)
Wind (Out of State)
Geothermal
Ba ttery Storage (4hr)
Mid Duration Storage (10hr)
Long Duration Storage (100hr)
Demand Response
Load Flexibility
Green Hydrogen
Transmission
Minimize
costs
Ensure
reliability
Achieve
carbon-
free
energy
Constraints
29  Fixed costs of new investments included in objective function  Embedded costs of existing infrastructure are treated as sunk costs Capacity Expansion Optimizes Incremental Investments
Baseline Resources: Existing and
planned resources assumed to remain
in service through planned retirement;
costs assumed to be sunk
Selected Resources: New resource
investments optimized by capacity
expansion to minimize cost while
meeting reliability and policy goals
MW
Year
Portfolios will vary in
what resources can
be selected based on
technology set.
30  What is PLEXOS?
 A system simulation tool based on optimization
 Allows us to represent complex energy systems to explore questions and inform decision making
 Why do we use PLEXOS?
 Detailed unit-level representations of resources and their interactions with other resources and load
 Can represent the evolution of policy and new technologies  Flexible tool that can help solve many real -world problems  What questions can PLEXOS help us answer?
 Optimal resource portfolio mix
 Future system operations
 And more!
Long-term Capacity Expansion (LTCE) Model: PLEXOS
https://www.energyexemplar.com/plexos
E3 will use PLEXOS, an industry
standard LTCE model, which has
functionality similar to EnCompass
(used in 2023 IRP)
31 Capacity Expansion Formulation
Objective Function ConstraintsDecisions
Fixed Costs
• Renewables
• Energy storage
• Thermal
Variable Costs
• Variable O&M*
• Fuel Costs
• Market purchases
Investment Costs
• New resources
• New transmission
(optional) Investments
System
Operations
Clean energy targets
Resource adequacy
requirements
Operations
Resource Limits
Transmission
*Operation and Maintenance
32 Key inputs and assumptions required for LTCE
Load Forecast
PWP baseload
EV adoption forecast
Energy efficiency
Baseline Resources
Glenarm and existing DER portfolio
Owned & contracted renewable, storage, and thermal resources Renewable profiles, thermal operating parameters, storage round-trip efficiency Candidate Resources
Capacity expansion resource options and availability, including DR resources Resource costs, fuel costs, and renewable profiles
Portfolio Constraints
CAISO Resource Adequacy (RA) requirement (applying forward-looking marginal ELCC) Requirements for local resources (from Glenarm Replacement/Conversion Study) Clean energy policy targets
Market Interactions
CAISO wholesale market price forecast
Restrictions on gross/net imports
Simulation Settings
Modeling horizon
Day sampling settings
PWP weighted average cost of capital (WACC)
33 Topology for Capacity Expansion Model
280 MW limit
(expanding to 336
MW by 2035)
TM Goodrich
Receiving Station
External
CAISO
System
Owned & Contracted Resources
Palo Verde Nuclear 10 MW
Intermountain Power Plant1 108 MW
Magnolia Power Plant 14 MW
Chiquita Canyon Landfill Gas1 7 MW
Puente Hills Landfill Gas1 13 MW
Hoover Hydro 15 MW
Azusa Hydro 15 MW
Calwind2 20 MW
Co so Ge otherm al2 10 MW
Geysers Geothermal2 25 MW
Antelope Big Sky Ranch Solar 7 MW
Big Sky Summer Solar 7 MW
Columbia 2 Solar 3 MW
Kingbird Solar 20 MW
Bonanza Solar2 105 MW
Sapphire Solar2 39 MW
Bonanza BESS2 55 MW
Sapphire BESS2 20 MW
Candidate Resource Options
Utility-Scale Solar PV
Wind (California)
Wind (Out of State)
Geothermal
Ba ttery Storage (4hr)
Mid Duration Storage (10hr)3
Long Duration Storage (100hr)3
Wholesale Energy Market
CAISO Whole sale Market (SP15)
Utility Load
2024 Peak Demand 4 332 MW
2030 Peak Demand 5 361 MW
Owned & Contracted Resources
Glenarm Power Plant 198 MW
Winds or Res ervoir Solar 0.7 MW
Glenarm BESS 2 25 MW
Existing Customer -Owned Resources
Cu stom er-Owned Solar 28 MW
Cu stom er Owned Storag e 1 MW
Cand idate Res ource Opti on s
Di stri but ed Solar PV
Battery Storage (4hr)
Mid Duration Storage (10hr) 3
Long Duration Storage (100hr 3
Demand Response
Load Flexibility
Green Hydrogen 3
Local
PWP
System
Notes:

1. Resources with established plans for retirement or contract expirations prior to end of 2030

2. Resources not yet online today

3. Candidate resources included in a select set of portfolios

4. Based on 2023 IRP forecast.

5. Based on 2023 IRP forecast with blended EV charging developed by E3. 34  Outputs of the portfolio development phase will be multiple cost-optimized resource portfolios reflecting different pathways to achieving Res. 9977. Key outputs for each portfolioinclude:
• T otal Installed Capacity (includes existing, planned, and new generation and battery storage) • T otal Generation (annual generation from all resources) • Annualized Build Cost (total fixed costs (capital and fixed O&M) of candidate resources) • Operational Costs (variable O&M, fuel costs)
• Market Purchases (market net purchases and cost/revenue)  In the Cost Impacts phase of analysis, we will further analyze the compare the cost of each portfolio.  In the Action Plan, we will synthesize findings across portfolios, identifying common themes, no-regrets actions, and next steps.

LTCE Outputs and next steps after portfolio development Technical Advisory Panel Meeting #6
October 16, 2024
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Mike Sontag, Director
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Sr. Managing Consultant
2  Optimized Strategic Plan progress update
 Demand Response and Flexible Load Study Results
 Update on Transmission Options
 Glenarm Conversion and Replacement – Resource Adequacy Consideration
 Appendix: Resource Cost Assumptions (requested by TAP) Agenda
3 Status Updates: Optimized Strategic Plan Supporting Studies Local Solar & Storage
•Geospatial analysis of technical potential complete (ground mount, parking canopy, rooftop) •Adoption modeling in progress
Demand Response & Flexible Loads
•Downscaling LBNL DR supply curves complete
•EV charging load shaping complete, under review by PWP New & Emerging Technologies
•Review of emerging technology characteristics and risk factors complete •Developed cost and performance assumptions for portfolio modeling Glenarm Conversion & Replacement
•Preliminary modeling underway
Long-Term Capacity Expansion
•Reviewing data provided by PWP, compiling additional inputs and assumptions needed for modeling Transmission Options
•Discussing options. Identifying key decision points and considerations Production Simulation
Distribution System Impacts
Cost Impacts
Active Workstreams
Not yet started
4 Phase 2: Portfolio DevelopmentPhase 1:

Resource Options
Technical Analysis Plan for Portfolio Development
Distributed
Solar and
Storage
New &
Emerging Tech
Demand
Response +
Flex Loads
Transmission
Options
Long-Term
Capacity
Expansion
Glenarm
Conversion
and
Replacement
Stochastic simulations of
system conditions
accounting for load and
renewable variability,
generator and transmission
outages across 1000
“draws,” each representing a
plausible calendar year of
conditions
Co-optimization of new
resource investments and
system operations to
minimize costs while
meeting clean energy targets
and reliability requirements
Local solar &
storage
potential
Supply-side
resource
characteristics
Supply curves
for load
management
Options for
transmission
expansion
Phase 3: Impact
Assessments
Production Cost
Modeling
Distribution
System
Analysis
Cost Impacts
Evaluation
Portfolio 1
Portfolio 2
…
Portfolio n
5 Phase 1
Preparatory Studies
Identify and characterize all
potential resource options
Phase 2
Portfolio Development
Use detailed power system
modeling to construct multiple
portfolios that meet clean
energy goals and maintain
reliability
Phase 3
Impact Assessment
Calculate cost metrics;
identify feasibility concerns,
dependencies, and risks of
each portfolio
Result
Optimized Strategic
Plan
Informed by technical
analysis, develop an action
plan.

Glenarm
Conversion/
Replacement
(on deck)
Long-Term
Capacity
Expansion
(on deck)
Production Cost
Modeling
Distribution
System Analysis
Cost Impacts
Updates from Preparatory Studies
Action Plan/
Final Report
Technical Analysis Synthesis
Distributed Solar
and Storage
(in progress)
New & Emerging
Tech
(in progress)
✓ Discussed at September 10 MSC & EAC meetings
✓ Discussed at September 10 MSC & EAC meetings
Demand Response
+ Flex Loads
(in progress)
Transmission
Options
(in progress)
✓ Draft supply curves for “shed” and “shift” demand response in Pasadena
✓ Description of options for transmission expansion explored 6 1A. Evaluate infrastructure investments and options for converting Glenarm to H2.
1B. Identify multiple portfolios of local resources
that that could replace Glenarm while providing
a similar levels of reliability.

2. Identify resources needed in addition to those
found in step 1.

Two Phase of Portfolio Development
1 Identify resources needed to
ensure local reliability 2
Identify additional resources
needed to meet PWP long-term
system needs, the goals of Res.
9977, and resource adequacy
requirements.

Glenarm Conversion and Replacement Long-Term Capacity Expansion Each portfolio will be optimized for least cost independently. The cost of each portfolios will be compared in the Cost Impacts study.

Demand Response & Flexible
Loads
8 Scope of DR and Flexible Loads Study
 Objective: Assess cost and potential for demand response in PWP’s service territory.  Motivation: Given constraints on PWP to leverage utility-scale resources due to limited import capability and in-zone resource availability, demand response is one of the zero-carbon demand-side resources that can contribute to meeting PWP’s capacity needs for maintaining reliability.

LBNL (2024), Figure 15, https://eta-publications.lbl.gov/sites/default/files/phase_4_dr_potential_study_final_2024-05-21.pdf Questions answered in this study:

1. How much demand response potential is
available from PWP customers?

2. What are the costs of demand response?

3. How can PWP leverage managed electric
vehicle (EV) charging to reduce grid impacts of
electrification?
Questions not answered in this study:

1. How should PWP design programs and tariffs to
procure demand response?
How much?
At what cost?
9  2023 CPUC IRP1 assumes 1,842 MW of Shed
Demand Response in CAISO with an additional 582
MW of interruptible pumping load (from CA
Department of Water Resources):
• 2,424 MW in total, which is ~5% of CAISO’s peak load of 50 GW
 California’s Energy Commission has set a statewide
target of 7,000 MW of load-shiftability by 20302
• This represents ~15% of California’s peak load
 Advanced Metering Infrastructure plays a critical
role in achieving this target, both through advanced
rate design, and being able to measure and
incentivize load shifting
 The resources are some of the building blocks of
VPPs, along with distributed solar and storage
Framing the Opportunity: Demand Response & Flexible Loads in California
1) CPUC 2023 IRP Inputs and Assumptions,
2) CEC 2023 Senate Bill 846 Load-Shift Goal Report:

CEC 2030 Load-Shift Goals
10  LBNL produces supply curves for the achievable potential for shed and shift demand response curves characterizing the resource availability at a given cost. • Shed (“conventional”) – Loads that can be curtailed to provide capacity reductions • Shift – Loads that can be shifted between hours
Lawerence Berkeley National Laboratory (LBNL) Potential Study Overview
1 3 5 7 9 11 13 15 17 19 21 23
Baseline Load Post-Shift Load
Shift DR
1 3 5 7 9 11 13 15 17 19 21 23
Baseline Load Shed Load
Shed DR
1 3 5 7 9 11 13 15 17 19 21 23
11 Downscaled Supply Curves of DR Potential for Pasadena 2030: Pasadena Shed DR Potential by Customer Grouping
Procurement Price ($)
2030: Pasadena Shift DR Potential by Customer Grouping
Procurement Price ($)
Shift potential in units of MWh to show the
amount of energy can be shifted
Shed potential in units of MW to show the
amount of load that can be reduced.
4hr storage levelized cost
$139/kW-yr
4hr storage levelized cost
$35/kWh-yr
~10 MW of shed DR
cheaper than storage
~4 MWH of shift DR
cheaper than storage
Commercial load makes up the majority of PWP’s load, creating the largest opportunity to shift and shed load. Electric vehicles are expected to emerge as a large source of shift DR. 12  Advanced Metering Infrastructure is necessary to enable TOU rates, and measure/incentivize demand response – AMI is currently expected roll out in PWP in 2028  LBNL’s supply curve is working from a reasonably mature and developed demand response market in CA’s investor-owned utilities which may be optimistic for PWP . • PWP will be starting from scratch and may need some time to build up base of participants, technologies, and dependable DER service providers.
• Point-of-sale recruitment is seen as much more effective than trying to enroll customers after device installation – may be challenging to leverage devices installed before AMI metering.  Establishing good customer relationships will be critical for ramping up DR programs, and PWP can start building those before the AMI project through behavioral demand response and customer engagement. • Customer-focused nature also presents an opportunity to build strong relationships and let more people participate in the clean energy transition
 Important to target end uses and customers that have load present during hours with high loss of load probability
• Majority of PWP’s load (~70%) is from commercial customers – programs should look to tap into this sector Considerations for Enabling this Resource
Transmission Options
14 The Power Delivery Master Plan (2022) Identified Three Priorities to Address Constraints on Pasadena’s Import Limitations PDMP Priority Current Status
Replace and upgrade internal
35kV subtransmission lines
Replacement projects currently in
progress (planning phase)
Evaluate potential to install
phase shifting transformer
at interconnection with LADWP
Discussed with CAISO; option not
pursued further due to CAISO
concerns of PWP operating
interconnection to another balancing
authority without CAISO control
Upgrade transformers at TM
Goodrich to enable increased
import limit of 336 MW
(contractual limit of
interconnection agreement
with SCE)
Recent events have raised question
of whether expansion to 336 MW is
the right long-term solution for
PWP or whether higher levels of
expansion should be considered

15  Replacement of TM Goodrich transformers presents an opportunity for PWP to redesign the power system for long-term needs
 Equipment lifetimes for transformers and other key electrical equipment are 50-70 years, meaning that these decisions will set the foundation for what Pasadena’s electrical system looks like through the remainder of this century
 Multiple key uncertainties and decisions have direct implications for right-sizing of transformers:
• What are long-term expectations for load growth, accounting for potential for transportation and building electrification associated with state’s ambitious decarbonization goals? • What are Pasadena’s long-term plans to build or maintain internal generation resources within the system? • How would increased import limits impact Pasadena’s subtransmission system? • What are the costs associated with various replacement options (and the corresponding infrastructure needs)? Right-Sizing Import Capability for Long -Term Needs Depends on Load Growth and the Role of Internal Generation
1970s 1980s 1990s 2000s 2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s Anticipated Lifetime of Current Transformers
Potential Lifetime of Replacement Transformers
Potential window for
replacement
Long-Term
Load
Forecast
Internal
Generation
Import
Capability
16 Roadmap for TM Goodrich Expansion
Stage Estimated Timeframe
Necessary for
Upgrade to
336 MW (Option 1)
Necessary for
Upgrade above 336
MW (Option 2)
Upgrades to Internal Subtransmission System Identified in PDMP Replace and upgrade existing 35 kV subtransmission lines to enable greater cross-town power flow and improve reliability 3-4 years Yes (in progress)
Yes
(in progress)
Right-Sizing Study for TM Goodrich
Determine ideal sizing of interconnection considering long -term load growth and plans for internal generation 1 year No Yes Technical & Engineering Studies (TM Goodrich Station)
Develop detailed project plans for selected expansion option 1-2 years Yes Yes Technical & Engineering Studies (Internal System)
Evaluate whether additional internal improvements are necessary at higher import levels 1-2 years No Yes Renegotiation of Interconnection Agreement with SCE*
Renegotiate agreement to allow maximum interchange above 336 MW Uncertain No Yes Competitive Procurement Processes
Conduct request for proposals, evaluate responses, select vendor, negotiate contracts 1-2 years Yes Yes Equipment Procurement Lead Time
Place orders transformers and other necessary specialized equipment 3-5 years Yes Yes Additional Upgrades to Internal Subtransmission System
Reconfigure PWP subtransmission system to higher voltage level consistent with higher TMG rating Uncertain No Maybe (depending on rating)
Upgrades to CAISO/SCE Transmission System*
Study upgrades necessary to SCE system, obtain permits & CPCNs, complete project Uncertain No Maybe (depending on rating)
Project Construction
Complete construction outside of summer peak seasons and Rose Bowl event moratorium 2-4 years Yes Yes Stages are organized roughly chronologically, but not all stages must occur sequentially; stages marked with asterisks (*) re flect stages involving or led by other parties (SCE & CAISO) 17  Riverside Public Utilities is connected to CAISO via a single point of interconnection at the Vista Substation which has a capacity lower than RPU’s peak load.  Project involves a new substation and several miles of transmission lines operated by SCE/CAISO and new switchyard and subtransmission lines operated by RPU. • Transmission lines had to be undergrounded.

Riverside Transmission Reliability Project (RTRP) as a Case Study for Second CAISO Interconnection
2006 2009 2012 2015 2018 2021 2024 2027 2030
CAISO conducted studies and concluded need for project
Legal challenges from Jarupa Valley
CPUC review of SCE’ s Certificate of Public Convenience and Necessity and Environmental Impact Review
CPUC Decision Issued
Construction
Riverside Scoping and Environment Impact Review
18  In the near term (by 2030), relieving the import
constraint at TM Goodrich poses a significant
challenge – so portfolios developed in OSP will reflect the continued need for internal generation to
maintain reliability
 At the same time that it pursues the ambitious goal of carbon-free by 2030, Pasadena faces a pivotal
decision of how to expand TM Goodrich, a decision
that will have long -term implications on its power
system
 Key results from OSP portfolio analysis can help
inform future efforts to examine right -sizing of intertie • Long-term demand forecasts
• Insights on the potential long-term role of different internal generating resources
 OSP can explore a sensitivity on long -term
transmission expansion to provide insight to
Pasadena.

How Transmission Options Tie into the Optimized Strategic Plan TM Goodrich
Receiving Station
External
CAISO
System
PWP
System

Appendix: Resource Costs
20 Summary of Techno-Economic Modeling Assumptions
Recommended for OSP
Year Characteristic Solar PV Wind Geothermal
Lithium Ion
Battery
(4 hour)
MDES
Archetype (10
hour)*
LDES
Archetype (100
hour)*
2030 Capital Cost
($/kW)
$1,275
$1,180-$1,370
$1,570
$1,430-$1,720
$9,120
$8055-$10,070
$1,420
$1,055-$1,790
- -
Levelized Cost of Energy
($/kW-yr)
$30
$20-$40
$35
$25-$45
$95
$77-$120
- - -
Levelized Fixed Cost
($/kW-yr)
- - - $140
$100 – $185
- -
Delivered Fuel Cost
($/MMBtu)
- - - - - -
2035 Capital Cost
($/kW)
$1,206
$965-$1,210
$1,445
$1260-$1630
$8,605
$7,050-$9,820
$1,310
$933-$1,690
$4,000
$3,140-$4,640
$2,780
$2,120-$3,440
Levelized Cost of Energy
($/kW-yr)
$20
$10-$35
$30
$20-$40
$90
$70-$115
- - -
Levelized Fixed Cost
($/kW-yr)
- - - $130
$90 – $180
$310
$225-$400
$245
$177-$335
Delivered Fuel Cost
($/MMBtu)
- - - - - -
* Numbers are rounded to nearest tenth.

DRAFT
21 Levelized Cost of Energy, Utility-Scale Solar PV (2024 $/MWh) Present-day cost assumptions reflect range observed across a range of data sources (with adjustments for California labor & materials costs):
• 2024 Annual T echnologies Baseline (NREL)
• 2023 Annual Energy Outlook (EIA)
• 2024 Levelized Cost of Energy + (Lazard)
• Solar Market Insight Report Q2 2024 (SEIA)
• Generation Technology Options – 2024 (EPRI)
 Future cost reduction trajectories derived from NREL ATB assuming exponential cost reductions between present day and 2050
 Levelized cost of energy calculated for informational purposes based on standard performance and financing
assumptions:
• Capacity factor of 31%-36% for high and low cost trajectories • IRA production tax credit available throughout horizon • Costs of debt and equity tied to current market indices Utility-Scale Solar PV: OSP Modeling Assumptions
E3 Low
E3 Mid
E3 High
$-
$5
$10
$15
$20
$25
$30
$35
$40
$45
$50
2025 2030 2035 2040
DRAFT
22 Levelized Cost of Energy, Land-Based Wind (2024 $/MWh) E3 Low
E3 Mid
E3 High
$0
$10
$20
$30
$40
$50
$60
2025 2030 2035 2040
 Present-day cost assumptions reflect range observed across a range of data sources (with adjustments for California labor & materials costs):
• 2024 Annual T echnologies Baseline (NREL)
• 2023 Annual Energy Outlook (EIA)
• 2024 Levelized Cost of Energy + (Lazard)
• Land-Based Wind Market Report (DOE)
• Generation Technology Options – 2024 (EPRI)
 The High trajectory is derived from NREL ATB assuming exponential cost reductions between present day and 2050; the Low trajectory assumes the CAPEX holds flat in nominal terms throughout 2050
 Levelized cost of energy calculated for informational purposes based on standard performance and financing
assumptions:
• Capacity factor of 33% - 38% for high and low cost trajectories • 30 years useful life
• IRA production tax credit available throughout horizon • Costs of debt and equity tied to current market indices Land-Based Wind: OSP Modeling Assumptions
DRAFT
23  Present-day cost assumptions reflect range observed across a range of data sources (with adjustments for California labor & materials costs):
• 2024 Annual T echnologies Baseline (NREL)
 Future cost reduction trajectories, derived from 2024 NREL ATB, assume 13% in Mid, 30% in Low and 0.5% in High case between present day and 2035
 Levelized cost of energy calculated for informational purposes based on standard performance and financing
assumptions:
• 80% capacity factor (for E3 Mid) of hydro binary type with a 30-year useful life
• IRA production tax credit available throughout horizon • Costs of debt and equity tied to current market indices Conventional Geothermal: OSP Modeling Assumptions
E3 Low
E3 Mid
E3 High
$0
$20
$40
$60
$80
$100
$120
$140
2025 2030 2035 2040
Levelized Cost of Energy, Conventional Geothermal (2024 $/MWh) DRAFT
24  E3 benchmarked recent year li -ion CAPEX
• 2024 Annual T echnologies Baseline (NREL)
• 2023 Annual Energy Outlook (EIA)
• 2024 Levelized Cost of Energy + (Lazard)
• Generation T echnology Options – 2024 (EPRI)
• Energy Storage Cost and Performance Database (PNNL)
 Cost reduction trajectory for E3 High case is derived from NREL ATB assuming exponential cost
reductions between present day and 2050
 The E3 Low trajectory assumes the capital cost
remains flat in nominal terms through 2050
 The Mid trajectory is the average of Low and High
 National average costs are adjusted for higher
California labor & materials costs
Cost assumptions for Lithium -Ion Batteries (Four Hour Duration) $0
$50
$100
$150
$200
$250
2025 2030 2035 2040
Levelized Fixed Cost, 4hr Li-Ion Battery Storage (2024 $/kW-yr) DRAFT
25  The rapidly evolving technology landscape
presents a challenge to representing storage
technologies in resource planning:
• Significant uncertainty in which technologies will
mature and at what pace
• High levels of cost uncertainty
 For the development of the OSP , three options
for energy storage are considered:
o Short-duration lithium-ion batteries (4 hr)
o A generic medium-duration storage resource with
10 hours of duration
o A generic multi-day duration energy
storageresource with 100 hours of duration
 Use of MDES and LDES archetypes allows
exploration of the role of different storage
technologies without predetermining a
preferred technology prior to a market test
Energy Storage Archetypes Considered in Development of OSP Characteristic Lithium Ion
Battery
MDES
Archetype
LDES
Archetype
Duration (hrs) 4 10 100
Round-Trip
Efficiency (%) 85% 70% 45%
Min Charging State
(%) 10% 10% 10%
Lifetime (years) 20 20 20
2035 Total Capital
Cost (2024 $/kW)
$1,310
($933 – $1,690)
$4,000
($3,140 – $4,640)
$2,780
($2,120-$3,440)
2035 Levelized Fixed
Cost (2024 $/kW-yr)
$130
($92 – $178)
$310
($225 – $400)
$245
($180 – $330)
Portfolio Suitability
Include as option
across all
portfolios
Include as option
in select
portfolios
Include as option
in select
portfolios
DRAFT
26 PNM Mid
Form Energy
CPUC Mid
CPUC Low
CPUC High
OSP Low
$2,120
OSP Mid
$2,780
OSP High
$3,440
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
$4,500
$5,000
PNM Mid
PNNL Mid
PNNL Low
PNNL High
CPUC Mid
CPUC Low
CPUC High
OSP Low
$3,140
OSP Mid
$4,000
OSP High
$4,640
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
$4,500
$5,000
 Because of a lack of technological maturity,
cost data for longer-duration storage
resources is inherently sparse and uncertain
 Public data sources span wide ranges and
inherently capture varying degrees of
technological optimism
 Cost assumptions developed for OSP
represent a plausible range of market
outcomes by 2035:
• High: limited technological improvements from
today; low likelihood that technology will be
competitive with alternatives
• Mid: evolutionary technological improvements
that enable market readiness
• Low: technology breakthrough that allows
competition with existing mature technologies
Development of Cost Assumptions, Mid - and Long-Duration Storage Archetypes
Capital Costs Assumptions for 10-
Hour Storage Resources Installed
2030-2040 (2024 $/kW)
Public Sources OSP Assumptions
Capital Costs Assumptions for 100-
Hour Storage Resources Installed
2030-2040 (2024 $/kW)
Public Sources OSP Assumptions
DRAFT
Technical Advisory Panel Meeting #7
December 16, 2024
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Mike Sontag, Director
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Sr. Managing Consultant
2  Summary of key findings from preparatory studies
 Brief update on DR and Flexible Load Study
 Glenarm Conversion and Replacement
• Replacement portfolio analysis
• Conversion pathways
Agenda
3 Phase 1
Preparatory Studies
Identify and characterize all
potential resource options
Phase 2
Portfolio Development
Use detailed power system
modeling to construct multiple
portfolios that meet clean
energy goals and maintain
reliability
Phase 3
Impact Assessment
Calculate cost metrics;
identify feasibility concerns,
dependencies, and risks of
each portfolio
Result
Optimized Strategic
Plan
Informed by technical
analysis, develop an action
plan.Distributed Solar
and Storage
New & Emerging
Tec h
Demand Response
+ Flex Loads
Transmission
Options
Glenarm
Conversion/
Replacement
Long-Term
Capacity
Expansion
Production Cost
Modeling
Distribution
System Analysis
Cost Impacts
Optimized Strategic Plan: Study Workflow
Action Plan/
Final Report
Te chni cal Analysi s Synthesis
4 Highlights from Preparatory Studies
Phase 1
Preparatory Studies
Identify and characterize all
potential resource options
Phase 2
Portfolio Development
Use detailed power system
modeling to construct multiple
portfolios that meet clean
energy goals and maintain
reliability
Phase 3
Impact Assessment
Calculate cost metrics;
identify feasibility concerns,
dependencies, and risks of
each portfolio
Result
Optimized Strategic
Plan
Informed by technical
analysis, develop an action
plan.Distributed Solar
and Storage
New & Emerging
Tec h
Demand Response
+ Flex Loads
Transmission
Options
Glenarm
Conversion/
Replacement
Long-Term
Capacity
Expansion
Production Cost
Modeling
Distribution
System Analysis
Cost Impacts Action Plan/
Final Report
Te chni cal Analysi s Synthesis
✓ Potential for ground-mount solar within the city is severely limited by land use constraints (10 MWAC).
✓ Potential for rooftop and parking canopy solar is much more significant (470 MWAC) ✓ Wind, solar , and battery storage are carbon-free resources commercially available today.

These technologies have experienced recent upward cost pressures. ✓ Emerging long-duration energy storage technologies are unlikely to reach commercial viability before the early 2030s.
✓ Deploying hydrogen for peaking capacity is technically feasible but will require technological advancement and significant new infrastructure development. ✓ Demand response and managed EV charging can reduce the need for incremental generation and distribution capacity.
✓ AMI deployment will be critical for scaling this resource. ✓ In the near term (by 2030), increasing import capacity poses a significant challenge due to the long-lead times and multistakeholder approval processes for transmission projects. ✓ Upgrading TM Goodrich is a critical long-term decision for PWP which will have significant implications for the system for the remainder of the century.

Demand Response & Flexible
Loads
6 Previously…
 Presented shift and shed demand response
supply curves.
 TAP provided feedback that PWP should model
high DR and load flexibility scenarios.

Today…
 Deep dive on managed EV charging and impact on
PWP's load forecast.
 Range of load flexibility sensitivities modeled in
the Portfolio Development phase of the OSP .

Demand Response and Flexible Loads Study Recap
2030: Pasadena Shed DR Potential by Customer Grouping
Procurement Price ($)
4hr storage levelized cost
139 $/kW-yr
7  In addition to Demand Response and BTM Solar/Storage, managed EV Charging is another key demand side resource.  Two key goals for managed EV charging include:
• Supporting the grid and reducing electric system costs for accommodating electrification.
• Lowering the costs of EV ownership and operation.
 A range of technologies are needed to enable and scale managed charging.
• Advanced Metering Infrastructure is foundational.
 The managed charging resource can be procured though several pathways including:
• Retail rates
• Demand response programs
• Customer education programs
• Virtual Power Plants / 3rd party aggregated demand management providers
Managed electric vehicle charging overview

0.00 0.50

1.00 1.50

2.00 2.50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Greatly reduces
system peak-
coincident charging
Net kW
Baseline Load Net Load with Managed Charging
 Retail rates are the most readily available
pathway for enabling managed charging
and a large portion of the resource can be
accessed via this pathway.
8 E3’s RESHAPE-EV model can be leveraged to model
custom scenarios of EV charging load shapes:
 Base or unmanaged charging load shapes are created
based on drivers’ travel needs and access to different
charger types.
 Managed charging load shapes are then developed by
optimizing load in response to price signals, such as
time-of-use rates, wholesale market prices, or utilities’ avoided costs.
 Managed charging can be passive, in response to time
varying rates, or active with participation in demand
response programs.

9  2023 IRP is derived from the California
Energy Commission's 2022 Integrated
Energy Policy Report Update (IEPR)
IEPR load shapes generally assume
75-85% of customers are price
responsive.

Transportation electrification load shapes
2023 IRP has decreased
load during system peak
hours
2023 IRP has large
rebound peaks
overnight after TOU
periods end.

Average Daily EV Charging Load, 2030
10 % of residential customers enrolled in a TOU rate

1.7% (Utility Dive, 2019)
3% (Brattle, 2019)
5% (American Public Power Association, 2019)
Average = 3%
Managed charging uptake with opt -in time-of-use
Opt-in TOU participation rates are low. Managed charging behavior amongst customer enrolled in TOU rates is high.
% of customers managing charging on a TOU rate
50% (Uplight, 2024)
68% (National Bureau of Economic Research, 2024)
72% (Enel X Way, 2020)
80% (E3 assumption used in various projects)
Average = 68%
Percentage of all residential customers managing their charging = 3% * 68% = 2% In an opt-in regime, customer must choose to be billed according to TOU rates while in an opt-out regime, customers are automatically enrolled in a TOU and must choose to be billed under a flat rate or other tariff structure. 11  2023 IRP is derived from the California
Energy Commission's 2022 Integrated
Energy Policy Report Update (IEPR)
IEPR load shapes generally assume
75-85% of customers are price
responsive.
 OSP Forecast is E3’s modeling
reflecting the expected uptake of
managed charging with opt -in TOU
rates of 2%.

Transportation electrification load shapes
2023 IRP has decreased
load during system peak
hours
Unmanaged charging
mostly occurs when
drivers get home
2023 IRP has large
rebound peaks
overnight after TOU
periods end.

Average Daily EV Charging Load, 2030
12  2023 IRP is derived from the California
Energy Commission's 2022 Integrated
Energy Policy Report Update (IEPR)
IEPR load shapes generally assume
75-85% of customers are price
responsive.
 OSP Forecast is E3’s modeling
reflecting the expected uptake of
managed charging with opt -in TOU
rates of 2%.
 OSP Managed Forecast is E3’s
modeling reflecting 100% customer
responsiveness to TOU rates and a
VGI aggregator coordinating charging.

Transportation electrification load shapes
OSP Managed
mitigates rebound
peaks by
coordinating
charging with help
of an aggregator
2023 IRP and OSP
Managed have decreased
load during system peak
hours
Unmanaged charging
mostly occurs when
drivers get home
2023 IRP has large
rebound peaks
overnight after TOU
periods end.

Average Daily EV Charging Load, 2030
13 Transportation electrification load impacts with charging management
 With highly managed EV charging, where 100% of drivers respond to time -of-use rates and a VGI aggregator coordinates charging, peak load growth from electrification could be significantly mitigated. • The 2023 IRP forecast relied upon IEPR which assumed 75-85% of drives of responsive to time-of-use rates.  Without managed charging, system peak load could be much higher than the scenario with managed charging.  Additional uncertainties in load growth not modeled include new large customers (e.g. data centers). -5%
2023 IRP Net Peak
OSP Net Peak (with 2023 IRP PV)
OSP Net Peak with EV Load
Management (with 2023 IRP PV)
400 MW
365 MW
345 MW
-8%
357 MW
14 Demand Response and Load Flexibility Sensitivities in the OSP , 2031
Unmanaged
Load forecast: OSP Forecast
Demand response:

Shed: 0 MW
Shift: 0 MW
Managed EV + DR
Load forecast: OSP Managed
(~20 MW)
Demand response:

Shed: 11 MW
Shift: 4 MW
Managed EV + High DR
Load forecast: OSP Managed
(~20 MW)
Demand response:

Shed: 38 MW
Shift: 11 MW
0 50
100
150
200
250
300
350
400
1 3 5 7 9 11 13 15 17 19 21 23
MW
Hour of the Day
1 3 5 7 9 11 13 15 17 19 21 23
Hour of the Day
1 3 5 7 9 11 13 15 17 19 21 23
Hour of the Day
Illustrative 2031 Peak Day
System Load
Managed EV
DR
Pre-Flexible Load
Post-Flexible
Load
Update on Glenarm Conversion &
Replacement Analyses
16 Portfolio development process comprises
two phases of analysis:

1. Glenarm Replacement and Conversion
Study: Identify a range of internal
resource solutions to meet local
reliability needs of PWP system given
limitations of transmission system
• Focus on a single specific challenge on the
path to Resolution 9977 goals
• Not yet considering relative cost of
different options

2. Long-Term Capacity Expansion: Create
complete resource portfolios that
consider objectives of clean energy,
reliability, affordability, and equity
• Focus on holistic view of resource portfolio
to support Resolution 9977 goals
Portfolio Development Process
Full Resource Portfolio
Local Resources
Li-ion
Li-ion
LDES
Glenarm
Replacement and
Conversion Study
Long T erm Capacity
Expansion
Utility-scale
solar
Battery Storage
Long duration
energy storage Geothermal
Wind
Distributed
solar
Long duration
energy storage
Battery Storage
(Front of Meter and
Be hin d the Meter)
Glenarm
DR and Flexible
Loads
LDES
17 Step Approach
Development of
Replacement
Portfolios
Use loss-of-load-probability
modeling to identify
alternative generation
portfolios that yield similar
levels of reliability within PWP
system
Review of
Regulatory
Considerations
Review requirements
associated with PWP’s
participation in CAISO market
Options for
Hydrogen
Conversion
Assess infrastructure
investments needed to convert
Glenarm to H2, including pilot
project and milestones
 Pasadena’s Glenarm Power Plant is a 200 MW
peaking facility comprising five units fueled by
natural gas
 While operations of Glenarm are limited (<5%
annual capacity factor), the power plant plays a
crucial role in maintaining local reliability
 Achieving PWP’s goals of carbon-free supply
requires a long-term transition plan for
Glenarm that either:

1. Results in continued operations in a limited
fashion using a carbon -free fuel

2. Provides for the replacement of Glenarm with a
portfolio of local resources that results in
comparable levels of local reliability
Scope of Glenarm Conversion & Replacement Study
Local System Reliability
Analysis
19 Glenarm ourly Generation uration urves
W  Historical operational patterns of Glenarm
Power Plant consistent with a resource whose
primary purpose is supporting reliability:
• Low capacity factor, frequently not operated
• Dispatched up to full capacity in a select number of
hours per year
 Conditions that currently require operations
of Glenarm:
• Peak demand conditions (above import capability)
• Transmission/distribution contingencies
• High wholesale electricity prices in California
Independent System Operator (CAISO)
• CAISO resource deficiencies
A Historical Perspective on the Role of Glenarm
2018 4.6% capacity factor
2019 3.9%
2020 3.5%
2021 1.2%
2022 3.3%
2023 2.6%
Throughout most of the year , PWP serves loads
by importing from CAISO without dispatching
Glenarm power plant
In a small number
of hours, Glenarm
operates at or
near full capacity,
playing a critical
role in meeting
local reliability
needs
# Hours
A long-term reliability solution will require local
resources that can operate reliably under very specific circumstances
20 Maintaining reliability in Pasadena’s
service territory requires internal
resources that can meet needs
under a range of extreme
conditions:

1. During peak demand conditions
(above import capability)

2. During transmission or
distribution contingencies that
reduce intertie capability
Visualizing Reliability Needs for Internal Generation
Full intertie
capability
available
Intertie
capability
derated
Relatively small quantities of
internal resources can help limit
reliance on Glenarm during peak
conditions when intertie is at full
capability
Relatively large quantities of
internal resources are needed
to ensure reliability during
transmission contingency
conditions
System load on a
high demand day
280 MW
~140 MW
Transmission/
Distribution
contingency
Hour of Day

21 Internal resources
considered
Three Frames for Internal Reliability Analysis
Augment
Glenarm
Mitigate
Glenarm
Replace
Glenarm
What internal resources are needed to meet growing loads while maintaining reliability if Glenarm remains in service? What additional internal resources can mitigate the need to operate Glenarm under "normal" operating conditions
(i.e. when import capability is available up to full 280 MW)? What are the total resource needs to maintain reliability even in the event of transmission contingency?
Solar
Storage
Increased Load
Flexibility & Managed
Charging
Long Duration Storage
Sensitivities also explore
increased import capability
22 280
200
+56
28 +11
+560 +539 +521
+353
+417
+51
+353 +319 +307
+93
+263
+124
+33 +68
0 200
400
600
800
1000
1200
1400
Glenarm Replacement Portfolio Summary
With Solar +
Storage + DR &
Flex Load
With Solar +
Storage + LDES
With Solar + Storage
+ Tx Expansion (336
MW)
Replacement Portfolio Augment
Portfolio
With Solar +
Storage
Existing System
DG PV
4hr Storage
LDES
Tx Expansion
DR and
Managed EV
With Solar + Storage
+ Additional DR &
Flex Load
Glenarm
Imports
Glenarm Supplement and Replacement Portfolios, Incremental Nameplate MW These values represent the minimum quantities of resources needed to ensure local reliability. Additional local resources will be included in portfolios to the extent that (a) the rate of naturally occurring customer adoption exceeds this level or (b) additional resources are found to be economic in LTCE. E3’s modeling indicates that naturally occurring adoption could result in an additional ~35 MW of customer-owned resources by 2031.

MW
With Solar +
Storage
23  Large sizing of solar in replacement portfolios driven by need to generate energy within Pasadena across sustained periods during transmission contingency conditions
• Limited imports are insufficient to recharge internal storage resources • Solar must be sized to serve daytime load (above import capability) and recharge storage – across a wide range of weather conditions
Solar output
(low solar day)
Goodrich import capability during contingency
Glenarm at full capacity
Visualizing Resource Needs for Internal Reliability
System with Glenarm in Service
Goodrich import capability during contingency
Solar output (typical day)
Surplus
solar energy
needed to
charge
storage
System with Replacement Portfolio
Daily load
Storage
24  RECAP is a loss-of-load-probability model that uses a time- sequential simulation approach to assess the availability of supply to meet system needs on an hour-to-hour basis
• Simulation approach designed to focus on challenges resulting from increasing penetrations of variable & energy-limited resources  Each simulation analyzes conditions across hundreds or thousands of possible years using a Monte Carlo approach to capture year-to-year variations in:
• Underlying weather , load, wind & solar profiles
• Power plant outage patterns
• Energy-limited resource dispatch
 Primary results include an array of indicators of system resource adequacy, including statistics of loss of load frequency, duration, and magnitude
RECAP: E3’s Renewable Energy Capacity Planning model
irm esources

ariable esource

ystem emand

Transmission apability

torage esources

Programs

nserved Energy

25 Process for Developing Alternative “Replacement” Portfolios Evaluate local reliability of
current PWP system (2024)
Calculate reliability metrics to
establish reliability baseline
Include simulations of hourly load &
outages at Glenarm consistent with
historical levels
Remove Glenarm and rerun
LOLP simulations in 2031
Frequency and magnitude of
unserved energy events increases,
particularly during peak periods and
outages at Goodrich
Add portfolios of new resources
to restore original level of
reliability
Add solar, storage, flexible load
resources, as well as EV load
management until reliability is restored
Maintain same sampling of load and
outage conditions as Step 1
Removing Glenarm from the
system results in increased
frequency/magnitude of
insufficient generation
Iterative addition of
resources restores
system to original
level of reliability
Reliability
Metric
27 0
200
400
600
0 24 48 72 96 120 144
2024 System
Snapshot Simulated Weeks during TMG Outage
The 2024 PWP system is simulated with
reduced intertie capacity across 500
years of conditions. Across this
sample:
5 Number of days per year on which a
reduction in import availability at Goodrich
would put the system at risk of loss of load
441
Total amount of load shed (MWh) across
reliability events that occur during
Goodrich outages
Reliability events are most likely to
occur on the days of highest peak
demand when at least one unit at
Glenarm experiences an outage
0 200
400
600
0 24 48 72 96 120 144
2024 Example week
(MW)
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
2024 Example “stress week”
(MW)
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Import derate + partial
Glenarm outage
Glenarm serves system
loads in Goodrich outage
CAISO Import s DG PV Gross L oadGlenarmLoss of Load 4hr Storage 28 0
200
400
600
0 24 48 72 96 120 144
2031 System with Glenarm
Additional Internal Resource Needs to Maintain Reliability with Glenarm 2031 Example “stress week” with Glenarm
(MW)
The 2031 PWP system is simulated
across the same 500 years of
conditions with Glenarm present.

Across this sample:
17 Number of days per year on which a
reduction in import availability at Goodrich
would put the system at risk of loss of load
without internal generation resources (a 3x
relative to the 2024 baseline)
1,777
Total amount of load shed (MWh) across
reliability events that occur during
Goodrich outages (4x to 2024 baseline)
With forecasted load growth,
additional internal resource are need
to meet loads on summer days even
when Glenarm is in service
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
0 200
400
600
0 24 48 72 96 120 144
2031 Example week with Glenarm
(MW)
Load growth between 2024 and 2031
make this week more challenging
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
CAISO Import s DG PV Gross L oadGlenarmLoss of Load 4hr Storage 29 0
200
400
600
0 24 48 72 96 120 144
2031 System without Glenarm
Same Snapshot Weeks show Large Needs during TMG Outage
2031 Example “stress week” without Glenarm
(MW)
The 2031 PWP system is simulated
across the same 500 years of
conditions without Glenarm present.

Across this sample:
365
Number of days per year on which a
reduction in import availability at Goodrich
would put the system at risk of loss of load
without internal generation resources (a
68x increase relative to the 2024 baseline)
228,712
Total amount of load shed (MWh) across
reliability events that occur during
Goodrich outages (a 519x increase relative
to the 2024 baseline)
Without internal resources, PWP would be
unable to meet loads on most days in the
event of an outage at Goodrich
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
0 200
400
600
0 24 48 72 96 120 144Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 2031 Example week without Glenarm
(MW)
CAISO Import s DG PV Gross L oadGlenarmLoss of Load 4hr Storage Large need for internal resources
without Glenarm
30 Adding Distributed Solar & 4 -hr Storage to Restore Reliability Adding 170 MW local solar & 160 MW
local storage to the system without
Glenarm improves reliability – but
significant risks remain:
66 Number of days per year on which a
reduction in import availability at Goodrich
would put the system at risk of loss of load
(still 11 times higher than the 2024
baseline)
31,970
Total amount of load shed (MWh) across
reliability events that occur during
Goodrich outages (a 72x increase relative
to the 2024 baseline)
System remains at risk of experiencing loss
of load if imports are limited during (a)
nighttime hours on high load days and (b)
across multi-day periods of low solar output
0 200
400
600
0 24 48 72 96 120 144
2031 Example “stress week”
(MW)
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
0 200
400
600
0 24 48 72 96 120 144
2031 Example week
(MW)
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
CAISO Import s DG PV Gross L oadGlenarmLoss of Load 4hr Storage 31 Adding Distributed Solar & 4 -hr Storage to Restore Reliability 0 200
400
600
0 24 48 72 96 120 144
2031 Example “stress week”
(MW)
Adding 560 MW local solar & 350 MW
local storage to the system without
Glenarm reduces the magnitude of
loss of load risk to 2024 levels:
2 Number of days per year on which a
reduction in import availability at Goodrich
would put the system at risk of loss of load
(less frequent than 2024 baseline)
441
Total amount of load shed (MWh) across
reliability events that occur during
Goodrich outages (equal to 2024 baseline)
Risks on hot, sunny days are limited; multi -
day periods of low solar output pose
greatest risk as storage resources are
unable to recharge from surplus resources
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
CAISO Import s DG PV Gross L oadGlenarmLoss of Load 4hr Storage Remains a challenging week due to
high load and low solar
0 200
400
600
0 24 48 72 96 120 144
2031 Example week
(MW)
32  While large quantities of internal renewables and storage are needed to replace all reliability services currently provided by Glenarm, smaller quantities can reduce utilization of Glenarm during peak periods Visualizing Reliability Needs for Internal Generation
Full intertie capability
available
Intertie capability derated
System load on a
high demand day
Hour of Day
Local resources needed to replace
Glenarm during contingencies while
maintaining reliability:
500 MW+ solar
300 MW+ storage
>5% DR peak load reductions
Local resources needed to mitigate reliance on
Glenarm during peak periods during “normal”
transmission conditions (in addition to flexible
loads & demand response):
95% of energy needs above full intertie limit:
43 MW solar
50 MW storage
99% of energy needs above full intertie limit:
53 MW solar
101 MW storage
100% of energy needs above full intertie limit:
75 MW solar
160 MW storage
Additional resources would also improve
resilience under contingency conditions
33  Additional internal resources can limit need to
rely on Glenarm, reducing its capacity factor
and frequency of operations.
 “Mitigate” portfolios assume retention of
Glenarm as a backstop resource
predominantly for transmission contingencies
– but would not allow for its retirement
• Glenarm’s role is reduced to ensuring reliability
during the most extreme conditions (extreme loads
or transmission contingencies)
Adding Internal Resources to Mitigate Reliance on Glenarm During Peak Periods
Modeling ramework to evelop “Mitigate” Portfolios:
• Use same Monte Carlo modeling tools used in replacement analysis, capturing a range of weather and peak load conditions • Assume normal operating conditions (i.e. intertie at 280 MW) • Add solar, storage, and load flexibility to meet remaining energy needs above 280 MW

Without flexible loads & DR
With flexible loads & DR
Flexible loads & DR reduce reliance on
internal resources to serve loads above
import capability
53 MW of DG and 101 MW of storage can meet 99% of
energy needs above the full Goodrich intertie limit
53 MW of DG and 151 MW of storage can meet 99% of
energy needs above the full Goodrich intertie limit
4hr Storage
DG PV
34 These values represent the minimum quantities of resources needed to ensure local reliability. Additional local resources will be included in portfolios to the extent that (a) the rate of naturally occurring customer adoption exceeds this level or (b) additional resources are found to be economic in LTCE. E3’s modeling indicates that naturally occurring adoption could result in an additional ~35 MW of customer-owned resources by 2031. 280
200
28 +11 +53 +53
+560 +539 +521
+51
+151 +101
+353 +319 +307
+33
+33 +68
0 200
400
600
800
1000
1200
1400
Glenarm Replacement and Mitigate Portfolio Summary
With Solar
+ Storage
Replacement Portfolio Augment
Portfolio
Existing
System
DG PV
4hr Storage
DR and
Managed EV
Glenarm Augment, Mitigate, and Replacement Portfolios, Incremental Nameplate MW 99% Mitigate Portfolio
With Solar
+ Storage
Glenarm
Imports
With Solar + Storage +
DR & Flex Load
With Solar + Storage +
Additional DR & Flex
Load
With Solar +
Storage
With Solar + Storage +
DR & Flex Load
MW
Glenarm Conversion Pathways
36 Fuel Options for Glenarm
Emissions reductions Fuel Price Premium Infrastructure Needs Uncertainty / Risk Emissions reductions
driven only by decreased
operation.

Up to 100% carbon
neutral depending upon
production pathway
Larger Premium No infrastructure
upgrades Moderate
Up to 100% carbon-free
(with full conversion)
Premium
(with tax credits)
Infrastructure upgrades at
Glenarm +
Infrastructure development
outside of PWP control
High
Fossil Gas
Renewable
Natural Gas
Hydrogen
PWP faces three fuel supply options for Glenarm with different emissions impacts, infrastructure needs, costs, and uncertainties.

RNG may be an effective short-term substitute for natural gas while longer-term options for replacement or conversion remain uncertain.
37 Key Questions for Glenarm Hydrogen Conversion
Conversion
Pathways
What are the options for combusting hydrogen at Glenarm? What are the options for producing and delivering hydrogen fuel to Glenarm?
What conversion pathway(s) are most viable for Glenarm? What are the interim steps to conversion?
Infrastructure
Requirements
What are the required infrastructure investments would PWP need to enable hydrogen conversion?
What are the required infrastructure investments and technology developments outside of Pasadena’s control that would be needed to enable conversion? Costs For the Cost Impacts study in the OSP , what are the costs associated with the most viable conversion pathway(s)?
38 Geologic
Tanks
Green Hydrogen and Production Pathways
Production Delivery Combustion
Electrolysis
Other emerging
production
methods utilizing
carbon capture
Trucking
Purpose built
pipeline
Storage
Blended
pipeline
Retrofit
New H2 Capable
Current pathways Emerging pathways
39 Evaluating Options to Supply Hydrogen at Glenarm
On-site storage of trucked in hydrogen or on-site production of hydrogen at Glenarm are not viable options for 100% conversion due to scale of plant fuel needs Delivering hydrogen to Glenarm would require...

To store fuel supply for… 1 day 3 days
T ons of H2 Fuel 375 1,125
Acres for storage 25 75
Truck trips to fill storage tank 375 1,125
H2
H2 To produce hydrogen on-site...

To generate enough fuel within
a summer week for.. 1 day 3 days
MW of electolyzers 125 375
Acres for electrolyzers <1 <1
MW of solar for electrolysis 300 900
Acres for solar for electrolysis 1,650 5,000
Acres for storage 25 75
Land use requirements for on -site hydrogen storage or production are prohibitive. 50-100% of
existing
system peak
load.

Ground-mount
PV potential in
Pasadena is
~10 MW.
40 General Roadmap for Hydrogen Conversion
Today Pilot Phase Full Conversion
Operations fueled entirely by
natural gas Natural gas remains primary fuel Connection to natural gas pipeline maintained for redundancy and resiliency
Two turbines retrofit (87 MW) to allow up to
100% hydrogen combustion
All turbines retrofit or replaced to allow
100% hydrogen combustion
Green hydrogen produced via electrolysis Green hydrogen produced via electrolysis Smaller volumes of hydrogen transported by
truck and stored on site
Larger volumes of hydrogen transported by
dedicated pipeline
Hydrogen blending up to 100%
demonstrated at retrofit turbines Hydrogen is primary fuel used in all turbines Factors outside of PWP control for full conversion
• Development of dedicated H 2 pipeline
• Scaling of Green H 2 production for delivery
• Development of viable turbine retrofit / replacement option. • Permitting
41  Implementing H2 conversion at Intermountain
Power Plant in Delta, Utah (in partnership with
BWP and GWP)
 Currently pursuing Scattergood modernization
project with plans to operate with 30% blending
by 2029 and 100% hydrogen by 2035
• One of two California power plants identified
explicitly in ARCHES plan for conversion to
hydrogen
 Strategic Long-Term Reso urce Plan p rovi des
schedule for long-term conversion of remaining
peak power plants to hydrogen by 2035
 2024 IRP identifies green hydrogen as
“most probable” option for clean firm
resource while acknowledging
infrastructure challenges
 Current Preferred Plan includes ~165 MW
of hydrogen CTs beginning in 2035-’36
• From which 35 MW is the share of GWP from
hydrogen-repowered Intermountain power
plant
Neighboring Utilities Planning for a Transition to Hydrogen Fuel in 2030s
Los Angeles Department of
Water & Power Glendale Water & Power
N h ’ p h pp , w h w ( 2030s) than goals set by Resolution 9977 (2030); the timing of a viable transition to hydrogen at scale will depend upon how quickly supporting infrastructure (including pipelines and storage) in the region can be developed  The Angeles Link project is proposed to
explore development of a dedicated
hydrogen pipeline system of 200 to 750
miles to transport clean renewable
hydrogen from production sites to various
users in central and southern California
 In December 2022, the CPUC approved
SoCalGas to commence Phase 1
feasibility studies and the creation of a
memorandum account to record cost
Southern California Gas
Company
42  In 2022, the California Public Utilities Commission (CPUC) authorized SoCalGas to start Phase 1 (2022-
2024) of the Angeles Link project which included
stakeholder engagement activities and feasibility
studies.
• PWP and Southern California Public Power Authority have been engaging with SoCalGas on project development.
 The Phase 1 routing/configuration identified several
potential routes considering:
• Utilization of existing right-of-ways
• Areas of hydrogen production and offtake.
• Land use limitations, environmental considerations,
disadvantaged communities
 Phase 2 (est. 2025-2026) will identify a preferred route and conduct refined design, engineering, and
environmental studies.

Angeles Link: Proposed Hydrogen Pipeline
 Phase 3 (est. 2025-2029) will involve final refinements to the design, permitting, and regulatory applications. 43 1. If Glenarm remains in service, the need for additional resources to meet growing loads is relatively modest and is largely consistent with resources already procured by PWP and naturally occurring customer adoption of solar.

2. Replacing Glenarm with a combination of internal renewables, storage, and demand -side resources requires new internal generation resources at a significant scale that approaches technical potential. • Sizing of replacement portfolio largely driven by need to maintain reliability during (a) transmission contingency conditions even when (b) solar output is limited (short-term cloudy days)
• Demand-side resources, emerging longer-duration storage technologies and incremental transmission upgrades can reduce scale of resource needs. None offer a “silver bullet” solution to the challenge of replacing firm generation. • A larger transmission upgrade, potentially constructed in the 2030s, could reduce the challenge of replacing Glenarm. • Consistent with broader literature and experience in the industry showing that meeting reliability needs without “firm” resources (short- term resources that can be dispatched on demand for as long as needed) is prohibitively challenging.

3. While the scale of resources needed to replace Glenarm entirely is significant, smaller quantities of new internal resources can reduce the reliance on Glenarm except for the most extreme conditions.

4. ull hydrogen conversion of Glenarm requires significant infrastructure developments that are beyond PWP’s control and are unlikely to built before 2030.
• RNG may be an effective short-term substitute for natural gas while longer-term options for replacement or conversion remain uncertain.

Insights & Initial Learnings from Glenarm Replacement and Conversion Study.

Technical Advisory Panel #8
February 10, 2025
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Mike Sontag, Director
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Sr. Managing Consultant
2  Update on Glenarm Conversion and Replacement Study
 Long-Term Capacity Expansion (LTCE) Methodology and Inputs  LTCE Results – Mature Technologies Only
 Additional LTCE Case Studies
• Long-Duration Energy Storage
• Glenarm Hydrogen Conversion
 Next steps
Agenda
3 Portfolio development process comprises
two phases of analysis:

1. During peak demand conditions
(above import capability)

6 Internal resources
considered
Three Frames for Internal Reliability Analysis
Augment
Glenarm
Mitigate
Glenarm
Replace
Glenarm
What internal resources are needed to meet growing loads while maintaining reliability if Glenarm remains in service? What additional internal resources can mitigate the need to operate Glenarm under "normal" operating conditions
(i.e. when import capability is available up to full 280 MW)? What are the total resource needs to maintain reliability even in the event of transmission contingency?
Solar
Storage
Increased Load
Flexibility & Managed
Charging
Long Duration Storage
Sensitivities also explore
increased import capability
7 280
200
+56
28 +11 +53 +53
+560 +539 +521
+353
+417
+51
+151 +101
+353 +319 +307
+93
+263
+124
+33
+33 +68
0 200
400
600
800
1000
1200
1400
These values represent the minimum quantities of resources needed to ensure local reliability. Additional local resources will be included in portfolios to the extent that (a) the rate of naturally occurring customer adoption exceeds this level or (b) additional resources are found to be economic in LTCE. E3’s modeling indicates that naturally occurring adoption could result in an additional ~35 MW of customer-owned resources by 2031.

Reminder of Glenarm Replacement Analysis Presented at Last TAP Meeting
With Solar
+ Storage
Replacement Portfolio Augment
Portfolio
Existing
System
DG PV
4hr Storage
DR and
Managed EV
Glenarm Augment, Mitigate, and Replacement Portfolios, Incremental Nameplate MW 99% Mitigate Portfolio
With Solar
+ Storage
Glenarm
Imports
With Solar +
Storage + DR &
Flex Load
With Solar +
Storage +
Additional DR &
Flex Load
With Solar +
Storage + DR &
Flex Load
MW
With Solar
+ Storage
With Solar +
Storage + LDES
With Solar +
Storage + Tx
Expansion (336
MW)
Tx Expansion
100-hr Storage
8 280
200
28 +539 +503
+396
+535 +535 +449 +513
+319
+93
+25
+119
+221
+51
+187+56
+101
+111
+56
+56
+111 +111
+56
+33
+33
+33
33 +33
+33
+33
0 200
400
600
800
1000
1200
1400
1600
Glenarm Replacement Portfolio Summary with MDES and LDES With Solar + SDES
Existing
System
DG PV
SDES (4-hr)
LDES (100-hr)
DR and
Managed EV
Glenarm
Imports
Glenarm Replacement Portfolios, Incremental Nameplate MW These values represent the minimum quantities of resources needed to ensure local reliability. Additional local resources will be included in portfolios to the extent that (a) the rate of naturally occurring customer adoption exceeds this level or (b) additional resources are found to be economic in LTCE. E3’s modeling indicates that naturally occurring adoption could result in an additional ~35 MW of customer-owned resources by 2031.

MW
With Solar + SDES + MDES + LDES
MDES (10-hr)
With Solar + SDES + MDES With Solar + SDES + LDES
Modeled in today’s
LTCE results
LDES = Long -Duration Energy Storage
MDES = Medium -Duration Energy Storage
SDES = Short -Duration Energy Storage
9 Scale of Local Solar Need and Resource Potential
300 MW 600 MW0 MW 100 MW 200 MW 400 MW 500 MW
478 MW
Developable Potential
(Rooftops + Parking Lots + Ground Mount)
638 MW
T echnical Potential
(Rooftops + Parking Lots + Ground Mount)
66 MW
Forecasted
Customer Solar
in 2031
(existing policy)
+66 MW
Developable Potential on Municipal Property
Glenarm
Replacement with
Mature Tech Only
549-588 MWGlenarm
Replacement with
Mature Tech +
LDES
424-563 MW
+358 - 522 MW
Additional Solar Need to Replace Glenarm
Mitigate
Glenarm
81 MW
Total Local Solar Capacity
LTCE Modeling Background
12 Long-Term Capacity Expansion Modeling Overview
Objective Function ConstraintsDecisions
Fixed Costs
• Renewables
• Energy storage
• Thermal
Variable Costs
• Variable O&M*
• Fuel Costs
• Market purchases
Investment Costs
• New resources
• New transmission
(optional) Investments
System
Operations
Clean energy targets
Resource adequacy
requirements
Operations
Resource Limits
Transmission
*Operation and Maintenance
13 Common Assumptions Across Cases Modeled in LTCE
Load Forecast Customer Resources Existing Portfolio Transmission Resource Potential 2031 peak demand:
360 MW
(reflects managed EV charging)
Adoption of customer
resources by 2031 based on
current NEM structure:

Solar: 65 MW
Storage: 1 MW
All resources currently owned
or under contract to PWP
included across portfolios
(retirement dates vary across
cases)
280 MW
import limit at TM Goodrich,
expanding to 336 MW by 2035
External resource options
informed by CPUC IRP planning
assumptions; internal resource
options informed by
preparatory studies
Resource Costs Commodity Pricing Clean Energy Local Reliability Resource Adequacy Informed by the OSP New and
Emerging T ech Study
Natural gas, carbon allowance,
and CAISO wholesale
electricity prices based on E3
fundamentals-based forecast
All portfolios include at least
enough carbon-free energy to
meet PWP’s annual energy
needs by 2031
All portfolios include a
minimum requirement for local
resources informed by LOLP
modeling
All portfolios must meet future
RA requirements based on
“marginal ELCC” accreditation
framework
14 Topology for Capacity Expansion Model
280 MW limit
(expanding to 336
MW by 2035)
TM Goodrich
Receiving Station
External
CAISO
System
Owned & Contracted Resources
Palo Verde Nuclear 10 MW
Intermountain Power Plant1 108 MW
Magnolia Power Plant 14 MW
Chiquita Canyon Landfill Gas1 7 MW
Puente Hills Landfill Gas1 13 MW
Hoover Hydro 15 MW
Azusa Hydro 15 MW
Calwind2 20 MW
Coso Geothermal2 10 MW
Geysers Geothermal2 25 MW
Antelope Big Sky Ranch Solar 7 MW
Big Sky Summer Solar 7 MW
Columbia 2 Solar 3 MW
Kingbird Solar 20 MW
Bo nanza Solar2 105 MW
Grace Solar2 50 MW
Bo nanza BESS2 55 MW
Candidate Resource Options
Utility-Scale Solar PV
Wind (California)
Wind (Out of State)
Geothermal
Ba ttery Storage (4hr)
Mid Duration Storage (10hr)3
Long Duration Storage (100hr)3
Wholesale Energy Market
CAISO Wholesale Market (SP15)
Utility Load
2031 Peak Demand 5 361 MW
Owned & Contracted Resources
Glenarm Power Plant 198 MW
Winds or Res ervoir Solar 0.7 MW
Glenarm BESS 2 25 MW
2031 Customer -Owned Resources
Cu stom er-Owned Solar 66 MW
Cu stom er Owned Storag e 1 MW
Cand idate Res ource Opti on s
Local Solar PV
Local Battery Storage (4hr)
Local Mid Duration Storage (10hr) 3
Local Long Duration Storage (100hr) 3
Demand Response
Load Flexibility
Green Hydrogen 3
Local
PWP
System
Notes:

1. Resources with established plans for retirement or contract expirations prior to end of 2030

2. Resources not yet online today

3. Candidate resources included in a select set of portfolios

4. Based on 2023 IRP forecast.

5. Based on 2023 IRP forecast with blended EV charging developed by E3. 15 Additional variations explored to provide PWP
and City Council with robust analyses to inform
the Optimized Strategic Plan:
 “Accelerated Local Resources”: What are the
comparative impacts of portfolios that
accelerate the deployment of local resources
while maintaining Glenarm Power Plant as a
backup for reliability?
 Timing: How does each strategy change if
transition to carbon-free occurs less rapidly?
• Opportunity to synchronize transition with
transmission expansion
• More plausible timelines for technology readiness
for emerging technologies
 Markets: How does short-term market
transaction flexibility impact these case
studies?
 Renewable Natural Gas: What are the cost
impacts of utilizing RNG to reduce carbon
emissions from Glenarm?
Three Core Case Studies to Achieve Resolution 9977 Goals Common methods & assumptions across all three case studies:
• Natural gas combustion at Glenarm ceases by end of 2030 (either converted to H2 or replaced) • No reliance on wholesale market purchases (“24x7 carbon free electricity”) • Quantities of each resource optimized in each case study to meet reliability needs and carbon-free objectives Case Studies New Resources Considered to Meet Resolution 9977 Goals Mature
Technologies
Only
Solar PV
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Mature
Technologies +
Green Hydrogen
Solar PV
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Green
Hydrogen
(H2) Conversion
at Glenarm
Mature
Technologies +
Long-Duration
Storage
Solar PV
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Long-Duration
Energy Storage
16 Case-Specific Modeling Assumptions
No. Portfolio Name Glenarm Local resourceportfolio (2031) Market Purchases(2031)
1 100% Annual Matching Retained* 66 MW local solar
26 MW 4-hr storage
No net
market purchases
2 100% Annual Matching
(Accelerated Local Resources)
Retained* 81 MW local solar
101 MW 4-hr storage
No net
market purchases
3 100% Annual Matching
(Glenarm Replacement)
Replaced 567 MW local solar
319 MW 4-hr storage
No net
market purchases
4 100% Hourly Matching Replaced 567 MW local solar
319 MW 4-hr storage
No market purchases
5 100% Annual Matching
(with Hydrogen)
H2 Conversion 66 MW local solar
26 MW 4-hr storage
No net market
purchases
6 100% Hourly Matching
(with Hydrogen)
H2 Conversion 66 MW local solar
26 MW 4-hr storage
No market purchases
7 100% Annual Matching
(with LDES)
Replaced 396 MW local solar, 25 MW 4-hr storage,
101 MW MDES, 111 MW LDES
No net market
purchases
8 100% Hourly Matching
(with LDES)
Replaced 396 MW local solar, 25 MW 4-hr storage,
101 MW MDES, 111 MW LDES
No market purchases
Mature
T echnologies
only
Mature
Technologies +
LDES
Mature
T echnologies +
Hydrogen
*Sensitivity analysis will explore use of renewable natural gas at Glenarm. 17 Different Procurement Strategies Will Lead to Large Differences in PWP’s Position in the CAISO Market
Clean energy
deficit in some
hours…
…offset by clean
energy surplus in
others
“100% Annual Matching”
Utility maintains a balanced market
position, offsetting market purchases with
sales of surplus clean energy
Carbon-free
generation
“100% Hourly Matching”
Utility takes a long market position,
resulting in significant off-system sales
and/or curtailment so carbon-free resources
can meet load in most constrained periods
Carbon-free
generation
Clean energy
portfolio sized to
meet needs in
most
constrained
hour…
…requires large
surplus of clean
energy throughout
the year
Hourly
demand
Hourly
demand
Clean energy deficit
balanced by market
purchases & fossil resources
Hourly
demand
Carbon-free
generation
Current Portfolio (2025)
Utility has a short market position, relying
on market purchases (and fossil resources)
to serve a portion of load in all hours
Figures are illustrative – not a modeling result
18 Cost metrics presented today:
 Incremental costs include the following components:
• Fixed Operations and Maintenance for existing utility -owned generation • Operating costs (fuel, operations and maintenance) for new and existing resources • Procurement costs for new resources
• Market purchases and sales
• Transmission Access Charge
 Costs are presented relative to the lowest cost case (100% Annual Matching)  Costs reflect annual costs in 2031 to serve loads; additional on-going costs are associated with the portfolio in each case study
 Next steps: Calculate total system cost including existing or embedded costs and develop average system cost metrics
Relative Incremental Cost Metric
LTCE Modeling Results:

Mature Technologies Only
20 Initial Learnings & Portfolio Highlights:
 Modest incremental additions of renewables and storage allow PWP to meet 100% of annual energy needs with carbon-free sources  Glenarm retained and operated infrequently for local reliability  “Annual matching”: sales of surplus carbon-free resources offset market purchases and limited Glenarm operations (mostly summer) 100% Annual Matching, Mature Technologies Only
Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
96%
Metric 3:
90%*
2031 Incremental Cost: +$0 million per year
Hourly Carbon -Free Electricity vs. Hourly Load
0% 100% >200%
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 21 100% Annual Matching, Mature Technologies Only
(Accelerated Local Resources)
Initial Learnings & Portfolio Highlights:
 Increased focus on local solar & storage development further reduces frequency of Glenarm’s use to meet local reliability needs and reduces imports from CAISO at limited incremental cost
 Increased internal resource development reduces level of external resources developed
Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
97%
Metric 3:
90%*
2031 Incremental Cost: +$3 million per year
Hourly Carbon -Free Electricity vs. Hourly Load
0% 100% >200%
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 22 100% Annual Matching, Mature Technologies Only
(Glenarm Replacement)
Initial Learnings & Portfolio Highlights:
 Local solar and storage at significant scale needed to replace Glenarm while maintaining local reliability, resulting in very high incremental costs
 Even with the scale of local resource development, there are still some periods where this portfolio relies on the market to meet PWP needs
Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
100%
Metric 3:
90%*
2031 Incremental Cost: +$101 million per year
Hourly Carbon -Free Electricity vs. Hourly Load
0% 100% >200%
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 23 100% Hourly Matching, Mature T echnologies Only
(Glenarm Replacement)
Initial Learnings & Portfolio Highlights:
 Achieving 100% hourly matching requires (1) significant local resource additions to replace Glenarm, (2) additional external resources to eliminate reliance on market, and (3) self-scheduling of energy storage resources to meet PWP needs (rather than maximizing value) Forecasted Clean
Energy Metrics:

26 $- $3
$101
$112
$-
$20
$40
$60
$80
$100
$120
100% Annual Matching 100% Annual Matching +
Accelerated Local
Resources
100% Annual Matching +
Glenarm Replacement
100% Hourly Matching +
Glenarm Replacement
2031 Relative Incremental Total System Cost
($ million)
Drivers of Cost Differences Among Cases:
 Procurement costs for new renewables and
storage resources
• NOTE: Costs associated with naturally occurring
customer adoption of solar & storage are not
included in system cost metrics.
 Fuel and O&M costs for PWP -owned
resources
 Differences in market purchase costs
Outstanding Cost Categories to
Incorporate:
 Existing resource fixed costs/PPA costs
 Other revenue requirement components
(e.g. dx costs)
Relative Total System Costs in 2031, Mature Technologies Only Note: PWP’s current revenue requirement is approximately $200M. Absent robust public data, costs for developing local parking canopy solar were assumed to comparable to commercial rooftop solar. If parking canopy solar were closer to the cost of residential rooftop solar, the cost for the 100% Annual Matching + Glenarm Replacement case would be $38M higher. 27  With high levels of procurement of one
resource types, there is significant risks for
high levels of curtailment of generation
that cannot be sold to the market.
 When PWP’s has an excess of solar
generation, the utility may not be able to
sell it to the CAISO market which is highly
saturated with solar.

Resource Diversity is an Important Consideration to Manage Market & Curtailment Risks
+1 MW of
solar gener ation
added to PWP’s
portfolio
80% of energy
produced when PWP has
clean energy surplus
40% of energy
produced when CAISO
prices are likely zero or
negative (i.e. at risk of
curtailment)
40% of energy
produced when off-
system sales can
provide additional
revenue
20% of energy
produced when PWP
has a clean energy
deficit
Based on results from 100% Annual Matching, Mature T echnologies Only, 2031 28 Forecasted Carbon Metrics, 2031, Mature Technologies Only Metric Planned + Existing
Resources2
100% Annual
Matching
100% Annual
Matching +
Accelerated Local
Resources
100% Annual
Matching +
Glenarm
Replacement
100% Hourly
Matching +
Glenarm
Replacement
Metric 1 103% 107% 108% 115% 128%
Metric 2 94% 96% 97% 100% 100%
Metric 3 88% 90% 90% 90% 100%
Metric 1: Share of PWP’s annual retail sales that is carbon-free Metric 2: Share of PWP’s total annual generation1 that is carbon-free Metric 3: Share of PWP’s hourly energy needs1 that is carbon-free

1. Includes retail sales, T&D losses, and storage losses

2. https://pwp.cityofpasadena.net/clean-energy-tracker/ Technical Advisory Panel #9
February 26, 2025
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Mike Sontag, Director
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Sr. Managing Consultant
2  Follow-up on TAP #8
 Recap of LTCE Case Studies for Mature Technologies Only  Additional LTCE Case Studies
• Long-Duration Energy Storage
• Glenarm Hydrogen Conversion
 Fuel price sensitivities
 Next steps
Agenda
Hydrogen Fuel Cell Analysis
4 280
200
28 +53
+539
+396
+43
+101
+319
+25
+34
+111
+101
+156+33
+33
+33
+33
0 200
400
600
800
1000
1200
1400
Glenarm Replacement Portfolio with Fuel Cells
Existing
System
DG PV
SDES (4-hr)
LDES (100-hr)
Fuel Cell
DR and
Managed EV
Glenarm
Imports
Glenarm Mitigate and Replacement Portfolios, Incremental Nameplate MW These values represent the minimum quantities of resources needed to ensure local reliability. Additional local resources will be included in portfolios to the extent that (a) the rate of naturally occurring customer adoption exceeds this level or (b) additional resources are found to be economic in LTCE. E3’s modeling indicates that naturally occurring adoption could result in an additional ~35 MW of customer-owned resources by 2031.

MW
With Solar + SDES + DR
MDES (10-hr)
With Solar + SDES +
MDES + LDES + DR
With Solar + SDES + H2
Fuel Cell + DR
Replacement Portfolio99% Mitigate Portfolio
With Solar + SDES + DR
5 Hydrogen Combustion Hydrogen Fuel Cell
Nameplate Capacity 200 MW 156 MW
Efficiency (%) 32% 55%
Fuel consumption for 1 hour 16 tons 7 tons
1 Day of Operations
Run time 8 hours 24 hours
Generation 1.6 GWh 3.7 GWh
Fuel consumption 125 tons 175 tons
Annual Operations
Capacity Factor 3% 90%
Generation 55 GWh 1,230 GWh
Fuel Consumption 4,155 tons 56,940 tons
Hydrogen consumption for combustion vs fuel cells
At scale, hydrogen fuel cells would consume more fuel than combusting hydrogen in a low-capacity factor generator.

Supplying hydrogen for fuel cells would require a dedicated pipeline.

LTCE Modeling Results:

Mature Technologies Only
7 Case-Specific Modeling Assumptions
No. Portfolio Name Glenarm Local resource portfolio (2031) Market Purchases (2031)
1 100% Annual Matching Retained* 66 MW local solar
26 MW 4-hr storage
No net
market purchases
2 100% Annual Matching
(Accelerated Local Resources)
Retained* 81 MW local solar
101 MW 4-hr storage
No net
market purchases
3 100% Annual Matching
(Glenarm Replacement)
Replaced 567 MW local solar
319 MW 4-hr storage
No net
market purchases
4 100% Hourly Matching Replaced 567 MW local solar
319 MW 4-hr storage
No market purchases
5 100% Annual Matching
(with Hydrogen)
H2 Conversion 66 MW local solar
26 MW 4-hr storage
No net market
purchases
6 100% Hourly Matching
(with Hydrogen)
H2 Conversion 66 MW local solar
26 MW 4-hr storage
No market purchases
7 100% Annual Matching
(with LDES)
Replaced 396 MW local solar, 25 MW 4-hr storage,
101 MW MDES, 111 MW LDES
No net market
purchases
8 100% Hourly Matching
(with LDES)
Replaced 396 MW local solar, 25 MW 4-hr storage,
101 MW MDES, 111 MW LDES
No market purchases
Mature
Technologies
Only
Mature
Technologies +
LDES
Mature
Technologies +
Hydrogen
*Sensitivity analysis will explore use of renewable natural gas at Glenarm. 8 Total Installed Capacity (MW) in 2031
9 Annual Generation (GWh) in 2031

10 1. Modest additions of renewable resources and storage by 2031 allow PWP to meet all system needs and serve >100% of annual energy needs with carbon-free generation.

2. Accelerating the development of local resources reduces the need to procure external resources and reduces the operations of Glenarm

3. Local solar and storage at significant scale is needed to replace Glenarm while maintaining local reliability, resulting in very high incremental costs

4. To achieve 100% hourly matching, additional external resources are needed to eliminate reliance on the market and energy storage resources must be self-scheduled to meet PWP’s need for clean energy on an hourly basis (rather than maximizing the value of storage resources).

Mature Technologies Only: Initial Learnings and Portfolio Highlights
LTCE Modeling Results:

Long Duration Storage &
Hydrogen
12 100% Annual Matching, Mature Technologies + LDES
Hourly Carbon-Free Electricity vs. Hourly Load
0% 100% >200%
Initial Learnings & Portfolio Highlights:
 Local solar and storage at significant scale needed to replace Glenarm even with long-duration and medium-duration storage technologies available.
 Even with scale of local resource development, there are still some periods where this portfolio relies on the market to meet PWP needs Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
100%
Metric 3:
90%*
2031 Incremental Cost: +$92 million per year
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 13 100% Hourly Matching, Mature Technologies + LDES
Hourly Carbon-Free Electricity vs. Hourly Load
0% 100% >200%
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions.

Initial Learnings & Portfolio Highlights:
 Achieving 100% hourly matching requires significant local and external resources to match clean generation with load.  Higher incremental cost relative to mature technologies only case indicates that utilizing emerging LDES in 2031 may not be cost- effective.

Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
100%
Metric 3:
100%*
2031 Incremental Cost: +$126 million per year
14 100% Annual Matching, Mature Technologies + Hydrogen Conversion
Hourly Carbon-Free Electricity vs. Hourly Load
0% 100% >200%
Initial Learnings & Portfolio Highlights:
 With Glenarm retained, a modest incremental additions of renewables and storage allow PWP to meet 100% of annual energy needs with carbon-free sources.
 Higher incremental costs are driven primarily by the retrofit costs for Glenarm hydrogen conversion.

Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
100%
Metric 3:
90%*
2031 Incremental Cost: +$17 million per year
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 15 100% Hourly Matching, Mature Technologies + Hydrogen Conversion
Hourly Carbon-Free Electricity vs. Hourly Load
0% 100% >200%
Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
100%
Metric 3:
100%*
2031 Incremental Cost: +$28 million per year
Initial Learnings & Portfolio Highlights:
 Conversion of Glenarm to hydrogen eliminates direct fossil fuel consumption while supporting local reliability needs
 Lower incremental cost (vs. Mature Technologies Only) indicates potential benefits of emerging technologies – but significant uncertainty remains
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 16 Total Installed Capacity (MW) in 2031
Mature Technologies Only Mature Technologies +
Hydrogen
Mature Technologies +
LDES
17 Annual Generation (GWh) in 2031
Mature Technologies Only Mature Technologies +
Hydrogen
Mature Technologies + LDES

18 Mature Technology Only Mature Technology + Hydrogen Mature Technology + LDES Metric
Planned +
Existing
Resources2
100% Annual
Matching
100% Annual
Matching +
Accelerated
Local
Resources
100% Annual
Matching +
Glenarm
Replacement
100% Hourly
Matching +
Glenarm
Replacement
100% Annual
Matching
100% Hourly
Matching
100% Annual
Matching
100% Hourly
Matching
Metric 1 103% 107% 108% 115% 128% 107% 118% 126% 142%
Metric 2 94% 96% 97% 100% 100% 100% 100% 100% 100%
Metric 33 88% 90% 90% 90% 100% 90% 100% 90% 100%
Forecasted Carbon Metrics, 2031
Metric 1: Share of PWP’s annual retail sales that is carbon-free Metric 2: Share of PWP’s total annual generation1 that is carbon-free Metric 3: Share of PWP’s hourly energy needs1 that is carbon-free

1. Includes retail sales, T&D losses, and storage losses

2. https://pwp.cityofpasadena.net/clean-energy-tracker/

3. Analysis of metric 3 is under refinement and subject to changed. Values presented for OSP portfolios here represent a lower bound. 19 $- $3
$101
$112
$17
$28
$92
$126
$-
$20
$40
$60
$80
$100
$120
$140
100% Annual
Matching
100% Annual
Matching +
Accelerated
Local
Resources
100% Annual
Matching +
Glenarm
Replacement
100% Hourly
Matching +
Glenarm
Replacement
100% Annual
Matching
100% Hourly
Matching
100% Annual
Matching
100% Hourly
Matching
2031 Relative Incremental Total System Cost
($ million)
Mature Technologies Only Mature
Technologies +
Hydrogen
Mature Technologies
+ LDES
Drivers of Cost Differences Among Cases:
 Procurement costs for new renewables
and storage resources
• NOTE: Costs associated with naturally
occurring customer adoption of solar &
storage are not included in system cost
metrics.
 Fuel and O&M costs for PWP-owned
resources
 Differences in market purchase costs
Outstanding Cost Categories to Incorporate:
 Existing resource fixed costs/PPA costs
 Other revenue requirement components
(e.g. dx costs)
Relative Total System Costs in 2031
Note: PWP’s current revenue requirement is approximately $200M. Absent robust public data, costs for developing local parking canopy solar were assumed to comparable to commercial rooftop solar. If parking canopy solar were closer to the cost of residential rooftop solar, the cost for the 100% Annual Matching + Glenarm Replacement case would be $38M higher. 20 $-
$17
$28
$-
$5
$10
$15
$20
$25
$30
$35
$40
100% Annual Matching 100% Annual Matching + H2
Conversion
100% Hourly Matching + H2
Conversion
2031 Relative Incremental Total System Cost
($ million)
 With higher hydrogen costs ($31/MMBtu
nominal vs $12) in 2031, incremental
total system costs could be between $5-
13 M/year higher
• High marginal fuel prices have lower impact
on total costs due to low capacity factors at
Glenarm
 To achieve hourly matching, Glenarm
would likely operate more exposing PWP
to greater risk if hydrogen fuel prices are
higher.

Green Hydrogen Cost Sensitivity
$23 M
Cost with higher
H2 prices
$40 M
Annual generation from Glenarm:
200 MW x 2% CF x 8760 hr/yr = 31 GWh/year
Additional cost with high H2 prices:
31 GWh/year x 9 MMBTu/MWh * ($31/MMBtu
- $12/MMBtu) = $6M
21 $- $3
$101
$112
$-
$20
$40
$60
$80
$100
$120
100% Annual Matching 100% Annual Matching +
Accelerated Local
Resources
100% Annual Matching +
Glenarm Replacement
100% Hourly Matching +
Glenarm Replacement
2031 Relative Incremental Total System Cost
($ million)
 The incremental total system cost of
procuring renewable natural gas for
Glenarm is small ($15-20 M/year)
compared to the costs of replacing
Glenarm with a portfolio of local
resources.
 Adding local resources to reduce
Glenarm’s operations lowers the
incremental fuel costs PWP would incur
if purchasing RNG for Glenarm.

Cost Sensitivity: Impact of Renewable Natural Gas Procurement $17 M $18 M
Cost with RNG Annual generation from Glenarm:
200 MW x 3% CF x 8760 hr/yr = 56 GWh/year
Approximate incremental cost for RNG in
2031:
56 GWh/year x 9 MMBTu/MWh * ($44/MMBtu
RNG - $10/MMBtu Fossil Gas) = $17M/yr
22 Metric Planned + Existing
Resources2
100% Annual
Matching
100% Annual
Matching +
Accelerated Local
Resources
100% Annual
Matching +
Glenarm
Replacement
100% Hourly
Matching +
Glenarm
Replacement
Metric 1 103% 107% (112%) 108% (112%) 115% 128%
Metric 2 94% 96% (100%) 97% (100%) 100% 100%
Metric 33 88% 90% (94%) 90% (94%) 90% 100%
Forecasted Carbon Metrics, 2031, with Renewable Natural Gas Metric 1: Share of PWP’s annual retail sales that is carbon-free Metric 2: Share of PWP’s total annual generation1 that is carbon-free Metric 3: Share of PWP’s hourly energy needs1 that is carbon-free

1. Includes retail sales, T&D losses, and storage losses

2. https://pwp.cityofpasadena.net/clean-energy-tracker/

3. Analysis of metric 3 is under refinement and subject to changed. Values presented for OSP portfolios here represent a lower bound.

Values in parenthesis show results if RNG is substituted for natural gas and is counted toward the three clean energy metrics Recap and Next Steps
24 1. Current forecasts indicate that PWP has procured carbon-free resources (including existing projects and projects under development) sufficient to match 100% of forecasted retail sales annually (Metric 1).
• Range of cases studied illustrate commonly-observed principle that closing the gap between 90-100% for Metrics 2 & 3 becomes increasingly challenging, and cases that achieve “100% hourly matching” exhibit hockey-stick increases in cost

2. Replacing Glenarm with mature technologies by 2031 while maintaining reliability would require additions of local solar and storage at a level far beyond what is plausibly achievable, resulting in high incremental costs and presenting significant implementation challenges • Despite increasingly infrequent operations, Glenarm currently provides significant reliability value as a firm resource that can (a) operate during extreme events and transmission contingencies and (b) contribute to CAISO local & system resource adequacy needs • Scale of solar resources included in replacement portfolios for Glenarm would require utilization of almost all viable parking lots and rooftops across the city, exceeding the naturally occurring adoption of customer solar by an order of magnitude

3. Development of additional local carbon-free resources provides multiple benefits at limited incremental net cost even if Glenarm is needed for reliability:
• Reduced utilization of Glenarm, a benefit magnified if plant is operating using a high marginal cost fuel (e.g. RNG or hydrogen) • Improved local system reliability and resilience during extreme events • Additional opportunities for community engagement

4. Emerging technologies may present alternative options to eliminate natural gas use at Glenarm in the long run but require significant technological advances and present additional costs and risks.

5. Consideration of PWP’s position in the CAISO wholesale market will be important to manage cost and risk • Additional renewable procurement will lead to “oversupply” in some periods (e.g. solar in spring), resulting in increasing risk of curtailment or sales at negative prices • Maintaining a small amount of flexibility to purchase from the market (while meeting 100% of annual needs with carbon-free energy) allows PWP to maximize the value of its resources and reduce costs relative to an “hourly matching” strategy that eliminates all reliance on the market Initial Observations and Takeaways from LTCE Modeling
25  Refinement of cases presented today
 Additional portfolio sensitivity analyses (e.g. transmission expansion)  Technology cost sensitivities
 Cost impacts analyses
Next Steps
Appendix
27 Portfolio development process comprises
two phases of analysis:

1. During peak demand conditions
(above import capability)

29 Internal resources
considered
Three Frames for Internal Reliability Analysis
Augment
Glenarm
Mitigate
Glenarm
Replace
Glenarm
What internal resources are needed to meet growing loads while maintaining reliability if Glenarm remains in service? What additional internal resources can mitigate the need to operate Glenarm under "normal" operating conditions
(i.e. when import capability is available up to full 280 MW)? What are the total resource needs to maintain reliability even in the event of transmission contingency?
Solar
Storage
Increased Load
Flexibility & Managed
Charging
Long Duration Storage
Sensitivities also explore
increased import capability
30 280
200
+56
28 +11 +53 +53
+560 +539 +521
+353
+417
+51
+151 +101
+353 +319 +307
+93
+263
+124
+33
+33 +68
0 200
400
600
800
1000
1200
1400
These values represent the minimum quantities of resources needed to ensure local reliability. Additional local resources will be included in portfolios to the extent that (a) the rate of naturally occurring customer adoption exceeds this level or (b) additional resources are found to be economic in LTCE. E3’s modeling indicates that naturally occurring adoption could result in an additional ~35 MW of customer-owned resources by 2031.

Reminder of Glenarm Replacement Analysis Presented at Last TAP Meeting
With Solar
+ Storage
Replacement Portfolio Augment
Portfolio
Existing
System
DG PV
4hr Storage
DR and
Managed EV
Glenarm Augment, Mitigate, and Replacement Portfolios, Incremental Nameplate MW 99% Mitigate Portfolio
With Solar
+ Storage
Glenarm
Imports
With Solar +
Storage + DR &
Flex Load
With Solar +
Storage +
Additional DR &
Flex Load
With Solar +
Storage + DR &
Flex Load
MW
With Solar
+ Storage
With Solar +
Storage + LDES
With Solar +
Storage + Tx
Expansion (336
MW)
Tx Expansion
100-hr Storage
31 280
200
28 +539 +503
+396
+535 +535 +449 +513
+319
+93
+25
+119
+221
+51
+187+56
+101
+111
+56
+56
+111 +111
+56
+33
+33
+33
33 +33
+33
+33
0 200
400
600
800
1000
1200
1400
1600
Glenarm Replacement Portfolio Summary with MDES and LDES With Solar + SDES
Existing
System
DG PV
SDES (4-hr)
LDES (100-hr)
DR and
Managed EV
Glenarm
Imports
Glenarm Replacement Portfolios, Incremental Nameplate MW These values represent the minimum quantities of resources needed to ensure local reliability. Additional local resources will be included in portfolios to the extent that (a) the rate of naturally occurring customer adoption exceeds this level or (b) additional resources are found to be economic in LTCE. E3’s modeling indicates that naturally occurring adoption could result in an additional ~35 MW of customer-owned resources by 2031.

To store fuel supply for… 1 day 3 days
Tons of H2 Fuel 375 1,125
Acres for storage 25 75
Truck trips to fill storage tank 375 1,125
H2
H2 To produce hydrogen on-site...

To generate enough fuel within
a summer week for.. 1 day 3 days
MW of electolyzers 125 375
Acres for electrolyzers <1 <1
MW of solar for electrolysis 300 900
Acres for solar for electrolysis 1,650 5,000
Acres for storage 25 75
Land use requirements for on-site hydrogen storage or production are prohibitive. 50-100% of
existing
system peak
load.

Ground-mount
PV potential in
Pasadena is
~10 MW.
34 Long-Term Capacity Expansion Modeling Overview
Objective Function ConstraintsDecisions
Fixed Costs
• Renewables
• Energy storage
• Thermal
Variable Costs
• Variable O&M*
• Fuel Costs
• Market purchases
Investment Costs
• New resources
• New transmission
(optional) Investments
System
Operations
Clean energy targets
Resource adequacy
requirements
Operations
Resource Limits
Transmission
*Operation and Maintenance
35 Common Assumptions Across Cases Modeled in LTCE
Load Forecast Customer Resources Existing Portfolio Transmission Resource Potential 2031 peak demand:
360 MW
(reflects managed EV charging)
Adoption of customer
resources by 2031 based on
current NEM structure:

Solar: 65 MW
Storage: 1 MW
All resources currently owned
or under contract to PWP
included across portfolios
(retirement dates vary across
cases)
280 MW
import limit at TM Goodrich,
expanding to 336 MW by 2035
External resource options
informed by CPUC IRP planning
assumptions; internal resource
options informed by
preparatory studies
Resource Costs Commodity Pricing Clean Energy Local Reliability Resource Adequacy Informed by the OSP New and
Emerging Tech Study
Natural gas, carbon allowance,
and CAISO wholesale
electricity prices based on E3
fundamentals-based forecast
All portfolios include at least
enough carbon-free energy to
meet PWP’s annual energy
needs by 2031
All portfolios include a
minimum requirement for local
resources informed by LOLP
modeling
All portfolios must meet future
RA requirements based on
“marginal ELCC” accreditation
framework
36 Topology for Capacity Expansion Model
280 MW limit
(expanding to 336
MW by 2035)
TM Goodrich
Receiving Station
External
CAISO
System
Owned & Contracted Resources
Palo Verde Nuclear 10 MW
Intermountain Power Plant1 108 MW
Magnolia Power Plant 14 MW
Chiquita Canyon Landfill Gas1 7 MW
Puente Hills Landfill Gas1 13 MW
Hoover Hydro 15 MW
Azusa Hydro 15 MW
Calwind2 20 MW
Coso Geothermal2 10 MW
Geysers Geothermal2 25 MW
Antelope Big Sky Ranch Solar 7 MW
Big Sky Summer Solar 7 MW
Columbia 2 Solar 3 MW
Kingbird Solar 20 MW
Bonanza Solar2 105 MW
Grace Solar2 50 MW
Bonanza BESS2 55 MW
Candidate Resource Options
Utility-Scale Solar PV
Wind (California)
Wind (Out of State)
Geothermal
Battery Storage (4hr)
Mid Duration Storage (10hr)3
Long Duration Storage (100hr)3
Wholesale Energy Market
CAISO Wholesale Market (SP15)
Utility Load
2031 Peak Demand5 361 MW
Owned & Contracted Resources
Glenarm Power Plant 198 MW
Windsor Reservoir Solar 0.7 MW
Glenarm BESS2 25 MW
2031 Customer-Owned Resources
Customer-Owned Solar 66 MW
Customer Owned Storage 1 MW
Candidate Resource Options
Local Solar PV
Local Battery Storage (4hr)
Local Mid Duration Storage (10hr)3
Local Long Duration Storage (100hr)3
Demand Response
Load Flexibility
Green Hydrogen3
Local
PWP
System
Notes:

1. Resources with established plans for retirement or contract expirations prior to end of 2030

2. Resources not yet online today

3. Candidate resources included in a select set of portfolios

4. Based on 2023 IRP forecast.

5. Based on 2023 IRP forecast with blended EV charging developed by E3. 37 Additional variations explored to provide PWP
and City Council with robust analyses to inform
the Optimized Strategic Plan:
 “Accelerated Local Resources”: What are the
comparative impacts of portfolios that
accelerate the deployment of local resources
while maintaining Glenarm Power Plant as a
backup for reliability?
 Timing: How does each strategy change if
transition to carbon-free occurs less rapidly?
• Opportunity to synchronize transition with
transmission expansion
• More plausible timelines for technology readiness
for emerging technologies
 Markets: How does short-term market
transaction flexibility impact these case
studies?
 Renewable Natural Gas: What are the cost
impacts of utilizing RNG to reduce carbon
emissions from Glenarm?
Three Core Case Studies to Achieve Resolution 9977 Goals Common methods & assumptions across all three case studies:
• Natural gas combustion at Glenarm ceases by end of 2030 (either converted to H2 or replaced) • No reliance on wholesale market purchases (“24x7 carbon free electricity”) • Quantities of each resource optimized in each case study to meet reliability needs and carbon-free objectives Case Studies New Resources Considered to Meet Resolution 9977 Goals Mature
Technologies
Only
Solar PV
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Mature
Technologies +
Green Hydrogen
Solar PV
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Green
Hydrogen
(H2) Conversion
at Glenarm
Mature
Technologies +
Long-Duration
Storage
Solar PV
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Long-Duration
Energy Storage
38 Different Procurement Strategies Will Lead to Large Differences in PWP’s Position in the CAISO Market
Clean energy
deficit in some
hours…
…offset by clean
energy surplus in
others
“100% Annual Matching”
Utility maintains a balanced market
position, offsetting market purchases with
sales of surplus clean energy
Carbon-free
generation
“100% Hourly Matching”
Utility takes a long market position,
resulting in significant off-system sales
and/or curtailment so carbon-free resources
can meet load in most constrained periods
Carbon-free
generation
Clean energy
portfolio sized to
meet needs in
most
constrained
hour…
…requires large
surplus of clean
energy throughout
the year
Hourly
demand
Hourly
demand
Clean energy deficit
balanced by market
purchases & fossil resources
Hourly
demand
Carbon-free
generation
Current Portfolio (2025)
Utility has a short market position, relying
on market purchases (and fossil resources)
to serve a portion of load in all hours
Figures are illustrative – not a modeling result
39 Cost metrics presented today:
 Incremental costs include the following components:
• Fixed Operations and Maintenance for existing utility-owned generation • Operating costs (fuel, operations and maintenance) for new and existing resources • Procurement costs for new resources
• Market purchases and sales
• Transmission Access Charge
 Costs are presented relative to the lowest cost case (100% Annual Matching)  Costs reflect annual costs in 2031 to serve loads; additional on-going costs are associated with the portfolio in each case study
 Next steps: Calculate total system cost including existing or embedded costs and develop average system cost metrics
Relative Incremental Cost Metric
40 Initial Learnings & Portfolio Highlights:
 Modest incremental additions of renewables and storage allow PWP to meet 100% of annual energy needs with carbon-free sources  Glenarm retained and operated infrequently for local reliability  “Annual matching”: sales of surplus carbon-free resources offset market purchases and limited Glenarm operations (mostly summer) 100% Annual Matching, Mature Technologies Only
Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
96%
Metric 3:
90%*
2031 Incremental Cost: +$0 million per year
Hourly Carbon-Free Electricity vs. Hourly Load
0% 100% >200%
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 41 100% Annual Matching, Mature Technologies Only
(Accelerated Local Resources)
Initial Learnings & Portfolio Highlights:
 Increased focus on local solar & storage development further reduces frequency of Glenarm’s use to meet local reliability needs and reduces imports from CAISO at limited incremental cost
 Increased internal resource development reduces level of external resources developed
Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
97%
Metric 3:
90%*
2031 Incremental Cost: +$3 million per year
Hourly Carbon-Free Electricity vs. Hourly Load
0% 100% >200%
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 42 100% Annual Matching, Mature Technologies Only
(Glenarm Replacement)
Initial Learnings & Portfolio Highlights:
 Local solar and storage at significant scale needed to replace Glenarm while maintaining local reliability, resulting in very high incremental costs
 Even with the scale of local resource development, there are still some periods where this portfolio relies on the market to meet PWP needs
Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
100%
Metric 3:
90%*
2031 Incremental Cost: +$101 million per year
Hourly Carbon-Free Electricity vs. Hourly Load
0% 100% >200%
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 43 100% Hourly Matching, Mature Technologies Only
(Glenarm Replacement)
Initial Learnings & Portfolio Highlights:
 Achieving 100% hourly matching requires (1) significant local resource additions to replace Glenarm, (2) additional external resources to eliminate reliance on market, and (3) self-scheduling of energy storage resources to meet PWP needs (rather than maximizing value) Forecasted Clean
Energy Metrics:

Metric 1:
100%+
Metric 2:
100%
Metric 3:
100%*
2031 Incremental Cost: +$112 million per year
Hourly Carbon-Free Electricity vs. Hourly Load
0% 100% >200%
* Metric 3 calculated based on limited deterministic set of conditions represented in long-term capacity expansion modeling. Real world results would necessarily vary and likely be lower due to a broader set of potential conditions. 44 $- $3
$101
$112
$-
$20
$40
$60
$80
$100
$120
100% Annual Matching 100% Annual Matching +
Accelerated Local
Resources
100% Annual Matching +
Glenarm Replacement
100% Hourly Matching +
Glenarm Replacement
2031 Relative Incremental Total System Cost
($ million)
Drivers of Cost Differences Among Cases:
 Procurement costs for new renewables and
storage resources
• NOTE: Costs associated with naturally occurring
customer adoption of solar & storage are not
included in system cost metrics.
 Fuel and O&M costs for PWP-owned
resources
 Differences in market purchase costs
Outstanding Cost Categories to
Incorporate:
 Existing resource fixed costs/PPA costs
 Other revenue requirement components
(e.g. dx costs)
Relative Total System Costs in 2031, Mature Technologies Only Note: PWP’s current revenue requirement is approximately $200M. Absent robust public data, costs for developing local parking canopy solar were assumed to comparable to commercial rooftop solar. If parking canopy solar were closer to the cost of residential rooftop solar, the cost for the 100% Annual Matching + Glenarm Replacement case would be $38M higher. 45  With high levels of procurement of one
resource types, there is significant risks for
high levels of curtailment of generation
that cannot be sold to the market.
 When PWP’s has an excess of solar
generation, the utility may not be able to
sell it to the CAISO market which is highly
saturated with solar.

Resource Diversity is an Important Consideration to Manage Market & Curtailment Risks
+1 MW of
solar generation
added to PWP’s
portfolio
80% of energy
produced when PWP has
clean energy surplus
40% of energy
produced when CAISO
prices are likely zero or
negative (i.e. at risk of
curtailment)
40% of energy
produced when off-
system sales can
provide additional
revenue
20% of energy
produced when PWP
has a clean energy
deficit
Based on results from 100% Annual Matching, Mature Technologies Only, 2031 46 Forecasted Carbon Metrics, 2031, Mature Technologies Only Metric Planned + Existing
Resources2
100% Annual
Matching
100% Annual
Matching +
Accelerated Local
Resources
100% Annual
Matching +
Glenarm
Replacement
100% Hourly
Matching +
Glenarm
Replacement
Metric 1 103% 107% 108% 115% 128%
Metric 2 94% 96% 97% 100% 100%
Metric 33 88% 90% 90% 90% 100%
Metric 1: Share of PWP’s annual retail sales that is carbon-free Metric 2: Share of PWP’s total annual generation1 that is carbon-free Metric 3: Share of PWP’s hourly energy needs1 that is carbon-free

1. Includes retail sales, T&D losses, and storage losses

2. https://pwp.cityofpasadena.net/clean-energy-tracker/

3. Analysis of metric 3 is under refinement and subject to changed. Values presented for OSP portfolios here represent a lower bound.
47 Multiple Layers of Reliability Requirements
CAISO System RA Requirements
CAISO Local RA
Requirements
PWP Local
Reliability
PWP Local
Reliability
CAISO
Local RA
CAISO
System RA
Internal Resources ✓ ✓ ✓
Transmission Upgrades ✓  
External Resources
(in LCR Zones in SCE TAC)  ✓ ✓
External Resources
(outside LCR Zones)   ✓
Potential solutions contribute differently to meeting
PWP’s reliability needs and obligations
48 Marginal ELCCs for the OSP
Firm resources like geothermal and gas CTs generally have higher ELCCs that do not decline over time.

Variable and energy limited resources have ELCCs that generally decline over time as more are added to the portfolio leading to saturation effects.

DRAFT
Resource 2024 2025 2026 2027 2028 2029 2030 2035 2040 2045 Utility-Scale Solar 12% 13% 13% 12% 12% 11% 11% 8% 6% 12% Short Duration Storage 64% 60% 56% 53% 50% 47% 44% 36% 28% 15% Mid Duration Storage 66% 62% 59% 58% 56% 56% 56% 59% 56% 30% Long Duration Storage 90% 90% 90% 90% 90% 90% 90% 90% 90% 90% In-State Wind 11% 11% 11% 11% 11% 11% 11% 11% 11% 11%
Out-of-State Wind 23% 23% 23% 22% 22% 21% 21% 20% 19% 19% Geothermal 92% 92% 92% 92% 92% 92% 92% 92% 92% 92%
Gas or Hydrogen CT 83% 83% 83% 83% 83% 83% 83% 83% 83% 83% Marginal ELCC assumptions used in PWP OSP derived from modeling results in CPUC Integrated Resource Planning proceeding 49  As penetration of variable & storage resources
increases across CAISO, marginal ELCCs
decline (particularly for energy storage)
 As a result, capacity accredited to solar &
storage replacement portfolio that initially has
the same accreditation as Glenarm may
decline significantly below that value over
time.
 Long-term declines in capacity accreditation of
solar & storage replacement portfolio means
that increasing quantities of additional
resources may be needed to meet CAISO RA
obligations
Long-Term Resource Adequacy Risks Associated with Solar & Storage Replacement
Glenarm Marginal ELCC
Marginal ELCC of Storage (300 MW)
Marginal ELCC of Solar (400 MW)
Illustrative portfolio of solar
and storage
50 Pasadena’s Carbon Metrics
Metric 1: Share of PWP’s annual retail sales that is carbon free Forecasted Annual Carbon − Free Generation
Forecasted Annual Load
Metric 2: Share of PWP’s total annual generation1 that is carbon free Forecasted Total Carbon − Free Generation
Forecasted Total of All Generation
Metric 3: Share of PWP’s hourly energy needs1 that is carbon free Forecasted Carbon − Free Generation Capped at Hourly Load Forecast Forecasted Annual Load

1. Includes retail sales, T&D losses, and storage losses

2. https://pwp.cityofpasadena.net/clean-energy-tracker/ 51 Cumulative Capacity Additions (MW) 2025-2031

52 CAISO System Resource Adequacy (MW) in 2031

53 1. If Glenarm remains in service, the need for additional resources to meet growing loads is relatively modest and is largely consistent with resources already procured by PWP and naturally occurring customer adoption of solar.

2. Replacing Glenarm with a combination of internal renewables, storage, and demand-side resources requires new internal generation resources at a significant scale that approaches technical potential. • Sizing of replacement portfolio largely driven by need to maintain reliability during (a) transmission contingency conditions even when (b) solar output is limited (short-term cloudy days)
• Demand-side resources, emerging longer-duration storage technologies and incremental transmission upgrades can reduce scale of resource needs. None offer a “silver bullet” solution to the challenge of replacing firm generation. • A larger transmission upgrade, potentially constructed in the 2030s, could reduce the challenge of replacing Glenarm. • Consistent with broader literature and experience in the industry showing that meeting reliability needs without “firm” resources (short- term resources that can be dispatched on demand for as long as needed) is prohibitively challenging.

4. Full hydrogen conversion of Glenarm requires significant infrastructure developments that are beyond PWP’s control and are unlikely to built before 2030.
• RNG may be an effective short-term substitute for natural gas while longer-term options for replacement or conversion remain uncertain.

Insights & Initial Learnings from Glenarm Replacement and Conversion Study.
54  Installation costs for solar and storage systems naturally adopted by customers in response to current rate design are not included in Total System Cost metric
 For additional local resources beyond this level of naturally occurring adoption, costs for solar and storage are included in Total System Cost  There are multiple mechanisms that PWP could utilize to procure or develop additional local resources
• Bill credits (as with current Net Energy Metering)
• Direct incentives to customers
• Feed-in tariffs
• Power purchase agreements with third party developers • Utility ownership
 All mechanisms require some financial incentive – in some cases explicit, in others implicit – to the counterparty making the investment that impacts costs borne by other utility customers
 Including costs for solar and storage at a level that would allow the owner to recover the costs of the investment over its lifetime represents a lower boundof the potential size of the the financial incentive and corresponding impact to PWP customers
Treatment of Local Solar & Storage Costs in Total System Cost

Technical Advisory Panel #10
March 27, 2025
Development of an Optimized
Strategic Plan for Pasadena Water
and Power
Nick Schlag, Partner
Mike Sontag, Director
Nathan Lee, Sr. Managing Consultant
Michaela Levine, Sr. Managing Consultant
2  Increasing Metric 3
 Additional case studies
 Distribution study methodology
 Next steps
Agenda
3 The Optimized Strategic Plan is…
…a roadmap that lays out the key steps and future decision points that will best position PWP to achieve its goal to source all electricity from carbon-free sources by the end of 2030 while maintaining reliability and limiting cost impacts to customers
The Optimized Strategic Plan will…
…consider how new generation resources, investments in transmission and distribution infrastructure, and customer programs can facilitate transition to Pasadena’s carbon-free goal
Defining “Optimized Strategic Plan”
4 Balance of Carbon-Free Energy Resources based on Currently Executed Contracts
Hourly Carbon-Free Electricity vs. Hourly Load
0% 25% 50% 75% 100% 125% 150% 175% 200%
2031 Carbon-Free Electricity Supply (Executed Contracts Only) Additions: Coso Geothermal (10 MW), Geysers Geothermal (25 MW), Bonanza Solar/BESS (105 MW/55MW), Glenarm BESS (25 MW), Calwind (20 MW)
2025 Carbon-Free Electricity Supply
Jan 1 Day of Year Dec 31
12am
12pm
12am
12am
12pm
12am
Jan 1 Day of Year Dec 31
Annual average (“Metric 3”): 25%
Annual average (“Metric 3”): 81%
5 Local External
Solar
Wind
Geothermal
Battery Storage (4-hr)
Demand Response
Managed EV Charging
Mid Duration Storage (10-hr)
Long Duration Storage (100-hr)
H2-Fired Gas Turbine
H2 Fuel Cell
Resource Options Available to Close the Gap on Metric 3 Multiple options for carbon-free generation and storage resources exist to increase Metric 3 and each have their own considerations or limitations.
6 80%
82%
84%
86%
88%
90%
92%
94%
96%
98%
100%
0 50 100 150 200 250 300 350 400
Metric #3
Incremental Capacity from the Base Portfolio (MW)
Closing the Gap: Internal Renewable Resource Options
+50 MW of solar increases Metric 3 1%
Incremental
Solar Only
+50 MW solar
+50 MW storage
increases
Metric 3 5%
Solar +
Storage (1:1)
Solar +
Storage (2:1)
Metric 3 reaches 95% with
+180 MW solar, +180 MW storage
Metric 3 reaches 95% with
+280 MW solar, +140 MW
storage
7 80%
82%
84%
86%
88%
90%
92%
94%
96%
98%
100%
0 50 100 150 200 250
Metric #3
Incremental Capacity from the Base Portfolio(MW)
Closing the Gap: External Renewable Resource Options
Wind
Geothermal
+50 MW Geothermal
increases Metric 3 16%
+50 MW Wind increases
Metric 3 10%
Reaching 95% on Metric 3
would require 45 MW
Geothermal
Reaching 95% on Metric 3
would require 125 MW Wind
External Solar +
Storage (1:1)
Reaching 95% on Metric 3
would require 140 MW External
Storage, 140 MW External
Wind
+50 MW Solar ,
+ 50 MW Storage
increases Metric 3 6%
8 1. Additions of both local and external carbon-free resources to PWP’s portfolio can increase Metric 3.

2. No single resource type is required for PWP to increase Metric 3 toward 100%, but PWP cannot approach 100% on Metric 3 by adding variable generation (solar and wind) alone.

3. Adding increasing amounts of one resource type to PWP’s portfolio has a diminishing impact on increasing in Metric 3.
• Clean firm resources such as geothermal can increase Metric 3 significantly with relatively smaller incremental capacity additions but also exhibit diminishing increases with further capacity additions.

4. A diverse portfolio of resources can increase Metric 3 with lower total capacity additions.

Analysis Summary
Additional Case Studies
10 Additional variations explored to provide
PWP and City Council with
robust analyses to inform the Optimized
Strategic Plan:
 “Accelerated Distributed Energy
Resources (DER)”: What are the
comparative impacts of portfolios that
accelerate the deployment of DER
while maintaining Glenarm Power Plant
as a backup for reliability?
 Timing: How does each strategy change
if transition to carbon-free occurs less
rapidly?
• Opportunity to synchronize transition with
transmission expansion
• More plausible timelines for technology
readiness for emerging technologies
 Markets: How does short-term market
transaction flexibility impact these
case studies, if PWP's owned and
contracted generation is carbon-free?
Three Core Case Studies to Achieve Resolution 9977 Goals Common methods & assumptions across all three case studies:
• Natural gas combustion at Glenarm ceases by end of 2030 (either converted to H2 or replaced) • No reliance on wholesale market purchases (“24x7 carbon free electricity”) • Quantities of each resource optimized in each case study to meet reliability needs and carbon -free objectives Case Studies New Resource Options to Meet 2030 Goals
Mature
Technologies Only
Solar PV
(Distributed
& Utility)
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Mature
Technologies +
Green Hydrogen
Solar PV
(Distributed
& Utility)
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Green
Hydrogen
(H2) Conversion
at Glenarm
Mature
Technologies +
Long-Duration
Storage
Solar PV
(Distributed
& Utility)
Land-
Based
Wind
Geother-
mal
Load
Flexibility
Battery
Storage
Long-Duration
Energy Storage
11 Mathematical optimization problem:

Minimize NPV Investment Costs + Operating Costs
Subject to constraints:
 Minimum requirements for local resources
 Resource adequacy requirements
 Carbon-free energy targets
 Plant-specific operational limitations
• Thermal: Maximum power, ramp rate
• Storage: Maximum power, state of charge
• Renewables: hourly availability
Using Long Term Capacity Expansion to Develop Portfolios 2025 2031 2050
Investment decisions:

New investments selected in each year across 25-year horizon Jan Feb Mar Apr May Ju n Ju l Aug Sep Oct Nov Dec
Operational decisions:

Hourly operations simulated across 48 representative days for each year arbon free
generation
ourly
demand
12 Topology for Capacity Expansion Model
280 MW limit
(expanding to 336
MW by 2035)
TM Goodrich
Receiving Station
External
CAISO
System
Owned & Contracted Resources
Palo Verde Nuclear 10 MW
Intermountain Power Plant1 108 MW
Magnolia Power Plant 14 MW
Chiquita Canyon Landfill Gas1 7 MW
Puente Hills Landfill Gas1 13 MW
Hoover Hydro 15 MW
Azusa Hydro 15 MW
Calwind2 20 MW
Coso Geothermal2 10 MW
Geysers Geothermal2 25 MW
Antelope Big Sky Ranch Solar 7 MW
Big Sky Summer Solar 7 MW
Columbia 2 Solar 3 MW
Kingbird Solar 20 MW
Bo nanza Solar2 105 MW
Grace Solar2 50 MW
Bo nanza BESS2 55 MW
Candidate Resource Options
Utility-Scale Solar PV
Wind (California)
Wind (Out of State)
Geothermal
Ba ttery Storage (4hr)
Mid Duration Storage (10hr)3
Long Duration Storage (100hr)3
Wholesale Energy Market
CAISO Wholesale Market (SP15)
Utility Load
2030 Peak Demand 5 361 MW
Owned & Contracted Resources
Glenarm Power Plant 198 MW
Winds or Res ervoir Solar 0.7 MW
Glenarm BESS 2 25 MW
Existing Customer -Owned Resources
Cu stom er-Owned Solar 28 MW
Cu stom er Owned Storag e 1 MW
Cand idate Res ource Opti on s
Di stri but ed Solar PV
Battery Storage (4hr)
Mid Duration Storage (10hr) 3
Long Duration Storage (100hr 3
Demand Response
Load Flexibility
Green Hydrogen 3
Local
PWP
System
Notes:

1. Resources with established plans for retirement or contract expirations prior to end of 2030

2. Resources not yet online today

3. Candidate resources included in a select set of portfolios

4. Based on 2023 IRP forecast.

5. Based on 2023 IRP forecast with blended EV charging developed by E3. 13 Case Studies Previously Presented
Technolog y
Set
Portfolio Name Glenarm Total Mini mum Local Resource Portfolio (2031) Market Purchases (2031) Mature Technologies Only
100% Annual Matching Retained 66 MW local solar
26 MW 4-hr storage
No net market purchases
100% Annual Matching
(Accelerated Local Resources)
Retained 81 MW local solar
101 MW 4-hr storage
No net market purchases
100% Annual Matching
(Glenarm Replacement)
Replaced 567 MW local solar
319 MW 4-hr storage
No net market purchases
100% Hourly Matching
(Glenarm Replacement)
Replaced 567 MW local solar
319 MW 4-hr storage
No market purchases
+ Hydrogen
Conversion
100% Annual Matching
(with Hydrogen)
H2 Conversion 66 MW local solar
26 MW 4-hr storage
No net market purchases
100% Hourly Matching
(with Hydrogen)
H2 Conversion 66 MW local solar
26 MW 4-hr storage
No market purchases
+ Long
Duration
Energy
Storage
100% Annual Matching
(with LDES)
Replaced 424 MW local solar , 26 MW 4-hr storage, 101 MW MDES, 111 MW LDES
No net market purchases
100% Hourly Matching
(with LDES)
Replaced 424 MW local solar , 26 MW 4-hr storage, 101 MW MDES, 111 MW LDES
No market purchases
14 New Case Studies, Case-Specific Modeling Assumptions Technolog y
Set
Portfolio Name Glenarm Total Mini mum Local Resource Portfolio (2031)
Market Purchases (2031)
Mature Technologies Only
100% Annual Matching Retained 66 MW local solar
26 MW 4-hr storage
No net market purchases
100% Annual Matching
(Accelerated Local Resources)
Retained 81 MW local solar
101 MW 4-hr storage
No net market purchases
100% Annual Matching
(Accelerated Local Resources Plus)
Retained 130 MW local solar
125 MW 4-hr storage
No net market purchases
100% Hourly Matching
(Accelerated Local Resources)
Retained 81 MW local solar
101 MW 4-hr storage
No market purchases
100% Hourly Matching
(Accelerated Local Resources Plus)
Retained 130 MW local solar
125 MW 4-hr storage
No market purchases
100% Annual Matching
(Glenarm Replacement)
Replaced 567 MW local solar
319 MW 4-hr storage
No net market purchases
100% Hourly Matching
(Glenarm Replacement)
Replaced 567 MW local solar
319 MW 4-hr storage
No market purchases
15 Portfolio and Metric Comparison
Current 2031
Resource
Portfolio*
Metric 1 86%
Metric 2 78%
Metric 3 83%
Portfolios Designed to Meet 100% of Hourly
Needs
(Glenarm Replaced/Converted)
167% 183% 127%
100% 100% 100%
100% 100% 100%
Portfolios Designed to Meet
100% of Hourly Needs
(Glenarm as Backup)
170% 171%
100% 100%
100% 100%
Portfolios Designed to Meet
100% of Annual Needs
(Glenarm Reduced Operations)
108% 108%
96% 96%
94% 95%
Local
Local Local
Local Local
Local Local
External
External
External External
External
Local
Local
Local Local
Local Local
External
External
External
ExternalLocal
Local
-100
100
300
500
700
900
1100
1300
1500
1700
1900
Existing Portfolio Solar + Storage
Glenarm
Replacement
LDES Glenarm
Replacement
Glenarm Hydrogen
Conversion
Accelerated Local
Resources (Glenarm
Backup)
Accelerated Local
Resources Plus
(Glenarm Backup)
Annual Matching
Accelerated Local
Resources
Annual Matching
Accelerated Local
Resources Plus
Installed Capacity (MW)
Managed EV
DR
Storage (100hr)
Storage (10hr)
Storage (4hr)
Solar
Wind
Geothermal
Hydrogen
Natural Gas
Natural Gas
Retirement
Note: Metric calculations are based on a single deterministic year of normal weather conditions and do not capture events where transmission or distribution contingencies would require Glenarm to operate to ensure local reliability. Actual outcomes may vary due to impacts of natura l year-to-year weather variability, transmission and distribution contingencies, and dispatch instructions provided by CAISO. *Metrics currently reported on the PWP clean energy tracker for Owned/Contracted resources 16 Local
Local Local
Local Local
Local Local
External
External
External External
External
Local
Local
Local Local
Local Local
External
External
External
ExternalLocal
Local
-100
100
300
500
700
900
1100
1300
1500
1700
1900
Existing Portfolio Solar + Storage
Glenarm
Replacement
LDES Glenarm
Replacement
Glenarm Hydrogen
Conversion
Accelerated Local
Resources (Glenarm
Backup)
Accelerated Local
Resources Plus
(Glenarm Backup)
Annual Matching
Accelerated Local
Resources
Annual Matching
Accelerated Local
Resources Plus
Installed Capacity (MW)
Managed EV
DR
Storage (100hr)
Storage (10hr)
Storage (4hr)
Solar
Wind
Geothermal
Hydrogen
Natural Gas
Natural Gas
Retirement
Portfolio and Metric Comparison
Current 2031
Resource
Portfolio*
Metric 1 86%
Metric 2 78%
Metric 3 83%
Portfolios Designed to Meet 100% of Hourly
Needs
(Glenarm Replaced/Converted)
167%
100%
100%
Note: Metric calculations are based on a single deterministic year of normal weather conditions and do not capture events where transmission or distribution contingencies would require Glenarm to operate to ensure local reliability. Actual outcomes may vary due to impacts of natura l year-to-year weather variability, transmission and distribution contingencies, and dispatch instructions provided by CAISO.

Resources
ad ded
Resources
removed
Without Glenarm, large
quantities of new local solar
and storage resources needed
to ensure local reliability during
transmission contingency
conditions
*Metrics currently reported on the PWP clean energy tracker for Owned/Contracted resources 17 Local
Local Local
Local Local
Local Local
External
External
External External
External
Local
Local
Local Local
Local Local
External
External
External
ExternalLocal
Local
-100
100
300
500
700
900
1100
1300
1500
1700
1900
Existing Portfolio Solar + Storage
Glenarm
Replacement
LDES Glenarm
Replacement
Glenarm Hydrogen
Conversion
Accelerated Local
Resources (Glenarm
Backup)
Accelerated Local
Resources Plus
(Glenarm Backup)
Annual Matching
Accelerated Local
Resources
Annual Matching
Accelerated Local
Resources Plus
Installed Capacity (MW)
Managed EV
DR
Storage (100hr)
Storage (10hr)
Storage (4hr)
Solar
Wind
Geothermal
Hydrogen
Natural Gas
Natural Gas
Retirement
Portfolio and Metric Comparison
Current 2031
Resource
Portfolio*
Metric 1 86%
Metric 2 78%
Metric 3 83%
Portfolios Designed to Meet 100% of Hourly
Needs
(Glenarm Replaced/Converted)
167% 183%
100% 100%
100% 100%
Portfolios Designed to Meet
100% of Hourly Needs
(Glenarm as Backup)
170% 171%
100% 100%
100% 100%
Portfolios Designed to Meet
100% of Annual Needs
(Glenarm Reduced Operations)
108% 108%
96% 96%
94% 95%
Note: Metric calculations are based on a single deterministic year of normal weather conditions and do not capture events where transmission or distribution contingencies would require Glenarm to operate to ensure local reliability. Actual outcomes may vary due to impacts of natura l year-to-year weather variability, transmission and distribution contingencies, and dispatch instructions provided by CAISO.

Resources
ad ded
Resources
removed
Availability of emerging long-
duration storage technologies
would reduce need for local
resources, but scale of new
additions required within PWP
service area remains large
*Metrics currently reported on the PWP clean energy tracker for Owned/Contracted resources 18 Local
Local Local
Local Local
Local Local
External
External
External External
External
Local
Local
Local Local
Local Local
External
External
External
ExternalLocal
Local
-100
100
300
500
700
900
1100
1300
1500
1700
1900
Existing Portfolio Solar + Storage
Glenarm
Replacement
LDES Glenarm
Replacement
Glenarm Hydrogen
Conversion
Accelerated Local
Resources (Glenarm
Backup)
Accelerated Local
Resources Plus
(Glenarm Backup)
Annual Matching
Accelerated Local
Resources
Annual Matching
Accelerated Local
Resources Plus
Installed Capacity (MW)
Managed EV
DR
Storage (100hr)
Storage (10hr)
Storage (4hr)
Solar
Wind
Geothermal
Hydrogen
Natural Gas
Natural Gas
Retirement
Portfolio and Metric Comparison
Current 2031
Resource
Portfolio*
Metric 1 86%
Metric 2 78%
Metric 3 83%
Portfolios Designed to Meet 100% of Hourly
Needs
(Glenarm Replaced/Converted)
167% 183% 127%
100% 100% 100%
100% 100% 100%
Portfolios Designed to Meet
100% of Hourly Needs
(Glenarm as Backup)
170% 171%
100% 100%
100% 100%
Portfolios Designed to Meet
100% of Annual Needs
(Glenarm Reduced Operations)
108% 108%
96% 96%
94% 95%
Note: Metric calculations are based on a single deterministic year of normal weather conditions and do not capture events where transmission or distribution contingencies would require Glenarm to operate to ensure local reliability. Actual outcomes may vary due to impacts of natura l year-to-year weather variability, transmission and distribution contingencies, and dispatch instructions provided by CAISO.

Resources
ad ded
Resources
removed
Conversion of Glenarm to
green hydrogen results in
limited need for additional
resources, serving as a “clean
firm” resource that can be
dispatched when needed to fill
gaps in portfolio and maintain
local reliability
*Metrics currently reported on the PWP clean energy tracker for Owned/Contracted resources 19 Local
Local Local
Local Local
Local Local
External
External
External External
External
Local
Local
Local Local
Local Local
External
External
External
ExternalLocal
Local
-100
100
300
500
700
900
1100
1300
1500
1700
1900
Existing Portfolio Solar + Storage
Glenarm
Replacement
LDES Glenarm
Replacement
Glenarm Hydrogen
Conversion
Accelerated Local
Resources (Glenarm
Backup)
Accelerated Local
Resources Plus
(Glenarm Backup)
Annual Matching
Accelerated Local
Resources
Annual Matching
Accelerated Local
Resources Plus
Installed Capacity (MW)
Managed EV
DR
Storage (100hr)
Storage (10hr)
Storage (4hr)
Solar
Wind
Geothermal
Hydrogen
Natural Gas
Natural Gas
Retirement
Portfolio and Metric Comparison
Current 2031
Resource
Portfolio*
Metric 1 86%
Metric 2 78%
Metric 3 83%
Portfolios Designed to Meet 100% of Hourly
Needs
(Glenarm Replaced/Converted)
167% 183% 127%
100% 100% 100%
100% 100% 100%
Portfolios Designed to Meet
100% of Hourly Needs
(Glenarm as Backup)
170% 171%
100% 100%
100% 100%
Portfolios Designed to Meet
100% of Annual Needs
(Glenarm Reduced Operations)
108% 108%
96% 96%
94% 95%
Note: Metric calculations are based on a single deterministic year of normal weather conditions and do not capture events where transmission or distribution contingencies would require Glenarm to operate to ensure local reliability. Actual outcomes may vary due to impacts of natura l year-to-year weather variability, transmission and distribution contingencies, and dispatch instructions provided by CAISO.

Resources
added
Resources
removed
With Glenarm preserved
as a backup resource,
multiple diverse portfolios
of local and external
resources are capable of
supplying close to 100%
of energy needs on an
hourly basis
*Metrics currently reported on the PWP clean energy tracker for Owned/Contracted resources 20 Local
Local Local
Local Local
Local Local
External
External
External External
External
Local
Local
Local Local
Local Local
External
External
External
ExternalLocal
Local
-100
100
300
500
700
900
1100
1300
1500
1700
1900
Existing Portfolio Solar + Storage
Glenarm
Replacement
LDES Glenarm
Replacement
Glenarm Hydrogen
Conversion
Accelerated Local
Resources (Glenarm
Backup)
Accelerated Local
Resources Plus
(Glenarm Backup)
Annual Matching
Accelerated Local
Resources
Annual Matching
Accelerated Local
Resources Plus
Installed Capacity (MW)
Managed EV
DR
Storage (100hr)
Storage (10hr)
Storage (4hr)
Solar
Wind
Geothermal
Hydrogen
Natural Gas
Natural Gas
Retirement
Portfolio and Metric Comparison
Current 2031
Resource
Portfolio*
Metric 1 86%
Metric 2 78%
Metric 3 83%
Portfolios Designed to Meet 100% of Hourly
Needs
(Glenarm Replaced/Converted)
167% 183% 127%
100% 100% 100%
100% 100% 100%
Portfolios Designed to Meet
100% of Hourly Needs
(Glenarm as Backup)
170% 171%
100% 100%
100% 100%
Portfolios Designed to Meet
100% of Annual Needs
(Glenarm Reduced Operations)
108% 108%
96% 96%
94% 95%
Note: Metric calculations are based on a single deterministic year of normal weather conditions and do not capture events where transmission or distribution contingencies would require Glenarm to operate to ensure local reliability. Actual outcomes may vary due to impacts of natura l year-to-year weather variability, transmission and distribution contingencies, and dispatch instructions provided by CAISO.

Resources
added
Resources
removed
*Metrics currently reported on the PWP clean energy tracker for Owned/Contracted resources 21 2031 Balance of Carbon -Free Energy Resources
Hourly Carbon-Free Electricity vs. Hourly Load
0% 25% 50% 75% 100% 125% 150% 175% 200%
Glenarm Replacement Solar + Storage Only
12am
12pm
12am
Jan 1 Day of Year Dec 31
Annual average (“Metric 3”): 99.95%
12am
12pm
12am
Jan 1 Day of Year Dec 31
Annual average (“Metric 3”): 99.92%
Accelerated Local Resources + Glenarm Backup
22 +$81
+$96
+$22
+$44 +$44
+$4 +$11
+$142
+$154
+$53
+$85 +$87
+$13
+$23
$-
$20
$40
$60
$80
$100
$120
$140
$160
$180
Solar + Storage Glenarm
Replacement
LDES Glenarm
Replacement
Glenarm Hydrogen
Conversion
Accelerated Local
Resources (Glenarm
Backup)
Accelerated Local
Resources Plus (Glenarm
Backup)
Annual Matching
Accelerated Local
Resources
Annual Matching
Accelerated Local
Resources Plus
Portfolio Cost Comparison
2031 Relative Incremental Total System Cost
($ million per year)
Metric 1
Metric 2
Metric 3
Portfolios Designed to Meet 100% of Hourly Needs
(Glenarm Replaced/Converted)
167% 183% 127%
100% 100% 100%
100% 100% 100%
Portfolios Designed to Meet 100% of
Hourly Needs
(Glenarm as Backup)
170% 171%
100% 100%
100% 100%
Portfolios Designed to Meet 100% of
Annual Needs
(Glenarm Reduced Operations)
108% 108%
96% 96%
94% 95%
Note: PWP’ s current power fund
operating expenses are ~$250
million/year
Note: Incremental cost measured relative to a “least-cost” portfolio designed to meet 100% of Pasadena’s annual energy needs with carbon-free resources 23 Summary of Portfolio Analyses
Portfolios Designed to Meet 100% of Hourly Needs
(Glenarm Replaced/Converted)
Portfolios Designed to Meet 100% of
Hourly Needs
(Glenarm as Backup)
Portfolios Designed to Meet 100% of
Annual Needs
(Glenarm Reduced Operations)
Solar-Storage
Replacement
LDES
Replacement
Hydrogen
Conversion
Accel Local
Resources
Accel Local
Resources Plus
Accel Local
Resources
Accel Local
Resources Plus
New Resource Needs by 2031
New Renewables (MW) 563 531 129 326 380 87 114
New Storage (MW) 339 215 - 173 214 76 100
New DR & Load Flex (MW) 35 37 35 35 36 35 35
Clean Energy Metrics by 2031
Metric 1 (%) 167% 183% 127% 170% 171% 108% 108%
Metric 2 (%) 100% 100% 100% 100% 100% 96% 96%
Metric 3 (%) 100% 100% 100% 100% 100% 94% 95%
Relative Costs in 2031
Incremental Cost ($M/yr) +$81-142 +$96-154 +$22-53 +$44-85 +$44-87 +$4-13 +$11-23 Other Considerations
Siting & Land Availability
Technology Readiness
Upstream Infrastructure Need
Wholesale Market Exposure
Resource Adequacy Risk
Local Resilience
Note: Metric calculations are based on a single deterministic year of normal weather conditions and do not capture events where transmission or distribution contingencies would require Glenarm to operate to ensure local reliability. Actual outcomes may vary due to impacts of natura l year-to-year weather variability, transmission and distribution contingencies, and dispatch instructions provided by CAISO.

Higher risk Lower risk
24 Opportunities:

1. A wide range of new resource options can contribute to meeting Pasadena’s clean energy goals under Resolution 9977 • Local solar and storage, demand-side flexibility, external renewables and storage, and additional emerging technologies may all play a role • Analysis does not indicate a specific “need threshold” for any single type of resource to meet Pasadena’s goals • There are multiple viable portfolios of resources that can meet any prescribed clean energy target, and the least-cost option among them will depend upon a range of market factors that are uncertain today

2. By relieving the local reliability constraint, maintaining Glenarm as a backup resource unlocks additional opportunities to diversify Pasadena’ s supply of carbon-free energy with external renewables and storage resources, reducing costs to integrate additional resources • Wind, geothermal, and utility-scale solar & storage are all selected as least-cost resources in multiple cases with Glenarm in the portfolio • Retaining Glenarm in the near-term creates an opportunity to leverage emerging technologies that may not be commercially viable by 2031.

Challenges:

1. The OSP case studies illustrate three contrasting visions for how Pasadena could fully decarbonize its power supply on an hourly basis, but each one faces at least one significant technical barrier to implementation • Land constraints and local siting challenges pose challenges in portfolios that replace Glenarm • Lack of technology readiness is likely to limit the feasibility of portfolios including long-duration storage or hydrogen by 2031

2. How the city chooses to meet local reliability needs within the city even under stress conditions has significant ramifications on the portfolio composition and cost
• Portfolios that seek to replace Glenarm with carbon-free resources result in large needs for new local resource capacity, where options for diversity are limited • All portfolios that maintain Glenarm in some form as a firm resource in the load pocket exhibit lower costs than portfolios in which it is retired Analysis Findings: Opportunities and Challenges
Distribution study
methodology
26 Distribution Study Process Overview
Define system-wide
scenarios of DER
adoption
Geospatially downscale
DER forecasts to
distribution feeders
Quantify bounds of
potential distribution
system costs
27 Distribution System Study Scenarios
Net Energy Metering
Managed light-duty EV charging
Demand Response
Solar (66 MW)
NEM forecast for customer solar
Storage (26 MW)
NEM forecast for customer storage (1 MW) +
Utility-scale storage (25 MW)
Accelerated Local Resources
Managed light-duty EV charging
Demand Response
Solar (130 MW)
Accelerated adoption of customer solar
Storage (127 MW)
Accelerated adoption of customer storage (51
MW) + Utility-scale storage (75 MW)
Glenarm Replacement
Managed light-duty EV charging
Demand Response
Solar (567 MW)
All technical solar potential (rooftop +
parking + ground mount)
Storage (319 MW)
Customer storage (119 MW) + Utility-scale
storage (200 MW)
28 Calculate propensity
(likelihood) to adopt
2 Businesses
Proximity to POI
Existing adoption
Propensity score
Income
Forecasting Anywhere Overview
Data, reports,
visualizations
6 Model Step
Calculate load
impacts
5 Hourly, annual, peak
load impact
Hourly load
shapes
Inputs Outputs
Determine technical
potential
1 Parcels
Businesses
# of chargers,
square footage, MW
capacity
Parking lots
Tells you where DERs could be installed
Tells you where DERs are likely to be
located
Determine local level
adoption
Allocate utility-level adoption
forecast
3 Units adopted
Power Supply
Forecast
OSP Glenarm
Study Results
OSP Customer
Solar Forecast
Geospatial
aggregation
Feeder area
4 Units adopted
Power Delivery
Diagram
29  Collect and incorporate feedback from MSC and TAP on portfolio development phase  Cost impacts phase
• Cost impacts study
– Transition to electric rate study
• Distribution system analysis
 Synthesis and plan development
Next Steps
Appendix
31 Power supply cost break down, 2031
$ Million/Y ear 100% Annual
Matching
Solar + Storage
Glenarm
Replacement
LDES Glenarm
Replacement
Glenarm
Hydrogen
Conversion
Accelerated
Local Resources
(Glenarm
Backup)
Accelerated
Local Resources
Plus (Glenarm
Backup)
Annual Matching
Accelerated
Local Resources
Annual Matching
Accelerated
Local Resources
Plus
Existing Utility-Owned
Generation Fixed O&M $12 - $12 $0 - $0 $ 0- $0 $0 - $0 $11 - $11 $11 - $11 $12 - $12 $12 - $12 Generic New Resources $18 - $23 $165 - $230 $174 - $237 $59 - $84 $103 - $150 $104 - $151 $30 - $43 $37 - $54 Generic New Solar $0 - $0 $103 - $129 $73 - $92 $0 - $0 $13 - $21 $25 - $37 $3 - $4 $13 - $16 Local $0 - $0 $103 - $129 $69 - $86 $0 - $0 $3 - $4 $13 - $16 $3 - $4 $13 - $16 External $0 - $0 $0 - $0 $3 - $6 $0 - $0 $9 - $17 $12 - $21 $0 - $0 $0 - $0 Generic Storage $0 - $0 $45 - $82 $62 - $98 $0 - $0 $23 - $42 $29 - $52 $10 - $18 $13 - $24 Local $0 - $0 $39 - $71 $61 - $97 $0 - $0 $10 - $18 $13 - $24 $10 - $18 $13 - $24 External $0 - $0 $6 - $11 $ - $1 $0 - $0 $13 - $23 $15 - $28 $0 - $0 $0 - $0 Generic New Other $18 - $22 $16 - $20 $40 - $48 $59 - $84 $68 - $87 $50 - $63 $16 - $21 $11 - $14 Operating Costs (Fuel, CO2, O&M) $10 - $10 $1 - $1 $1 - $1 $11 - $11 $1 - $1 $1 - $1 $8 - $8 $8 - $8 Market Purchases (Revenues) ($4) – ($4) ($32) – ($32) ($35) – ($35) ($12) – ($12) ($37) – ($37) ($36) – ($36) ($10) – ($10) ($9) – ($9) Transmi ssi on Access Ch arge
(TAC) $27 - $27 $10 - $10 $19 - $19 $27 - $27 $28 - $28 $27 - $27 $27 - $27 $25 - $25 Note: The table above shows only components of the power supply cost that differ between case studies. 32 Metric 3 Calculation
Net Carbon-Free GenerationH =
Carbon-Free GenerationH + Storage DischargeH - Storage ChargeH, Sum(Minimum[Net Carbon-Free GenerationH, LoadH] for H in 1…8760) Annual Load
Calculate the net carbon-free generation in each hour:

Cap net carbon-free generation at load in each hour, sum each hour of the year, divide by annual load:

Pasadena City CouncilMay 5, 2025

Pasadena City Council
May 5, 2025