Attachment 1 - Electrical Capacity Study — Foster City City Council, March 18, 2024

Foster City Electrical Capacity Study

January 11, 2024

PREPARED BY
FRONTIER ENERGY , INC.

AUTHORS
Alea German
Tom Vagts
Claudia Pingatore
Chris White

IN PARTNERSHIP WITH
ID360

PREPARED FOR
The City of Foster City

Table of Contents
Acknowledgements ....................................................................................................................................... 3 DeﬁniƟons & Acronyms ................................................................................................................................ 3 1 IntroducƟon .......................................................................................................................................... 7 2 Methodology ......................................................................................................................................... 8

2.1 ExisƟng Building Stock ................................................................................................................. 8

2.1.1 ExisƟng ResidenƟal Buildings .................................................................................................. 8

2.1.2 ExisƟng Non-ResidenƟal Buildings ........................................................................................ 10

2.2 New Development ..................................................................................................................... 10

2.2.1 Biomedical ............................................................................................................................. 11

2.3 Zero Emissions Vehicles (ZEVs) .................................................................................................. 12

2.4 Phasing of ElectriﬁcaƟon ........................................................................................................... 14

2.5 Capacity Gap Analysis ................................................................................................................ 15

2.6 Cost-EﬀecƟveness of ResidenƟal ElectriﬁcaƟon ........................................................................ 16

2.6.1 Cost-EﬀecƟveness Methodology ........................................................................................... 16

2.6.2 Incremental Costs .................................................................................................................. 17 3 Results ................................................................................................................................................. 19

3.1 ExisƟng Buildings ....................................................................................................................... 19

3.1.1 ResidenƟal ............................................................................................................................. 20

3.1.2 Non-ResidenƟal ..................................................................................................................... 23

3.2 New Development ..................................................................................................................... 24

3.3 Zero Emission Vehicles ............................................................................................................... 26

3.4 Capacity Gap Analysis ................................................................................................................ 28

3.5 Cost-EﬀecƟveness & Service Upgrades ...................................................................................... 32

3.5.1 Service Upgrades ................................................................................................................... 35 4 Conclusions & RecommendaƟons....................................................................................................... 36

4.1 Conclusions ................................................................................................................................ 36

4.2 RecommendaƟons & MiƟgaƟon Strategies ............................................................................... 37 5 References ........................................................................................................................................... 39

3 | P a g e

Acknowledgements
The authors wish to acknowledge the following organizaƟons for their assistance in the development of this report, including the City of Foster City Community Development Department, the City of Foster City Public Works Department, the Statewide Reach Codes Program (administered by Paciﬁc Gas and Electric Company, San Diego Gas & Electric Company, and Southern California Edison Company), the PG&E Energy Data Request Program, the NaƟonal Renewable Energy Lab, and the California Energy Commission.

DeﬁniƟons & Acronyms
2022 Scoping Plan – A plan developed by the California Air Resources Board that lays out a path to achieve targets for carbon neutrality and reduce anthropogenic greenhouse gas emissions by 85 percent below 1990 levels no later than 2045, as directed by Assembly Bill 1279.

ADU – Accessory Dwelling Unit – A secondary dwelling unit with complete independent living faciliƟes for one or more persons.

Assembly Bill 1236 – A bill requiring local jurisdicƟons to approve electric vehicle charging staƟons through a non-discreƟonary administraƟve permit process, unless the charging staƟon would have speciﬁc, adverse impact, supported by substanƟal evidence, on public health or safety.

CEC – California Energy Commission – California’s state energy oﬃce responsible for energy policy and planning. The agency was established by the California Legislature through the Warren-Alquist Act in 1974.

CARE – California Alternate Rates for Energy – A program that oﬀers a 30-35% electric bill discount and a 20% natural gas bill discount for income-qualiﬁed customers.

CASE – Codes and Standards Enhancement – The Codes and Standards Enhancement (CASE) iniƟaƟve presents recommendaƟons to support the California Energy Commission’s eﬀorts to update the California Energy Code (Title 24, Part 6) to include new requirements or to upgrade exisƟng requirements for various technologies. (California Energy Codes and Standards Program).

CBECC – Computer program developed by the California Energy Commission for use in demonstraƟng compliance with the California Energy Eﬃciency Standards Circuit Sharing Device – A device that allows two appliances, such as an electric clothes dryer and an electric vehicle charger, to share the same circuit. This can help avoid the need for costly and Ɵme- consuming circuit panel upgrades.

Coeﬃcient of Performance – the raƟo between the useful heaƟng or cooling of a heat pump and the power that is supplied to operate the system. Heat pumps typically operate at COPs greater than 2.

Cost-EﬀecƟveness – The overall value of a measure assessed by combining the cost of the measure and the measure’s energy savings.

4 | P a g e

DCFC – Direct Current Fast Chargers, or Level 3 Chargers – A type of electric vehicle charger available from certain public charging staƟons, oﬀering the fastest available charging rates.

DER – Distributed Energy Resources – Small scale energy generaƟon resources situated behind the customer’s meter such as rooŌop solar panels and baƩery storage systems.

DIDF – DistribuƟon Investment Deferral Framework – An ongoing annual process to idenƟfy, review, and select opportuniƟes for compeƟƟvely sourced distributed energy resources to defer or avoid uƟlity tradiƟonal distribuƟon capital investments.

DMV – California Division of Motor Vehicles
E-ELEC Electricity Tariﬀ – A PG&E residenƟal TOU electricity rate with peak pricing at 4-9 p.m. daily, aimed for homes with heat pump or baƩery systems.

Energy Use Intensity – The amount of energy used per square foot annually. It's calculated by dividing the total energy consumed by the building in a year by the total gross ﬂoor area.

EV – Electric Vehicles – A broad category that includes all vehicles that are fully powered by electricity, usually provided by baƩeries but may also be provided by photovoltaic (solar) cells or a fuel cell. In this report, EV is used interchangeably with ZEV.

EVI-Pro 2 – NREL's Electric Vehicle Infrastructure – ProjecƟon tool which esƟmates how much electric vehicle charging infrastructure is needed in a designated area to meet a given demand. Developed in collaboraƟon with the California Energy Commission, EVI-Pro draws on detailed data about personal vehicle travel paƩerns, EV aƩributes, and charging staƟon characterisƟcs to esƟmate the required quanƟty and type of charging infrastructure.

ExecuƟve Order B-55-18 – LegislaƟon establishing a goal for the State of California to achieve carbon neutrality no later than 2045.

G-1 Gas Tariﬀ – A PG&E residenƟal gas rate.

GHG – Greenhouse Gas
GWh – GigawaƩ-Hour – One million kilowaƩ-hours of electric power. California's electric uƟliƟes generated a total of about 302,072 gigawaƩ-hours in 2007.

HEEPI – Higher EducaƟon for Environmental Performance Improvement HVAC – HeaƟng, VenƟlaƟon, and Air CondiƟoning
ICA – IntegraƟon Capacity Analysis – A type of analysis that quanƟﬁes the maximum amount of capacity available to a connected distribuƟon system requiring minimal to no distribuƟon upgrades or operaƟonal restricƟons.

Internal Gains – The heat produced by people, lighƟng, and appliances inside a building. In winter, internal gains reduce the heat the HVAC system must supply; in summer, they add to the cooling load.

5 | P a g e

kWh – KilowaƩ Hour – The most commonly-used unit of measure telling the amount of electricity consumed over Ɵme. It means one kilowaƩ of electricity (approximately 3,412 Btus) supplied for one hour. In 1989, a typical California household consumes 534 kWh in an average month.

LCC – Life-Cycle Cost – All costs associated with the deﬁned life cycle of a product, including capital costs, installaƟon costs, operaƟng costs, maintenance costs, and disposal costs. This deﬁniƟon does not include external costs (i.e., those not borne directly by the enƟty that owns and operates a product/service, such as environmental costs to society at large).

LED – Light Emiƫng Diode
Level 2 Chargers – EV chargers that oﬀer higher-rate AC charging than Level 1 chargers. Charging occurs through 240V (in residenƟal applicaƟons) or 208V (in commercial applicaƟons) electrical service, and is common for home, workplace, and public charging.

Load ShiŌing/Management – Steps taken to reduce power demand at peak load Ɵmes or to shiŌ some of it to oﬀ-peak Ɵmes. This may be with reference to peak hours, peak days or peak seasons. The main thing aﬀecƟng electric peaks is air-condiƟoning usage, which is therefore a prime target for load management eﬀorts. Load management may be pursued by persuading consumers to modify behavior or by using equipment that regulates some electric consumpƟon.

LSC – Long-term System Cost – A metric used to determine the dollar value of energy eﬃciency measures in the Energy Code. LSC hourly factors help the state account for long-term beneﬁts associated with policies needed to meet the statewide climate acƟons goals. Today’s energy costs do not adequately account for these long-term values to California’s energy system. LSC hourly factors weigh the long-term value of each hour diﬀerently, where Ɵmes of peak demand are more valuable, and Ɵmes oﬀ-peak demand are less valuable. LSC hourly factors are not uƟlity rates or energy rate forecasts. LSC is not a predicted uƟlity bill. LSC is the proposed primary compliance metric for the 2025 Title 24 code cycle.

MF – MulƟfamily
MW – MegawaƩ – One-thousand kilowaƩs (1,000 kW) or one million (1,000,000) waƩs. One megawaƩ is enough electrical capacity to power 1,000 average California homes. (Assuming a loading factor of 0.5 and an average California home having a 2-kilowaƩ peak capacity.) NREL – NaƟonal Renewable Energy Laboratory
NPV – Net Present Value – A measure used to help decide whether or not to proceed with an investment. Net means that both the costs and beneﬁts of the investment are included. To calculate net present value (NPV), ﬁrst add together all the expected beneﬁts from the investment, now and in the future. Then add together all the expected costs. Then work out what these future beneﬁts and costs are worth now by adjusƟng future cashﬂow using an appropriate discount rate. Then subtract the costs from the beneﬁts. If the NPV is negaƟve, then the investment cannot be jusƟﬁed by the expected returns. If the NPV is posiƟve, it can, although it pays to make comparisons with the NPVs of alternaƟve investment opportuniƟes before going ahead.

6 | P a g e

On-Bill LCC – A customer-based lifecycle cost (LCC) approach that values energy based upon esƟmated site energy usage and customer uƟlity bill savings using today’s electricity and natural gas uƟlity tariﬀs.

PCE – Peninsula Clean Energy – A community choice aggregator PG&E – Paciﬁc Gas and Electric Company
Process Loads – The load on a building resulƟng from energy consumed in support of a manufacturing, industrial, or commercial process. Process loads do not include energy consumed maintaining comfort and ameniƟes for the occupants of the building (including space condiƟoning for human comfort).

RASS – ResidenƟal Appliance SaturaƟon Survey
Real Discount Rate – Discount or interest rates adjusted for inﬂaƟon.

S-Lab – Safe, Successful and Sustainable Laboratories
SF – Single Family
Statewide Reach Code Team – The California Statewide Investor-Owned UƟliƟes (IOUs) Codes and Standards Program and its key consultants and engaged ciƟes that provide technical support to local governments considering adopƟng a local ordinance (reach code) intended to support meeƟng local and/or statewide energy eﬃciency and greenhouse gas reducƟon goals.

Team – The FronƟer Energy staﬀ that contributed to this analysis.

Title 24 – Title 24, Part 6 – The California Energy Code; which contains energy conservaƟon standards applicable to most residenƟal and nonresidenƟal buildings throughout California.

Thermal Eﬃciency – The eﬃciency of a heat engine measured by the raƟo of the work done by it to the heat supplied to it.

TDV – Time Dependent ValuaƟon – TDV is the predecessor to LSC used in the 2005 through 2022 Title 24 code cycles as the primary compliance metric.

TOU – Time-Of-Use Rates – The pricing of electricity based on the esƟmated cost of electricity during a parƟcular Ɵme block. Time-of-use rates are usually divided into three or four Ɵme blocks per twenty-four hour period (on-peak, mid-peak, oﬀ-peak and someƟmes super oﬀ-peak) and by seasons of the year (summer and winter). Real-Ɵme pricing diﬀers from TOU rates in that it is based on actual (as opposed to forecasted) prices which may ﬂuctuate many Ɵmes a day and are weather-sensiƟve, rather than varying with a ﬁxed schedule.

TOU-C Electricity Tariﬀ – A PG&E residenƟal TOU electricity rate with peak pricing at 4-9 p.m. daily.

ZEV – Zero-Emission Vehicle – Vehicles which produce no emissions from the on-board source of power (e.g., an electric vehicle). In this report, EV is used interchangeably with ZEV.

7 | P a g e

1 IntroducƟon
As the City of Foster City conƟnues to develop and grow economically, its future focuses on innovaƟon, technology, and sustainable development. The City is commiƩed to being a leader in environmental sustainability and has adopted a local Climate AcƟon Plan. The City is considering the adopƟon of a local energy reach code that would prioriƟze cost-eﬀecƟve building electriﬁcaƟon to achieve broad decarbonizaƟon. However, full electriﬁcaƟon of buildings and transportaƟon comes with pracƟcal challenges. To support the City in understanding the feasibility of this transiƟon, FronƟer Energy analyzed three components: 1) esƟmated increase in electricity use from buildings and transportaƟon that must be supported by electric uƟlity infrastructure, 2) potenƟal capacity gaps in exisƟng infrastructure, and 3) the cost-eﬀecƟveness of residenƟal building electriﬁcaƟon.

8 | P a g e

2 Methodology
This secƟon describes data sources and analysis methodology to esƟmate the increased (or incremental) electricity use that would result from city-wide electriﬁcaƟon for each of the three components analyzed: exisƟng buildings, new development, and transportaƟon.

2.1 Existing Building Stock
To esƟmate the increased electricity due to city-wide electriﬁcaƟon of buildings, energy models were used alongside metered energy use data for gas and electricity throughout the City. Modeling provides details on energy use including hourly proﬁles by end use and city metered data provides a true total of energy use by building type for a parƟcular year. As such, modeling results of natural gas use were calibrated with meter data to piece together a likely scenario of changing electrical loads under a full electriﬁcaƟon scenario.

Building energy modeling involves simulaƟons of various building prototypes with standardized features, layout, and equipment, so that speciﬁc changes can be analyzed in terms of energy use. The soŌware uƟlized in this analysis – CBECC – is approved by the California Energy Commission and is used to inform updates to the state’s building energy codes. SimulaƟon results used in this analysis were based on the analysis of exisƟng buildings performed by the Statewide Reach Code Team.

Modeled data was used to compare energy use of mixed fuel and all-electric buildings for residenƟal (single family and mulƟfamily) and commercial buildings. This simulated energy use data is broken down by all hours of the year, allowing for annual, seasonal, and hourly views of the impacts.

Metered energy use was provided by PG&E for the major customer secƟons for 2019 and 2020. Gas energy use for the two years was within 2%. The 2020 data is presented in Table 1.

Table 1: Measured City Annual Energy Use in 2020
Customer
Sector
Measured Annual
Electricity (kWh)
Measured Annual
Natural Gas (Therms)
Gas Energy
Percent of Total
Residential 64,866,649 4,532,418 49.2%
Commercial 138,734,264 4,667,495 50.6%
Industrial 891,803 15,453 0.2%
Agricultural 215,694 5,662 0.1%
Total 204,708,410 9,221,029

2.1.1 ExisƟng ResidenƟal Buildings
The analysis of exisƟng single family and mulƟfamily buildings uƟlized modeling results developed by the Statewide Reach Code Team (Statewide Reach Code Team, 2021) (Statewide Reach Code Team, 2022b), speciﬁcally for Climate Zone 3 in which Foster City is located. The analysis included three categories of home vintages: pre-1978, 1978-1991, 1992-2010. Hourly energy use data for the four main gas end-uses among exisƟng residenƟal buildings – water heaƟng, space heaƟng, cooking, and clothes drying – was

9 | P a g e

averaged across the three home vintages. For the electriﬁcaƟon proﬁles, heat pumps were assumed for water heaƟng and space heaƟng and electric resistance for cooking and clothes drying.

ResulƟng hourly energy use proﬁles were mulƟplied by the number of impacted residenƟal units for an esƟmate of citywide electricity and gas use. The number of housing units was taken from 2020 Census data1 for Foster City as well as the City’s single family parcel data, revealing approximately 6,464 single family units and 7,094 mulƟfamily units for a total of 13,558 housing units. Of these units, the number that would be impacted by electriﬁcaƟon were esƟmated using data from the 2019 ResidenƟal Appliance SaturaƟon Survey (RASS) (DNV, 2022) showing the prevalence of homes in Climate Zone 3 with gas versus electric major appliances. This is shown for single family and mulƟfamily homes in Table 2 and Table 3, respecƟvely.

Table 2: Single Family Appliance Breakdown
Appliance Type
Gas
Appliance
Percentage
# of Homes
with Gas
Appliances
Electric
Appliance
Percentage
# of Homes
with Electric
Appliances
Homes
with no
appliance
Space Heating 85% 5,516 15% 948 -
Water Heating 96% 6,202 4% 262 -
Dryer 38% 2,425 48% 3,128 991
Stove Top 70% 4,493 30% 1,971 -

Table 3: MulƟfamily Appliance Breakdown
Appliance Type
Gas
Appliance
Percentage
# of Units
with Gas
Appliances
Electric
Appliance
Percentage
# of Units
with Electric
Appliances
Units
with no
appliance
Space Heating 56% 3,987 44% 3,107 -
Water Heating 92% 6,494 8% 600 -
Dryer 10% 724 24% 1,687 4,682
Stove Top 41% 2,894 59% 4,200 -

The total esƟmated modeled natural gas use was then compared to 2020 city meter data provided by PG&E for residenƟal buildings (see Table 1). Total gas energy use was then scaled to reasonably align the annual modeled data with annual metered energy use. For buildings with gas appliances, gas use load proﬁles were subsƟtuted with electric load proﬁles for each appliance. The same scaling used in the gas calibraƟon was then applied to determine the total electrical load of the residenƟal building stock if all units were electriﬁed. From this hourly data the increase in annual and seasonal electricity use was esƟmated.

1 Foster City, California - Census Bureau Proﬁle

10 | P a g e

2.1.2 ExisƟng Non-ResidenƟal Buildings
Analysis of the non-residenƟal sector’s exisƟng building stock focused on commercial buildings, as they make up the vast majority of the City’s non-residenƟal building gas use (see Table 1). Industrial and agricultural buildings were also evaluated, although their combined gas loads represent only 0.3% of total annual gas use citywide.

2.1.2.1 Commercial Buildings
The analysis of exisƟng commercial buildings uƟlized modeling results developed by the Statewide Reach Code Team (Statewide Reach Code Team, 2022a) for Climate Zone 3. Unlike exisƟng residenƟal buildings, there is not as much data qualifying the type and total square footage of non-residenƟal buildings within Foster City’s exisƟng building stock. Foster City parcel data was reviewed, and the large majority of commercial parcels were classiﬁed as “Town Center”, “Service Commercial” and “Civic Center.” In coordinaƟon with the City it was decided to apply two building categories from the reach code work in this analysis – “medium oﬃce” and “stand-alone retail” – to be representaƟve of the citywide gas consumpƟon for the commercial sector. Results from modeled simulaƟons were averaged across three building vintage groups (1980s, 1990s, 2000s), similar to the approach used in the residenƟal analysis.

Space heaƟng and water heaƟng modeled hourly proﬁles were developed for the two building types. For the electriﬁcaƟon proﬁles, heat pumps with electric resistance backup heaƟng were assumed for water heaƟng and space heaƟng.

This methodology generally followed that used for exisƟng residenƟal buildings. However, there were no building counts or total ﬂoor area of commercial buildings so the sum of the gas use for the two building types was compared to the total measured gas use from Table 1 and a scaling factor calculated. This same scaling factor was then mulƟplied by the sum of the associated electricity load proﬁles to esƟmate the total increase in electrical load of non-residenƟal buildings.

2.1.2.2 Industrial and Agricultural Buildings
For industrial and agricultural energy use, modeling results were not available to esƟmate incremental electricity demand. These load types tend to be made up of process loads. Process loads can be more challenging to electrify than space heaƟng or water heaƟng where heat pumps may not always be appropriate. As such, electric resistance technology was assumed for this small load and annual metered gas usage data was simply converted to an electrical demand, accounƟng for 80% thermal eﬃciency of gas combusƟon equipment and an electric constant coeﬃcient of performance of 1.

The use of process loads also assumes negligible variaƟon in hourly demand, therefore this analysis assumed even distribuƟon of electricity demand throughout both the year and the day.

2.2 New Development
Planned developments were documented in Appendix D of Foster City’s Water Capacity Study as part of the Foster City Housing and Safety Elements Updates EIR. These developments include both commercial and residenƟal buildings and are summarized in Table 4.

For occupancies other than biomedical, annual and peak loads were esƟmated based on new construcƟon modeled analysis conducted by the Statewide Reach Code Team for Climate Zone 3 [ (Statewide Reach Code Team, 2022), (Statewide Reach Code Team, 2023), (Statewide Reach Code Team,

11 | P a g e

2022b)]. Based on the project descripƟon, the closest building category from the reach code studies was selected as described in Table 4. All new buildings were assumed to be all-electric. Hourly energy data from the reach code studies for all-electric buildings was then applied on a square foot basis for commercial buildings and a per dwelling unit basis for residenƟal buildings.

Table 4: Proposed New Development
Development Project Name
Completion
Schedule
Number of
Dwelling
Units
Commercial
Floor Area
(ft2)
Closest Modeling
Category
1001 E. Hillsdale (Parkside Towers) 2025-2030 n/a 317,599 Biomed 1065 E. Hillsdale R&D Conversions (Century
Plaza) 2020-2025 n/a 87,000 Biomed
Gilead Integrated Corporate Campus 2030-2035 n/a 745,250 Biomed, Office Biomed Phase 2 2020-2025 n/a 235,000 Biomed, Office
388 Vintage Park 2023 n/a 95,931 Biomed, Office
New Hotel in Metro Center (VISA) 2025-2030 n/a 83,000 Hotel Schooner Bay II Redevelopment 2029 350 n/a Multifamily 15-Acres Project (Foster Square) 2020-2025 397 30,000 Multifamily, Retail Schooner Bay I Redevelopment 2028 408 n/a Hotel
Lantern Cove Apartments Redevelopment 2025-2030 420 n/a Multifamily 2023-2031 Residential Development to Achieve
RHNA 2025-2030 663 n/a Multifamily
Other/Additional Residential Development 2032-2045 1,184 n/a Multifamily, Office Pilgrim Triton Project Completion 2020-2025 805 70,057 Multifamily, Office 1010 Metro Center Blvd (OSH Redevelopment) 2025-2030 111 58,300 Multifamily, Retail Chess/Hatch Drive Offices Project 2025-2030 n/a 800,000 Office 1601 Beach Park Blvd/Sea Island 2020-2025 n/a 3,330 Office Charter Square Demo/Beach Park Elementary
School 2021 n/a 58,479 Office
1065 E. Hillsdale Retail Pavilion (Century Plaza) 2020-2025 n/a 5,200 Retail Other/Additional Non-Residential Growth 2030-2040 n/a 81,000 Biomed, Office, Retail
ADUs for Eaves and Single-Family Homes 2023-2031 56 n/a Single Family

2.2.1 Biomedical
Biomedical buildings were not evaluated as part of the reach code studies. EsƟmaƟng energy use for labs and research faciliƟes is challenging and there are limited exisƟng studies or other resources to inform esƟmates. A study based on audit data published by S-Lab (Safe, Successful and Sustainable Laboratories) iniƟaƟve of HEEPI (Higher EducaƟon for Environmental Performance Improvement) was used to idenƟfy a typical energy use intensity (EUI or energy use per square foot) for labs. The study provided electricity and gas EUIs. The gas EUI was converted to an electric EUI using an average

12 | P a g e

coeﬃcient of performance from the statewide reach code study for oﬃces. For the biomedical developments where a porƟon of the development was idenƟﬁed as oﬃce, the EUI from the statewide reach code study was applied. The HEEPI report did not provide any details on hourly proﬁles. In the absence of beƩer data, the hourly proﬁle for oﬃce from the statewide reach code study was used.

2.3 Zero Emissions Vehicles (ZEVs)
The added electrical load from full vehicle electriﬁcaƟon through 2045 (the assumed year of full electriﬁcaƟon, addressed in SecƟon 2.4 Phasing of ElectriﬁcaƟon) depends on the number of zero emissions vehicles (ZEVs) in Foster City and the amount of electrical energy consumed and replaced each day. Because vehicles are transitory by nature, the load will vary from day to day and place to place. For example, an EV driver might drive 20 miles one day, 180 the next day, and not at all on the third day.

That driver’s daily electricity use for charging will vary wildly. The methodology used to esƟmate this added load marries models developed by the California Energy Commission (CEC) and the NaƟonal Renewable Energy Laboratory (NREL) and uses the Team’s own forecasts based on a combinaƟon of factors that are described below.

First, a baseline was created for ZEV adopƟon. For this step, two data sources were used: 1) historical data from the California Division of Motor Vehicles (DMV)2 which shows the current populaƟon of light- duty vehicles3 registered in Foster City zip codes as of December 31, 2022; and 2) the current populaƟon (individuals and households) in the same zip codes using the U.S. Census Bureau’s American Community Survey. The following steps were applied to this data to project the transiƟon of combusƟon-engine vehicles to zero-emission vehicles by 2045:
 EsƟmate populaƟon growth (individuals and households) using historical growth from the U.S.

Census and Foster City’s projected growth from Foster City’s Housing Element4 report in the General Plan.
 EsƟmate the total number of light-duty vehicles based on historical DMV registraƟon data, new car sales data from the California New Car Dealers AssociaƟon, and projecƟons from the Federal Reserve Bank.
 EsƟmate the percentage of new cars that will be ZEVs each year in alignment with California Air Resources Board’s Advanced Clean Cars II regulaƟon.5 This regulaƟon requires that all new cars are ZEVs in 2035.

FronƟer Energy created three growth curves: slow, medium, and fast. The medium growth curve assumes that all regulaƟons stay as they are now, and that automakers meet their self-imposed

2 hƩps://data.ca.gov/dataset/vehicle-fuel-type-count-by-zip-code/resource/9aa5b4c5-252c-4d68-b1be-ﬀe19a2f1d26 3 Although DMV data shows about 45 heavy-duty trucks registered in Foster City zip codes, FronƟer’s ﬂeet databases indicate that those registraƟons are business oﬃces and that the trucks park at other locaƟons. Outside of the municipal and Caltrans ﬂeet, the Team’s ﬂeet database found fewer than 10 trucks at Foster City addresses and several of those are registered by MoƟv Power Systems, a manufacturer of EV trucks. Because the number of heavy-duty vehicles is so low, this study did not evaluate their energy use.
4 hƩps://engagefostercity.org/housing-element
5 hƩps://ww2.arb.ca.gov/news/california-moves-accelerate-100-new-zero-emission-vehicle-sales-2035

13 | P a g e

deadlines for EV producƟon and cost decreases. Slow growth assumes that one or more of those factors change and fast growth assumes that low-cost EVs are available sooner than currently projected.

All three scenarios assume that all ZEVs will be 100% baƩery electric (no fuel cell electric or plug-in hybrids) 6 and that all ZEVs purchased in early years will remain in Foster City. In other words, when an owner trades in a 2022 ZEV that car will be registered to a diﬀerent Foster City resident. By 2035, the forecast assumes that older model EVs will be replaced with newer model EVs.

Next, daily electricity consumpƟon was esƟmated for the medium-growth scenario. Based on U.S.

Census’ Origin-DesƟnaƟon Data for personally owned vehicles in Foster City zip codes, ZEVs were assumed to drive an average of 27 miles a day and need 0.32 kWh/mile. The assumed kWh/mile accounts for ZEV pickups, SUVs, and vans that are coming to market soon and will be slightly less eﬃcient than lighter-weight ZEV sedans. Using these numbers, each vehicle will need about 8.6 kWh a day to completely recharge.

Finally, daily electric load and load shape were esƟmated using results from EVI-Pro 2, which the CEC uses to project charging staƟon and electricity needs statewide. EVI-Pro 2 assumes that:
 Every car will recharge from empty to full every day.  82% of vehicles will charge at home, with most charging occurring between 12:00 am and 5:00 am.
 Vehicles that travel from outside the area charge at a hotel, workplace, or public charging staƟon.
 Public Level 2 chargers:
o Will have two charging session each day.
o Use 11.2 kW.
 Public DC Fast Chargers (DCFCs):
o Will have 6 to 9 charging sessions each day.
o Use 50 kW.
 Most public charging (Level 2 and DCFC) will be between 8:00 am and 3:00 pm.  UƟliƟes or some other enƟty will manage the charging load, like how smart meters control electricity to homes and businesses.

Regarding the ﬁrst bullet above, today’s EVs have a range of 200 miles or greater and it is very unlikely that every ZEV will have an empty baƩery each Ɵme it charges. Therefore, in consultaƟon with NREL, it was assumed that Foster City ZEVs will charge one third of the Ɵme of the EVI-Pro 2 assumpƟon. CEC and NREL are developing the third version of EVI-Pro that changes this assumpƟon.

In the third step, the number of chargers within Foster City over Ɵme were esƟmated. For this, CEC’s projecƟon of charging staƟons in San Mateo County was used, where 4% were assigned to Foster City based on U.S. Census data showing that the City has about 4% of San Mateo County’s populaƟon and jobs. The resulƟng number of charging staƟons is shown in Table 5.

6 Due to this assumpƟon, this report uses “electric vehicles (EVs)” and “zero emissions vehicles (ZEVs)” interchangeably.

14 | P a g e

Table 5: Projected number of ZEV chargers in Foster City Charger Seƫng Foster City 2030 Foster City 2035
MulƟfamily 324 505
Workplace 297 503
Public 331 583
DCFC 25 50
Total 977 1,641

This approach provided esƟmates for 2030 and 2035. For 2040, the Housing Element report was used to determine populaƟon size and the number of single family and mulƟfamily residences that could support residenƟal charging; this projecƟon was extrapolated to esƟmate the increases in chargers up to 2045. For the 2040 and 2045 load calculaƟons, minimal growth in the numbers of public, workplace, and DCFC was assumed, and an increase in the number of daily charging sessions was also assumed.

Lastly, annual EV electricity use was calculated using EquaƟon 1.

Equation 1

2.4 Phasing of Electrification
In 2019 ExecuƟve Order B-55-18 was signed establishing a goal for the State of California to achieve carbon neutrality no later than 2045. As part of this analysis, 2045 is set as the year for compliance with city-wide electriﬁcaƟon. The esƟmated incremental electricity use that would result from city-wide electriﬁcaƟon was then apporƟoned over the next 20 years.

For exisƟng buildings, this phasing was based on the California Air Resources Board’s 2022 scoping plan to reach carbon neutrality (California Air Resources Board, 2022). The plan idenƟﬁes that of the around 14 million exisƟng homes in California, by 2030 there shall be 3 million all-electric homes, and by 2035 7 million all-electric homes. This trajectory was used to esƟmate the percent of the total addiƟonal electriﬁcaƟon load esƟmated as part of this study that would be expected in 2030 (3 of 14 million homes) and 2035 (7 of 14 million homes). The 2040 target was set as one-third of the way moving from the 2035 target to the 100% 2045 target, assuming that substanƟal electriﬁcaƟon is achieved in the last ﬁve years. The scoping plan does not idenƟfy a similar number of exisƟng commercial buildings to be electriﬁed by 2030 and 2035, but it does establish the same appliance sales goals for both residenƟal and commercial buildings. As such, the same phasing trajectory was used for commercial buildings as can be seen in Table 6.

New development phasing was based on the compleƟon schedule reported in Table 4. Where the compleƟon schedule spans mulƟple years the total esƟmated electricity use was split evenly across

15 | P a g e

those years. The assumpƟons for EV charging are described above in SecƟon 2.3 Zero Emissions Vehicles (ZEVs).

Table 6: Assumptions for Phasing of Electrification Through 2045 2030 2035 2040 2045
ExisƟng ResidenƟal Buildings 21% 50% 67% 100%
ExisƟng Commercial Buildings 21% 50% 67% 100%
New Development 77% 95% 98% 100%
Electric Vehicles 19% 51% 66% 100%

2.5 Capacity Gap Analysis
Foster City electricity distribuƟon service is provided by PG&E and served by the Bay Meadows, Belmont and San Mateo substaƟons and the following seven feeders.  Bay Meadows 1108, 1106, 2106, 2105, 2103
 Belmont 1105
 San Mateo 2101
The following PG&E resources were reviewed to evaluate the exisƟng capacity and loading of the feeders. Data accessed from the PG&E resources included rated capacity in MW by feeder, current and projected loading by feeder, Ɵme of year of peak demand and number of customers per feeder by customer type (residenƟal, commercial, industrial, and other).  DistribuƟon Investment Deferral Framework (DIDF) map7  IntegraƟon Capacity Analysis (ICA) tool8
Bay Meadows Feeder 2103 and San Mateo Feeder 2101 are considered conﬁdenƟal and do not show loading on the DIDF map or ICA tool. To esƟmate the loading on these two feeders, the average loading of the other ﬁve feeders as a percent of feeder capacity was calculated and applied to the capacity of the conﬁdenƟal feeders.

ExisƟng esƟmated feeder loads were then extrapolated to future loads from building and transportaƟon electriﬁcaƟon. These projecƟons were based on the customer type distribuƟon per feeder. For example, if a feeder served 35% of the total residenƟal customers, then 35% of the projected residenƟal building load was apporƟoned to that feeder. In this same example, 35% of projected electricity use from residenƟal charging would be applied to the same feeder. For new development, projecƟons were apporƟoned based on their proposed locaƟon in Foster City. If this data was unavailable (for example “Other/Additional Residential Development”), the loads were distributed in the same fashion as for the exisƟng building loads.

7 hƩps://www.pge.com/b2b/distribuƟon-resource-planning/grid-needs-assessment-map.html 8 hƩps://www.pge.com/b2b/distribuƟon-resource-planning/integraƟon-capacity-map.shtml

16 | P a g e

2.6 Cost-Effectiveness of Residential Electrification
The team leveraged the Statewide Reach Code Team exisƟng and ongoing studies to inform cost- eﬀecƟveness of exisƟng residenƟal building electriﬁcaƟon in Foster City. A report was published in 2021 that studied the cost-eﬀecƟveness of electriﬁcaƟon of space heaƟng and water heaƟng in single family exisƟng homes (Statewide Reach Code Team, 2021). The Statewide Reach Codes Team is currently in the process of updaƟng this study; the results were used to inform the custom scenario analyses reﬂected in this report. In addiƟon to electriﬁcaƟon of individual appliances, this analysis also evaluated a complete building electriﬁcaƟon scenario where all gas appliances in the home were converted to electric appliances. The three scenarios are described below, and all replacements are evaluated at the end of the life of the exisƟng equipment, when the equipment is being replaced.

1. Space HeaƟng: Ducted heat pump space heater replacing a tradiƟonal central system (ducted gas furnace and air condiƟoner).

2. Water HeaƟng: Heat pump water heaƟng located in a garage replacing a gas storage tank water heater.

3. Complete Building ElectriﬁcaƟon:
a. Space heaƟng as described above.
b. Water heaƟng as described above.
c. Electric resistance range/oven replacing a gas range/oven. d. Electric resistance clothes dryer replacing a gas clothes dryer.

The complete building electriﬁcaƟon package was evaluated alongside the need for electrical service upgrades. Some electriﬁcaƟon upgrades trigger upgrading a home’s electrical panels either in the case where the exisƟng panel presents a safety concern or has insuﬃcient capacity to support full home electriﬁcaƟon, either based on the total amperage or limited breaker space for new circuits. To analyze the impact of this on cost-eﬀecƟveness, the analysis considered three service upgrade scenarios, described below.

1. No electrical service upgrades are necessary.

2. The electrical panel is upgraded.

2.6.1 Cost-EﬀecƟveness Methodology
Consistent with the Statewide Reach Codes Team’s statewide studies, this analysis applied two diﬀerent metrics to assess cost-eﬀecƟveness of the electriﬁcaƟon scenarios. Both life-cycle cost (LCC) methodologies require esƟmaƟng and quanƟfying the incremental costs and energy savings associated with the electriﬁcaƟon upgrades. The main diﬀerence between the methodologies is the way they value energy and thus the cost savings of reduced or avoided energy use. The two methods are described below.
 UƟlity Bill Impacts (On-Bill): Customer-based LCC approach that values energy based upon esƟmated site energy usage and customer uƟlity bill savings using today’s electricity and natural gas uƟlity rate schedules and esƟmaƟng impacts over the analysis period accounƟng for discount rate and energy cost inﬂaƟon.

17 | P a g e

 Long-term System Cost (LSC): The California Energy Commission’s LCC methodology (formerly referred to as Time Dependent ValuaƟon (TDV)), which is intended to capture current and long- term societal value or cost of energy use, such as the cost of providing energy during peak periods of demand, projected costs for carbon emissions, as well as grid transmission and distribuƟon impacts. This is the methodology used in evaluaƟng the cost-eﬀecƟveness of upgrades to the Title 24, Part 6 energy code.

Cost-eﬀecƟveness is presented using lifecycle net present value (NPV), represenƟng the cost- eﬀecƟveness of an upgrade measure over a 30-year lifeƟme. This approach discounts future savings and costs at a three percent real discount rate. NPV is calculated as the present value of the beneﬁts minus the present value of the costs over the lifeƟme, as expressed in EquaƟon 2. If the NPV of a measure or package is posiƟve, it is considered cost-eﬀecƟve.

Equation 2
𝑵𝒆𝒕 𝑷𝒓𝒆𝒔𝒆𝒏𝒕 𝑽𝒂𝒍𝒖𝒆 = 𝑷𝒓𝒆𝒔𝒆𝒏𝒕 𝑽𝒂𝒍𝒖𝒆 𝒐𝒇 𝑩𝒆𝒏𝒆𝒇𝒊𝒕𝒔 − 𝑷𝒓𝒆𝒔𝒆𝒏𝒕 𝑽𝒂𝒍𝒖𝒆 𝒐𝒇 𝑪𝒐𝒔𝒕𝒔 Analysis was conducted using a 1,665 square foot three-bedroom single family prototype home. The prototype is a single-story with an aƩached two-car garage. Three building vintages were evaluated to determine sensiƟvity of exisƟng building performance on cost-eﬀecƟveness of upgrades: pre-1978, 1978-1991, and 1992-2010. Results in this report are presented for the pre-1978 home to best reﬂect the exisƟng building stock of Foster City.

For further details on the cost-eﬀecƟveness approach see the statewide study (Statewide Reach Code Team, 2021).

UƟlity bill impacts are calculated based on Peninsula Clean Energy (PCE) electricity tariﬀs and PG&E gas tariﬀs. Based on review of the PG&E - PCE San Mateo joint rate comparison9, PCE rates were calculated as $0.01/kWh less than PG&E rates. The exisƟng mixed fuel home was evaluated under the TOU-C electricity tariﬀ. Homes with heat pumps were applied either the TOU-C or the new E-ELEC tariﬀ, whichever resulted in a lower annual uƟlity bill for the customer. The G-1 gas tariﬀ was used for calculaƟng gas costs.

2.6.2 Incremental Costs
Table 7 summarizes the esƟmated incremental costs at Ɵme of replacement for electric appliances based on the scenarios described above. The costs include equipment and labor as well as any electrical work that is required within the building. For example, electric appliances typically require 220-Volt service and costs include running electrical wiring to service the appliance. These costs do not include other electrical service upgrades. Cost-eﬀecƟveness results over the 30-year lifeƟme also include future replacement costs over the 30-year analysis period where applicable.

9 hƩps://www.peninsulacleanenergy.com/wp-content/uploads/2023/08/PCE-San-Mateo-Jul-2023-Comparison.pdf

18 | P a g e

Table 7: Incremental Costs for Single Family Home Electrification Measures Measure First Incremental Cost
Space HeaƟng $1,020
Water HeaƟng $4,497
Range/Oven $475
Clothes Dryer $475

A 2022 PG&E report Service Upgrades for ElectriﬁcaƟon Retroﬁts Study (NV5, Inc, 2022) was used to esƟmate the costs of other electric service upgrades. The study, based on costs reported by electricians, found that electrical panel upgrades costs between $2,000 and $4,500 with an average cost of $2,780.

Service upgrades upstream of the home electrical panel are typically paid for by the uƟlity, but the costs for transformer upgrades may be passed onto the homeowner in certain situaƟons. In cases where the transformer serves a single home the homeowner will be responsible for 100% of the transformer upgrade costs. If the transformer serves two homes, the cost is split evenly between the homes. In all other cases the UƟlity takes on 100% of the upgrade cost. The study reported average costs for transformer upgrades between $6,000 and $8,000. The costs used in this study are summarized in Table

8. Table 8: Incremental Costs for Electric Service Upgrades in Single Family Homes Measure First Incremental Cost
New Panel $2,780
Transformer $7,000

19 | P a g e

3 Results
ElectriﬁcaƟon of exisƟng buildings, new building development, and vehicles will add an esƟmated 228.5 GWh of annual electricity consumpƟon in addiƟon to the exisƟng 204.7 GWh electricity use in 2022, more than doubling current electricity use. The relaƟve contribuƟon of these groups is shown in Figure

1. Further details on incremental electricity use from these groups, impacts on the City’s exisƟng grid, and cost-eﬀecƟveness of electriﬁcaƟon are discussed in the following secƟons. Results are presented under a full electriﬁcaƟon scenario, which is assumed to occur in 2045 (see SecƟon 2.4 Phasing of ElectriﬁcaƟon).

Figure 1: AddiƟonal Annual Electricity Use in GWh, 2045 Full ElectriﬁcaƟon Scenario

3.1 Existing Buildings
Of the total added (incremental) electrical load, 42% (88.1 GWh) is due to the electriﬁcaƟon of exisƟng residenƟal (single family and mulƟfamily) and non-residenƟal (commercial, industrial, and agricultural) buildings; Figure 2 presents an overview of the contribuƟon of these various building types. Details, including hourly electricity use breakdowns, are provided throughout this secƟon.

20 | P a g e

Figure 2: AddiƟonal Annual Electricity Use in GWh for ExisƟng Buildings, 2045 Full ElectriﬁcaƟon Scenario

3.1.1 ResidenƟal
Added annual electricity use among the residenƟal sector due to conversion from gas appliances in exisƟng building is esƟmated to be 28.7 GWh. 72% (20.7 GWh) of this added load is aƩributed to single family, while 28% (8.0 GWh) is from mulƟfamily. This esƟmate builds from current building condiƟons, including 1) exisƟng housing unit counts, 2) measured energy use in 2020, and 3) an esƟmated penetraƟon of exisƟng electric appliances (that don’t need to be converted from gas to electricity).

Forecasted electricity use for the electriﬁed future is based on combining these exisƟng condiƟons with modeled energy esƟmates (see SecƟon 2.1.1 ExisƟng ResidenƟal Buildings). The esƟmate does not reﬂect potenƟal changes in electricity use over Ɵme, as discussed in SecƟon 4 Conclusions & RecommendaƟons, such as from technology advances and building code updates that improve the eﬃciency and performance of buildings. The breakdown by end-use is shown in Figure 3. Heat pumps were assumed for water heaƟng and space heaƟng electriﬁcaƟon while electric resistance was assumed for cooking and clothes drying electriﬁcaƟon.

21 | P a g e

Figure 3: Annual Incremental Electrical Load by End-Use for Single Family and MulƟfamily Buildings.

Single family space heaƟng is clearly the largest electricity-consuming end-use, followed by water heaƟng for both dwelling types. Space heaƟng for mulƟfamily is substanƟally less due to fewer units, less condiƟoned ﬂoor area and lower loads due to shared walls and higher internal gains as compared to single family homes. Today’s mulƟfamily building stock also has more units with electric space heaƟng, clothes drying, and cooking as compared to single family.

The spread of added electricity loads throughout the year are shown in Figure 4 and Figure 5. This shows anƟcipated peaks in electricity use where the grid may be most constrained, and thus where load shiŌing eﬀorts would be most valuable. In these ﬁgures, electricity loads are broken down by end use equipment and by hour of the year, where 1 represents midnight on January 1 and 8760 represents 11:00 pm on December 31.

Figure 4 illustrates the seasonal peaks in winter months due to space heaƟng—reversing the current, familiar summer peaks associated with air condiƟoning. While water heaƟng follows a similar seasonal paƩern, the associated electricity use is much lower than space heaƟng. Clothes drying and cooking represent relaƟvely smaller and more consistent loads. Remember that these results show added electrical demands; summer peaks due to air condiƟoning, for example, will sƟll be present.

Hourly electrical loads for mulƟfamily dwelling units (Figure 5) diﬀer from single-family in terms of magnitude, as discussed above, as well as distribuƟon. Due to the signiﬁcantly lower space heaƟng needs, seasonal peaks are drasƟcally reduced, leaving a more even spread of electricity use throughout the year.

22 | P a g e

Figure 4: Single-Family Incremental Electricity Use (kWh) by Hour.

Figure 5: MulƟfamily Incremental Electricity Use (kWh) by Hour.

23 | P a g e

3.1.2 Non-ResidenƟal
ExisƟng non-residenƟal buildings are esƟmated to add 59.4 GWh annually to the grid as a result of electriﬁcaƟon. This is dominated by commercial buildings—esƟmated to use over 99% of the added load, with industrial and agricultural faciliƟes consuming less than 1% combined. This is shown in Figure 6 for oﬃces and Figure 7 for retail buildings. EsƟmated electricity use for commercial buildings uƟlizes basically the same approach as that for residenƟal buildings – the key diﬀerences are that 1) scaling to annual metered energy use was less granular as less data was available for exisƟng commercial buildings, and 2) oﬃce and retail were used as the representaƟve building categories to develop hourly proﬁles.

Analysis of industrial and agricultural buildings uƟlized a much simpler approach, where an assumed constant electricity demand was applied to annual metered energy usage. For more informaƟon on the non-residenƟal methodology, see SecƟon 2.1.2 ExisƟng Non-ResidenƟal Buildings.

Figure 6: Oﬃce Incremental Electricity Use (kWh) by Hour.

24 | P a g e

Figure 7: Retail Incremental Electricity Use (kWh) by Hour.

Like residenƟal buildings, these ﬁgures show winter electricity peaks associated with space heaƟng.

Water heaƟng, on the other hand, is relaƟvely constant unlike residenƟal buildings which typically have larger and more dispersed water heaƟng needs. Of course, commercial buildings other than oﬃces and retail can have various electricity demands not represented here.

3.2 New Development
ResidenƟal and commercial construcƟon projects planned or underway within Foster City are expected to add 67.7 GWh to the grid by the Ɵme of compleƟon, assuming all development is all-electric. Table 9 shows the esƟmated amount that each development project contributes to this total. These esƟmates are not representaƟve of the design for any parƟcular project and are based on modeling of prototype buildings only – speciﬁcally the categories listed in Table 910. However, annual electricity use was informed by project-speciﬁc data as listed in Foster City’s Water Capacity Study including ﬂoor area and/or number of units and development Ɵmespan, which is discussed in SecƟon 2.2 New Development.

10 Biomedical faciliƟes do not have a readily available prototype for modeling, therefore a diﬀerent source was used to esƟmate associated electricity use. See SecƟon 2.2 New Development.

25 | P a g e

Table 9: Annual electricity use of planned new development projects.

Development Project Name Completion Schedule Closest Modeling Category Annual kWh
1001 E. Hillsdale (Parkside
Towers) 2025-2030 Biomed 18,457,222
1065 E. Hillsdale R&D
Conversions (Century Plaza) 2020-2025 Biomed 5,055,993
Gilead Integrated Corporate
Campus 2030-2035 Biomed 11,099,576
Office 893,864
Biomed Phase 2 2020-2025 Biomed 9,559,895
Office 305,879
388 Vintage Park 2023 Biomed 1,393,754
Office 104,054
New Hotel in Metro Center
(VISA) 2025-2030 Hotel 871,956
Schooner Bay II Redevelopment 2029 Multifamily 1,096,103 15-Acres Project (Foster Square) 2020-2025 Multifamily 1,243,294 Retail 137,081
Schooner Bay I Redevelopment 2028 Multifamily 1,277,743 Lantern Cove Apartments
Redevelopment 2025-2030 Multifamily 1,315,323
2023-2031 Residential
Development to Achieve RHNA 2025-2030 Multifamily 2,076,332 Other/Additional Residential
Development 2032-2045 Multifamily 3,707,959
Pilgrim Triton Project
Completion 2020-2025 Multifamily 2,521,036
Office 303,957
1010 Metro Center Blvd (OSH
Redevelopment) 2025-2030 Multifamily 347,621
Retail 266,394
Chess/Hatch Drive Offices
Project 2025-2030 Office 3,470,968
1601 Beach Park Blvd/Sea Island 2020-2025 Office 14,448 Charter Square Demo/Beach
Park Elementary School 2021 Office 253,723
1065 E. Hillsdale Retail Pavilion
(Century Plaza) 2020-2025 Retail 23,761
Other/Additional Non-
Residential Growth 2030-2040
Office 117,145
Biomed 1,569,101
Retail 123,373
ADUs for Eaves and Single-
Family Homes 2023-2031 Single Family 56,183

26 | P a g e

Because the esƟmated electricity use is based on the best-ﬁƫng model data, the hourly proﬁles of new development projects mostly reﬂect the proﬁles already shown for single-family, mulƟfamily, oﬃces, and retail. This is also the case for the relaƟve load distribuƟon throughout the year; the magnitude of hourly loads, however, will vary based on the total annual electricity use of a given building. Total (instead of building type-speciﬁc) hourly proﬁles for collecƟve new development are shown in Figure 8.

Figure 8: Hourly Electricity Use, Total New Development (kWh)

3.3 Zero Emission Vehicles
ElectriﬁcaƟon of vehicles residing in and traveling through Foster City is expected to add 72.8 GWh annually to the grid by 2045. This accounts for the largest added load due to city-wide electriﬁcaƟon. As discussed in SecƟon 2.3 Zero Emissions Vehicles (ZEVs), this includes only light-duty vehicles due to the minimal number of heavy-duty vehicles registered to and parked in Foster City locaƟons.

The hourly electricity use proﬁles are averages that are subject to change over Ɵme as more data is collected on EV usage; results for the average charging proﬁle are presented in Figure 9. The distribuƟon of electricity use throughout the day is reﬂecƟve of the assumpƟons used by the EVI-Pro soŌware—with some adjustments by the Team—as discussed in SecƟon 2.3 Zero Emissions Vehicles (ZEVs). This includes the majority (82%) of vehicles being charged at homes overnight using Level 2 charging staƟons, with most charging between midnight and 5:00am. The remainder of charging will occur during the day (8:00am to 3:00pm) and mostly at public or commercial DC fast chargers. The Team assumes that most resident vehicles will not charge every day (e.g., topping oﬀ), but follow the automakers’ recommendaƟon to charge when the baƩery is between 20 and 40 percent full.

27 | P a g e

Figure 9: ZEV Daily Charging Proﬁle (kWh) in 2045
Reﬁnements to the assumpƟons made by this state-uƟlized soŌware are ongoing. Such reﬁnements include near term adjustments to reﬂect today’s variaƟons in charging pracƟces, such as parƟal instead of full daily baƩery depleƟon. They will surely also include longer term changes to reﬂect increases in baƩery eﬃciency and use of load shiŌing (see SecƟon 4 Conclusions & RecommendaƟons).

Figure 10 presents the addiƟonal electricity use over Ɵme for residenƟal chargers and commercial chargers.

Figure 10: Charging needs over Ɵme for residenƟal versus non-residenƟal charging staƟons.

28 | P a g e

3.4 Capacity Gap Analysis
Figure 11 shows the neighborhoods of Foster City as provided by city staﬀ. Table 10 idenƟﬁes which of the seven feeders providing electrical service to Foster City serve which neighborhoods.

Figure 11: Foster City Neighborhoods

29 | P a g e

Table 10: Foster City Neighborhoods with PG&E Feeders IdenƟﬁed Neighborhoods Feeders
1 Bay Meadows 1106, 1108
2 Bay Meadows 1106
3 Bay Meadows 1106, 1108
4 Bay Meadows 1108
5 Bay Meadows 1108
6 Bay Meadows 1108, Belmont 1105
7 Belmont 1105
8 Belmont 1105
9 Bay Meadows 1108, Belmont 1105
TC - Town Center Bay Meadows 2106
PT - Pilgrim Triton Bay Meadows 1106
C - Chess Hatch Bay Meadows 1106
L - Lincoln Centre SM 2101, Bay Meadows 2105
VP - Vintage Park Bay Meadows 2105, 2103, San Mateo 2101

Many of the neighborhoods are served by more than one feeder; in fact all but one feeder serves mulƟple neighborhoods. The feeders are also not limited to Foster City. Table 11 shows the number of customers on each feeder. The total number of residenƟal customers exceeds the total number of Foster City residenƟal homes by almost 5,000 homes, indicaƟng that some of those customers are outside the City limits.

Table 11: PG&E Number of ExisƟng Customers by Type per Feeder Substation Feeder Residential Commercial Industrial Other Bay Meadows 2401 1106 5,704 359 156 14
Bay Meadows 2401 2106 570 39 40 6
Bay Meadows 2401 2105 145 30 65 6
Bay Meadows 2401 1108 5,533 133 39 8
Bay Meadows 2401 2103 1,036 58 42 10
Belmont 2403 1105 3,613 132 34 13
San Mateo 2101 2,000 95 41 20

Bay Meadows Feeder 1106 serves the highest number of residenƟal, commercial, and industrial customers of all the feeders. Consequently, this feeder also has the highest load at 9.1 MW and is already using 89% of its capacity raƟng. Table 12 shows the capacity raƟng (MW) of each feeder and the maximum load recorded in 2022.

30 | P a g e

Table 12: PG&E Feeder Capacity and Loading from 2022
Substation Feeder
Feeder
Capacity
Rating MW
2022
Loading
MW
% of
Capacity
Rating
Bay Meadows 2401 1106 10.3 9.1 89%
Bay Meadows 2401 2106 22.2 8.9 40%
Bay Meadows 2401 2105 16.8 6.8 41%
Bay Meadows 2401 1108 10.3 4.8 47%
Bay Meadows 2401 2103 16.8 Confidential
Belmont 2403 1105 10.8 4.8 45%
San Mateo 2101 20.0 Confidential

Bay Meadows Feeder 1106 extends throughout neighborhoods 1, 2, 3, PT (Pilgrim Triton), and C (Chess Hatch) as shown in Figure 12. Figure 13, on the other hand, shows the area outside of Foster City that is served by Feeder 1106.

Figure 12: Bay Meadows Feeder 1106 Shown in Yellow

31 | P a g e

Figure 13: Bay Meadows Feeder 1106 Shown in Yellow
The expected load increase (MW) from the analysis acƟviƟes described above are summarized by customer type in Table 13 below. The new system peak is expected to occur during winter mornings.

Table 13: New Load ProjecƟons (MW) by Customer Type
New Electrical Load (MW) by Year
Category 2030 2035 2040 2045
Existing Residential Buildings 6 15 19 29
Existing Commercial Buildings 17 40 54 80
Electric Vehicles Residential 5 12 18 30
Electric Vehicles Commercial 1 2 1 1
Electric Vehicles Other 1 2 3 3
Electric Vehicles Industrial 0 1 1 2
New Residential Development 5 6 7 7
New Commercial
Development
4 5 5 5
New Industrial Development 27 35 35 35

The load projecƟons were added to the exisƟng feeder loads from PG&E data and are shown in Table 14.

These load projecƟons overload the exisƟng distribuƟon system by 2030.

32 | P a g e

Table 14: Load ProjecƟons by Feeder
Substation Feeder
Feeder
Capacity
(MW)
2030
Forecast
Load (MW)
2035
Forecast
Load (MW)
2040
Forecast
Load (MW)
2045
Forecast
Load (MW)
Bay Meadows 2401 1106 10 38 58 68 86
Bay Meadows 2401 2106 22 24 29 30 33
Bay Meadows 2401 2105 17 8 9 9 11
Bay Meadows 2401 1108 10 15 24 30 41
Bay Meadows 2401 2103 17 11 14 16 19
Belmont 2403 1105 11 13 21 25 34
San Mateo 2101 20 14 19 22 28
Totals 107 123 174 200 250

This analysis does not consider the eﬀects of solar photovoltaics or other distributed energy resources (DERs) on the grid. The electric grid was designed for power ﬂow in one direcƟon, from transmission to distribuƟon. GeneraƟon plants connect to the transmission system, which delivers power and electricity to distribuƟon grids, which then delivers power and electricity to all end users. The exisƟng control systems that provide grid reliability and safety are designed around this unidirecƟonal setup. While voltage regulators are used to increase voltage for heavily loaded feeders, there is no method for reducing voltages if too many DERs are connected. DER inverters create harmonics, increased feeder voltages, and the potenƟal for reverse power ﬂow. The NaƟonal Renewable Energy Laboratory (NREL) has been studying these issues for years and is currently developing new strategies to control distribuƟon systems with a high penetraƟon of DERs.

While DERs can produce signiﬁcant amounts of renewable electricity and reduce reliance on coal, natural gas and nuclear fueled energy sources, they do not help reduce overloading on transmission and distribuƟon systems. In the long term, new control systems may be able to take advantage of these DERs to create a more versaƟle and dynamic electrical grid.

3.5 Cost-Effectiveness & Service Upgrades
The primary objecƟve of this part of the study is to evaluate the cost-eﬀecƟveness of electriﬁcaƟon upgrade measures and packages for exisƟng single-family buildings in Foster City, to support the design of local ordinances, incenƟve programs, or other iniƟaƟves.

Table 15 presents the impacts of electriﬁcaƟon of space heaƟng and water heaƟng individually, alongside a whole house electriﬁcaƟon package that upgrades space heaƟng, water heaƟng, clothes drying, and cooking to electric appliances (“All-Electric”). In all cases converƟng from gas to electric appliances results in an increase in electricity use alongside a decrease in natural gas use. The net overall impact is greenhouse gas emissions savings. UƟlity costs are esƟmated to increase as a result of electriﬁcaƟon, although the increase is lower under CARE rates. The increase is about $170 per

33 | P a g e

appliance under standard uƟlity rates and about $80 per appliance under CARE rates. Based on LSC all measures are cost-eﬀecƟve over the analysis period with a posiƟve NPV.

In some cases, electriﬁcaƟon will require that the electrical panel be upgraded. This may be due to insuﬃcient capacity to serve the electric appliances at the exisƟng panel, or it may also be triggered for exisƟng panels that pose safety concerns. Panel upgrades will most oŌen be from a 100 Amp panel to a 200 Amp panel. See SecƟon 3.5.1 Service Upgrades for further discussion. Table 16 compares the whole house electriﬁcaƟon package (“All-Electric”) shown in Table 15 with whole house electriﬁcaƟon where the electrical panel needs to be upgraded. While the energy impacts between these two cases are idenƟcal, the costs incurred by the owner are higher in the case where the panel must be replaced. Table 16 shows the impact on LSC cost-eﬀecƟveness of these costs. Even aŌer accounƟng for an average $2,780 cost for a panel upgrade, electriﬁcaƟon conƟnues to be cost-eﬀecƟve based on LSC.

34 | P a g e

Table 15: Savings Impacts and Cost-EﬀecƟveness for ElectriﬁcaƟon Measures Measure
Electricity
Savings
(kWh/yr)
Gas
Savings
(therm/yr)
GHG
Savings
(mtons/yr)
First
Incremental
Cost
LSC
NPV
Standard
Year 1 Utility
Cost
Savings
Standard
On-Bill
NPV
CARE
Year 1
Utility
Cost
Savings
CARE
On-Bill
NPV
Space Heating (2,214) 253 1.1 $1,020 $10,772 ($180) ($2,445) ($53) ($329) Water Heating (1,361) 170 0.9 $4,497 $4,080 ($158) ($7,959) ($61) ($6,249) All-Electric (4,630) 465 2.1 $6,468 $11,988 ($688) ($19,451) ($321) ($12,317)

Table 16: Cost-EﬀecƟveness SensiƟvity with Electrical Service Upgrades Measure
First
Incremental
Cost LSC NPV
All-Electric $6,468 $11,988
All-Electric + Panel Upgrade $9,248 $9,208

35 | P a g e

3.5.1 Service Upgrades
The 2022 PG&E study on service upgrades (NV5, Inc, 2022) invesƟgated what set of condiƟons is most likely to trigger the need for service upgrades, and it recommended strategies to eliminate the need for upgrades in some cases. Solar photovoltaic installaƟon, EV charger installaƟon, and HVAC upgrades were the top reasons for service upgrades. In homes with exisƟng air condiƟoning, the upgrade to a heat pump may require an addiƟonal circuit for the air handler or may not require any addiƟonal infrastructure. Where air condiƟoning is being added, it’s more likely that a panel upgrade may be triggered.

The 2022 PG&E study idenƟﬁed various opƟons for homes with 100-amp and 150-amp electric service.

Soon aŌer 1965, California code began to require 100-amp electrical service for single family homes.

Therefore, a subset of homes built prior to this will have less than 100-amp service and will likely require service upgrades when undergoing electriﬁcaƟon. Most homes built aŌerwards will have at least 100- amp service. 200-amp service has been required as of the 2019 code. The 2022 PG&E study esƟmated based on US Census data for the California housing stock that 71% of exisƟng homes have at least 100- amp service.

Following is a list of strategies to consider when electrifying homes with a 100-amp panel.  Reduce heaƟng and cooling loads. This provides a mulƟtude of beneﬁts including lower cost of heat pump equipment from reduced capacity, lower uƟlity bills, improved occupant comfort, and improved home resiliency to ride out power outage events. o Simple measures include insulaƟon and air sealing. Other measures include new windows in older homes with single pane windows, replacing aged duct systems, and venƟlaƟon cooling systems such as whole house fans.
 Select appliances with lower amperage and voltage requirements. o Most heat pump water heaters require 240-volt service, but the major manufactures all have developed or are developing 120-volt, 15-amp opƟons. A recent study by New Buildings InsƟtute showed that these products are excellent soluƟons for households with low to medium hot water demands, provided the tanks are appropriately sized and located (New Buildings InsƟtute, 2023).
o Standard split air handlers oŌen require 240-volt service both to the outdoor unit and the indoor air handler. Many variable capacity mini-split heat pumps power the indoor units from the outdoor units, only requiring a single 240-volt service to the outdoor unit.  Where appropriate, select appliances that combine two funcƟons. o Combined ranges require less electrical capacity than separate ovens and cooktops. o Stacked washer and dryers require less electrical capacity than separate appliances.  PrioriƟzed circuit sharing devices for EV charging.
o These devices share circuit across appliances that don’t typically have coincident loads (loads that occur at the same Ɵme), such as EV charging and washing machines.

36 | P a g e

4 Conclusions & RecommendaƟons

4.1 Conclusions
Following are a set of conclusions from this study.

1. The exisƟng electric distribuƟon system will not fully support the future development and electriﬁcaƟon of Foster City.

2. Bay Meadows Feeder 1106 was the feeder with the highest loading in 2022 at 89%. This feeder serves porƟons of neighborhoods 1, 2, 3, PT (Pilgrim Triton), and C (Chess Hatch) as well as porƟons of San Mateo. 30% of Foster City residenƟal homes are esƟmated to be served by this feeder. ElectriﬁcaƟon of exisƟng homes alone is esƟmated to add 1.8 MW of new load on this feeder, which has only 0.6 MW of capacity remaining.

3. The resultant peak load due to electriﬁcaƟon is expected to occur during winter morning hours due to the addiƟon of electric space heaƟng.

4. The electrical load esƟmates presented in this analysis should be interpreted as a maximum potenƟal for planning purposes. There are many uncertainƟes with developing projecƟons at this scale and the Team erred on the side of over-projecƟon rather than under-projecƟon. The projecƟons don’t consider technology development over Ɵme that is expected to improve system eﬃciency such as for heaƟng equipment and electric vehicles. They also don’t consider load reducƟon measures in exisƟng buildings, load shiŌing opportuniƟes, or the impacts of behind the meter solar and baƩery storage.

5. Behind-the-meter solar photovoltaics or other DERs are not considered as part of the capacity analysis. While these systems have many beneﬁts including reducing reliance on natural gas fueled energy sources with renewal energy, they do not reduce overloading on transmission and distribuƟon systems. When peak loads are non-coincident with solar producƟon, as is the case with a winter morning peak, storage strategies must be introduced to successfully alleviate grid overloading. In the long term, new control systems may be able to take advantage of DERs to create a more versaƟle and dynamic electrical grid.

6. ElectriﬁcaƟon in single family homes is cost-eﬀecƟve based on the California Energy Commission’s societal based long-term system cost (LSC) metric. However, cost-eﬀecƟveness from a customer perspecƟve is driven by the relaƟve cost of natural gas compared to electricity.

UƟlity bills based on today’s tariﬀs are esƟmated to be higher with electric appliances as compared to gas appliances. Over Ɵme, if gas rates increase substanƟally, as some suspect they may, and more electriﬁcaƟon-friendly electric tariﬀs are introduced this may shiŌ making all- electric homes lower cost.

7. Even when service upgrades are necessary, it is sƟll cost-eﬀecƟve to electrify single family homes based on the LSC metric. However, service upgrades present a high ﬁrst cost to owners and can be a deterrent to equitable decarbonizaƟon.

8. With anƟcipated changes in ZEV use and technology, it is ideal to periodically revisit assumpƟons used in terms of grid impacts due to ZEVs. For example:
 Current ZEV drivers tend to ﬁt one demographic: Homeowners that have a driveway or garage for parking (and charging) a car. As ZEVs are to become more widespread, the

37 | P a g e

number of chargers will likely increase and charging paƩerns may favor day Ɵmes when public chargers are more widely used.
 EV travel mile ranges have substanƟally increased in the last three years and is expected to conƟnue to improve in coming years. Improvements in baƩery eﬃciency are also likely in the future. Both will likely impact future charging duraƟons and Ɵmes.

4.2 Recommendations & Mitigation Strategies
Following are a set of recommendaƟons from this study.

1. The City can use this study as a resource to re-engage with PG&E to coordinate and plan miƟgaƟon eﬀorts before loads exceed exisƟng distribuƟon capaciƟes. PG&E has stated they are commiƩed to adding the necessary resources to meet load growth. This can be accomplished through adding substaƟons, adding feeders, and/or moving lines between feeders to beƩer distribute loads.

2. Bay Meadows Feeder 1106 has the highest loading currently and electriﬁcaƟon in these neighborhoods should be monitored closely. The Team recommends that The City sƟll encourage electriﬁcaƟon in these neighborhoods; however, resource planning with PG&E will need to prioriƟze addressing this feeder.

3. Load shiŌing and load reducƟon programs can miƟgate infrastructure upgrade needs. This eﬀort can leverage exisƟng programs available through PG&E and PCE and the federal Energy Eﬃcient Home Improvement tax credit11. New funding sources may be available in the future as a result of the InﬂaƟon ReducƟon Act12.
o HeaƟng and cooling loads can be reduced with measures such as adding insulaƟon, air sealing, window replacements, duct system upgrades, and whole house fans. This provides a mulƟtude of beneﬁts including lower cost of heat pump equipment from reduced capacity, lower uƟlity bills, improved occupant comfort, and improved home resiliency to ride out power outage events.
o Other building loads such as lighƟng, which can be signiﬁcant in retail and oﬃce buildings, can be reduced through the installaƟon of high eﬃciency light emiƫng diode (LED) lamps.
o Behind the meter baƩery storage systems coupled with solar photovoltaics can miƟgate grid impacts if controlled to minimize imports from the electric grid during peak Ɵmes.

In Foster City, electriﬁcaƟon is expected to impact the grid most during winter mornings due to the addiƟon of electric space heaƟng. To have an eﬀect on infrastructure capacity in the winter, baƩeries would need to be setup to store energy overnight and limit grid imports during the morning hours.
o EducaƟon can help ensure that space heaƟng heat pumps are installed, commissioned, and controlled properly to miƟgate grid impacts and maximize occupant saƟsfacƟon.

The Quality ResidenƟal HVAC Services Program13 is an incenƟve program to train

11 hƩps://www.irs.gov/credits-deducƟons/energy-eﬃcient-home-improvement-credit 12 hƩps://www.energy.gov/scep/home-energy-rebates-programs 13 hƩps://qualityhvac.fronƟerenergy.com/

38 | P a g e

California contractors in providing quality installaƟon and maintenance while advancing energy-eﬃcient technologies in the residenƟal HVAC industry. Foster City could market this to local contractors to increase the penetraƟon of contractors skilled in heat pump design and installaƟon.
 Heat pumps installed without electric resistance backup and which are controlled to a constant thermostat setpoint during the winter will also help alleviate the winter morning peak load.

4. SecƟon 3.5.1 Service Upgrades provides addiƟonal recommendaƟons regarding service upgrades. Foster City may consider strategies to encourage projects that avoid service upgrades when possible. This may include training, resources, and/or incenƟve programs geared toward contractors.

5. When developing electriﬁcaƟon strategies, consider ways to remove barriers to deploying EV charging staƟons, such as zoning codes and permiƫng pracƟces. Speciﬁcally:
 Many ciƟes and counƟes have exisƟng codes that restrict development of new automoƟve-related businesses, prohibit the renovaƟon or rehabilitaƟon of gas staƟons, and/or restrict development in the right-of-way. Each of these can prohibit a business from adding public charging staƟons as well as development of a charging hub.  California’s AB 1236 requires streamlined permiƫng for residenƟal EV charging staƟon installaƟon, which Foster City has fully adopted and implemented. It’s important to adopt a similar process for commercial charging staƟons and for ﬂeet depots, which may include an exempƟon from condiƟonal use permits for a maximum number of charging staƟons and a method to accept and record PG&E’s engineering analysis, which is a separate process.
 In 2015, California Building Code required that all new construcƟon and major renovaƟons include a level of EV readiness. Since then, state code and local codes have required greater degrees of compliance. However, none of the codes required that either the property owner or the permiƫng authority keep track of the locaƟons that have raceways for EV charging and/or stub outs for EVSEs because these charging locaƟons do not have separate building permits. Reviewing permits issued since 2015 and cataloging the locaƟons of exisƟng infrastructure could speed deployment of charging staƟons and might be a great project for a CivicSpark fellow.14

14 hƩps://civicspark.civicwell.org/fellow/

39 | P a g e

5 References
California Air Resources Board. (2022). 2022 Scoping Plan for Achieving Carbon Neutrality. Retrieved from hƩps://ww2.arb.ca.gov/sites/default/ﬁles/2022-11/2022-sp.pdf DNV. (2022). RASS SaturaƟon Tables. Retrieved from 2019 California Statewide ResidenƟal Appliance SaturaƟon Study: hƩps://webtools.dnv.com/CA_RASS/
Higher EducaƟon for Environmental Performance Improvement. (2011). Energy ConsumpƟon of University Laboratories: Detailed Results from S-Lab Audits.

NaƟonal Renewable Energy Laboratory. (2023). Highly Resolved ProjecƟons of Passenger Electric Vehicle Charging Loads for the ConƟguous United States. Retrieved from hƩps://www.nrel.gov/docs/fy23osƟ/83916.pdf
New Buildings InsƟtute. (2023). Plug-In Heat Pump Water Heater Field Study Findings & Market CommercializaƟon RecommendaƟons. Retrieved from hƩps://newbuildings.org/resource/plug- in-heat-pump-water-heater-ﬁeld-study-ﬁndings-market-commercializaƟon-recommendaƟons/ NV5, Inc. (2022). Service Upgrades for ElectriﬁcaƟon Retroﬁts Study Final Report. Sponsored by Paciﬁc Gas and Electric Company and San Diego Gas and Electric Company. Retrieved from hƩps://pda.energydataweb.com/api/view/2635/Service%20Upgrades%20for%20ElectriﬁcaƟon% 20Retroﬁts%20Study%20FINAL.pdf
Statewide Reach Code Team. (2021). 2019 Cost-EﬀecƟveness Study: ExisƟng Single Family ResidenƟal Building Upgrades. Prepared by FronƟer Energy. Retrieved from hƩps://localenergycodes.com/download/875/ﬁle_path/ﬁeldList/2019%20V2- ResidenƟal%20Retroﬁt%20Cost-eﬀ%20Report-2021-08-27.pdf
Statewide Reach Code Team. (2022, September). 2022 Cost-EﬀecƟveness Study: Single Family New ConstrucƟon. Prepared for Paciﬁc Gas and Electric Company. Prepared by FronƟer Energy.

Retrieved from
hƩps://localenergycodes.com/download/1240/ﬁle_path/ﬁeldList/2022%20Single%20Family%20 NewCon%20Cost-eﬀ%20Study.docx
Statewide Reach Code Team. (2022a). 2021 Reach Code Cost-EﬀecƟveness Analysis: Non-ResidenƟal AlteraƟons. Prepared by TRC. Retrieved from
hƩps://localenergycodes.com/download/960/ﬁle_path/ﬁeldList/NonresidenƟal%20AlteraƟons% 20Cost-eﬀecƟveness%20Report.pdf
Statewide Reach Code Team. (2022b). 2019 Cost-EﬀecƟveness Study: ExisƟng MulƟfamily ResidenƟal Building Upgrades. Prepared by FronƟer Energy. Retrieved from hƩps://localenergycodes.com/download/986/ﬁle_path/ﬁeldList/Low- rise%20MulƟfamily%20Retroﬁts-Cost-eﬀ%20Report.pdf

40 | P a g e

Statewide Reach Code Team. (2022b). NonresidenƟal New ConstrucƟon Reach Code Cost-eﬀecƟveness Study. Prepared by Avani Goyal, Farhad Farahmand, TRC Companies. Retrieved from California Energy Codes & Standards: hƩps://localenergycodes.com/content/resources Statewide Reach Code Team. (2023). 2022 Cost-EﬀecƟveness Study: MulƟfamily New ConstrucƟon.

Prepared for Paciﬁc Gas and Electric Company. Prepared by FronƟer Energy. Retrieved from hƩps://localenergycodes.com/download/1552/ﬁle_path/ﬁeldList/2022%20MulƟfamily%20New Con%20Cost-Eﬀ%20Report.pdf

Foster City City CouncilMarch 18, 2024

Foster City City Council
March 18, 2024