GFO-25-304 - Modeling and Monitoring Air Quality and Co-Benefits of Energy Interventions

GRANT FUNDING OPPORTUNITY

Modeling and Monitoring Air Quality and Co-Benefits of Energy Interventions

EPIC Program

GFO-25-304

https://www.energy.ca.gov/funding-opportunities/solicitation

State of California

California Energy Commission

Table of Contents

I. Introduction 1

A. Purpose of Solicitation 1

B. Key Words/Terms 2

C. Project Focus 5

D. Funding 12

E. Key Activities Schedule 13

F. Notice of Pre-Application Workshop 14

G. Questions 15

H. Applicants’ Admonishment 16

I. Additional Requirements regarding environmental review 16

J. Background 17

K. Match Funding 23

L. Funds Spent in California 25

II. Eligibility Requirements 25

A. Applicant Requirements 25

B. Project Requirements 27

III. Application Submission Instructions 28

A. Application Format, Page Limits 28

B. Method For Delivery 29

C. Application Content 30

IV. Evaluation and Award Process 34

A. Application Evaluation 34

B. Ranking, Notice of Proposed Award, and Agreement Development 34

C. Grounds to Reject an Application or Cancel an Award 35

D. Miscellaneous 36

E. Stage One: Application Screening 38

F. Stage Two: Application Scoring 39

Attachments Attachment Number Title of Section 1 Executive Summary Form 2 Project Narrative Form 3 Project Team Form 4 Scope of Work Template 5 Project Schedule 6 Budget 7 CEQA Compliance Form 8 Past Projects Information Form 9 Commitment and Support Letters Form (requires signature) 10 Project Performance Metrics 11 Applicant Declarations (requires signature)

I. Introduction

A. Purpose of Solicitation

The purpose of this solicitation is to fund applied research and development projects that inform California's transition to an equitable, zero-carbon energy system that is climate-resilient and meets environmental goals. Applied research supported by this solicitation will improve existing ambient air quality modeling approaches, advance low-cost measurement technologies, and conduct analyses to quantify the air quality implications and related human health impacts of clean energy interventions across energy use sectors. Ultimately, this research will contribute to a foundation for accurate monetization of non-energy impacts of clean energy interventions.

Improvements in air quality and related human health outcomes are important direct benefits of decarbonizing energy sectors that currently rely on fossil fuels. Capturing these impacts is important to motivate investments in decarbonization strategies but is also a challenging task. Robust air quality modeling and field measurements can help to accurately project the air quality-related benefits and ultimately monetize the human health impacts of clean energy interventions such as implementation of Senate Bill (SB) 100 ( SB 100, De León, Chapter 312, Statutes of 2018 ), transportation electrification, and building electrification.

This solicitation addresses public interest research opportunities at the nexus of human health, air quality monitoring and modeling, and California’s decarbonizing electricity system. Existing ambient air quality simulation models require rigorous evaluation for accuracy of estimates, specific tailoring to California’s complex geography and meteorology, and expanded capability to simulate pollutants such as ozone and secondary particulate matter. Additionally, improvements in the spatial resolution and computational efficiency of existing models are critical to illuminate impacts of decarbonization strategies, and applied research is needed related to low-cost air quality sensors for in-situ household pollution measurement. While existing sensors enable collection of abundant data, they can be afflicted by performance issues and associated inaccuracies.

Projects must fall within the following project groups:

• Group 1: Advancing Ambient Air Quality Modeling Capabilities and Analysis; and

• Group 2: Developing a Low-cost Air Quality Sensor to Assess Household Air Pollution.

See Section II of this solicitation for eligibility requirements. Applications will be evaluated as described in Section IV of this solicitation.

Applicants may submit multiple applications, though each application must address only one of the project groups identified above. If an applicant submits multiple applications that address the same project group, each application must be for a distinct project (i.e., no overlap with respect to the technical tasks described in the Scope of Work).

Prospective applicants looking for partnering opportunities for this funding opportunity should register on the California Energy Commission’s Empower Innovation website at www.empowerinnovation.net

B. Key Words/Terms

Word/Term	Definition
Applicant	An entity that submits an application to this solicitation.
Application	An applicant’s written response to this solicitation.
Authorized Representative	The person submitting the application who has authority to enter into an agreement with the CEC.
California Native American Tribe	A Native American Tribe located in California that is on the contact list maintained by the Native American Heritage Commission for the purposes of Chapter 905 of the Statutes of 2004 (Pub. Resources Code, § 21073).
California Tribal Organization	A corporation, association, or group controlled, sanctioned, or chartered by a California Native American tribe that is subject to its laws, the laws of the State of California, or the laws of the United States.
CAM	Commission Agreement Manager, the person designated by the CEC to oversee the performance of an agreement resulting from this solicitation and to serve as the main point of contact for the grant recipient.
CAO	Commission Agreement Officer, the person designated by the CEC to oversee the internal administrative processes and to serve as the main point of contact for solicitation applicants.
CBO	Community Based Organization, a public or private nonprofit organization of demonstrated effectiveness that: a) Has deployed projects and/or outreach efforts within the region (e.g., air basin or county) of the proposed disadvantaged or low-income community or similar community. b) Has an official mission and vision statements that expressly identifies serving disadvantaged and/or low-income communities. c) Currently employs staff member(s) who specialized in and are dedicated to – diversity, or equity, or inclusion, or is a 501(c)(3) non-profit.
CEC	State Energy Resources Conservation and Development Commission or the California Energy Commission.
CEC funds	CEC funds are EPIC grant funds awarded under this solicitation. Also referred to as grant funds.
CEQA	California Environmental Quality Act, California Public Resources Code Section 21000 et seq.
Days	Days refers to calendar days.
Disadvantaged Community	Communities designated pursuant to Health and Safety Code section 39711 as representing the top 25% scoring census tracts from CalEnviroScreen along with other areas with high amounts of pollution and low populations as identified by the California Environmental Protection Agency. (https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40)
Disadvantaged and Vulnerable Communities	Communities identified as disadvantaged under CalEnviroScreen, low-income communities with median household incomes at or below 60% of the statewide median, and all California Native American Tribes (federally and non-federally recognized).
Energy Equity	The fair distribution of benefits and burdens from energy production and consumption.
EPIC	Electric Program Investment Charge, the source of funding for the projects awarded under this solicitation.
IOU	Investor-owned utility, an electrical corporation as defined in California Public Utilities Code section 218. For purposes of this solicitation, it includes Pacific Gas and Electric Co., San Diego Gas and Electric Co., and Southern California Edison Co.
Low Income Community	Communities within census tracts with median household incomes at or below 80 percent of the statewide median income or the applicable low-income threshold listed in the state income limits updated by the Department of Housing and Community Development. (https://www.hcd.ca.gov/grants-and-funding/income-limits)
Major Subrecipient	A Subrecipient that is budgeted to receive $100,000 or more of CEC funds, not including any equipment or match funds that may be provide by the Subrecipient.
NOPA	Notice of Proposed Award, a public notice by CEC staff that identifies proposed grant recipients.
Pre-Commercial Technology	A technology that has not reached commercial maturity or been deployed at scales sufficiently large and in conditions sufficiently reflective of anticipated actual operating environments to enable the appraisal of operational and performance characteristics, or of financial risks.
Pilot Test	Small scale testing in a laboratory or testing on a small portion of the production line of the affected industry. Pilot tests help verify the design and validity of an approach, and adjustments can be made at this stage before full-scale demonstrations
Principal Investigator	The technical lead for the applicant’s project, who is responsible for overseeing the project; in some instances, the Principal Investigator and Project Manager may be the same person.
Project Manager	The person designated by the applicant to oversee the project and to serve as the main point of contact for the CEC.
Project Partner	A person or entity that contributes financially or otherwise to the project (e.g., match funding, provision of a test, demonstration or deployment site) and does not receive CEC funds.
Recipient	A person or entity receiving a grant award under this solicitation. “Recipient” may be used interchangeably with “grant recipient”.
SOAs	Secondary Organic Aerosols (SOAs), fine airborne particles formed when volatile organic compounds (VOCs) from natural or human sources undergo chemical reactions in the atmosphere, producing low-volatility compounds that condense into particulate matter.
Solicitation	This entire document, including all attachments, exhibits, addenda, written notices, and questions and answers (“solicitation” may be used interchangeably with “Grant Funding Opportunity” or “GFO”).
Subrecipient	A person or entity that receives grant funds directly from a grant Recipient and is entrusted to make decisions about how to conduct some of the grant’s activities. A Subrecipient’s role involves discretion over grant activities and is not merely just selling goods or services.
Sub-Subrecipient	Has the same meaning as a Subrecipient except that it receives grant funds from a Subrecipient or any lower tier level of a Sub-Subrecipient.
State	State of California
TRL	Technology readiness levels are a method for estimating the maturity of technologies during the acquisition phase of a program. Source: U.S. Department of Energy, “Technology Readiness Assessment Guide”. https://www2.lbl.gov/dir/assets/docs/TRL%20guide.pdf
Vendor	A person or entity that sells goods or services to the grant Recipient, Subrecipient, or any lower-tiered level of Sub-Subrecipient, in exchange for some of the grant funds, and does not make decisions about how to perform the grant’s activities. The Vendor’s role is ministerial and does not involve discretion over grant activities.
VOCs	Volatile Organic Compounds (VOCs), chemicals with high vapor pressure and low water solubility that readily evaporate into the air. They are precursors to harmful pollutants such as fine particulate matter and ozone, contributing significantly to air pollution and associated health risks. Common examples include benzene, toluene, and formaldehyde.

C. Project Focus

Group 1: Advancing Ambient Air Quality Modeling Capabilities and Analysis.

Group 1 will support applied research to advance modeling capabilities of ambient air quality models, test optimal and equitable placement of sensors within California’s current air quality sensor network, and perform analysis using data and air quality models to answer policy-relevant questions related to understanding non-energy impacts and benefits of energy system decarbonization scenarios.

The successful applicant for this group must, at a minimum, propose a project that will:

1. Identify an existing ambient air quality model to improve, or justify development of a new model. The applicant must contextualize its proposed focus based on peer reviewed literature.

2. Use novel methodologies, such as machine learning and artificial intelligence approaches, to improve the existing model or develop the new model, per the proposed project focus. Proposals must include a description of the methods that will be used to improve an existing model or develop a new one.

3. Use the improved or new model[s] to perform analyses illuminating important policy-relevant questions, including, at a minimum:

a) Quantify air quality and health impacts of clean energy transitions in California, namely SB 100 implementation, building electrification, transportation electrification, bioenergy deployments, and distributed generation, with particular attention to health-damaging pollutants such as fine particulate matter (PM2.5) and ozone (O3). Applicants must delineate specific questions that merit inquiry and will drive the analyses.

b) Quantify distributed air quality impacts of the current energy system (for benchmarking) and future clean energy scenarios to Disadvantaged and Vulnerable Communities.

c) Quantify impacts of residential electrification on outdoor air quality and human health.

d) Investigate marginal emission impacts of interventions such as electric vehicles and renewable energy generation.

e) Quantify impacts of wildfire-generated aerosols on solar photovoltaic (PV) generation in California.

f) Develop preliminary estimates that monetize impacts of clean energy interventions to clarify, at a minimum, net costs of home electrification by factoring in the non-energy costs with upfront, operational, and maintenance costs. The goal is to provide a credible and empirically grounded assessment of the net cost impact on households—particularly those in Disadvantaged and Vulnerable Communities—when transitioning from fossil gas to electric options. Emphasis should be placed on capturing the distribution of costs and benefits across different household types, moving beyond reliance on average cost metrics. Proposals must outline a robust methodology for this analysis.

g) Perform air quality and geographic analysis of existing and planned gas power plants—such as those identified in the California Independent System Operator (CAISO) interconnection queue and SB 100 planning—to reduce impact of health-damaging pollutants on Disadvantaged and Vulnerable Communities.

4. Create a high-resolution spatial tool (e.g., an interactive dashboard) that provides estimates of air quality-related health and monetized impacts for Disadvantaged and Vulnerable Communities of future electrification scenarios across energy sectors. The tool should include estimates of health endpoints (e.g., mortality, respiratory effects) as well as monetized health impacts or benefits.

5. Test optimal and equitable placement of air quality sensors within California’s current regulatory air quality sensor network for criteria pollutants such as PM2.5, nitrogen dioxide (NO2), and O3.

a) Identify high-priority sensor locations to capture air quality benefits of future clean energy interventions.

b) Identify what (if any) improvements are needed to the air quality monitoring network.

Additionally, it is desirable that funded research addresses additional policy-relevant items below:

6. Clarify how air quality and health implications of clean energy transitions are affected by climate change and community-specific vulnerability to air pollution.

7. Estimate the combined climate and health impacts of clean energy interventions.

8. Assess potential air quality impacts of CEC-proposed activities in Lithium Valley.

Background information (Group 1)

The atmosphere is a complex system in which numerous physical and chemical processes occur simultaneously across a range of spatial and temporal scales. Ambient monitors measure conditions at a particular time and location. Scientists developed air quality (or “atmospheric”) models in part to simulate concentrations more comprehensively than limited observational measurements support. These models are useful for illuminating air quality issues and supporting science-based policy decisions.

Air quality models can be broadly classified into two types: (1) statistical or empirical models and (2) mechanistic models. Statistical models are based on observations and predictions (examples: land-use regression models). Mechanistic models explicitly describe physics, chemistry, and meteorology to simulate atmospheric processes that govern the transport and transformation of air pollutants (examples: AERMOD, CMAQ). Mechanistic models can be further classified as Lagrangian, where the reference frame moves with the wind/particles; or Eulerian, where the reference frame is fixed. Eulerian Chemical Transport Models (Eulerian CTMs; examples:CMAQ, CAMx, WRF-Chem) are powerful tools that simulate atmospheric processes to predict concentrations of atmospheric constituents and their spatiotemporal variability.

Many air quality models are used in regulatory and research communities. Complex CTMs represent state-of-the-science atmospheric models and have historically provided the most robust estimates available when time and computational constraints are not limiting.4-7 However, because complex CTMs are time- and resource-intensive, in some cases modelers may prefer reduced-complexity air quality models (RCMs). RCMs can take a CTM-based,, Gaussian,, Lagrangian, or chemical mass balance approach. Although less accurate than complex CTMs, RCMs can have the flexibility to allow for a greater number of sensitivity analyses, Monte Carlo approaches, an understanding of source and receptor effects, use of smaller-sized grid cells, and longer simulated periods.,

Commonly used RCMs that can provide comprehensive estimates covering the contiguous U.S. at relatively high spatial resolution (approximately county level or finer) include: (1) the Air Pollution Emission Experiments and Policy (APEEP/AP2) model,8 (2) the Estimating Air pollution Social Impacts Using Regression (EASIUR) model, and (3) the Intervention Model for Air Pollution (InMAP). Gilmore et al. (2019) compare these three models. Among the three, APEEP/AP2 has county-level spatial resolution, EASIUR uses a 36 km × 36 km grid covering the contiguous U.S., and InMAP employs a variable spatial grid, where the grid cell size is a function of the gradient in population density and pollutant concentrations, varying from 1 km × 1 km (typically in urban areas) to 48 km × 48 km (typically in rural areas).

Each of these three models has specific strengths and weaknesses. While APEEP/AP2 can model concentrations of PM2.5 that is formed in the atmosphere from emissions of precursors, EASIUR cannot model formation of secondary organic aerosols (SOA) from volatile organic compound (VOC) emissions; hence, EASIUR may not be suitable to estimate total PM2.5. APEEP/AP2 has coarser spatial resolution (county-level) than InMAP does, so it may not be desirable for addressing questions related to environmental justice and other distributional concerns. Currently, InMAP does not have the capability to model other (beyond PM2.5) health-damaging pollutants such as O3 and nitrogen oxides. Many of these models do not offer uncertainty analysis. Additionally, Paolella et al. (2018) demonstrated the importance of fine spatial resolution for identifying and quantifying exposure disparities in energy systems.

There are many areas in which air quality models can be improved for greater efficiency, inclusion of multiple pollutants, and improved accuracy of results. For example, complex CTMs can be made computationally faster. RCMs, such as InMAP, have been widely used in recent years owing to their computational speed. For example, Wei et al. (2023) estimated that more than $40 million in public health benefits would accrue from transitioning all vehicles in Fresno County to zero-emission vehicles, and in another study, InMAP modeling showed transportation-related air pollution disparities in California. However, Tessum et al. (2017) recommends that further improvements in InMAP are needed. Further recommendations include tailoring the model to a specific geography such as California, expanding it to other pollutants such as O3, and enhancing it to produce uncertainty analysis. Machine learning approaches have recently gained attention in the scientific community to train models for efficiency and accuracy in predicting air quality. Models trained by machine learning can adequately account for nonlinear relationships between emissions, atmospheric chemistry, and meteorological factors., These approaches can be used to further improve existing modeling capabilities or to create new models by managing a large quantity of datasets and ultimately achieving desired model computational efficiency for research and regulatory use.

There are several policy-relevant questions that warrant attention and can be addressed using improved air quality models. For example, understanding air quality impacts and related health implications of gas-fueled peaker electricity plants and refineries in Disadvantaged and Vulnerable Communities is an important environmental justice issue. A 2021 report from the Bay Area Air Quality Management District used the California Puff (CALPUFF) AQ model and U.S. Environmental Protection Agency’s (EPA) BenMAP health analysis model to show three to six premature deaths per year – associated with an economic cost of $29-65 million per year – attributed to PM2.5 emissions from one refinery in the San Francisco Bay Area. Further research is needed to elucidate environmental impacts of transport electrification by evaluating various factors including growth in electric vehicles (both passenger and freight), charging infrastructure, and clean electricity production, as well as human behaviors. Implementation of SB 100 scenarios also require assessing air quality and the related health costs and benefits of the additional clean electricity generation capacity and storage needed to achieve SB 100 goals.

Modeling air quality helps inform future policy decisions, while monitoring air quality using ground sensors plays an important role in capturing real-time, empirically grounded impacts of energy interventions and in validating modeling outputs. Kelp et al. (2022) suggested that the current U.S. EPA monitoring network for PM2.5 in the San Joaquin Valley and northern California is relatively sparse in the context of capturing impacts of high-pollution events such as wildfires. The study identified the need for significantly more sensors in these regions than were in the current U.S. EPA sensor network to better monitor concentrations from such events. Another study, Marlier et al. (2022) showed that existing air quality monitoring networks in California do not provide adequate coverage of PM2.5 exposure in wildfire-prone regions, particularly those with large populations of agricultural workers who are at heightened risk under both current and future climate conditions. These studies show there is a mismatch between where the sensors are and where air pollution and populations are concentrated, though these studies are largely focused on wildfire pollution. The importance of incorporating location-specific emissions reductions into the US air quality regulatory framework to eliminate exposure disparity has been documented in a recent study by Wang et al. (2022). Although low-cost air sensors exist (e.g., from PurpleAir), the adequacy of crowd-sourced observational networks to portray exposure disparities remains uncertain. Environmental justice advocates have recently raised issues with the placement of new monitors (in terms of number and location) in other states.

California is making significant efforts to improve ambient air quality by decarbonizing economic sectors including electricity generation, transportation, and buildings. To capture the benefits of improved air quality from clean energy interventions such as electrification of end-uses and to identify related equity gaps in allocation of benefits, it is important to develop an optimal and equitable air quality monitoring network across California’s complex geography, with particular attention to characterizing air quality among both rural and urban populations. A key question to evaluate is whether the existing air quality monitoring network (for health-damaging pollutants such as PM2.5, NO2, and O3) in California is optimally and equitably placed to capture the benefits of air quality improvements from future clean energy interventions. There is a need to characterize air quality monitoring networks’ abilities to detect changes with statistical rigor and to understand what improvements are needed.

Group 2: Developing a Low-cost Air Quality Sensor to Assess Household Air Pollution.

Group 2 will support research to identify gaps and challenges in low-cost sensor technology, develop a new low-cost air quality sensor, and validate it in the indoor environment.

The successful applicant for this group must, at a minimum, propose a project that will:

1. Identify pollutant[s] of concern (e.g., PM2.5, NO2) for developing the low-cost air quality sensor. The applicant must justify its choice of pollutant[s] of concern.

2. Identify key performance challenges in the ability of existing low-cost sensors to adequately quantify the pollutant[s] of concerns based on scientific studies, including the peer-reviewed literature.

3. Develop an improved low-cost air quality sensor addressing the identified challenges. The applicant must describe its approach to developing the improved low-cost air quality sensor. The new sensor will comply to Technology Readiness Level (TRL) 6 (validated in relevant environment).

4. Validate the improved low-cost air quality sensor. The proposal must describe the approach to validating the new sensor.

It is desirable that funded research include development of:

1. Wearable air quality sensors to advance research for dynamic and real-time measurements of environmental exposures.

2. A compact, low-cost NO2 sensor that can be deployed inside homes for monitoring exposure to NO2, especially during cooking activities.

3. Methods to link population-based air quality-related health data back into the healthcare system.

Additional background for Group 2

Indoor air quality (IAQ) monitoring provides crucial data to support the health and comfort of building occupants. In recent years, revised energy efficiency requirements for buildings have led to tightening of building envelopes to reduce infiltration of pollution from outdoors. This has, however, exacerbated concerns about health effects from exposures to air pollutants that are generated indoors (e.g., cooking-associated pollutants). On the other hand, in older buildings, where new building standards are not applicable, indoor air can be degraded from increasing high outdoor pollution events such as wildfires. Also, building decarbonization interventions such as cooking electrification can have varying impacts on residents’ exposure to pollutants based on home size and availability of mechanical ventilation. There is a need to fill data gaps on IAQ in homes to understand air quality-related health consequences of clean energy and energy efficiency interventions, identify disparities in air quality impacts, and track progress of non-energy benefits from interventions.

To date, efforts to collect such data are limited by the costs of instrumentation and sample analysis. Low-cost sensors can reduce the hardware costs associated with acquiring time-resolved pollutant concentration data. For example, Clark et al. (2020) designed a specialized 3D-printed passive air quality sampler by combining low-cost methods (passive sample collection; digital image-based analysis). Tryner et al. (2021) assembled small “Home Health Boxes” (HHBs) to measure indoor PM2.5, NO2, and O3 concentrations using filter samplers and low-cost sensors.

While the above-mentioned advancements in low-cost sensors are promising, it is important to note that low-cost sensor data must be interpreted with care. Relative to reference instruments, low-cost sensors are still lagging in accuracy. A number of prior studies have assessed the performance of low-cost sensors and documented limitations or inaccuracies in their measurements.,, These studies suggest that the low-cost sensors tested are not yet ready to replace more established exposure assessment methods in long-term household air pollution epidemiologic studies. A recently published California Air Resources Board (CARB) white paper, “Low-Cost Sensors for Healthier Indoor Air Quality in Impacted Communities,” recommends continuous validation of low-cost sensor performance, especially for gas pollutants such as NO₂, O₃, and Volatile Organic Compounds (VOCs). It also emphasizes the need for ongoing research and development to advance low-cost sensor technology, as well as the need to adapt sensor features to emerging IAQ issues and trends.

Wearable air quality sensors have recently gained attention for their potential role in facilitating citizen science research on exposure and advancing understanding of precision environmental health. These sensors can be pivotal in promoting clean energy solutions by providing quantitative information that facilitates management of indoor conditions for improved wellbeing. These sensors track real-time data on pollutants like particulates and VOCs while also monitoring the pollutants’ impact on stress, mobility, and sleep quality. This data can guide parameters important for energy efficiency strategies—such as optimizing air filtration, ventilation, humidity, or temperature control—and ultimately help modify indoor environments (e.g., adjusting air filtration rate or capture efficiency of kitchen ventilation system) to enhance individuals’ health and comfort.

An improved low-cost air quality sensor could help California residents confidently track their exposures to health-damaging pollutants such as NO2 and PM2.5. This is especially timely, as the CARB is working to update the California 2005 IAQ guidelines for NO2. Additionally, the research may help inform the efforts of the South Coast Air Quality Management District’s program, Air Quality Sensor Performance Evaluation Center, which aims to evaluate commercially available low-cost air quality sensors for measuring criteria pollutants.

D. Funding

1. Amount Available and Minimum/ Maximum Funding Amounts

There is up to $6,000,000 available for grants awarded under this solicitation. The total, minimum, and maximum funding amounts for each project group are listed below.

Project Group	Available CEC funding	Minimum CEC award	Maximum CEC award	Minimum total match share percentage
Group 1: Advancing Ambient Air Quality Modeling Capabilities and Analysis	$3,000,000	$2,900,000	$3,000,000	5%
Group 2: Developing a Low-cost Air Quality Sensor to Assess Household Air Pollution	$3,000,000	$2,900,000	$3,000,000	5%

2. Match Funding Requirement

Applications for groups 1 and 2 must include a minimum 5% total match share percentage for this solicitation.

Total match share percentage is calculated by dividing the total match share contributions by the total CEC funds requested plus total match share contributions:

X 100 = Total Match Share percentage

For the definition of match funding see Section I K.

3. Change in Funding Amount

Along with any other rights and remedies available to it, the CEC reserves the right to:

• Increase or decrease the available funding and the minimum/maximum grant award amounts described in this section.

• Allocate any additional or unawarded funds to passing applications, in rank order.

• Reallocate funding between any of the groups

• Aggregate funds from multiple groups to fully fund the highest ranked passing applications, regardless of group.

• Reduce funding to an appropriate amount if the budgeted funds do not provide full funding for agreements. In this event, the proposed grant recipient and Commission Agreement Manager (CAM) will attempt to reach agreement on a reduced Scope of Work commensurate with available funding.

E. Key Activities Schedule

Key activities, dates, and times for this solicitation and for agreements resulting from this solicitation are presented below. An addendum will be released if the dates change for activities that appear in bold.

ACTIVITY	DATE	TIME
Solicitation Release	January 27, 2026
Pre-Application Workshop	February 10, 2026	10:00 a.m.-12:00 p.m.
Deadline for Written Questions	February 17, 2026	5:00 p.m.
Anticipated Distribution of Questions and Answers	Week of February 23, 2026
Support for Application Submission in ECAMS	April 30, 2026	5:00 p.m.
Deadline to Submit Applications	April 30, 2026	11:59 p.m.
Anticipated Notice of Proposed Award Posting Date	Week of June 22, 2026
Anticipated Energy Commission Business Meeting Date	September 9, 2026
Anticipated Agreement Start Date	October 1, 2026
Anticipated Agreement End Date	March 31, 2031

F. Notice of Pre-Application Workshop

CEC staff will hold one Pre-Application Workshop to discuss this solicitation with potential applicants. Participation is optional but encouraged. The Pre-Application Workshop will be held remotely. Applicants may attend the workshop via the internet (Zoom, see instructions below), or via conference call on the date and at the time and location listed below. Please refer to the CEC's website at www.energy.ca.gov/contracts/index.html to confirm the date and time. Please be aware that the meeting will be recorded.

Date and time: February 10, 2026, 10:00 AM Pacific Time

Zoom Instructions:

To join the Zoom meeting, go to https://zoom.us/joinand enter the Meeting ID below and select “join from your browser.” Participants will then enter the meeting password listed below and their name. Participants will select the “Join” button.:

Meeting ID: 865 3759 2878

Meeting Password: 728478

Topic: GFO-25-304 Pre-Application Workshop

Telephone Access Only:

Call 1-888 475 4499 (Toll Free) or 1-877 853 5257 (Toll Free). When prompted, enter the meeting number above. International callers may select a number from the Zoom International Dial-in Number List at: https://energy.zoom.us/u/adjzKUXvoy. To comment, dial *9 to “raise your hand” and *6 to mute/unmute your phone line.

Access by Mobile Device:

Download the application from the Zoom Download Center, https://energy.zoom.us/download.

Technical Support for Pre-Application Workshop:

• For assistance with problems or questions about joining or attending the meeting,

please call Zoom Technical Support at 1-888-799-9666 ext. 2. You may also contact the CEC’s Public Advisor’s Office at publicadvisor@energy.ca.gov, or (916) 957-7910.

• System Requirements: To determine whether your computer is compatible, visit:

https://support.zoom.us/hc/en-us/articles/201362023-System-requirements-for-Windows-macOS-and-Linux.

• If you need a reasonable accommodation to participate, please Erica Rodriguez by e-mail at Erica.Rodriguez@energy.ca.gov or (916) 764-5705 at least five days in advance.

G. Questions

During the solicitation process, for questions only related to submission of application in the new ECAMS system, please contact ECAMS.SalesforceSupport@energy.ca.gov . Through that email address applicants will be able to access a team of technical assistants who can answer questions about application submission. Please also see Section III.B for additional information about the ECAMS system.

For all other questions, including all technical and administrative questions that are not related to submission of applications in the ECAMS system, please contact the Commission Agreement Officer listed below:

Natalie Johnson, Commission Agreement Officer

California Energy Commission

715 P, MS-18

Sacramento, California 95814

E-mail: Natalie.Johnson@energy.ca.gov

Applicants may ask questions at the Pre-Application Workshop and may submit written questions via email. However, all technical questions must be received by the deadline listed in the “Key Activities Schedule” above. Questions received after the deadline may be answered at the CEC's discretion. Non-technical questions (e.g., administrative questions concerning application format requirements or attachment instructions) may be submitted to the CAO at any time prior to 5:00 p.m. of the application deadline date. Similarly, questions related to submission of applications in the ECAMS system may be submitted to ECAMS.SalesforceSupport@energy.ca.gov at any time prior to 5:00 p.m. of the application deadline date.

The questions and answers will also be posted on the CEC’s website at: https://www.energy.ca.gov/funding-opportunities/solicitations .

If an applicant discovers a conflict, discrepancy, omission, or other error in the solicitation at any time prior 5:00 p.m. of the application deadline date, the applicant may notify the CAO in writing and request modification or clarification of the solicitation. The CEC, at its discretion will provide modifications or clarifications by either an addendum to the solicitation or by written notice to all entities that requested the solicitation. At its discretion, the CEC may, in addition to any other actions it may choose, re-open the question/answer period to provide all applicants the opportunity to seek any further clarification required.

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