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Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Community Solar Project Models: An Introduction to Basic Financial and Administrative Relationships [Slides]

This resource is a collection of visual aids and descriptions to help explain the basic financial and administrative relationships underpinning common community solar project models. The graphics in this resource depict the flow of electricity from generation to consumption and the financial exchanges between the community solar project owner, subscribers, and distribution utility. Community solar models vary based on ownership (e.g., third party, utility), customer type (e.g., multifamily, single-household), and solar array location (e.g., on-site, off-site).

14 SOLAR ENERGY↗

Guideline for Characterizing and Evaluating a Candidate Project Site for Solar Thermal Applications

This document presents a structured procedure for characterizing and evaluating candidate project sites for concentrating solar power (CSP) and solar heat for industrial processes (SHIP) applications. The objective is to provide project developers, researchers, and other stakeholders with a consistent, technology-agnostic framework for early-stage site assessment, enabling informed decision-making prior to significant investment in project development. Site selection is a critical factor in project success or failure for both CSP and SHIP projects. Key factors such as solar resource availability, land characteristics, environmental and regulatory constraints, infrastructure availability, and community context are determined by the choice of project site and can materially impact project performance, cost, schedule, and overall viability. This procedure is designed to systematically evaluate these factors, identify potential fatal flaws, and prioritize the most favorable candidate sites for further development. The process begins with rapid screening-level evaluation, using publicly available data to assess solar resource, land availability and suitability, zoning and land-use compatibility, and exclusion zones such as protected lands or sensitive habitats. Sites that meet the minimum screening criteria advance to a more detailed characterization. Subsequent sections of this report provide guidance for a next-level assessment of the most important technical and environmental parameters, including: 1) Solar resource quality, variability, and uncertainty using multiyear datasets and, where appropriate, on-site measurement campaigns; 2) Meteorological conditions such as wind, temperature, extreme weather events, and soiling impacts; 3) Land characteristics including slope, shading, and geotechnical conditions; and 4) Environmental and regulatory considerations, including permitting processes, endangered species, cultural resources, and visual impacts. The procedure also addresses infrastructure and integration considerations, including: 1) Grid interconnection requirements for CSP power generation projects; 2) Electrical and operational integration for SHIP facilities; 3) Water availability, quality, and permitting constraints, which are particularly critical for CSP in arid regions; and 4) Site access, construction logistics, and availability of workforce and supporting services. Recognizing the importance of social and economic context, the procedure includes evaluation of community engagement factors, such as stakeholder sentiment, proximity to sensitive visual receptors, workforce development opportunities, and local economic incentives. The outputs of these assessments are synthesized in a cost and risk evaluation, translating site characteristics into expected impacts on capital cost, operating cost, schedule, and technical risk. This is complemented by screening-level performance modeling, including 8760 simulations and long-term projections, to quantify expected energy or thermal output, assess variability thereof, and support comparison between candidate sites. Finally, the procedure provides high-level guidance on a structured go/no-go decision framework, categorizing sites based on identified risks and constraints, and outlining a clear path forward to feasibility studies and front-end engineering design for viable projects. By standardizing the site characterization process across both CSP and SHIP applications, this guideline aims to: 1) Improve consistency and transparency in early-stage project evaluation; 2) Reduce development risk and avoid investment in nonviable project sites; 3) Support collaboration between developers, researchers, and public agencies; and 4) Accelerate successful deployment of concentrating solar technologies for both power generation and industrial process heat.

14 SOLAR ENERGY↗

Demonstration of Electrolyzer Operation at a Nuclear Plant to Allow for Dynamic Participation in an Organized Electricity Market and In-House Hydrogen Supply

This document details the execution of Cooperative Agreement DE-EE0008849, “demonstration of electrolyzer operation at a nuclear plant to allow for dynamic participation in an organized electricity market and in-house hydrogen supply” during the performance period of 10/1/2019 – 9/30/2024. The project was funded by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE). Constellation Energy Generation, LLC (formerly Exelon Generation Company, LLC) is the prime recipient of the award. Other members of the project team are INL, NREL, ANL and Nel Hydrogen. The main project objective was to demonstrate an end-to-end integrated grid-scale carbon-free H 2 production, storage and utilization pilot plant at a nuclear generating facility. The project also aimed to evaluate market opportunities and regulatory requirements related to the participation of integrated hydrogen production and nuclear plant facilities in organized power markets, by demonstrating dynamic control and operation of the electrolyzer and assessing the economics of dynamic participation combined with the revenue streams from hydrogen production. On March 7th , 2023 Constellation started hydrogen production at it’s Nine Mile Point Nuclear Plant in Oswego, New York. The PEM electrolyzer operating at Nine Mile Point uses 1.25 megawatt of nuclear electricity to produce 560 kilograms of clean hydrogen per day, more than enough to meet the plant’s operational hydrogen use. It will also help set the stage for possible large-scale deployments at other clean energy centers in Constellation’s fleet that would couple clean hydrogen production with storage and other on-site uses. Employing the lessons learned from the 1.25 MW demonstration-scale, nuclear-powered clean hydrogen production facility at Nine Mile Point, Constellation was a major participant in the MachH2 hydrogen hub recently selected for up to $\$$1 billion by the Department of Energy (DOE) as part of the bipartisan Infrastructure Investment and Jobs Act. Constellation will use a portion of the hub funding to build the world’s largest nuclear-powered clean hydrogen production facility at its LaSalle Clean Energy Center in Illinois. The project was featured in a number of news articles and press releases and received 2 awards. At the 2023 DOE HFTO’s Annual Merit Review meeting, the P.I. Dr. Uuganbayar Otgonbaatar and project manager Robert Beaumont were recognized for “outstanding achievements in the development and demonstration of a first-of-a-kind clean hydrogen production facility, powered by carbon-free nuclear energy, at the Nine Mile Point Nuclear Station in Oswego, New York.” The project was also awarded 2023 Nuclear Energy Institute’s Top Innovative Practice award.

08 HYDROGEN↗

Upgrading Biogas through in situ Conversion of Carbon Dioxide to Biomethane in Anaerobic Digesters

Organic waste streams generated by wastewater treatment plants, agricultural operations, and food processing industries represent an important yet underutilized opportunity for renewable energy production in the United States. Through anaerobic digestion, these waste streams can produce biogas, a mixture primarily composed of methane (CH4) and carbon dioxide (CO2), that can be upgraded to pipeline-quality natural gas. However, most existing upgrading technologies remove CO2 from biogas rather than utilizing it, leaving a significant portion of the potential energy unused. This project investigates a novel biological upgrading approach that converts CO2 into additional CH4 by supplying hydrogen (H2) to specialized microorganisms capable of performing hydrogenotrophic methanation. The main challenges associated with biological biogas upgrading are related to hydrogen supply, gas-liquid mass transfer, and process stability. First, due to the high cost of hydrogen gas, it is preferable that H2 be produced on-site using renewable energy sources such as wind or solar power. Second, hydrogen has low solubility in liquids, which limits its availability to microorganisms and requires strategies to improve gas dissolution and transfer within the reactor. Third, process inhibition may occur as a result of increased pH caused by CO2 consumption or elevated H2 partial pressure, both of which can negatively affect methanogenic activity. Although research in these areas has advanced during the course of this project, these challenges have not yet been fully resolved. To date, the biological systems that have achieved the highest methane concentrations are typically ex-situ reactors, where operational conditions can be more easily controlled. For this reason, the findings of the present project remain highly relevant. The project goal was to develop an innovative system that can accomplish biogas upgrading via biological conversion of CO2 to CH4, in a novel hybrid approach that combines the advantages of both in-situ and ex-situ systems. The proposed system employs a three-phase upflow anaerobic bioreactor with H2 delivery through a gas-permeable membrane, enabling efficient hydrogen transfer and microbial conversion. Under optimized operating conditions, the system achieved 99% H2 consumption and 90% CO2 conversion. A subsequent gas cleaning stage was implemented to further improve gas quality and meet target purity standards. The upgraded gas composition reached 97.7% CH4, 2.2% CO2, and 0.97% O2, while H2S concentrations remained below detection limits. In addition, a flue gas-driven inorganic thermoelectric generator (TEG) system was designed and experimentally validated as a potential source of electricity for H2 production. The system consisted of six TEG modules connected in series and achieved an open-circuit voltage of 4.5 V and a maximum power output of 224 mW at a temperature difference of approximately 53.5 °C, demonstrating effective conversion of waste heat into electrical power under simulated flue gas conditions. Finally, a comprehensive techno-economic analysis was completed to evaluate the capital and operating costs associated with the proposed system. The results provide important insights to guide future scale-up, optimization, and potential deployment of integrated biological biogas upgrading technologies.

09 BIOMASS FUELS↗

Industrial Conduit Hydropower Opportunities in the United States: Scoping Assessment

Industrial conduit hydropower involves generating electricity from flowing water in existing industrial water supply, process flow, or wastewater discharges. The broader range of conduit hydropower involves powering existing water infrastructure in municipal, industrial, or agricultural systems. Whereas municipal and agricultural water systems have been retrofitted somewhat broadly in the United States, there are very few examples of industrial conduit hydropower. As stated in Kao et al. (2022), “conduit hydropower opportunities associated with industrial conduits are the least understood […] industrial developments are likely to be particularly efficient and cost-effective since they are typically eligible for on-site net-metering.” Therefore, additional insight into industrial opportunities is needed.

13 HYDRO ENERGY↗