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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation: Preprint

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

distribution system operator↗
DOE OSTI2025

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

adaptation models↗
DOE OSTI2025

Game Theory Approaches for System-level Incentive Design

This report presents a generalized Stackelberg game framework for designing and evaluating financial incentives that enhance power system resilience through strategic deployment of distributed energy resources(DERs) under various contingencies. The proposed approach addresses the challenge of coordinating individual community investment decisions to meet system-wide resilience objectives. The framework is demonstrated in a three-community test system subjected to two transmission contingency scenarios: inter-community line failure (Case 1) and complete main grid disconnection (Case 2). In both cases, three incentive levels are compared: a Base case with no financial incentives, and low and high incentive cases. In Case 1, the Base case (no incentives) results in a total installed DER capacity of 217.2 MW, with no load shedding due to alternative routing, but community costs remain high. Increasing incentives raises DER deployment to 286.9 MW, lowers aggregate community costs by $22M annually, and completely avoids the need for costly new transmission line construction. In Case 2, the Base case results in 24.3 MWh of unserved load; introducing incentives eliminates all load shedding and ensures up to 89 MWh of battery storage is available for emergency reserve. These results demonstrate that targeted incentives can dramatically improve grid resilience and cost-effectiveness. The framework thus offers policymakers and system planners a robust tool to quantify and compare the effectiveness of incentive programs for multi-community transmission networks behavior, system resilience, and economic efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Distribution Grid Incentive Design with Unknown Agent Behavior

Motivation: During extreme events, traditional grid regulation methods (e.g., energy prices, net power injection limits) may be insufficient. While system operators typically lack control over end-user grid interactions, (e.g., energy demand), incentives can influence behavior - for example, a user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. Problem: Optimize for the best incentive subject to system stability constraints. However, user behavior is unknown to the SO - i.e., for a given incentive, the amount of curtailed load or control variables exposed is unknown.

feedback based control↗
DOE OSTI2016

Financial Incentives to Enable Clean Energy Deployment: Policy Overview and Good Practices

Financial incentives have been widely implemented by governments around the world to support scaled up deployment of renewable energy and energy efficiency technologies and practices. As of 2015, at least 48 countries have adopted financial incentives to support renewable energy and energy efficiency deployment. Broader clean energy strategies and plans provide a crucial foundation for financial incentives that often complement regulatory policies such as renewable energy targets, standards, and other mandates. This policy brief provides a primer on key financial incentive design elements, lessons from different country experiences, and curated support resources for more detailed and country-specific financial incentive design information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗
DOE OSTI2014

Using Cost-Effectiveness Tests to Design CHP Incentive Programs

This paper examines the structure of cost-effectiveness tests to illustrate how they can accurately reflect the costs and benefits of CHP systems. This paper begins with a general background discussion on cost-effectiveness analysis of DER and then describes how cost-effectiveness tests can be applied to CHP. Cost-effectiveness results are then calculated and analyzed for CHP projects in five states: Arkansas, Colorado, Iowa, Maryland, and North Carolina. Based on the results obtained for these five states, this paper offers four considerations to inform regulators in the application of cost-effectiveness tests in developing CHP programs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Workshop Summary for Maintaining Innovation and Security in Biotechnology: Lessons Learned from Nuclear, Chemical, and Informational Technologies

In the fast-paced field of biotechnology where innovation has such far-reaching impacts on human health and the environment, dealing with the implications of possible illicit activities, accidents or unintended research consequences with potential detrimental societal impacts tends to remain in the background. While controls may be inevitable for the biotech industry, workshop attendees agreed that the way in which controls are implemented will play a major role in the agility and innovation of the biotechnology industry. There is little desire to slow down the pace of the gains while dealing with the security issues that arise. As was seen from the brief examinations of the Nuclear, Chemical, and Information Technology sectors explored in this workshop, establishing a regulatory regime needs to be a partnership between the public, corporate interests, scientists, and the government. Regulation is often written to combat perceived risk rather than actual risk—the public’s perceptions (occasionally even fictional portrayals) can spur regulatory efforts. This leads to the need for a thorough and continuing assessment of the risks posed by modern biotechnology. Inadequate or minimal risk assessment might expedite development in the short term but has potential negative long-term security and economic consequences. Industry and the technical community also often have a large role in setting regulatory policy, especially when well-crafted incentives are incorporated into the regulations. Such incentives might actually lead to enhanced innovation while poorly designed incentives can actually reduce safety and security. Any regulations should be as agile and flexible as the technology they regulate and when applied to biotechnologies they will need a new framework for thinking and implementing. The new framework should consider biotechnology as a technology and not simply a science since it is an extremely complex and adaptive system. This suggests the need to invest in social and political systems, rather than exclusively in technology, as a part of maintaining not only robust, but agile regulation. Modeling and simulations to illustrate how the biotech ecosystem might respond to new policies may be a promising approach to crafting regulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

On the Path to SunShot - Emerging Opportunities and Challenges in Financing Solar

Financial innovations—independent of technology-cost improvements—could cut the cost of solar energy to customers and businesses by 30%–60% (see Feldman and Bolinger 2016). Financing is critical to solar deployment, because the costs of solar technologies are paid up front, while their benefits are realized over decades. Solar financing has been shaped by the government incentives designed to accelerate solar deployment. This is particularly true for federal tax incentives, which have spawned complex tax-equity structures that monetize tax benefits for project sponsors who otherwise could not use them efficiently. Although these structures have helped expand solar deployment, they are relatively costly and inefficient. This has spurred solar stakeholders to develop lower-cost financing solutions such as securitization of solar project portfolios, solar-specific loan products, and methods for incorporating residential PV’s value into home values. To move solar further toward an unsubsidized SunShot future, additional financial innovation must occur. Development of a larger, more mature U.S. solar industry will likely increase financial transparency and investor confidence, which in turn will enable simpler, lower-cost financing methods. Utility-scale solar might be financed more like conventional generation assets are today, non-residential solar might be financed more like a new roof, and residential solar might be financed more like an expensive appliance. Assuming a constant, SunShot-level installed PV system price, such financing innovations could reduce PV’s LCOE by an estimated 30%–60% (depending on the sector) compared with historical financing approaches.

14 SOLAR ENERGY↗
DOE OSTI2022

Rooftop solar incentives remain effective for low- and moderate-income adoption

Financial incentives for rooftop solar photovoltaic (PV) adoption have declined in the United States over time by policy design. Incentive phase-down can efficiently promote early adoption and avoid ineffective payments to late adopters. Furthermore, incentive phase-down may exclude low- and moderate-income (LMI) households from realizing the same financial benefits from PV adoption as high-income early adopters. Here, data from two state-level LMI PV incentive programs are analyzed to test whether incentives still drive PV adoption among LMI households. As a first order approximation, the analysis suggests that incentives drove adoption that would not otherwise have happened in about 80% of cases. To the extent that policymakers prioritize PV adoption equity as part of the emerging energy justice policy agenda, the results suggest that ongoing incentive support for LMI adoption may be merited.

14 SOLAR ENERGY↗
DOE OSTI2014

Strengthening Building Retrofit Markets

The Business Energy Financing (BEF) program offered commercial businesses in Michigan affordable financing options and other incentives designed to support energy efficiency improvements. We worked through partnerships with Michigan utilities, lenders, building contractors, trade associations, and other community organizations to offer competitive interest rates and flexible financing terms to support energy efficiency projects that otherwise would not have happened. The BEF program targeted the retail food market, including restaurants, grocery stores, convenience stores, and wholesale food vendors, with the goal of achieving energy efficiency retrofits for 2 percent of the target market. We offered low interest rates, flexible payments, easy applications and approval processes, and access to other incentives and rebates. Through these efforts, we sought to help customers strive for energy savings retrofits that would save 20 percent or more on their energy use. This program helped Michigan businesses reduce costs by financing energy efficient lighting, heating and cooling systems, insulation, refrigeration, equipment upgrades, and more. Businesses completed the upgrades with the help of our authorized contractors, and, through our lending partners, we provided affordable financing options.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Data-driven occupant-behavior analytics for residential buildings

Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. Here, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

The impact of market design and clean energy incentives on strategic generation investments and resource adequacy in low-carbon electricity markets

Well-designed electricity markets play a crucial role in maintaining reliable electric power systems, which are critical in modern society. Here, this study examines the impact of different electricity market designs and clean energy incentive schemes on supporting renewable energy integration and achieving clean energy goals. To this end, we utilize a game-theoretical generation expansion planning model where generation companies make investment and retirement decisions to maximize their expected profit. The model is structured as an equilibrium problem with equilibrium constraints (EPEC) and solved using a diagonalization approach combined with progressive hedging. We analyze three types of electricity market designs: an energy-only market, a capacity market, and a clean energy market, and consider a wide range of market parameters resulting in 14 total scenarios. Wind and solar capacity comprise the majority of new investments in all considered scenarios, but the resultant system planning reserve margin (PRM) can differ significantly depending on market parameters. We also find that profit-driven investments lead to lower PRMs than a traditional system cost minimization approach. These individual scenario results further demonstrate how different market designs and clean energy incentive schemes may influence investor decision-making and impact resource adequacy throughout the clean energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Incorporate day-ahead robustness and real-time incentives for electricity market design

In this paper, we propose a two-stage electricity market framework to explore the participation of distributed energy resources (DERs) in a day-ahead (DA) market and a real-time (RT) market. The objective is to determine the optimal bidding strategies of the aggregated DERs in the DA market and generate online incentive signals for DER-owners to optimize the social-welfare taking into account network operational constraints. Distributionally robust optimization is used to explicitly incorporate data-based statistical information of renewable forecasts into the supply/demand decisions in the DA market. We evaluate the conservativeness of bidding strategies distinguished by different risk aversion settings. In the RT market, a bi-level time-varying optimization problem is proposed to design the online incentive signals to tradeoff the RT imbalance penalty for distribution system operators (DSOs) and the costs of individual DER-owners. This enables tracking their optimal dispatch to provide fast balancing services, in the presence of time-varying network states while satisfying the voltage regulation requirement. Simulation results on both DA wholesale market and RT balancing market demonstrate the necessity of this two-stage design, and its robustness to uncertainties, the performance of convergence, the tracking ability and the feasibility of the resulting network operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Incentivizing Distributed Solar: Best Practices (Presentation)

The presentation discussed findings of a recent NREL technical report on best practices for designing and implementing incentives for small and mid-sized residential and commercial distributed solar energy projects. Topics covered included: factors to consider in setting and modifying incentive levels over time, differentiating incentives to encourage various market segments, and administrative issues such as providing equitable access to incentives and customer protection. In addition, the presentation explored how incentive programs can be designed to respond to changing market conditions while attempting to provide a longer-term and stable environment for the solar industry. The findings of the report were based on interviews with program administrators, regulators, and industry representatives as well as data from numerous incentive programs nationally, particularly the largest and longest-running programs.

14 SOLAR ENERGY↗
DOE OSTI2017

Incentive-Based Voltage Regulation in Distribution Networks

This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltage constraints. Stability of the proposed schemes is analytically established and numerically corroborated.

24 POWER TRANSMISSION AND DISTRIBUTION↗