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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.
Dynamically Learning Incentives for Load Control
As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid operators lack direct control over end-users' grid interactions, such as energy usage, but incentives can influence behavior -- for example, an end-user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. A key challenge in studying such incentives is the lack of data about human behavior, which usually motivates strong assumptions, such as distributional assumptions on compliance or rational utility-maximization. In this paper, we propose a general incentive mechanism in the form of a constrained optimization problem -- our approach is distinguished from prior work by modeling human behavior (e.g., reactions to an incentive) as an arbitrary unknown function. We propose feedback-based optimization algorithms to solve this problem that each leverage different amounts of information and/or measurements. We show that each converges to an asymptotically stable incentive with (near)-optimality guarantees given mild assumptions on the problem. Finally, we evaluate our proposed techniques in voltage regulation simulations on standard test beds. We test a variety of settings, including those that break assumptions required for theoretical convergence (e.g., convexity, smoothness) to capture realistic settings. In this evaluation, our proposed algorithms are able to find near-optimal incentives even when the reaction to an incentive is modeled by a theoretically difficult (yet realistic) function.
Show Me the Money! Evaluating A Graduated Incentive Structure to Keep Respondents Engaged Through 101 Surveys in 28 Days
We report the results of a survey test conducted in advance of a series of community response tests (CRTs) to evaluate response to noise from NASA’s X-59 aircraft. The CRTs will require a substantial number of observations to generate a dose response curve for noise exposure and related annoyance levels and the timeframe is limited due to resource and scheduling constraints with an experimental aircraft. Within each CRT area, we will recruit a sample of residents in advance and ask them to fill out a brief survey each time the aircraft passes over. Respondents will be asked to fill out the survey either on the web or as part of an application they are able to download onto their mobile phones. Respondents will be notified each time the plane flies over, asking for their reactions. On many days, respondents will be asked to fill out the survey for multiple flights. Typical incentives are used during the recruitment phase (pre-incentive for a household screener and post-paid for a background survey completed by the selected respondent) and post-paid for an end of study survey. To improve response rates over such a demanding survey schedule (101 surveys in 28 days) we will implement a weekly incentive based upon participation rates, with higher incentives for greater participation and increasing rates each week. The survey test will follow the proposed methodology of the CRTs. We will report on the participation rates by incentive amount and the effectiveness of the incentive structure in obtaining and maintaining response rates over the 4 weeks. We will examine how quickly participation fades over the 4-week period and consider whether the incentive schedule could be improved by altering the distribution of payments. Finally, we will compare how these rates varied across web and app respondents. We will use the results of this work to confirm or improve the design of the CRTs, ensuring the collection of data needed by NASA to evaluate the impact of this innovative technology.
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.
Driving Investment in Wind Energy: An Introduction to Incentives and the Inflation Reduction Act [Slides]
In a webinar hosted by the U.S. Department of Energy's WINDExchange initiative, experts from the North Carolina Clean Energy Technology Center and the National Renewable Energy Laboratory introduce attendees to the key incentives supporting investment in wind energy deployment and manufacturing in the United States, as well as the role that the Inflation Reduction Act (IRA) plays in shaping those investments. Over the past few decades, incentives like the production tax credit and investment tax credit have supported the growth of wind energy deployment, while manufacturing-related incentives have helped scale up domestic manufacturing of wind energy components. With its passage in 2022, the IRA ushered in a new wave of investment in wind energy and other renewable technologies, as well as introducing new workforce requirements and equity provisions. This presentation explores the history and impact of major incentives, unpacks some of the complex provisions of the IRA, and highlights the ways federal incentives and policies will continue to shape the wind energy industry.
Calculating Behind-the-Meter Energy Storage Incentives on an Avoided Cost Basis
Behind-the-meter (BTM) energy storage offers the potential for shared investment by utilities and their customers, in which both parties share in the costs and benefits of battery investment. Several utilities and a handful of states have begun providing incentives to help customers purchase BTM energy storage, and in exchange, operate that battery on behalf of the customer to realize grid benefits such as meeting peak capacity needs. But there is wide variation in the value and structure of these incentives, suggesting a lack of clarity on the value of BTM storage to the grid and to non-participating customers who fund the incentives for participating customers. This paper provides an objective framework for establishing BTM energy storage incentives based on the avoided cost of generation from a marginal, gas-fired peaking plant. It also conducts several sensitivity analyses to see how those avoided costs vary with emissions pricing, gas prices, plant heat rates, and utility capital structures. This work may be of use to utilities, regulators, and energy system stakeholders in providing a value-based framework for BTM incentive programs that can be used in the absence of policy guidance.
Climate change as an incentive for future human migration
Human migration is both motivated and constrained by a multitude of socioeconomic and environmental factors, including climate-related factors. Climatic factors exert an influence on local and regional population density. Here, we examine the implications of future motivation for humans to migrate by analyzing today's relationships between climatic factors and population density, with all other factors held constant. Such “all other factors held constant” analyses are unlikely to make quantitatively accurate predictions, but the order of magnitude and spatial pattern that come out of such an analysis can be useful when considering the influence of climate change on the possible scale and pattern of future incentives to migrate. Our results indicate that, within decades, climate change may provide hundreds of millions of people with additional incentive to migrate, largely from warm tropical and subtropical countries to cooler temperate countries, with India being the country with the greatest number of people with additional incentive to migrate. These climate-driven incentives would be among the broader constellation of incentives that influence migration decisions. Areas with the highest projected population growth rates tend to be areas that are likely to be most adversely affected by climate change.
Multi-technology building system retrofits for utility incentive programs: Savings, costs and baseline considerations
Utility incentive programs are an important channel to support the deployment of energy efficiency in buildings. To date, these programs have largely been limited to single-component strategies. However, many utilities are now motivated to identify and develop multi-component system retrofits to achieve deeper energy savings, which are essential to achieving broader energy and greenhouse gas reduction goals in the buildings sector. In this paper we present the energy savings, demand reductions, and cost-effectiveness of 16 systems retrofit packages in six utility regions in the United States. These results are being used by these utilities to inform and develop incentive programs for systems retrofits. Our analysis shows that packages with proven lighting and HVAC measures can provide 5–22% whole building annual energy savings, and 13–22% annual energy costs savings, using utility incentive program baselines (code and existing building). The packages are reasonably cost effective for replace-on-burnout but generally not for a retrofit scenario prior to end of equipment life. Demand response can increase both the energy savings and energy cost savings, further improving the cost effectiveness of these packages. We analyzed the impact of using existing building vs. code baselines for calculating savings, showing that the choice of baseline in developing utility incentive programs has a substantial impact on the attributable energy savings to a program, with significant implications for the overall viability of a program (generally savings against existing building condition are higher and improve project and program cost-effectiveness).
Comparative Analysis of Natural Gas Cogeneration Incentives on Electricity Production in Latin America
An analysis to assess the influence of country-dependent variables and incentives on the feasibility of natural gas cogeneration projects in Latin America is presented in this work. The analysis is performed using a hypothetical industrial plant, where the cogeneration solution consists in the recovery of waste heat from the power generation for steam production. The feasibility is evaluated by calculating the Return of Investment (ROI) and the Internal Rate of Return (IRR) of the project. Eight Latin American countries are studied considering their specific natural gas markets, regulation, and macroeconomic variables. Two scenarios, electricity production for self-consumption and electricity production with power surplus sale, are independently analyzed considering the effects of available incentives. In the countries where the project is feasible, the application of incentives leads to a significant reduction in the ROI and, consequently, to an increment in the IRR. The effect of the interest rate and environmental impact were also analyzed. In general terms, along the region, the regulation for cogeneration is incipient while incentives are very standard and similar respect to lowering of import, Value-Added Tax (VAT), and income taxes, which seems to be designed purely to promote capital investment. Following the results from this study, it is of paramount importance to create new policy instruments in the future to advance the regulatory framework for cogeneration in Latin America.
Evaluating the incentive for soil organic carbon sequestration from carinata production in the Southeast United States
Soil organic carbon (SOC) can be increased by cultivating bioenergy crops to produce low-carbon fuels, improving soil quality and agricultural productivity. This study evaluates the incentives for farmers to sequester SOC by adopting a bioenergy crop, carinata. Two agricultural management scenarios – business as usual (BaU) and a climate-smart (no-till) practice – were simulated using an agent-based modeling approach to account for farmers’ carinata adoption rates within their context of traditional crop rotations, the associated profitability, influences of neighboring farmers, as well as their individual attitudes. Here, using the state of Georgia, US, as a case study, the results show that farmers allocated 1056 × 10 3 acres (23.8%; 2.47 acres is equivalent to 1 ha) of farmlands by 2050 at a contract price of $\$6.5$ per bushel of carinata seeds and with an incentive of $50Mg -1 CO2e SOC sequestered under the BaU scenario. In contrast, at the same contract price and SOC incentive rate, farmers allocated 1152 × 10 3 acres (25.9%) of land under the no-till scenario, while the SOC sequestration was 483.83 × 10 3 Mg CO2e, which is nearly four times the amount under the BaU scenario. Thus, this study demonstrated combinations of seed prices and SOC incentives that encourage farmers to adopt carinata with climate-smart practices to attain higher SOC sequestration benefits.
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.
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.
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.
Rethinking the Price Formation Problem–Part 1: Participant Incentives under Uncertainty
Operators of organized wholesale electricity markets attempt to form prices in such a way that the private incentives of market participants are consistent with a socially optimal commitment and dispatch schedule. In the U.S. context, several competing price formation schemes have been proposed to address the non-convex production cost functions characteristic of most generation technologies. Here, this paper considers how the design and analysis of price formation policies for non-convex markets are affected by the uncertainty inherent in electricity demand and supply. We argue that by excluding uncertainty, the analytical framework underlying existing policies mischaracterizes the incentives of market participants, leading to inefficient price formation and poor incentives for flexibility. We establish favorable theoretical properties of a new construct, ex ante convex hull pricing , and demonstrate the difference between this idealized benchmark and existing methods on a large-scale test system. Given increased operational uncertainty with a transition to wind and solar generation, distortions caused by poor incentives for flexibility are likely to grow without improved price formation in organized wholesale markets.
Analysis of incentive policies for phosphorus recovery at livestock facilities in the Great Lakes area
Livestock operations have been highly intensified over the last decades, resulting in the advent of large concentrated animal feeding operations (CAFOs). Intensification decreases production costs but also leads to substantial environmental impacts. Specifically, nutrient runoff from livestock waste results in eutrophication, harmful algal blooms, and hypoxia. The implementation of nutrient recovery systems in CAFOs can abate nutrient releases and negative ecosystem responses, although they might negatively affect the economic performance of CAFOs. We design and analyze potential incentive policies for the deployment of phosphorus recovery technologies at CAFOs considering the geospatial vulnerability to nutrient pollution. The case study demonstration consists of 2217 CAFOs in the U.S. Great Lakes area. The results reveal that phosphorus recovery is more economically viable in the largest CAFOs due to economies of scale, although they also represent the largest eutrophication threats. For small and medium-scale CAFOs, phosphorus credits progressively improve the profitability of nutrient management systems. The integration of biogas production does not improve the economic performance of phosphorus recovery systems at most of CAFOs, as they lack enough size to be cost-effective. Furthermore, phosphorus recovery proves to be economically beneficial by comparing the net costs of nutrient management systems with the negative economic impact derived from phosphorus releases. The incentives necessary for avoiding up to 20.7.10 3 ton/year phosphorus releases and achieve economic neutrality in the Great Lakes area are estimated at $223 million/year. Additionally, the fair distribution of limited incentives is studied using a Nash allocation scheme, determining the break-even point for allocating monetary resources.
Improving the economics of battery storage for industrial customers: Are incentives enough to increase adoption?
As adoption of behind-the-meter battery energy storage increases across the United States, implementation continues to lag in the industrial sector. This analysis considers two manufacturing facilities with potential for load shifting to reduce peak demand. Although both facilities have load profiles that demonstrate great potential for regular and programmed demand reduction during peak hours, battery energy storage was deemed prohibitively expensive. A review of several existing utility and state-level policies and incentives determined that few may be rightsized for the industrial customer class. Furthermore, this analysis further considers multiple incentive structures and finds that although incentives increase viability of energy storage, developers must also consider optimization, unique load profiles, and use case to effectively increase adoption of battery energy storage by industrial customers.
Feedback Optimization of Incentives for Distribution Grid Services
Energy prices and net power injection limitations regulate the operations in distribution grids and typically ensure that operational constraints are met. Nevertheless, unexpected or prolonged abnormal events could undermine the grid's functioning. During contingencies, customers could contribute effectively to sustaining the network by providing services. Herein this paper proposes an incentive mechanism that promotes users' active participation by essentially altering the energy pricing rule. The incentives are modeled via a linear function whose parameters can be computed by the system operator (SO) by solving an optimization problem. Feedback-based optimization algorithms are then proposed to seek optimal incentives by leveraging measurements from the grid, even in the case when the SO does not have a full grid and customer information. Numerical simulations on a standard testbed validate the proposed approach.