ABSTRACT

For most electricity markets in the U.S., the marginal cost and carbon emission intensity of electricity generation exhibit opposite diurnal trends: during peak demand hours, the electricity price is high while the carbon marginal emission rate is low due to the operation of costly but less polluting natural gas ?peaker? plants. Energy consumers may respond to the conflicting price and emission signals quite differently. Understanding the behavioral heterogeneity in energy use and its impact on sustainability of the electrical infrastructure is of critical importance to accelerating global energy system decarbonization. This project will establish an altruistic game theoretic framework to understand the interplay of an individual?s financial and environmental goals in shaping their energy use behaviors and to evaluate the impact of behavioral heterogeneity on system-level performances of the electric grid. The altruistic game framework models each energy consumer as a partially altruistic entity whose perceived cost is a weighted sum of his/her direct electricity cost and the social cost of energy-related carbon emission. The weighting factor characterizes an individual?s valuation of energy-related carbon emission, which in turn influences their energy use behaviors. Customer behavioral models will be developed from data collected through (1) online human subject tests with a custom designed demand response game and (2) sociotechnical experiments in a multi-family apartment complex within a historic African American community in downtown Oklahoma City.

Project goals include: (1) generation of new knowledge on residential customers? valuation of energy-related carbon emission and its impact on energy use behaviors; (2) development of a statistical behavioral model that characterizes how climate altruism correlates with socio-demographic variables; (3) synthesis of learning-based model predictive control strategies to enable automated demand response; (4) establishment of an altruistic game theoretic framework to facilitate impact analysis of behavioral heterogeneity on financial and environmental performances of the electric grid; and (5) design of distributed and privacy-preserving Nash equilibrium solution algorithms to accommodate distributed decision making for flexible load control in electricity markets. The project aims to increase public awareness of load flexibility and the associated environmental impact through a series of interrelated research, educational and outreach activities. The data-driven predictive control strategies are designed to unlock the residential flexibility potential through technological development, while the game theoretic framework supports design and assessment of demand-side carbon reduction technologies, programs and policies with socio-economic insights. The field experiment will directly engage 50 and indirectly affect more than 300 households in Oklahoma City providing technology solutions to and educating a population that does not usually engage in the early stages of technology adoption. Through collaboration with the community, developers and other partners, the project will demonstrate social drivers that affect large infrastructure systems, with the results potentially scalable and applicable on a national level in the residential sector. Through the education program development, this project will provide opportunities for K-12, underrepresented, undergraduate, and graduate students to acquire cross-disciplinary skills that are critical to addressing future engineering challenges.

This project is jointly funded by the CBET Environmental Sustainability program and the Established Program to Stimulate Competitive Research (EPSCoR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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