2026 Theses Doctoral

Behavior Modeling and Analysis in Energy Systems

Chen, Liudong

Decision-making behavior is central to modern energy systems, where growing uncertainty, strategic interdependence, and the need for market inclusiveness pose new modeling and design challenges. Prevailing analytical approaches often simplify these behaviors by relying on assumptions that sacrifice realism, overlook strategic interactions, and leave the choice and consequences of fairness definitions underexplored.

This dissertation presents three main results to address these gaps: (1) a theory explaining the first principles of risk-averse behavior in demand response, which originates from non-quadratic cost function structures and price uncertainties with skewed distributions. The degree of risk aversion is proportional to the skewness of the price distribution, which motivates the design of risk-averse control methods that can be readily applied with non-anticipatory or decomposition-based methods; (2) a game-theoretic model demonstrating that strategic interactions in peak demand reduction programs can preserve the peak reduction effect while generating efficiency losses. The sources of efficiency loss arise from greater consumers’ demand-shifting flexibility and heterogeneity in their marginal shifting costs, motivating targeted policy design to improve system efficiency. The analysis with respect to the number of consumers suggests that market segmentation is another effective way to improve efficiency; and (3) a framework for fair energy aggregator pricing design that evaluates the impact of fairness under alternative fairness definitions and enforcement levels. The framework considers energy fairness, price fairness, and utility fairness, and develops a systematic map linking fairness design choices to operational and welfare outcomes.

Together, these results provide insights into capturing realistic decision-making behavior and policy design for a more efficient, interactive, and sustainable energy future.