Distributed Demand-Side Management in Renewable Energy Communities Under Generation Uncertainty: A Bayesian Game-Theoretic Approach

This paper investigates decentralized demand-side management in renewable energy communities with limited and uncertain renewable energy resources. Consumer interactions are modeled as a Bayesian game in which self-interested consumers schedule flexible loads between daytime and nighttime periods to minimize electricity costs under time-of-use tariffs. Consumer heterogeneity is captured through private information describing both risk preferences and forecasts of renewable energy availability. Analytical conditions under which dominant strategies or mixed-strategy Bayesian Nash equilibria (BNE) exist are derived. Based on this analysis, two distributed algorithms that operate without a central coordinator are developed. The first is an iterative best-reply (BR) algorithm, while the second is a novel Demand Agreement (DA) algorithm that directly exploits the equilibrium conditions to reduce computation and communication requirements. The proposed decentralized mechanisms are compared against a centralized social-cost minimization benchmark. The results further demonstrate that the DA algorithm consistently converges to the minimum-cost equilibrium whenever a BNE exists, while requiring substantially lower communication overhead than BR. In contrast, the BR algorithm may converge even when the BNE conditions are not satisfied. Finally, the analysis quantifies the impact of consumer risk preferences and renewable generation uncertainty on BNE existence, scheduling decisions, and overall system performance.