Risk-aware distributed chance constrained energy coordination in energy communities

The concept of energy communities is drawing particular attention, given their potential to bridge energy gaps by fostering energy transition through community-led initiatives. Although the literature offers well-established business models that ensure long-term sustainability, several improvement opportunities exist to manage demand-side risks that could jeopardize the program’s effectiveness. Accordingly, this paper proposes to evaluate potential customer hazards given end-users’ concerns about thermal discomfort given uncertainty sources in weather forecasting and subsequent renewable production. The strategy employs a distributed energy coordination framework for residential prosumers within a hierarchical control structure governed by a benevolent coordinator. Prosumers maximize their welfare using a stochastic chance-constrained programming approach, while the coordinator minimizes planned grid costs through a cost-sharing approach. Each customer is integrated with electric thermal storage and renewable power. Extensive simulations were performed with accurate energy consumption and weather forecast data in the province of Quebec. Results demonstrate that demand-side risk-aversion preferences can significantly impact the expected aggregated demand profile, creating power congestion situations. The price of robustness as a consequence of anticipating forecasting errors ranged from 9.06 to 15.15% compared to the optimal solution with perfect information. Eventually, the proposal was validated through a cyber–physical infrastructure meeting scalability requirements.