Risk-Based Constraints for the Optimal Operation of an Energy Community

This paper formulates an energy community’s centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates portfolio’s aggregated flexibility. First, it is emphasized in stylized analysis that risk-based constraints are highly beneficial (compared to chance-constraints) coordinating distributed assets unknown costs of constraint violation, they limit both violation magnitude probability. The research extends state-of-the-art models by implementing worst-case conditional value at risk (WCVaR) based for SoC bounds. Then, extensive numerical comparison conducted analyze trade-off between out-of-sample violations expected objective values, revealing proposed WCVaR shields significantly better against extreme outcomes than equivalent. To bypass non-trivial task capturing underlying time asset-dependent processes, data directly leveraged imbalance market uncertainty load forecast errors. For this purpose, shape-based clustering method implemented capture input scenarios’ temporal characteristics.