Addressing Wind Power Forecast Errors in Day-Ahead Pricing With Energy Storage Systems: A Distributionally Robust Joint Chance-Constrained Approach

The rapid integration of renewable energy sources (RESs) has imposed substantial uncertainty and variability on the operation of power markets, which calls for unprecedentedly flexible generation resources such as batteries. In this paper, we develop a novel pricing mechanism for day-ahead electricity markets to adeptly accommodate the uncertainties stemming from RESs. First, a distributionally robust joint chance-constrained (DRJCC) economic dispatch model that incorporates energy storage systems is presented, ensuring that the DRJCCs are satisfied across a moment-based ambiguity set enriched with unimodality-skewness characteristics. Second, by applying the Bonferroni approximation method to tackle the DRJCCs, we show that the proposed model can be transformed into a second-order cone programming (SOCP) problem. Building on the SOCP reformulation, we then precisely derive the electricity prices, including the energy, reserve, and uncertainty prices. Furthermore, we prove that the obtained pricing mechanism supports a robust competitive equilibrium under specific premises. Finally, a PJM 5-bus test system and the IEEE 118-bus test system are used to demonstrate the effectiveness and superiority of the suggested approach, underscoring its potential contributions to modern power market operations.