Day-Ahead Joint Chance-Constrained Optimal Scheduling for Distribution Networks Based on Optimal Risk Allocation

With the integration of renewable energy sources and the increasing penetration of electric vehicles (EVs) into the distribution network, the system has transitioned from a passive network to an active one capable of operational scheduling. To address the challenges posed by the uncertainty of renewable energy on system security, this paper develops a day-ahead joint chance-constrained (JCC) optimization scheduling model. Compared with deterministic optimization and individual chance-constrained (ICC) optimization, the proposed JCC model ensures that the overall violation probability of the system meets the required threshold. To mitigate the conservatism introduced by the Bonferroni approximation (BA), which equally divides risk probabilities when decomposing JCCs into ICCs, this paper introduces an optimal risk allocation algorithm. This algorithm optimizes the allocation of risk across different buses, adjusting to the system's operating conditions. Verification through a modified IEEE 33-bus distribution network case study demonstrates that the proposed optimal risk allocation enhances economic performance by 9% compared to the BA method, significantly reducing operational costs while maintaining system security.