发表机构
University of Santiago of Chile（智利圣地亚哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于预测残差的机会约束调度框架，联合优化光伏与储能运行，并通过事后分配最大化内部匹配，在近5%预测误差下性能接近随机规划且计算高效。
AI 中文摘要
本地能源社区（LECs）的日前运行受到光伏发电和电力需求不确定性的影响，这可能危及与上游电网承诺交换的可行性。本文提出了一种基于预测残差的机会约束优化（CCO）框架，该框架通过预测及相关的残差分布来刻画不确定参数。在采用零均值高斯假设的情况下，所得机会约束具有精确的确定性等价重构形式，其中不确定性通过解析安全裕度嵌入，保持了原始调度问题的混合整数线性结构。该模型联合协调光伏和电池储能系统（BESS）的运行、低压配电网约束、内部能量共享以及日前电网交换承诺。在物理调度确定后，事后分配阶段根据预定义的参与系数将可用的社区能量池分配给各用户，识别分配后的盈余和赤字，并在保持CCO获得的聚合电网交换的同时最大化其内部匹配。该框架在一个包含多达55个社区用户的简化206节点欧洲低压馈线上进行了评估，并与两阶段随机规划公式进行了基准比较。结果表明，对于接近5%的预测误差水平，所提出的CCO实现了与随机基准相当的操作结果，同时保持了接近确定性公式的计算需求。事后分配结果进一步表明，内部匹配可以减少需要与上游电网结算的总能量交换。
英文摘要
Day-ahead operation of local energy communities (LECs) is affected by uncertainty in PV generation and electricity demand, which may compromise the feasibility of committed exchanges with the upstream grid. This paper proposes a forecast-residual-based chance-constrained optimization (CCO) framework that characterizes uncertain parameters through their forecasts and associated residual distributions. Under the adopted zero-mean Gaussian assumption, the resulting chance constraints admit an exact deterministic-equivalent reformulation in which uncertainty is embedded through analytical safety margins, preserving the mixed-integer linear structure of the original scheduling problem. The model jointly coordinates PV and BESS operation, low-voltage distribution network constraints, internal energy sharing, and day-ahead grid-exchange commitments. Once the physical schedule is determined, an ex-post allocation stage distributes the available community energy pool among users according to predefined participation coefficients, identifies post-allocation surpluses and deficits, and maximizes their internal matching while preserving the aggregate grid exchanges obtained from the CCO. The framework is evaluated on a reduced 206-node European low-voltage feeder with up to 55 community users and benchmarked against a two-stage stochastic programming formulation. Results show that, for forecast-error levels close to 5\%, the proposed CCO achieves operating outcomes comparable to the stochastic benchmark while retaining computational requirements close to the deterministic formulation. The ex-post allocation results further show that internal matching can reduce gross energy exchanges that require settlement with the upstream grid.
