【目的】针对高比例风光能源接入配电网导致的运行经济性与用户满意度之间的协调优化问题以及风光出力不确定，提出一种需求响应中考虑用户满意度约束的主动配电网两阶段分布鲁棒优化调度方法，以实现经济效益与用户满意度的协调优化。【方法】考虑需求响应和用户满意度，建立综合网损成本、弃风弃光成本、购电成本、用户削减成本及开关操作惩罚成本的配电网重构优化模型；引入Frank-Copula函数刻画风光出力相关性，结合K-means聚类生成典型场景集，构建基于1-范数和∞-置信区间的分布鲁棒优化模型，第一阶段确定支路开断及储能状态，第二阶段确定功率分配与需求响应策略，采用列与约束生成算法求解。【结果】以IEEE 33节点配电网为例，首先设置4种场景进行仿真，结果表明，本文方法能有效提升清洁能源消纳率，降低运行成本，平缓负荷曲线，兼顾用户用电满意度；其次，进行分布鲁棒模糊集置信度灵敏性分析，结果表明，置信区间增大，风光出力不确定性增大，模型保守度变高，成本也随之上升。【结论】考虑需求响应中用户满意度的主动配电网分布式鲁棒优化模型，显著提升了配电网的运行经济性和用户满意度，为解决高比例风光能源接入问题提供了有效途径。

[Objective] Aiming at the coordination and optimization problem between operation economy and user satisfaction caused by the high proportion of wind and solar energy access to the distribution network, as well as the uncertainty of wind and solar output, a two-stage distributionally robust optimal dispatch method considering user satisfaction constraints in demand response for active distribution networks is proposed, to achieve coordinated optimization of economic efficiency and user satisfaction. [Methods] An optimization model for distribution network reconfiguration was developed, which considered demand response and user satisfaction. The model aimed to minimize comprehensive costs, including network loss cost, wind and solar curtailment cost, electricity purchase cost, user compensation cost, and switch operation penalty cost. The Frank-Copula function was introduced to characterize the correlation between wind and photovoltaic power output. Typical scenario sets were generated using the K-means clustering method. A distributionally robust optimization model was then constructed based on confidence intervals of 1-norm and ∞-norm. A two-stage solution approach was adopted: the first stage determined the status of branch switching and energy storage, while the second stage optimized the power dispatch and demand response strategies. The column-and-constraint generation algorithm was employed to solve the established model. [Results] Taking the IEEE 33-node distribution network as an example, four scenarios were set up for simulation, and the results showed that the proposed method could effectively improve the clean energy consumption rate, reduce the operating cost, smooth the load curve, and take into account the user’s power satisfaction. Secondly, the sensitivity of the fuzzy set of the split blue rod was analyzed, and the results showed that the confidence interval increased, the uncertainty of wind and solar output increased, the conservatism of the model becomed higher, and the cost also increased. [Conclusion] The distribution network sub-blue rod optimization model considering user satisfaction in demand response significantly improves the operation economy and user satisfaction of the distribution network, and provides an effective way to solve the problem of high proportion of wind and solar energy access.

国家自然科学基金(62476153)