考虑源荷功率矩不确定性的新型互联电力系统省内-省间分布鲁棒协调优化调度

Distributed Robust Optimal Scheduling of the New Interconnected Power System with the Inter-province and Intra-province Considering Moment Uncertainty of Source Load Power

为推动我国高比例清洁能源接入和高比例跨区电力输送的新型互联电力系统建设,降低源荷不确定性对清洁能源跨区消纳和省内-省间电力平衡的影响,提出考虑源荷功率矩不确定性的新型互联电力系统优化调度方法。首先,设计了计划-市场双轨制下新型互联电力系统省内-省间双层优化调度模式。其次,采用矩不确定集合刻画风光发电和负荷功率的不确定性,并提出由此引起的系统弃风弃光分布鲁棒条件风险度量模型。最后,以省间和省内总购电成本最小为目标,建立新型互联电力系统省内-省间分布鲁棒协调优化调度模型,并通过对偶优化理论将该模型转化为易求解的半定规划问题。数值仿真结果表明,所提模型促进了西部地区清洁能源跨省跨区消纳,降低受端系统运行成本的同时,有效提高了系统应对源荷功率不确定性波动的能力。

To promote the construction of a novel interconnected power system in China—one featuring a high proportion of clean energy access and significant trans-regional power transmission proportion—a new optimal dispatching method is proposed that considers the uncertainty of source load power moment.This approach aims to mitigate the impact of source load uncertainty on cross-regional clean energy consumption and intra-provincial power balance.First,a two-level optimal dispatching mode for an innovative interconnected power system between and within provinces was designed under the planning market dual-track system.Second,the uncertainties of wind and solar power generation and load power were characterized by the moment uncertainty set,and the resulting robust conditional risk measurement model of wind and light abandonment distributions was proposed.Finally,to minimize total power purchase cost between and within provinces,a novel distributed robust,coordinated optimal dispatching model for an interconnected power system was established,and the model was transformed into a semi-definite programming problem,more readily solvable by applying the dual optimization theory.Numerical simulation results show that the proposed model promotes the cross-provincial and cross-regional consumption of clean energy in the western region,reduces the operational cost of the receiving-end system,and effectively improves the ability of the system to handle uncertain fluctuations in the source-load power.