计及灵活性供需的多场景分布式电源双层规划

高渗透率可再生能源并网对电力系统提出了更高的灵活性要求；在可再生能源系统规划阶段计及多种灵活性资源协同优化可有效提升系统灵活性，为此，基于灵活性调节能力分析，提出计及灵活性的配电网分布式电源双层规划模型，将经济目标和灵活性目标作为优化目标，构建了多场景协调优化规划模型；考虑到风光场景集过大所带来求解效率较低的问题，在仿射传播(affinity propagation,AP)聚类算法的基础上提出一种基于AP-Kmedoids的双层场景缩减技术，并对缩减后的场景进行校验。最后通过算例采用整数自适应粒子群算法(adaptiveparticleswarm optimization,APSO)-混沌粒子群算法(chaos particle swarm optimization,CPSO)混合求解策略对双层规划模型进行仿真，结果验证了所提规划方法在提升经济性和灵活调节能力方面的有效性。

A Bi-level Programming of Multi Scenario Distributed Generation Considering Flexible Supply and Demand

The grid-connection of high penetration renewable energy makes a higher request on the flexibility of power grid. During the planning stage of renewable energy system taking collaborative optimization of multiple flexible resources into consideration can effectively improve the system flexibility. For this reason, based on the analysis on flexible regulating ability, considering flexibility a bi-level planning model of distributed generation in distribution network was proposed. Taking economic goals and flexible goals as optimization objectives, a multi scenario coordinated optimization planning model was constructed. Considering the defect of low solution efficiency due to too large scenery scene set, on the basis of affinity propagation (abbr. AP) clustering algorithm an AP-Kmedoids-based bi-level scene reduction technology was put forward, and the reduced scene was verified. Finally, by use of the mixed solution strategy of integer adaptive particle swarm optimization (abbr. APSO) and chaos particle swarm optimization (abbr. CPSO) the simulation of the proposed bi-level programming model was implemented. Simulation results show that the proposed programming method is effective in improving economy and flexible regulation ability.