基于可拓距K-均值聚类和正弦微分进化算法的风储联合系统优化配置  

Optimal allocation of wind-energy storage combined system based on extension distance K-means clustering and sine differential evolution algorithm

作　　者：孙惠娟 方杜 彭春华 SUN Huijuan;FANG Du;PENG Chunhua(School of Electrical&Automation Engineering,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学电气与自动化工程学院,江西南昌330013

出　　处：《电力自动化设备》2021年第10期20-27,共8页Electric Power Automation Equipment

基　　金：国家自然科学基金资助项目(51867008);江西省自然科学基金资助项目(20192ACBL20007,20202BAB204024);江西省主要学科学术和技术带头人项目(20204BCJL22038)。

摘　　要：以最大化风储联合投资商的收益和风电就地消纳率为目标,考虑源网荷协同优化和需求响应构建了风储联合系统的多目标优化配置模型;采用可拓距K-均值聚类算法对分布式风电出力和负荷需求的不确定性进行多场景分析,以实现更为准确而均衡的场景缩减;通过引入多核并行运行环境与正弦函数的思想,提出基于并行多目标正弦微分进化算法对优化配置模型进行高效求解;以IEEE 33节点配电系统为算例进行风储联合系统的优化配置,仿真结果验证了所建模型的有效性和优越性。Aiming at maximizing the return of wind-energy storage joint investor and the local consumption rate of wind power,a multi-objective optimal allocation model of wind-energy storage combined system is established considering the collaborative optimization of source-network-load and demand response.Extension distance K-means clustering is used to analyze the uncertainties of distributed wind power output and load demand in multiple scenarios,so as to achieve more accurate and balanced scenario reduction.By introducing the idea of multi-core parallel operation environment and sine function,a parallel multi-objective sine differential evolution algorithm is proposed to efficiently solve the optimal allocation model.Taking the IEEE 33-bus distribution system as an example to conduct the optimal allocation of wind-energy storage combined system,and the simulative results verify the effectiveness and superiority of the proposed model.

关 键 词：风储联合系统 优化配置 源网荷协同 需求响应 可拓距K-均值聚类 并行多目标正弦微分进化算法 

分 类 号：TM761[电气工程—电力系统及自动化] TM614