资源聚合商模式下的分布式电源、储能与柔性负荷联合调度模型

袁晓冬; 费骏韬; 胡波; 张友旺; 葛乐

引用本文:	袁晓冬,费骏韬,胡波,等.资源聚合商模式下的分布式电源、储能与柔性负荷联合调度模型[J].电力系统保护与控制,2019,47(22):17-26.
	YUAN Xiaodong,FEI Juntao,HU Bo,et al.Joint scheduling model of distributed generation, energy storage and flexible load under resource aggregator mode[J].Power System Protection and Control,2019,47(22):17-26

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资源聚合商模式下的分布式电源、储能与柔性负荷联合调度模型
袁晓冬¹,费骏韬¹,胡波²,张友旺³,葛乐³
(1.国网江苏省电力有限公司电力科学研究院，江苏南京 211103;2.国网苏州市吴江区供电公司，江苏苏州 215200;3.南京工程学院电力工程学院，江苏南京 211167)

摘要:

分布式电源、储能、柔性负荷等分布式资源数量众多、布局分散，难以直接被电网调度。资源聚合商可通过内部整合各类分布式资源执行电网调度指令。基于资源聚合商运行模式，建立了结合大容量资源直接调度与小容量资源电价响应间接调度的联合调度模型。在此基础上，以资源聚合商利润最大为优化目标，对大容量资源调度性能差异进行滚动在线评估，设置动态综合调度优先级。针对小容量资源间接调度的不确定性，提出了包含模糊参数的机会调度约束。应用改进的粒子群算法将模糊机会约束清晰化并求解调度模型。基于IEEE33节点配电网络，验证了所提模型和算法的有效性和科学性。

关键词: 资源聚合商联合调度动态综合调度优先级模糊机会约束改进粒子群算法

DOI：10.19783/j.cnki.pspc.181559

分类号:

基金项目:国家自然科学基金项目资助(51707089);国网总部科技项目资助(5210EF17001C);国网江苏省电力有限公司科技项目资助

Joint scheduling model of distributed generation, energy storage and flexible load under resource aggregator mode

YUAN Xiaodong¹,FEI Juntao¹,HU Bo²,ZHANG Youwang³,GE Le³

(1. State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103, China;2. State Grid Suzhou Wujiang Power Supply Company, Suzhou 215200, China;3. School of Electrical Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

Abstract:

Distributed generation, energy storage, flexible load and other distributed resources are numerous and scattered, making it difficult to be directly scheduled by the power grid. Resource aggregators can execute power grid scheduling instructions by integrating various distributed resources internally. Based on the resource aggregator operation mode, a joint scheduling model for direct scheduling of large-capacity resources and indirect scheduling of electricity price response of small-capacity resources is constructed. On this basis, the resource aggregator's profit is the maximum scheduling goal, and the scheduling performance difference of large-capacity resources is evaluated by rolling online, and the dynamic integrated scheduling priority is set. In view of the uncertainty of indirect scheduling of small capacity resources, an opportunistic scheduling constraint with fuzzy parameters is proposed. The improved particle swarm optimization algorithm is applied to clarify the fuzzy chance constraints and solve the scheduling model. Combining with IEEE33 node distribution network, the validity and scientific nature of the proposed model and algorithm are verified. This work is supported by National Natural Science Foundation of China (No. 51707089), Science and Technology Project of the Headquarter of State Grid Corporation of China (No. 5210EF17001C), and State Grid Jiangsu Electric Power Co., Ltd.

Key words: resource aggregator joint scheduling dynamic integrated scheduling priority fuzzy opportunity constraints improved particle swarm optimization

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