引用本文:郑重,苗世洪,张松岩,姚福星,张迪,韩佶.基于极限学习机的输配一体储能系统选址定容协同优化策略[J].电力自动化设备,2022,42(2):
ZHENG Zhong,MIAO Shihong,ZHANG Songyan,YAO Fuxing,ZHANG Di,HAN Ji.Collaborative optimization strategy for location and capacity determination of energy storage system considering transmission and distribution integration based on extreme learning machine[J].Electric Power Automation Equipment,2022,42(2):
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基于极限学习机的输配一体储能系统选址定容协同优化策略
郑重1,2, 苗世洪1,2, 张松岩1,2, 姚福星1,2, 张迪1,2, 韩佶1,2
1.华中科技大学 电气与电子工程学院 强电磁工程与新技术国家重点实验室,湖北 武汉 430074;2.华中科技大学 电气与电子工程学院 电力安全与高效湖北省重点实验室,湖北 武汉 430074
摘要:
针对传统电网储能选址定容模型所存在的资源协同不足、求解复杂等问题,提出了一种基于极限学习机(ELM)的输配一体储能系统选址定容协同优化策略。首先,综合考虑输配电网的安全运行约束和经济性优化目标,分别建立输电网及配电网储能系统选址定容模型;引入二阶锥松弛转化模型非凸约束,建立基于二阶锥松弛的输配一体储能系统选址定容优化模型。其次,充分计及输配电网协同运行机制,推导含二阶锥约束的输电网节点边际电价;构建基于ELM的输配电网状态表征模型,实现输配电网状态快速响应。然后,提出一种基于ELM的输配一体储能系统选址定容协同优化算法,获取输配电网储能系统的全局最优配置。最后,以一个T6D7D9系统为例进行仿真分析,仿真结果表明所提优化策略能够充分协同输配电网资源,促进清洁能源安全消纳,有效提升输配电网运行经济性,实现“互利共赢”的目标。
关键词:  输配一体  储能系统  选址定容  二阶锥松弛  极限学习机  协同优化
DOI:10.16081/j.epae.202111007
分类号:TM715
基金项目:国家自然科学基金资助项目(51777088)
Collaborative optimization strategy for location and capacity determination of energy storage system considering transmission and distribution integration based on extreme learning machine
ZHENG Zhong1,2, MIAO Shihong1,2, ZHANG Songyan1,2, YAO Fuxing1,2, ZHANG Di1,2, HAN Ji1,2
1.State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2.Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract:
Aiming at the problems of insufficient resource coordination and complex solution in the traditional model of energy storage location and capacity determination, a collaborative optimization strategy for location and capacity determination of energy storage system considering transmission and distribution integration based on ELM(Extreme Learning Machine) is proposed. Firstly, considering the security operation constraints and economic optimization objectives of the transmission and distribution network, the location and capacity determination models of the transmission and distribution network are established respectively. Then, the nonconvex constraint of the second-order cone relaxation transformation model is introduced to establish the location and capacity optimization model of energy storage system considering transmission and distribution integration based on the second-order cone relaxation. Secondly, considering the coordinated operation mechanism of transmission and distribution grids, the locational marginal price of transmission grid with second-order cone constraint is derived. Then, the state representation model of transmission and distribution grids based on ELM is constructed to realize the fast response of states for transmission and distribution grids. Thirdly, the collaborative optimization algorithm for location and capacity determination of energy storage system considering transmission and distribution integration based on ELM is proposed, so as to obtain the global optimal allocation of energy storage system in transmission and distribution grids. Finally, taking a T6D7D9 system as an example for simulation analysis, simulative results show that the proposed strategy can fully coordinate the resources of transmission and distribution grids, promote the safe consumption of clean energy, improve the operation economy of transmission and distribution grids effectively, and achieve the goal of “mutual benefit”.
Key words:  transmission and distribution integration  energy storage system  location and capacity determination  second-order cone relaxation  extreme learning machine  collaborative optimization

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