文章摘要
郑宝敏,余涛,瞿凯平,等.多区域并行协同的多目标分布式帕累托最优潮流算法[J].电力系统自动化,2018,42(20):93-101. DOI: 10.7500/AEPS20171026009.
ZHENG Baomin,YU Tao,QU Kaiping, et al.Pareto Optimized Algorithm for Distributed Multi-objective Power Flow Based on Multi-area Parallel Cooperation[J].Automation of Electric Power Systems,2018,42(20):93-101. DOI: 10.7500/AEPS20171026009.
多区域并行协同的多目标分布式帕累托最优潮流算法
Pareto Optimized Algorithm for Distributed Multi-objective Power Flow Based on Multi-area Parallel Cooperation
DOI:10.7500/AEPS20171026009
关键词: 最优潮流  法线边界交叉  多目标  并行协同  虚拟目标因子  电力市场
KeyWords: optimal power flow  normal boundary intersection  multi-objective  parallel cooperation  virtual objective coefficients  electricity market
上网日期:2018-09-04
基金项目:国家自然科学基金资助项目(51777078)
作者单位E-mail
郑宝敏 华南理工大学电力学院, 广东省广州市 510640
广东省绿色能源技术重点实验室(华南理工大学), 广东省广州市 510640 
 
余涛 华南理工大学电力学院, 广东省广州市 510640
广东省绿色能源技术重点实验室(华南理工大学), 广东省广州市 510640 
taoyu1@scut.edu.cn 
瞿凯平 华南理工大学电力学院, 广东省广州市 510640
广东省绿色能源技术重点实验室(华南理工大学), 广东省广州市 510640 
 
张孝顺 华南理工大学电力学院, 广东省广州市 510640
广东省绿色能源技术重点实验室(华南理工大学), 广东省广州市 510640 
 
殷林飞 华南理工大学电力学院, 广东省广州市 510640
广东省绿色能源技术重点实验室(华南理工大学), 广东省广州市 510640 
 
摘要:
      在开放电力市场的环境下,各区域电网合作与利益博弈共存,区域电网之间的信息保密问题显得愈发重要。现有的帕累托最优潮流求解方法均属于集中式算法,在优化时需要获知全网的信息,无法满足高私密性以及高可靠性的要求。在该背景下,寻求一种去中心化的分布式优化方法以保障系统内各区域电网的信息安全显得尤为重要。基于此,文中提出了一种多区域并行协同的多目标分布式帕累托最优潮流求解算法。该算法以法线边界交叉法为基础,将整个系统的多目标潮流优化问题分解为与多个子区域对应的子优化问题。每个子区域采用独立的优化器完成子问题的优化,区域之间仅交换联络线上的边界变量以及虚拟目标因子进行全局调整,不断逼近原问题的帕累托最优解集。IEEE 118节点算例仿真结果表明:所提算法可在有效实现多目标帕累托最优潮流分布式并行求解的同时,还可提高求解精度和减小计算内存,从而适用于在开放电力市场背景下各区域电网合作与利益博弈共存的运营模式。
Abstract:
      Under the situation of open electricity market, the cooperation and interest games among regional grids coexist, and the issues of information security among regional grids become more and more important. The current Pareto optimized power flow algorithms belong to centralized algorithms, and need to get the global information about the whole power system in the optimization process, thus it is hard to meet the high privacy and the high reliability requirements. In this context, it is particularly important to seek a decentralized distributed optimization method to secure the information security of the regional grids within the power system. To solve these issues, a Pareto optimized algorithm for distributed multi-objective power flow based on multi-area parallel cooperation is proposed. In the proposed algorithm, the normal boundary intersection is used as basis, and the multi-objective power flow optimization problem of the whole power system is decomposed into the sub-optimal problems corresponding to the sub regions. To continuosly approach the Pareto optimal solution set of the original problem, each sub region uses independent optimizer to optimize sub problems, and only the boundary variables between the interconnecting regions and the virtual objective coefficients are exchanged for the global regulation. The simulation results of the IEEE 118-bus power system verify that the proposed algorithm can effectively realize the distributed parallel solving of multi-objective Pareto optimal power flow, and simultaneously enhance the solution precision enhancement and reduce the calculation storage, which is suitable for the operation mode of the coexistence of the regional cooperation and interest games in the current background of open electricity market.
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