南方电网技术2017，Vol.11Issue(10)：103-114,12.DOI:10.13648/j.cnki.issn1674-0629.2017.10.010
快速负荷波动下支持最优潮流的进化型多任务处理架构
Evolutionary Multitasking Framework to Support Optimal Power Flow Under Rapid Load Variations
摘要
Abstract
Operation planning of power systems is mostly carried out in hourly intervals using unit commitment (UC).Nowadays,increasing penetrations of renewable energy sources at consumer side have caused large load variations on power system.Therefore,intra-hour optimal power flow (OPF) calculations considering most likely scenarios are mandatory to maintain the economy and the security requirements of power system operations to ensure the reliability of the electricity supply,On the other hand,traditional meta-heuristic methods which are more suitable as per the structure of OPF problems,may not be practical due to longer execution times.To address these issues,this paper presents an evolutionary multitasking framework for parallel execution of multiple OPF problems at different load demands.Simulation results show that multitasking substantially improves the utilization of evolutionary algorithms in OPF problems showing the potential for computations in fast timescale in comparison to canonical evolutionary algorithms (EA).关键词
进化算法/进化型多任务处理/元启发/最优潮流Key words
evolutionary algorithms/evolutionary multitasking/meta-heuristics/optimal power flow分类
信息技术与安全科学引用本文复制引用
L.P.M.I.Sampath,Abhishek Gupta,Yew-Soon Ong,H.B.Gooi..快速负荷波动下支持最优潮流的进化型多任务处理架构[J].南方电网技术,2017,11(10):103-114,12.基金项目
This work is funded by the International Center of Energy Research (ICER),established by Nanyang Technological University,Singapore and Technische Universit(a)t München,Germany. （ICER）
