Paper Info
Title
Species and Memory Enhanced Differential Evolution for Optimal Power Flow Under Double-Sided Uncertainties
Abstract
Considering the uncertainty of power generations, in addition to the uncertainty of loads, is more and more important because of the increasing use of renewable energy sources. Most existing works on dynamic optimal power flow (DOPF) have only focused on either the uncertainty of loads (called demand-side uncertainty) or the uncertainty of power generations (called supply-side uncertainty). As far as we know, only a little work on the dynamic OPF problems considered both uncertainties simultaneously. It might be because the combination of variable uncertainties could lead to a huge problem size for existing methods. In this paper, inspired by the ideas in the field of evolutionary dynamic optimization (EDO), we attempt to deal with uncertain parameters from the perspective of tracking the moving optimum. A species and memory enhanced differential evolutionary algorithm (called SMDE) is specially designed to solve the DOPF with double-sided uncertainties. Specifically, in order to deal with the double-sided uncertainties, a modified memory strategy and an improved multi-population strategy were introduced, where the multi-population strategy includes two versions. The experimental results on the modified IEEE 57-bus and 118-bus systems show that the proposed algorithms perform much better than the comparison algorithms for most cases.
Year
DOI
Venue
2020
10.1109/TSUSC.2019.2929811
IEEE Transactions on Sustainable Computing
Keywords
DocType
Volume
Dynamic optimal power flow,renewable power generation,evolutionary algorithm,differential evolution,double-sided uncertainties
Journal
5
Issue
Citations 
PageRank 
3
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Chenyang Bu1479.18
Wenjian Luo235640.95
Tao Zhu35812.63
Ruikang Yi431.04
Bin Yang541.05