文献类型：期刊文献

英文题名：Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging

作者：Ma, Haiping[1,2];Yang, Zhile[3];You, Pengcheng[1];Fei, Minrui[4]

机构：[1]Shaoxing Univ, Dept Elect Engn, Shaoxing, Zhejiang, Peoples R China;[2]Zhejiang Univ, Dept Control, State Key Lab Ind Control Technol, Hangzhou, Zhejiang, Peoples R China;[3]Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland;[4]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai, Peoples R China

年份：2017

卷号：135

起止页码：101

外文期刊名：ENERGY

收录：SCI-EXPANDED(收录号：WOS:000412043300011)、、EI(收录号：20172603846481)、Scopus(收录号：2-s2.0-85021210708)、WOS

基金：This work is financially supported by the National Natural Science Foundation of China under Grant Nos. 61640316, 61633016 and 61533010.

语种：英文

外文关键词：Economic emission load dispatch; Plug-in electric vehicles; Dynamic multi-objective optimization; Biogeography-based optimization; Non-dominated sorting

外文摘要：The climate change is addressing unprecedented pressures on conventional power system regarding the significant fossil fuel consumptions and carbon emissions, which largely challenges the conventional power system operation. This paper proposes a novel dynamic non-dominated sorting multi-objective biogeography-based optimization (Dy-NSBBO) to solve multi-objective dynamic economic emission load dispatch considering the mass integration of plug-in electric vehicles (PEVs), namely MO-DEELDP problem. First, a real-world economic emission load dispatch considering PEVs charging is first formulated as a constrained dynamic multi-objective optimization problem. Then a new multi-objective BBO is proposed adopting the non-dominated solution sorting technique, change detection and memory-based selection strategies in the multi-objective BBO method to strengthen the dynamic optimization performance. The proposed Dy-NSBBO is applied to solve three different dynamic economic emission load dispatch cases integrating four plug-in electric vehicle charging scenarios respectively. Comprehensive analysis shows that the novel algorithm is promising to bring considerable economic and environmental benefits to the power system operators and provides competitive charging strategies for policy makers and PEV5 aggregators. (C) 2017 Elsevier Ltd. All rights reserved.