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

被引：92
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
来源：
ENERGY | 2017年 / 135卷
基金：
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词：
Economic emission load dispatch; Plug-in electric vehicles; Dynamic multi-objective optimization; Biogeography-based optimization; Non-dominated sorting; GENETIC ALGORITHM; POWER-SYSTEMS; SEARCH;
D O I：
10.1016/j.energy.2017.06.102
中图分类号：
O414.1 [热力学];
学科分类号：
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.
引用
收藏
页码：101 / 111
页数：11
相关论文
共 30 条
[1]   Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems [J].
Abdelaziz, A. Y. ;
Ali, E. S. ;
Abd Elazim, S. M. .
ENERGY, 2016, 101 :506-518
[2]   Environmental/economic power dispatch using multiobjective evolutionary algorithms [J].
Abido, MA .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2003, 18 (04) :1529-1537
[3]  
[Anonymous], 2013, Evolutionary Optimization Algorithms
[4]  
[Anonymous], 2012, Power Generation, Operation, and Control
[5]  
[Anonymous], 2013, ENERGY
[6]  
[Anonymous], EPRI EX SUMM ASS PLU
[7]  
Azzouz R, 2015, SOFT COMPUT, P1
[8]   Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II [J].
Basu, M. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2008, 30 (02) :140-149
[9]  
Deb, 1994, EVOLUTIONARY COMPUTA, V2, P221, DOI DOI 10.1162/EVCO.1994.2.3.221
[10]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
123