Multi-objective membrane search algorithm: A new solution for economic emission dispatch

被引:24
作者
Lai, Wenhao [1 ]
Zheng, Xiaoliang [1 ]
Song, Qi [1 ]
Hu, Feng [1 ]
Tao, Qiong [1 ]
Chen, Hualiang [1 ]
机构
[1] Anhui Univ Sci & Technol, Sch Elect & Informat Engn, Huainan 232000, Peoples R China
关键词
Global Climate Issues; Combined Heat and Power; Economic Emission Dispatch; Multi -objective Membrane Search Algorithm; Large Scale Power System; PARTICLE SWARM OPTIMIZATION; COMBINED HEAT; GENETIC ALGORITHM; DIFFERENTIAL EVOLUTION; POWER DISPATCH; ENERGY; FLOW; STRATEGIES; IMPACTS; COLONY;
D O I
10.1016/j.apenergy.2022.119969
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Many countries or regions are committed to reducing emissions in response to global climate issues. As an in-dustry with a large proportion of emissions, power generation companies are facing increasing pressure to reduce emissions. Based on the Membrane Search Algorithm (MSA) designed by us, this paper proposes a multi-objective problem-solving algorithm, denoted as multi-objective MSA (MOMSA), in which the constrain-handling rules are designed to solve the Combined Heat and Power Economic Emission Dispatch (CHPEED) problem in a nonconvex and nonlinear space. The proposed method obtains the Pareto front of CHPEED 5-unit and 7-unit systems, and the recommended compromise solution has fewer emissions for the same fuel cost. In addition, the extremely challenging ultra large scale Combined Economic Emission Dispatch (CEED)problem is also studied, and the fuel cost and emissions of the compromise solution are more competitive. The research results show that MOMSA has excellent space exploration ability and can provide better emission reduction dispatching for CEED and CHPEED problems without complex parameter optimization.
引用
收藏
页数:22
相关论文
共 98 条
[91]   Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization [J].
Xiong, Guojiang ;
Shuai, Maohang ;
Hu, Xiao .
ENERGY, 2022, 244
[92]   Review on Multi-objective Joint Economic Dispatching of Microgrid in Power System [J].
Xue, Mengya ;
Xie, Jun ;
Chen, Fei ;
Ke, Xianbin ;
Xu, Tao ;
Hou, Hui .
9TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2018) / THE 8TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2018) / AFFILIATED WORKSHOPS, 2018, 130 :1152-1157
[93]   The race to zero emissions: Can renewable energy be the path to carbon neutrality? [J].
Yuan, Xi ;
Su, Chi-Wei ;
Umar, Muhammad ;
Shao, Xuefeng ;
Lobont, Oana-Ramona .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 308
[94]   Long-term transition of China's power sector under carbon neutrality target and water withdrawal constraint [J].
Zhang, Chao ;
He, Gang ;
Johnston, Josiah ;
Zhong, Lijin .
JOURNAL OF CLEANER PRODUCTION, 2021, 329
[95]   An adaptive differential evolutionary algorithm incorporating multiple mutation strategies for the economic load dispatch problem [J].
Zhang, Qiang ;
Zou, Dexuan ;
Duan, Na ;
Shen, Xin .
APPLIED SOFT COMPUTING, 2019, 78 :641-669
[96]   Economic environmental dispatch using an enhanced multi-objective cultural algorithm [J].
Zhang, Rui ;
Zhou, Jianzhong ;
Mo, Li ;
Ouyang, Shuo ;
Liao, Xiang .
ELECTRIC POWER SYSTEMS RESEARCH, 2013, 99 :18-29
[97]   A novel cascade heating system for waste heat recovery in the combined heat and power plant integrating with the steam jet pump [J].
Zhang, Youjun ;
Xiong, Nian ;
Ge, Zhihua ;
Zhang, Yichen ;
Hao, Junhong ;
Yang, Zhiping .
APPLIED ENERGY, 2020, 278 (278)
[98]   An improved quantum particle swarm optimization algorithm for environmental economic dispatch [J].
Zhao Xin-gang ;
Liang Ji ;
Meng Jin ;
Zhou Ying .
EXPERT SYSTEMS WITH APPLICATIONS, 2020, 152