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

被引:25
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
[81]   A new Kho-Kho optimization Algorithm: An application to solve combined emission economic dispatch and combined heat and power economic dispatch problem [J].
Srivastava, Abhishek ;
Das, Dushmanta Kumar .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 94
[82]   Indicator & crowding distance-based evolutionary algorithm for combined heat and power economic emission dispatch [J].
Sun, Jiaze ;
Deng, Jiahui ;
Li, Yang .
APPLIED SOFT COMPUTING, 2020, 90
[83]   Multiobjective multi-verse optimization algorithm to solve combined economic, heat and power emission dispatch problems [J].
Sundaram, Arunachalam .
APPLIED SOFT COMPUTING, 2020, 91
[84]   Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGA II-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism [J].
Sundaram, Arunachalam .
IEEE ACCESS, 2020, 8 :13748-13768
[85]   Climate change impacts on tropical cyclones and extreme sea levels in the South Pacific - A regional assessment [J].
Walsh, Kevin J. E. ;
McInnes, Kathleen L. ;
McBride, John L. .
GLOBAL AND PLANETARY CHANGE, 2012, 80-81 :149-164
[86]   GENETIC ALGORITHM SOLUTION OF ECONOMIC-DISPATCH WITH VALVE POINT LOADING [J].
WALTERS, DC ;
SHEBLE, GB .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1993, 8 (03) :1325-1332
[87]   Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search [J].
Wang, Lingfeng ;
Singh, Chanan .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2009, 22 (02) :298-307
[88]   China's city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces [J].
Wang, Shaojian ;
Liu, Xiaoping .
APPLIED ENERGY, 2017, 200 :204-214
[89]   Optimal GWCSO-based home appliances scheduling for demand response considering end-users comfort [J].
Waseem, Muhammad ;
Lin, Zhenzhi ;
Liu, Shengyuan ;
Sajjad, Intisar Ali ;
Aziz, Tarique .
ELECTRIC POWER SYSTEMS RESEARCH, 2020, 187 (187)
[90]  
Wolpert D. H., 1997, IEEE Transactions on Evolutionary Computation, V1, P67, DOI 10.1109/4235.585893