Multi-objective Economic Dispatch Considering Wind Power Penetration Using Stochastic Weight Trade-off Chaotic NSPSO

被引:14
作者
Man-Im, Anongpun [1 ]
Ongsakul, Weerakorn [1 ]
Singh, Jai Govind [1 ]
Boonchuay, Chanwit [2 ]
机构
[1] Asian Inst Technol, Sch Environm Resources & Dev, Dept Energy Environm & Climate Change, Energy Program, 58 Moo 9, Klongluang 12120, Pathumthani, Thailand
[2] Rajamangala Univ Technol Rattanakosin, Fac Ind & Technol, Dept Elect Engn Technol, Prachuap Khiri Khan, Thailand
关键词
Particle Swarm Optimization; multi-objective economic dispatch; wind power; system risk; PARTICLE SWARM OPTIMIZATION; LOAD DISPATCH; ALGORITHM; SYSTEM; MODEL; RISK;
D O I
10.1080/15325008.2017.1362067
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, a stochastic weight trade-off chaotic nondominated sorting particle swarm optimization (SWTC_NSPSO) is proposed for solving multi-objective economic dispatch considering wind power penetration. Multi-objective functions including generator fuel cost and system risk are considered. The SWTC_NSPSO algorithm improves the solution search capability by balancing between global best exploration and local best utilization through the stochastic weight trade-off technique combining dynamistic coefficients trade-off methods. The proposed algorithm cooperates with the freak, lethargy factors, and chaotic mutation to enhance diversity and search capability. Non-dominated sorting and crowding distance techniques efficiently provide the optimal Pareto front. The fuzzy function is used to select the local compromise best solution. Using a two stage approach, the global best compromise solution is selected from a large number of local best compromise trial solutions. Simulation results on the modified IEEE 30-bus test system indicate that SWTC_NSPSO can provide a lower and wider Pareto front than nondominated sorting genetic algorithm II (NSGAII), non-dominated sorting particle swarm optimization (NSPSO), non-dominated sorting chaotic particle swarm optimization (NS_CPSO), and a stochastic weight trade-off non-dominated sorting particle swarm optimization (SWT_NSPSO) in a less computation effort, leading to a lower generator fuel cost and a higher system reliability trade-off solution.
引用
收藏
页码:1525 / 1542
页数:18
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