A comprehensive study of practical economic dispatch problems by a new hybrid evolutionary algorithm

被引:68
|
作者
Naderi, Ehsan [1 ]
Azizivahed, Ali [2 ]
Narimani, Hossein [3 ]
Fathi, Mehdi [3 ]
Narimani, Mohammad Rasoul [4 ]
机构
[1] Razi Univ, Fac Engn & Technol, Eslam Abad Gharb, Kermanshah, Iran
[2] Shiraz Univ Technol, Dept Elect Engn, Shiraz, Iran
[3] Islamic Azad Univ, Kermanshah Branch, Dept Elect Engn, Kermanshah, Iran
[4] Missouri Univ Sci & Technol, Elect & Comp Engn, Rolla, MO USA
关键词
Multi-Objective Economic Dispatch (MOED); Fuzzy Based Hybrid Particle Swarm Optimization-Differential Evolution (FBHPSO-DE); Practical constraints; Pareto-optimal technique; OPTIMAL POWER-FLOW; PARTICLE SWARM OPTIMIZATION; LEARNING BASED OPTIMIZATION; GROUP SEARCH OPTIMIZATION; LOAD DISPATCH; DIFFERENTIAL EVOLUTION; EMISSION DISPATCH; WIND POWER; GLOBAL OPTIMIZATION; COMBINED HEAT;
D O I
10.1016/j.asoc.2017.06.041
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Economic Dispatch (ED) is the bare bone essential of power system operation and control from economic aspect point of view. In this paper, ED has been investigated from different points of view including power losses, emission, and generation cost. To this end, a multi-objective solution methodology and a fuzzy decision making strategy are implemented to find and sort the Pareto-optimal solutions. Yet another, this paper introduces a powerful, robust and hybrid configuration of evolutionary-based algorithm namely Fuzzy Based Hybrid Particle Swarm Optimization-Differential Evolution (FBHPSO-DE) algorithm for solving the proposed Multi-Objective ED (MOED) problem. The practical conditions including Multi-Fuels Operation (MFO), Valve-Point Effect (VPE) and Prohibited Operation Zones (POZs) are investigated in this study which makes the obtained results more practicable. The proposed algorithm is investigated on 10-unit, 40-unit, and 160-unit test systems in order to evaluate its capabilities. The obtained results are compared with the outcomes of other algorithms which proves the superiority of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:1186 / 1206
页数:21
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