Environmental and economic power dispatch of thermal generators using modified NSGA-II algorithm

被引:30
|
作者
Muthuswamy, Rajkumar [1 ]
Krishnan, Mahadevan [2 ]
Subramanian, Kannan [3 ]
Subramanian, Baskar [4 ]
机构
[1] Natl Coll Engn, Dept EEE, Tirunelveli 627151, Tamil Nadu, India
[2] PSNA Coll Engn & Technol, Dept EEE, Dindigul 624622, Tamil Nadu, India
[3] Kalasalingam Univ, Dept EEE, Krishnankoil 626126, Tamil Nadu, India
[4] Thiagarajar Coll Engn, Dept EEE, Madurai 625015, Tamil Nadu, India
来源
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS | 2015年 / 25卷 / 08期
关键词
environmental and economic power dispatch; valve-point effect; prohibited operating zones; multi-objective evolutionary algorithms; modified non-dominated sorting genetic algorithm-II; TOPSIS; non-dominated solutions; PARTICLE SWARM OPTIMIZATION; EMISSION LOAD DISPATCH; NONSMOOTH FUEL COST; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; UNITS;
D O I
10.1002/etep.1918
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
This paper presents the solution to the problem in fabricating Environmental and Economic Power Dispatch (EEPD) of thermal generators with valve-point loading effect and multiple prohibited operating zones (POZ). The valve-point effect introduces ripples in the input-output characteristics of generating units, and the existence of POZ breaks the operating region of a generating unit into isolated sub-regions, thus forms a nonconvex decision space. The EEPD problem becomes a nonsmooth optimization problem because of these valve-point effect and POZ. Accuracy of the solution for a practical system is improved by considering the nonlinearities of valve-point loading effect and multiple POZ in the EEPD problem. The multi-objective evolutionary algorithms, namely non-dominated sorting genetic algorithm-II (NSGA-II) and modified NSGA-II (MNSGA-II) have been applied for solving the multi-objective nonlinear optimization EEPD problem. To improve the uniform distribution of non-dominated solutions, dynamic crowding distance is considered in the NSGA-II and developed MNSGA-II. These multi-objective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus and IEEE 118-bus test systems. Real-coded genetic algorithm is used to generate reference Pareto-front, which is used to compare with the Pareto front obtained using NSGA-II and MNSGA-II. Numerical results reveal that MNSGA-II is effectively capable for appreciable performance than NSGA-II to solve the different power system nonsmooth EEPD problem. Moreover, three different performance metrics such as convergence, diversity and Inverted Generational Distance are calculated for the evaluation of closeness of obtained Pareto fronts to the reference Pareto-front. In addition, an approach based on Technique for ordering Preferences by Similarity to Ideal Solution is applied to extract best compromise solution from the obtained non-domination solutions. Copyright (c) 2014 John Wiley & Sons, Ltd.
引用
收藏
页码:1552 / 1569
页数:18
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