Population variant differential evolution–based multiobjective economic emission load dispatch

• Published in: International transactions on electrical energy systems Vol. 27; no. 10; pp. e2378 - n/a
• Main Authors: Swain, Rajkishore, Sarkar, Pallab, Meher, Krishna Chandra, Chanda, Chandan Kumar
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi Limited 01.10.2017
• Subjects: Algorithms; Clustering; Computing time; deregulated power market; Deregulation; Emission; Evolutionary algorithms; Evolutionary computation; Fossil fuels; Genetic algorithms; Industrial plant emissions; interquartile range; Linear programming; multiobjective economic emission load dispatch; Multiple objective analysis; Particle swarm optimization; Pollutants; population variant differential evolution; Sorting algorithms; Test systems; Thermal power plants; valve‐point loading effect
• ISSN: 2050-7038; 2050-7038
• DOI: 10.1002/etep.2378

Summary This paper presents a novel heuristic optimization approach using population variant differential evolution algorithm for solving the multiobjective economic emission load dispatch (EELD) problem. The EELD problem simultaneously takes into consideration the effect of gaseous pollutants like NOx, SOx, etc, including the cost of the fossil fuel used in the thermal power plants. A population refreshment mechanism, based on the concept of interquartile range, has been applied to the classical differential evolution method to compute the EELD problem successfully. The algorithm has been tested on IEEE‐30 bus 6‐generator test system and a standard 40‐generator test system, taking valve point loading effects into consideration for effective solutions. The results have been compared with the standard existing techniques in the literature such as linear programming, multiobjective stochastic search technique, nondominated sorting genetic algorithm, fuzzy clustering particle swarm optimization, and modified bacterial foraging algorithm, which authenticate the capability of the proposed algorithm. A novel concept of applying the proposed methodology to a deregulated power market has also been presented in this paper, where successful results have been obtained. This method appears as an efficient and robust optimization algorithm in terms of minimizing the total cost, emission, and computational time.