Multi-objective Differential Evolution for Dynamic Economic Emission Dispatch

ENERGYO ◽  
2018 ◽  
Author(s):  
M. Basu





Author(s):  
M. F. Mehdi ◽  
A. Ahmad ◽  
S. S. Ul Haq ◽  
M. Saqib ◽  
M. F. Ullah

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.



2014 ◽  
Vol 15 (2) ◽  
pp. 141-150 ◽  
Author(s):  
M. Basu

Abstract Dynamic economic emission dispatch is an important optimization task in fossil fuel–based power plant operation for allocating generation among the committed units with predicted load demands over a certain period of time such that fuel cost and emission level are optimized simultaneously. It is a highly constrained dynamic multi-objective optimization problem involving conflicting objectives. This paper proposes multi-objective differential evolution for dynamic economic emission dispatch problem. Numerical results for a sample test system have been presented to demonstrate the performance of the proposed algorithm. The results obtained from the proposed algorithm are compared with those obtained from nondominated sorting genetic algorithm-II (NSGA-II).



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