Abstract
This paper presents the optimization of fuel cost, emission of NOX, COX, and SOX gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.
Highlights
The primary objective of an independent electric power producer is to generate electricity at the minimum possible cost
Combined economic emission dispatch using particle swarm optimization (PSO) and genetic algorithm (GA) for 500 iterations were implemented on MATLAB on six generators of IEEE 30 bus system and eight committed units of an independent power plant (IPP) in Pakistan for load demands of 1500 and 2000 MW, and 500 and 700 MW, respectively
3, the power output of all six generators was presented as the sum of which is equal to Discussion In Table 3, the power output of all six generators was presented as the sum of which is equal to the load demand (1500 and 2000 MW) for both algorithms (PSO and GA)
Summary
The primary objective of an independent electric power producer is to generate electricity at the minimum possible cost. Conventional methods are optimization techniques being employed to solve multiobjective problems like CEED. Their hybrid versions are reported widely in every discipline including power systems [3,4,5,6] Their hybrid versions are reported in in the literature [7,8,9], where qualities of both are combined to solve a particular problem. This work will contribute the results of combined dispatch of fuel and gas emissions (economic and environmental aspects, respectively) carried out on a power plant with two leading algorithms (PSO and GA) using MATLAB and comparing their performance in terms of better solution, convergence characteristics (3D plots), and computation time.
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