Abstract
In recent years, power companies have shown increasing interest in making strategic decisions to maintain profitable energy systems. Economic Load Dispatch (ELD) is a complex decision-making process where the output power of the entire power generating units must be set in a way that results in the overall economic operation of the power system. Moreover, it is a constrained multi-objective optimization problem. Now a days, there is a tendency to use metaheuristic methods to deal with the complexity of the ELD problem. Particle swarm optimization (PSO) is a subclass of metaheuristic methods inspired by fish schooling and bird flocking behaviors. However, the optimization performance of the PSO is highly dependent on fitness landscapes and can lead to local optima stagnation and premature convergence. Therefore, in the proposed study, two new variants of the PSO called global particle swarm optimizer with inertial weights (GPSO-w) and quasi-oppositional population based global particle swarm optimizer with inertial weights (QPGPSO-w) are proposed to address the complexity of the ELD problem. The ELD problem is formulated as an optimization problem and validation of the proposed methods is performed on IEEE standards (3, 6, 13, 15, 40 & 140) unit Korean grid ELD test systems under numerous constraints and the obtained results are compared with the several recent techniques presented in the literature. The results obtained with convex systems showed excellent cost-effectiveness, while for non-convex systems sequential quadratic programming (SQP) optimizer was added to discover global minima even more efficiently. The proposed techniques were successful in solving the ELD problem and yielded better results compared to the reported results in the selected cases. It is further inferred that the proposed techniques with less algorithmic parameters reflected improved exploration and convergence characteristics.
Highlights
The world is currently going through uncertain times
Eq 12 is solved for 100 iterations by both global particle swarm optimizer with inertia weights (GPSO-w) and QPGPSO-w
From Figure. 1 it can be seen that QPGPSO-w performs better than GPSO-w by achieving optimal results in 32 iterations compared to 60 iterations taken by the latter technique. 2 shows the solution transition in QPSPSO-w during iteration 2 and iteration 4 respectively
Summary
The world is currently going through uncertain times. The corona pandemic has halted economic progress worldwide. Economic load dispatch of thermal resources deals with non-linear, non-convex fuel cost curves under constraints such as energy balance, valve point effect, generators limits and prohibited operating zones. All these limitations make the ELD problem extremely challenging for optimization engineers, making it ideal for research. A, b, c, e and f represent cost coefficients; Nx indicates the total number of generating units available for scheduling, Pi represents the ith power output of the generating unit and Pil shows the minimum power generating limit of the ith generating unit [9] These objective functions are subjected to following equality and inequality constraints. 1: Initialize N, d dimensional velocity vectors Vi (j) and solutions Xi (j) within search space range
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