Quasi Oppositional Population Based Polar Bear Optimization Algorithm for Solution of Economic Dispatch Problem

Fayyaz, Saqib; Rehman, Fahad Ur; Sattar, Muhammad Kashif; Hussain, Hafiz Ashiq

doi:10.1109/icecce49384.2020.9179464

Cited by 1 publication

(2 citation statements)

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“…2. In the above equations, a, b, c, e and f represent cost coefficients; Nx indicates the total number of generating units available for scheduling, P i represents the ith power output of the generating unit and P il shows the minimum power generating limit of the ith generating unit [9]. .…”

Section: Problem Formulationmentioning

confidence: 99%

“…To ensure energy supply, these thermal generation sources cannot be overlooked and must be utilized in an efficient and cost-effective manner. Economic load dispatch (ELD) is a well-documented optimization problem related to the economy of the power system, which involves the control of thermal resources so that maximum power can be extracted at the lowest possible fuel cost [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

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Quasi Oppositional Population Based Global Particle Swarm Optimizer With Inertial Weights (QPGPSO-W) for Solving Economic Load Dispatch Problem

Salaria

Menhas²,

Manzoor

2021

IEEE Access

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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 decisionmaking 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 inertia weights (GPSO-w) and quasi-oppositional population based global particle swarm optimizer with inertia 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.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%