Hybrid Approach to Solve Thermal Power Plants Fuel Cost Optimization Using Ant Lion Optimizer with Newton-Based Local Search Technique

Ejaz Ahmed; Shahbaz Khan; Abdul Wadood; Husan Ali; Babar Sattar Khan

Authors

Ejaz Ahmed Air University Kamra Campus
Shahbaz Khan Department of Electrical Engineering Aero Space and Aviation, Air University Islamabad Kamra Campus Attock, Pakistan
Abdul Wadood Department of Electrical Engineering Aero Space and Aviation, Air University Islamabad Kamra Campus Attock, Pakistan
Husan Ali Department of Electrical Engineering Aero Space and Aviation, Air University Islamabad Kamra Campus Attock, Pakistan
Babar Sattar Khan Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Attock, Pakistan

Keywords:

Economic Load Dispatch (ELD), Ant Lion Optimization (ALO), Valve point loading (VPLE), Fuel cost., Objective function

Abstract

Introduction/Importance of Study: The optimization of the power system is a complicated problem that is extremely non-convex, nonlinear, and important for reducing the cost of production.

Novelty Statement: Despite the fact that several metaheuristic algorithms are proposed for solving power system optimization problems, the strength of hybridized global search-based techniques has not commonly been applied to power system optimization.

Material and Method: Deterministic power system optimization strategies are unable to yield global optimal outcomes because of the entrapment in local optimum zones. Stochastic approaches like those in which Ant-Lion Optimizer is used and hybridization algorithms with local search methods SQP, IPA, and active set give better results.

Result and Discussion: Hybridized global search-based techniques have been successfully applied to power system optimization with economic load dispatch in particular. Results from findings hybridized-ALO outperforms modern optimization methods.

Concluding Remarks: Results from findings show 3 and 13 generator systems that hybridized-ALO outperforms modern optimization methods.

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