Angbas Donald Orame
Nigeria’s socioeconomic development is constrained by inadequate and inefficient electricity generation and dispatch, largely due to challenges in the power sector. This paper addresses the Economic Load Dispatch (ELD) problem for six thermal power generators, comparing Particle Swarm Optimization (PSO) with the traditional Lambda Iteration method. The objective is to minimize total fuel cost while satisfying a 600 MW power demand and generator operating constraints, using quadratic cost functions derived from historical input-output data and implemented in MATLAB. Results show that PSO yields a slightly lower total generation cost (₦507,103,545) than Lambda Iteration (₦507,232,335), representing a 0.0254% cost improvement and a savings of about ₦128,790, which can be significant at large scales. PSO also exhibits faster convergence and greater flexibility in handling the non-linear, non-convex nature of practical ELD problems. The study concludes that advanced optimization techniques like PSO offer improved cost-effectiveness and operational efficiency for modern thermal power systems.
@article{25eebf83-7ff7-4e0f-af5c-94a71e1af4ef,
title={Economic Load Dispatch Optimization for Thermal Power Plant Using Particle Swarm Optimization and Lambda Iteration },
author={Angbas Donald Orame},
year={2025},
language={en}
}TY - JOUR TI - Economic Load Dispatch Optimization for Thermal Power Plant Using Particle Swarm Optimization and Lambda Iteration AU - Angbas Donald Orame PY - 2025 LA - en ER -
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