Abstract
We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA.
Highlights
In recent years, energy deregulation of the electricity industry has been deployed in many countries to improve the economic efficiency of power system operation
We propose new algorithm to solve the Multi-Area Economic Dispatch problem (MAEDP), called Hybrid Particle Swarm Optimization (HGAPSO), which is the combination of Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) model
We proposed new hybrid particle swarm optimization algorithm for solving MAEDP
Summary
Energy deregulation of the electricity industry has been deployed in many countries to improve the economic efficiency of power system operation. It has been used widely in a more competitive market of power industry. We focus on solving ED problem in multi-area environment, so we call this problem MAEDP (MultiArea Economic Dispatch problem). The tie-lines are the ways to transport electricity between two areas. The objective of MAEDP is to determine the generation levels and the power interchange between two areas which would minimize total fuel costs in all areas while satisfying power balance, generating limit and transmission capacity constraints. If an area with excess power is not adjacent to a power deficient area, or the tie-line between the two areas is at the transmission limit, it is necessary to find an alternative path between these two areas in order to transmit additional power
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