Solve Multi-area Economic Dispatch Problem Research Articles

Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm

Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm

Fast Convergence Evolutionary Programming for Multi-area Economic Dispatch

This paper develops and suggests fast convergence evolutionary programming (FCEP) for solving multi-area economic dispatch problem with tie-line constraints, transmission losses, multiple fuels, valve point effect, and disallowed operating region. Evolutionary programming (EP), a class of evolutionary algorithm, is based on the basic genetic operation of human chromosomes. EP has the ability to seek out the global or close to the global optima. FCEP has been developed to boost convergence speed and solution quality. The efficacy of the developed technique has been tested on three different types of test systems. Test results have been compared with those acquired from improved fast evolutionary programming, EP and other stated evolutionary techniques. It has been observed from the comparison that the developed FCEP has the ability to offer superior solution.

Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem

Water Wave Optimization Algorithm for Solving Multi-Area Economic Dispatch Problem

The Auxiliary Problem Principle with Self-Adaptive Penalty Parameter for Multi-Area Economic Dispatch Problem

The auxiliary problem principle is a powerful tool for solving multi-area economic dispatch problem. One of the main drawbacks of the auxiliary problem principle method is that the convergence performance depends on the selection of penalty parameter. In this paper, we propose a self-adaptive strategy to adjust penalty parameter based on the iterative information, the proposed approach is verified by two given test systems. The corresponding simulation results demonstrate that the proposed self-adaptive auxiliary problem principle iterative scheme is robust in terms of the selection of penalty parameter and has better convergence rate compared with the traditional auxiliary problem principle method.

The Engineering Application of Improved Alternating Direction Method for Solving Power Systems Multi-Area Dispatch Problem

The alternating direction method has been widespread used to solve multi-area economic dispatch problem. Compared with traditional centered economic dispatch, alternating direction method divides centered optimal problem into completely independent sub-problems while the corresponding equality and inequality constraints are satisfied. However, plenty of applications show that the choice of penalty parameter for the consistency constraint has an important influence on the convergence performance of alternating direction method. In this paper, we proposed a novel improved alternating direction method. To be more exact, the key is to adjust penalty parameter based on iterative information of alternating direction method. Numerical results illustrate the proposed method has better stability in convergence.

Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch Problem

Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch Problem

Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch Problem

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.