Dynamic Economic Load Dispatch Considering Renewable Energy Sources using Multiswarm Statistical Particle Swarm Optimization

In current scenario, it is encouraged to employ renewable resources to meet energy needs. This leads to increased focus on power planning and economic management of resources. This paper presents a comparison between various meta heuristics methods employed to solve economic load dispatch problem with a novel multiswarm statistical particle swarm optimization (MSPSO) method. It is tested on various benchmark functions and statistical analysis is carried out. The performance is found better than the existing metaheuristics approaches. The proposed method is tested on six conventional diesel generators and a solar photovoltaic power generation system to minimize cost for economic load dispatch. Most of the nonlinear characteristics of the generators such as ramp rate limits, transmission losses, valve point loading and prohibited operating zones for economic operation are considered along with fuel cost function for constrained economic load dispatch. It is observed that the MSPSO performs better than standard PSO as well as other meta heuristics methods with faster convergence rate and accuracy.