Hybrid DE-NSO for Multi Type Economic Load Dispatch Problem (Economic Load Dispatch: An Approach using Differential Evolution Algorithm with Neighborhood Search Operator)
This paper discusses a novel and efficient algorithm to solve Economic Load Dispatch Problem (EDP) constructed with a non-smooth fuel cost function. Due to non-linear generator constraints such as prohibited operating zones, valve point loading and spinning reserve, optimization problem arises in the formulation of more realistic EDP. To solve this complex limitations of EDP, a new technique known as Differential Evolution (DE) is proposed in which a Neighborhood Search Operation (NSO) is performed for each population member, which is accelerated towards finding the global solution. The weak solutions are replaced by randomly selected individuals thus exploring the search space for new optimum regions. The main concept of this method is to stabilize the exploitation and exploration ability of DE. Thus a method known as NSO-DE is proposed such that, DE runs as the main optimizer while NSO fine tunes the solution and acts as a local optimizer. The effective and robust characteristics of the proposed NSO-DE method is evaluated based on three standard test EDP cases comprising 10, 13 and 15 thermal units. Based on the quality of obtained final solution, performance comparison with other DE methods is done.
Economic load dispatch, Prohibited operating zone, Spinning reserve, Differential evolution, Neighborhood search operator..