Economic Load Dispatch Solution Using Improved Time Variant MOPSO Algorithm Considering Generator Constraints

摘要

In this paper, an improved time variant Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is proposed to find the feasible optimal solution of the Economic Load Dispatch (ELD) problem with considering operational constraints of the generators. For practical generator operation, many nonlinear constraints of the generator, such as ramp rate limits, prohibited operating zone, generation limits, transmission line loss and non-smooth cost functions are all considered using the proposed method. Time Variant MOPSO (TV-MOPSO) uses a new adaptive strategy to change its vital parameters i.e.: inertia weight and acceleration coefficients with iterations. This adaptiveness guides the particles to efficiently explore the search space process in algorithm within a reasonable computation effort. In addition, it preserves the diversity of Pareto optimality by a crowding entropy diversity measure tactic. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately and efficiently. For this reason, TV-MOPSO algorithm is used for the solution of ELD problem in order to efficiently control the local search and convergence to the global optimum solution. To solve ELD problem by the proposed method it is converted to a nonlinear multi-objective optimization problem with competing and non-commensurable objectives of fuel cost and transmission losses. Effectiveness of the proposed method is investigated on three different systems, including 6, 15 and 40 units generating in comparison with the performance of the other recently reported optimization techniques in the literature in terms of the solution superiority and computation efficacy. The results analysis reveals that the proposed multi objective method is effective and achieves good ability to find optimal solution for ELD problems over the other existing approaches and improves significantly the solutions quality of the power systems.