Based On Improved Particle Swarm Algorithm Of Power System Economic Load Distribution Environment

Economic load dispatch(ELD) of power system is an important research topic in power economic dispatch. In recent years, environmental issues become increasingly prominent. The environmental pollution caused by nitrogen dioxide and carbon dioxide gas emissions become a growing concern and attention. In this context, only considering minimizing the cost of fuel for power generation can not meet the actual requirements in power economic dispatch. It is called Economic Emission Load Dispatch(EELD). It need consider both power generation cost and environmental pollution control costs, without changing the current state of systems. It has become a hot topic in power system studies.In order to solve EELD problem better, this paper first built a EELD mathematical model,then select the particle swarm optimization(PSO) algorithm to solve EELD based on comparing several commonly used algorithms. Particle swarm algorithm is easily trapped into local optimum and its global convergence performance is poor. For these two disadvantages, a improved particle swarm optimization is introduced to solve EELD in this paper, which adopts quadratic improvement strategy and adaptive inertia weight and acceleration factor improvement strategy. This improve balance of the global and local search capabilities well, and advoid falling into local optimum. Its global search capability is enhanced obviously. This algorithm is proved to be effective by simulating EELD example and comparing results with other optimization algorithms.The formation process of the pareto optimal set is complex in multi-objective particle swarm optimization. This paper presents a virtual ideal particle multi-objective particle swarm optimization. This optimization construct a virtual ideal particle first and then select particles according to geometric distance between selected particle and virtual ideal particle. This algorithm is proved to be effective by simulating EELD example and comparing results with other optimization algorithms.