Application Of Multiobjective Optimization Methods In Plant-level Optimal Load Dispatching Of Thermal Power Plant

Most of the actual problems are in the field of multiobjective optimization problems.Each of the objectives may be affected and conflicted by each other.Thus,it is difficult to deal with these problems.Traditional ways to solve the problems are limited to the factors of the problems and the subjective factors of the human who makes the decisions.Evolutionary algorithms have high ability of higher accuracy and efficiency to solve multiobjective problems.An improved algorithm was proposed.Finally the algorithm was applied in a thermal power plant.For the research of plant-level load dispatch in thermal power plant,the characteristics of coal-consumption and pollutant emission were established based on daily data.The improved swarm algorithms were applied to simulating,in order to find economic and environmentally friendly methods of load dispatching.Firstly,the fundamental and the development of evolutionary algorithms are introduced,and the features of evolutionary algorithms are concluded;secondly,considering the low convergence and efficiency,an improved particle swarm optimization method is proposed,which relies on particle swarm optimization for its fast and powerful search ability and relies on extremal optimization.The proposed algorithm was applied in a thermal power plant for its plant-level load dispatch and achieved good results,and the results could show a great reference for the load dispatch problem.