Alexandria Engineering Journal (Mar 2022)
Power supply system scheduling and clean energy application based on adaptive chaotic particle swarm optimization
Abstract
The research fields in the fields of information science and computer are very extensive, and there are many research hotspots. Intelligent optimization algorithm is one of the research hotspots. One of the key issues found in research papers in this field is how to effectively improve the search quality of intelligent optimization algorithms in a complex work environment. As a multi-functional swarm algorithm, particle swarm optimization has relatively few parameters, good convergence, and relatively fast working speed. It is a popular intelligent algorithm. In the process of studying the optimal operation of the combined cooling, heating and power system, in order to better dispatch the output of each device in the combined cooling, heating and power system, a combination of chaotic search based on Tent mapping and nonlinear adaptive particle swarm optimization is proposed Optimization algorithm. Taking the operating cost, pollutant emission and energy efficiency of the cogeneration system as the goals, a multi-objective optimization model was established. The simulation results show that the proposed cooling, heating and power cogeneration system optimization method can effectively improve economic benefits, reduce pollution emissions, and improve energy utilization. The traditional model cannot accurately analyze the environmental pollution mitigation effect. In order to avoid this problem, a modeling analysis of the impact of wind power photovoltaic clean energy on the mitigation of environmental pollution is proposed, thereby building a wind power photovoltaic clean energy organization structure and maintaining the application of wind power photovoltaic clean energy the stability of the range. Set wind power photovoltaic clean energy to alleviate environmental pollution factors, and determine the degree of environmental pollution limited by natural numbers under continuous use of polynomial constraints.
