Research On Energy Management Strategy Optimization Algorithm Of Photovoltaic Microgrid For Smart Factory

With the rapid progress and development of technology,“smart manufacturing” has become a development trend of the global manufacturing industry in recent years.Smart factory will become an important role in the future industrial system.At the same time,the rapid development of society has led to a substantial increase in power demand.However,energy shortage and environmental pollution problems are becoming increasingly serious.In such a case,the utilization of solar energy has been vigorously developed.Micro-grid is one main way to organize the solar power generation.The current photovoltaic(PV)micro-grid system for smart factory mainly has the following problems: most optimization algorithms for energy management strategies have defects such as “premature”;and there are such problems as high power supply costs and chaotic energy management in smart factories.The solution of these problems will have important practical significance for the application and promotion of PV micro-grid in smart factory in the future.Based on the above problems,this paper takes the energy management of PV micro-grid of smart factory as the research direction,which mainly includes the following work:1.Considering the energy cost between the interaction of micro-grid and power grid and the utilization rate of PV generation,a photovoltaic micro-grid energy management scheduling strategy for smart factory is established.Meanwhile,a mathematical model is established.2.Introduce the principle of standard brain storm optimization(BSO)algorithm.Then,in order to avoid the ‘prematurity' defect,using the kernel fuzzy clustering(FCM)algorithm takes the place of the k-means clustering in the standard brainstorming optimization algorithm to improve the performance of the BSO algorithm.Simulate the improved BSO algorithm by using the intelligent algorithm test function.The simulation results show that the improved BSO algorithm's performance is better than particle swarm optimization algorithm,genetic algorithm and standard BSO algorithm.3.Verify the improved algorithm and the optimized scheduling model of PV micro-grid by the simulation,and the results show the effectiveness.4.A web platform is built for a PV micro-grid energy management system for smart factory.The platform is capable of monitoring PV generation and compliance with electricity usage data,generating annual,monthly,and daily reports,as well as query scheduling functions.The constructed platform can improve the management efficiency of the PV micro-grid energy management system and achieve a good display effect.