Research On Energy Management Of Photovoltaic-Storage Microgrid For Smart Building

With the economy and industry developed rapidly in the world,the necessity for energy has also increased for human.Energy consumption accounts has a large proportion of the power system for buildings,and energy pressure urgently requires us to improve energy use methods and increase energy efficiency.In this context,microgrids composed of renewable energy sources such as photovoltaics have been vigorously promoted in building energy management.At present,the operation of optical storage microgrid for buildings still has problems such as insufficient reliability,high power supply cost,and power supply and demand mismatch;most intelligent optimization algorithms suitable for microgrid energy management have local convergence and are prone to defects such as "premature".This paper focuses on the optimization and economic dispatch related issues of the optical storage microgrid.The main work is as follows:1.Analyzing the operating characteristics and respective mathematical models of the distributed power generation units in the optical storage micro-grid,consider the depreciation and maintenance costs in the operation of the distributed power generation unit,and establish the economic operation of the optical storage micro-grid on this basis Objective function and constraint conditions;based on the expense of electricity price mechanism,an energy management strategy for the optical storage microgrid is proposed.2.Aiming at the problem that particle swarm optimization algorithm that is easy to form the local optimum.The idea of Gaussian normal distribution is introduced to update the inertia weights,and mutation links are added to improve the accuracy of the algorithm,forming an Adaptive Mutation Particle Swarm Optimization(AMPSO)based on the economic scheduling of photovoltaic-storage microgrid.The test function is used to evaluate the effectiveness of the algorithm,and compared with Particle Swarm Optimization,Adaptive Genetic Algorithm,Genetic Particle Swarm Optimization,the AMPSO proposed in this paper has excellent performance in convergence and efficiency.3.The optimization model of the optical storage microgrid was simulated and solved through the MATLAB platform,and three scenarios with different initial values of energy storage devices were set,and AMPSO was used to optimize the charge and discharge capacity of the energy storage device and the interactive power between the system and the tie line.By comparing the operating cost and optimization efficiency of the optical storage microgrid under four different algorithms,the validity of the constructed mathematical model and the improved algorithm is verified.The results show that the daily operating costs of AMPSO are reduced to varying degrees compared with other algorithms,and the comparison with traditional PSO is the most obvious.The comprehensive daily operating costs of the three scenarios are reduced by 10.7%,11.5%,and 21.9% respectively compared with traditional PSO.4.Set up the management platform containing photovoltaics and energy storage devices,which is used to collect and process the data of smart air conditioners,lighting,electricity meters,photovoltaic controllers and other equipment in the building in real time,and develop interface functions for equipment control.The improved algorithm and energy management strategy proposed in this paper improves the energy utilization rate and operating economy of the optical storage microgrid,which is of great significance for the promotion and application of the optical storage microgrid in buildings.