Research On Model And Optimal Dispatch Of Residential Household Energy Management System In Southern Anhui Province

With the rapid development of my country’s economy,the living standards of rural residents are improving day by day,and the types and quantities of household electrical equipment continue to increase,and the demand for electricity in rural areas is showing a trend of rapid growth.At the same time,distributed power generation such as small wind turbines,rooftop photovoltaics,and smart grids are widely used in rural areas.How distributed power generation can meet rural electricity demand and improve the utilization rate of renewable energy has attracted more and more attention.According to the electricity load characteristics of the residential buildings in southern Anhui,residential and restaurants are used as the research objects,and the daily electricity demand is analyzed by field investigation and numerical simulation.Under the conditions of real-time electricity prices,a household energy management system is established.Dispatching optimization guides users to change the original power mode,effectively utilizes distributed power generation,and reduces power purchase costs.The energy consumption of residential buildings in southern Anhui was simulated to obtain the air-conditioning energy consumption of residential buildings in southern Anhui,the energy system of southern Anhui residential buildings was designed,and a household energy management system composed of distributed power generation,household electrical equipment and energy storage equipment was established.According to the load characteristics of household electrical equipment,the electrical load is divided into rigid load and flexible load,and the time distribution model of flexible load is established.Analyze the influencing factors of wind power and photovoltaic power generation,and determine that wind speed,wind direction,temperature,and historical power generation are the main influencing factors of wind power generation;temperature,solar radiation intensity,and historical power generation are the main influencing factors of photovoltaic power generation.As a result,a BP neural network model for photovoltaic and wind power generation with the structures of 5-5-1 and7-10-1 is established to predict power generation.The genetic algorithm is used to optimize the BP neural network model,and the optimized BP neural network prediction results are more accurate.On the basis of real-time electricity prices,with the minimum electricity purchase cost as the optimization goal,the distributed power generation forecast is compared with the household electricity demand,and the genetic algorithm is used for load optimization scheduling.Electric equipment is adjusted to a relatively rich time period for distributed power generation to effectively realize user-side demand response,reduce the power consumption of the power grid during peak power consumption,and improve the stability of the power grid and the utilization rate of renewable energy.Compared with unused load dispatching,the utilization rate of renewable energy has increased by 16.5% and 36.15% respectively after adopting load optimization dispatching for residences and restaurants on a typical day.Finally,add energy storage equipment to the energy system to achieve optimal energy storage scheduling.With the minimization of electricity purchase costs as the optimization goal,the load is optimally dispatched based on the difference between the renewable energy generation and the building load demand,the real-time electricity price and the state of charge of the battery.Optimal dispatch of energy storage can make greater use of renewable energy power generation.After using energy storage optimization scheduling for residences and restaurants on a typical day,the renewable energy utilization rates of residences and restaurants increased by11.49% and 34.24%,respectively,compared with before unloaded scheduling.