Research On The Optimal Management And Control Strategy Of Microgrid In The Energy Internet Environment

In the context of global energy conservation and environmental protection,the power system has begun to adopt a large number of renewable new energy sources,such as wind power generation and solar power generation,to gradually reduce or even replace the dependence on fossil energy power generation.However,distributed power sources are not easily transmitted over long distances and these distributed power sources use the operating mode of the nearest consumption.The best solution for adopting this model is to set up a micro-grid under the energy Internet,and optimize scheduling management and control these distributed power sources in the micro-grid.This not only optimizes the energy allocation within the network,but also facilitates the realization of energy-saving and emission reduction targets for the power system.For the energy management of micro-grid,distributed energy in the micro-grid is intermittent and varies timely.It is impossible to control the stability the power generation and installed capacity of power generation like thermal power.Therefore,traditional grid energy management control method is not suitable for solving the energy management control problem of the micro grid.In order to effectively manage the distributed energy in the micro-grid,this paper develops the micro-grid energy management model and its control method under the background of energy internet.The contents of the research are as follows:Firstly,based on the respective characteristics of distributed energy in the micro-grid,the cost model of distributed power supply and mathematical expression of constraints are established.Then,based on the distributed power cost model and constraints,the micro-grid energy management control cost optimization model is established,and the model is used to study the micro-grid energy optimization management control.Secondly,based on the micro-grid energy management control cost optimization model,a deep learning neural network is combined with adaptive dynamic programming to solve the real-time optimal scheduling problem of micro-grid energy management.As a result,a real-time management framework for distributed energy in micro-grid is proposed,and a deep learning adaptive dynamic programming algorithm is proposed for the framework.The algorithm inherits the original advantages and characteristics of deep learning and adaptive dynamic programming,and effectively solves the real-time problem of energy management scheduling of micro-grid.By carrying out the simulation data from historical data obtained by the grid company,the statistical analysis results are obtained,which verifies the validity,real-time,and stability of the model and algorithm.Thirdly,in the micro-grid energy optimization management control,since the amount of renewable energy generated is insufficient to meet the needs of users in the network,the flexible load is removed.Thus,demand response theory and real-time electricity price theory are introduced to the energy management optimization of the micro-grid,which makes the energy management control strategy more reasonable.In the micro-grid,users are mostly rational,and most users in the network are sensitive to electricity price.To this end,a real-time electricity price micro-grid management control model was established.Real-time electricity price is used to adjust the relationship between distributed energy supply and demands within the network,which not only realizes dynamic balance of supply and demand,but also realizes peak load shifting.By simulating the demand response real-time electricity price management control model of micro-grid,the effectiveness and feasibility of real-time electricity price strategy in micro-grid energy management control are verified.Finally,the proposed energy management and control strategy for the micro-grid is tested through simulations considering a regional micro-grid of China Southern Grid Corporation.The results show that the real-time energy management and control strategy is more suitable for the energy management and scheduling of the distributed multi-source power supply in the micro-grid.The micro-grid will inevitably be affected by some uncertain factors such as load disturbance,weather conditions and so on,and furthermore will be affected by the inaccuracy of the model,the measurement error of sensing devices and the parameter error of the mathematical model.These uncertainties and generalized disturbances may degrade the performance of the actual control,making it difficult to get the optimal control strategy.However,the evolutionary game theory only requires that the players in the micro-grid(decision makers of the system and virtual participants with uncertainties)have bounded rationality.In this paper,both approximation theorems of bounded rationality are applied to the energy management and control for the micro-grid,and moreover criteria of applying both the theorems in electricity market are determined.The real-time energy management and control strategy is proved to be a specific application of bounded rationality approximating perfect rationality.The results also suggest that the real-time energy management and control strategy is feasible and optimal.In this paper,the real-time dispatch optimization control problem of DSRM in industrial engineering and management of micro-grid is taken as the research object,and the real-time dispatch problem of demand response management in micro-grid is solved by using the related theory of complex scientific optimization decision-making,which provides theory and methodological support for demand response management in micro-grid.Moreover,the micro-grid energy management control strategy proposed in this paper is in line with the current development direction of the green energy of the power system,which helps the power system to improve the utilization rate of distributed power in the regional micro-grid.The benefit will be that carbon emissions and environmental pollution caused by the extensive use of fossil fuels can be reduced effectively,and finally achieve the goal of energy conservation and emission reduction,which eliminates thermal power and completely uses clean energy.