Research On Energy Management Strategy Of Microgrid

In the new era,China has actively promoted the transformation and adjustment of energy structure towards clean and low-carbon.In the development and construction of power engineering projects,the technical level of photovoltaic power generation has been significantly improved and plays a vital role in promoting the development of distributed generation.When connecting intermittent distributed generation equipment,the use of microgrid can effectively improve the utilization rate of renewable energy,and can support and cooperate with large power grid to supply power flexibly.However,there are many problems in the microgrid,such as the inability to accurately predict the power of power generation equipment and the uncertainty in load forecasting.It is difficult to effectively control the energy flow process of the microgrid and ensure the safety,stability and reliability of the microgrid operation.Based on this,this thesis selects the optical hydrogen storage microgrid system as the research object to explore the photovoltaic power prediction mode and the optical hydrogen storage energy management strategy in detail.Firstly,in the aspect of photovoltaic power prediction and analysis,PSO(Particle Swarm Optimization)is selected to improve the PSO-BP model of BP neural network(Back Propagation).In order to analyze the correlation between meteorological factors and photovoltaic power generation,select the correlation quantity,create a PSO-BP model based on this,and then conduct a comparative analysis with the BP model,and select multiple weather examples for verification.By optimizing and adjusting the combined model,the prediction results of the PSO-BP model and the BP model are compared.square error are relatively small.By carrying out 10 predictions and analyzing the average error of the obtained results,MAPE and RMSE are reduced by 17.57% and 1.30% respectively.Therefore,the results obtained by the PSO-BP prediction model have high accuracy and can significantly improve the network.convergence speed.Secondly,the energy management model of the optical hydrogen storage microgrid is created,and the models of photovoltaic power generation equipment,storage battery,fuel cell,electrolyzer,and hydrogen storage tank are used to conduct research on storage battery,fuel cell,fuel cell and hydrogen storage equipment,and finally optimize the system.Then,the principle of intelligent algorithm is analyzed,the particle swarm algorithm is adjusted,and the results obtained by comparison are analyzed.It is found that after the algorithm is improved,the optimization speed is significantly improved.In addition,the working characteristics and constraints of distributed units such as photovoltaic power generation,storage batteries,fuel cells,electrolyzers and hydrogen storage tanks are described in detail.Finally,the energy management strategy is formulated based on the multi-time scale of day-a-day,and the optimization and adjustment plan of the day-a-day bipolarity is formulated at the same time.In the day-ahead scheduling management,the battery and fuel cell charging and discharging rules of the peak-level-valley time-of-use electricity price are adopted,so that the battery and fuel cell can operate efficiently and reasonably within the range of [0,1].According to this rule,there are It is beneficial to reduce the operating cost of the micro-grid,and at the same time,it can ensure the rationality of the charging and discharging times of the battery and the fuel cell,and prolong the life of the battery and the fuel cell.During the day,the MPC rolling optimization method is used to adjust the day-ahead optimization to avoid a large deviation between the actual operation of the system and the scheduling plan,thereby causing unnecessary losses.In addition,the accuracy and economy of the strategy are verified by practical examples.