融合自适应高精度负荷预测的微电网动态能量管理策略OA

[Objective]Under the"Dual Carbon"goals,microgrids serve as key carriers for improving energy efficiency and promoting renewable energy consumption.However,they face challenges regarding system stability and economic operation due to significant load fluctuations and difficulties in multi-agent coordination.To address these issues,this paper proposes a load forecasting method integrating a parameter-adaptive long short-term memory(LSTM)network,along with a dynamic energy management method for microgrids based on the whale optimization algorithm(WOA).[Methods]First,the quantum particle swarm pptimization(QPSO)algorithm is employed to globally optimize the key hyperparameters of the LSTM network.This significantly improves the accuracy of short-term load forecasting and effectively captures the characteristics of load mutations during peak and valley periods.Second,based on the load forecasting results,an economic dispatch model for microgrids containing various distributed generators and energy storage devices is established.With the objective of minimizing daily operating costs and subject to constraints on power balance and equipment output,the WOA is utilized to achieve global optimal dispatch.[Results]The proposed load forecasting method demonstrates high accuracy in short-term predictions and effectively identifies load peak-valley fluctuation characteristics.Based on these forecasting results,the WOA achieves lower operating costs in the economic dispatch model.Furthermore,it outperforms traditional optimization algorithms in improving the utilization rate of local distributed generators and maintaining cost stability.[Conclusions]The synergistic strategy established in this study,combining a high-precision prediction model with a whale global optimization algorithm,provides a reference for the economic operation of microgrids under source-load uncertainty.