【摘要】 【目的】针对“双碳”目标下微电网作为提升能源利用效率与促进可再生能源消纳的关键载体，系统稳定与经济运行面临负荷波动大和多主体协同难的挑战，提出一种融合参数自适应长短期记忆网络的负荷预测方法，以及基于鲸鱼优化算法的微电网动态能量管理方法。【方法】首先，通过量子粒子群算法对长短期记忆网络关键超参数进行全局寻优，有效提升短期负荷预测精度和有效捕捉峰谷时段负荷突变特征；其次，基于长短期记忆网络下的负荷预测结果建立含多种分布式电源与储能装置的微电网经济调度模型，以单日内运行成本最小化为目标，结合功率平衡和设备出力的约束关系，利用鲸鱼优化算法实现全局优化调度。【结果】所提负荷预测方法在短期预测中具有较高精度，并能有效识别负荷峰谷波动特征；基于该预测结果，鲸鱼优化算法在经济调度模型中实现了更低的运行成本，同时在提升本地分布式电源利用率与维持成本稳定性方面，表现优于传统优化算法。【结论】所建立的高精度预测模型与鲸鱼全局优化算法的协同策略可为源-荷不确定性下的微电网经济运行提供参考。更多还原

【Abstract】 [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.更多还原