针对目前风电功率预测结果常以“功率点”的形式提供未来时刻预测值、可靠性普遍较低的现状,提出一种基于“风电功率区间预测”的风储系统优化控制策略。首先,建立基于双重注意力机制和胡伯分位数损失函数的风电功率区间预测神经网络模型;然后,基于得到的风电功率区间预测结果,构建综合微网能量平衡、储能状态可靠性以及与外部电网交互经济性等关键因素的风储微网系统多目标优化模型,并设计基于深度强化学习算法的优化控制策略;最后,通过对典型工况的仿真,证明所提控制策略在调节微网能量平衡以及保障备电性能方面均具有良好效果。
Abstract
To solve the problem of low reliability caused by typical wind power forecasting provided in the form of ‘power points’, an optimized control strategy for energy storage charging and discharging decisions based on ‘wind power interval prediction’ is proposed. Firstly, a wind power interval prediction model based on the dual attention mechanism and the Huber quantile loss function （DA-BiLSTM-QH） is established. Secondly, by integrating interval prediction information, microgrid load, energy storage status and real-time electricity price, a multi-objective optimization model of wind-storage microgrid system is established, and a solution strategy based on deep reinforcement learning DQN algorithm is designed. The effectiveness of the proposed strategy is verified through simulation of typical operating conditions.
关键词
风电功率 /
预测 /
储能 /
优化控制 /
微网 /
多目标优化
Key words
wind power /
prediction /
energy storage /
optimization control /
microgrid /
multi-objective optimization
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