Deep reinforcement learning based energy storage management strategy considering prediction intervals of wind power
Deep reinforcement learning based energy storage management strategy considering prediction intervals of wind power
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DOI：
10.1016/j.ijepes.2022.108608
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发表时间：
2022-10-07
影响因子：
5.2
通讯作者：
Sidorov, Denis
中科院分区：
文献类型：
--
作者：
Liu, Fang;Liu, Qianyi;Sidorov, Denis
风力发电与储能相结合，能够保持能量平衡，实现稳定运行。本文提出了一种考虑风电预测区间的数据驱动储能管理策略。首先，建立了基于长短期记忆和上下界估计（LUBE）的功率区间预测模型，对风电的不确定性进行量化，解决了传统LUBE无法采用梯度下降法的问题；其次，将能量存储管理转化为马尔可夫决策过程，并采用深度强化学习进行求解。建立了智能体与环境交互作用的状态空间、动作空间和奖励函数，并通过深度Q网络逼近了价值函数。然后，根据风电、电力预测区间、局部负荷、动态电价和充电状态等实时状态，自动制定充放电计划。最后，基于实际风电场数据验证了所提储能管理策略的有效性和优越性。决策错误率为零，储能管理系统的日常交易成本和损耗成本显著降低。
Wind power generation combined with energy storage is able to maintain energy balance and realize stable operation. This article proposes a data-driven energy storage management strategy considering the prediction intervals of wind power. Firstly, a power interval prediction model is established based on long-short term memory and lower and upper bound estimation (LUBE) to quantify the uncertainty of wind power, which solves the issue that traditional LUBE cannot adopt gradient descent method. Secondly, the energy storage management is transformed into Markov decision process and solved by deep reinforcement learning. The state space, action space and reward function of the interaction between agent and environment are established, and the value function is approximated through the deep Q network. Then, according to the real-time state, such as wind power, power prediction intervals, local load, dynamic electricity price and state of charge, the proposed strategy can make the charge/discharge schedule automatically. Finally, the effectiveness and superiority of the proposed energy storage management strategy are verified based on real wind farm dataset. The proportion of wrong decisions is zero, and daily transaction cost and wear cost of energy storage management system decrease significantly.