Deep reinforcement learning based energy storage management strategy considering prediction intervals of wind power

• A power interval prediction model is established based on LSTM and LUBE to quantify the uncertainty of wind power. • The energy storage management is transformed into Markov decision process and solved by deep reinforcement learning. • According to the real-time state, the proposed strategy can make the charge/discharge schedule automatically. 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.