Deep Reinforcement Learning Based Energy Storage Management Strategy Considering Prediction Intervals of Wind Power

2021 ◽  
Author(s):  
Fang Liu ◽  
Qing Tao ◽  
Dechang Yang ◽  
Denis Sidorov
Keyword(s):  
Reinforcement Learning ◽  
Energy Storage ◽  
Wind Power ◽  
Management Strategy ◽  
Storage Management ◽  
Prediction Intervals

scholarly journals Energy storage management strategy in distribution networks utilised by photovoltaic resources

2018 ◽  
Vol 12 (21) ◽  
pp. 5627-5638 ◽  
Author(s):  
Ali Azizivahed ◽  
Mostafa Barani ◽  
Seyed-Ehsan Razavi ◽  
Sahand Ghavidel ◽  
Li Li ◽  
...  
Keyword(s):  
Energy Storage ◽  
Management Strategy ◽  
Distribution Networks ◽  
Storage Management

scholarly journals Reinforcement-Learning-Based Virtual Energy Storage System Operation Strategy for Wind Power Forecast Uncertainty Management

2020 ◽  
Vol 10 (18) ◽  
pp. 6420
Author(s):  
Eunsung Oh
Keyword(s):  
Reinforcement Learning ◽  
Energy Storage ◽  
Wind Power ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Uncertainty Management ◽  
Operation Strategy ◽  
Forecast Uncertainty ◽  
State Action ◽  
Multiple Units

Uncertainties related to wind power generation (WPG) restrict its usage. Energy storage systems (ESSs) are key elements employed in managing this uncertainty. This study proposes a reinforcement learning (RL)-based virtual ESS (VESS) operation strategy for WPG forecast uncertainty management. The VESS logically shares a physical ESS to multiple units, while VESS operation reduces the cost barrier of the ESS. In this study, the VESS operation model is suggested considering not only its own operation but also the operation of other units, and the VESS operation problem is formulated as a decision-making problem. To solve this problem, a policy-learning strategy is proposed based on an expected state-action-reward-state-action (SARSA) approach that is robust to variations in uncertainty. Moreover, multi-dimensional clustering is performed according to the WPG forecast data of multiple units to enhance performance. Simulation results using real datasets recorded by the National Renewable Energy Laboratory project of U.S. demonstrate that the proposed strategy provides a near-optimal performance with a less than 2%-point gap with the optimal solution. In addition, the performance of the VESS operation is enhanced by multi-user diversity gain in comparison with individual ESS operation.

scholarly journals Reinforcement learning for fluctuation reduction of wind power with energy storage

2021 ◽  
pp. 100030
Author(s):  
Zhen Yang ◽  
Xiaoteng Ma ◽  
Li Xia ◽  
Qianchuan Zhao ◽  
Xiaohong Guan
Keyword(s):  
Reinforcement Learning ◽  
Energy Storage ◽  
Wind Power

Design of Energy Storage Station Grouping Energy Management Strategies to Balance Short-Term Wind Power Fluctuations

2013 ◽  
Vol 448-453 ◽  
pp. 2866-2871 ◽  
Author(s):  
Jun Hui Li ◽  
Xing Xu Zhu ◽  
Gan Gui Yan ◽  
Gang Mu ◽  
Wei Hua Luo
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Energy Management ◽  
Management Strategy ◽  
Wind Farm ◽  
Management Strategies ◽  
Wind Farms ◽  
Energy Management Strategy ◽  
Storage Station ◽  
Power Fluctuations

This paper designs a grouping energy management strategy to reduce the influence of wind power fluctuations on the power system. To improve operational technicality and economy of energy storage stations, this paper designs a grouping energy management strategy with SOC correction. According to physical constrains of battery energy storage systems, technical and economic evaluation index of energy storage stations are established. Reasonable limit bands to an energy storage station installed 5MW×2h can balance the output power of a wind farm installed 49.3MW achieved through example analysis. Then the energy management strategy designed is proved to be able to control the change range of the battery SOC and distribute control tasks efficaciously, improving operational technicality and economy of the station effectively. This research provides a theoretical reference to design of energy management strategies for energy storage stations installed small at wind farms.

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