Abstract
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.
Highlights
The use and development of renewables has grown continuously in the power sector owing to climate change, with renewable power generation becoming second in the electricity mix in 2018 [1].Renewables installed more than 200 gigawatts in 2019, which is the largest increase to date [2].It is expected that renewable-based power capacity will grow by 50% between 2019 and 2024 [3].In particular, forecasts predict that wind capacity installations will triple by 2024 [3].Wind power generation (WPG) is subject to high fluctuations and intermittent properties.The characteristics of WPG make it difficult to ensure power system reliability [4,5]
This study focuses on reinforcement learning (RL)-based virtual Energy storage systems (ESSs) (VESS) operation to manage the uncertainty of WPG
Five WPG datasets that were recorded by the National Renewable Energy Laboratory to develop eastern wind resources in the United States of America from 2004 to 2006 were employed [38]
Summary
The use and development of renewables has grown continuously in the power sector owing to climate change, with renewable power generation becoming second in the electricity mix in 2018 [1].Renewables installed more than 200 gigawatts in 2019, which is the largest increase to date [2].It is expected that renewable-based power capacity will grow by 50% between 2019 and 2024 [3].In particular, forecasts predict that wind capacity installations will triple by 2024 [3].Wind power generation (WPG) is subject to high fluctuations and intermittent properties.The characteristics of WPG make it difficult to ensure power system reliability [4,5]. Renewables installed more than 200 gigawatts in 2019, which is the largest increase to date [2]. It is expected that renewable-based power capacity will grow by 50% between 2019 and 2024 [3]. Forecasts predict that wind capacity installations will triple by 2024 [3]. Wind power generation (WPG) is subject to high fluctuations and intermittent properties. The characteristics of WPG make it difficult to ensure power system reliability [4,5]. Various wind power forecasting methods such as the ensemble method [6], aggregated probabilistic method [7], and machine learning-based method [8] have been researched, uncertainty cannot be completely eliminated owing to the nature of wind-resource phenomena
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