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Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment
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Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment
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Journal Article
Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment
2019
Overview
Over the past years, wind energy has been considered as a promising solution for clean and sustainable energy development, but wind curtailment remains a challenge to wind power development. On this basis, utilization of non‐grid‐connected wind power becomes crucial and necessary as it can mitigate wind curtailment and improve energy efficiency. This paper proposes the collaboration strategy and optimization model of wind farm‐hybrid energy storage system (WF‐HESS) for non‐grid‐connected wind power based on battery and superconducting magnetic energy storage (SMES) whose combination can effectively cope with fluctuation and intermittence of wind input. The optimization problem is simultaneously investigated by the minimization of total cost, wind curtailment magnitude, and loss of power supply probability (LPSP). The multi‐objective particle swarm optimization (MOPSO) is introduced to find available solutions, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to determine the optimal one. Results are obtained for a case study, and the effectiveness and the feasibility of proposed model are verified by a comparative analysis and a sensitivity analysis. Results analysis and discussion show that the WF‐HESS model and the application of HESS have important influence on promoting utilization of non‐grid‐connected wind power and mitigating wind curtailment. This paper proposes a collaboration strategy and optimization model of wind farm‐hybrid energy storage system (WF‐HESS) for non‐grid‐connected wind power based on battery and superconducting magnetic energy storage (SMES). The optimization problem is investigated by minimization of total cost, wind curtailment magnitude, and loss of power supply probability (LPSP) simultaneously. The multi‐objective particle swarm optimization (MOPSO) is introduced to find available solutions, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to determine the optimal one.
Publisher
John Wiley & Sons, Inc,Wiley
