Abstract
Many uncertain factors in wind power forecasting lead to large prediction errors. Various prediction technologies have been developed to reduce errors and improve the dispatch-ability of grid-connected wind power. To install energy storage systems is an effective approach to reduce the scheduling deviation in dispatching the grid-connected wind power. This paper considers the optimal capacity allocation, a key issue in smoothing the grid wind power generation and integration. Based on the analysis of wind power prediction technologies and the resultant prediction deviations, the relationship between the distribution characteristics of wind power prediction errors and energy storage capacity demand is first investigated. Then, an optimization method is proposed, considering the stability of grid operation and the relationship between compensation necessity and load changes. Further, load fitness factor is introduced in processing the deviation data samples, together with an economic dispatch model for the deviation compensation, considering the operation costs. Finally, based on the analysis of various factors, the technical route to achieve energy storage capacity allocation for scheduling deviation compensation is proposed. Case studies are also presented to demonstrate the effectiveness of the proposed approach.
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Acknowledgements
This work is a part of the National Natural Science Research Programs of China (No. 61673226), considerable Natural Science Research Projects of Colleges and Universities in Jiangsu Province of China (No. 18KJA470003), Natural Science Foundation of Jiangsu Province (BK20200969), Nantong Science and Technology Bureau Project (JC2018116), and Jiangsu Province's fifth ‘333 high-level talent training objects’ Project. The authors would like to thank for the supports from both the Ministry of Science and Technology and National Natural Science Foundation of China.
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Zhu, Jh., Li, K., Xu, R., Gu, J., Zhang, L., Sun, C. (2021). Energy Storage Capacity Optimization for Deviation Compensation in Dispatching Grid-Connected Wind Power. In: Li, K., Coombs, T., He, J., Tian, Y., Niu, Q., Yang, Z. (eds) Recent Advances in Sustainable Energy and Intelligent Systems. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1468. Springer, Singapore. https://doi.org/10.1007/978-981-16-7210-1_10
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DOI: https://doi.org/10.1007/978-981-16-7210-1_10
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