Abstract
The paper proposes a hierarchical optimization control concept for wind power cluster control, which divided the wind power cluster into cluster layer, group layer, and wind farm layer. The wind farms within the cluster were grouped based on their ram** rates, and then the wind farms within each group were sorted based on their load rates. The power grid command was first issued from the cluster layer to the group layer, and then to the wind farms within the group, with scheduling carried out sequentially in a layered manner. Firstly, constructed a scheduling process based on control objectives and constraints. Then, through simulation of a large-scale base example and comparison with the traditional proportional allocation method, verified the feasibility and effectiveness of the proposed cluster allocation strategy in reducing the regulation frequency and volatility of wind farms in terms of power grid command tracking and scheduling sequence. Lastly, evaluated the performance of power reduction for each wind farm using the entropy method and variable weight theory, and the results demonstrate that the proposed strategy effectively improve the cluster volatility.
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Acknowledgments
This research was partially funded by the National Natural Science Foundation of China under Grant 52106238, and by the Fundamental Research Funds for the Central Universities under Grant No. B230201051.
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Gao, Y. et al. (2024). A Layered Scheduling Strategy for Wind Power Cluster Considering Entropy Variable Weight Evaluation. In: Yang, Q., Li, Z., Luo, A. (eds) The Proceedings of the 18th Annual Conference of China Electrotechnical Society. ACCES 2023. Lecture Notes in Electrical Engineering, vol 1168. Springer, Singapore. https://doi.org/10.1007/978-981-97-1068-3_57
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DOI: https://doi.org/10.1007/978-981-97-1068-3_57
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