Real-Time Cooperative Control Algorithm for Regional Power Grid Based on Long and Short-Term Memory Networks

Youfei Lu, Luhao Liu, Hongwei Zhao, Shirong Zou, Xu Liang, Tao Bao
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Abstract

With the increasing proportion of new energy resources connected, the power fluctuation of the regional power grid presents higher stochasticity property, and the real-time power balance faces greater challenges. To address this problem, a cooperative control model is constructed for real-time power balance in the regional grid with the high participation of multiple frequency regulation resources. In order to quickly generate the high- quality of cooperative control strategies, a long and short-term memory network (LSTM) is proposed to learn knowledge of the historical cooperative control strategies. Meanwhile, an infeasible solution correction method based on the ideal point method is proposed to modify the infeasible solution created by the network into a high-quality feasible solution. Finally, based on the extension model of the IEEE standard two-area frequency control model, the effectiveness and performance of the proposed method are verified by comparing various cooperative control optimization algorithms.
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基于长短期记忆网络的区域电网实时协同控制算法
随着新能源并网比例的增加,区域电网的功率波动呈现出更高的随机性,实时功率平衡面临更大的挑战。针对这一问题,构建了多频调节资源高度参与的区域电网实时功率均衡协同控制模型。为了快速生成高质量的协同控制策略,提出了一种长短期记忆网络(LSTM)来学习历史协同控制策略的知识。同时,提出了一种基于理想点法的不可行解修正方法,将网络生成的不可行解修正为高质量的可行解。最后,基于IEEE标准双区域频率控制模型的扩展模型,通过比较各种协同控制优化算法,验证了所提方法的有效性和性能。
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