Multi-time-scale Scheduling for Regional Power Grid Considering Flexibility of Electric Vehicle and Wind Power Accommodation

被引：0
|
作者：
Hu J. [1 ]
Lai X. [1 ]
Guo W. [2 ]
Zhang Y. [3 ]
Yang Y. [4 ]
机构：
[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing
[2] Economic Research Institute of State Grid Hebei Electric Power Supply Co., Ltd., Shijiazhuang
[3] Shijiazhuang Tonhe Electronics Technologies Co., Ltd., Shijiazhuang
[4] State Grid Electric Vehicle Service Co., Ltd., Beijing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 16期
基金：
中国国家自然科学基金;
关键词：
bidirectional long short-term memory neural network; electric vehicle; empirical mode decomposition; model predictive control; multi-time-scale power system scheduling; wind power prediction;
D O I：
10.7500/AEPS20220321020
中图分类号：
学科分类号：
摘要：
As the number of electric vehicles (EVs) gradually increases, the integration of EVs to power grids brings the difficulties to the operation and control of power grids. At the same time, the new power system with renewable energy as the main body faces the challenges of power balance. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM) neural network, a multi-time-scale scheduling method for regional power grid considering EV flexibility and wind power accommodation is proposed. Firstly, the intrinsic mode functions at different frequencies are obtained by CEEMMDAN of historical wind power and load data. Secondly, the intrinsic mode functions are reconstructed according to the criterion of the number of maximum value points. Thirdly, BiLSTM is used to predict the reconstructed components to obtain the predicted data of wind power and load data. Based on the predicted data, the multi-timescale scheduling model for the regional power grid is established based on the model predictive control (MPC) method. Finally, the simulation results show that the proposed prediction method has universal applicability. The proposed multi-time-scale scheduling method is effective and economic, which can not only suppress the load fluctuation and reduce the influence of wind power grid-connection, but also use the flexibility of EVs to perform the real-time deviation compensation of wind power prediction, and to maintain the system balance. © 2022 Automation of Electric Power Systems Press. All rights reserved.
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页码：52 / 60
页数：8
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