Online Decision-Making Method for Wind Power Integrated Power System Restoration Based on Hierarchical Model Predictive Control
Gu Xueping1, Wei Jiajun1, Bai Yansong1, Li Shaoyan1, Liu Yutian2
1. School of Electrical & Electronic Engineering North China Electric Power Univesity Baoding 071003 China; 2. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan 250061 China
Abstract：Under the background of 'carbon peaking and carbon neutrality', the wind power penetration rate in the power system is constantly increasing, and the impact of wind turbine units on the power system restoration process after blackout is becoming more and more significant. Reasonably utilizing wind power resources can effectively accelerate the restoration process and reduce the blackout losses. However, the traditional power system restoration optimization methods only depend on the prediction data at the initial restoration stage, which have weak applicability to real-time conditions and are not suitable for the restoration scenario of high wind power proportion. To address the impact of wind power output uncertainty on the restoration process, this paper proposes an online rolling optimization method for power system restoration based on a hierarchical model predictive control framework. Firstly, to meet various restoration decision-making requirements, a hierarchical control structure is employed to formulate the restoration task as a two-layer optimization model, where a rolling optimization strategy with two different rolling mechanisms is proposed based on dynamically updated wind power forecast information, to coordinate the foresight and information accuracy in rolling optimization for different decision objectives. Considering the aftereffect of the component restoration decision-making, the upper layer adopts the forward-to-the-end rolling mechanism to optimize the component restoration sequence within the entire restoration process. With the aim of maximizing the overall restoration efficiency, this layer utilizes the full-time wind power prediction data to determine the component restoration sequence. At the lower layer, based on the component restoration sequence determined by the upper layer, taking into account the practical scenario where the accuracy of wind power forecasts decreases with increase of the lead time, a sliding time window rolling mechanism is employed for decision-making in both generator output planning and load restoration planning. It takes the subsequent finite time step as the optimization domain to ensure the accuracy of the prediction information within the decision domain, avoiding direct impact of the remote prediction errors on power scheduling schemes. Meanwhile, this layer reserves enough adjustment space for feedback correction link. Then, in the feedback correction link, based on the measured wind power data, a real-time scheduling model of flexible resources such as energy storage is established and the wind power prediction is corrected. Finally, the proposed method is verified in the modified New England 39-bus system and a real power system. The results indicate that the method can formulate a safe and efficient restoration scheme for the wind power integrated power system. The following conclusions can be drawn from the simulation analysis: (1) The online rolling optimization decision can track the continuously updated wind power forecast data with increasing accuracy to adjust the component restoration sequence, generator output plans, and load restoration schemes dynamically. (2) Compared to the traditional model predictive control, the hierarchical model predictive control which employs the rolling optimization strategy in this paper, can improve the restoration efficiency of the whole process while ensuring the calculation speed and restoration safety. (3) The real-time scheduling models of the flexible resources can not only fully leverage the rapid response advantage of the flexible resources to ensure system power balance during the restoration process but also improve the utilization rate of the wind power resources.
顾雪平, 魏佳俊, 白岩松, 李少岩, 刘玉田. 基于分层模型预测控制的含风电电力系统恢复在线决策方法[J]. 电工技术学报, 2025, 40(5): 1471-1486.
Gu Xueping, Wei Jiajun, Bai Yansong, Li Shaoyan, Liu Yutian. Online Decision-Making Method for Wind Power Integrated Power System Restoration Based on Hierarchical Model Predictive Control. Transactions of China Electrotechnical Society, 2025, 40(5): 1471-1486.
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2025, Vol. 40 
