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

doi:10.19595/j.cnki.1000-6753.tces.240331

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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.

Key words： Model predictive control blackout wind power uncertainty rolling mechanism online restoration real-time correction

Received: 03 March 2024

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	Gu Xueping
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Gu Xueping,Wei Jiajun,Bai Yansong等. Online Decision-Making Method for Wind Power Integrated Power System Restoration Based on Hierarchical Model Predictive Control[J]. Transactions of China Electrotechnical Society, 0, (): 2492923-2492923.

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[1] 辛保安, 李明节, 贺静波, 等. 新型电力系统安全防御体系探究[J]. 中国电机工程学报, 2023, 43(15): 5723-5731.
Xin Baoan, Li Mingjie, He Jingbo, et al.Research on security defense system of new power system[J]. Proceedings of the CSEE, 2023, 43(15): 5723-5731.
[2] 孟荣涛, 李少岩, 顾雪平, 等. 光热电站作为黑启动电源时计及机组恢复效益的运行优化[J]. 电工技术学报, 2023, 38(13): 3486-3498.
Meng Rongtao, Li Shaoyan, Gu Xueping, et al.Operation optimization considering unit recovery effect when concentrating solar power station acts as black-start power source[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3486-3498.
[3] 曾辉, 孙峰, 李铁, 等. 澳大利亚“9·28” 大停电事故分析及对中国启示[J]. 电力系统自动化, 2017, 41(13): 1-6.
Zeng Hui, Sun Feng, Li Tie, et al.Analysis of “9·28” blackout in South Australia and its enlightenment to China[J]. Automation of Electric Power Systems, 2017, 41(13): 1-6.
[4] 易俊, 卜广全, 郭强, 等. 巴西“3·21” 大停电事故分析及对中国电网的启示[J]. 电力系统自动化, 2019, 43(2): 1-6.
Yi Jun, Bu Guangquan, Guo Qiang, et al.Analysis on blackout in Brazilian power grid on March 21, 2018 and its enlightenment to power grid in China[J]. Automation of Electric Power Systems, 2019, 43(2): 1-6.
[5] 严道波, 文劲宇, 杜治, 等. 2021年得州大停电事故分析及其对电网规划管理的启示[J]. 电力系统保护与控制, 2021, 49(9): 121-128.
Yan Daobo, Wen Jinyu, Du Zhi, et al.Analysis of Texas blackout in 2021 and its enlightenment to power system planning management[J]. Power System Protection and Control, 2021, 49(9): 121-128.
[6] 叶茂, 刘艳, 顾雪平, 等. 基于动态风电穿透功率极限的黑启动方案制定[J]. 中国电机工程学报, 2018, 38(3): 744-752.
Ye Mao, Liu Yan, Gu Xueping, et al.Black start scheme formation considering dynamic wind power penetration limit[J]. Proceedings of the CSEE, 2018, 38(3): 744-752.
[7] 顾雪平, 白岩松, 李少岩, 等. 电力系统黑启动恢复问题的研究评述[J]. 电工技术学报, 2022, 37(13): 3183-3200.
Gu Xueping, Bai Yansong, Li Shaoyan, et al.Research review of power system black-start restoration[J]. Transactions of China Electrotechnical Society, 2022, 37(13): 3183-3200.
[8] 张璨, 孙磊, 林振智, 等. 含风电场的电力系统最优网络重构策略[J]. 电力系统及其自动化学报, 2016, 28(2): 22-29.
Zhang Can, Sun Lei, Lin Zhenzhi, et al.Optimal network reconfiguration strategy for power systems with integrated wind farms[J]. Proceedings of the CSU-EPSA, 2016, 28(2): 22-29.
[9] 荣俊杰, 周明, 李庚银. 考虑动态频率安全的风电参与负荷恢复优化调度[J]. 电网技术, 2022, 46(4): 1335-1345.
Rong Junjie, Zhou Ming, Li Gengyin.Optimal dispatch of wind power participating in load restoration considering dynamic frequency security[J]. Power System Technology, 2022, 46(4): 1335-1345.
[10] 韩讴竹, 陈志铭, 丁涛, 等. 计及风电不确定性的电力系统黑启动恢复模型[J]. 电网技术, 2023, 47(8): 3289-3301.
Han Ouzhu, Chen Zhiming, Ding Tao, et al.Power system black-start restoration model considering wind power uncertainties[J]. Power System Technology, 2023, 47(8): 3289-3301.
[11] 刘力卿, 杜平, 万玉良, 等. 储能型风电场作为局域电网黑启动电源的可行性探讨[J]. 电力系统自动化, 2016, 40(21): 210-216.
Liu Liqing, Du Ping, Wan Yuliang, et al.Feasibility discussion on using storage-based wind farm as black-start power source in local power grid[J]. Automation of Electric Power Systems, 2016, 40(21): 210-216.
[12] 闫来清, 曹丽源, 薛太林, 等. 风电场黑启动储能容量优化配置: 一种考虑储能运行策略的方法[J]. 电力系统保护与控制, 2022, 50(16): 131-139.
Yan Laiqing, Cao Liyuan, Xue Tailin, et al.Optimal configuration of wind farm black start energy storage capacity: a method considering energy storage operation strategy[J]. Power System Protection and Control, 2022, 50(16): 131-139.
[13] 刘艳, 季晨阳, 马艺瑄, 等. 基于生产可能性边界的含规模风电电力系统网架重构策略[J]. 电网技术, 2022, 46(8): 2986-2995.
Liu Yan, Ji Chenyang, Ma Yixuan, et al.Production-possibility frontier-based network reconfiguration strategy for power system integrated with scaled wind farm[J]. Power System Technology, 2022, 46(8): 2986-2995.
[14] 赵瑾, 王洪涛, 曹曦. 计及风电条件风险价值的负荷恢复双层优化[J]. 中国电机工程学报, 2017, 37(18): 5275-5285.
Zhao Jin, Wang Hongtao, Cao Xi.Bi-level optimization of load restoration considering the conditional value at risk of wind power[J]. Proceedings of the CSEE, 2017, 37(18): 5275-5285.
[15] Golshani A, Sun Wei, Zhou Qun, et al.Incorporating wind energy in power system restoration planning[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 16-28.
[16] 顾雪平, 白岩松, 李少岩, 等. 考虑风电不确定性的电力系统恢复全过程两阶段鲁棒优化方法[J]. 电工技术学报, 2022, 37(21): 5462-5477.
Gu Xueping, Bai Yansong, Li Shaoyan, et al.Two stage robust optimization method for the whole-process power system restoration considering wind power uncertainty[J]. Transactions of China Electrotechnical Society, 2022, 37(21): 5462-5477.
[17] 王扬, 赵书强, 徐岩, 等. 基于机会约束目标规划的风火储系统滚动调度[J]. 电网技术, 2017, 41(1): 192-199.
Wang Yang, Zhao Shuqiang, Xu Yan, et al.Rolling dispatch of wind/thermal/storage system based on chance constrained goal programming[J]. Power System Technology, 2017, 41(1): 192-199.
[18] 叶林, 李智, 孙舶皓, 等. 基于随机预测控制理论和功率波动相关性的风电集群优化调度[J]. 中国电机工程学报, 2018, 38(11): 3172-3183.
Ye Lin, Li Zhi, Sun Bohao, et al.Optimal dispatch of system integrated wind farm clusters based on stochastic model predictive control considering temporal correlation of wind power[J]. Proceedings of the CSEE, 2018, 38(11): 3172-3183.
[19] 刘立阳, 吴军基, 孟绍良. 基于预测控制的含风电滚动优化调度[J]. 电工技术学报, 2017, 32(17): 75-83.
Liu Liyang, Wu Junji, Meng Shaoliang.A rolling dispatch model for wind power integrated power system based on predictive control[J]. Transactions of China Electrotechnical Society, 2017, 32(17): 75-83.
[20] 叶林, 路朋, 赵永宁, 等. 含风电电力系统有功功率模型预测控制方法综述[J]. 中国电机工程学报, 2021, 41(18): 6181-6197.
Ye Lin, Lu Peng, Zhao Yongning, et al.Review of model predictive control for power system with large-scale wind power grid-connected[J]. Proceedings of the CSEE, 2021, 41(18): 6181-6197.
[21] 李军徽, 尤宏飞, 李翠萍, 等. 基于模型预测控制的风光储黑启动功率协调策略[J]. 电网技术, 2020, 44(10): 3700-3708.
Li Junhui, You Hongfei, Li Cuiping, et al.Power coordination strategy based on model predictive control for black start with \rPV-wind-battery system[J]. Power System Technology, 2020, 44(10): 3700-3708.
[22] 谢云云, 杨正婷, 蔡胜, 等. 基于鲁棒模型预测控制的配电网供电恢复策略[J]. 电力系统自动化, 2021, 45(23): 123-131.
Xie Yunyun, Yang Zhengting, Cai Sheng, et al.Power supply restoration strategy for distribution network based on robust model prediction control[J]. Automation of Electric Power Systems, 2021, 45(23): 123-131.
[23] 李少岩, 曹珂, 顾雪平, 等. 多直流馈入受端系统与直流联络线协调恢复的一体化建模与求解[J]. 电工技术学报, 2023, 38(21): 5862-5877.
Li Shaoyan, Cao Ke, Gu Xueping, et al.Integrated modeling and solution for coordinated restoration of multi-infeed receiving-end systems and HVDC Tie lines[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5862-5877.
[24] 国家能源局. 风电功率预测技术规定: NB/T 10205-—2019[S]. 北京: 中国电力出版社, 2020.
[25] 刘玉田, 王洪涛, 叶华. 电力系统恢复理论与技术[M]. 北京: 科学出版社, 2014.
[26] 张伯明, 陈建华, 吴文传. 大规模风电接入电网的有功分层模型预测控制方法[J]. 电力系统自动化, 2014, 38(9): 6-14.
Zhang Boming, Chen Jianhua, Wu Wenchuan.A hierarchical model predictive control method of active power for accommodating large-scale wind power integration[J]. Automation of Electric Power Systems, 2014, 38(9): 6-14.
[27] 伊昆明, 孙磊, 丁江, 等. 基于凸包理论的含风电电力系统负荷恢复方案优化[J]. 电力系统自动化, 2024, 48(5): 77-87.
Yi Kunming, Sun Lei, Ding Jiang, et al.Load restoration scheme optimization for power system with wind power based on convex hull theory[J]. Automation of Electric Power Systems, 2024, 48(5): 77-87.
[28] Patsakis G, Rajan D, Aravena I, et al.Optimal black start allocation for power system restoration[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6766-6776.
[29] Yang Jingwei, Zhang Ning, Kang Chongqing, et al.A state-independent linear power flow model with accurate estimation of voltage magnitude[J]. IEEE Transactions on Power Systems, 2017, 32(5): 3607-3617.
[30] 徐询, 谢丽蓉, 梁武星, 等. 考虑风电预测误差时序性及风电可信度的双层优化模型[J]. 电工技术学报, 2023, 38(6): 1620-1632, 1661.
Xu Xun, Xie Lirong, Liang Wuxing, et al.Bi-level optimization model considering time series characteristic of wind power forecast error and wind power reliability[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1620-1632, 1661.
[31] 林莉, 林雨露, 谭惠丹, 等. 计及SOC自恢复的混合储能平抑风电功率波动控制[J]. 电工技术学报, 2024, 39(3): 658-671.
Lin Li, Lin Yulu, Tan Huidan, et al.Hybrid energy storage control with SOC self-recovery to smooth out wind power fluctuations[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 658-671.
[32] 李翠萍, 司文博, 李军徽, 等. 基于集合经验模态分解和多目标遗传算法的火-多储系统调频功率双层优化[J]. 电工技术学报, 2024, 39(7): 2017-2032.
Li Cuiping, Si Wenbo, Li Junhui, et al.Two-layer optimization of frequency modulated power of thermal generation and multi-storage system based on ensemble empirical mode decomposition and multi-objective genetic algorithm[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 2017-2032.
[33] Gu Xueping, Bai Yansong, Li Shaoyan, et al.An optimisation method of whole-process restoration decision-making of power systems considering disturbance-resisting ability of the restored network[J]. IET Generation, Transmission & Distribution, 2023, 17(7): 1638-1651.
[34] Wei Wei, Liu Feng, Mei Shengwei, et al.Two-level unit commitment and reserve level adjustment considering large-scale wind power integration[J]. International Transactions on Electrical Energy Systems, 2014, 24(12): 1726-1746.