基于置信间隙决策的输电网络不确定性最优传输切换研究

doi:10.19595/j.cnki.1000-6753.tces.221375

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摘要最优传输切换(OTS)是一种应用较为广泛的通过改变网络拓扑结构提高输电网络灵活性的控制机制。然而,输电网络中往往存在不确定风能及负荷需求,这使得常规最优传输切换方案无法达到预期效果。为此,提出将置信间隙决策理论（CGDT）应用于寻求不确定条件下的鲁棒最优传输切换方案。该文分别采用高斯混合模型和正态分布对风电出力及负荷需求预测误差的概率分布函数进行拟合,将不确定性纳入最优传输切换,并以总发电成本最优为目标,建立了基于置信间隙决策理论的输电网络最优传输切换鲁棒优化模型。采用改进的IEEE 24节点系统进行了仿真实验,结果表明,使用所提方法求取的最优传输切换方案具有较强的鲁棒性。

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	王东风
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关键词 ：不确定性, 风能发电, 负荷需求, 最优传输切换, 置信间隙决策理论

Abstract：Optimal Transmission Switching （OTS） is a control mechanism that improves the transmission network's flexibility by changing the network's topology. OTS is widely used in transmission networks. OTS schemes are usually determined in the day-ahead scheduling stage. However, there are often uncertain wind power and load demands in the transmission network, which makes OTS schemes unable to achieve the desired effect. In order to solve these problems, this paper proposes to apply the Confidence Gap decision theory （CGDT） to find the OTS scheme under the condition of uncertainty and improve its robustness of OTS scheme.
Firstly, the Gaussian mixture model and normal distribution are used to fit the probability distribution function of wind power output and load demand prediction errors, and the uncertainty is incorporated into the OTS model of the transmission network. Secondly, CGDT based on the robust drive is introduced to solve the uncertainty, and a robust optimization method based on CGDT is proposed to solve the uncertainty OTS scheme. A mixed integer nonlinear programming model based on CGDT for OTS robust optimization of the transmission network is constructed to minimize the total generation cost as the optimization goal. Thirdly, the chance constraints in the model are transformed into equivalent deterministic constraints according to the uncertainty theory. Finally, this paper uses the improved IEEE-24 node power network to analyze the proposed CGDT-based OTS robust optimization model. In the proposed OTS robust optimization method, the influence of uncertainty parameters on OTS optimization results is maximized by maximizing the confidence level to provide a robust OTS scheme.
According to the OTS solution, the transmission network topology is changed. The operation results show that the proportion of the power generation of coal-fired units in the total power output of the power network is reduced from 84.96% to 60.13%. The power output of generating units that meet the load demand is transferred from thermal power units to gas units with high generation efficiency and wind turbines with low generation cost, which improves the power generation structure of the system and reduces the total power generation cost by 3.9%. In wind power and load demand robust range using the Monte Carlo method in extracting 1 000 groups of random wind power and load demand scenario, the total cost by probability distribution results show that the approximate probability distribution is normal, the corresponding scenarios of the total cost is less than the set value. Thus the solving scheme of OTS has good adaptability. Under the same operation condition, CGDT, IGDT, and multi-scene methods are used to obtain the corresponding OTS scheme. One thousand scenarios were randomly selected within the fluctuation range of wind power and load demand prediction errors to solve the total power generation cost corresponding to different scenarios. The statistical results show that the CGDT method has a relatively short computation time, its corresponding maximum, mean, and median values are optimal, and the OTS scheme is more robust. In addition, this method can give a robust fluctuation interval of uncertain parameters, which is of great significance to decision makers and has good scalability in practical applications.
The following conclusions can be drawn from the simulation analysis: （1） Compared with LSTM and GRU, the SRU in the proposed model not only significantly reduces the computational cost, but also effectively overcomes the gradient disappearance problem. Therefore, it is appropriate to apply SRU to the multi-turbine local wind speed forecast. （2） The proposed model only needs historical wind speed and direction measurements. In this sense, it is more practical than physical-based forecasting models, which depends on atmospheric motion equation. （3） The proposed model extracts informative features by firstly learning spatial correlations using CNN and subsequently learning temporal correlations using SRUs. Compared with traditional deep learning models, it formulates wind data as RGB images.

Key words： Uncertainty wind power generation load demand optimal transmission switching （OTS） confidence gap decision theory （CGDT）

收稿日期: 2022-07-16

PACS:

TM734

基金资助:基中央高校基本科研业务费专项资金资助项目（2021MS089）

通讯作者: 黄宇男,1982年生,副教授,研究生导师,研究方向为多类异质综合能源系统分析与优化。E-mail：huangyufish@ncepu.edu.cn

作者简介: 王东风男,1971年生,教授,博士生导师,研究方向为先进控制理论及应用。E-mail：wangdongfeng@ncepu.edu.cn

引用本文:

王东风, 陈江丽, 黄宇, 孙赫宇, 杨明叶. 基于置信间隙决策的输电网络不确定性最优传输切换研究[J]. 电工技术学报, 0, (): 49-49. Wang Dongfeng, Chen Jiangli, Huang Yu, Sun Heyu, Yang Mingye. Research on the Uncertain Optimal Transmission Switching in Transmission Networks Based on Confidence Gap Decision. Transactions of China Electrotechnical Society, 0, (): 49-49.

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[1] 陆明璇, 周明, 黄瀚燕, 等. 含快速线路筛选的电力系统发输电协同优化运行[J]. 中国电机工程学报, 2021, 41(20): 6949-6959.
Lu Mingxuan, Zhou Ming, Huang Hanyan, et al.Synergetic operational optimization of generation and transmission in power systems with rapid line screening[J]. Proceedings of the CSEE, 2021, 41(20): 6949-6959.
[2] He Hongjie, Du Ershun, Zhang Ning, et al.Enhancing the power grid flexibility with battery energy storage transportation and transmission switching[J]. Applied Energy, 2021, 290: 116692.
[3] 赵冬梅, 王浩翔, 陶然. 计及风电-负荷不确定性的风-火-核-碳捕集多源协调优化调度[J]. 电工技术学报, 2022, 37(3): 707-718.
Zhao Dongmei, Wang Haoxiang, Tao Ran.A multi-source coordinated optimal scheduling model considering wind-load uncertainty[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 707-718.
[4] 侯慧, 刘鹏, 黄亮, 等. 考虑不确定性的电-热-氢综合能源系统规划[J]. 电工技术学报, 2021, 36(增刊1): 133-144.
Hou Hui, Liu Peng, Huang Liang, et al.Planning of electricity-heat-hydrogen integrated energy system considering uncertainties[J]. Transactions of China Electrotechnical Society, 2021, 36(S1): 133-144.
[5] 王雪纯, 陈红坤, 陈磊. 提升区域综合能源系统运行灵活性的多主体互动决策模型[J]. 电工技术学报, 2021, 36(11): 2207-2219.
Wang Xuechun, Chen Hongkun, Chen Lei.Multi-player interactive decision-making model for operational flexibility improvement of regional integrated energy system[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2207-2219.
[6] Peker M, Kocaman A S, Kara B Y.Benefits of transmission switching and energy storage in power systems with high renewable energy penetration[J]. Applied Energy, 2018, 228: 1182-1197.
[7] Zhou Yuqi, Zhu Hao, Hanasusanto G A.Transmission switching under wind uncertainty using linear decision rules[C]//2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada, 2020: 1-5.
[8] Dehghan S, Amjady N, Conejo A J.Adaptive robust transmission expansion planning using linear decision rules[J]. IEEE Transactions on Power Systems, 2017, 32(5): 4024-4034.
[9] 那广宇, 魏俊红, 王亮, 等. 基于Gram-Charlier级数的含风电电力系统静态电压稳定概率评估[J]. 电力系统保护与控制, 2021, 49(3): 115-122.
Na Guangyu, Wei Junhong, Wang Liang, et al.Probabilistic evaluation of power system static voltage stability with wind power uncertainty based on the Gram-Charlier expansion[J]. Power System Protection and Control, 2021, 49(3): 115-122.
[10] Ahmed R, Nawaz A, Javid Z, et al.Optimal transmission switching based on probabilistic load flow in power system with large-scale renewable energy integration[J]. Electrical Engineering, 2022, 104(2): 883-898.
[11] Pasupathy R, Song Yongjia.Adaptive sequential sample average approximation for solving two-stage stochastic linear programs[J]. SIAM Journal on Optimization, 2021, 31(1): 1017-1048.
[12] 张靠社, 冯培基, 张刚, 等. 考虑机会约束的多能源微电网双层优化配置[J]. 太阳能学报, 2021, 42(8): 41-48.
Zhang Kaoshe, Feng Peiji, Zhang Gang, et al.Bi-level optimization configuration method for multienergy microgrid considering chance constraints[J]. Acta Energiae Solaris Sinica, 2021, 42(8): 41-48.
[13] 曾博, 胡强, 刘裕, 等. 考虑需求响应复杂不确定性的电-气互联系统动态概率能流计算[J]. 中国电机工程学报, 2020, 40(4): 1161-1171, 1408.
Zeng Bo, Hu Qiang, Liu Yu, et al.Dynamic probabilistic energy flow calculation for interconnected electricity-gas system considering complex uncertainties of demand response[J]. Proceedings of the CSEE, 2020, 40(4): 1161-1171, 1408.
[14] Dehghanian P, Kezunovic M.Probabilistic decision making for the bulk power system optimal topology control[J]. IEEE Transactions on Smart Grid, 2016, 7(4): 2071-2081.
[15] Yuan Yang, Cheng Haozhong, Zhang Heng, et al.Transmission expansion planning with optimal transmission switching considering uncertain n-k contingency and renewables[J]. Energy Reports, 2022, 8: 573-583.
[16] 侯慧, 刘鹏, 黄亮, 等. 考虑不确定性的电-热-氢综合能源系统规划[J]. 电工技术学报, 2021, 36(增刊1): 133-144.
Hou Hui, Liu Peng, Huang Liang, et al.Planning of electricity-heat-hydrogen integrated energy system considering uncertainties[J]. Transactions of China Electrotechnical Society, 2021, 36(S1): 133-144.
[17] Dehghan S, Amjady N.Robust transmission and energy storage expansion planning in wind farm-integrated power systems considering transmission switching[J]. IEEE Transactions on Sustainable Energy, 2016, 7(2): 765-774.
[18] 夏鹏, 刘文颖, 张尧翔, 等. 考虑风电高阶不确定性的分布式鲁棒优化调度模型[J]. 电工技术学报, 2020, 35(1): 189-200.
Xia Peng, Liu Wenying, Zhang Yaoxiang, et al.A distributionally robust optimization scheduling model considering higher-order uncertainty of wind power[J]. Transactions of China Electrotechnical Society, 2020, 35(1): 189-200.
[19] 彭春华, 郑聪, 陈婧, 等. 基于置信间隙决策的综合能源系统鲁棒优化调度[J]. 中国电机工程学报, 2021, 41(16): 5593-5604.
Peng Chunhua, Zheng Cong, Chen Jing, et al.Robust optimal dispatching of integrated energy system based on confidence gap decision[J]. Proceedings of the CSEE, 2021, 41(16): 5593-5604.
[20] 汪超群, 韦化, 吴思缘. 基于信息间隙决策理论的多源联合优化机组组合[J]. 中国电机工程学报, 2018, 38(12): 3431-3440, 2.
Wang Chaoqun, Wei Hua, Wu Siyuan.Multi-power combined unit commitment based on information gap decision theory[J]. Proceedings of the CSEE, 2018, 38(12): 3431-3440, 2.
[21] 张亚超, 郑峰, 乐健, 等. 考虑风电高阶不确定性的电气综合系统分布鲁棒协同优化调度[J]. 中国电机工程学报, 2020, 40(24): 8012-8026, 8238.
Zhang Yachao, Zheng Feng, Le Jian, et al.A distributionally robust coordinated optimization scheduling of integrated electricity and natural gas systems considering higher-order uncertainty of wind power[J]. Proceedings of the CSEE, 2020, 40(24): 8012-8026, 8238.
[22] 杨智超. 考虑可再生能源接入的电力系统恢复协同优化策略[D]. 合肥: 合肥工业大学, 2021.
[23] 姚琦, 胡阳, 柳玉, 等. 考虑载荷抑制的风电场分布式自动发电控制[J]. 电工技术学报, 2022, 37(3): 697-706.
Yao Qi, Hu Yang, Liu Yu, et al.Distributed automatic generation control of wind farm considering load suppression[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 697-706.
[24] Saavedra R, Street A, Arroyo J M.Day-ahead contingency-constrained unit commitment with Co-optimized post-contingency transmission switching[J]. IEEE Transactions on Power Systems, 2020, 35(6): 4408-4420.
[25] Sheikh M, Aghaei J, Letafat A, et al.Security-constrained unit commitment problem with transmission switching reliability and dynamic thermal line rating[J]. IEEE Systems Journal, 2019, 13(4): 3933-3943.
[26] Soroudi A.Power System Optimization Modeling in GAMS[M]. Cham: Springer International Publishing, 2017
[27] 彭春华, 陈露, 张金克, 等. 基于分类概率机会约束IGDT的配网储能多目标优化配置[J]. 中国电机工程学报, 2020, 40(9): 2809-2819.
Peng Chunhua, Chen Lu, Zhang Jinke, et al.Multi-objective optimal allocation of energy storage in distribution network based on classified probability chance constraint information gap decision theory[J]. Proceedings of the CSEE, 2020, 40(9): 2809-2819.
[28] 谢敏, 罗文豪, 吉祥, 等. 随机风电接入的电力系统动态经济调度多场[J]. 电力自动化设备, 2019, 39(11): 7.
Xie Min, Luo Wenhao, Jixiang, et al. Multi-scenario collaborative optimization of dynamic economic dispatching in power system with random wind power access[J]. Electric Power Automation Equipment, 2019, 39(11): 7.