计及温度不确定性的配电网广义储能分层调控策略

doi:10.19595/j.cnki.1000-6753.tces.221152

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摘要

为发挥空调负荷的调控潜力,该文将变频空调视为广义储能,提出一种计及温度不确定性的配电网广义储能分层调控策略。首先,建立变频空调广义储能模型,基于广义储能特征参数,将广义储能聚合为多个集群。在此基础上,构建广义储能集群分层调控框架,将广义储能调控分为广义储能集群调控与广义储能群内调控两层。在广义储能集群调控层面,提出配电网广义储能集群分布鲁棒优化调控策略,避免了室外温度不确定性影响用户舒适度;在广义储能群内调控层面,提出了广义储能集群群内功率分配策略,实现广义储能等效荷电状态变化的一致性。最后,基于改进的IEEE 33节点系统开展算例仿真,仿真结果表明所提的调控策略在保障用户舒适度和调控公平性的前提下降低了配电网的运行成本,提高了可再生能源的消纳水平。

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关键词 ：广义储能, 变频空调, 不确定性, 分层调控

Abstract：

The air-conditioning load has the ability to store thermal energy, and can be converted into generalized energy storage through direct load control, which can effectively promote the consumption of renewable energy and improve the economics of the distribution network. However, most of the existing researches on the control strategy of air-conditioning load ignore the influence of ambient temperature uncertainty on the thermodynamic process of the air-conditioning room, which could not guarantee user's comfort. Aiming at this problem, this paper proposes a generalized energy storage hierarchical regulation strategy for distribution network considering temperature uncertainty.
Firstly, a generalized energy storage model is established, and the generalized energy storage is aggregated into multiple clusters based on the characteristic parameters. On this basis, a hierarchical regulation framework is constructed, and generalized energy storage regulation is divided into two layers: generalized energy storage cluster regulation and generalized energy storage regulation. At the generalized energy storage cluster regulation layer, aiming at the lowest operating cost of the distribution network, a distribution network generalized energy storage cluster distributionally robust optimization model is established. Distributionally robust chance constraints are used to deal with the uncertainty of outdoor temperature, and are transformed into deterministic linear constraints to realize the effective solution of the model. At the generalized energy storage regulation layer, aiming at the consistency of the equivalent state of charge of each generalized energy storage in the cluster, a power distribution model within the generalized energy storage cluster is established.
The simulation results based on the improved IEEE 33 node system show that when the generalized energy storage cluster can not be controlled, the abandonment of wind energy and solar energy in the system is large, the network loss is high, and the total operating cost is 8 758.61 $. While when controlling generalized energy storage clusters, the cost of abandoning wind energy and solar energy in the system is significantly reduced, the cost of network loss is lower, and the total operating cost is reduced to 8 700.80 $. Comparing the simulation results without considering the uncertainty of outdoor temperature, the average user's comfort level increases from 80.41% to 99.71% when the outdoor temperature uncertainty is considered. In addition, according to the power distribution results in the generalized energy storage cluster, the power of each generalized energy storage in the cluster has a similar trend of change, and the equivalent state of charge tends to be consistent.
The following conclusions can be drawn from the simulation analysis: (1) Controlling the inverter air-conditioning as generalized energy storage can effectively reduce the operating cost of the distribution network and improve consumption rate of renewable energy. (2) The proposed generalized energy storage cluster distributionally robust optimization strategy can effectively avoid the indoor temperature exceeding the limit caused by the uncertainty of outdoor temperature, thereby ensuring user's comfort. (3) The proposed generalized energy storage cluster power distribution strategy can achieve consistent changes in the equivalent state of charge of each generalized energy storage, thereby ensuring the fairness of control.

Key words： Generalized energy storage inverter air-conditioning uncertainty hierarchical regulation

收稿日期: 2022-06-18

PACS:

TM73

基金资助:

国家电网有限公司总部管理科技项目资助（5419-202199551A-0-5-ZN）

通讯作者: 苗世洪男,1963年生,教授,博士生导师,研究方向为电力系统保护、控制及计算,储能系统及应用、配电网及微电网新技术等。E-mail：shmiao@hust.edu.cn

作者简介: 刘志伟男,1998年生,硕士研究生,研究方向为广义储能的建模于控制、微电网的控制等。E-mail：liuzhiwei_hust@126.com

引用本文:

刘志伟, 苗世洪, 杨炜晨, 姚福星, 王廷涛. 计及温度不确定性的配电网广义储能分层调控策略[J]. 电工技术学报, 2023, 38(21): 5794-5807. Liu Zhiwei, Miao Shihong, Yang Weichen, Yao Fuxing, Wang Tingtao. Generalized Energy Storage Hierarchical Regulation Strategy for Distribution Network Considering Temperature Uncertainty. Transactions of China Electrotechnical Society, 2023, 38(21): 5794-5807.

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[1] Song Meng, Gao Ciwei, Shahidehpour M, et al.Multi-time-scale modeling and parameter estimation of TCLs for smoothing out wind power generation variability[J]. IEEE Transactions on Sustainable Energy, 2019, 10(1): 105-118.
[2] 李宏仲, 房宇娇, 肖宝辉. 考虑广义储能的区域综合能源系统优化运行研究[J]. 电网技术, 2019, 43(9): 3130-3138.
Li Hongzhong, Fang Yujiao, Xiao Baohui.Research on optimized operation of regional integrated energy system considering generalized energy storage[J]. Power System Technology, 2019, 43(9): 3130-3138.
[3] 李宏仲, 张仪, 孙伟卿. 小波包分解下考虑广义储能的风电功率波动平抑策略[J]. 电网技术, 2020, 44(12): 4495-4504.
Li Hongzhong, Zhang Yi, Sun Weiqing.Wind power fluctuation smoothing strategy with generalized energy storage under wavelet packet decomposition[J]. Power System Technology, 2020, 44(12): 4495-4504.
[4] 郝金宝, 张勇军, 刘泽槐, 等. 基于空调负荷温差-功率特性的博弈分层优化调度[J]. 电力系统自动化, 2020, 44(7): 70-78.
Hao Jinbao, Zhang Yongjun, Liu Zehuai, et al.Hierarchical game optimization scheduling based on temperature difference and power characteristics of air-conditioning load[J]. Automation of Electric Power Systems, 2020, 44(7): 70-78.
[5] 范睿, 孙润稼, 刘玉田. 考虑空调负荷需求响应的负荷恢复量削减方法[J]. 电工技术学报, 2022, 37(11): 2869-2877.
Fan Rui, Sun Runjia, Liu Yutian.A load restoration amount reduction method considering demand response of air conditioning loads[J]. Transactions of China Electrotechnical Society, 2022, 37(11): 2869-2877.
[6] Song Meng, Gao Ciwei, Shahidehpour M, et al.State space modeling and control of aggregated TCLs for regulation services in power grids[J]. IEEE Transactions on Smart Grid, 2019, 10(4): 4095-4106.
[7] Wang Dongxiao, Meng Ke, Gao Xiaodan, et al.Coordinated dispatch of virtual energy storage systems in LV grids for voltage regulation[J]. IEEE Transactions on Industrial Informatics, 2018, 14(6): 2452-2462.
[8] 王怡岚, 童亦斌, 黄梅, 等. 基于需求侧响应的空调负荷虚拟储能模型研究[J]. 电网技术, 2017, 41(2): 394-401.
Wang Yilan, Tong Yibin, Huang Mei, et al.Research on virtual energy storage model of air conditioning loads based on demand response[J]. Power System Technology, 2017, 41(2): 394-401.
[9] Song Meng, Gao Ciwei, Yan Huaguang, et al.Thermal battery modeling of inverter air conditioning for demand response[J]. IEEE Transactions on Smart Grid, 2018, 9(6): 5522-5534.
[10] Song Meng, Gao Ciwei, Yang Jianlin, et al.Energy storage modeling of inverter air conditioning for output optimizing of wind generation in the electricity market[J]. CSEE Journal of Power and Energy Systems, 2018, 4(3): 305-315.
[11] Jiang Tingyu, Ju Ping, Wang Chong, et al.Coordinated control of air-conditioning loads for system frequency regulation[J]. IEEE Transactions on Smart Grid, 2021, 12(1): 548-560.
[12] 许刚, 张丙旭, 张广超. 电动汽车集群并网的分布式鲁棒优化调度模型[J]. 电工技术学报, 2021, 36(3): 565-578.
Xu Gang, Zhang Bingxu, Zhang Guangchao.Distributed and robust optimal scheduling model for large-scale electric vehicles connected to grid[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 565-578.
[13] 孔顺飞, 胡志坚, 谢仕炜, 等. 含电动汽车充电站的主动配电网二阶段鲁棒规划模型及其求解方法[J]. 电工技术学报, 2020, 35(5): 1093-1105.
Kong Shunfei, Hu Zhijian, Xie Shiwei, et al.Two-stage robust planning model and its solution algorithm of active distribution network containing electric vehicle charging stations[J]. Transactions of China Electrotechnical Society, 2020, 35(5): 1093-1105.
[14] 贺帅佳, 阮贺彬, 高红均, 等. 分布鲁棒优化方法在电力系统中的理论分析与应用综述[J]. 电力系统自动化, 2020, 44(14): 179-191.
He Shuaijia, Ruan Hebin, Gao Hongjun, et al.Overview on theory analysis and application of distributionally robust optimization method in power system[J]. Automation of Electric Power Systems, 2020, 44(14): 179-191.
[15] 殷爽睿, 艾芊, 王大鹏, 等. 考虑空调负荷虚拟储能的产消者鲁棒日前申报策略[J]. 电力系统自动化, 2020, 44(4): 24-34.
Yin Shuangrui, Ai Qian, Wang Dapeng, et al.Day-ahead robust bidding strategy for prosumer considering virtual energy storage of air-conditioning load[J]. Automation of Electric Power Systems, 2020, 44(4): 24-34.
[16] 随权, 林湘宁, 童宁, 等. 基于改进两阶段鲁棒优化的主动配电网经济调度[J]. 中国电机工程学报, 2020, 40(7): 2166-2179, 2396.
Sui Quan, Lin Xiangning, Tong Ning, et al.Economic dispatch of active distribution network based on improved two-stage robust optimization[J]. Proceedings of the CSEE, 2020, 40(7): 2166-2179, 2396.
[17] 叶畅, 曹侃, 丁凯, 等. 基于广义储能的多能源系统不确定优化调度策略[J]. 电工技术学报, 2021, 36(17): 3753-3764.
Ye Chang, Cao Kan, Ding Kai, et al.Uncertain optimal dispatch strategy based on generalized energy storage for multi-energy system[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3753-3764.
[18] 朱兰, 李孝均, 唐陇军, 等. 考虑相变储能与建筑蓄能特性的微网分布鲁棒优化调度[J]. 电网技术, 2021, 45(6): 2308-2319.
Zhu Lan, Li Xiaojun, Tang Longjun, et al.Distributionally robust optimal operation for microgrid considering phase change storage and building storage[J]. Power System Technology, 2021, 45(6): 2308-2319.
[19] 漆淘懿, 郑朝明, 叶承晋, 等. 面向光伏一体化楼宇的电池-变频空调复合储能运行控制策略研究[J]. 电网技术, 2022, 46(11): 4247-4255.
Qi Taoyi, Zheng Chaoming, Ye Chengjin, et al.Complementary energy storage operation strategy of battery and inverter air conditioners for buildings with integrated photovoltaic system[J]. Power System Technology, 2022, 46(11): 4247-4255.
[20] Kim Y.Experimental study of grid frequency regulation ancillary service of a variable speed heat pump[C]//2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 2016: 1.
[21] 向明旭, 杨知方, 余娟, 等. 配电网线性潮流模型通式及误差分析[J]. 中国电机工程学报, 2021, 41(6): 2053-2064.
Xiang Mingxu, Yang Zhifang, Yu Juan, et al.Linear power flow model in distribution network: unified expression and error analysis[J]. Proceedings of the CSEE, 2021, 41(6): 2053-2064.
[22] Zymler S, Kuhn D, Rustem B.Distributionally robust joint chance constraints with second-order moment information[J]. Mathematical Programming, 2013, 137(1): 167-198.
[23] Van Parys B P G, Kuhn D, Goulart P J, et al. Distributionally robust control of constrained stochastic systems[J]. IEEE Transactions on Automatic Control, 2016, 61(2): 430-442.
[24] Du Y F, Jiang L, Duan C, et al.Energy consumption scheduling of HVAC considering weather forecast error through the distributionally robust approach[J]. IEEE Transactions on Industrial Informatics, 2018, 14(3): 846-857.