考虑负荷静态电压特性的虚拟电厂精准调控方法

doi:10.19595/j.cnki.1000-6753.tces.241169

摘要
图/表
参考文献
相关文章 (8)

全文: PDF (1129 KB)
输出: BibTeX | EndNote (RIS)

摘要

虚拟电厂(VPP)是挖掘需求侧灵活性的重要手段。现有研究多将需求侧资源划分为可控和不可控两类,在优化模型中将后者视为与决策无关的参数。然而,由于负荷客观存在静态电压特性,VPP调控引发的节点电压变化必然影响不可控资源的实际功率,导致VPP难以实现精准调控。为此,本文提出考虑负荷静态电压特性的VPP精准调控方法。首先,建立了考虑负荷静态电压特性的VPP运行模型,在优化模型中精细化地考虑不可控资源功率受决策变量被动变化的影响关系;其次,针对负荷静态电压特性引入所产生的模型求解难题,建立了适应于VPP控制策略的负荷静态电压特性方程,实现高效地模型在线求解。基于实际系统的算例分析发现,本文方法可以在VPP运行问题中准确考虑不可控资源受其它资源调控而带来的影响,进而以更小的调节代价达到合格的功率目标跟踪精度。该研究解决了当前VPP因调节粗略而仅在削峰填谷等单一场景应用的不足,为VPP发挥负荷跟踪、自动功率控制、运行备用等多类型调节作用奠定基础。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	田济源
	范帅
	杨冬阳
	黄任可
	何光宇

关键词 ：静态电压特性, 虚拟电厂, 精准调控, 需求侧资源

Abstract：

Virtual Power Plant (VPP) serves as an effective mechanism for exploiting flexibility from demand-side resources in modern power systems. Current research typically categorizes demand-side resources into controllable and uncontrollable types, treating the latter as parameters independent of decision variables in optimization models. However, loads inherently possess static voltage characteristics, meaning voltage variations induced by VPP regulation inevitably affect the actual power consumption of uncontrollable resources. This phenomenon causes difficulties in achieving accurate VPP regulation and limits its application scope to simple scenarios such as peak shaving.
This study proposes an accurate regulation method for VPPs that accounts for the static voltage load characteristics. The objective is to overcome the limitations of conventional approaches where uncontrollable resources are inaccurately modeled, leading to suboptimal regulation performance. The research addresses a critical gap in VPP operation strategies by recognizing that uncontrollable loads should not be considered as fixed parameters but as functions related to decision variables through network voltage profiles.
The methodology involves three main components. First, a comprehensive VPP operation model is developed that incorporates the topology of the low-voltage distribution network within the park area. This model explicitly represents how uncontrollable loads passively respond to voltage changes caused by control actions on other resources. Second, to address the computational challenges introduced by load static voltage characteristics, a model linearization method and a measurement feedback-based model calibration mechanism are proposed. These approaches enable efficient online solution of the optimization problem while maintaining modeling accuracy. Third, an integrated control framework is established that minimizes adjustment utility losses while meeting regulation targets. This framework optimizes the dispatch of various controllable resources including distributed generators, energy storage systems, and flexible loads, while accounting for their impact on uncontrollable loads through voltage variations.
Results from case studies demonstrate that the proposed method significantly improves regulation accuracy compared to conventional approaches. Specifically, compared to traditional methods that treat loads as rigid in control strategies, the proposed method reduces the average tracking relative error from 4.82% to 0.78%. Additionally, the adjustment utility loss is reduced by 35% compared to the energy storage droop control correction strategy based on measurement feedback. The advantages of the proposed method become more pronounced as the load static voltage characteristics in the network become more significant. Under high equivalent Conservation Voltage Reduction (CVR) coefficients, the proposed method maintains an average tracking relative error of only 0.09%, while traditional methods reach 5.74%. Even under low equivalent CVR coefficients, the proposed method still maintains a tracking error of 2.22%. The method demonstrates good adaptability by maintaining stable tracking performance under various regulation depths and tracking accuracy requirements.
The proposed methodology resolves limitations of current VPP implementations that typically offer only single-scenario applications such as peak shaving. By enabling more precise control, this approach establishes a foundation for VPPs to provide multiple types of regulation services including load following, automatic generation control, and operating reserves. The method enhances the overall efficiency and reliability of VPP operations in distribution networks with high penetration of demand-side resources.
This research primarily focuses on the impact mechanism of loads with static voltage characteristics on VPP regulation accuracy, particularly their passive voltage response characteristics. The current study has several limitations that warrant future research. The control period is relatively short, thus neglecting the constraints from energy storage State of Charge (SoC). In longer control periods or normalized control scenarios, the temporal and spatial coupling constraints from energy storage SoC cannot be ignored. More complex Distributed Energy Resources (DERs) models would introduce new complexities to the control method. For instance, considering the actual curtailment costs of photovoltaic (PV) systems would require addressing uncertainties in PV forecasting through methods such as Monte Carlo simulation. Additionally, implementing continuous data collection and coordinated control of various dispersed resources throughout the network poses new challenges to resource measurement systems and control systems. Future research should focus on VPP precise control methods that consider longer time periods, more DER types, more accurate DER models, finer granularity, and collaborative optimization of flexible loads with static voltage characteristics.

Key words： Static Voltage Load Characteristics Virtual Power Plant Accurate Regulation Demand-side Resources

收稿日期: 2024-07-04

PACS:

TM715

基金资助:

国家自然科学基金（52207123）和国家重点研发计划（2021YFB1507104）资助项目

通讯作者: 范帅男,1993年生,博士,研究方向为需求响应与虚拟电厂等。E-mail：fanshuai@sjtu.edu.cn

作者简介: 田济源男,2000年生,硕士研究生,研究方向为智能用电与虚拟电厂。E-mail：aveckbeko@sjtu.edu.cn

引用本文:

田济源, 范帅, 杨冬阳, 黄任可, 何光宇. 考虑负荷静态电压特性的虚拟电厂精准调控方法[J]. 电工技术学报, 0, (): 250424-. Tian Jiyuan, Fan Shuai, Yang Dongyang, Huang Renke, He Guangyu. Accurate Regulation Method for Virtual Power Plants Considering Static Voltage Load Characteristics. Transactions of China Electrotechnical Society, 0, (): 250424-.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.241169 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/250424

[1] 辛保安, 张智刚. 奋进新时代新征程展现新气象新作为[N]. 国家电网报, 2022-01-01(2).
[2] 范帅, 危怡涵, 何光宇, 等. 面向新型电力系统的需求响应机制探讨[J]. 电力系统自动化, 2022, 46(7): 1-12.
Fan Shuai, Wei Yihan, He Guangyu, et al.Discussion on demand response mechanism for new power systems[J]. Automation of Electric Power Systems, 2022, 46(7): 1-12.
[3] 范帅, 郏琨琪, 王芬, 等. 基于负荷准线的大规模需求响应[J]. 电力系统自动化, 2020, 44(15): 19-27.
Fan Shuai, Jia Kunqi, Wang Fen, et al.Large-scale demand response based on customer directrix load[J]. Automation of Electric Power Systems, 2020, 44(15): 19-27.
[4] 孟琰, 肖居承, 洪居华, 等. 计及需求响应不确定性的节点负荷准线: 概念与模型[J]. 电力系统自动化, 2023, 47(13): 28-39.
Meng Yan, Xiao Jucheng, Hong Juhua, et al.Nodal customer directrix load considering demand response uncertainty: concept and model[J]. Automation of Electric Power Systems, 2023, 47(13): 28-39.
[5] 李翔宇, 赵冬梅. 分散架构下多虚拟电厂分布式协同优化调度[J]. 电工技术学报, 2023, 38(7): 1852-1863.
Li Xiangyu, Zhao Dongmei.Distributed coordinated optimal scheduling of multiple virtual power plants based on decentralized control structure[J]. Transactions of China Electrotechnical Society, 2023, 38(7): 1852-1863.
[6] 李宗晟, 张璐, 张志刚, 等. 考虑柔性资源多维价值标签的交直流配电网灵活调度[J]. 电工技术学报, 2024, 39(9): 2621-2634.
Li Zongsheng, Zhang Lu, Zhang Zhigang, et al.A flexible scheduling method of AC/DC hybrid distribution network considering the multi-dimensional value tags of flexible resources[J]. Transactions of China Electrotechnical Society, 2024, 39(9): 2621-2634.
[7] Xiao Jucheng, He Guangyu, Fan Shuai, et al.Decentralized transfer of contingency reserve: framework and methodology[J]. Applied Energy, 2020, 278: 115703.
[8] 王健, 郑峻峰, 黄际元, 等. 虚拟电厂关键技术综述与发展展望[J]. 供用电, 2023, 40(12): 43-54, 86.
Wang Jian, Zheng Junfeng, Huang Jiyuan, et al.Key technology and development prospects of virtual power plants[J]. Distribution & Utilization, 2023, 40(12): 43-54, 86.
[9] 吴潇雨, 曹雨辰, 时庆.一味追捧虚拟电厂概念不可取[N].中国能源报,2023-02-06(06).
Wu Xiaoyu, Cao Yuchen, Shi Qing.It is not desirable to pursue the concept of virtual power plants indiscriminately[N]. China Energy News, 2023-02-06(06).
[10] 李翔宇, 赵冬梅. 基于模糊-概率策略实时反馈的虚拟电厂多时间尺度优化调度[J]. 电工技术学报, 2021, 36(7): 1446-1455.
Li Xiangyu, Zhao Dongmei.Research on multi-time scale optimal scheduling of virtual power plant based on real-time feedback of fuzzy-probability strategy[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1446-1455.
[11] 王明月, 刘东, 魏力鹏, 等. 电动汽车参与虚拟电厂优化调控的状态推演策略研究[J]. 供用电, 2024, 41(1): 14-25, 41.
Wang Mingyue, Liu Dong, Wei Lipeng, et al.Research on state inference strategies for electric vehicles participating in the optimal regulation of virtual power plants[J]. Distribution & Utilization, 2024, 41(1): 14-25, 41.
[12] 戴睿鹏, 窦晓波, 喻洁, 等. 含光储充的配网虚拟电厂二次调频随机模型预测控制策略[J]. 电网技术, 2024, 48(8): 3228-3237.
Dai Ruipeng, Dou Xiaobo, Yu Jie, et al.Secondary frequency control strategy for photovoltaic-storage-charging distribution-level virtual power plant based on stochastic model predictive control[J]. Power System Technology, 2024, 48(8): 3228-3237.
[13] Yamashita K, Djokic S, Matevosyan J, et al.Modelling and aggregation of loads in flexible power networks-scope and status of the work of CIGRE WG C4.605[J]. IFAC Proceedings Volumes, 2012, 45(21): 405-410.
[14] Khodayar M, Wang Jianhui.Probabilistic time-varying parameter identification for load modeling: a deep generative approach[J]. IEEE Transactions on Industrial Informatics, 2021, 17(3): 1625-1636.
[15] AlMuhaini M, Yahaya A, AlAhmed A. Distributed generation and load modeling in microgrids[J]. Sustainability, 2023, 15(6): 4831.
[16] Lee S H, Son S E, Lee S M, et al.Kalman-filter based static load modeling of real power system using K-EMS data[J]. Journal of Electrical Engineering and Technology, 2012, 7(3): 304-311.
[17] Schneider K P, Fuller JC, Tuffner F K, et al.Evaluation of Conservation Voltage Reduction (CVR) on a National Level[R]. Pacific Northwest National Lab.(PNNL), Richland, WA(United States), 2010.
[18] 朱星阳, 张建华, 刘文霞, 等. 考虑负荷电压静特性的含分布式电源的配电网潮流计算[J]. 电网技术, 2012, 36(2): 217-223.
Zhu Xingyang, Zhang Jianhua, Liu Wenxia, et al.Power flow calculation of distribution system with distributed generation considering static load characteristics[J]. Power System Technology, 2012, 36(2): 217-223.
[19] Arif A, Wang Zhaoyu, Wang Jianhui, et al.Load modeling: a review[J]. IEEE Transactions on Smart Grid, 2018, 9(6): 5986-5999.
[20] Yang Jing, Wu Min, He Yong, et al.Identification and application of nonlinear dynamic load models[J]. Journal of Control Theory and Applications, 2013, 11(2): 173-179.
[21] Shair J, Li Haozhi, Hu Jiabing, et al.Power system stability issues, classifications and research prospects in the context of high-penetration of renewables and power electronics[J]. Renewable and Sustainable Energy Reviews, 2021, 145: 111111.
[22] Wu Hao, Li Jing, Yang Haibo.Research methods for transient stability analysis of power systems under large disturbances[J]. Energies, 2024, 17(17): 4330.
[23] 毛文博, 曹冬志, 姚良忠, 等. 考虑感应电机负荷特性的主动配电网电压模型预测控制[J].电力自动化设备, 2024, 44(8): 92-99.
Mao Wenbo, Cao Dongzhi, Yao Liangzhong, et al.Voltage model predictive control of active distribution network considering load characteristics of induction motor[J]. Electric Power Automation Equipment, 2024, 44(8): 92-99.
[24] 田壁源, 常喜强, 戚红燕, 等. 基于混合博弈的园区虚拟电厂广义储能共享与协同优化调度[J]. 电力需求侧管理, 2023, 25(4): 8-14.
Tian Biyuan, Chang Xiqiang, Qi Hongyan, et al.Generalized energy storage sharing and collaborative optimal scheduling of park-level vritual power plants based on mixed game[J]. Power Demand Side Management, 2023, 25(4): 8-14.
[25] 李山山, 李华强, 金智博, 等. 基于共享经济理念的园区分布式能源共享服务机制[J]. 中国电机工程学报, 2022, 42(1): 56-71.
Li Shanshan, Li Huaqiang, Jin Zhibo, et al.Distributed energy sharing service mechanism for park based on the concept of sharing economy[J]. Proceedings of the CSEE, 2022, 42(1): 56-71.
[26] 刘雨佳, 樊艳芳, 白雪岩, 等. 基于优化计算型区块链系统的虚拟电厂模型与调度策略[J]. 电工技术学报, 2023, 38(15): 4178-4191.
Liu Yujia, Fan Yanfang, Bai Xueyan, et al.Virtual power plant model and scheduling strategy based on optimized computing block-chain system[J]. Transactions of China Electrotechnical Society, 2023, 38(15): 4178-4191.
[27] 杨宜龙, 范帅, 蔡思烨, 等. 内嵌调节目标的分布式能源调节能力分层分类聚合方法[J/OL]. 电力系统自动化, 1-18 [2025-04-07]. http://kns.cnki.net/kcms/detail/32.1180.TP.20250122.1641.004.html.
Yang Yilong, Fan Shuai, Cai Siye, et al. A hierarchical and classified aggregation method for distributed energy resources regulation capability with embedded regulation targets[J/OL]. Automation of Electric Power Systems, 1-18 [2025-04-07]. http://kns.cnki.net/kcms/detail/32.1180.TP.20250122.1641.004.html.
[28] 周欢, 王芬, 李志勇, 等. 虚拟电厂自趋优负荷跟踪控制策略[J]. 中国电机工程学报, 2021, 41(24): 8334-8349.
Zhou Huan, Wang Fen, Li Zhiyong, et al.Load tracking control strategy for virtual power plant via self-approaching optimization[J]. Proceedings of the CSEE, 2021, 41(24): 8334-8349.
[29] Fan Shuai, Liu Jiang, Wu Qing, et al.Optimal coordination of virtual power plant with photovoltaics and electric vehicles: a temporally coupled distributed online algorithm[J]. Applied Energy, 2020, 277: 115583.
[30] Bokhari A, Alkan A, Dogan R, et al.Experimental determination of the ZIP coefficients for modern residential, commercial, and industrial loads[J]. IEEE Transactions on Power Delivery, 2014, 29(3): 1372-1381.
[31] 孙充勃, 李敬如, 罗凤章, 等. 考虑分布式电源接入的配电系统典型算例设计[J]. 电力建设, 2020, 41(10): 47-62.
Sun Chongbo, Li Jingru, Luo Fengzhang, et al.Typical case design of distribution system considering DG integration[J]. Electric Power Construction, 2020, 41(10): 47-62.
[32] Baran M E, Wu F F.Network reconfiguration in distribution systems for loss reduction and load balancing[J]. IEEE Transactions on Power Delivery, 1989, 4(2): 1401-1407.
[33] 国家能源局南方能源监管局. 关于印发《南方区域电力并网运行管理实施细则》《南方区域电力辅助服务管理实施细则》的通知[EB/OL]. [2022-6-13]. https://nfj.nea.gov.cn/xxgk/fdzdgknr/scjg/202402/t20240208_240258.html
[34] Jha R R, Dubey A, Liu C C, et al.Bi-level volt-VAR optimization to coordinate smart inverters with voltage control devices[J]. IEEE Transactions on Power Systems, 2019, 34(3): 1801-1813.
[35] Short T A, Mee R W.Voltage reduction field trials on distributions circuits[C]//PES T&D 2012, Orlando, FL, USA, 2012: 1-6.
[36] Lefebvre S, Gaba G, Ba Ao, et al.Measuring the efficiency of voltage reduction at Hydro-Québec distribution[C]//2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, 2008: 1-7.
[37] Diaz-Aguiló M, Sandraz J, Macwan R, et al.Field-validated load model for the analysis of CVR in distribution secondary networks: energy conservation[J]. IEEE Transactions on Power Delivery, 2013, 28(4): 2428-2436.
[38] Wang Zhaoyu, Wang Jianhui.Time-varying stochastic assessment of conservation voltage reduction based on load modeling[J]. IEEE Transactions on Power Systems, 2014, 29(5): 2321-2328.
[39] Wang Chong, Wang Zhaoyu, Wang Jianhui, et al.Robust time-varying parameter identification for composite load modeling[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 967-979.
[40] Cui Mingjian, Khodayar M, Chen Chen, et al.Deep learning-based time-varying parameter identification for system-wide load modeling[J]. IEEE Transactions on Smart Grid, 2019, 10(6): 6102-6114.
[41] Anderson K S, Hansen C W, Holmgren W F, et al.Pvlib Python: 2023 project update[J]. Journal of Open Source Software, 2023, 8(92): 5994.
[42] Thurner L, Scheidler A, Schäfer F, et al.Pandapower: an open-source Python tool for convenient modeling, analysis, and optimization of electric power systems[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6510-6521.