Dynamic Construction and Trustworthy Quantification of Virtual Power Plant: Theoretical Analysis and Key Technologies

Qixin CHEN; Hongchao GAO; Cheng FENG; Songsong CHEN; Xiangyu KONG; Chongqing KANG

doi:10.7500/AEPS20220427008

您当前的位置：

首页 >

文章列表页 >

Dynamic Construction and Trustworthy Quantification of Virtual Power Plant: Theoretical Analysis and Key Technologies

Special Session on Virtual Power Plant with Enormous Flexible Resources | 更新时间：2025-06-16

- Dynamic Construction and Trustworthy Quantification of Virtual Power Plant: Theoretical Analysis and Key Technologies
- Automation of Electric Power Systems Vol. 46, Issue 18, Pages: 26-36(2022)
- 作者机构：
  
  1.新型电力系统运行与控制全国重点实验室(清华大学),北京市 100084
  2.需求侧多能互补优化与供需互动技术北京重点实验室(中国电力科学研究院有限公司),北京市 100192
  3.天津大学电气自动化与信息工程学院,天津市 300072
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China(2021YFB2401200)
- DOI：10.7500/AEPS20220427008
  CLC：
- Received：27 April 2022，
  
  Revised：2022-07-14，
  
  Published Online：29 August 2022，
  
  Published：25 September 2022
- 稿件说明：
移动端阅览
陈启鑫,高洪超,冯成等.虚拟电厂动态构建与可信量化：理论分析与关键技术[J].电力系统自动化,2022,46(18):26-36.

CHEN Qixin,GAO Hongchao,FENG Cheng,et al.Dynamic Construction and Trustworthy Quantification of Virtual Power Plant: Theoretical Analysis and Key Technologies[J].Automation of Electric Power Systems,2022,46(18):26-36.
陈启鑫,高洪超,冯成等.虚拟电厂动态构建与可信量化：理论分析与关键技术[J].电力系统自动化,2022,46(18):26-36. DOI： 10.7500/AEPS20220427008.

CHEN Qixin,GAO Hongchao,FENG Cheng,et al.Dynamic Construction and Trustworthy Quantification of Virtual Power Plant: Theoretical Analysis and Key Technologies[J].Automation of Electric Power Systems,2022,46(18):26-36. DOI： 10.7500/AEPS20220427008.

摘要

建设新型电力系统需适应大规模、高比例新能源接入，对系统运行灵活性提出更高要求，亟待发掘闲散灵活性资源并进行有效调控。虚拟电厂作为分散资源的高效聚合方式，可为新型电力系统安全运行提供调峰、备用、调频等多种灵活性服务，而如何建设虚拟电厂以充分释放海量分布式资源的可调潜力，是其中关键的技术难点。文中聚焦于虚拟电厂中灵活资源的运行特性、动态构建与可信量化这一关键问题，对虚拟电厂的外特性建模、资源优化组合、服务能力的可信量化等问题进行了详细综述。首先，总结了虚拟电厂灵活资源运行特性对其外特性的差异化支撑能力，并提出了通用的规范化描述方法；然后，立足物理-信息-社会耦合视角，分别分析了虚拟电厂动态构建与可信量化的关键问题及核心技术，并从个体资源和资源集群两个维度分析了不同视角下的耦合关系及关键技术的支撑逻辑；最后，归纳总结了文中主要贡献与虚拟电厂服务新型电力系统的展望与挑战。

Abstract

The construction of new power system needs to adapt to the access of large-scale high-proportion renewable energy

which puts forward higher requirements for the flexibility of system operation. It is urgent to explore and effectively control the idle flexible energy resources. As an efficient way of aggregating distributed energy resources

virtual power plants can provide various flexibility services such as peak shaving

reserving capacity and frequency regulation for the safe operation of new power system. The key technical challenge is how to construct the virtual power plants to fully unlock the adjustable potential of massive distributed energy sources. This paper focuses on the critical issues of operation characteristics

dynamic construction

and trustworthy quantification of flexible energy resources in virtual power plants. It provides a detailed review of the modeling of external characteristics

optimal combination of energy resources

and trustworthy quantification of service capabilities in virtual power plants. Firstly

this paper summarizes the differentiated supporting capabilities of the operation characteristics of flexible energy resources in virtual power plants for their external characteristics and puts forward a general standardized description method. Then

based on the perspective of physics-cyber-social coupling

the key issues and core technologies of dynamic construction and trustworthy quantification of virtual power plants are analyzed respectively

and the coupling relationship from different perspectives and the supporting logic of key technologies from the two dimensions of individual resources and resource clusters are analyzed. Finally

the vital contributions of this paper and the prospects and challenges of virtual power plants serving the new power system are summarized.

关键词

Keywords

references

康重庆，姚良忠 . 高比例可再生能源电力系统的关键科学问题与理论研究框架［J］. 电力系统自动化， 2017 ， 41 （ 9 ）： 2 - 11 .

KANG Chongqing ， YAO Liangzhong . Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy ［J］. Automation of Electric Power Systems ， 2017 ， 41 （ 9 ）： 2 - 11 .

鲁宗相，李海波，乔颖 . 高比例可再生能源并网的电力系统灵活性评价与平衡机理［J］. 中国电机工程学报， 2017 ， 37 （ 1 ）： 9 - 20 .

LU Zongxiang ， LI Haibo ， QIAO Ying . Flexibility evaluation and supply/demand balance principle of power system with high-penetration renewable electricity ［J］. Proceedings of the CSEE ， 2017 ， 37 （ 1 ）： 9 - 20 .

国家能源局 . “十四五”现代能源体系规划［EB/OL］.［ 2022-04-15 ］. https：//www.ndrc.gov.cn/xxgk/zcfb/ghwb/202203/t20220322_1320016.html https://www.ndrc.gov.cn/xxgk/zcfb/ghwb/202203/t20220322_1320016.html .

National Energy Administration . The “14th Five-Year Plan” of modern energy system ［EB/OL］. ［ 2022-04-15 ］. https：//www.ndrc.gov.cn/xxgk/zcfb/ghwb/202203/t20220322_1320016.html https://www.ndrc.gov.cn/xxgk/zcfb/ghwb/202203/t20220322_1320016.html .

WANG S Y ， WU W C . Stochastic flexibility evaluation for virtual power plant by aggregating distributed energy resources ［J/OL］. Probability Theory ［ 2022-04-15 ］. https：//doi.org/10.485 50/arXiv.2006.16170 https://doi.org/10.48550/arXiv.2006.16170 .

HUANG S ， YE Y Y ， WU D ， et al . An assessment of power flexibility from commercial building cooling systems in the United States ［J］. Energy ， 2021 ， 221 ： 119571 .

QI N ， CHENG L ， XU H L ， et al . Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads ［J］. Applied Energy ， 2020 ， 279 ： 115708 .

赵本源，熊岑，张沛超，等 . 信息物理融合的负荷型虚拟电厂聚合方法［J］. 电力需求侧管理， 2020 ， 22 （ 1 ）： 15 - 20 .

ZHAO Benyuan ， XIONG Cen ， ZHANG Peichao ， et al . Aggregation method of load virtual power plant based on cyber-physical system ［J］. Power Demand Side Management ， 2020 ， 22 （ 1 ）： 15 - 20 .

FANG F ， YU S Y ， XIN X L . Data-driven-based stochastic robust optimization for a virtual power plant with multiple uncertainties ［J］. IEEE Transactions on Power Systems ， 2022 ， 37 （ 1 ）： 456 - 466 .

RAHIMI M ， ARDAKANI F J ， ARDAKANI A J . Optimal stochastic scheduling of electrical and thermal renewable and non-renewable resources in virtual power plant ［J］. International Journal of Electrical Power & Energy Systems ， 2021 ， 127 ： 106658 .

VAHEDIPOUR-DAHRAIE M ， RASHIDIZADEH-KERMANI H ， SHAFIE-KHAH M ， et al . Risk-averse optimal energy and reserve scheduling for virtual power plants incorporating demand response programs ［J］. IEEE Transactions on Smart Grid ， 2021 ， 12 （ 2 ）： 1405 - 1415 .

YANG X L ， NIU D X ， SUN L J ， et al . Participation of electric vehicles in auxiliary service market to promote renewable energy power consumption： case study on deep peak load regulation of auxiliary thermal power by electric vehicles ［J］. Energy Science & Engineering ， 2021 ， 9 （ 9 ）： 1465 - 1476 .

PAL P ， PARVATHY A K ， DEVABALAJI K R ， et al . IoT-based real time energy management of virtual power plant using PLC for transactive energy framework ［J］. IEEE Access ， 2021 ， 9 ： 97643 - 97660 .

屈尹鹏，孙元章，徐箭，等 . 基于解析灵敏度的不平衡配电系统分布式发电出力优化［J］. 电力系统自动化， 2021 ， 45 （ 19 ）： 117 - 125 .

QU Yinpeng ， SUN Yuanzhang ， XU Jian ， et al . Optimization of distributed generation output in unbalanced distribution system based on analytical sensitivity ［J］. Automation of Electric Power Systems ， 2021 ， 45 （ 19 ）： 117 - 125 .

GU Y X ， WANG J X ， CHEN Y B ， et al . A simplified electro-chemical lithium-ion battery model applicable for in-situ monitoring and online controlling ［J/OL］. Electrical Engineering and Systems Science ［ 2021-11-05 ］. https：//doi.org/10.48550/arXiv.2111.03288 https://doi.org/10.48550/arXiv.2111.03288 .

李翔宇，赵冬梅 . 计及可调资源动态特性的虚拟电厂多级优化配置［J］. 电力系统自动化， 2020 ， 44 （ 13 ）： 17 - 24 .

LI Xiangyu ， ZHAO Dongmei . Multi-level optimal configuration of virtual power plant considering dynamic characteristics of adjustable resources ［J］. Automation of Electric Power Systems ， 2020 ， 44 （ 13 ）： 17 - 24 .

唐庆虎，张银，钱瞳 . 考虑配电网络重构和楼宇群主动需求响应的集成楼宇群配电网优化控制［J/OL］. 中国电机工程学报［ 2022-04-13 ］. https：//kns.cnki.net/kcms/detail/11.2107.tm.20220411.1720.002.html https://kns.cnki.net/kcms/detail/11.2107.tm.20220411.1720.002.html .

TANG Qinghu ， ZHANG Yin ， QIAN Tong . Optimal control of buildings-to-distribution-network integration system considering reconfiguration of distribution network and active demand response of buildings ［J/OL］. Proceedings of the CSEE ［ 2022-04-13 ］. https：//kns.cnki.net/kcms/detail/11.2107.tm.20220411.1720.002.html https://kns.cnki.net/kcms/detail/11.2107.tm.20220411.1720.002.html .

FENG C ， WANG Y ， WANG X Y ， et al . Device access optimization for virtual power plants in heterogeneous networks ［J］. IEEE Transactions on Smart Grid ， 2022 ， 13 （ 2 ）： 1478 - 1489 .

邱兰馨，王彦波，卢杉，等 . 基于5G电力虚拟专网的秒级可中断负荷快速响应系统［J］. 电力信息与通信技术， 2021 ， 19 （ 9 ）： 51 - 56 .

QIU Lanxin ， WANG Yanbo ， LU Shan ， et al . A second-level interruptible load fast response system based on 5G power virtual private network ［J］. Electric Power Information and Communication Technology ， 2021 ， 19 （ 9 ）： 51 - 56 .

KONG X Y ， KONG D Q ， YAO J T ， et al . Online pricing of demand response based on long short-term memory and reinforcement learning ［J］. Applied Energy ， 2020 ， 271 ： 114945 .

云秋晨，田立亭，齐宁，等 . 基于投资组合理论的虚拟电厂资源优化组合方法［J］. 电力系统自动化， 2022 ， 46 （ 1 ）： 146 - 154 .

YUN Qiuchen ， TIAN Liting ， QI Ning ， et al . Optimization method of resource combination for virtual power plant based on modern portfolio theory ［J］. Automation of Electric Power Systems ， 2022 ， 46 （ 1 ）： 146 - 154 .

高宇，李昀，曹蓉蓉，等 . 基于多代理Double DQN算法模拟发电侧竞价行为［J］. 电网技术， 2020 ， 44 （ 11 ）： 4175 - 4183 .

GAO Yu ， LI Yun ， CAO Rongrong ， et al . Simulation of generators’ bidding behavior based on multi-agent Double DQN ［J］. Power System Technology ， 2020 ， 44 （ 11 ）： 4175 - 4183 .

王毅，陈启鑫，张宁，等 . 5G通信与泛在电力物联网的融合：应用分析与研究展望［J］. 电网技术， 2019 ， 43 （ 5 ）： 1575 - 1585 .

WANG Yi ， CHEN Qixin ， ZHANG Ning ， et al . Fusion of the 5G communication and the ubiquitous electric Internet of Things： application analysis and research prospects ［J］. Power System Technology ， 2019 ， 43 （ 5 ）： 1575 - 1585 .

WANG J X ， ZHONG H W ， QIN J J ， et al . Incentive mechanism for sharing distributed energy resources ［J］. Journal of Modern Power Systems and Clean Energy ， 2019 ， 7 （ 4 ）： 837 - 850 .

武昕，严萌，郭一凡，等 . 基于结构化特征图谱的组合支持向量机非侵入式负荷辨识［J］. 电力系统自动化， 2022 ， 46 （ 12 ）： 210 - 219 .

WU Xin ， YAN Meng ， GUO Yifan ， et al . Non-intrusive load identification by combined support vector machine based on structured characteristic spectrum ［J］. Automation of Electric Power Systems ， 2022 ， 46 （ 12 ）： 210 - 219 .

盛裕杰，郭庆来，刘梦洁，等 . 多源数据融合的用户充电行为分析与充电设施规划实践［J］. 电力系统自动化， 2022 ， 46 （ 12 ）： 151 - 162 .

SHENG Yujie ， GUO Qinglai ， LIU Mengjie ， et al . User charging behavior analysis and charging facility planning practice based on multi-source data fusion ［J］. Automation of Electric Power Systems ， 2022 ， 46 （ 12 ）： 151 - 162 .

YOUSEFI S ， MOGHADDAM M P ， MAJD V J . Optimal real time pricing in an agent-based retail market using a comprehensive demand response model ［J］. Energy ， 2011 ， 36 （ 9 ）： 5716 - 5727 .

GUO H Y ， CHEN Q X ， GU Y X ， et al . A data-driven pattern extraction method for analyzing bidding behaviors in power markets ［J］. IEEE Transactions on Smart Grid ， 2020 ， 11 （ 4 ）： 3509 - 3521 .

胡帅，向月，刘俊勇，等 . 基于参数化隶属度匹配函数的含分布式发电配电网模糊潮流计算［J］. 中国电机工程学报， 2019 ， 39 （ 18 ）： 5370 - 5379 .

HU Shuai ， XIANG Yue ， LIU Junyong ， et al . Fuzzy power flow calculation in distribution networks with distributed generation based on parameterized membership matching function ［J］. Proceedings of the CSEE ， 2019 ， 39 （ 18 ）： 5370 - 5379 .

方晓涛，严正，王晗，等 . 考虑源-荷随机-模糊特征的配电网潮流不确定性量化方法［J/OL］. 中国电机工程学报［ 2022-04-27 ］. http：//kns.cnki.net/kcms/detail/11.2107.TM.20220118.1409.012.html http://kns.cnki.net/kcms/detail/11.2107.TM.20220118.1409.012.html .

FANG Xiaotao ， YAN Zheng ， WANG Han ， et al . Quantification method of distribution network power flow uncertainty considering random-fuzzy characteristics of source and load ［J/OL］. Proceedings of the CSEE ［ 2022-04-27 ］. http：//kns.cnki.net/kcms/detail/11.2107.TM.20220118.1409.012.html http://kns.cnki.net/kcms/detail/11.2107.TM.20220118.1409.012.html .

TANG Q H ， GUO H Y ， CHEN Q X . Multi-market bidding behavior analysis of energy storage system based on inverse reinforcement learning ［J］. IEEE Transactions on Power Systems ， 2022 ， 99 ： 0518 .

凌万水，刘刚 . 基于鲁棒优化的主动配电网分布式电源优化配置方法［J］. 电力系统保护与控制， 2020 ， 48 （ 15 ）： 141 - 148 .

LING Wanshui ， LIU Gang . Distributed generation optimal configuration method for active distribution networks based on robust optimization ［J］. Power System Protection and Control ， 2020 ， 48 （ 15 ）： 141 - 148 .

GUO H Y ， CHEN Q X ， XIA Q ， et al . Deep inverse reinforcement learning for objective function identification in bidding models ［J］. IEEE Transactions on Power Systems ， 2021 ， 36 （ 6 ）： 5684 - 5696 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Flexibility Demand Decomposition and Operation Optimization for Power System Based on Hybrid Mathematical Morphology

Review on Aggregation Characteristics and Trading Mechanisms of Virtual Power Plant for Enhancing System Response Capability

Prospect of Research on Communication Network Architecture and Key Technologies for Virtual Power Plant

Scientific Problems and Research Framework of Virtual Power Plant with Enormous Flexible Distributed Energy Resources in New Power System

Related Author

Chongqing KANG

Qixin CHEN

Jian SU

Qian AI

Yu JI

Weiwei PAN

Hongchao GAO

Ming WU

Related Institution

State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment, Tsinghua University

China Electric Power Research Institute

Key Laboratory of Control of Power Transmission and Conversion Control of Ministry of Education, Shanghai Jiao Tong University

State Grid Shanghai Energy Internet Research Institute Co. Ltd.

State Grid Zhejiang Electric Power Co. Ltd

⁰