Construction Method for Probability-based Feasible Region of Virtual Power Plant Considering Uncertainty of Distributed Renewable Energy Output

JIAO Zhijie; WANG Xiaojun; LIU Zhao; HE Jinghan; SI Fangyuan; GUAN Jinyu

doi:10.7500/AEPS20240407002

您当前的位置：

首页 >

文章列表页 >

Construction Method for Probability-based Feasible Region of Virtual Power Plant Considering Uncertainty of Distributed Renewable Energy Output

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

- Construction Method for Probability-based Feasible Region of Virtual Power Plant Considering Uncertainty of Distributed Renewable Energy Output
- Automation of Electric Power Systems Vol. 48, Issue 18, Pages: 129-138(2024)
- 作者机构：
  
  1.北京交通大学电气工程学院，北京市 100044
  2.国电南瑞科技股份有限公司，江苏省南京市 211106
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China(2022YFB2403400)
- DOI：10.7500/AEPS20240407002
  CLC：
- Received：07 April 2024，
  
  Revised：2024-06-13，
  
  Published Online：12 August 2024，
  
  Published：25 September 2024
- 稿件说明：
移动端阅览
焦治杰,王小君,刘曌等.考虑分布式新能源出力不确定性的虚拟电厂概率可行域构建方法[J].电力系统自动化,2024,48(18):129-138.

JIAO Zhijie,WANG Xiaojun,LIU Zhao,et al.Construction Method for Probability-based Feasible Region of Virtual Power Plant Considering Uncertainty of Distributed Renewable Energy Output[J].Automation of Electric Power Systems,2024,48(18):129-138.
焦治杰,王小君,刘曌等.考虑分布式新能源出力不确定性的虚拟电厂概率可行域构建方法[J].电力系统自动化,2024,48(18):129-138. DOI： 10.7500/AEPS20240407002.

JIAO Zhijie,WANG Xiaojun,LIU Zhao,et al.Construction Method for Probability-based Feasible Region of Virtual Power Plant Considering Uncertainty of Distributed Renewable Energy Output[J].Automation of Electric Power Systems,2024,48(18):129-138. DOI： 10.7500/AEPS20240407002.

摘要

分布式新能源出力的不确定性严重影响虚拟电厂聚合可行域的准确刻画，需精准计算虚拟电厂的可调能力以提升电力系统的平衡能力。文中提出了面向虚拟电厂的分布式新能源出力不确定性概率模型及基于多概率变量的可信概率可行域构建方法。首先，基于集成学习与共形分位数回归方法，提出了能够描述新能源出力异方差时间序列的不确定性概率模型。其次，研究了闵可夫斯基和方法与优化方法求解可行域的特点，分析了新能源出力约束和网络约束对虚拟电厂可行域的影响，进一步提出了基于分布式新能源出力概率区间的虚拟电厂概率可行域构建方法。最后，考虑风光耦合特性，优化了虚拟电厂概率可行域构建方法，通过协调各分布式新能源出力概率区间置信度，得出虚拟电厂概率可行域整体的置信水平。算例验证结果表明，所提方法能够准确刻画虚拟电厂的可调能力。

Abstract

The uncertainty of distributed renewable energy output significantly affects the accurate characterization of the feasible region for virtual power plant aggregation. It is necessary to accurately calculate the adjustable capacity of virtual power plants to enhance the balancing capability of the power system. This paper proposes a probability model for the uncertainty of distributed renewable energy output in virtual power plants and a construction method of the credible probability-based feasible region based on multiple probability variables. Firstly

based on the ensemble learning and conformal quantile regression methods

an uncertainty probability model is proposed to describe the heteroscedastic time series of renewable energy output. Secondly

the characteristics of Minkowski sum and optimization methods for solving feasible region are studied

and the influences of renewable energy output constraints and network constraints on the feasible region of the virtual power plant are analyzed. Then

this paper proposes a construction method for the probability-based feasible region of the virtual power plant based on output probability intervals of distributed renewable energy. Finally

considering the coupling characteristics of wind and photovoltaic power

the construction method for the probability-based feasible region of the virtual power plant is optimized. By coordinating the confidence coefficients of the output probability intervals of each distributed renewable energy

an overall confidence level of the probability-based feasible region of the virtual power plant is obtained. The case verification results show that the proposed method can accurately characterize the adjustable capacity of virtual power plants.

关键词

Keywords

references

PAN C C ， SHAO C Z ， HU B ， et al . Modeling the reserve capacity of wind power and the inherent decision-dependent uncertainty in the power system economic dispatch ［J］. IEEE Transactions on Power Systems ， 2023 ， 38 （ 5 ）： 4404 - 4417 .

LEI C ， BU S Q ， WANG Q G ， et al . Look-ahead rolling economic dispatch approach for wind-thermal-bundled power system considering dynamic ramping and flexible load transfer strategy ［J］. IEEE Transactions on Power Systems ， 2024 ， 39 （ 1 ）： 186 - 202 .

国家发展改革委、国家能源局关于印发《“十四五”现代能源体系规划》的通知［EB/OL］.（ 2022-01-29 ）［ 2024-03-27 ］. https：//www.gov.cn/zhengce/zhengceku/2022-03/23/content_5680759.htm https://www.gov.cn/zhengce/zhengceku/2022-03/23/content_5680759.htm .

Notice of the National Development and Reform Commission and the National Energy Administration on issuing the “14th Five-year Plan for Modern Energy System ”［EB/OL］. （ 2022-01-29 ）［ 2024-03-27 ］. https：//www.gov.cn/zhengce/zhengceku/2022-03/23/content_5680759.htm https://www.gov.cn/zhengce/zhengceku/2022-03/23/content_5680759.htm .

国家发展改革委等部门关于印发《电力需求侧管理办法（2023年版）》的通知［EB/OL］.（ 2023-09-15 ）［ 2024-03-27 ］. https：//www.gov.cn/zhengce/zhengceku/202310/content_6907311.htm https://www.gov.cn/zhengce/zhengceku/202310/content_6907311.htm .

The National Development and Reform Commission and other departments on the issuance of the “Power Demand Side Management Measures （2023 version）” notice ［EB/OL］. （ 2023-09-15 ）［ 2024-03-27 ］. https：//www.gov.cn/zhengce/zhengceku/202310/content_6907311.htm https://www.gov.cn/zhengce/zhengceku/202310/content_6907311.htm .

WANG S Y ， WU W C . Aggregate flexibility of virtual power plants with temporal coupling constraints ［J］. IEEE Transactions on Smart Grid ， 2021 ， 12 （ 6 ）： 5043 - 5051 .

WU C Y ， GU W ， ZHOU S Y ， et al . Coordinated optimal power flow for integrated active distribution network and virtual power plants using decentralized algorithm ［J］. IEEE Transactions on Power Systems ， 2021 ， 36 （ 4 ）： 3541 - 3551 .

王炳乾 . 考虑灵活性资源的多虚拟电厂优化调度研究［D］. 北京：华北电力大学， 2023 .

WANG Bingqian . Research on optimal scheduling of multi-virtual power plants considering flexible resources ［D］. Beijing ： North China Electric Power University ， 2023 .

张杰 . 含分布式能源的虚拟电厂优化调度及收益分配策略研究［D］. 吉林：东北电力大学， 2023 .

ZHANG Jie . Virtual power plant with distributed energy resources research on optimal scheduling and revenue distribution strategy ［D］. Jilin ： Northeast Electric Power University ， 2023 .

陈艺华，张炜，张成刚，等 . 促进新能源消纳的省间、省内两级电力现货市场运行机制［J］. 电力系统自动化， 2021 ， 45 （ 14 ）： 104 - 113 .

CHEN Yihua ， ZHANG Wei ， ZHANG Chenggang ， et al . Operation mechanism of inter-and intra-provincial electricity spot markets to promote renewable energy accommodation ［J］. Automation of Electric Power Systems ， 2021 ， 45 （ 14 ）： 104 - 113 .

AHMADI A ， NABIPOUR M ， TAHERI S ， et al . A new false data injection attack detection model for cyberattack resilient energy forecasting ［J］. IEEE Transactions on Industrial Informatics ， 2023 ， 19 （ 1 ）： 371 - 381 .

TANG G ， WU Y F ， LI C S ， et al . A novel wind speed interval prediction based on error prediction method ［J］. IEEE Transactions on Industrial Informatics ， 2020 ， 16 （ 11 ）： 6806 - 6815 .

高洪超，李楚一，王冠雄，等 . 聚合规模化5G基站的虚拟电厂动态构建与响应实证［J/OL］. 电力系统自动化［ 2024-03-27 ］. https：//kns.cnki.net/kcms/detail/32.1180.TP.20240205.1451.006.html https://kns.cnki.net/kcms/detail/32.1180.TP.20240205.1451.006.html .

GAO Hongchao ， LI Chuyi ， WANG Guanxiong ， et al . Dynamic construction and response demonstration of virtual power plants with aggregated large-scale 5G base stations ［J/OL］. Automation of Electric Power Systems ［ 2024-03-27 ］. https：//kns.cnki.net/kcms/detail/32.1180.TP.20240205.1451.006.html https://kns.cnki.net/kcms/detail/32.1180.TP.20240205.1451.006.html .

许泽凯，和敬涵，刘曌，等 . 基于耦合约束解耦的虚拟电厂动态可行域求解方法［J］. 中国电机工程学报， 2024 ， 44 （ 9 ）： 3440 - 3452 .

XU Zekai ， HE Jinghan ， LIU Zhao ， et al . Solution method of virtual power plant dynamic feasible region based on decoupling of coupling constraints ［J］. Proceedings of the CSEE ， 2024 ， 44 （ 9 ）： 3440 - 3452 .

张天策，李庚银，王剑晓，等 . 基于可行域投影理论的新能源电力系统协同运行方法［J］. 电工技术学报， 2024 ， 39 （ 9 ）： 2784 - 2796 .

ZHANG Tiance ， LI Gengyin ， WANG Jianxiao ， et al . Coordinated operation method of renewable energy power systems based on feasible region projection theory ［J］. Transactions of China Electrotechnical Society ， 2024 ， 39 （ 9 ）： 2784 - 2796 .

陈会来 . 基于成本-效益分析的虚拟电厂可调节功率域聚合算法［D］. 北京：华北电力大学， 2023 .

CHEN Huilai . Aggregation algorithm for adjustable power domain of virtual power plant based on cost-benefit analysis ［D］. Beijing ： North China Electric Power University ， 2023 .

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 .

BABAEI S ， ZHAO C Y ， FAN L . A data-driven model of virtual power plants in day-ahead unit commitment ［J］. IEEE Transactions on Power Systems ， 2019 ， 34 （ 6 ）： 5125 - 5135 .

田书欣，朱峰，杨喜军，等 . 基于Vague软集的海上风电功率区间预测［J/OL］. 中国电机工程学报［ 2024-03-24 ］. https：//doi.org/10.13334/j.0258-8013.pcsee.231682 https://doi.org/10.13334/j.0258-8013.pcsee.231682 .

TIAN Shuxin ， ZHU Feng ， YANG Xijun ， et al . Offshore wind power interval prediction based on Vague soft set ［J/OL］. Proceedings of the CSEE ［ 2024-03-24 ］. https：//doi.org/10.13334/j.0258-8013.pcsee.231682 https://doi.org/10.13334/j.0258-8013.pcsee.231682 .

RUAN J Q ， WANG Q H ， CHEN S C ， et al . On vulnerability of renewable energy forecasting： adversarial learning attacks ［J］. IEEE Transactions on Industrial Informatics ， 2024 ， 20 （ 3 ）： 3650 - 3663 .

万灿，宋永华 . 新能源电力系统概率预测理论与方法及其应用［J］. 电力系统自动化， 2021 ， 45 （ 1 ）： 2 - 16 .

WAN Can ， SONG Yonghua . Theories， methodologies and applications of probabilistic forecasting for power systems with renewable energy sources ［J］. Automation of Electric Power Systems ， 2021 ， 45 （ 1 ）： 2 - 16 .

马伟，乔颖，鲁宗相，等 . 基于敏感气象特征因子筛选与优化组合的短期风电功率预测［J］. 电网技术， 2023 ， 47 （ 7 ）： 2897 - 2908 .

MA Wei ， QIAO Ying ， LU Zongxiang ， et al . Short-term wind power prediction based on combination of screening and optimizing sensitive meteorological characteristics ［J］. Power System Technology ， 2023 ， 47 （ 7 ）： 2897 - 2908 .

毛嘉铭，刘光宇，罗凯元 . 结合数据增强及组合算法的短期光伏功率预测［J/OL］. 电力系统及其自动化学报［ 2024-03-27 ］. https：//doi.org/10.19635/j.cnki.csu-epsa.001430 https://doi.org/10.19635/j.cnki.csu-epsa.001430 .

MAO Jiaming ， LIU Guangyu ， LUO Kaiyuan . Short term photovoltaic power prediction using data augmentation and combined algorithms ［J/OL］. Proceeding of the CSU-EPSA ［ 2024-03-27 ］. https：//doi.org/10.19635/j.cnki.csu-epsa.001430 https://doi.org/10.19635/j.cnki.csu-epsa.001430 .

赵耀，高少炜，李东东，等 . 基于天气相似聚类与QRNN的短期光伏功率区间概率预测［J］. 电力系统自动化， 2023 ， 47 （ 23 ）： 152 - 161 .

ZHAO Yao ， GAO Shaowei ， LI Dongdong ， et al . Short-term interval probability prediction of photovoltaic power based on weather similarity clustering and quantile regression neural network ［J］. Automation of Electric Power Systems ， 2023 ， 47 （ 23 ）： 152 - 161 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Dynamic Construction and Response Demonstration of Virtual Power Plant with Aggregated Large-scale 5G Base Stations

含电动汽车的虚拟电厂鲁棒随机优化调度

基于投资组合的虚拟电厂多电源容量配置

含风光水的虚拟电厂与配电公司协调调度模型

Related Author

GAO Hongchao

LI Chuyi

WANG Guanxiong

JIN Tai

HU Jiye

ZHU Jingkai

CHEN Qixin

KANG Chongqing

Related Institution

National Key Laboratory of New Power System Operation and Control (Tsinghua University)

Tsinghua Sichuan Energy Internet Research Institute

Beijing Chuangzhixinke Technology Co., Ltd.

State Grid Shanghai Municipal Electric Power Company

College of Energy and Electrical Engineering, Hohai University

⁰