High-proportion Renewable Energy Accommodation Method Based on Customer Directrix Load and Nash Bargaining

XU Boqiang; ZHAO Jianli; ZHANG Peichao; FAN Shuai; HE Guangyu

doi:10.7500/AEPS20221130016

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High-proportion Renewable Energy Accommodation Method Based on Customer Directrix Load and Nash Bargaining

Special Topic on Power Balance and Optimal Dispatching Technology for Power System with High Proportion of Renewable Energy | 更新时间：2025-06-16

- High-proportion Renewable Energy Accommodation Method Based on Customer Directrix Load and Nash Bargaining
- Automation of Electric Power Systems Vol. 47, Issue 15, Pages: 36-45(2023)
- 作者机构：
  
  1.电力传输与功率变换控制教育部重点实验室（上海交通大学），上海市 200240
  2.上海市智能电网需求响应重点实验室，上海市 200063
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51877134);National Key R&D Program of China(2021YFB2401200)
- DOI：10.7500/AEPS20221130016
  CLC：
- Received：30 November 2022，
  
  Revised：2023-06-08，
  
  Published Online：28 June 2023，
  
  Published：10 August 2023
- 稿件说明：
移动端阅览
徐博强,赵建立,张沛超等.基于负荷准线和纳什谈判的高比例新能源消纳方法[J].电力系统自动化,2023,47(15):36-45.

XU Boqiang,ZHAO Jianli,ZHANG Peichao,et al.High-proportion Renewable Energy Accommodation Method Based on Customer Directrix Load and Nash Bargaining[J].Automation of Electric Power Systems,2023,47(15):36-45.
徐博强,赵建立,张沛超等.基于负荷准线和纳什谈判的高比例新能源消纳方法[J].电力系统自动化,2023,47(15):36-45. DOI： 10.7500/AEPS20221130016.

XU Boqiang,ZHAO Jianli,ZHANG Peichao,et al.High-proportion Renewable Energy Accommodation Method Based on Customer Directrix Load and Nash Bargaining[J].Automation of Electric Power Systems,2023,47(15):36-45. DOI： 10.7500/AEPS20221130016.

摘要

新型电力系统中新能源占比的迅速提高将导致发电侧调节能力严重匮乏。基于负荷准线的需求响应能常态化激励负荷整形，从而提升电网平衡能力、促进新能源消纳。针对准线型需求响应中的利益分配问题，提出基于纳什谈判的网-荷合作博弈机制；针对纳什谈判问题的难解性，提出和建立其近似问题，并提出能有效保证各方信息隐私的分布式求解方法；以电动汽车集群为例，提出可调负荷灵活性的概率预测模型，反映其全时段的整形能力。最后，建立含电动汽车、热泵热水器两类灵活资源以及高比例新能源的区域电网算例。仿真结果表明，以上两类负荷均具有良好的整形能力。基于所提的收益分配机制，准线型需求响应能够显著增强对聚合商的激励，有效促进新能源消纳，实现电网和用户的双赢。

Abstract

The rapid increase in the proportion of renewable energy in new power systems will lead to a serious lack of regulation capacity on the power generation side. The demand response based on customer directrix load (CDL) can normalize the motivation of load shaping

thereby improving the balance capability of the power grid and promoting the accommodation of renewable energy. A network-load cooperative game mechanism based on Nash bargaining is proposed to address the issue of profit allocation in CDL-based demand response. To address the intractability of the Nash bargaining

its approximation is proposed and established

and its distributed solution method is proposed to ensure the information privacy of each entity. Taking the electric vehicle cluster as an example

a flexibility probability prediction model for adjustable load is proposed to reflect its full-time shaping capability. Finally

a regional power grid case that includes flexible resources such as electric vehicles and heat pump water heaters as well as high proportion of renewable energy is established. The simulation results show that both types of loads have good shaping capability. Based on the proposed profit allocation mechanism

the CDL-based demand response can significantly enhance the incentives of aggregators

effectively promote renewable energy accommodation

and achieve a win-win situation for the power grid and the users

关键词

Keywords

references

辛保安 . 为实现“碳达峰、碳中和”目标贡献智慧和力量［N］. 中国电力报， 2021-02-24 （1）.

XIN Baoan . Contribute wisdom and strength to achieve the goal of “carbon peak and carbon neutral” ［N］. China Electric Power News ， 2021-02-24 （1）.

MA Y ， LI Y P ， HUANG G H . Planning China’s non-deterministic energy system （2021—2060） to achieve carbon neutrality ［J］. Applied Energy ， 2023 ， 334 ： 120673 .

LI G ， YANG J ， WU F Z ， et al . A market framework for a 100% renewable energy penetration spot market ［J］. IEEE Transactions on Sustainable Energy ， 2023 ， PP（ 99 ）： 1 - 15 .

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 .

徐博强，张沛超，何光宇，等 . 基于主从博弈的热水器集群的负荷准线控制方法［J］. 中国电机工程学报， 2022 ， 42 （ 21 ）： 7785 - 7797 .

XU Boqiang ， ZHANG Peichao ， HE Guangyu ， et al . Load alignment control method of water heater cluster based on master-slave game ［J］. Proceedings of the CSEE ， 2022 ， 42 （ 21 ）： 7785 - 7797 .

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 .

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 .

MOHAJERYAMI S ， DOOSTAN M ， ASADINEJAD A ， et al . Error analysis of customer baseline load （CBL） calculation methods for residential customers ［J］. IEEE Transactions on Industry Applications ， 2017 ， 53 （ 1 ）： 5 - 14 .

FAN S ， LI Z Y ， YANG L ， et al . Customer directrix load-based large-scale demand response for integrating renewable energy sources ［J］. Electric Power Systems Research ， 2020 ， 181 ： 106175 .

姚垚，张沛超，王永权 . 温控负荷参与快速频率调整的双层控制方法［J］. 中国电机工程学报， 2018 ， 38 （ 17 ）： 4987 - 4998 .

YAO Yao ， ZHANG Peichao ， WANG Yongquan . A two-layer control method for thermostatically controlled loads to provide fast frequency regulation ［J］. Proceedings of the CSEE ， 2018 ， 38 （ 17 ）： 4987 - 4998 .

ZHU L L ， HONG J H ， YANG S Y ， et al . Online modeling of virtual energy storage for inverter air conditioning clusters in CDL-based demand response ［J］. Energy Reports ， 2023 ， 9 ： 2024 - 2034 .

吴浩天，孙荣富，廖思阳，等 . 基于改进气象聚类分型的短期风电功率概率预测方法［J］. 电力系统自动化， 2022 ， 46 （ 15 ）： 56 - 65 .

WU Haotian ， SUN Rongfu ， LIAO Siyang ， et al . Short-term wind power probability prediction method based on improved meteorological clustering classification ［J］. Automation of Electric Power Systems ， 2022 ， 46 （ 15 ）： 56 - 65 .

FAN S L ， AI Q ， PIAO L J . Bargaining-based cooperative energy trading for distribution company and demand response ［J］. Applied Energy ， 2018 ， 226 ： 469 - 482 .

LIU S Y ， ZHANG T H ， LIN Z Z ， et al . Controlled islanding strategy considering uncertainty of renewable energy sources based on chance-constrained model ［J］. Journal of Modern Power Systems and Clean Energy ， 2021 ， 10 （ 2 ）： 471 - 481 .

NASH J F . The bargaining problem ［J］. Econometrica ， 1950 ， 18 （ 2 ）： 155 .

BOYD S P ， VANDENBERGHE L . Convex optimization ［M］. Cambridge， UK ： Cambridge University Press ， 2004 .

王枭，何怡刚，马恒瑞，等 . 面向电网辅助服务的虚拟储能电厂分布式优化控制方法［J］. 电力系统自动化， 2022 ， 46 （ 10 ）： 181 - 188 .

WANG Xiao ， HE Yigang ， MA Hengrui ， et al . Distributed optimization control method of virtual energy storage plants for power grid ancillary services ［J］. Automation of Electric Power Systems ， 2022 ， 46 （ 10 ）： 181 - 188 .

BOYD S ， PARIKH N ， CHU E ， et al . Distributed optimization and statistical learning via the alternating direction method of multipliers ［J］. Foundations and Trends in Machine Learning ， 2011 ， 3 （ 1 ）： 1 - 122 .

VANDAEL S ， CLAESSENS B ， HOMMELBERG M ， et al . A scalable three-step approach for demand side management of plug-in hybrid vehicles ［J］. IEEE Transactions on Smart Grid ， 2013 ， 4 （ 2 ）： 720 - 728 .

XU Z W ， SU W C ， HU Z C ， et al . A hierarchical framework for coordinated charging of plug-in electric vehicles in China ［J］. IEEE Transactions on Smart Grid ， 2016 ， 7 （ 1 ）： 428 - 438 .

LEE Z J ， LEE G ， LEE T ， et al . Adaptive charging networks： a framework for smart electric vehicle charging ［J］. IEEE Transactions on Smart Grid ， 2021 ， 12 （ 5 ）： 4339 - 4350 .

LIU S Y ， JIANG Y C ， LIN Z Z ， et al . Data-driven two-step day-ahead electricity price forecasting considering price spikes ［J］. Journal of Modern Power Systems and Clean Energy ， 2022 ， 11 （ 2 ）： 523 - 533 .

RASMUSSEN C E ， WILLIAMS C K I . Gaussian processes for machine learning ［M］. Cambridge， USA ： MIT Press ， 2006 .

陈吕鹏，潘振宁，余涛，等 . 基于动态非合作博弈的大规模电动汽车实时优化调度［J］. 电力系统自动化， 2019 ， 43 （ 24 ）： 32 - 40 .

CHEN Lüpeng ， PAN Zhenning ， YU Tao ， et al . Real-time optimal dispatch for large-scale electric vehicles based on dynamic non-cooperative game theory ［J］. Automation of Electric Power Systems ， 2019 ， 43 （ 24 ）： 32 - 40 .

ACN-Data： a public EV charging dataset ［EB/OL］. （ 2021-09-30 ）［ 2022-11-30 ］. https：//ev.caltech.edu/dataset https://ev.caltech.edu/dataset .

ElaadNL open datasets ［EB/OL］. （ 2019-12-31 ）［ 2022-11-30 ］. https：//platform.elaad.io/download-data/ https://platform.elaad.io/download-data/ .

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