广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (02): 64-73.doi: 10.12052/gdutxb.210170

• 综合研究 • 上一篇    下一篇

一种电网需求响应资源参与天然气市场的协调框架

符政鑫1, 刘霜2, 段意强1, 朱伟东1   

  1. 1. 广东工业大学 自动化学院，广东 广州 510006;
    2. 南华大学 机械工程学院，湖南 衡阳 421000
  • 收稿日期:2021-11-08 出版日期:2023-03-25 发布日期:2023-04-07
  • 作者简介:符政鑫(1996-),男,硕士研究生,主要研究方向为气-电跨网需求侧响应、能源互联网
  • 基金资助:
    广东省自然科学基金资助项目(2021A1515010742)

A Coordinated Framework for Power Demand Response Resources Participating in Gas Market

Fu Zheng-xin1, Liu Shuang2, Duan Yi-qiang1, Zhu Wei-dong1   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. College of Mechanical Engineering, University of South China, Hengyang 421000, China
  • Received:2021-11-08 Online:2023-03-25 Published:2023-04-07

HTML

PDF (PC)

982

摘要： 能源互联网的提出与发展使不同的能源彼此转换成为可能。本文将电力系统的需求侧响应拓展到天然气网络中，建立了一个电网需求响应资源跨网支持天然气网络需求的协调市场与运营框架。在提出的框架中，以燃气轮机作为电−气双网的耦合点，利用电网需求响应资源替代耦合点燃气机组的部分出力，使燃气轮机省下部分天然气量，相当于将电力等效转化为天然气。此外，建立了电网需求响应资源跨网支持的成本模型，获取该资源的 “成本−等效天然气量”，参与天然气市场的竞争。同时，该需求响应资源在天然气市场的中标等效天然气量也将按照成本最低的原则分配给参与该过程的资源拥有者。最后，通过数值分析验证了所提模型和框架的可行性。该框架为电网需求响应资源支持天然气网络运行提供了良好的建议。

关键词: 能源互联网, 电力系统, 需求侧响应, 跨网, 天然气市场

Abstract: The proposal and development of the Energy Internet makes it possible to convert different energy network. Therefore, by expanding the demand response of the power system to the gas network, a coordinated market and operation framework for power demand response resources is established to support the demand of gas networks cross grid. In the proposed framework, the gas turbine is used as the coupling point of the electricity-gas dual grid, and the grid demand response resources are used to replace part of the output of the coupling point gas unit, so that the gas turbine saves part of the amount of natural gas, which is equivalent to converting power into gas. A cost model for cross-grid support of power demand response resources has been established to obtain the “Cost-Equivalent Gas Volume” of these resources to participate in the gas market competition. At the same time, the bid-winning equivalent gas volume of this demand response resources in the gas market will also be allocated to the resource owners participating in the process in accordance with the principle of lowest cost. Finally, the feasibility of the proposed model and framework is verified through numerical analysis. The framework provides good suggestions for power grid demand response resources to support natural gas network operation.

Key words: energy internet, power system, demand response, cross-grid, gas market

中图分类号: 

  • TM734
[1] 杨晚生, 郭开华. 分布式供能系统的定义及其内涵[J]. 广东工业大学学报, 2010, 27(3): 76-82.
YANG W S, GUO K H. Definition and connotation for distributed energy system [J]. Journal of Guangdong University of Technology, 2010, 27(3): 76-82.
[2] 赵军, 王妍, 王丹, 等. 能源互联网研究进展: 定义、指标与研究方法[J]. 电力系统及其自动化学报, 2018, 30(10): 1-14.
ZHAO J, WANG Y, WANG D, et al. Research progress in energy internet: definition, indicator and research method [J]. Proceedings of the CSU-EPSA, 2018, 30(10): 1-14.
[3] 王毅, 张宁, 康重庆. 能源互联网中能量枢纽的优化规划与运行研究综述及展望[J]. 中国电机工程学报, 2015, 35(22): 5669-5681.
WANG Y, ZHANG N, KANG C Q. Review and prospect of optimal planning and operation of energy hub in energy internet [J]. Proceedings of the CSEE, 2015, 35(22): 5669-5681.
[4] SALDARRIAGA C A, HINCAPIÉ R A, SALAZAR H. A holistic approach for planning natural gas and electricity distribution networks [J]. IEEE Transactions on Power Systems, 2013, 28(4): 4052-4063.
[5] CHEN Y B, ZHANG L X, PENG X, et al. Electricity demand response schemes in China: pilot study and future outlook[J]. Energy, 2021, 224: 120042.
[6] PALENSKY P, DIETRICH D. Demand side management: demand response, intelligent energy systems, and smart loads [J]. IEEE Transactions on Industrial Informatics, 2011, 7(3): 381-388.
[7] 张磊, 秦光宇, 刘亚玲. 含电转气的多能源中心协调优化运行策略[J]. 中国电力, 2020, 53(10): 113-122.
ZHANG L, QIN G Y, LIU Y L. Coordinated optimization operation strategy for multi-energy center with power-to-gas devices [J]. Electric Power, 2020, 53(10): 113-122.
[8] 卢志刚, 乞胜静, 蔡瑶, 等. 基于改进MOBCC算法的热电联合系统经济环境调度[J]. 中国电力, 2021, 54(2): 164-174.
LU Z G, QI S J, CAI Y, et al. Economic and environmental dispatch of combined heat and power system based on improved MOBCC algorithm [J]. Electric Power, 2021, 54(2): 164-174.
[9] 陈凯炎, 牛玉刚. 基于V2G技术的电动汽车实时调度策略[J]. 电力系统保护与控制, 2019, 47(14): 1-9.
CHEN K Y, NIU Y G. Real-time scheduling strategy of electric vehicle based on vehicle-to-grid application [J]. Power System Protection and Control, 2019, 47(14): 1-9.
[10] 罗金满, 赵善龙, 封祐钧, 等. 考虑综合需求响应不确定性的电-气综合能源系统优化运行[J]. 中国电力, 2020, 53(12): 119-126.
LUO J M, ZHAO S L, FENG Y J, et al. Optimal operation of integrated electricity-gas system considering uncertainty of integrated demand response [J]. Electric Power, 2020, 53(12): 119-126.
[11] 崔杨, 闫石, 仲悟之, 等. 含电转气的区域综合能源系统热电优化调度[J]. 电网技术, 2020, 44(11): 4254-4264.
CUI Y, YAN S, ZHONG W Z, et al. Optimal thermoelectric dispatching of regional integrated energy system with power-to-gas [J]. Power System Technology, 2020, 44(11): 4254-4264.
[12] NI L N, LIU W J, WEN F S, et al. Optimal operation of electricity, natural gas and heat systems considering integrated demand responses and diversified storage devices [J]. Journal of Modern Power Systems and Clean Energy, 2018, 6(3): 423-437.
[13] 陈冠廷, 张利, 刘宁宁, 等. 基于区块链的面向居民用户需求响应交易机制[J]. 电力自动化设备, 2020, 40(8): 9-17.
CHEN G T, ZHANG L, LIU N N, et al. Blockchain-based transaction mechanism for residential users demand response [J]. Electric Power Automation Equipment, 2020, 40(8): 9-17.
[14] 高赐威, 李倩玉, 李慧星, 等. 基于负荷聚合商业务的需求响应资源整合方法与运营机制[J]. 电力系统自动化, 2013, 37(17): 78-86.
GAO C W, LI Q Y, LI H X, et al. Methodology and operation mechanism of demand response resources integration based on load aggregation [J]. Automation of Electric Power Systems, 2013, 37(17): 78-86.
[15] 杜江, 董超, 邸鹏宇, 等. 电力市场期货交易的分析与应用[J]. 机电信息, 2018(30): 114-115+117.
[16] XU F Y, TANG R X, XU S B, et al. Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification[J]. Energy, 2021, 223: 119748.
[17] LI D S, GAO C W, SHI X M. Study on coupled planning of power grid and gas network considering P2G device [C]// 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) , Beijing: IEEE, 2017.
[18] ZHANG Y C, HUANG Z H, ZHENG F, et al. Interval optimization based coordination scheduling of gas–electricity coupled system considering wind power uncertainty, dynamic process of natural gas flow and demand response management[J]. Energy Reports, 2020, 6: 216-227.
[1] 邱明晋, 陈璟华, 唐俊杰. 含风电场最优潮流及其关键技术研究综述[J]. 广东工业大学学报, 2018, 35(02): 63-68,94.
[2] 陈璟华, 邱明晋, 郭经韬, 唐俊杰. 模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化[J]. 广东工业大学学报, 2018, 35(01): 35-40.
[3] 郭壮志，吴杰康，周永旺，徐亮. 考虑互动特性的水火电力系统优化调度策略[J]. 广东工业大学学报, 2013, 30(2): 47-56.
[4] 胡廷鹤，孟安波. 基于虚拟极值粒子群的电网无功优化研究[J]. 广东工业大学学报, 2012, 29(4): 49-53.
[5] 陈少华； 陈璟华； 杨宜民； . 基于多Agent技术的自适应继电保护系统研究[J]. 广东工业大学学报, 2005, 22(1): 78-82.
[6] 石莹； 李日隆； . 发-输电系统可靠性评估及计算程序研究[J]. 广东工业大学学报, 1998, 15(s1): 43-48.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发