计及实时碳减排的产消群价格型需求响应机制

doi:10.16183/j.cnki.jsjtu.2022.062

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (4): 452-463.doi: 10.16183/j.cnki.jsjtu.2022.062

所属专题：《上海交通大学学报》2023年“新型电力系统与综合能源”专题

• 新型电力系统与综合能源 • 上一篇下一篇

计及实时碳减排的产消群价格型需求响应机制

朱月尧¹, 祁佟¹, 吴星辰², 刘迪³(), 华昊辰⁴

1.国网江苏省电力有限公司淮安供电分公司,江苏淮安 210000
2.山东科技大学智能装备学院, 山东泰安 271019
3.清华大学自动化系,北京 100084
4.河海大学能源与电气学院,南京 211100

收稿日期:2022-03-14 修回日期:2022-07-07 接受日期:2022-07-27 出版日期:2023-04-28 发布日期:2023-05-05
通讯作者: 刘迪,博士,助理研究员;E-mail:kfliudi@mail.tsinghua.edu.cn.
作者简介:朱月尧(1990-),工程师,从事电网运行及规划研究工作.
基金资助:
国家自然科学基金(52107089);中国博士后科学基金(2021M700040)

Price-Based Demand Response Mechanism of Prosumer Groups Considering Real-Time Carbon Emission Reduction

ZHU Yueyao¹, QI Tong¹, WU Xingchen², LIU Di³(), HUA Haochen⁴

1. Huaian Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd., Huaian 210000, Jiangsu, China
2. College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, Shandong, China
3. Department of Automation, Tsinghua University, Beijing 100084, China
4. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

Received:2022-03-14 Revised:2022-07-07 Accepted:2022-07-27 Online:2023-04-28 Published:2023-05-05

摘要/Abstract

摘要：

随着电力市场不断发展,电价机制不断得到完善,但在电价机制制定的过程中未将碳排放因素考虑在内,电价和碳排放成本在需求侧耦合方面的研究尚有欠缺.以更好地推动碳减排为目标,在产消者负荷调节优化过程中充分考虑产消者差异化的特性,提出考虑碳排放的价格型需求响应机制及边云协同优化策略.在保障公平性的前提下,利用所提出的碳-电折扣因子能使积极参与碳减排调节的产消者获得更多电价折扣.仿真实验表明:基于所提出的碳-电折扣因子的需求响应机制,在光伏发电量大于和小于需求量的情况下,售电商的效益分别可以提高2.4倍和0.9倍.

关键词: 碳减排, 需求响应, 边云协同, 光伏发电

Abstract:

With the continuous development of electricity market, electricity price mechanism has been continuously improved, but carbon emission factor has not been considered in the formulation of electricity price mechanism, and research on the demand side coupling of electricity price and carbon emission cost is still limited. Aiming at promoting carbon emission reduction, this paper fully considers the characteristics of prosumer differentiation in the process of load regulation and optimization of producers and consumers, and proposes a price demand response mechanism considering carbon emission and the edge cloud collaborative optimization strategy. By utilizing the proposed carbon electricity discount factor on the premise of fairness, prosumers who actively participate in the regulation of carbon emission reduction can obtain more electricity price discounts. The simulation results show that based on the proposed demand response mechanism with carbon electricity discount factor, the efficiency of electricity retailers can be increased by 2.4 and 0.9 times respectively when photovoltaic power generation is more or less than demand.

Key words: carbon emission reduction, demand response, edge cloud collaboration, photovoltaic power generation

中图分类号:

TM732

朱月尧, 祁佟, 吴星辰, 刘迪, 华昊辰. 计及实时碳减排的产消群价格型需求响应机制[J]. 上海交通大学学报, 2023, 57(4): 452-463.

ZHU Yueyao, QI Tong, WU Xingchen, LIU Di, HUA Haochen. Price-Based Demand Response Mechanism of Prosumer Groups Considering Real-Time Carbon Emission Reduction[J]. Journal of Shanghai Jiao Tong University, 2023, 57(4): 452-463.

图/表 11

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

参考文献 37

[1]	新华社. 中共中央国务院关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见[J]. 中华人民共和国国务院公报, 2021(31): 33-38.
	Xinhua News Agency. Opinions of the central committee of the CPC and the state council on the complete, accurate and comprehensive implementation of the new development concept to do a good job in carbon peak and carbon neutrality[J]. Gazette of the State Council of the People’s Republic of China, 2021(31): 33-38.
[2]	陈皓勇. “双碳”目标下的电能价值分析与市场机制设计[J]. 发电技术, 2021, 42(2): 141-150. doi: 10.12096/j.2096-4528.pgt.21008
	CHEN Haoyong. Electricity value analysis and market mechanism design under carbon-neutral goal[J]. Power Generation Technology, 2021, 42(2): 141-150. doi: 10.12096/j.2096-4528.pgt.21008
[3]	李嘉龙, 陈雨果, 刘思捷, 等. 考虑碳排放成本的电力市场均衡分析[J]. 电网技术, 2016, 40(5): 1558-1563.
	LI Jialong, CHEN Yuguo, LIU Sijie, et al. Electricity market equilibrium analysis considering carbon emission cost[J]. Power System Technology, 2016, 40(5): 1558-1563.
[4]	SAEBI J, THANH NGUYEN D. Distributed demand response market model for facilitating wind power integration[J]. IET Smart Grid, 2020, 3(3): 394-405. doi: 10.1049/stg2.v3.3 URL
[5]	GUO Y F, WU Q W, GAO H L, et al. MPC-based coordinated voltage regulation for distribution networks with distributed generation and energy storage system[J]. IEEE Transactions on Sustainable Energy, 2019, 10(4): 1731-1739. doi: 10.1109/TSTE.5165391 URL
[6]	NOJAVAN S, NOUROLLAHI R, PASHAEI-DIDANI H, et al. Uncertainty-based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange[J]. International Journal of Electrical Power & Energy Systems, 2019, 105: 237-248. doi: 10.1016/j.ijepes.2018.08.041 URL
[7]	JIA Q G, CHEN S J, YAN Z, et al. Optimal incentive strategy in cloud-edge integrated demand response framework for residential air conditioning loads[J]. IEEE Transactions on Cloud Computing, 2022, 10(1): 31-42. doi: 10.1109/TCC.2021.3118597 URL
[8]	FU W M, WAN Y N, QIN J H, et al. Privacy-preserving optimal energy management for smart grid with cloud-edge computing[J]. IEEE Transactions on Industrial Informatics, 2022, 18(6): 4029-4038. doi: 10.1109/TII.2021.3114513 URL
[9]	张巍, 王丹. 基于云边协同的电动汽车实时需求响应调度策略[J]. 电网技术, 2022, 46(4): 1447-1458.
	ZHANG Wei, WANG Dan. Real-time demand response scheduling strategy for electric vehicles based on cloud edge collaboration[J]. Power System Technology, 2022, 46(4): 1447-1458.
[10]	张钦, 王锡凡, 王建学, 等. 电力市场下需求响应研究综述[J]. 电力系统自动化, 2008, 32(3): 97-106.
	ZHANG Qin, WANG Xifan, WANG Jianxue, et al. Survey of demand response research in deregulated electricity markets[J]. Automation of Electric Power Systems, 2008, 32(3): 97-106.
[11]	JORDEHI A R. Optimisation of demand response in electric power systems, a review[J]. Renewable & Sustainable Energy Reviews, 2019, 103: 308-319.
[12]	刘秋华, 胡苏晨, 周维初. 售电商参与现货市场下的售电套餐优化设计[J]. 电力工程技术, 2022, 41(1): 19-25.
	LIU Qiuhua, HU Suchen, ZHOU Weichu. Optimal design of electricity plans based on electricity retailers’ participation in spot market[J]. Electric Power Engineering Technology, 2022, 41(1): 19-25.
[13]	胡鹏, 艾欣, 张朔, 等. 基于需求响应的分时电价主从博弈建模与仿真研究[J]. 电网技术, 2020, 44(2): 585-592.
	HU Peng, AI Xin, ZHANG Shuo, et al. Modelling and simulation study of TOU stackelberg game based on demand response[J]. Power System Technology, 2020, 44(2): 585-592.
[14]	姚钢, 王旭, 周荔丹. 基于模糊控制的光储微网实时电价能量管理策略[J]. 电力系统及其自动化学报, 2022, 34(2): 1-8.
	YAO Gang, WANG Xu, ZHOU Lidan. Real-time electricity price energy management strategy for photovoltaic storage microgrid based on fuzzy control[J]. Proceedings of the CSU-EPSA, 2022, 34(2): 1-8.
[15]	HUA H C, QIN Z M, DONG N Q, et al. Data-driven dynamical control for bottom-up energy Internet system[J]. IEEE Transactions on Sustainable Energy, 2022, 13(1): 315-327. doi: 10.1109/TSTE.2021.3110294 URL
[16]	刘迪, 孙毅, 李彬, 等. 计及调节弹性差异化的产消群价格型需求响应机制[J]. 电网技术, 2020, 44(6): 2062-2070.
	LIU Di, SUN Yi, LI Bin, et al. Price-based demand response mechanism of prosumer groups considering adjusting elasticity differentiation[J]. Power System Technology, 2020, 44(6): 2062-2070.
[17]	吉斌, 昌力, 陈振寰, 等. 基于区块链技术的电力碳排放权交易市场机制设计与应用[J]. 电力系统自动化, 2021, 45(12): 1-10.
	JI Bin, CHANG Li, CHEN Zhenhuan, et al. Blockchain technology based design and application of market mechanism for power carbon emission allowance trading[J]. Automation of Electric Power Systems, 2021, 45(12): 1-10.
[18]	侯建朝, 史丹. 中国电力行业碳排放变化的驱动因素研究[J]. 中国工业经济, 2014(6): 44-56.
	HOU Jianchao, SHI Dan. Driving factors for the evolution of carbon dioxide emissions from electricity sector in China[J]. China Industrial Economics, 2014(6): 44-56.
[19]	ANKE C P, H HOBBIE, SCHREIBER S, et al. Coal phase-outs and carbon prices: Interactions between EU emission trading and national carbon mitigation policies[J]. Energy Policy, 2020, 144: 111647. doi: 10.1016/j.enpol.2020.111647 URL
[20]	刘洋, 崔雪, 谢雄, 等. 电碳联动环境下考虑社会效益最优的发电权交易研究[J]. 电测与仪表, 2020, 57(13): 112-117.
	LIU Yang, CUI Xue, XIE Xiong, et al. Research on the trading of clean energy power generation right with the best social benefit under the electric-carbon linkage environment[J]. Electrical Measurement & Instrumentation, 2020, 57(13): 112-117.
[21]	康重庆, 杜尔顺, 李姚旺, 等. 新型电力系统的“碳视角”: 科学问题与研究框架[J]. 电网技术, 2022, 46(3): 821-833.
	KANG Chongqing, DU Ershun, LI Yaowang, et al. Key scientific problems and research framework for carbon perspective research of new power systems[J]. Power System Technology, 2022, 46(3): 821-833.
[22]	LIU N, YU X H, WANG C, et al. Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers[J]. IEEE Transactions on Power Systems, 2017, 32(5): 3569-3583. doi: 10.1109/TPWRS.2017.2649558 URL
[23]	窦春霞, 罗维, 岳东, 等. 基于多智能体的微网群内电力市场交易策略[J]. 电网技术, 2019, 43(5): 1735-1744.
	DOU Chunxia, LUO Wei, YUE Dong, et al. Multi-agent system based electricity market trading strategy within microgrid groups[J]. Power System Technology, 2019, 43(5): 1735-1744.
[24]	张婕, 孙伟卿, 刘唯. 考虑需求响应收益的售电商实时电价决策模型[J]. 电网技术, 2022, 46(2): 492-504.
	ZHANG Jie, SUN Weiqing, LIU Wei. Real time pricing considering demand response revenue of electricity sellers[J]. Power System Technology, 2022, 46(2): 492-504.
[25]	王勇, 王丁玉, 陈彦汝. 不同配额分配方式对中国碳交易市场运行的影响: 基于流动性、波动性与有效性视角的考察[J]. 资源科学, 2021, 43(12): 2503-2513. doi: 10.18402/resci.2021.12.12
	WANG Yong, WANG Dingyu, CHEN Yanru. Impact of different quota allocation methods on the operation of China’s carbon trading market: From the perspective of liquidity, volatility, and effectiveness[J]. Resources Science, 2021, 43(12): 2503-2513. doi: 10.18402/resci.2021.12.12
[26]	彭春华, 张金克, 陈露, 等. 计及差异化需求响应的微电网源荷储协调优化调度[J]. 电力自动化设备, 2020, 40(3): 1-7.
	PENG Chunhua, ZHANG Jinke, CHEN Lu, et al. Source-load-storage coordinated optimal scheduling of microgrid considering differential demand response[J]. Electric Power Automation Equipment, 2020, 40(3): 1-7.
[27]	涂京, 周明, 宋旭帆, 等. 居民用户参与电网调峰激励机制及优化用电策略研究[J]. 电网技术, 2019, 43(2): 443-453.
	TU Jing, ZHOU Ming, SONG Xufan, et al. Research on incentive mechanism and optimal power consumption strategy for residential users’ participation in peak shaving of power grid[J]. Power System Technology, 2019, 43(2): 443-453.
[28]	HU Q R, LI F X, FANG X, et al. A framework of residential demand aggregation with financial incentives[J]. IEEE Transactions on Smart Grid, 2018, 9(1): 497-505. doi: 10.1109/TSG.2016.2631083 URL
[29]	World Steel Association. Life cycle inventory 2020 data release[R/OL]. (2021-05-01)[2022-02-18]. https://worldsteel.org/publications/bookshop/life-cycle-inventory-study-report-2020-data-release/.
[30]	GITARSKIY M L. The refinement to the 2006 ipcc guidelines for national greenhouse gas inventories[J]. Fundamental & Applied Climatology, 2019, 2: 5-13.
[31]	刘迪, 孙毅, 李彬, 等. 计及调节弹性差异化的产消群价格型需求响应机制[J]. 电网技术, 2020, 44(6): 2062-2070.
	LIU Di, SUN Yi, LI Bin, et al. Price-based demand response mechanism of prosumer groups considering adjusting elasticity differentiation[J]. Power System Technology, 2020, 44(6): 2062-2070.
[32]	翁桂荣, 何志勇. 基于自适应符号函数的主动轮廓模型[J]. 软件学报, 2019, 30(12): 3892-3906.
	WENG Guirong, HE Zhiyong. Active contour model based on adaptive sign function[J]. Journal of Software, 2019, 30(12): 3892-3906.
[33]	OKUR Ö, VOULIS N, HEIJNEN P, et al. Aggregator-mediated demand response: Minimizing imbalances caused by uncertainty of solar generation[J]. Applied Energy, 2019, 247: 426-437. doi: 10.1016/j.apenergy.2019.04.035 URL
[34]	司羽飞, 谭阳红, 汪沨, 等. 面向电力物联网的云边协同结构模型[J]. 中国电机工程学报, 2020, 40(24): 7973-7979.
	SI Yufei, TAN Yanghong, WANG Feng, et al. Cloud-edge collaborative structure model for power Internet of things[J]. Proceedings of the CSEE, 2020, 40(24): 7973-7979.
[35]	冯茜, 李擎, 全威, 等. 多目标粒子群优化算法研究综述[J]. 工程科学学报, 2021, 43(6): 745-753.
	FENG Qian, LI Qing, QUAN Wei, et al. Overview of multiobjective particle swarm optimization algorithm[J]. Chinese Journal of Engineering, 2021, 43(6): 745-753.
[36]	张伟, 谢源海, 王亚刚. 基于收敛性分析的偏差粒子群算法及PID仿真应用[J]. 控制工程, 2021, 28(7): 1466-1473.
	ZHANG Wei, XIE Yuanhai, WANG Yagang. A deviation particle swarm optimization algorithm based on convergence analysis and its application on PID tuning[J]. Control Engineering of China, 2021, 28(7): 1466-1473.
[37]	张占强, 窦春霞, 岳东, 等. 考虑通信时延的事件触发电压分布式协同控制[J]. 中国电机工程学报, 2020, 40(17): 5426-5435.
	ZHANG Zhanqiang, DOU Chunxia, YUE Dong, et al. Event-triggered voltage distributed cooperative control with communication delay[J]. Proceedings of the CSEE, 2020, 40(17): 5426-5435.

计及实时碳减排的产消群价格型需求响应机制

Price-Based Demand Response Mechanism of Prosumer Groups Considering Real-Time Carbon Emission Reduction

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 37

相关文章 8

编辑推荐

Metrics

本文评价

[1]	胡壮丽, 罗毅初, 蔡航. 城市电力行业碳排放测算方法及减碳路径[J]. 上海交通大学学报, 2024, 58(1): 82-90.
[2]	姜恩宇, 陈宇, 施峥靖, 吴哲城, 林顺富, 李东东. 考虑碳配额引导需求响应的微电网能量管理策略[J]. 上海交通大学学报, 2023, 57(9): 1126-1136.
[3]	王精, 邢海军, 王华昕, 彭思佳. 考虑电动汽车及负荷聚合商参与的综合能源系统优化调度[J]. 上海交通大学学报, 2023, 57(7): 814-823.
[4]	顾慧杰, 彭超逸, 孙书豪, 刘明涛, 谢俊, 施雄华, 鲍永. 风电-光伏-电制氢-抽蓄零碳电力系统短期生产模拟模型[J]. 上海交通大学学报, 2023, 57(5): 505-512.
[5]	刘可真, 陈雪鸥, 陈镭丹, 林铮, 沈赋. 光伏发电系统动态离散等值模型研究[J]. 上海交通大学学报, 2023, 57(4): 412-421.
[6]	秦文萍, 杨镜司, 景祥, 姚宏民, 李晓舟, 张信哲. 计及需求响应的微能网综合能源多时空尺度优化调度[J]. 上海交通大学学报, 2023, 57(12): 1583-1596.
[7]	曾博, 穆宏伟, 董厚琦, 曾鸣. 考虑5G基站低碳赋能的主动配电网优化运行[J]. 上海交通大学学报, 2022, 56(3): 279-292.
[8]	吕祥梅, 刘天琪, 刘绚, 何川, 南璐, 曾红. 考虑高比例新能源消纳的多能源园区日前低碳经济调度[J]. 上海交通大学学报, 2021, 55(12): 1586-1597.