考虑多重不确定性与电碳耦合交易的多微网合作博弈优化调度

doi:10.19595/j.cnki.1000-6753.tces.232184

摘要
图/表
参考文献
相关文章 (6)

全文: PDF (1172 KB)
输出: BibTeX | EndNote (RIS)

摘要

双碳背景下,构建低碳运行的能源系统是实现双碳目标的重要方向与实施路径。为了促进多微网系统内部能源的本地消纳以及低碳经济运行,本文对不确定性环境下多微网系统的合作运行及电碳耦合交易展开研究。首先,对于每个微网,构建了电转气碳捕集系统相耦合的热电联产运行模式,基于地方碳交易市场和阶梯碳交易机制,提出了多能源微网的低碳运行模型。其次,考虑到新能源发电和电力市场电价都存在不确定性的实际情况,采用机会约束和鲁棒优化的方法,以降低不确定性影响。在此基础上,基于纳什谈判理论,建立了多个微网电碳耦合的合作博弈模型,各微网主体可同时参与到上级能源市场和地方能源市场来进行电能和碳排放配额的交易。最后,将非凸的合作博弈问题分解为两个线性可求解的子问题,进一步采用交替方向乘子法对问题进行求解。通过算例验证,本文所提方法可以有效提升各微网经济效益并减少碳排放。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	董雷
	李扬
	陈盛
	乔骥
	蒲天骄

关键词 ：电碳耦合交易, 纳什谈判, 合作博弈, 多微网优化调度

Abstract：

In the context of achieving dual carbon goals, establishing a multi-microgrid system with low-carbon operations is crucial for attaining energy conservation and emission reduction goals. In recent years, as the energy market evolves, microgrids have the capability to trade both carbon quotas and electric energy concurrently, which has greatly promoted the local consumption of internal resources in microgrids. However, for multi-microgrid systems, there are challenges arising from differing time scales and topological structures during the trading of electric energy and carbon quotas; at the same time, they will also encounter the influence of various uncertain factors, including the integration of new energy sources and fluctuations in electricity market prices. In order to solve the above problems, this paper constructs a multi-microgrid electricity-carbon coupling trading model that considers multiple uncertainties to proficiently oversee multi-microgrid systems engaged in electricity-carbon coupling trading
First, this paper builds a microgrid low-carbon operation model including P2G and CCS for a single microgrid. In each microgrid, in order to limit the carbon emissions of CHP units, a CHP unit operation mode coupling P2G and CCS is proposed. CCS captures CO₂ generated by CHP units, and P2G equipment uses CO₂ to generate CH₄, which reduces carbon emissions and realizes energy recycling. On this basis, building upon the tiered carbon trading mechanism, we constructed a carbon emission cost model for a single microgrid that incorporates inter-microgrid carbon quota trading. Furthermore, accounting for the variability in new energy output and the uncertainty of electricity prices in the power market during practical operations, opportunity constraints and robust optimization methods are used to reduce the impact of uncertainty. Then, for multi-microgrid systems, utilizing Nash bargaining theory, we established a cooperative game model for the electricity-carbon coupling of multiple microgrids. Each microgrid entity can simultaneously engage in transactions within the central energy market and local energy markets, conducting both electricity and carbon emission quota transactions. In the final step, we decompose the non-convex cooperative game problem into two linearly solvable sub-problems, employing the ADMM algorithm for iterative problem resolution.
In the case analysis, simulation analysis was conducted on three microgrids integrating multiple energy sources, including electricity, gas, and heat, to validate the effectiveness of the proposed method. At the same time, establishing a local energy trading market holds positive significance for enhancing the operation of the system's low-carbon economy. The case study yields the following conclusions: (1) The CHP unit operation mode coupled with P2G and CCS proposed in this article can successfully reduce carbon emissions within the IES. (2) Contrasted with the independent operation of each microgrid, cooperative operation through electricity-carbon coupling enhances the overall system benefits. Simultaneously, by engaging in carbon quota trading between microgrids, the multi-microgrid system can decrease the quantity of carbon quotas procured from external markets, thereby effectively lowering the system's carbon trading costs. (3) Taking into account the uncertainty of new energy output and electricity market prices enhances the resilience of multi-microgrid systems to operational risks.

Key words： Electricity-carbon coupling transaction Nash bargaining cooperative game optimized scheduling of multiple microgrids

收稿日期: 2024-01-02

PACS:

TM73

基金资助:

国家电网公司科技项目资助（5108-202218280A-2-233-XG）

通讯作者: 李扬男,2000年生,硕士研究生,研究方向为综合能源系统优化调度运行。E-mail：HdLiyang2021@163.com

作者简介: 董雷女,1967年生,副教授,博士生导师,主要从事电网优化调度与运行控制、综合能源系统优化、人工智能在电力系统中的应用等领域的研究。E-mail：hbdldl@126.com

引用本文:

董雷, 李扬, 陈盛, 乔骥, 蒲天骄. 考虑多重不确定性与电碳耦合交易的多微网合作博弈优化调度[J]. 电工技术学报, 0, (): 2024031410-2024031410. Dong Lei, Li Yang, Chen Sheng, Qiao Ji, Pu Tianjiao. Multi-Microgrid Cooperative Game Optimization Scheduling Considering Multiple Uncertainties and Coupled Electricity-Carbon Transactions. Transactions of China Electrotechnical Society, 0, (): 2024031410-2024031410.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.232184 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/2024031410

[1] Wang Zibo, Dong Lei, Shi Mengjie, et al.Market power modeling and restraint of aggregated prosumers in peer-to-peer energy trading: a game-theoretic approach[J]. Applied Energy, 2023, 348: 121550.
[2] Dong Lei, Li Mengting, Hu Junjie, et al.Hierarchical game approach for optimization of regional integrated energy system clusters considering bounded rationality[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 302-313.
[3] Wang Zibo, Yu Xiaodan, Mu Yunfei, et al.Peer-to-Peer energy trading strategy for energy balance service provider (EBSP) considering market elasticity in community microgrid[J]. Applied Energy, 2021, 303: 117596.
[4] 董雷, 杨子民, 乔骥, 等. 基于分层约束强化学习的综合能源多微网系统优化调度[J/OL]. 电工技术学报报, 2023: 1-19. https://doi.org/10.19595/j.cnki.1000-6753.tces.230015.
Dong Lei, Yang Zimin, Qiao Ji, et al. Optimal Scheduling of Integrated Energy Multi-Microgrid System Based on Hierarchical Constraint Reinforcement Learning[J/OL]. Transactions of China Electrotechnical Society2023: 1-19. https://doi.org/10.19595/j.cnki.1000-6753.tces.230015.].
[5] 吴锦领, 楼平, 管敏渊, 等. 基于非对称纳什谈判的多微网电能共享运行优化策略[J]. 电网技术, 2022, 46(7): 2711-2723.
Wu Jinling, Lou Ping, Guan Minyuan, et al.Operation optimization strategy of multi-microgrids energy sharing based on asymmetric Nash bargaining[J]. Power System Technology, 2022, 46(7): 2711-2723.
[6] 刘念, 赵璟, 王杰, 等. 基于合作博弈论的光伏微电网群交易模型[J]. 电工技术学报, 2018, 33(8): 1903-1910.
Liu Nian, Zhao Jing, Wang Jie, et al.A trading model of PV microgrid cluster based on cooperative game theory[J]. Transactions of China Electrotechnical Society, 2018, 33(8): 1903-1910.
[7] 王再闯, 陈来军, 李笑竹, 等. 基于合作博弈的产销者社区分布式光伏与共享储能容量优化[J]. 电工技术学报, 2022, 37(23): 5922-5932.
Wang Zaichuang, Chen Laijun, Li Xiaozhu, et al.Capacity optimization of distributed PV and shared energy storage of prosumer community based on cooperative game[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5922-5932.
[8] 丛昊, 王旭, 蒋传文, 等. 基于联盟博弈的综合能源系统优化运行方法[J]. 电力系统自动化, 2018, 42(14): 14-22.
Cong Hao, Wang Xu, Jiang Chuanwen, et al.Coalition game based optimized operation method for integrated energy systems[J]. Automation of Electric Power Systems, 2018, 42(14): 14-22.
[9] 马腾飞, 裴玮, 肖浩, 等. 基于纳什谈判理论的风-光-氢多主体能源系统合作运行方法[J]. 中国电机工程学报, 2021, 41(1): 3, 25-39.
Ma Tengfei, Pei Wei, Xiao Hao, et al. Cooperative operation method for wind-solar-hydrogen multi-agent energy system based on Nash bargaining theory[J]. Proceedings of the CSEE, 2021, 41(1): 3, 25-39.
[10] 胡健, 于娣, 张晓杰. 电力P2P交易中计及社会福利的产消者合作联盟[J]. 中国电机工程学报, 2024, 44(3): 960-971.
Hu Jian, Yu Di, Zhang Xiaojie.Prosumer alliance in P2P transaction of electricity considering social welfare[J]. Proceedings of the CSEE, 2024, 44(3): 960-971.
[11] 韩丽, 王冲, 于晓娇, 等. 考虑风电爬坡灵活调节的碳捕集电厂低碳经济调度[J/OL]. 电工技术学报, 2023: 1-11. https://doi.org/10.19595/j.cnki.1000-6753.tces.230188.
Han Li, Wang Chong Yu, Xiaojiao, et al. Low-carbon and economic dispatch considering the carbon capture power plants with flexible adjustment of wind power ramp[J/OL]. Transactions of China Electrotechnical Society, 2023: 1-11. https://doi.org/10.19595/j.cnki.1000-6753.tces.230188.
[12] 刘英培, 黄寅峰. 考虑碳排权供求关系的多区域综合能源系统联合优化运行[J]. 电工技术学报, 2023, 38(13): 3459-3472.
Liu Yingpei, Huang Yinfeng.Joint optimal operation of multi-regional integrated energy system considering the supply and demand of carbon emission rights[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3459-3472.
[13] 李欣, 李涵文, 陈德秋, 等. 储液式CCS耦合P2G的综合能源系统低碳经济调度[J/OL]. 电力系统及其自动化学报, 2023: 1-8. https://doi.org/10.19635/j.cnki.csu-epsa.001301.
Li Xin, Li Hanwen, Chen Deqiu, et al. Low carbon economic dispatch of integrated energy system with coupling solvent-storaged CCS and P2G[J/OL]. Proceedings of the CSU-EPSA, 2023: 1-8. https://doi.org/10.19635/j.cnki.csu-epsa.001301.
[14] Chen Maozhi, Lu Hao, Chang Xiqiang, et al.An optimization on an integrated energy system of combined heat and power, carbon capture system and power to gas by considering flexible load[J]. Energy, 2023, 273: 127203.
[15] 陈锦鹏, 胡志坚, 陈颖光, 等. 考虑阶梯式碳交易机制与电制氢的综合能源系统热电优化[J]. 电力自动化设备, 2021, 41(9): 48-55.
Chen Jinpeng, Hu Zhijian, Chen Yingguang, et al.Thermoelectric optimization of integrated energy system considering ladder-type carbon trading mechanism and electric hydrogen production[J]. Electric Power Automation Equipment, 2021, 41(9): 48-55.
[16] Zhong Xiaoqing, Zhong Weifeng, Liu Yi, et al.Cooperative operation of battery swapping stations and charging stations with electricity and carbon trading[J]. Energy, 2022, 254: 124208.
[17] Zhong Xiaoqing, Zhong Weifeng, Liu Yi, et al.A communication-efficient coalition graph game-based framework for electricity and carbon trading in networked energy hubs[J]. Applied Energy, 2023, 329: 120221.
[18] 金国彬, 潘狄, 陈庆, 等. 考虑源荷不确定性的直流配电网模糊随机日前优化调度[J]. 电工技术学报, 2021, 36(21): 4517-4528.
Jin Guobin, Pan Di, Chen Qing, et al.Fuzzy random day-ahead optimal dispatch of DC distribution network considering the uncertainty of source-load[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4517-4528.
[19] 徐湘楚, 米增强, 詹泽伟, 等. 考虑多重不确定性的电动汽车聚合商参与能量-调频市场的鲁棒优化模型[J]. 电工技术学报, 2023, 38(3): 793-805.
Xu Xiangchu, Mi Zengqiang, Zhan Zewei, et al.A robust optimization model for electric vehicle aggregator participation in energy and frequency regulation markets considering multiple uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(3): 793-805.
[20] 王开艳, 梁岩, 贾嵘, 等. 不确定环境下基于纳什谈判的含掺氢燃气综合能源多微网两阶段优化调度[J]. 电网技术, 2023, 47(8): 3141-3159.
Wang Kaiyan, Liang Yan, Jia Rong, et al.Two-stage optimal scheduling of Nash negotiation-based integrated energy multi-microgrids with hydrogen-doped gas under uncertain environment[J]. Power System Technology, 2023, 47(8): 3141-3159.
[21] 徐艳春, 章世聪, 张涛, 等. 基于混合两阶段鲁棒的多微网合作优化运行[J]. 电网技术, 2024, 48(1): 247-267.
Xu Yanchun, Zhang Shicong, Zhang Tao, et al.Cooperative optimal operation of multi-microgrids based on hybrid two-staged robustness[J]. Power System Technology, 2024, 48(1): 247-267.
[22] 李懿鑫, 李正烁, 刘聪聪. 考虑光伏与电价不确定性的综合能源生产单元自调度报价策略研究[J]. 电网技术, 2023, 47(7): 2713-2727.
Li Yixin, Li Zhengshuo, Liu Congcong.Self-scheduling offering strategy of integrated energy production unit considering uncertainty of photovoltaic and electricity prices[J]. Power System Technology, 2023, 47(7): 2713-2727.
[23] 杜佳男, 韩肖清, 李廷钧, 等. 考虑电价不确定性和博弈欺诈行为的多微网电能合作运行优化策略[J]. 电网技术, 2022, 46(11): 4217-4230.
Du Jianan, Han Xiaoqing, Li Tingjun, et al.Optimization strategy of multi-microgrid electric energy cooperative operation considering electricity price uncertainty and game cheating behaviors[J]. Power System Technology, 2022, 46(11): 4217-4230.
[24] Mansour-Saatloo A, Pezhmani Y, Mirzaei M A, et al.Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies[J]. Applied Energy, 2021, 304: 117635.
[25] 陈金富, 孙鑫, 段献忠, 等. 基于机会约束规划的含风电场电力系统可用输电能力计算[J]. 中国电机工程学报, 2019, 39(23): 6804-6814, 7094.
Chen Jinfu, Sun Xin, Duan Xianzhong, et al.A chance-constrained approach for available transfer capability evaluation for power systems with wind farm integration[J]. Proceedings of the CSEE, 2019, 39(23): 6804-6814, 7094.
[26] Jia Yanbo, Wan Can, Cui Wenkang, et al.Peer-to-peer energy trading using prediction intervals of renewable energy generation[J]. IEEE Transactions on Smart Grid, 2023, 14(2): 1454-1465.
[27] Wang Hao, Huang Jianwei.Incentivizing energy trading for interconnected microgrids[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 2647-2657.
[28] Yan Mingyu, Shahidehpour M, Alabdulwahab A, et al.Blockchain for transacting energy and carbon allowance in networked microgrids[J]. IEEE Transactions on Smart Grid, 2021, 12(6): 4702-4714.