Collaborative Optimal Scheduling and Cost Allocation of Multiload Aggregator Considering Ladder-Type Carbon Trading

doi:10.12204/j.issn.1000-7229.2024.02.015

Abstract

Abstract:

The large-scale penetration of renewable energy sources poses significant challenges to the stable operation of power systems. Driven by double uncertainties on both the supply and demand sides, demand response resources based on terminal flexible loads need to be explored. Considering the load differentiation characteristics of different types of users, multitype load aggregators based on cooperation and win-win were introduced. Flexible dispatching of the power system was performed based on the complementary characteristics of the heterogeneous load response behaviors. Moreover, each load aggregator was assigned the dual status of a carbon trading integrator to enter the carbon trading market. A carbon trading model based on a reward-punishment ladder was constructed using the electricity load forecasting method to allocate carbon emission quotas for a system free of charge. Based on this, to minimize the sum of the operating costs of a cooperative alliance of multiple load aggregators, a pre-day optimization model of the interaction and cooperation among multiple aggregators was developed and solved. The Shapley value method was introduced for the cooperative game, and the cost was shared according to the contribution of each participant to the operation of the cooperative alliance. The results show that the overall and individual operational costs and the carbon emissions of the alliance are significantly reduced under the cooperative operation mechanism.

Key words: demand response, carbon trading, load aggregator, cost allocation, cooperative game

CLC Number:

TM73

REN Hongbo, WANG Nan, WU Qiong, SHI Shanshan, FANG Chen, WAN Sha. Collaborative Optimal Scheduling and Cost Allocation of Multiload Aggregator Considering Ladder-Type Carbon Trading[J]. ELECTRIC POWER CONSTRUCTION, 2024, 45(2): 171-182.

Figures/Tables 15

Fig.1 Operating structure of LA

Fig.2 Relationship between carbon trading price and carbon trading volume

Fig.3 Image of non-cooperative and cooperative operations of multiple LAs

Table 1 Energy price parameters

Table 2 Carbon trading parameter setting

Table 3 Load shedding contract parameters

Table 4 Transferable load parameters

Table 5 Shiftable load parameters

Table 6 Operating parameters of distributed generation units

Fig.4 Comparison of operation strategies under two operation modes

Fig.5 Comparison of demand response strategies under two operation models

Fig.6 Comparison of carbon trading strategies under two operating models

Table 7 Comparison of overall affiliate costs under different operating models 元

Table 8 Analysis of the marginal contribution of potential alliance strategies

Table 9 Cost analysis of each operation mode 元

References 30

[1]	周天舒, 迟东训, 艾明晔. 双碳背景下可再生能源面临的挑战及对策建议[J]. 宏观经济管理, 2022(7): 59-65.
	ZHOU Tianshu, CHI Dongxun, AI Mingye. Challenges of and countermeasures for renewable energy sources against the background of carbon peaking and carbon neutralization[J]. Macroeconomic Management, 2022(7): 59-65.
[2]	卓振宇, 张宁, 谢小荣, 等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(9): 171-191.
	ZHUO Zhenyu, ZHANG Ning, XIE Xiaorong, et al. Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(9): 171-191.
[3]	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.
[4]	张晓东, 艾欣, 潘玺安. 考虑用户可调度潜力的负荷聚合商优化调度策略[J]. 华北电力大学学报(自然科学版), 2023, 50(1): 28-37, 47, 127.
	ZHANG Xiaodong, AI Xin, PAN Xi’an. Optimal scheduling strategy of load aggregators considering user’s scheduling potential[J]. Journal of North China Electric Power University (Natural Science Edition), 2023, 50(1): 28-37, 47, 127.
[5]	LI S R, ZHANG L H, NIE L, et al. Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: a hierarchical Stackelberg game[J]. Energy, 2022, 249: 123678. doi: 10.1016/j.energy.2022.123678 URL
[6]	WANG J J, QIU R J, XU B, et al. Aggregated large-scale air-conditioning load: modeling and response capability evaluation of virtual generator units[J]. Energy, 2023, 276: 127570. doi: 10.1016/j.energy.2023.127570 URL
[7]	LI C Y, ZHAO R S, WANG D, et al. Optimal spatio-temporal scheduling for electric vehicles and load aggregators considering response reliability[J]. Electric Power Systems Research, 2018, 162: 183-193. doi: 10.1016/j.epsr.2018.05.007 URL
[8]	龚诚嘉锐, 林顺富, 边晓燕, 等. 基于多主体主从博弈的负荷聚合商经济优化模型[J]. 电力系统保护与控制, 2022, 50(2): 30-40.
	GONG Chengjiarui, LIN Shunfu, BIAN Xiaoyan, et al. Economic optimization model of a load aggregator based on the multi-agent Stackelberg game[J]. Power System Protection and Control, 2022, 50(2): 30-40.
[9]	冷钊莹, 陈中, 邢强, 等. 基于负荷类型细分的负荷聚合商日前投标非合作博弈模型[J]. 电网与清洁能源, 2020, 36(5): 17-28.
	LENG Zhaoying, CHEN Zhong, XING Qiang, et al. Day-ahead bidding model of load aggregators based on load type subdivision and non-cooperative game[J]. Power System and Clean Energy, 2020, 36(5): 17-28.
[10]	孙伟卿, 刘晓楠, 向威, 等. 基于主从博弈的负荷聚合商日前市场最优定价策略[J]. 电力系统自动化, 2021, 45(1): 159-167.
	SUN Weiqing, LIU Xiaonan, XIANG Wei, et al. Master-slave game based optimal pricing strategy for load aggregator in day-ahead electricity market[J]. Automation of Electric Power Systems, 2021, 45(1): 159-167.
[11]	周宁, 刘文学, 李嘉媚, 等. 基于合作博弈论与综合需求响应的负荷聚合商集群优化运营策略[J]. 水电能源科学, 2020, 38(8): 202-206.
	ZHOU Ning, LIU Wenxue, LI Jiamei, et al. Load aggregator cluster optimization operation strategy based on cooperative game theory and comprehensive demand response[J]. Water Resources and Power, 2020, 38(8): 202-206.
[12]	曾艾东, 邹宇航, 郝思鹏, 等. 考虑阶梯式碳交易机制的园区工业用户综合需求响应策略[J]. 高电压技术, 2022, 48(11): 4352-4363.
	ZENG Aidong, ZOU Yuhang, HAO Sipeng, et al. Comprehensive demand response strategy of industrial users in the park considering the stepped carbon trading mechanism[J]. High Voltage Engineering, 2022, 48(11): 4352-4363.
[13]	白宏坤, 尹硕, 李虎军, 等. 计及碳交易成本的多能源站综合能源系统规划[J]. 电力科学与技术学报, 2019, 34(1): 11-19.
	BAI Hongkun, YIN Shuo, LI Hujun, et al. Optimal planning of multi-energy stations considering carbon-trading cost[J]. Journal of Electric Power Science and Technology, 2019, 34(1): 11-19.
[14]	肖秋瑶, 杨騉, 宋政湘. 考虑碳交易和电动汽车充电负荷的工业园区综合能源系统调度策略[J]. 高电压技术, 2023, 49(4): 1392-1401.
	XIAO Qiuyao, YANG Kun, SONG Zhengxiang. Scheduling strategy of industrial parks integrated energy system considering carbon trading and electric vehicle charging load[J]. High Voltage Engineering, 2023, 49(4): 1392-1401.
[15]	刘光宇, 韩东升, 刘超杰, 等. 考虑双重需求响应及阶梯型碳交易的综合能源系统双时间尺度优化调度[J]. 电力自动化设备, 2023, 43(5): 218-225.
	LIU Guangyu, HAN Dongsheng, LIU Chaojie, et al. Dual time scale optimal scheduling of integrated energy system considering dual demand response and stepped carbon trading[J]. Electric Power Automation Equipment, 2023, 43(5): 218-225.
[16]	JIAO P H, CAI X, WANG L L, et al. Flexibility operation for integrated energy system considering hydrogen energy under inertia characteristics and stepped carbon trading mechanism[J]. Sustainable Cities and Society, 2023, 98: 104809. doi: 10.1016/j.scs.2023.104809 URL
[17]	MONYEI C G, ADEWUMI A O. Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads-South Africa in focus[J]. Electric Power Systems Research, 2018, 158: 92-104. doi: 10.1016/j.epsr.2017.12.033 URL
[18]	KATZ J, ANDERSEN F M, MORTHORST P E. Load-shift incentives for household demand response: evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system[J]. Energy, 2016, 115: 1602-1616. doi: 10.1016/j.energy.2016.07.084 URL
[19]	张晓辉, 刘小琰, 钟嘉庆. 考虑奖惩阶梯型碳交易和电-热转移负荷不确定性的综合能源系统规划[J]. 中国电机工程学报, 2020, 40(19): 6132-6142.
	ZHANG Xiaohui, LIU Xiaoyan, ZHONG Jiaqing. Integrated energy system planning considering a reward and punishment ladder-type carbon trading and electric-thermal transfer load uncertainty[J]. Proceedings of the CSEE, 2020, 40(19): 6132-6142.
[20]	任德军, 刘自发, 高峰, 等. 考虑碳交易机制与需求响应的园区综合能源系统电热协同运行优化研究[J]. 热力发电, 2022, 51(3): 119-130.
	REN Dejun, LIU Zifa, GAO Feng, et al. Electrothermal coordinated operation optimization of park integrated energy system considering carbon trading mechanism and demand response[J]. Thermal Power Generation, 2022, 51(3): 119-130.
[21]	魏震波, 马新如, 郭毅, 等. 碳交易机制下考虑需求响应的综合能源系统优化运行[J]. 电力建设, 2022, 43(1): 1-9. doi: 10.12204/j.issn.1000-7229.2022.01.001
	WEI Zhenbo, MA Xinru, GUO Yi, et al. Optimized operation of integrated energy system considering demand response under carbon trading mechanism[J]. Electric Power Construction, 2022, 43(1): 1-9. doi: 10.12204/j.issn.1000-7229.2022.01.001
[22]	2006至2017年度减排项目中国区域电网的基准线排放因子[EB/OL]. [2023-02-18]. http://www/tanjiaoyi.com/article-25420-1.html.
[23]	MAMOUNAKIS I. A pricing scheme for electric utility’s participation in day-ahead and real-time flexibility energy markets[J]. Journal of Modern Power Systems and Clean Energy, 2019, 7(5): 1294-1306. doi: 10.1007/s40565-019-0537-2
[24]	钱佳慧, 尹鹏, 邓学华, 等. 分时电价激励下考虑负荷聚集商的日前经济调度[J]. 现代电力, 2019, 36(4): 31-37.
	QIAN Jiahui, YIN Peng, DENG Xuehua, et al. The day-ahead economic dispatch considering time-of-use pricing incentives for load aggregators[J]. Modern Electric Power, 2019, 36(4): 31-37.
[25]	何蕾. 基于需求侧综合响应的热电联供型微网运行优化[J]. 电测与仪表, 2018, 55(7): 47-52, 61.
	HE Lei. Optimal operation of combined heat and power micro-grid based on integrated demand response[J]. Electrical Measurement & Instrumentation, 2018, 55(7): 47-52, 61.
[26]	任文诗, 高红均, 刘友波, 等. 智能建筑群电能日前优化共享[J]. 电网技术, 2019, 43(7): 2568-2577.
	REN Wenshi, GAO Hongjun, LIU Youbo, et al. Optimal day-ahead electricity scheduling and sharing for smart building cluster[J]. Power System Technology, 2019, 43(7): 2568-2577.
[27]	张峰, 杨志鹏, 张利, 等. 计及多类型需求响应的孤岛型微能源网经济运行[J]. 电网技术, 2020, 44(2): 547-557.
	ZHANG Feng, YANG Zhipeng, ZHANG Li, et al. Optimal operation of islanded micro energy grid with multi-type demand responses[J]. Power System Technology, 2020, 44(2): 547-557.
[28]	侯慧, 徐焘, 肖振锋, 等. 计及可调控负荷的发用电一体化综合优化调度[J]. 电网技术, 2020, 44(11): 4294-4304.
	HOU Hui, XU Tao, XIAO Zhenfeng, et al. Generation and load integrated optimal scheduling considering adjustable load[J]. Power System Technology, 2020, 44(11): 4294-4304.
[29]	王志南, 张宇华, 黄珂, 等. 计及多种柔性负荷的虚拟电厂热电联合鲁棒优化调度模型[J]. 电力建设, 2021, 42(7): 1-10. doi: 10.12204/j.issn.1000-7229.2021.07.001
	WANG Zhinan, ZHANG Yuhua, HUANG Ke, et al. Robust optimal scheduling model of virtual power plant combined heat and power considering multiple flexible loads[J]. Electric Power Construction, 2021, 42(7): 1-10. doi: 10.12204/j.issn.1000-7229.2021.07.001
[30]	魏大钧, 张承慧, 孙波, 等. 基于分时电价的电动汽车充放电多目标优化调度[J]. 电网技术, 2014, 38(11): 2972-2977.
	WEI Dajun, ZHANG Chenghui, SUN Bo, et al. A time-of-use price based multi-objective optimal dispatching for charging and discharging of electric vehicles[J]. Power System Technology, 2014, 38(11): 2972-2977.