Low Carbon Economic Dispatch of Virtual Power Plants Considering Ladder-Type Carbon Trading in Multiple Uncertainties

doi:10.16183/j.cnki.jsjtu.2022.185

Abstract

Abstract:

Virtual power plant (VPP) with a carbon capture system provides a new path to improve energy efficiency and achieve the target of carbon peaking and carbon neutrality. At the same time, flexible coordination of multiple uncertainties in the VPP system is a key premise to realize low-carbon operation of the system. A low-carbon economic dispatch model of VPP considering ladder-type carbon trading in multiple uncertainties is proposed. The carbon capture system and demand response are modeled, and a carbon trading mechanism is introduced into the optimal dispatch model to build a ladder-type carbon trading cost model to restrict system carbon emissions. A variety of uncertain factors in VPP is modeled, including wind power generation, photovoltaic, load and electric vehicle, and a low carbon economic dispatch model of VPP is established considering opportunity constraints. The uncertainty of electric vehicles is dealt with by using adjustable robust optimization. Based on the sequence operation theory, the uncertain model with opportunity constraints is transformed into a mixed integer linear programming model. The decision optimization technology CPLEX solution is used to verify the effectiveness of the proposed model in an actual VPP example.

Key words: virtual power plant (VPP), carbon capture, opportunity constrained programming, stepped carbon trading, low carbon economic dispatch, tunable Robust optimization

CLC Number:

TM732

PENG Sijia, XING Haijun, CHENG Mingyang. Low Carbon Economic Dispatch of Virtual Power Plants Considering Ladder-Type Carbon Trading in Multiple Uncertainties[J]. Journal of Shanghai Jiao Tong University, 2023, 57(12): 1571-1582.

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References 25

[1]	ROUZBAHANI H M, KARIMIPOUR H, LEI L. A review on virtual power plant for energy management[J]. Sustainable Energy Technologies and Assessments, 2021, 47: 101370. doi: 10.1016/j.seta.2021.101370 URL
[2]	JU L W, LI H H, ZHAO J W, et al. Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response[J]. Energy Conversion and Management, 2016, 128: 160-177. doi: 10.1016/j.enconman.2016.09.072 URL
[3]	孙国强, 袁智, 耿天翔, 等. 含电动汽车的虚拟电厂鲁棒随机优化调度[J]. 电力系统自动化, 2017, 41(6): 44-50.
	SUN Guoqiang, YUAN Zhi, GENG Tianxiang, et al. Robust stochastic optimal dispatching of virtual power plant containing plug-in electric vehicles[J]. Automation of Electric Power Systems, 2017, 41(6): 44-50.
[4]	卢志刚, 王荟敬, 赵号, 等. 含V2G的虚拟电厂双层逆鲁棒优化调度策略[J]. 电网技术, 2017, 41(4): 1245-1252.
	LU Zhigang, WANG Huijing, ZHAO Hao, et al. Strategy of bilevel inverse robust optimization dispatch of virtual power plant containing V2G[J]. Power System Technology, 2017, 41(4): 1245-1252.
[5]	ALAHYARI A, EHSAN M, MOUSAVIZADEH M. A hybrid storage-wind virtual power plant (VPP) participation in the electricity markets: A self-scheduling optimization considering price, renewable generation, and electric vehicles uncertainties[J]. Journal of Energy Storage, 2019, 25: 100812. doi: 10.1016/j.est.2019.100812 URL
[6]	SHEIDAEI F, AHMARINEJAD A. Multi-stage stochastic framework for energy management of virtual power plants considering electric vehicles and demand response programs[J]. International Journal of Electrical Power & Energy Systems, 2020, 120: 106047. doi: 10.1016/j.ijepes.2020.106047 URL
[7]	刘祚宇, 齐峰, 文福拴, 等. 含电动汽车虚拟电厂参与碳交易时的经济与环境调度[J]. 电力建设, 2017, 38(9): 45-52. doi: 10.3969/j.issn.1000-7229.2017.09.007
	LIU Zuoyu, QI Feng, WEN Fushuan, et al. Economic and environmental dispatching in electric vehicles embedded virtual power plants with participation in carbon trading[J]. Electric Power Construction, 2017, 38(9): 45-52. doi: 10.3969/j.issn.1000-7229.2017.09.007
[8]	LIU Z Y, ZHENG W M, QI F, et al. Optimal dispatch of a virtual power plant considering demand response and carbon trading[J]. Energies, 2018, 11(6): 1488. doi: 10.3390/en11061488 URL
[9]	LIU X O. Research on bidding strategy of virtual power plant considering carbon-electricity integrated market mechanism[J]. International Journal of Electrical Power & Energy Systems, 2022, 137: 107891. doi: 10.1016/j.ijepes.2021.107891 URL
[10]	张立辉, 戴谷禹, 聂青云, 等. 碳交易机制下计及用电行为的虚拟电厂经济调度模型[J]. 电力系统保护与控制, 2020, 48(24): 154-163.
	ZHANG Lihui, DAI Guyu, NIE Qingyun, et al. Economic dispatch model of virtual power plant considering electricity consumption under a carbon trading mechanism[J]. Power System Protection and Control, 2020, 48(24): 154-163.
[11]	周任军, 孙洪, 唐夏菲, 等. 双碳量约束下风电-碳捕集虚拟电厂低碳经济调度[J]. 中国电机工程学报, 2018, 38(6): 1675-1683.
	ZHOU Renjun, SUN Hong, TANG Xiafei, et al. Low-carbon economic dispatch based on virtual power plant made up of carbon capture unit and wind power under double carbon constraint[J]. Proceedings of the CSEE, 2018, 38(6): 1675-1683.
[12]	孙惠娟, 刘昀, 彭春华, 等. 计及电转气协同的含碳捕集与垃圾焚烧虚拟电厂优化调度[J]. 电网技术, 2021, 45(9): 3534-3545.
	SUN Huijuan, LIU Yun, PENG Chunhua, et al. Optimization scheduling of virtual power plant with carbon capture and waste incineration considering power-to-gas coordination[J]. Power System Technology, 2021, 45(9): 3534-3545.
[13]	仲悟之, 黄思宇, 崔杨, 等. 考虑源荷不确定性的风电-光热-碳捕集虚拟电厂协调优化调度[J]. 电网技术, 2020, 44(9): 3424-3432.
	ZHONG Wuzhi, HUANG Siyu, CUI Yang, et al. W-S-C capture coordination in virtual power plant considering source-load uncertainty[J]. Power System Technology, 2020, 44(9): 3424-3432.
[14]	周任军, 邓子昂, 徐健, 等. 碳捕集燃气热电机组碳循环及其虚拟电厂优化运行[J]. 中国电力, 2020, 53(9): 166-171.
	ZHOU Renjun, DENG Ziang, XU Jian, et al. Optimized operation using carbon recycling for benefit of virtual power plant with carbon capture and gas thermal power[J]. Electric Power, 2020, 53(9): 166-171.
[15]	卫志农, 张思德, 孙国强, 等. 基于碳交易机制的电—气互联综合能源系统低碳经济运行[J]. 电力系统自动化, 2016, 40(15): 9-16.
	WEI Zhinong, ZHANG Side, SUN Guoqiang, et al. Carbon trading based low-carbon economic operation for integrated electricity and natural gas energy system[J]. Automation of Electric Power Systems, 2016, 40(15): 9-16.
[16]	王琦, 李宁, 顾欣, 等. 考虑碳减排的综合能源服务商合作运行优化策略[J]. 电力系统自动化, 2022, 46(7): 131-140.
	WANG Qi, LI Ning, GU Xin, et al. Optimization strategy for cooperative operation of integrated energy service providers considering carbon emission reduction[J]. Automation of Electric Power Systems, 2022, 46(7): 131-140.
[17]	崔杨, 曾鹏, 仲悟之, 等. 考虑阶梯式碳交易的电-气-热综合能源系统低碳经济调度[J]. 电力自动化设备, 2021, 41(3): 10-17.
	CUI Yang, ZENG Peng, ZHONG Wuzhi, et al. Low-carbon economic dispatch of electricity-gas-heat integrated energy system based on ladder-type carbon trading[J]. Electric Power Automation Equipment, 2021, 41(3): 10-17.
[18]	LI Y, YANG Z, LI G Q, et al. Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties[J]. IEEE Transactions on Industrial Electronics, 2019, 66(2): 1565-1575. doi: 10.1109/TIE.2018.2840498 URL
[19]	夏鹏, 刘文颖, 蔡万通, 等. 基于风电离散化概率序列的机会约束规划优化调度方法[J]. 电工技术学报, 2018, 33(21): 5069-5079.
	XIA Peng, LIU Wenying, CAI Wantong, et al. Optimal scheduling method of chance constrained programming based on discrete wind power probability sequences[J]. Transactions of China Electrotechnical Society, 2018, 33(21): 5069-5079.
[20]	蒋向兵, 汤波, 余光正, 等. 面向新能源就地消纳的园区储能与电价协调优化方法[J]. 电力系统自动化, 2022, 46(5): 51-64.
	JIANG Xiangbing, TANG Bo, YU Guangzheng, et al. Coordination and optimization method of park-level energy storage and electricity price for local accommodation of renewable energy[J]. Automation of Electric Power Systems, 2022, 46(5): 51-64.
[21]	LI Y, HAN M, YANG Z, et al. Coordinating flexible demand response and renewable uncertainties for scheduling of community integrated energy systems with an electric vehicle charging station: A Bi-level approach[J]. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2321-2331. doi: 10.1109/TSTE.2021.3090463 URL
[22]	周任军, 肖钧文, 唐夏菲, 等. 电转气消纳新能源与碳捕集电厂碳利用的协调优化[J]. 电力自动化设备, 2018, 38(7): 61-67.
	ZHOU Renjun, XIAO Junwen, TANG Xiafei, et al. Coordinated optimization of carbon utilization between power-to-gas renewable energy accommodation and carbon capture power plant[J]. Electric Power Automation Equipment, 2018, 38(7): 61-67.
[23]	陈锦鹏, 胡志坚, 陈嘉滨, 等. 考虑阶梯式碳交易与供需灵活双响应的综合能源系统优化调度[J]. 高电压技术, 2021, 47(9): 3094-3106.
	CHEN Jinpeng, HU Zhijian, CHEN Jiabin, et al. Optimal dispatch of integrated energy system considering ladder-type carbon trading and flexible double response of supply and demand[J]. High Voltage Engineering, 2021, 47(9): 3094-3106.
[24]	林楷东, 陈泽兴, 张勇军, 等. 含P2G的电—气互联网络风电消纳与逐次线性低碳经济调度[J]. 电力系统自动化, 2019, 43(21): 23-33.
	LIN Kaidong, CHEN Zexing, ZHANG Yongjun, et al. Wind power accommodation and successive linear low-carbon economic dispatch of integrated electricity-gas network with power to gas[J]. Automation of Electric Power Systems, 2019, 43(21): 23-33.
[25]	高山, 邹子卿, 刘宇. 考虑多类型需求响应负荷的热电联供系统协调优化运行[J]. 电力建设, 2019, 40(10): 9-17. doi: 10.3969/j.issn.1000-7229.2019.10.002
	GAO Shan, ZOU Ziqing, LIU Yu. Coordination and optimization of combined heat and power system considering multi-type demand-response load[J]. Electric Power Construction, 2019, 40(10): 9-17. doi: 10.3969/j.issn.1000-7229.2019.10.002

功率/kW	概率
0	e(0)
q	e(1)
u_eq	e(u_e)
N_e_,_tq	e(N_e_,_t)

时段	特定时间段	电价/ (元·kW^-1·h^-1)
高峰期	8:00—11:00, 18:00—21:00	0.964
中峰期	6:00—7:00, 12:00—17:00	0.575
低峰期	1:00—5:00, 22:00—24:00	0.273

ζ/元	ψ/元	w_re,GT/(元·kW^-1)	w_on/(元·kW^-1)
1.2	0.35	0.04	1.6
P^GT,max/kW		λ_T/(kg·kW^-1·h^-1)	Q_G/(kg·kW^-1·h^-1)
500		0.156	0.4

π	α/%	F_risk/元	不计风险成本的收益/元	计及风险成本的收益/元
2	99	251.91	5916.40	5664.49
	97	316.77	6160.00	5843.23
	95	723.68	6366.31	5642.64
	93	1162.82	6515.82	5353.01
	91	1517.52	6684.76	5167.25
3	99	629.77	5916.40	5286.63
	97	791.92	6160.00	5368.08
	95	1809.2	6366.31	4557.12
	93	2907.05	6515.82	3608.78
	91	3793.8	6684.76	2890.97
5	99	1007.64	5916.40	4908.76
	97	1267.08	6160.00	4892.92
	95	2894.72	6366.31	3471.60
	93	4651.28	6515.82	1864.55
	91	6070.08	6684.76	614.69

模式	碳排放量/kg	碳捕集量/kg	碳交易成本/元	碳捕集运行成本/元	CO₂出售收益/元
一	4831.40	0	2162.0	0	0
二	520.56	3533.5	-983.8	989.30	3180.1
三	599.16	3521.5	-894.2	986.02	3169.3
四	4895.40	0	2177.0	0	0