Research on Robust Bidding Model of Virtual Power Plant with Multi-type Resources

doi:10.12204/j.issn.1000-7229.2020.09.003

Abstract

Abstract:

Virtual power plants (VPP) can effectively aggregate decentralized demand-side resources. It is an effective way for demand-side resources to participate in electricity market. However, the market behavior of VPP cannot be expressed by the traditional bidding model of the thermal power plant, meanwhile the transmission constraints will also impact the interior resource management strategy of the VPP. Under the aforementioned background, a robust bidding model of multi-type resources in virtual power plant in power market is proposed and it takes into account renewable energy output uncertainty and the adjusting ability of aggregated units. The virtual power plant studied in this paper consists of wind power, energy storage, gas turbine and demand response load. The model takes Karush-Kuhn-Tucker (KKT) equivalent condition of market liquidation model as constraints to represent the relationship between market liquidation process and VPP decision-making. The results show that the model can optimize internal resource combination of VPP according to the market situation, provide an economic and reliable bidding strategy, and effectively improve the economic benefits of VPP.

Key words: demand side resources, bidding, virtual power plant, power market, profit

CLC Number:

TM73

HAN Shuai, WU Wanlu, GUO Xiaoxuan, SUN Leping. Research on Robust Bidding Model of Virtual Power Plant with Multi-type Resources[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(9): 20-29.

Figures/Tables 15

Fig.1 Structure of VPP's bidding problem in power market

Table 1 Parameters of conventional generators

Table 2 Parameters of gas turbine

Fig.2 6-node system

Fig.3 Forecast value of load and wind power

Fig.4 Locational marginal prices

Fig.5 Clearing results of units

Fig.6 Power of lines with transmission restriction

Fig.7 Power of lines without transmission restriction

Table 3 Comparison of output results of VPPMW·h

Table 4 Comparison of main power flowsMW

Fig.8 Combined output results in VPP

Table 5 Cost and benefit analysis of VPP $

Table 6 Results comparison under different γ $

Fig.9 Cost and benefit of VPP under different γ

References 23

[1]	陈春武, 李娜, 钟朋园 , 等. 虚拟电厂发展的国际经验及启示[J]. 电网技术, 2013,37(8):2258-2263.
	CHEN Chunwu, LI Na, ZHONG Pengyuan , et al. Review of virtual power plant technology abroad and enlightenment to China[J]. Power System Technology, 2013,37(8):2258-2263.
[2]	刘吉臻, 李明扬, 房方 , 等. 虚拟发电厂研究综述[J]. 中国电机工程学报, 2014,34(29):5103-5111.
	LIU Jizhen, LI Mingyang, FANG Fang , et al. Review on virtual power plants[J]. Proceedings of the CSEE, 2014,34(29):5103-5111.
[3]	卫志农, 余爽, 孙国强 , 等. 虚拟电厂的概念与发展[J]. 电力系统自动化, 2013,37(13):1-9.
	WEI Zhinong, YU Shuang, SUN Guoqiang , et al. Concept and development of virtual power plant[J]. Automation of Electric Power Systems, 2013,37(13):1-9.
[4]	邢龙, 张沛超, 方陈 , 等. 基于广义需求侧资源的微网运行优化[J]. 电力系统自动化, 2013,37(12):7-12, 133. doi: 10.7500/AEPS201209160 URL
	XING Long, ZHANG Peichao, FANG Chen , et al. Optimal operation for microgrid using generalized demand side resources[J]. Automation of Electric Power Systems, 2013,37(12):7-12, 133. doi: 10.7500/AEPS201209160 URL
[5]	孙国强, 袁智, 许晓慧 , 等. 碳排放约束下虚拟电厂鲁棒优化竞标模型[J]. 电力自动化设备, 2017,37(2):97-103.
	SUN Guoqiang, YUAN Zhi, XU Xiaohui , et al. Bidding model based on robust optimization for virtual power plant under carbon emission constraint[J]. Electric Power Automation Equipment, 2017,37(2):97-103.
[6]	闫园, 林鸿基, 文福拴 , 等. 考虑电价和碳价间Copula风险依赖的虚拟电厂竞标策略[J]. 电力建设, 2019,40(11):106-115.
	YAN Yuan, LIN Hongji, WEN Fushuan , et al. Bidding strategy of A virtual power plant considering copula risk dependence of electricity and carbon prices[J]. Electric Power Construction, 2019,40(11):106-115.
[7]	周博, 吕林, 高红均 , 等. 基于两阶段随机规划的虚拟电厂优化交易策略[J]. 电力建设, 2018,39(9):70-77.
	ZHOU Bo, LÜ Lin, GAO Hongjun , et al. Optimal bidding strategy based on two-stage stochastic programming for virtual power plant[J]. Electric Power Construction, 2018,39(9):70-77.
[8]	刘思源, 艾芊, 郑建平 , 等. 多时间尺度的多虚拟电厂双层协调机制与运行策略[J]. 中国电机工程学报, 2018,38(3):753-761.
	LIU Siyuan, AI Qian, ZHENG Jianping , et al. Bi-level coordination mechanism and operation strategy of multi-time scale multiple virtual power plants[J]. Proceedings of the CSEE, 2018,38(3):753-761.
[9]	孙国强, 周亦洲, 卫志农 , 等. 基于混合随机规划/信息间隙决策理论的虚拟电厂调度优化模型[J]. 电力自动化设备, 2017,37(10):112-118.
	SUN Guoqiang, ZHOU Yizhou, WEI Zhinong , et al. Dispatch optimization model of virtual power plant based on hybrid stochastic programming and information gap decision theory[J]. Electric Power Automation Equipment, 2017,37(10):112-118.
[10]	马春艳, 董春发, 吕志鹏 , 等. 计及随机因素的商业型虚拟发电厂短期交易与优化运行策略[J]. 电网技术, 2016,40(5):1543-1549.
	MA Chunyan, DONG Chunfa, LÜ Zhipeng , et al. Short-term trading and optimal operation strategy for commercial virtual power plant considering uncertainties[J]. Power System Technology, 2016,40(5):1543-1549.
[11]	KARDAKOS E G, SIMOGLOU C K, BAKIRTZIS A G . Optimal offering strategy of a virtual power plant: A stochastic Bi-level approach[J]. IEEE Transactions on Smart Grid, 2016,7(2):794-806.
[12]	PANDŽIĆ H, MORALES J M, CONEJO A J , et al. Offering model for a virtual power plant based on stochastic programming[J]. Applied Energy, 2013,105:282-292.
[13]	周博, 吕林, 高红均 , 等. 多虚拟电厂日前鲁棒交易策略研究[J]. 电网技术, 2018,42(8):2694-2703.
	ZHOU Bo, LÜ Lin, GAO Hongjun , et al. Robust day-ahead trading strategy for multiple virtual power plants[J]. Power System Technology, 2018,42(8):2694-2703.
[14]	何奇琳, 艾芊 . 售电侧放开环境下含需求响应虚拟电厂的电力市场竞价策略[J]. 电力建设, 2019,40(2):1-10.
	HE Qilin, AI Qian . Bidding strategy of electricity market including virtual power plant considering demand response under retail power market deregulation[J]. Electric Power Construction, 2019,40(2):1-10.
[15]	方燕琼, 甘霖, 艾芊 , 等. 基于主从博弈的虚拟电厂双层竞标策略[J]. 电力系统自动化, 2017,41(14):61-69.
	FANG Yanqiong, GAN Lin, AI Qian , et al. Stackelberg game based Bi-level bidding strategy for virtual power plant[J]. Automation of Electric Power Systems, 2017,41(14):61-69.
[16]	PEREIRA M V, GRANVILLE S, FAMPA M H C , et al. Strategic bidding under uncertainty: A binary expansion approach[J]. IEEE Transactions on Power Systems, 2005,20(1):180-188.
[17]	NASROLAHPOUR E, KAZEMPOUR J, ZAREIPOUR H , et al. Impacts of ramping inflexibility of conventional generators on strategic operation of energy storage facilities[J]. IEEE Transactions on Smart Grid, 2018,9(2):1334-1344.
[18]	周亦洲, 孙国强, 黄文进 , 等. 计及电动汽车和需求响应的多类电力市场下虚拟电厂竞标模型[J]. 电网技术, 2017,41(6):1759-1767.
	ZHOU Yizhou, SUN Guoqiang, HUANG Wenjin , et al. Strategic bidding model for virtual power plant in different electricity markets considering electric vehicles and demand response[J]. Power System Technology, 2017,41(6):1759-1767.
[19]	KAZEMPOUR S J, CONEJO A J, RUIZ C . Strategic bidding for a large consumer[J]. IEEE Transactions on Power Systems, 2015,30(2):848-856.
[20]	RUIZ C, CONEJO A J . Pool strategy of a producer with endogenous formation of locational marginal prices[J]. IEEE Transactions on Power Systems, 2009,24(4):1855-1866.
[21]	FORTUNY-AMAT J, MCCARL B A . A representation and economic interpretation of a two-level programming problem[J]. Journal of the Operational Research Society, 1981,32(9):783-792. doi: 10.1057/jors.1981.156 URL
[22]	ZENG B, ZHAO L . Solving two-stage robust optimization problems using a column-and-constraint generation method[J]. Operations Research Letters, 2013,41(5):457-461. doi: 10.1016/j.orl.2013.05.003 URL
[23]	NASROLAHPOUR E, GHASEMI H . A stochastic security constrained unit commitment model for reconfigurable networks with high wind power penetration[J]. Electric Power Systems Research, 2015,121:341-350. doi: 10.1016/j.epsr.2014.10.014 URL