Internal Optimization Stochastic Model of Virtual Power Plant Participating in Gas and Electricity Market

doi:10.11930/j.issn.1004-9649.201902182

Abstract

Abstract: The virtual power plant (VPP) generally reduces the impact of uncertainties, but due to the conservative nature of its scheduling schemes, it is difficult for a VPP to obtain the highest economic benefits without considering the internal randomness in the process of participating in the electricity and gas market. In order to fully exploit the economic benefits of VPPs, a stochastic optimization scheduling model is proposed for electric-thermal-gas VPPs with consideration of the uncertainty of electricity price and wind-photovoltaic under gas and electricity market. The objective function of the model is the total benefit of VPPs, which is the difference between the sales of electricity, heat and gas and the cost for electricity to gas conversion, carbon capture, carbon emission and fuel. The superquantile method is introduced to convert the total benefit optimal model of VPPs with multiple random variables into a super-quantile random optimization model. For the convenience of calculation, the model is further processed into a discretization calculation model, and is solved with the spatial particle swarm optimization algorithm. The simulation results show that the VPP obtains the optimal benefits through optimizing the sale schemes of electricity and gas, and with consideration of various random variables in the process of participating in gas and electricity market, the VPP has more opportunities to obtain higher economic benefits after avoiding risks.

Key words: gas and electricity market, electric-thermal-gas virtual power plant, uncertainty, internal optimization, superquantile method, spatial particle swarm optimization algorithm

PENG Yuanyuan, ZHOU Renjun, ZENG Ziqi, FENG Jian, CHENG Yuanlin, FANG Shaofeng. Internal Optimization Stochastic Model of Virtual Power Plant Participating in Gas and Electricity Market[J]. Electric Power, 2020, 53(9): 181-188.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201902182

https://www.electricpower.com.cn/EN/Y2020/V53/I9/181

References

[1] GIUNTOLI M, POLI D. Optimized thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storages[J]. IEEE Transactions on Smart Grid, 2013, 4(2): 942–955.
[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] SOARES J, FOTOUHI GHAZVINI M A, VALE Z, et al. A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads[J]. Applied Energy, 2016, 162: 1074–1088.
[4] 张涛, 王成, 王凌云, 等. 考虑虚拟电厂参与的售电公司双层优化调度模型[J]. 电网技术, 2019, 43(3): 952–961
ZHANG Tao, WANG Cheng, WANG Lingyun, et al. A bi-level optimal dispatching model of electricity retailers integrated with VPPs[J]. Power System Technology, 2019, 43(3): 952–961
[5] 马腾飞, 吴俊勇, 郝亮亮, 等. 基于能源集线器的微能源网能量流建模及优化运行分析[J]. 电网技术, 2018, 42(1): 179–186
MA Tengfei, WU Junyong, HAO Liangliang, et al. Energy flow modeling and optimal operation analysis of micro energy grid based on energy hub[J]. Power System Technology, 2018, 42(1): 179–186
[6] KARIMYAN P, HOSSEINIAN S H, KHATAMI R, et al. Stochastic approach to represent distributed energy resources in the form of a virtual power plant in energy and reserve markets[J]. IET Generation, Transmission & Distribution, 2016, 10(8): 1792–1804.
[7] HOGAN S, MEADE R. Vertical integration and market power in electricity markets[R]. Wellington, New Zealand: New Zealand Institute for the Study of Competition and Regulation, 2009.
[8] ZAMANI A G, ZAKARIAZADEH A, JADID S. Day-ahead resource scheduling of a renewable energy based virtual power plant[J]. Applied Energy, 2016, 169: 324–340.
[9] 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
[10] 郑宇, 李杨, 焦丰顺, 等. 含风电机组与电转气设备的虚拟电厂竞价策略[J]. 电力建设, 2017, 38(7): 88–95
ZHENG Yu, LI Yang, JIAO Fengshun, et al. Bidding strategies for virtual power plants including wind power generation units and power-to-gas facilities[J]. Electric Power Construction, 2017, 38(7): 88–95
[11] 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.
[12] 范松丽, 艾芊, 贺兴. 基于机会约束规划的虚拟电厂调度风险分析[J]. 中国电机工程学报, 2015, 35(16): 4025–4034
FAN Songli, AI QIAN, HE Xing. Risk analysis on dispatch of virtual power plant based on chance constrained programming[J]. Proceedings of the CSEE, 2015, 35(16): 4025–4034
[13] 赵瑾, 王洪涛, 曹曦. 计及风电条件风险价值的负荷恢复双层优化[J]. 中国电机工程学报, 2017, 37(18): 5275–5285, 5526
ZHAO Jin, WANG Hongtao, CAO Xi. Bi-level optimization of load restoration considering the conditional value at risk of wind power[J]. Proceedings of the CSEE, 2017, 37(18): 5275–5285, 5526
[14] 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
[15] 卫志农, 张思德, 孙国强, 等. 计及电转气的电-气互联综合能源系统削峰填谷研究[J]. 中国电机工程学报, 2017, 37(16): 4601–4609, 4885
WEI Zhinong, ZHANG Side, SUN Guoqiang, et al. Power-to-gas considered peak load shifting research for integrated electricity and natural-gas energy systems[J]. Proceedings of the CSEE, 2017, 37(16): 4601–4609, 4885
[16] 卢志刚, 夏明昭, 张晓辉. 基于多阶段减排规划的发电厂碳捕集系统优化配置[J]. 中国电机工程学报, 2011, 31(35): 65–71
LU Zhigang, XIA Mingzhao, ZHANG Xiaohui. Carbon capture systems optimal allocation scheme for multi-stage emission reduction planning in power plants[J]. Proceedings of the CSEE, 2011, 31(35): 65–71
[17] 袁桂丽, 王琳博, 王宝源. 基于虚拟电厂“热电解耦”的负荷优化调度及经济效益分析[J]. 中国电机工程学报, 2017, 37(17): 4974–4985, 5217
YUAN Guili, WANG Linbo, WANG Baoyuan. Optimal dispatch of heat-power load and economy benefit analysis based on decoupling of heat and power of virtual power plant[J]. Proceedings of the CSEE, 2017, 37(17): 4974–4985, 5217
[18] ROCKAFELLAR R T, ROYSET J O. On buffered failure probability in design and optimization of structures[J]. Reliability Engineering and System Safety, 2010, 95(5): 499–510.
[19] 周任军, 李绍金, 李红英, 等. 空间粒子群优化算法及其在电力系统环保经济负荷分配中的应用[J]. 电力自动化设备, 2014, 34(9): 7–12
ZHOU Renjun, LI Shaojin, LI Hongying, et al. Space particle swarm optimization algorithm and its application in environmental & economic load distribution of power system[J]. Electric Power Automation Equipment, 2014, 34(9): 7–12