Near-Zero Carbon Dispatching Optimization Model of Virtual Power Plant Connected with Power-to-Gas Considering Uncertainties of Wind and Photovoltaic Power

doi:10.12204/j.issn.1000-7229.2020.09.002

Abstract

Abstract:

The uncertainty of wind and photovoltaic power output poses a threat to the stable operation of power system, and the power-gas coupling system including power to gas (P2G) can solve this problem. The paper integrates power to gas into virtual power plant (VPP), presents the basic concepts, general modeling, and dispatching optimization model of virtual power plant connected with power-to-gas. In terms of the uncertainty of wind and photovoltaic power, the conditional value-at-risk (CVaR) method and robust stochastic optimization method are introduced to characterize the uncertain variables in the objective function and constraint conditions, respectively. Then, the maximum total emission allowances (MTEA) is applied to achieve the near-zero carbon dispatching scheme of virtual power plant. To solve the multi-objective model, a model solving algorithm based on the input-output table of objective function and the check of weight deviation is constructed, and a 9-node energy hub system is selected for example analysis. The results show that: the virtual power plant connected with power-to-gas can realize the complementary utilization of distributed energy and form the electricity-gas-electricity recycling mode. The proposed model can take into account the high economic benefits and uncertainty risks of distributed energy.This work is supported by National Natural Science Foundation of China (No.71573084).

Key words: virtual power plant(VPP), power-to-gas(P2G), low carbon, conditional value at risk(CVaR), robust stochastic optimization, uncertainty

CLC Number:

TM73

LI Hongxia, FAN Wei, LI Nan, TAN Caixia, XU Decao, JU Liwei. Near-Zero Carbon Dispatching Optimization Model of Virtual Power Plant Connected with Power-to-Gas Considering Uncertainties of Wind and Photovoltaic Power[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(9): 10-19.

Figures/Tables 12

Fig.1 Basic structure of GVPP

Fig.2 P2G technical schematic diagram

Table 1 The input income table

目标函数	y₁	y₂	…	y_I
y₁	y₁₁	y₁₂	…
y₂	y₂₁	…	…
?	?	?	?
y_I	$y I 1$	$y I 2$	…	y_II
$y I + 1$	$y I + 1,1$	$y I + 1,2$	…	$y I + 1, I$

Table 1 The input income table

目标函数	y₁	y₂	…	y_I
y₁	y₁₁	y₁₂	…
y₂	y₂₁	…	…
?	?	?	?
y_I	$y I 1$	$y I 2$	…	y_II
$y I + 1$	$y I + 1,1$	$y I + 1,2$	…	$y I + 1, I$

Fig.3 9-node energy hub system

Fig.4 Forecast values of load,WPP and PV of the typical day

Fig.5 The trend of weight coefficient and objective function

Table 2 Dispatching results of GVPP operation in different cases

Fig.6 CH4 flow of PGST in Case 4

Fig.7 Value of revenue and CVaR in different confidence degree and robust coefficients

Fig.8 Output distribution of WPP,PV in different GST's capacity

Fig.9 Results of GVPP operation in different δMETA

Table 3 Comparative analysis of dispatching results of GVPP in different methods

References 20

[1]	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.
[2]	陆秋瑜, 杨银国, 王中冠 , 等. 基于次梯度投影分布式控制法的虚拟电厂经济性一次调频[J]. 电力建设, 2020,41(3):79-85.
	LU Qiuyu, YANG Yinguo, WANG Zhongguan , et al. Economic primary frequency control of virtual power plant applying distributed control method based on sub-gradient projection[J]. Electric Power Construction, 2020,41(3):79-85.
[3]	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):23-37.
	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):23-37.
[4]	于潇涵, 赵晋泉 . 含P2H、P2G电-气-热综合能源系统多能流算法[J]. 电力建设, 2018,39(12):13-21.
	YU Xiaohan, ZHAO Jinquan . Heat-gas-power flow calculation method for integrated energy system containing P2H and P2G[J]. Electric Power Construction, 2018,39(12):13-21.
[5]	CLEGG S, MANCARELLA P . Integrated modeling and assessment of the operational impact of power-to-gas (P2G) on electrical and gas transmission networks[J]. IEEE Transactions on Sustainable Energy, 2015,6(4):1234-1244.
[6]	CHAUDRY M, JENKINS N, STRBAC G . Multi-time period combined gas and electricity network optimisation[J]. Electric Power Systems Research, 2008,78(7):1265-1279.
[7]	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.
[8]	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.
[9]	郑益, 朱俊澎, 袁越 . 基于条件风险价值的风柴储孤岛微网经济风险评估[J]. 电力自动化设备, 2019,39(11):57-63.
	ZHENG Yi, ZHU Junpeng, YUAN Yue . Economic risk assessment of wind-diesel-storage islanded microgrid based on conditional value at risk[J]. Electric Power Automation Equipment, 2019,39(11):57-63.
[10]	崔惟 . 风电并网电力系统电压无功控制的概率决策方法研究[D]. 重庆: 重庆大学, 2017.
	CUI Wei . Probabilistic decision methods of voltage and reactive power control for wind power integrated power system[D]. Chongqing: Chongqing University, 2017.
[11]	阮传扬, 杨建辉, 韩莉娜 , 等. 考虑可信度和属性优先级的犹豫模糊决策方法[J]. 运筹与管理, 2016,25(3):125-131.
	RUAN Chuanyang, YANG Jianhui, HAN Lina , et al. Hesitant fuzzy decision making method with confidence levels and preference relations on attributes[J]. Operations Research and Management Science, 2016,25(3):125-131.
[12]	汪臻, 盛四清, 周庆捷 . 不确定环境下配电网变电站选址定容[J]. 电力建设, 2014,35(3):59-63. doi: 10.3969/j.issn.1000－7229.2014.03.011 URL
	WANG Zhen, SHENG Siqing, ZHOU Qingjie . Substation locating and sizing for distribution network in uncertain environment[J]. Electric Power Construction, 2014,35(3):59-63. doi: 10.3969/j.issn.1000－7229.2014.03.011 URL
[13]	周安平, 杨明, 赵斌 , 等. 电力系统运行调度中的高阶不确定性及其对策评述[J]. 电力系统自动化, 2018,42(12):173-183.
	ZHOU Anping, YANG Ming, ZHAO Bin . Higher-order uncertainty and corresponding strategies of operation and dispatching for power system[J]. Automation of Electric Power Systems, 2018,42(12):173-183.
[14]	付蓉 . 市场环境下基于阻塞激励机制的输电网扩展规划[D]. 南京: 东南大学, 2005.
	FU Rong . Congestion driven mechanism based transmission expansion planning under market environment[D]. Nanjing: Southeast University, 2005.
[15]	李坤 . 考虑风险偏好的虚拟电厂鲁棒优化运行[J]. 电力学报, 2018,33(3):190-196, 244.
	LI Kun . Risk-constrained virtual power plant robust optimization operation[J]. Journal of Electric Power, 2018,33(3):190-196, 244.
[16]	卫志农, 陈妤, 黄文进 , 等. 考虑条件风险价值的虚拟电厂多电源容量优化配置模型[J]. 电力系统自动化, 2018,42(4):39-46.
	WEI Zhinong, CHEN Yu, HUANG Wenjin , et al. Optimal allocation model for multi-energy capacity of virtual power plant considering conditional value-at-risk[J]. Automation of Electric Power Systems, 2018,42(4):39-46.
[17]	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.
[18]	董军, 聂麟鹏, 聂仕麟 , 等. 考虑需求响应与风电不确定性的两阶段鲁棒旋转备用容量优化模型[J]. 电力建设, 2019,40(11):55-64.
	DONG Jun, NIE Linpeng, NIE Shilin , et al. Two-stage robust optimization model for spinning reserve capacity considering demand response and uncertainty of wind power[J]. Electric Power Construction, 2019,40(11):55-64.
[19]	邓创, 鞠立伟, 刘俊勇 , 等. 基于模糊CVaR理论的水火电系统随机调度多目标优化模型[J]. 电网技术, 2016,40(5):1447-1454.
	DENG Chuang, JU Liwei, LIU Junyong , et al. Stochastic scheduling multi-objective optimization model for hydro-thermal power systems based on fuzzy CVaR theory[J]. Power System Technology, 2016,40(5):1447-1454.
[20]	朱兰, 牛培源, 唐陇军 , 等. 考虑直接负荷控制不确定性的微能源网鲁棒优化运行[J]. 电网技术, 2020,44(4):1400-1409.
	ZHU Lan, NIU Peiyuan, TANG Longjun , et al. Robust optimal operation for micro-energy grid considering uncertainties of direct load control[J]. Power System Technology, 2020,44(4):1400-1409.