Welcome to Francis Academic Press

International Journal of Frontiers in Engineering Technology, 2021, 3(4); doi: 10.25236/IJFET.2021.030401.

Power-to-gas-based VPP Stochastic Scheduling Optimization Model in View of Carbon Emission Constraints


Da Xing, Xiaohua Song

Corresponding Author:
Da Xing

Economics and Management School, North China Electric Power University, Beijing, 102200, China


Combining P2G and virtual power plant (Virtual power plant, VPP), this paper proposes a new concept of electrical interconnection virtual power plant (Power-to-gas-based VPP, GVPP). In addition, this paper proposes a GVPP low-carbon economic dispatch optimization model considering carbon emission rights trading. Furthermore, in view of the strong uncertainty of wind power and PV power generation in GVPP, the information gap decision theory is used to measure the uncertainty tolerance threshold of decision makers under different expected target deviations. In addition, a GVPP near-zero carbon random scheduling optimization model is established under the conventional and worst-case scenarios. In order to verify the feasibility and effectiveness of the proposed model, a 9-node energy hub was selected as the simulation system. The results show that: (1) GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. Moreover, it can use the two-way conversion of gas and electricity energy to form an electricity-gas-electricity cycle, thereby improving the system’s clean energy absorption capacity, reducing its own carbon emissions and the volatility of net output. (2) The IGDT method can be used to describe the impact of wind and wind uncertainty in GVPP. Decision makers can obtain the maximum tolerance for wind and wind uncertainty by setting a reasonable expected target deviation coefficient. For example, when the expected target deviation coefficient is 0.5, the corresponding degree of uncertainty is 0.142. In the worst scenario, the scheduling results obtained by this method are in line with the actual scheduling experience, which reflects the effectiveness of the method in this paper. In summary, the models and methods presented in this paper can be used to formulate optimal scheduling decision-making schemes for GVPP considering carbon trading and uncertainty.


Virtual power plant (VPP); Information Gap; Power-to-gas-based; Near Zero Carbon; Random Scheduling

Cite This Paper

Da Xing, Xiaohua Song. Power-to-gas-based VPP Stochastic Scheduling Optimization Model in View of Carbon Emission Constraints. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 4: 1-15. https://doi.org/10.25236/IJFET.2021.030401.


[1] National Development and Reform Commission, National Energy Administration.Notice on the issuance of the 13th Five-Year Plan for Energy Development [EB/OL]. http://www.nea.gov.cn/2017-01/17/c_135989417.htm, 2016-12-26/2020-04-02

[2] Ju Liwei, Tan Zhongfu, Yuan Jinyun, etal. A Bi-level Stochastic Scheduling Optimization Model for a Virtual Power Plant Connected to a Wind–Photovoltaic–Energy Storage System Considering the Uncertainty and Demand Response [J]. Applied Energy, 2016, 171: 184-199

[3] Yu Juan, Ma Mengnan, Guo Lin, Zhang Shuguo.Reliability Evaluation of Electric-Gas Interconnection System with Electricity to Gas [J]. Proceedings of the Csee, 2018,38(03):708-715.

Yu Juan, Ma Mengnan, Guo Lin, et al. Reliability Evaluation of Integrated Electrical and Natural-Gas System With Power-to-Gas [J]. Proceedings of the CSEE, 2018, 38(3): Proceedings of the CSEE

[4] Guandalini G, Campanari S, Romano MC. Power-to-gas plants and gas turbines for improved wind energy dispatch ability:energy and economic assessment [J]. Applied Energy,2015,147:117-130

[5] Paolo C, Giulio G, Stefano C. Modelling the integrated power and transport energy system: The role of power-to-gas and hydrogen in long-term scenarios for Italy[J].Energy, 2018,154: 592-601

[6] Manuel B, Begoña P, Pilar L, Luis M. R. Decision-making methodology for managing photovoltaic surplus electricity through Power to Gas: Combined heat and power in urban buildings[J]. Applied Energy, 2018,228:1032-1045

[7] David F, Florian K, Raphael H, Christopher V.Real live demonstration of MPC for a power-to-gas plant [J]. Applied Energy, 2018, 228: 833-842,

[8] Chaudry M,Jenkins N,Strbac G.Multi-time period combined gas and electricity network optimization [J].Electric Power Systems Research,2008,78(7):1265-1279

[9] Wang Mingyue, Liu Yu, Shi Wenqiang, Li Mengming, Zhong Chao.Research on the strategy of collaborative sharing of low-carbon technologies in different places under carbon trading policy and the benefits of emission reduction [J]. Systems Engineering Theory and Practice, 2019, 39(06):1419-1434.

Wang Mingyue, Liu Yu, Shi Wenqiang, etal. Research on technology remote synergic sharing strategy of low carbon under the ETS in China [J]. System Engineering-Theory& Practice, 2019, 39(06):1419-1434.

[10] Liu Sidong, Zhu Help.Optimal Scheduling of Power Generators Considering Carbon Emission Trading and Electricity Price Risks [J]. Systems Engineering Theory and Practice, 2015, 35(08):2054-2063.

[11] Liu Sidong, Zhu Bangzhu. Self-scheduling of generation company considering carbon trading and electricity price risk [J]. System Engineering-Theory& Practice, 2015,35(08):2054-2063.

[12] Qin Ting, Liu Huaidong, Wang Mianqiao et al.Low carbon economic scheduling of electricity-heat-gas integrated energy system based on carbon trading [J]. Automation of power system, 2018, 42(14): 8-14

[13] Qin Ting, Liu Huaidong, Wang Mianqiao, etal. Carbon Trading Based Low-carbon Economic Dispatch for Integrated Electricity-Heat-Gas Energy system [J]. Automation of Electric Power System, 2018, 42(14): 8-14

[14] Ju Liwei, Tan Qinliang, Lu Yan, etal. A CVaR-robust-based multi-objective optimization model and three-stage solution algorithm for a virtual power plant considering uncertainties and carbon emission allowances [J]. International Journal of Electrical Power and Energy Systems, 2019, 107:628-643