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

Abstract

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.