Dispatching optimization model of gas-electricity virtual power plant considering uncertainty based on robust stochastic optimization theory

Abstract With the rapid development of renewable energy, virtual power plant technology has gradually become a key technology to solve the large-scale development of renewable energy. This paper focuses on the stochastic dispatching optimization of gas-electric virtual power plant (GVPP). Based on this, wind power plant, photovoltaic power generation and convention gas turbines are used as the power generation side of GVPP. Power-to-gas (P2G) equipment and gas storage tank can realize the conversion and storage of electricity-gas energy. Price based demand response and incentive based demand response are introduced into the terminal load side to regulate the user’s electricity consumption behavior. GVPP bilaterally connects power network and natural gas network, which realizes the bidirectional flow of electricity-gas energy. Firstly, taking the maximization of economic benefits as the objective function, combined with the constraints of power balance, system reserve and so on, a dispatching optimization model of GVPP participating in multi-energy markets is constructed to determine the operation strategy. Secondly, wind, solar and other clean energy have the characteristics of random and fluctuation, which threaten the stable operation of the system. Therefore, a stochastic dispatching optimization model of GVPP considering wind and solar uncertainty is established based on robust stochastic optimization theory. Thirdly, the evaluation indicators of GVPP operation is determined, which can comprehensively evaluate the economic benefits, environmental benefits and system operation of virtual power plant. Finally, in order to verify the validity and feasibility of the model, a virtual power plant is selected for example analysis. The results show that: (1) After the implementation of price based demand response and incentive based demand response, the system load variance changes from 0.03 to 0.013. Through the comparison of load curves, it is found that demand response can play a role of peak-shaving and valley-filling and smooth the power load curve; (2) Stochastic optimization theory can overcome the uncertainty of wind and solar by setting different robust coefficients Γ which reflects the ability of the system to withstand risks; (3) The optimization effect after introducing the P2G subsystem makes the amount of abandoned clean energy close to zero. The operation risk of system is reduced, and the carbon emissions are reduced by 370 m3 too. The market space is expanded from electricity market mainly to natural gas market and carbon trading market.