Bidding strategy of virtual power plant considering carbon trading and conditional value at risk

Abstract

To explore the bidding strategy of a virtual power plant (VPP) that combines economy and low-carbon, a two-level Stackelberg game bidding model is proposed from the point of view of VPP as the price maker, which takes into account carbon trading and risk to participate in the energy market and spinning reserve market. Taking VPP including wind power and photovoltaic as the research object, the base-line method is firstly used to allocate a free carbon emission quota to VPP, and the carbon trading model of VPP is established. Secondly, a two-level bidding model based on the Stackelberg game theory is established. The upper-level is the VPP operator participating in the carbon, energy power, and spinning reserve markets. The lower-level follower is the electricity market operator. At the same time, conditional value at risk (CVaR) is used to transform the upper-level problem into a multi-objective optimization problem taking risk into account. Finally, the genetic algorithm and solver are combined to solve the problem. The example shows that the model can provide economical and low-carbon bidding strategies in multi-market environments, and it can also provide output plans for different markets. The influence of different market types, carbon trading and different risk aversion coefficients on VPP bidding results are analyzed, which provides a new way to improve the revenue of VPP operators.

Keywords

• virtual power plant (vpp)
• price maker
• carbon trading
• stackelberg game
• energy market
• spinning reserve market
• conditional value at risk (cvar)
• genetic algorithm