Electric vehicle power-carbon joint optimal dispatching based on carbon trading bidding game

Abstract

The large-scale promotion of electric vehicles provides strong support for the realization of the dual-carbon goal. However, in the process of carbon trading, how to consider the coupling relationship between electricity and carbon and the willingness to participate in electric vehicles are currently important challenges. To this end, in view of the complex correlation characteristics between the power market and the carbon trading market, the column-and constraint generation (C&CG) algorithm is used to decouple the power market model and the carbon trading market model, and iterative calculations are performed based on the coupled variables of the output of each power unit to coordinate and optimize the power generation scheduling cost and carbon footprint. In addition, considering the physical boundary constraints of charging and discharging power and individual interests when electric vehicles participate in the carbon trading market, the negotiation strategy of electric vehicles and conventional units when participating in carbon trading is studied, and a fuzzy Bayesian learning-based model is established. The carbon trading price bilateral negotiation function model is based on the carbon trading price, and the optimized carbon trading price is obtained in the final game, so as to enhance the enthusiasm of electric vehicles to participate in carbon emission reduction. The economic benefits and carbon emission reduction effects of the proposed model are compared and analyzed through various typical scenarios.

Keywords

• carbon trading
• carbon quota
• electric vehicle
• negotiation function
• fuzzy bayesian learning