Low-Carbon Incentive Guidance Strategy for Electric Vehicle Agents Based on Carbon Emission Flow

Abstract

The cleanliness of charging power determines whether electric vehicles can fully utilize their low-carbon properties. This paper, taking into account the impact of temperature on the energy consumption of electric vehicle air conditioning, uses the Monte Carlo algorithm to calculate the typical daily charging load of electric vehicle clusters in different seasons. Secondly, based on the Shapley value carbon responsibility allocation method, a reasonable range of carbon emission responsibilities for different electric vehicle agents is calculated, and a tiered carbon pricing method is proposed accordingly. Then, using carbon emission flow theory, we calculate the carbon emissions generated by the different agents’ charging amounts and corresponding carbon emission costs. Finally, a low-carbon incentive guidance model is constructed with the signal of tiered carbon prices and the goal of minimizing operating costs to re-optimize the charging load distribution of electric vehicles. Case studies showcase that the proposed method is effective in reducing power system carbon emissions and electric vehicle charging costs.