Low-carbon scheduling strategy for electric vehicles considering carbon emission flow and dynamic electricity prices

As the global environmental pollution problem intensifies, the carbon reduction transformation of the power system is urgent. In order to solve the problem of unclear carbon flow and distribution in the operation of the power grid, as well as the mismatch between static time-of-use electricity prices and peak and valley periods in the scheduling of electric vehicle charging loads, a multiperiod dynamic electricity price guidance strategy based on carbon emission flow theory is proposed. First, based on the accurate power flow results of the power system, a complex power distribution matrix of the power system is constructed to obtain the distribution of the power generated by the generator units in each node of the network. Then, the Monte Carlo random sampling method is used to simulate the load situation of electric vehicles in a disordered charging state. A mathematical model based on the carbon trading model is established to minimize the load difference at the grid end and maximize the cost of charging on the user side. Finally, the proposed ordered charging method with a multiperiod dynamic electricity pricing strategy is compared with unordered charging, and considering the participation of electric vehicles in carbon trading, this strategy effectively reduces the peak valley difference between the power grid and user charging costs.