Abstract: The transportation and electricity systems are two major parts of global fossil energy consumption, and are also the important targets for optimization under the “dual carbon” goal. Electric vehicles (EV) have the nature of both transportation and electricity-use, and feature zero on-road carbon emissions, while the upstream generation carbon emissions associated with EV charging demand are still accountable. It is therefore urgently needed to reduce the carbon emissions of the whole system through optimizing the EV charging loads. In this context, this paper proposes an EV charging demand low carbon optimization method in traffic-grid coupling networks towards the “dual carbon” goal. Firstly, the EV driving flows and their charging behavior in the coupling networks are modeled to obtain the aggregated EV charging loads. Then the carbon emissions flow is traced according to the optimal power flow results of grid. Given the above-mentioned model, the optimization problem is solved based on the simulated annealing algorithm. Under the circumstance of not sacrificing the charging demand of EV users, part of the charging power supported by thermal units will be transferred to renewable energy resources through temporal-spatial redistribution of charging loads, subsequently reducing the total carbon emissions of the coupling networks while improving the local consumption of renewable energy resources. Finally, case studies have demonstrated the effectiveness of the proposed low carbon scheduling method.

Key words: “dual carbon” goal, electric vehicle, traffic-grid coupling networks, low carbon optimization, carbon emission flow tracing, simulated annealing algorithm