Considering the spatiotemporal optimal scheduling strategy of electric vehicles entering the network

With the intensification of the contradiction between economic development, fossil fuel shortages, and severe environmental pollution, the development and widespread adoption of Electric Vehicles (EVs) have become an inevitable trend. The large-scale and disorderly charging of EVs connected to the grid will impose significant impacts on the power system, potentially leading to local overloads and threatening the security and economic operation of the grid. Therefore, this study investigates the coordinated optimization planning problem involving generators, EVs, and renewable energy sources (wind and solar). A spatiotemporal optimization strategy for EV charging scheduling is proposed. On the temporal scale, an optimal scheduling model based on unit commitment is established, aiming to minimize the operational costs of generators on the transmission grid side, PM2.5 emissions, total user charging costs, and the curtailment of wind and solar power. On the spatial scale, an optimal power flow-based scheduling model is developed to reduce distribution network losses, taking into account network security constraints and the spatial migration characteristics of EVs. The proposed EV charging scheduling strategy is simulated and analyzed on a power system model comprising a standard 10-machine transmission network and an IEEE 33-node distribution network. The results validate the effectiveness and superiority of the proposed spatiotemporal optimization scheduling strategy.