Joint Optimization of EV Charging and Renewable Distributed Energy With Storage Systems Under Uncertainty

Electric Vehicles (EVs) are essential to achieving the 2030 United Nations Sustainable Development Goals by reducing emissions and improving air quality. The strategic placement of Electric Vehicle Charging Stations (EVCSs) in urban areas is critical to supporting the transition to clean transportation. However, as EV adoption increases, challenges such as rising power losses, voltage profile degradation, and voltage instability emerge within microgrids. These issues can be mitigated by integrating Energy Storage Systems (ESSs) to enhance efficiency. This study presents an integrated planning approach to optimize the allocation of EVCSs based on the spatial-temporal distribution of traffic flows. A stochastic model is also introduced to determine the optimal placement of the energy storage system, accounting for uncertainty factors such as fluctuating electrical loads and the intermittency of renewable energy sources. The energy storage system allocation model is formulated as a multi-objective optimization problem aimed at improving voltage profiles, minimizing power losses, and maximizing voltage stability. The mathematical models of EVCSs and ESSs, and an economic analysis of the microgrid is included, considering the costs associated with energy storage system integration. The proposed model's effectiveness is validated through a case study on a benchmark transportation network, with results indicating its ability to mitigate the negative effects of EV integration on microgrids. Additionally, the study introduces a stochastic framework to simulate the inherent uncertainties in electrical loads and renewable energy sources.