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

Abstract

Electric vehicles 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 in urban areas is critical to support the transition to clean transportation. However, as electric vehicle 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 to enhance efficiency. This study presents an integrated planning approach to optimize the allocation of electric vehicle charging stations 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 model of electric vehicle charging stations and energy storage systems. 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 electric vehicle integration on microgrids. Additionally, the study introduces a stochastic framework to simulate the inherent uncertainties in electrical loads and renewable energy sources.