Optimal allocation and configuration of renewable energy sources, electric vehicle parking lots, and fixed and mobile batteries under uncertainty and demand response program

The global transition to renewable energy sources (RESs) is critical for mitigating environmental pollution and reducing dependence on fossil fuels. However, the inherent intermittency of RESs presents significant challenges to power system stability, which are further exacerbated by the anticipated rise in electric vehicles (EVs), projected to exceed 130 million by 2030. To support this transformation, a robust energy infrastructure that integrates RESs, smart plug-in EV parking lots (PEV-PLs), energy storage systems (ESSs), and demand response programs (DRPs) is essential. This paper proposes a stochastic multi-objective mixed-integer nonlinear programming (MINLP) model for the optimal planning and operation of distribution systems (PDS) with increasing RES and EV penetration. The model incorporates wind and photovoltaic (PV) distributed generators (DGs), PEV-PLs, fixed and mobile battery energy storage systems (FBESSs and MBESSs), and DRPs. It optimally determines the sizing and placement of key components while strategically managing the relocation of MBESSs to minimize economic costs (investment, maintenance, and operation), environmental impacts (carbon emissions from the PDS), power losses, and voltage deviations. The model also accounts for major uncertainties, including EV charging behavior, wind speed, solar irradiation, load demand, and energy price fluctuations. The proposed framework is validated using the IEEE 69-bus test system, with nine configurations evaluated, ranging from a baseline (Case 1) to a fully integrated setup (Case 9). These cases include various combinations of RESs, PEV-PLs, FBESSs, MBESSs, and DRPs. The analysis underscores the importance of coordinated integration of RESs, storage, and DRPs to achieve environmental and operational benefits. Among the evaluated configurations, Case 7, which includes RESs, PEV-PLs, MBESSs, and DRP, emerges as the most balanced solution, achieving the lowest emission cost ($323,516.65/year), the highest loss reduction (39.60%), and the greatest voltage deviation improvement (41.14%). Case 3 excels in reducing operational costs (35.66%) and achieving the highest RES penetration (2.65 MVA), demonstrating the benefits of combining wind-PV DGs with a moderate EV fleet. In contrast, Case 9, despite incorporating the highest EV penetration (179 EVs) and the most comprehensive mix of technologies, underperforms economically due to system complexity, with only a 16.54% reduction in operational costs and a 1.23% increase in total cost. Overall, the results indicate that configurations that balance RES integration with moderate EV deployment and strategic energy storage, such as Cases 3 and 7, offer the most effective trade-offs between economic and environmental objectives.