The integration of electric vehicles (EVs) into power systems worldwide will be challenged in many locations by grid constraints, such as load shedding in developing countries or active network management in developed countries. Nevertheless, an opportunity exists for EV fleets to alleviate financial and operational repercussions of these constraints while reducing carbon emissions through integrated photovoltaic-energy storage-charging station (PV-ES-CS) systems. However, the effectiveness of PV-ES-CS systems depends on the load they serve, and many commercial fleets may only partially electrify their vehicle stock, thus limiting their energy demand. Consequently, there is a need for methodologies that incorporate grid constraints into the design and technoeconomic assessments of PV-ES-CS systems, alongside an understanding of the impact of varying levels of fleet EV penetration on system performance. We develop a novel methodology that incorporates grid constraints into a PV-ES capacity optimization model, and investigate the impacts of optimistic and conservative grid constraint scenarios and different degrees of fleet EV penetration on PV-ES-CS system performance through a case study of a paratransit fleet in Dobsonville, South Africa. To determine the most cost-effective design of the systems, the PV and ES components are optimized to maximize net present value (NPV). Three generalized insights emerge from the analysis: The first relates to the importance of timing PV-ES investments based on the planned fleet electrification strategy. The second sheds light on the sensitivity of PV-ES system performance to grid constraint frequency and forecasting, emphasizing the need for nuanced understanding and adaptive strategies in regions with pervasive grid constraints. The third explores the influence of trickle charging on system feasibility and economic performance, particularly at lower levels of EV penetration. Enhanced utilization of trickle charging emerges as a promising avenue to improve the economic viability of PV-ES systems, especially for fleets with morning and evening peak loads. Collectively, these insights contribute to advancing the understanding of renewable energy integration in commercial fleet operations in grid and capital-constrained environments. Moreover, the methodology provides a robust framework for fleet owners in these environments to optimally plan PV-ES investments, fostering informed decision-making and propelling the transition towards sustainable and economically efficient transportation.
