Optimal scheduling and techno-economic analysis of electric vehicles by implementing solar-based grid-tied charging station

The integration of Electric Vehicles (EVs) and Photovoltaics (PVs) into the electric power distribution system is rising worldwide due to the environmental concerns and the rapid depletion of fossil fuels. However, the simultaneous integration of EVs and PVs in the power system poses challenges of uncertain power penetration, charging, and dynamic loading conditions, which requires a suitable control design and scheduling optimization to keep the modified electric system stable. This paper proposes a solar-based grid-tied charging station (SGTCS) that optimizes EV charging by enabling the scheduling technique resulting in maximum utilization of PV power. The proposed model also identifies and considers seasonal variation effects of power generation and EV charging connectivity. The SGTCS is implemented using HOMER Grid, and a case study of a practical locality in Islamabad was investigated; the conducted detailed analysis comprises the annual cost estimation, energy costs, EV scheduling, main grid power usage reduction, and the impact of environmental changes. Examining the integration of small and big EVs in the grid-tied solar-integrated charging system, a brief scheduling strategy of 250 sessions per day is analyzed. The resultant techno-economic implications are presented, depicting the proposed study's relevance and applicability.

Automated summary

The integration of Electric Vehicles (EVs) and Photovoltaics (PVs) into the electric power distribution system is increasing worldwide. However, the simultaneous integration of EVs and PVs poses challenges that require suitable control design and optimization strategies. This paper proposes a solar-based grid-tied charging station (SGTCS) that maximizes PV power utilization for EV charging. The proposed model considers seasonal variation effects and was implemented using HOMER Grid, including a case study in Islamabad. The results show the relevance and applicability of the study.