In Saudi Arabia, the energy sector is presently the most significant contributor to carbon emissions, followed by the transportation sector, which contributes about 26% of the gross greenhouse gas emissions. The adoption of electric vehicles (EVs) in the transportation sector worldwide is one way to bring about a global green solution that can support the decarbonization of the environment, which now constitutes a new electric power demand for the utility grid network. To preserve the environment, and reduce the pressure on the existing grid network, we propose the utilization of EV charging stations (EVCSs) in off-grid locations. It is essential to have an alternative stand-alone renewables-based electrification framework to secure the charging demand needed for the electric vehicles. The present study performs a techno-economic investigation of a novel off-grid scheme that combines renewable energy resources to provide clean electricity for EV charging stations. The optimized system for the EVCS is compared with the alternative option of grid extension using economic criteria evaluation metrics and distance limitations. The optimization and comparative analysis results reveal that the option of an optimum stand-alone hybrid charging station is an economical, sustainable, and eco-friendly alternative to the option of grid expansion.

The transportation sector accounts for more than 70% of Nigeria’s energy consumption. This sector has been the major consumer of fossil fuels in the past 20 years. In this study, the technical and economic feasibility of an electrical vehicle (EV) charging scheme is investigated based on the availability of renewable energy (RE) sources in six sites representing diverse geographic and climatic conditions in Nigeria. The HOMER Pro^® microgrid software with the grid-search and proprietary derivative-free optimization techniques is used to assess the viability of the proposed EV charging scheme. The PV/WT/battery charging station with a quantity of two WT, 174 kW of PV panels, a quantity of 380 batteries storage, and a converter of 109 kW located in Sokoto provide the best economic metrics with the lowest NPC, electricity cost, and initial costs of USD547,717, USD0.211/kWh, and USD449,134, respectively. The optimal charging scheme is able to reliably satisfy most of the EV charging demand as it presents a small percentage of the unmet load, which is the lowest when compared with the corresponding values of the other charging stations. Moreover, the optimal charging system in all six locations is able to sufficiently meet the EV charge requirement with maximum uptime. A sensitivity analysis was conducted to check the robustness of the optimum charging scheme. This sensitivity analysis reveals that the technical and economic performance indicators of the optimum charging station are sensitive to the changes in the sensitivity variables. Furthermore, the outcomes ensure that the hybrid system of RE sources and EVs can minimize carbon and other pollutant emissions. The results and findings in this study can be implemented by all relevant parties involved to accelerate the development of EVs not only in Nigeria but also in other parts of the African continent and the rest of the world.

The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. Most of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM), such as system inertia and load damping factors. This paper proposes an automatic generation control (AGC) method considering the uncertainties of these key parameters. First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline. Second, the offline identification results and the normal operation data right before the large power disturbance are used to train the online probability estimation model of the FRM key parameters. Third, the online estimation results of the FRM key parameters are used as the input, and the model predictive-based AGC optimization method is developed based on distributionally robust optimization (DRO) theory. Case studies conducted on the IEEE 118-bus system show that the proposed AGC method outperforms the widely utilized PI-based and PID-based control methods in terms of performance and efficiency.