Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP) index. The results obtained indicate the efficacy of the VRP index.

The ever-increasing population of India accompanied by the recent concerns regarding fossil fuel depletion and environmental pollution has made it indispensable to develop alternate mode of transportation. Electric Vehicle (EV) market in India is expanding. For acceptance of EVs among the masses, development of charging infrastructure is of paramount importance. This work formulates and solves the charging infrastructure-planning problem for Guwahati city, India that will develop as a smart city soon. The allocation of charging station problem was framed in a multi-objective framework considering the economic factors, power grid characteristics such as voltage stability, reliability, power loss as well as EV user’s convenience and random road traffic. The placement problem was solved by using a Pareto dominance based hybrid algorithm amalgamating Chicken Swarm Optimization (CSO) and Teaching Learning Based Optimization (TLBO) algorithm. Finally, the Pareto optimal solutions were compared by fuzzy decision-making.

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.

Global warming, pollution, and the depletion of fossil fuels have compelled human beings to explore alternate sources of energy and cleaner modes of transport. In recent years, renewable energy sources (RES) have been massively introduced to the grid. Furthermore, Electric Vehicles (EVs) are becoming popular as a cleaner mode of transport. However, the introduction of RESs and EVs to the grid has imposed additional challenges on the grid operators because of their random nature. This review aims to focus on the integration of RES and EVs to the grid, thereby presenting the global status of RESs and EVs, the impact of integrating RESs and EVs to the grid, the challenges of integrating RES and EV to the grid, optimization techniques for EV and RES integration to the grid, and mitigation techniques. A total of 153 research papers are meticulously reviewed, and the findings are put forward in this review. Thus, this review will put forward the latest developments in the area of EV and RES integration into the grid and will enlighten the researchers with the unsolved questions in the area that need investigation.

Optimal charge scheduling of electric vehicles in solar-powered charging stations based on day-ahead forecasting of solar power generation is proposed in this paper. The proposed algorithm’s major objective is to schedule EV charging based on the availability of solar PV power to minimize the total charging costs. The efficacy of the proposed algorithm is validated for a small-scale system with a capacity of 3.45 kW and a single charging point, and the annual cost analysis is carried out by modelling a 65 kWp solar-powered EV charging station The reliability and cost saving of the proposed optimal scheduling algorithm along with the integration and the solar PV system is validated for a charging station with a 65 kW solar PV system having charging points with different charging powers. A comprehensive comparison of uncontrolled charging, optimal charging without solar PV system, and optimal charging with solar PV system for different vehicles and different time slots are presented and discussed. From the results, it can be realized that the proposed charging algorithm reduces the overall charging cost from 10–20% without a PV system, and while integrating a solar PV system with the proposed charging method, a cost saving of 50–100% can be achieved. Based on the selected location, system size, and charging points, it is realized that the annual charging cost under an uncontrolled approach is AUS $28,131. On the other hand, vehicle charging becomes completely sustainable with net-zero energy consumption from the grid and net annual revenue of AUS $28,134.445 can be generated by the operator. New South Wales (NSW), Australia is selected as the location for the study. For the analysis Time-Of-Use pricing (ToUP) scheme and solar feed-in tariff of New South Wales (NSW), Australia is adopted, and the daily power generation of the PV system is computed using the real-time data on an hourly basis for the selected location. The power forecasting is carried out using an ANN-based forecast model and is developed using MATLAB and trained using the Levenberg–Marquardt algorithm. Overall, a prediction accuracy of 99.61% was achieved using the selected algorithm.