Charge Scheduling Optimization of Plug-In Electric Vehicle in a PV Powered Grid-Connected Charging Station Based on Day-Ahead Solar Energy Forecasting in Australia

Abstract

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.

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

“…For this, the present study demonstrates the utilization of real‐time metrological data and actual EVs charging load statistics in an optimal EMS. To maximize solar energy usage, reduce peak power consumption, and sudden spikes on the distribution grid during EV charging a coordinated control system is required [28]. On the basis of the extracted weather real data sets, PV electricity is forecasted as per the actual PV electricity generated data.…”

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

“…HESS technology faces multiple optimization problems, i.e., sizing, capacity, and power distribution, since it is still an emerging technology. Thus, several optimization techniques such as genetic algorithms (GA) and ant colony optimization (ACO) were adopted in the literature to fix these issues (see Table 1) [14][15][16][17][18]. In [14], the authors propose an optimal charge-scheduling algorithm for EV based on day-ahead PV power forecasts in order to minimize the total charging costs.…”

“…Thus, several optimization techniques such as genetic algorithms (GA) and ant colony optimization (ACO) were adopted in the literature to fix these issues (see Table 1) [14][15][16][17][18]. In [14], the authors propose an optimal charge-scheduling algorithm for EV based on day-ahead PV power forecasts in order to minimize the total charging costs. In [15], an optimization model and energy management schemes for microgrids to increase the efficiency of EV are suggested.…”

Coordination of Plug-In Electric Vehicle Charging in a Stochastic Framework: A Decentralized Tax/Incentive-Based Mechanism to Reach Global Optimality

“…However, even moderate PEVs market penetrations may create new demand peaks in the electricity grid [3][4][5] and lead to serious issues concerning the grid stability [6]. Consequently, the need has emerged to coordinate the charging schedule of PEVs so as to mitigate the demand peaks, or to use the Energy, stored in the PEVs batteries as a source for providing ancillary services [7][8][9][10][11]. This proposal belongs, more generally, to the wider concept of demand-side management in smart grids.…”