Optimal Operation of Smart Electric Vehicle Parking Lots Equipped with Renewable Energy Sources under Various Uncertainties

Document Type : Original Article


1 PhD Student in Power Electrical Engineering, University of Tabriz, Faculty of Electrical and Computer Engineering

2 Professor of Power Electrical Engineering, University of Tabriz, Faculty of Electrical and Computer Engineering


By the extensive presence of electric vehicles (EVs) in today’s communities, the smart management of EVs should be completely investigated. On the other hand, the integration of renewable energy sources (RESs) in the smart electric vehicles parking lot (SEVsPL) reduces the dependency on the upstream network and also the whole operation costs. So, under finite energy sources around the world, in order to supply various demands along with generating low greenhouse gas emissions (GHGEs), policymakers and different scholars seek to find cost-effective, eco-friendly, efficient, and sustainable solutions. With investigating the literature review in the field of management and optimal operation of SEVsPL can be obviously concluded that multiple approaches and strategies from different perspectives were used to improve the efficient usage of energy based on smart grids (SGs) framework in SEVsPL. It was indicated that none of the analyzed references, addressee the simultaneous effects of different uncertainties, i.e., the random behavior of EVs owners, RES, and the upstream network electricity price on the charging and discharging processes for the operation of SEVsPL. Certainly, applying the nature of such uncertainty sources in an appropriate path will have significant impacts on the bringing results as close as possible to the real-world solutions.
Model description and method of solution
Therefore, in the current paper, a stochastic scheduling methodology for the optimal operation of SEVsPL has been proposed as an interactive user interface between the respective operator and EVs owners to facilitate charging and discharging activities and operation costs. The total capacity of SEVsPL is 50 in which three various EVs’ models with 10 scenarios for the arrival time, departure time, and initial state of energy (SoE) were considered. Furthermore, the other uncertainties oriented from RES and the upstream network electricity price have also taken into account with relative 10 scenarios. It is worth to mention that Mont Carlo simulation (MCS) implemented in MATLAB software to produce 1000 scenarios and then reduce them to 10 scenarios with the assistance of SCENRED algorithm in GAMS software. This stochastic optimization model of SEVsPL was formulated as mixed integer linear programming (MILP) problem, which was solved by CPLEX solver in GAMS software.
Simulation results and discussion
According to the obtained results in different scenarios, it was obviously seen that the lowest profit of SEVsPL was occurred in scenario 8, which can be considered as the worst case scenario, although, the highest profit has been achieved in scenario 9 and can be taken as the best case scenario. So, it can be mentioned that the optimal utilization of SEVsPL in the presence of RES uncertainty and the unpredictable behavior of EVs can be different. By considering the probabilities of the aforementioned uncertainties, the expected profit for SEVsPL was $ 1571.31. For the plotted figures of photovoltaic (PV) and wind turbine (WT) power plants in scenario 9, the generated WT power can be sold to the upstream network within the period in which EVs have not yet entered the SEVsPL and also for the PV generated power can be utilized to charge EVs. To realize the aim of the SEVsPL operator in maximizing his/her profit, the smart operating strategy was accomplished in purchased /sold power from /to the upstream network, which were indicated in relative figures for two worst case and best case scenarios. These outcomings highlighted that sold power has happened when electricity price is high (e.g., 10 o'clock in scenario 8 and 17 o'clock in scenario 9); however, the purchased power has taken place when electricity price is low (e.g., 10 o'clock in Scenario 9 and 12 o'clock in Scenario 8). This strategy is true for all EVs in all scenarios such that results to gain profit from electricity price arbitrage by the SEVsPL operator. If the EVs owners are looking for the maximum charge when leaving the SEVsPL, the respective operator will earn less profit according to different gamma coefficients presented in relevant expected SEVsPL profit’s figure. In other words, the chance of discharging EVs will be less when attending SEVsPL.
The proposed stochastic optimal operation strategy of SEVsPL in this paper has been accomplished to flatten charging and discharging processes along with operation costs. In the presence of different considered uncertainties, the optimal operation strategy of SEVsPL can be diverse for different scenarios such that the highest and lowest profits were equal to $1740.84 and $1359.22, respectively. Although by considering the probability of all scenarios, the expected profit was equal to $ 1571.31. These various uncertainties affect the obtained profit of SEVsPL which the respective operator seeks to charge EVs at low price hours and discharge EVs at high price hours under the smart performance strategy.


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