# A Simple Multi-Parameter Method for Efficient Charging Scheduling of Electric Vehicles
###### Abstract
In [29], the authors aim to increase mainly the profits of the PL and secondarily the profits of the EV owners. PSO was used to schedule the charging and discharging of the EVs. They also consider random behavior for the owners of the EVs and a penalty is applied to the owners or the PL if they do not comply with the decided agreements. A number of studies related to the scheduling of EVs' charging using renewable energy can be found in the literature. In [30], mixed-integer linear programming was used to obtain the real-time charging of the EVs in a PL with a photovoltaics and energy storage system (ESS). DC charging and the overloading of the distribution grid were not considered in this work. The proposed charging method maximizes the satisfaction of the EV owners in terms of fulfilling all charging requests and minimizes the overall operational cost of the PL by suitably coordinating EVs, PV and ESS. A similar study was implemented in [31]. An optimization method is presented where chance-constrained programming and linear programming were used in order to reduce the charging cost by determining optimal contracts for the charging stations. V2G was considered as a last option when the power sold to the grid as PV and energy storage systems inject active power first. Modified electricity price patterns were used in order to avoid overload of the local transformer, and grid overloading has not been addressed. In [32], a two-stage model was developed to manage the integrated operation of PLs of EVs and RES, considering the uncertainties arising from the behavior of the owners of the EVs and the RES. The effects that RES location in the distribution grid and their size have on the profit of the PL were examined. Finally, only the studies in [22; 27] used real-word data of conventional vehicles for the arrival and departure to and from parking spots. Additionally, in the literature, the limitation of the number of charges and discharges during one charging process was not considered in order to prevent battery degradation. The method proposed in this article comprises the following features:  
* The charging scheduling of the EVs is performed at the EV level. However, the major constraints at EV, PL and distribution grid levels are taken into consideration. This allows for easier implementation of the method. * The proposed method can be easily applied as it does not require private data to be disclosed to other parties and it requires very few forecasts. The only forecasts that are required are those of the electricity price, distribution network loading and maximum/minimum total charging power of the EVs. It is noted that only the last forecast should be performed by the PL operator, while increased robustness to electricity price forecast errors is ensured by the proposed method as the charging scheduling is performed at the EV level. * The proposed charging schedule method has a minimal effect on the lifetime of the batteries of the EVs, while it allows V2G operation, at the same time. More specifically, it can keep the charging cycles of the EVs' batteries below a specific limit defined by the EV driver. * A simple and effective initialization technique for the PSO method used to solve the examined problem is introduced in this study. The proposed initialization process helps the method to converge fast to a very good solution with a high degree of certainty. * A simple and easily applied way to take into account the overloading of the PL transformer and the distribution network is proposed. * The developed method does not use aggregation techniques, and therefore, methods for optimal power dispatch at the EV level are not required. This greatly simplifies the application of the method, and reduces the complexity and the required computation capacity. Moreover, the sensitivity to electricity price forecast is limited. To the best of the authors' knowledge, there is not any other research work that jointly addresses the above list of technical issues and comprises the respective features.