1. Introduction
Many countries are trying to reduce their environmental footprint on the planet. In particular, Europe 2030 climate and energy framework targets are to reduce greenhouse gas emissions at least 40% with regard to 1990 levels, increase the share of renewable energy by at least 32% and improve the energy efficiency by at least 32.5% [
1]. In addition, Europe aims to be climate-neutral in 2050 [
2].
The transportation sector produces almost 25% of Europe’s greenhouse gas emissions [
3]. The deployment of EVs could be one of the key factors that will assist Europe and other countries to accomplish their environmental goals. The use of EVs has become a trend gaining more and more ground [
4], but with different acceptance in every country. The findings of the global EV outlook (2020) are a proof of the popularity gained so far by the EVs [
5]. Although the introduction of EVs has many benefits, such as zero emissions, no noise, better efficiency, bigger renewable energy penetration, etc., it has also created some challenges for the economy, technology, government policies, etc. [
6,
7,
8]. Most of these challenges could be addressed by smart charging and Vehicle-to-Grid (V2G) concepts [
9,
10]. In [
11], the authors estimate that the idle time of EVs is approximate 60.32% of their parking time. Idle time occurs when the EV has achieved its charging goals, but it is still plugged in. This time could be exploited for V2G services.
The suitable allocation of EV chargers, and more importantly, the allocation of parking lots (PLs) with EV chargers, could also be a solution for some of the aforementioned challenges. In [
12,
13], the authors study the optimal allocation of a PL for EVs, aiming to minimize the impacts on the grid and maximize the profitability of the PL.
There are several studies for the viability of V2G operation. V2G could help to address some of the issues arising from the large penetration of EVs, such as the exploitation of the energy stored in the batteries of the plug-in EVs (PEVs) for ancillary services to the grid. The willingness of EV owners to participate in V2G services is studied in [
14]. Several works conclude that range anxiety of the EV drivers is an important parameter for their participation in V2G operation. Range anxiety represents the anxiety of the EV owners from their uncertainty regarding the range of their next travel ensured by the energy stored in their EVs. In [
15], different V2G business models were studied and five major factors for a profitable market were identified. These factors are: the battery degradation because of V2G operation, the dispatch of the economic revenue from V2G to the involved parties, V2G services such as frequency regulation, the energy used to provide V2G services and the variables which should be taken into consideration when applying V2G and creating bids to the electricity market.
Smart and conventional charging of EVs have been studied taking into consideration several points of view, scenarios, methodologies and objectives. EVs’ load could be characterized as shiftable, interruptible and flexible. Hence, the charging of EVs could be coordinated with the operation of the local distribution network, preventing its overload and satisfying all local charging constraints, at the same time. The studies of the previous issues could be separated into two main categories. The first one considers smart charging in terms of cost minimization, while the second one includes smart charging and discharging (V2G and ancillary services to the grid). In most studies, the charging/discharging schedule of the PEVs is optimized. However, charging/discharging at the PL level has to address more constraints, such as PL transformer power limit, grid voltage at the common point of coupling, local distribution grid loading, etc.
In [
16], a particle swarm optimization (PSO)-based charging schedule is developed, and its comparison with priority charging-based algorithms is made. The main objective was a fast charging process with the minimum cost, and consequently, grid overloading was not considered. The authors claimed that the proposed method outperformed the other methods regarding the optimal dispatch of the charging power to the controlled EVs. In [
17], an improved PSO was introduced in order to charge/discharge EVs. Power losses of the electric network, frequent operation and smoothness of the power daily load curve were considered. At the same time, it was attempted to increase the satisfaction of EV owners’ charging requirements. One type of EV was considered with constant charging power of 3.12 kW (slow charging).
In [
18], an innovative method is presented to obtain an efficient charging schedule of PEVs. A hierarchical multi-agent system with small computation and communication times and able to satisfy state-of-charge (SoC) targets and all distribution network and EV constraints was designed. In [
19], a smart charging decision function focusing on EV user satisfaction and the minimization of grid investments was developed. Three main factors are considered for the charging and discharging of EVs: residual energy, charging efficiency and charging habit. In this case, communication is required between the charging stations and EVs in order to decide if the EVs will charge, discharge or wait at the charging station. It is also assumed that the EVs can store information about the residual energy of their battery and charging habits of the driver. In addition, the charging capacity and the distance of each charging station should be broadcasted from the charging stations. A method considering urgent charging demands is proposed in [
20]. EVs could charge in a fast or slow charging mode depending on the charging urgency of the EV. V2G was not considered as an option. It satisfies charging requests and smoothens the power exchanged by the PEVs and the microgrid. A comparison between the first-in-first-serve method, PSO and the Shuffled Frog Leaping Algorithm for dynamic charging of EVs is made in [
21]. The developed objective function aimed to minimize the EV charging cost considering the electricity price and charger booking. The effect of the autonomous operation of the microgrid on the electricity price or not is studied for operation scenarios of 20 EVs. The EV discharging scheme was not considered in this work.
In [
22], the authors took into consideration realistic mobility/parking patterns and developed a centralized charging scheduling system in order to maximize the profit of the PL and satisfy EVs’ requirements using the Advanced Interactive Multidimensional Modeling System software. Only slow charging was considered in this work. In [
23], the operation of a PL of EVs was studied under real-time pricing. An improved PSO method is proposed for charging and discharging 30 EVs which arrive at the PL at 8:00 and have different departure hours. In [
24], PSO is used to determine the charging schedule of PEVs in order to fulfill their requirements and minimize the charging cost at the PL level. Additionally, a heuristics and proportion-based assignment method was used to generate the initial population. Distribution grid loading was not considered in [
22,
23,
24].
In [
25], an autonomous real-time energy management system is proposed for a workplace microgrid with EV integration and time-of-use (TOU) pricing. Random forest methodology was used to forecast the EV travel pattern. The EVs and the workplace energy management system should exchange information in order to reduce the power consumption in the workplace and the charging costs for EV owners, however the overloading of the local transformer power was not considered. In [
26], the effects of coalitions between employer and employee, for the sake of efficient charging scheduling of EVs, are studied using cooperative game theory. It was assumed that in the workplace parking, the EVs could only discharge. The results showcased that the proposed approach is economically beneficial to EV owners (employees) and the employers. In [
27], peak shaving and valley filling of power consumption in a non-residential system are achieved with suitable EV charging/discharging scheduling, without minimizing the charging cost of the EVs. Only one type of EV was considered with a battery capacity of 24 kWh with a low charging/discharging rate. A real-time charging method with V2G capability using fuzzy logic was developed in [
28]. The aims of the method were the satisfaction of EV charging demands and the minimization of the charging cost. Linear programming was used to solve the EV charging schedule problem. The proposed fuzzy inference system computes the charging/discharging priority of each electric vehicle based on state-of-charging (SoC), remaining charging time and electricity price. 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.
The article is structured as follows. The case study, the creation and processing of the input data and the proposed charging scheduling method are described in
Section 2. In
Section 3, detailed simulation results obtained for several indicative operation scenarios are presented. The results are discussed, and the effectiveness of the proposed method is highlighted. Finally, the major conclusions drawn by this study are provided in the concluding section of the paper.
3. Results
In the following, the application of the proposed charging method to EVs that are parked in the PL presented in
Section 2 is examined. Six operation scenarios were carried out. The major information about the examined scenarios is tabulated in
Table 3.
The proposed charging scheduling method is not applied in scenarios 1 and 2 (SC1, SC2). In scenario 1 (SC1), the EVs are charged with the maximum power of their charging power converter, while in scenario 2 (SC2), they absorb the average power required to achieve their state-of-charge target. Discharging of the EVs and the power limit of the transformer of the PL were not considered as an option in SC1 and SC2.
In scenarios 3 (SC3), 4 (SC4), 5 (SC5) and 6 (SC6), the proposed charging scheduling method was applied. In SC4 and SC6, the proposed tariff was applied to the electricity price when the load of the local electric distribution network exceeds 80% of its maximum value. In SC5 and SC6, the power limit of the transformer of the PL was considered, and none of the aforementioned constraints were applied in SC3.
The nominal power of the PL transformer used in this study has been considered equal to 400 kVA. The PL operation scheduling time period was divided into time intervals of duration, Δt = 15 min. In total, 432 EVs participated in the proposed charging schedule method during the examined 24 h period. The total energy required to satisfy all EVs’ charging target (in all scenarios) was 2835 kWh. The major results obtained for the examined operation scenarios are depicted in
Figure 8,
Figure 9,
Figure 10,
Figure 11,
Figure 12 and
Figure 13.
3.1. Conventional Charging Scenarios
3.1.1. Scenario 1
In scenario 1, the charging cost was 136.95 EUR. The maximum and minimum values of PL’s power consumption were 461 kW at 11:30 and 15 kW at 7:30, respectively. The PL’s active power time series obtained in scenario 1 is shown in
Figure 8. As it was expected, PL’s active power and electricity price are uncorrelated in this scenario.
3.1.2. Scenario 2
In scenario 2, the charging cost was 135.56 EUR. The maximum and minimum values of PL’s power consumption were 273 kW at 11:30 and 45 kW at 7:30, respectively. The PL’s active power time series obtained in scenario 2 is shown in
Figure 9. As it was expected, PL’s active power and electricity price are uncorrelated in this scenario.
3.2. Charging Scenarios with the Proposed Charging Scheduling Method Being Applied
3.2.1. Scenario 3
In scenario 3, the energy absorbed by the PL was 3165 kWh, while the energy injected to the grid was 330 kWh. The PL’s charging cost was 122.73 EUR. The PL’s active power time series obtained in scenario 3 is shown in
Figure 10. It can be observed that the most intensive charging takes place when the electricity price is low, while discharging is preferred when the electricity price is high. The maximum and minimum values of PL’s active power were 470 kW at 12:15 and −231 kW at 20:00, respectively.
3.2.2. Scenario 4
In scenario 4, the energy absorbed by the PL was 3254 kWh, while the energy injected to the grid was 419 kWh. The PL’s charging cost was 121.59 EUR. The PL’s active power time series obtained in scenario 4 is shown in
Figure 11. The maximum and minimum values of PL’s power consumption obtained in this scenario were 501 kW at 12:15 and 312 kW at 20:15, respectively.
3.2.3. Scenario 5
In scenario 5, the energy absorbed by the PL was 3116 kWh, while the energy injected to the grid was 281 kWh. The PL’s charging cost was 124.06 EUR. The PL’s active power time series obtained in scenario 5 is shown in
Figure 12. The maximum and minimum values of PL’s power consumption were 400 kW during 12:00–12:45 and −223 kW at 20:00, respectively.
3.2.4. Scenario 6
In scenario 6, the energy absorbed by the PL was 3143 kWh, while the energy injected to the grid was 308 kWh. The PL’s charging cost was 123.78 EUR. The PL’s active power time series obtained in scenario 6 is shown in
Figure 13. The maximum and minimum values of PL’s power consumption were 400 kW during 12:00–12:45 and −242 kW at 20:00, respectively.
3.3. Comparison of the Examined Charging Scenarios
The energy that was bought from the grid and sold to it together with the respective costs for all examined scenarios are shown in
Table 4. In
Table 5, the comparison of the charging costs obtained in operation scenarios where the proposed method was applied with the respective costs of all examined scenarios is provided. The positive values indicate a lower charging cost reduction, and the reference used for the estimation of the percentage reduction was the charging cost obtained in SC4.
The application of an electricity tariff resulted in the reduction of the daily cost and the improvement of the performance in all examined scenarios, as in the examined case study, the time periods of high electric network load and high electricity price coincided. It is noted that usually, this is the case for electric power systems. Furthermore, the consideration of PL transformers’ power limit did not affect the daily charging cost considerably. However, the performance of the proposed charging scheduling method was slightly worse when the power limit constraint of the PL transformer was applied. Finally, it is highlighted that the proposed charging schedule method presented a considerably better performance (charging cost reduction bigger than 12% for the examined case studies) than the conventional charging strategies (SC1 and SC2), while it ensured the satisfaction of all operation and technical constraints at EV, PL and distribution network levels without negatively affecting the lifetime of EVs’ batteries.
Finally, some indicative trajectories of the EVs’ SoC and the respective charging powers are shown in
Figure 14,
Figure 15 and
Figure 16. In all cases, SoC targets and the battery lifetime constraint are fully satisfied.
3.4. Aggregation Model of the PL
In this scenario, an equivalent aggregation model of the EVs of the PL was developed and the obtained results are compared with those of the proposed charging scheduling method. The objective function used by the aggregation model of PL is the total charging cost of the PL,
, over the examined 24 h optimization period (96 quarters of the hour). Hence, the decision variable is the total active power,
, exchanged by the PL and the electric network over the examined 24 h optimization period. In order to ensure a fair comparison between the methods, the initial and final energy stored is the same in both cases, while the same battery lifetime constraint is applied. The solved optimization problem is defined in Equations (23)–(30):
Subject to:
Battery lifetime constraint
Stored energy constraints
Charging power constraints
Final stored energy constraint
Initial stored energy constraint
where is the total active power exchanged by the PL and the electrical network, is the total energy stored in the plug-in electric vehicles, is the energy stored in a plug-in electric vehicle and min(max) is the minimum (maximum) energy stored in a plug-in electric vehicle.
The energy absorbed and injected into the grid were 3900 and 1065 kWh when the aggregation PL model was applied, respectively. The obtained charging cost was 120.73 EUR and it is 2.76% lower than that obtained by the proposed charging schedule method. However, this is not the case in real-world applications, as the aggregation models are highly sensitive to electricity price forecast errors. Moreover, the low application requirements of the proposed method make it more suitable for such applications.
In
Figure 17 and
Figure 18, the PL active power and the total stored energy obtained by the aggregation PL model are presented, respectively.