**1. Introduction**

Many research works have been presented in the literature to overcome the issues related to the electric vehicles' (EVs) scheduling at parking lots (PLs), such as the number of charging points, time-varying electricity price, the capacity of chargers, and charging limit. However, few works addressed advanced technologies for online booking and location finding [1]. In this regard, the research works presented in the literature are broadly focused on three major categories: (i) EV battery charging technology, (ii) charging scheduling schemes, and (iii) charging station (CS) recommendation methods. In global environmental pollution, the transport sector has a significant role due to fossil fuel usage. Nowadays, a non-fuel or a partial fuel-based vehicle is emerging due to low fuel consumption, no environmental pollution, and reduction of greenhouse emissions, etc. [2,3]. Most countries migrate from fuel to electrical-based transport systems (EV-based systems), and more research and development is initiated in this direction. In Figure 1, the typical schematic diagram charging system is shown. By changing conventional vehicles to EVs, the electric power supply has to be maintained with power quality. However, in practice, a large amount of EV charging degrades the electric power system's performance due to unexpected demand, overload of transformers, and grid stability issues [4,5]. consumption, no environmental pollution, and reduction of greenhouse emissions, etc. [2,3]. Most countries migrate from fuel to electrical-based transport systems (EV-based systems), and more research and development is initiated in this direction. In Figure 1, the typical schematic diagram charging system is shown. By changing conventional vehicles to EVs, the electric power supply has to be maintained with power quality. However, in practice, a large amount of EV charging degrades the electric power system's performance due to unexpected demand, overload of transformers, and grid stability issues [4,5].

**Figure 1.** Typical electric vehicle (EV) charging system. **Figure 1.** Typical electric vehicle (EV) charging system.

The EV charger is one of the main components in determining the recharging time of the battery. In general, the commonly used EV chargers are classified into four types: The EV charger is one of the main components in determining the recharging time of the battery. In general, the commonly used EV chargers are classified into four types:


the user pay more. On the other hand, it worsens the distribution network during peak time. Hence, controlled EV charging is a prominent solution to minimize grid disturbances [10]. Most plug-in hybrid EV (PHEVs)/EV charging is predicted to occur in public CS. During the peak load period, the controlled EV charging is used to minimize the grid disturbances and charging costs [11,12]. Figure 2 shows an EV charging at a parking lot. The EV charging loads can double the average household electricity consumption and makes the user pay more. On the other hand, it worsens the distribution network during peak time. Hence, controlled EV charging is a prominent solution to minimize grid disturbances [10]. Most plug-in hybrid EV (PHEVs)/EV charging is predicted to occur in public CS. During the peak load period, the controlled EV charging is used to minimize the grid disturbances and charging costs [11,12]. Figure 2 shows an EV charging at a parking lot.

*Energies* **2020**, *13*, x FOR PEER REVIEW 3 of 25

**Figure 2.** EV charging at a parking lot. **Figure 2.** EV charging at a parking lot.

Controlled EV charging is classified as centralized charging and decentralized charging. The aggregator or a standard operator [13] will control individual PHEVs and make a universal control for cost reduction in the centralized coordination scheme. However, this scheme is not advisable for the customers who do not want any third party to control their EV usage and electric power consumption. Under the decentralized charging scheme, there is no restriction on using the charging point. The EVs can occupy the charging point directly until the battery is charged sufficiently. A PL with 2 to 6 chargers is highly recommended due to space and cost constraints [14]. Some demand response (DR) programs are considered for a smart grid to control the peak demand and minimize EV charging costs. A new intelligent load management scheme is proposed to reduce the charging cost. This coordinated charging scheme is implemented by controlling several EVs charging and taking load profiles of the residential area into account. This is investigated in multiple residential distribution systems with EVs. The charging time is shifted to midnight to minimize the charging cost and peak load without using a storage device. However, the proposed scheme does not discuss the charging infrastructure, uncertain arrival, and EVs [15]. A real-time power management program is recommended, and optimal scheduling is implemented using a genetic algorithm (GA). However, the uncertain arrival of EVs and real-time pricing are not considered [16,17]. A distributed DR program is proposed to manage EV charging demand and minimize the charging cost in a smart grid [18]. In this method, the forecasted electricity price is shared with the customer. Besides, several DR programs are presented in the energy and reserve market for optimal EVs schedule [18,19]. By deciding the charging and discharging time from each battery optimally, the PL's profit is maximized [20,21]. The suggested approach is compared with the time of use (TOU), critical demand price, and emergency DR programs. The choice of charging and discharging of EVs is completed at a particular time sequence with an unvarying rate. An aggregator supported centralized EV charging is proposed to minimize the overall purchasing cost of electricity. However, the provided solution requires a high-level communication infrastructure. Also, it is assumed that CSs have unlimited electric power to charge a considerable number of EVs together, which is not easy to implement in a real-time scenario. The charging cost variation in the CSs provides the EV users to choose between charging time. Considering this, a charging model is proposed for a PL equipped with a solar and energy storage system (ESS) [22]. This model also includes the PL's profit maximization, the capacity of distributed generation (DG) and ESS, PL's investment choices, and cost to charge the EVs. However, the basic first-in-first serve (FIFS) method is used for charging the EVs. Controlled EV charging is classified as centralized charging and decentralized charging. The aggregator or a standard operator [13] will control individual PHEVs and make a universal control for cost reduction in the centralized coordination scheme. However, this scheme is not advisable for the customers who do not want any third party to control their EV usage and electric power consumption. Under the decentralized charging scheme, there is no restriction on using the charging point. The EVs can occupy the charging point directly until the battery is charged sufficiently. A PL with 2 to 6 chargers is highly recommended due to space and cost constraints [14]. Some demand response (DR) programs are considered for a smart grid to control the peak demand and minimize EV charging costs. A new intelligent load management scheme is proposed to reduce the charging cost. This coordinated charging scheme is implemented by controlling several EVs charging and taking load profiles of the residential area into account. This is investigated in multiple residential distribution systems with EVs. The charging time is shifted to midnight to minimize the charging cost and peak load without using a storage device. However, the proposed scheme does not discuss the charging infrastructure, uncertain arrival, and EVs [15]. A real-time power management program is recommended, and optimal scheduling is implemented using a genetic algorithm (GA). However, the uncertain arrival of EVs and real-time pricing are not considered [16,17]. A distributed DR program is proposed to manage EV charging demand and minimize the charging cost in a smart grid [18]. In this method, the forecasted electricity price is shared with the customer. Besides, several DR programs are presented in the energy and reserve market for optimal EVs schedule [18,19]. By deciding the charging and discharging time from each battery optimally, the PL's profit is maximized [20,21]. The suggested approach is compared with the time of use (TOU), critical demand price, and emergency DR programs. The choice of charging and discharging of EVs is completed at a particular time sequence with an unvarying rate. An aggregator supported centralized EV charging is proposed to minimize the overall purchasing cost of electricity. However, the provided solution requires a high-level communication infrastructure. Also, it is assumed that CSs have unlimited electric power to charge a considerable number of EVs together, which is not easy to implement in a real-time scenario. The charging cost variation in the CSs provides the EV users to choose between charging time. Considering this, a charging model is proposed for a PL equipped with a solar and energy storage system (ESS) [22]. This model also includes the PL's profit maximization, the capacity of distributed generation (DG) and ESS, PL's investment choices, and cost to charge the EVs. However, the basic first-in-first serve (FIFS) method is used for charging the EVs.

The EV charging scheduling is presented in [23] with ESS from a power market perspective. The aggregator considered the day ahead and actual market price and involves the energy trade. The optimal charging improves the aggregator's revenue, and it can be further enhanced with ES's support. However, it is assumed that the EV charging demand is known. A charging scheme considering a real-time scenario to minimize the EV charging cost is presented [24]. This scheme The EV charging scheduling is presented in [23] with ESS from a power market perspective. The aggregator considered the day ahead and actual market price and involves the energy trade. The optimal charging improves the aggregator's revenue, and it can be further enhanced with ES's support. However, it is assumed that the EV charging demand is known. A charging scheme considering a real-time scenario to minimize the EV charging cost is presented [24]. This scheme

includes EV demand, which varies with power tariff and load reduction requests from service providers. The proposed system operates with a dynamic tariff provided by the operator. The EV includes EV demand, which varies with power tariff and load reduction requests from service providers. The proposed system operates with a dynamic tariff provided by the operator. The EV charging schedule is determined by turning on/off each charger available in the PL. An optimal charging scheduling scheme in an office PL is proposed in [25] using a two-stage relative dynamic program. The EV arrival pattern is modeled using the Poisson process. The Poisson process is a model for a series of discrete events where the average time between events is known, but the exact timing of events is random. The primary goal of optimal scheduling is to reduce the cost of EV charging. A penalty cost is also considered if the PL does not provide the requested power. A game-theoretic approach is used to schedule the EV charging [26]. The proposed method considers the variation of hourly energy costs to minimize the charging cost. However, the vehicle charging demand of EVs is very low. Optimal resource sharing to minimize the charging cost is proposed for the municipal PL [27]. A large number of EVs are scheduled in a PL by using a distribution algorithm. The available state of charge (SOC), the time required to reach full battery capacity, and the utility cost are considered to minimize the charging cost. The charging rate is regarded as a continuous variable. A two-layered parking lot for the EV recharging scheme is proposed in [28] to minimize the EV charging cost. The proposed system is compared with the basic charging scheduling scheme, such as FIFS and early deadline first (EDF). However, the recommended procedures require high-level communication network support between the users and aggregators for making optimal scheduling to minimize the charging cost.

Also, optimal scheduling for EVs with random arrival time is proposed in [29]. The battery's capacity, available SOC, charging interval, arrival and departure time, and charging cost are considered for the charging cost minimization. The price is kept constant throughout the charging locations. The EVs are grouped and managed by a local controller. The predicted demands are sent from the central controller to a local controller—the local controller schedules the EV based on the optimization algorithm to charge or discharge the EV. A smart charging method using a third-party agent is presented in [30]. The required power to charge the EV is shared with the aggregator. The aggregator considers the power allocated by the distribution system operator (DSO) and transmission system operator (TSO) to make optimal scheduling. However, this method only finds a monotype of charging. However, implementing this scheme in real-time is not economical due to technical challenges. In [31], smart charging is proposed to enable optimal EV charging. Two algorithms are developed to minimize the charging cost. By analyzing the predicted electricity cost, dynamic programming is designed to obtain the charging cost. However, the forecasted driving profile and power requirements are not always accurate. In [32], an agreement-based approach is proposed to minimize the charging cost. The EV user needs to sign an agreement with the aggregators for charging the EV. The drawback of this method is that the users are forced to charge the EV for a particular time every day. A flexible EV scheduling scheme is developed to minimize the charging cost [33]. In this scheme, the customer details are shared with several operators such as charging service provider (CSP), DSO, and a retailer. The system operator forecasts the EV charging load to find an optimal schedule. However, predicting the EV load is not always accurate. Furthermore, the privacy of the customer is affected. In [34], a price-response based EV scheduling method is proposed using modern communication infrastructure. A base-level aggregator and a central aggregator are involved in the EV scheduling for charging cost minimization. In this scheme, it is assumed that the demand for EVs, plug-in time, and charging time of EVs are known to the base level aggregator. In [35], a charging scheduling scheme is proposed by considering vehicle uncertainty. Bidirectional communication is used for monitoring and controlling the data exchange between aggregators and users. The global aggregator decides the charging management of EVs in the CSs. In [36], a model is developed for the PL operator to charge EVs in a deregulated power market. The objectives are to increase the service provider's revenue and the revenue from renewable resources. In this model, the service provider utilizes the EVs by discharging the power left at the battery. Because of battery power discharging, the expected SOC of the EV may not be reached when the EV customer wants to depart from the PL. A charging scheduling scheme is developed to minimize the EV charging cost in a PL. A bi-level approach to bid the electricity price is

introduced between the aggregator and DSO. This method considers the generation limit with the uncertainty of wind power and charging demand [37]. An optimal charging scheduled is proposed in [38] to minimize the charging cost of PL. The proposed scheme avoids grid disturbance at the distribution level.

An optimal energy management scheme is proposed in a commercial PL [39]. The energy management scheme reduces the cost of the PL operator with respect to the TOU tariff. However, it is assumed that the arrival time of EV and demand of EV is already known to the PL operator. Smart charging management for EVs in a PL is carried out to reduce the charging cost [40]. The PL is equipped with a photovoltaic system and an ESS. The proposed method minimizes the charging cost of the PL. However, the charging scheduling for the un-appointed EVs is not considered. A smart scheduling approach is proposed for the EVs to minimize the cost by reducing the waiting time with a limited charging infrastructure [41]. A simulation is carried out for the EVs to find the CS location on a highway. However, it focused only on travel time and did not consider the EV's energy consumption, varying with the EV speed. An optimal charging schedule for EVs is proposed in [42]. The charging cost variation was calculated by considering the uncertain arrival and departure of EVs. An aggregator controlled dynamic scheduling scheme is proposed in [43] to minimize the charging cost. The objective is derived from the total cost and a penalty cost to the operator if the charging is not completed before the user's timeline. An optimal centralized EV charging scheduling is developed in [44] for minimizing the overall charging cost. But it is assumed that the CS is having an unlimited number of charging points to avoid queuing. Also, if the EV is plugged in for charging, it cannot be plugged out until the battery is fully charged. However, in a dynamic tariff, the energy cost is variable, and hence the scheduling cannot be shifted when the energy price is low. In [45], a cost-effective charging scheme is proposed by considering the output power from a photovoltaic (PV) system. The user preferences, such as charging time, required demand, parking time, etc., are included in this scheme to minimize the charging cost. An optimal day ahead charging schedule is proposed in [46] to reduce the charging cost. The aggregator considers the demand for EVs and the energy price for the optimal scheduling of EVs. A transactive control method [47] proposed two-stage optimal scheduling of EVs for charging cost minimization. An aggregator collects the day-ahead electricity price and the real-time electricity price for the charging. It is assumed that the users give their exact travel patterns for the next day and reserves a charging slot. The customers with flexibility in EV charging time obtain benefits, whereas the other EV users do not benefit. An optimal charging schedule to minimize the charging cost through vehicle-to-grid (V2G) technology is proposed in [48].

The aggregator considers the charging and discharging of multiple EVs in the CSs and minimizes the overall cost. However, frequent charging and discharging will affect the battery's life. A risk-aware day ahead EV charging scheduling scheme is proposed in [49]. This scheme reduces the difference between the actual and forecasted EV load and allocates the power to optimize the cost. The change in forecasted EV load varies with the unexpected arrival of EV. However, the uncertain arrival of more EVs makes the computation more complex. In [50], a charging schedule for EVs is proposed by considering EV users' behavior and output from a PV system. The objective was to minimize the overall charging cost. The proposed scheme estimates the EVs demand as very low. An optimal scheduling scheme was proposed in [51] for EV charging in a CS to minimize charging costs. The service provider calculates the expected scheduling demand and actual scheduling demand. If the actual demand is more than the expected demand, then the service provider cannot meet some EVs' demands. In [52], optimal scheduling is used to minimize the charging cost of the CS operator. A central operator controls the CSs, in which the central operator receives the demand requests for every hour from the CSs. This may create computational complexity in the method.

An optimal scheduling scheme is proposed for EV charging in the CSs [53] to minimize the charging cost. Various renewable energy sources such as wind, solar, and local energy storage devices are used to charge the EVs. The basic FIFS scheme is used for the EV charging. Optimal cost-based scheduling is proposed in [54] by considering renewable energy. This scheme optimizes the EV charging cost by considering the energy price, renewable energy, the arrival, and departure time of EVs. An agent-based decentralized optimal charging scheme is proposed for minimizing the cost [55]. A two-way communication scheme is introduced between the customers and the operators for sharing information such as demand, energy price, etc. A dynamic stochastic optimization method is proposed in [56] to minimize the charging cost. The users have to request the aggregator in advance by using a communication network. The aggregator allocates electric power based on the energy price. A two-stage economic operation of a PL equipped with a microgrid is proposed in [57] to reduce the charging cost. The forecasted electricity price determines the PL operation for the next 24 h. A dynamic algorithm is proposed for a coordinated charging between the EV user and the aggregator in [58]. The proposed algorithm generates the next day's EV schedule based on an EV's previous days driving pattern. The charging schedule suggested by this scheme minimizes the charging cost. However, the actual driving pattern differs from the expected driving pattern. The profit maximization of CS is developed by using an admission control program [59]. The EV demand is modeled from past historical data, and the EVs are suggested to charge in any of the CS located nearby. An optimal cooperative charging strategy is developed for the smart CS to minimize the overall charging cost of the CS [60]. The available battery power and the demand is shared with the aggregator for optimal scheduling.

The literature discussed above shows various EV charging scheduling schemes that can benefit the CS owners. Many researchers solve the charging scheduling for PL cost minimization. However, many of the researchers focus on the fixed power range of chargers and vehicles. Besides, the charging limit of the PL is also not considered. Accordingly, in this paper, the EV charging schedule to minimize the charging cost of the PL is investigated considering controlled and uncontrolled EV charging. The nature of EV charging in the PL is different. In some cases, the charging time is fixed and flexible, whereas in some cases, the charging time is variable; an EV may come with and without appointments. This different nature of PL charging methods motivated the authors to work on an economical charging schedule to minimize the PL's charging cost.

The scheme proposed in this work is more suitable for the PLs with a limited number of charging points. The PL can accommodate more EVs for charging without enhancing the PL infrastructure. The advanced booking may help the customers to avoid unwanted waiting time at the PLs. Finally, the potential of renewable energy sources in the PL is considered to reduce the charging cost. The PL operator suffers from many uncertainties in terms of EVs' energy demand, uncertainties in electricity cost, different arrival and departure time of EVs, and resources available at the PL. Hence, a dynamic charging scheme is considered the main objective of this work to minimize the PL's electricity purchase cost. In general, the arrival and departure of EVs in a PL are unpredictable. The customer has their optional preference to charge the EVs either with a prior booking or without booking. By considering the uncertain arrival of EVs, the dynamic scheduling scheme is analyzed based on the FIFS method, particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA).

The rest of the paper is organized into five sections. The configuration of the system studied is introduced in Section 2. The problem formulation for the scheduling is given in Section 3, in which the objective function, constraints, and the three solution methods used in this work are presented in detail. The results obtained are presented and discussed in Section 4, and conclusions are presented in Section 5. Possible future works are presented in Section 6.
