Grid Integration of Electric Vehicles for Economic Benefits: A Review

Harshavardhan Patil; Vaiju Nago Kalkhambkar

网刊加载中。。。

使用Chrome浏览器效果最佳，继续浏览，你可能不会看到最佳的展示效果，

确定继续浏览么?

复制成功，请在其他浏览器进行阅读

OUTLINE

Abstract

Emissions from the internal combustion engine (ICE) vehicles are one of the primary cause of air pollution and climate change. In recent years, electric vehicles (EVs) are becoming a more sensible alternative to these ICE vehicles. With the recent breakthroughs in battery technology and large-scale production, EVs are becoming cheaper. In the near future, mass deployment of EVs will put severe stress on the existing electrical power system (EPS). Optimal scheduling of EVs can reduce the stress on the existing network while accommodating large-scale integration of EVs. The integration of these EVs can provide several economic benefits to different players in the energy market. In this paper, recent works related to the integration of EV with EPS are classified based on their relevance to different players in the electricity market. This classification refers to four players: generation company (GENCO), distribution system operator (DSO), EV aggregator, and end user. Further classification is done based on scheduling or charging strategies used for the grid integration of EVs. This paper provides a comprehensive review of technical challenges in the grid integration of EVs along with their solution based on optimal scheduling and controlled charging strategies.

Keywords

Electric vehicle (EV) scheduling; economic benefit; vehicle-to-grid (V2G); aggregator; EV grid integration; distribution system operator (DSO); cost minimization

I. Introduction

ELECTRIC vehicles (EVs) with their zero-tailpipe emission have attracted much interest in recent years as an eco-friendly and cost-effective alternative to traditional internal combustion engine (ICE) vehicles. Though ICE vehicles outperform EVs in terms of price and range availability of fuel, EVs has many advantages over ICE such as more efficiency, less noise, no emission, and low maintenance. An electric vehicle has an electrical motor powered by an energy source. In the case of battery electric vehicles (BEVs), the source is a battery, and in the case of plug-in hybrid electric vehicles (PHEVs), it is a combination of ICE and battery. Most of the EVs have the capability of regenerative braking, which can reduce external energy consumption by 20% in urban areas [

1].

Vehicles are parked 95% of the time, and are parked at home 80% of the time, mostly during night hours [

2]. Household EV charger allows EV owners to charge their EVs at night when they are parked, thereby increasing the electricity consumption of the household. Each new registered EV increases the total daily energy consumption by 4-5 kWh in a UK household [3]. Their aggregated effect increases power demand, especially during peak hours [4]. This additional demand has to be satisfied by installing new generation plants and upgrading the existing electrical network. The integration of renewable energy resources (RESs) and energy storage system (ESS) can alleviate these issues. A more novel approach of controlled EV charging flattens the load curve and increases the overnight charging to avoid the congestion in the power grid, additional generator costs of start-up, operation and shut-down [5]. EV chargers affect not only the quantity of power but also the quality of power in the distribution system [6]. These impacts of PHEV on the distribution system can be determined using the driving patterns, charging time, charging characteristics, and EV penetration [7]. In case of public charging, EVs are clustered through a local controller (LC) which acts as an aggregator. It is responsible for optimal charging of each EV and coordinating ancillary services provided by these EVs. EV owners may opt for public charging through this aggregator during day time and household charging through electrical outlet depending on their convenience and driving profile. Generally, level 1 or 2 charging is preferred at a household level, which has a lower power rating and hence requires more time to charge the vehicle. Level 2 or 3 DC fast charging is preferred to charge the vehicle at higher charging rate at public charging stations [8], [9].

Numerous review papers have been presented with grid integration and scheduling strategies for EV in recent years. Most of these review papers have focused on the impact of unidirectional [

9]-[11] and bidirectional [7], [12], [13] EV charging in the distribution system. Other works have classified scheduling methods based on the flow of power with centralized-decentralized control [14], computational scheduling techniques [15], numerical optimization methods [16], vehicle-to-grid (V2G) technology [17], and controlled-uncontrolled charging schemes [18]. However, they do not address the economic impact of EV charging on different participants in the energy market. Considering the above facts, this paper reviews the issue of grid integration of electric vehicles for economic benefits for various players in the energy market.

This paper classifies the existing works on grid integration and charging scheduling strategies of EV based on the economic benefits for different entities in electrical power system (EPS). The study mainly focuses on generation companies (GENCOs), distribution system operator (DSO), EV aggregator, and end user, since they bare most of the economic stakes. This classification of EV integration strategies for different players in the electricity market is shown in Fig. 1.

Fig. 1 Grid integration of EV for economic benefits.

The remainder contributions of the paper are listed below.

1) Discussion of different grid integration and scheduling strategies for PEV in conventional power system and smart grid.

2) A comprehensive review of integration strategies of EV for economic benefits for GENCOs, DSO, aggregator, and end user.

3) Classification of integration and scheduling techniques based on their performance objective.

4) Exploration of open issues and future research areas for grid integration of EV to achieve economic benefits.

The rest of this paper is organized as follows. Section II describes the economic benefits of GENCO. Section III deals with the economic benefits for DSO. Section IV describes revenue maximization techniques for the aggregator. Section V describes cost minimization for the end user. Section VI explores open issues and future research challenges. Finally, Section VII concludes this paper.

II. Economic Benefits for GENCO

In recent years, with the reduction in the cost of RES and distributed energy resources (DERs), conventional centralized generation is shifting towards distributed generation. GENCOs maximize their profits by integrating these cheaper RESs, optimally scheduling units of existing thermal plants and economically dispatching power. In the smart grid, DSM strategies are introduced to manage the load demand rather than power generation. These DSM strategies can be implemented to minimize the load during peak hours or shift the load to the off-peak hours [

19]. In this section, EV integration strategies are classified based on their role in achieving economic benefits for GENCOs.

EV integration strategies related to GENCOs are tabulated in Table I, where the rows indicate the literature referred and the columns indicate the parameters used for classification of strategies. Classification is done based on performance objective, solving method and particular strategy used along with EV integration to achieve the proposed objective, i.e., UC, integration of RESs, DSM, OPF, and use of V2G capability.

Table I Grid Integration of EV: Economic Benefits for GENCO

Reference	Performance objective	Solving method or algorithm used	UC	Integration of RES	DSM	OPF	V2G capability
[20], [21]	Generation cost and emission minimization	Particle swarm optimization	Yes	Yes	No	No	No
[22]	Total operation cost and emission minimization	Fireworks algorithm	Yes	Yes	No	No	Yes
[23], [24]	Total cost minimization	Mixed-integer linear programming	Yes	Yes	No	No	Yes
[25], [26]	Grid operation cost minimization	Mixed-integer programming	SCUC	Yes	No	No	Yes
[27]	Total cost of generation minimization	Non-convex optimization	Yes	No	No	No	Spinning reserve
[28], [29]	Generation cost and emission minimization	Mixed-integer linear programming	Yes	Yes	No	No	Spinning reserve
[30]	Total cost of system minimization	Simulated annealing algorithm	Yes	No	No	No	Spinning reserve
[31]	Generation cost minimization	Game theory	No	No	Valley filling	No	No
[32]	Optimal control for valley filling	Convex optimization	No	No	Valley filling	No	No
[33]	Generation cost minimization	Maximum sensitivities selection optimization	No	No	Valley filling	No	No
[34]	Demand fulfillment in microgrid	Game theory	No	Yes	Load shifting	No	Yes
[35], [36]	Generation cost minimization	Convex optimization	No	No	Valley filling	Yes	No
[37]	Generation cost minimization	Lagrange multiplier	No	No	No	Security constrained OPF (SCOPF)	No
[38]	System-wide generation cost minimization	Linear optimization	No	No	Valley filling	Yes	No
[39]-[41]	Frequency control	Simulation study	No	Yes	No	No	Frequency regulation
[42]	Total operation cost minimization	Monte Carlo simulations	No	Yes	No	No	Yes
[43]	Financial cost of supply minimization	Evolutionary optimization	No	Yes	No	No	Yes
[44]	Wind integration cost minimization	Rolling-horizon algorithm	Yes	Yes	Yes	No	No
[45]	Wind power mismatch and V2G cost minimization	Genetic algorithm coupled with Monte Carlo simulation	No	Yes	No	No	Yes
[46]	Expected cost minimization	Stochastic programming	No	Yes	No	No	Yes
[47]	Generation cost and emission minimization	Linear optimization	No	Yes	No	No	Frequency regulation
[48]	Viability VPP formation	Linear programming	No	Yes	No	No	Yes
[49]	Frequency control	Simulation study	No	Yes	No	No	Yes

A. UC

About 50% of world electricity generation is by coal and nuclear plant [

50]. It is more economical to operate these plants as baseload plants at a constant load level since they cannot change their output quickly [51]. Higher uncoordinated penetration of EVs leads to reshaping of the existing load profile, which results in more power plant start-ups and shutdowns and leads to higher operation costs [38]. Overnight charging of EVs shows various benefits. Optimal dispatch avoids the underloading of the units of baseload power plants, additional generation requirement, and start-up cost of existing units [5]. Controlled charging of EVs coordinated with the units of thermal power plants can achieve economic benefits for the GENCOs.

1)　UC Considering RES

Combined optimal scheduling of thermal power plants, RESs, and EVs can reduce the generation by the thermal plant. It can also minimize the running cost and emission from the thermal plants.

References [

20] and [21] have generated on/off schedules for the units of thermal power plants for UC problem while considering the integration of EVs and RESs. EVs are charged through RESs as loads and discharged to the EPS as a source during peak hours in a smart grid model [20]. Compared with load levelling model, the proposed smart grid model can achieve more savings in total cost and emissions from thermal plants. A similar approach has been used in [22], where thermal units in the smart grid are scheduled considering DERs and PEVs. However, in both of these cases uncertainties due to RESs, load, and EVs have not been considered. Valid scenarios for uncertainties of RESs, load, and EVs based on the study in [52] can be used to improve the UC problem in order to maximize the utilization of RES and EVs in the smart grid and minimize the cost and emission from thermal units [21]. Uncertainties due to wind can also be addressed by a two-stage stochastic unit commitment (SUC) model for hourly UC and economic dispatch. A smart charging technique can be implemented along with SUC to minimize the operation cost and commitment of expensive units [24]. Security constrained unit commitment (SCUC) model can coordinate the PEV fleet and large-scale wind power sources with a thermal generation unit [25], [26]. In hourly UC, a dispatch from different generation units and charging/discharging states of PEVs can be decided to minimize the grid operation cost and dispatching costs of expensive thermal units [25]. An aggregator model, along with standard SCUC constraints, can reduce the operation cost of thermal units and integration of large-scale RESs [26]. But for a more realistic scenario, the UC problem has to be modified for different generation mix, EVs, and RES penetration [23].

2)　UC Considering Spinning Reserves

Spinning reserve is generation capacity synchronized to the system equipped to respond immediately to serve the load in case of a system contingency [

51]. Spinning reserves are online but unloaded, and can reach the full capacity within 10 min. V2G capability of EV is an economically viable option for ancillary services such as spinning reserve and frequency regulation [53].

EVs can provide an operation reserve to optimize the day-ahead spinning reserve requirement (SRR). SSR reschedules the unit such that thermal generators can be turned off when EVs act as a spinning reserve. This optimization minimizes the scheduled spinning reserve and generation operation cost, and improves the reliability of EPS [

27]. Different operation models for the integration of EVs can be compared based on the power flow and reserve capabilities. It can be observed that the performances of both unidirectional and bidirectional power flow with the reserve are equivalent. Both models minimize the operation costs from the units by providing energy and reserve services [28]. These same models have been incorporated in [29] to present a mixed-integer linear model for flexibility studies of modern EPS with high penetration of RESs and EVs. Deterministic and probabilistic methods are generally used to model the reserve supply. These models optimize either the cost of spinning reserve or the value of risk. By considering the value of load loss (VOLL) of each significant customer, both the total risk and total cost of the system can be minimized [30]. Thus, V2G capability with the aggregator model is a viable alternative to the units of thermal power plants.

B. DSM

DSM is a modification in the consumer demand profile while considering their convenience.

The individual strategy of each EV can be updated with respect to the average charging strategy of the infinite EV fleet [

31]. Charging strategy varies based on the real-time marginal electricity price and homogeneity of the EV population. The control signal sent by the utility can also be used rather than marginal electricity price to update EV charging profiles [32]. A smart load management (SLM) system proposed in [54] reduces overall system overloads and power peaks. SLM can be implemented in real time (RT-SLM), which enables PEVs to begin charging as soon as possible, considering priority-charging time zones and network operation criteria. RT-SLM achieves cost reduction through the deferment of costly upgrades and building of new generation plants [33]. For a microgrid, a real-time decentralized DSM (RDCDSM) can be implemented using a home energy management system. RDCDSM allows microgrid to follow the predetermined purchase plan in real time and avoid the higher costs of activating new generators at instantaneous market price [34].

Most of the DSM strategies are decentralized and require less computation time and implementation cost. The decentralized nature and shorter computation time have enabled the service providers to implement these strategies in real time.

C. OPF Strategies

All generation units and loads are not connected to the same bus; hence, economic dispatch may result in unacceptable flows or voltages in the network. An OPF can solve this problem and minimize the total generation cost. Both equality and inequality constraints are considered in OPF.

EV scheduling problem for charging can be converted into the OPF problem to obtain a joint OPF-charging optimization [

35], [36]. A solution to this problem optimizes the network performance by minimizing the total generation and charging costs while satisfying the constraints. Dynamic OPF problem is associated with both price inelastic load (exact power requested by this load must be provided) and price elastic load (the power delivered is affected by current electricity price, i.e., the EV). PHEVs can be used to support the grid during contingencies by adding additional constraints to OPF problem to form an SCOPF. SCOPF solves a system for

N - 1

contingency and minimizes the overall operation cost of the power generation [37]. The centralized and decentralized schemes for optimal scheduling of EVs are proposed in [38]. In a centralized scheme, EV batteries are optimally dispatched with a multiperiod OPF; while in a decentralized scheme, EVs optimizes their charging based on day-ahead time-of-use (TOU) tariffs to minimize the generation costs.

D. Integration of RESs

In recent years, the penetration of RESs in the power system has increased rapidly and become the fastest developing renewable energy technology [

55]. Maximizing the utilization of RES reduces the costs of supply from the energy market and emission of pollutants such as CO₂ and NO_x [47]. Still, the intermittent nature of RESs makes it challenging for the utility to integrate them into EPS. When RESs are integrated with the power grid, their variable generation could cause frequency fluctuation in the power grid, which destabilizes the power system and gives rise to the power quality and power fluctuation issues [56]. Additional power balancing or regulation can mitigate the fluctuations and achieve a stable power system operation and control. In a conventional EPS, power balancing is provided by the connected power plants which are primarily thermal power plants. However, the integration of EVs along with RESs can form a virtual power plant (VPP), which acts like a conventional power plant [47]-[49]. Compared with spinning reserves, regulation service is invoked more frequently and requires an immediate response. It is required to continue its operation for shorter duration [57].

1)　Fluctuation Mitigation

An autonomous distributed V2G control (ADC) scheme can provide a distributed spinning reserve for unexpected frequency fluctuations caused by the RESs. V2G control provides distributed spinning reserve while smart charging control satisfies the charging requirements of EV owners [

39]. In an improved decentralized V2G control (DVC) method, the battery SoC holder (BSH) strategy maintains the residual battery energy, while the charging and frequency regulation (CFR) strategy schedules EV charging and participates in frequency regulation [40]. DVC dispatches V2G power for the scheduled duration of PEV, whereas ADC waits till the charging demand is reached to dispatch the V2G power. Thus, compared with ADC, the DVC is more beneficial for suppressing system frequency fluctuation and tie-line power deviation. Reference [42] proposes an optimization model for V2G dynamic regulation of the EVs connected to the distribution network with RESs. The proposed model uses V2G to smooth out the power fluctuation from RES penetration and minimizes the operation cost. A load frequency control (LFC) method for conventional power plants, battery energy storage systems (BESSs), EVs, and heat pump water heaters (HPWHs) is proposed in [41], which provides an alternative to BESS with the integration of EVs and HPWHs while suppressing the frequency fluctuation from the integration of RES. Similar LFC models with V2G capability are proposed in [49], [58] in order to minimize the power exchange deviations, and regulate power required from conventional power plants.

2)　Supply Cost Minimization

In a multiagent system, each EV acts according to the dynamic conditions in the surrounding environment while integrating the RESs. While forming a solution, this system considers static and a more accurate dynamic behaviour to minimize the cost of the power supply [

43]. A novel idea of the two-hour window of flexibility is proposed in [44]. PEV load is shifted around within two hours to reduce the ancillary cost of wind uncertainty and variability. Similarly, a collaborative strategy between the wind participants and EV owners is proposed in [45] to mitigate the effects of wind power uncertainties by optimal charging/discharging of EVs. This strategy minimizes the sum of the penalty cost associated with wind power imbalances and V2G expenses related to purchased energy, capital costs, and battery wear. Power management scheme proposed in [46] uses PHEV fleet as ESS to minimize the expected cost. This cost is the sum of total cost of thermal energy generation, cost of power from a diesel generator, cost of power waste of RESs and cost of battery degradation.

3)　VPP

VPP with its ability to deliver peak power in a short time with higher efficiency and more flexibility can replace conventional power plants. Reference [

47] proposes a VPP comprising RESs and EVs to reduce the cost by 29.5% and emission of CO₂ and NO_x by 79% and 83%, respectively. An agent-based approach is proposed in [48] where wind generators and EVs form a VPP for the day-ahead and real-time markets. EVs act as an energy storage and they are paid for storage in the form of charging credits. This approach maximizes the profits of the VPP and also calculates the amount of EV storage required to maximize this profit. A local controller (LC) is proposed by [49] to synchronize state of charge (SoC) of the EVs considering the convenience of customers. LC treats EVs as virtual BESSs and controls their charging/ discharging according to the load frequency signal. The EDISON project on the Danish island of Bornholm uses a centralized control scheme to aggregate EVs as a DER. In this project, the optimal integration of EVs allows the electric vehicle virtual power plant (EVPP) to support the island electric grid [59].

Most research works on economic benefits for GENCOS are related to the integration of RESs and UC of existing generators. With the increasing penetration of RES, future EV grid integration and economic strategies should focus on minimizing the disadvantages of RES integration. The V2G capability of EV can be used to provide ancillary services such as spinning reserve and frequency regulation in order to stabilize the grid and minimize the intermittency due to RESs.

III. Economic Benefits for DSO

A DSO is responsible for the operation and maintenance of the distribution system to ensure the long-term capability of the system to meet reasonable demands for the distribution of electricity [

60]. The distribution system has certain limitations including voltage limit, thermal limits of equipment, congestion and protection setting. In the case of direct or uncoordinated charging, EVs start charging when it is plugged into the power grid, and stop when the battery is fully charged. These uncoordinated and random charging activities create stress on the distribution system causing voltage fluctuations, suboptimal generation dispatch, and degraded system efficiency. It also increases the possibility of blackouts because of network overload [54]. Extensive penetration of EV also has a significant effect on the distribution system in terms of harmonics and losses, potential transformer overloads, feeder congestion, and undue circuit faults [61]. EV battery chargers can affect the transformer due to total harmonic distortion, cables due to ohmic losses, and circuit breakers and fuses due to harmonic distortion [62]. EV scheduling or coordinated charging can alleviate these ill effects and provide optimal and smooth operation of the distribution system.

EV integration strategies related to DSO are tabulated in Table II, where the rows indicate the literature referred for this study, and the columns indicate the parameters used for classification of strategies. Classification is based on the performance objective, solving method, and particular strategy used along with EV scheduling to achieve the proposed objective (i.e., loss minimization, cost benefits, DSM, and maximum power transfer) and provision of V2G.

Table II Grid Integration of EV: Economic Benefits for DSO

Reference	Performance objective	Solving method or algorithm used	Loss minimization	DSM	Maximum power transfer	V2G capability
[33]	Grid energy loss minimization	Maximum sensitivities selection optimization	Yes	Valley filling	No	No
[54]	Voltage profile improvement	MATLAB based algorithm	Yes	Peak shaving	No	No
[63]	Charging maximization and cost minimization	Linear optimization	Yes	No	No	-
[64]	PHEV impact minimization	Heuristic or sequential method	Yes	No	Yes	No
[65]	Power loss and charging cost minimization	Multi-objective particle swarm optimization	Yes	No	No	No
[66]	PEV charging impact estimation	General algebraic modelling system	Yes	No	No	No
[67]	Total energy consumption and PAR minimization	Game theory	No	Peak shaving	No	No
[68]	Peak power demand minimization	Linear and convex optimization	No	Peak shaving	No	Yes
[69]	Peak demand minimization	Two-stage V2G control algorithm	No	Peak shaving	No	Yes
[70]	Electricity demand cost minimization	Proposed control algorithm	No	Peak shaving	No	No
[71]	Total energy cost and peak demand minimization	Game theory	No	Peak shaving	No	Yes
[72]	EWH power consumption control	MATLAB simulation	No	Peak shaving	No	No
[73]	PHEV impact assessment	MATLAB simulation	No	Peak shifting, load shedding	No	No
[74], [75]	Distribution transformer utilization improvement	Proposed control algorithm	No	Peak shifting	No	No
[76]	Load curve flattening of LVT	Convex optimization	No	Peak shaving	No	No
[77]	Cost minimization	Heuristic-based	No	Load shedding	No	No
[78]	maximize power delivered to EV	Linear programming	No	No	Yes	No
[79]	Grid congestion minimization	Sequential quadratic programming	No	No	Yes	No
[80]	Congestion prevention	Lagrange multiplier	No	No	Yes	No
[81]	Multiple EV charge optimization	Lottery-based resource allocation	No	No	Yes	No

A. Loss Minimization

The losses in the distribution system increase when a system operates nearer to its limits. The increase in these power losses incurs substantial financial losses for DSO.

An optimal scheduling problem is formulated in [

63] considering distribution system constraints such as transformer rating, current rating of lines, voltage drop and phase unbalance. Among different proposed strategies, greedy charging allows the existing network to add 50% more vehicles while improving phase unbalance. Also, greedy charging with price consideration allows the consumer to charge at 90% of uncontrolled charging cost. The impact of uncoordinated and coordinated charging of PHEV can be obtained by constructing a load profile using historical data. Coordinated charging reduces power loss as well as voltage deviations compared with uncoordinated charging. This strategy can be extended to other objective functions such as voltage control by controlling EV reactive power output and grid balancing [64]. A relation between power loss, charging cost, and load demand can be used to form an optimal charging schedule for EV. This schedule minimizes the power loss as well as the charging cost of EV [65]. The loss-optimal strategy can be used with demographical data to estimate the number of vehicles during different hours of the day, for different areas to minimize the network losses [66]. Implementation of the loss minimization strategies improves the lifetime and efficiency of distribution system equipment.

B. Peak Shaving and Peak Shifting

In this subsection, peak shaving and peak shifting strategies of DSM used to reduce the stress on the distribution system have been reviewed.

EV scheduling algorithm can be implemented using an energy consumption scheduling (ECS) device to balance the load among residential customers sharing a mutual energy source. ECS reduces the peak-to-average ratio (PAR) as well as the total energy cost in the system [

67]. A fast-converging distributed demand response (DR) algorithm minimizes the PAR of the aggregated daily power demand of residential EVs. V2G capability provides energy during peaks so that the peak is reshaped towards 0% penetration of EV without affecting the users’ convenience [68]. A similar approach is proposed in [69], where a coordinated V2G control scheme adjusts charging-discharging rates of PEVs to reduce peak loads at the distribution level.

A power monitoring and control system (PMCS) can be used with V2G and vehicle-to-vehicle (V2V) charging capability for peak shifting and demand response during high-demand periods. It limits the disincentive due to uncontrolled charging of large-scale EV penetration [

70]. A smart load management (SLM) control strategy can schedule starting time and charging time of the individual EV. SLM maximizes the grid performance by shaving the peak demand, improving voltage profile, and minimizing power losses [54]. A centralized controller in a garage of a building can derive smart charging-discharging process for EVs. This approach allows the controller to optimize the energy consumption profile of the building while minimizing the peak load and total energy cost [71]. Electric water heaters (EWHs) can be used as thermal ESS which stores energy in hot water tanks in the absence of PEV and uses it when PEVs are being charged [72].

Significant research on DSM strategies for integration of EVs [

73]-[75]. A demand management strategy (DMS) can be implemented in the smart grid for normal and quick charging. DMS sets a specific setpoint in the case of normal charging. EV is charged only if the total demand drops below this setpoint. In the case of quick charging, non-critical loads are shed when EVs start charging [73]. Residential loads are classified into two categories: controllable loads (e.g., EVs) and critical loads [74], [75]. The load can be shifted or shaped to alleviate the overloading condition of a distribution transformer [74]. The same load reshaping technique is used in [75] to provide optimal charging of EVs while maintaining the original peak demand with different EV penetration levels. Load curve of low-voltage transformer (LVT) can be flattened while satisfying each PHEV consumer’s requirement within a specified time [76]. An EV manager is proposed in [77] to shed the charging loads using heuristics techniques when overloading of transformer and lines is detected. Most of the peak shifting and shaving strategies provide more economic benefits to DSO compared with an EV owner.

C. Maximum Power Transfer

Maximum power flow in the power grid without considering distribution system constraints can lead to voltage fluctuation and overloading of the equipment. Hence, strategies with maximum energy transfer to the consumer should be implemented while satisfying the constraints of distribution system.

Load factor (LF), load variance (LV), and distribution system losses are co-related. Maximizing LF and minimizing LV can reduce losses if the power consumed by EV along with baseload and total current are both below the optimal value. Different algorithms are formulated based on this assumption: minimizing losses, maximizing LF, and minimizing LV [

64]. A standard and a weighted objective function are proposed in [78] to maximize the total amount of energy delivered to all EVs while satisfying network limits. The standard objective function maximizes energy delivered to all EVs by optimizing the charging rate of each EV. The weighted objective function modifies the standard objective function by applying weights to each EV based on the SoC at the previous time step. This improvement distributes the charging in the system more evenly while prioritizing EVs with low SoC. Instead of a large group of EV, an individual charging plan for each EV allows the maximum power flow through the network while considering distribution system congestion and EV requirements [79]. A distribution system capacity market scheme regulates the energy schedule between DSO and fleet operator to avoid congestion [80]. A PEV allocation policy in the distribution system using the lottery scheduling algorithm is proposed in [81]. This allocation strategy enables multiple PEVs to charge simultaneously at different charging rates while ensuring fair treatment to all consumers. Implementation of these strategies can allow the optimal utilization of the existing distribution system for extensive penetration of EVs.

Most of the EV charging and scheduling strategies reviewed in this subsection provide short-term solutions. As the penetration of EVs increases, the implementation of proposed strategies either violates the customers’ comfort levels or network constraints. Thus, it becomes necessary to modify the existing network for large-scale integration of EVs. In recent years, DGs are being integrated at the distribution level. It creates a unique opportunity for DSO to generate energy locally with DERs and store it in ESS and EVs to minimize the cost of energy purchased from the transmission network.

IV. Economic Benefits for Aggregators

EV charging at public places such as large parking complex, charging stations, office, and apartment parking can be clustered through an aggregator. It acts as a centralized system operator who controls the charging and scheduling of each EV. The aggregator can also use several intelligent control units to implement decentralized charging of EV. A decentralized approach can provide high computation efficiency with similar results as centralized charging [

82]. Aggregators receive revenue from two sources, a fixed percentage of the ancillary services revenues, and a fixed mark up over price on energy consumed by EV owner [83], [84]. Based on this hypothesis, EV scheduling strategies are classified according to the revenue source. In the first case, the revenue is from the utility as payment for ancillary services; while in the second case, the revenue comes from EV owners or end-users as payment for EV charging.

EV integration strategies implemented by an EV aggregator are tabulated in Table III, where the rows indicate the literature referred for this study and the columns indicate the parameters based on which strategies are classified. Classification is done based on the performance objective, solving method, possibility of V2G capability, and the provision of ancillary service.

Table III Grid Integration of EV: Economic Benefits Aggregator

Reference	Performance objective	Solving method or algorithm used	V2G capability	Ancillary service
[85]	Aggregator revenue maximization	Linear programming	Yes	Frequency regulation
[86], [87]	Aggregator revenue maximization	Linear programming	Yes	Spinning reserve and frequency regulation
[88]	Aggregator profit maximization	Mixed-integer linear programming	Yes	Frequency regulation
[89]	Aggregator profit maximization	Stochastic linear programming	Yes	Frequency regulation
[90]	Online scheduling of EV	Convex optimization	Yes	Frequency regulation
[91]	Regulation quality improvement	Convex optimization	Yes	Frequency regulation
[92]	Energy trading profile maximization	Scheduling and dispatching algorithm	No	No
[93]	Aggregator revenue maximization	MILP model and heuristic algorithm	Yes	Frequency regulation
[94]	Aggregator revenue maximization	Dynamic programming	Yes	Frequency regulation
[95]	Charging discharging cost minimization	Linear and quadratic optimization	Yes	No
[96]	Charging cost minimization of PHEV	Mixed-integer linear programming	No	No
[97]	Energy trading cost minimization	Mixed-integer programming	Yes	No
[98]	Total electricity cost minimization	Linear programming	Yes	No
[99]	Aggregator revenue risk management	Lagrange relaxation	Yes	No
[100]	Aggregator revenue maximization	Linear programming	Yes	Frequency regulation
[101]	Aggregator profit maximization and charging cost minimization	Heuristic dynamic optimization	Yes	No
[102]	EV user cost minimization	Quadratic programming	Yes	Frequency regulation
[103]	Social wellfare maximization	Dynamic programming	No	No
[104]	Cost of electricity for PHEV minimization	k-nearest neighbors (kNN) classification	No	No
[105]	Aggregator revenue maximization	Mixed-integer linear programming	Yes	No

A. Revenue from Utility

Ancillary services such as spinning reserve and frequency regulation are necessary for grid reliability and voltage and frequency stability. Compared with spinning reserve, regulation services are more frequently used [

57]. V2G capability of the EV can be used to provide these ancillary services at the distribution level. Generally, revenue for ancillary services is obtained from two sources: capacity payment (regardless of V2G service) and energy payment (based on actual kWh supplied with V2G) [53]. The aggregator can cluster a large fleet of EV to increase their capacity payment, which depends on the capacity of the connected load. During peak hours, aggregator can maximize the revenue with energy payment for providing the reserve and regulation services.

References [

85]-[87] have proposed the concept of set point called preferred operating point (POP). This concept is based on studies from ancillary market [106]. The POP is the average power drawn. The aggregator varies the charging rate of an individual EV around POP to provide regulating power. The smart charging algorithm, namely price-based, load-based, maximum regulation (MaxReg) based, and their optimal counterparts, i.e., optimal price (OptPrice), optimal load (OptLoad), optimal maximum regulation (OptMaxReg), are formulated. These algorithms minimize the charging cost of EV while maximizing the profits of the aggregator through EV charging and providing regulation. Unconstrained OptMaxReg algorithm produces higher profits compared with other strategies [85]. Based on these sub-optimal algorithms, three optimal algorithms, i.e., OptPrice, OptLoad, and optimal combined bidding (OptComb), are formulated. These algorithms maximize profits for the aggregator while increasing the benefits of customers and utility. Unconstrained OptComb algorithm produces a higher profit for aggregator [86]. Restriction in [86] of combined bidding of energy sales and multiple ancillary services is addressed in [87]. This modified algorithm maximizes the aggregator profit while providing ancillary services and peak load shaving to the grid and low costs of EV charging to the customer.

The profit of aggregator from participating in competitive energy and regulation reserve markets can be maximized with optimal bidding strategy [

88], [89]. This profit can also be used to reimburse EV owner for battery degradation due to V2G operation [88]. A multi-level architecture for bidirectional EV regulation service is proposed in [90], [91]. This architecture consists of aggregators, EVs, and grid operators. The scheduling problem of V2G regulation can be formulated based on the current and past regulation profiles ignoring accurate forecasting of regulation demand [90]. Forecasting-based algorithm, which improves [90] and online scheduling algorithm proposed in [91], enhances the quality of the regulation service received by the EPS. These algorithms can be implemented in real time, and are compared with the algorithm proposed in [32] while considering the discharging of EV batteries. In contrast to previous research where the objective is to maximize the aggregator profit with ancillary services, [92] has proposed optimal scheduling and dispatch of PEV fleets to improve the profits related to energy trading.

Most of the existing aggregator models control the spinning reserve and frequency regulation service. In recent years, tremendous development has been made in regards to the capability of EV in providing ancillary services such as reactive power, voltage control, and black start capability. The use of EVs in providing reactive power compensation has been explored in [107]-[109], while [110]-[112] describe strategies to restore the EPS without black-start generators. Even though the implementation of these strategies are beneficial to utility, aggregator and EV users, their economic viability is yet to be explored.

B. Revenue from End User

EV aggregator is responsible for optimal charging of large fleet of EV. While satisfying the charging demand of each EV, an aggregator can buy energy from the power grid at a lower price and sell it to the customer at a higher price. It can also maximize the revenue by optimizing the charging of each EV and adding a fixed mark up over energy price consumed by that EV.

The scheduling of EVs and ESS can minimize additional cost caused by load mismatch of the aggregator in the real-time market. Aggregator schedules EVs with the aid of ESS while considering practical day-ahead and real-time electricity market trading. This strategy improves the aggregator’s revenue by 80.1% and a further increase of 7.8% with the aid of ESS [

93]. Charging rate can be treated as a control variable rather than charging sequence to maximize the aggregator’s revenue. The aggregator provides maximum power to EVs whenever the regulation price is low and maintains EV idle whenever the regulation price is high [94]. Charging-discharging of EV can be optimized while considering driving pattern and variation of the electricity price. It allows the aggregator to minimize the charging cost by charging EVs overnight during the low-tariff period. Also, V2G capability can be implemented if tax incentives are more favourable than the difference between day-time and night-time tariffs [95]. A bidding strategy for the day-ahead market allows the aggregator to minimize the cost of purchasing the charging energy for PEV [96]. A classification scheme is proposed in [97] to minimize the total cost of energy trading with different energy entities. In this scheme, EVs are classified into different classes with different pricing, charging rate, allotted power, and charging time. Aggregator maximizes its profit by optimally charging each class of EVs based on the strategy suitable for that class while saving on the energy purchased from the power grid. In the smart grid, an aggregator can jointly optimize the EVs and thermostatically controlled appliances (TCAs) so that EVs charge their batteries during off-peak hours and discharge the energy to supply the TCA system during peak hours [98]. The aggregator can also use an information gap decision theory (IGDP) based approach to meet desired profit target through effective risk management [99].

Scheduling strategies in [

85] and [94] maximize the revenue of the aggregators, but they ignore the welfare of consumers. For overall welfare, the scheduling strategy should maximize the revenue of aggregator, minimize the total charging cost for the consumer while satisfying the charging parameter specified by the consumer [100]. A multi-objective problem proposed in [101] maximizes the aggregator’s profit, minimizes the charging cost for EV users, and maximize target SoC for EV. The proposed Myopic aggregator model offers the highest aggregator profit while Myopic EV model provides the least total charging cost for an EV owner. In [102], a model predictive control (MPC) based charging, and frequency regulation algorithm is used to design a V2G aggregator. This V2G aggregator minimizes the cost of all the EVs in the system while simultaneously ensuring that vehicles are charged to the desired level at the plug-out time. A decision-making problem as a stochastic deadline scheduling problem is formed in [103] to maximize the overall social welfare, where the social welfare is a function of total customer utility, the sum of electricity cost associated with EV charging and the penalty for not meeting EVs’ charging requests. A prediction-based charging scheme predicts the market prices during the charging period in order to determine an appropriate time of charging (TOC) with low costs. If the predicted price is higher than a threshold, charging is shifted to more suitable TOC. This scheme reduces the operation cost of PHEV along with CO₂ tailpipe emissions [104]. A conceptual aggregator model proposed in [105] uses a different approach of V2V energy exchange mode where inter-vehicle communication is established through an aggregator to allow the exchange of energy. The use of V2V over V2G reduces the energy cost for the consumer while maximizing the aggregator revenue [113].

With reliable information on optimum penetration and EV mobility, an aggregator can purchase the bulk energy at a cheaper rate in a day-ahead market and implement ESS to minimize the energy purchasing at peak hours. Thus, the aggregator and EV owner can achieve economic benefits at optimum penetration of EV through the aggregator.

V. Economic Benefits for End User

End users are mainly the EV owners who purchase power from DSO directly. They charge their vehicles at home through household electrical outlets with Level 1 or 2 chargers mostly during the night time when they are parked. EV charging strategies can be classified into two types based on the flow of power: charging cost minimization and total cost minimization. In the first case, the power flow is unidirectional, i.e., EV is only charged from the power grid. Thus, only the charging cost is considered while formulating a strategy. In the second case, the flow of power is bidirectional, i.e., G2V and V2G, where EV is charged from the power grid as well as discharged to the power grid. V2G capability creates a unique opportunity for end users to minimize the charging cost by charging their EVs during off-peak hours (i.e., low tariff) and discharging during peak hour (i.e., high tariff).

EV integration strategies implemented by EV end users are tabulated in Table IV, where the rows indicate the literature referred for this work and the columns indicate the parameters based on which strategies are classified. Classification is based on performance objective, solving method and provision of V2G.

Table IV Grid Integration of EV: Economic Benefits for End User

Reference	Performance objective	Solving method or algorithm used	V2G capability
[66]	PEV charging impact estimation	General algebraic modelling system	No
[114]	Charging cost minimization	Heuristic method	No
[115]	Charging cost minimization	Linear and quadratic approximation	No
[116]	Charging cost minimization	Quadratic programming	No
[117]	Total cost of fuel and electricity minimization	Multi-objective genetic algorithm	No
[118]	Total charging cost minimization	Convex optimization	Yes
[119]	Total charging cost minimization	Electricity price based control algorithm	Yes
[120]	EV profit maximization	Non-linear programming	Yes
[121]	Total charging cost minimization	Proposed price based algorithm	Yes
[122]	Daily electricity cost minimization	Dynamic programming	Yes
[123]	Spinning reserve and user cost optimization	Proposed algorithm	Yes
[124]	EV scheduling considering battery wear cost	Mixed-integer linear problem	Yes

A. Charging Cost Minimization

EV owners charge their vehicles at home during the night time or at the workplace during the day time. In the unidirectional power flow, EVs are only charged from the power grid and the charging cost is due to the energy purchased from the power grid by end users.

In a regulated market, EV charging can be controlled in response to TOU electricity price. Single EV system avoids peak demands and chooses to charge intelligently during the valley time, thus reducing charging costs. Multi-EV optimized charging shifts the mass of peak load to valley load and flattens the load curve [

114]. Price-optimal charging strategy proposed in [66] uses demographical data to estimate the number of vehicles during different hours of the day for different areas, and shifts the charging to off-peak hours. This charging strategy minimizes the electricity cost paid by residential users by 15% compared with uncontrolled charging. In the case of a fleet of EV, the charging plan is formulated for each EV to minimize charging costs. optimized charging plan achieves satisfactory SoC levels, optimal power balance, and considers violations of the battery boundaries [115]. An optimization problem can be formulated for EV charging to minimize the charging cost by considering battery charging characteristics, charging demand, and energy price. This will lead to EVs charged during off-peak, which results in saving on low EV penetration [116]. The charging trajectory of PHEV is the timing and rate with which the PHEV draws power from the EPS. A delaying charging strategy can be used to delay the charging toward the TOU so that batteries are charged based on the need of users right before the TOU. It minimizes the total cost of fuel and electricity as well as battery health degradation [117].

B. Total Cost Minimization

Total cost is the difference between charging cost and discharging cost. Based on the tariff set by the utility and the overall power demand, EV owners can schedule their EVs to charge during off-peak hours and discharge during peak hours using the V2G capability. It provides the end-users incentives in their charging cost while mitigating the overloads in the network.

The charging/discharging of EVs can be implemented in centralized (globally optimal scheduling) or decentralized (locally optimal scheduling) manner. Globally optimal scheduling is impractical since it requires future information of EV mobility. In a more realistic locally optimal scheduling, an LC instructs each local EV to charge or discharge its battery with the optimal charging power based on information from the central controller [

118]. Charging-discharging control of EVs based on electricity price signal has been investigated in [119]. This control strategies take into account EV capacity, electricity price, SoC and EV movement within the network but neglects the battery degradation due to the fluctuation in SoC. To mitigate this issue, [120] has used the age of battery to impose a limitation on the number of switching between charging and discharging. This limitation minimizes the charging cost while safeguarding the battery health. Based on a real-time price signal, the aggregator can shift power consumption of EV to low-demand periods [121]. Smart charging can be implemented using two strategies in the deregulated electricity market. In the first strategy, smart charging is implemented without any regulation service as an alternative to fast charging, which reduces the daily electricity cost. In the second strategy, the provision of V2G regulation power is implemented along with the first strategy. End users can attain a daily profit, including the cost of driving with V2G regulation [122]. Uncoordinated scheduling increases spinning reserve and charging costs, whereas smart scheduling minimizes charging costs and the spinning reserve. With a more coordinated approach, spinning reserve power can be elevated above the requirement of the power grid while minimizing the charging cost of EV [123]. A practical lithium-ion battery wear model is incorporated within the optimal scheduling of EVs in [124] to minimize the total charging cost.

Various strategies reviewed in previous sections are multi-objective. Though their primary objectives are different, their implementation provides the added benefit of charging cost minimization for end users. The strategies proposed in [

96], [100], [102], [104] maximize the aggregator revenue while minimizing the charging cost of EV. The strategies in [32], [36], [63], [65] also minimize the charging cost for end users while satisfying their primary objectives. Total cost minimization strategies provide more cost savings compared with charging cost minimization. However, the implementation of these strategies is complicated since tariffs for V2G services for end users are not available, and smart grid infrastructure is necessary.

VI. Open Issues and Future Research

In this section, some of the significant research challenges and technical hurdles for the implementation of grid integration strategies to achieve economic benefits are explored.

A. Commercialization of V2G

The analysis of the recent works related to grid integration of electric vehicles indicates that the majority of the strategies use V2G capability of the EVs to achieve economic benefits. Most of the research work focuses on the technical aspects of V2G rather than the commercial implementation with the existing systems. The commercialization of V2G requires active participation of the EV manufacturers, utility and end users. Most of the EVs in the market use unidirectional chargers only to charge their vehicles. Though many strategies are proposed for V2G implementation, very few successful studies exist for commercialization of V2G system. Bidirectional chargers and sophisticated power electronics are the main challenges for the implementation of the V2G system. The switching between charging and discharging can degrade the battery of EV; however, this issue has already been addressed by researchers [

124]. Also, conventional distribution system needs to be reinforced for the bidirectional flow of power from EVs.

B. Connectivity and Security

Aggregator and utility require constant information regarding power grid conditions, EV mobility and customer preferences for optimal control of EVs. Most of the PEVs will be equipped with different communication channels like Zigbee, power line carrier, cellular data, WiMax. Nevertheless, one-way power and communication in the conventional power grid will make it challenging to integrate EVs. The communication in the smart grid between EV and different players increases the risk of a security breach. It can violate the confidentiality and privacy of the system and can pose a serious threat to EV owners. There is also a possibility of end users falsifying the information related to ancillary services and charging demand for the added profits.

C. Charging and Power Infrastructure

Large-scale integration of EVs requires the improvement in the current power infrastructure, mainly in the generation and distribution sectors. To accommodate the increasing demand from the EVs, the present generation capacity needs to be increased while the distribution network needs to be upgraded to allow the additional power flow. In addition to existing charging infrastructure, the introduction of the fast-charging stations and battery swapping stations will allow faster charging of the EVs while reducing the charging time.

D. Incentive Policies

Currently, EV users do not receive any economic incentives to control EV charging time, power demand and provision of V2G power. Even though researchers have proposed different strategies beneficial to different players in the energy market, government oversight is needed for the successful implementation of these strategies. Government policies can accelerate the adaption of EVs in the existing system while proposing a framework to provide economic incentives to different players. The utilities also need to set forth the V2G tariff, incentive policies for ancillary services and battery degradation due to V2G.

VII. Conclusion

Large-scale penetration of EVs can create a gap between supply and demand. The increase in the power demand from the EVs has to be satisfied by additional generation units, and current network infrastructure has to be updated to lessen the congestion. However, optimal scheduling or charging of EVs can alleviate these issues while providing economic incentives to different entities in EPS. Optimal scheduling of EVs can reduce the generation cost by the integration of RESs and optimizing UC of existing units. The stress on the distribution system can be minimized by implementing loss minimization and DSM strategies. Aggregators and end users can maximize their profits by optimizing the charging of EVs and providing power grid support services. Since most of the proposed strategies are multi-objective, their implementation can provide economic benefits to more than one player.

REFERENCES

S. van Sterkenburg, E. Rietveld, F. Rieck et al., “Analysis of regenerative braking efficiency–a case study of two electric vehicles operating in the Rotterdam area,” in Proceedings of 2011 IEEE Vehicle Power and Propulsion Conference, Chicago, USA, Sept. 2011, pp. 1-6. [百度学术]

J. Bates and D. Leibling. (2012, Jul.). Spaced out: perspectives on parking policy. [Online]. Available: https://www.racfoundation.org/assets/rac_foundation/content/downloadables/spaced_out-bates_leibling-ju l12.pdf [百度学术]

M. Bucher, Y. Vagapov, A. Davydova et al., “Estimation of electrical energy demand by electric vehicles from households: a UK perspective,” in Proceedings of 2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW), St. Petersburg, Russia, Feb. 2015, pp. 159-164. [百度学术]

A. Lojowska, D. Kurowicka, G. Papaefthymiou et al., “Stochastic modeling of power demand due to EVs using copula,” IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 1960-1968, Nov. 2012. [百度学术]

P. Denholm and W. Short. (2006, Oct.). Evaluation of utility system impacts and benefits of optimally dispatched plug-in hybrid electric vehicles. [Online]. Available: http://nrel.gov/docs/fy07osti/40293.pdf [百度学术]

J. C. Gomez and M. M. Morcos, “Impact of EV battery chargers on the power quality of distribution systems,” IEEE Power Engineering Review, vol. 22, no. 3, pp. 63-63, Oct. 2002. [百度学术]

R. C. Green, L. Wang, and M. Alam, “The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook,” Renewable and Sustainable Energy Reviews, vol. 15, no.1, pp. 544-553, Jan. 2011. [百度学术]

US Department of Energy. (2019, Aug.). Alternative fuels data center: developing infrastructure to charge plug-in electric vehicles. [Online]. Available: https://afdc.energy.gov/fuels/electricity_infrastructure.html [百度学术]

S. Bohn, M. Agsten, A. Dubey et al. (2015, Apr.). A comparative analysis of PEV charging impacts–an international perspective. [Online]. Available: https://www.researchgate.net/publication/282920053_A_Comparative_Analysis_of_PEV_Charging_Impacts_-An_International_ Perspective [百度学术]

A. Dubey and S. Santoso, “Electric vehicle charging on residential distribution systems: impacts and mitigations,” IEEE Access, vol. 3, pp. 1871-1893, Sept. 2015. [百度学术]

J. Y. Yong, V. K. Ramachandaramurthy, K. M. Tan et al., “A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects,” Renewable and Sustainable Energy Reviews, vol. 49, pp. 365-385, Sept. 2015. [百度学术]

M. Yilmaz and P. T. Krein, “Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces,” IEEE Transactions on Power Electronics, vol. 28, no. 12, pp. 5673-5689, Dec. 2013. [百度学术]

S. Habib, M. Kamran, and U. Rashid, “Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks - a review,” Journal of Power Sources, vol. 277, pp. 205-214, Mar. 2015. [百度学术]

J. C. Mukherjee and A. Gupta, “A review of charge scheduling of electric vehicles in smart grid,” IEEE Systems Journal, vol. 9, no. 4, pp. 1541-1553, Dec. 2015. [百度学术]

Z. Yang, K. Li, and A. Foley, “Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review,” Renewable and Sustainable Energy Reviews, vol. 51, pp. 396-416, Nov. 2015. [百度学术]

Z. Yang, K. Li, A. Foley et al., “Optimal scheduling methods to integrate plug-in electric vehicles with the power system: a review,” IFAC Proceedings Volumes, vol. 47, no. 3, pp. 8594-8603, Jul. 2014. [百度学术]

K. M. Tan, V. K. Ramachandaramurthy, and J. Y. Yong, “Integration of electric vehicles in smart grid: a review on vehicle to grid technologies and optimization techniques,” Renewable and Sustainable Energy Reviews, vol. 53, pp. 720-732, Jan. 2016. [百度学术]

P. Kong and G. K. Karagiannidis, “Charging schemes for plug-in hybrid electric vehicles in smart grid: a survey,” IEEE Access, vol. 4, pp. 6846-6875, Oct. 2016. [百度学术]

P. Palensky and D. Dietrich, “Demand side management: demand response, intelligent energy systems, and smart loads,” IEEE Transactions on Industrial Informatics, vol. 7, no. 3, pp. 381-388, Aug. 2011. [百度学术]

A. Y. Saber and G. K. Venayagamoorthy, “Plug-in vehicles and renewable energy sources for cost and emission reductions,” IEEE Transactions on Industrial Electronics, vol. 58, no. 4, pp. 1229-1238, Apr. 2011. [百度学术]

A. Y. Saber and G. K. Venayagamoorthy, “Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles,” IEEE Systems Journal, vol. 6, no.1, pp. 103-109, Mar. 2012. [百度学术]

K. S. Reddy, L. K. Panwar, R. Kumar et al., “Distributed resource scheduling in smart grid with electric vehicle deployment using fireworks algorithm,” Journal of Modern Power Systems and Clean Energy, vol. 4, no. 2, pp. 188-199, Apr. 2016. [百度学术]

I. Pavic, T. Capuder, N. Holjevac et al., “Role and impact of coordinated EV charging on flexibility in low carbon power systems,” in Proceedings of 2014 IEEE International Electric Vehicle Conference (IEVC), Florence, Italy, Dec. 2014, pp. 1-8. [百度学术]

C. Liu, J. Wang, A. Botterud et al., “Assessment of impacts of PHEV charging patterns on wind-thermal scheduling by stochastic unit commitment,” IEEE Transactions on Smart Grid, vol. 3, no. 2, pp. 675-683, Jun. 2012. [百度学术]

M. E. Khodayar, L. Wu, and M. Shahidehpour, “Hourly coordination of electric vehicle operation and volatile wind power generation in SCUC,” IEEE Transactions on Smart Grid, vol. 3, no.3, pp. 1271-1279, Sept. 2012. [百度学术]

C. Shao, X. Wang, X. Wang et al., “Cooperative dispatch of wind generation and electric vehicles with battery storage capacity constraints in SCUC,” IEEE Transactions on Smart Grid, vol. 5, no. 5, pp. 2219-2226, Sept. 2014. [百度学术]

J. Zhao, C. Wan, Z. Xu et al., “Spinning reserve requirement optimization considering integration of plug-in electric vehicles,” IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 2009-2021, Jul. 2017. [百度学术]

I. Pavic, T. Capuder, and I. Kuzle, “Value of flexible electric vehicles in providing spinning reserve services,” Applied Energy, vol. 157, pp. 60-74, Nov. 2015. [百度学术]

I. Pavic, T. Capuder, and I. Kuzle, “Fast charging stations–power and ancillary services provision,” in Proceedings of 2017 IEEE Manchester PowerTech, Manchester, UK, Jun. 2017, pp. 1-6. [百度学术]

M. Rahmani-Andebili, “Spinning reserve supply with presence of electric vehicles aggregator considering compromize between cost and reliability,” IET Generation, Transmission & Distribution, vol. 7, no. 12, pp. 1442-1452, Dec. 2013. [百度学术]

Z. Ma, D. S. Callaway, and I. A. Hiskens, “Decentralized charging control of large populations of plug-in electric vehicles,” IEEE Transactions on Control Systems Technology, vol. 21, no. 1, pp. 67-78, Jan. 2013. [百度学术]

L. Gan, U. Topcu, and S. H. Low, “Optimal decentralized protocol for electric vehicle charging,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 940-951, May 2013. [百度学术]

S. Deilami, A. S. Masoum, P. S. Moses et al., “Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile,” IEEE Transactions on Smart Grid, vol. 2, no. 3, pp. 456-467, Sept. 2011. [百度学术]

M. H. K. Tushar, A. W. Zeineddine, and C. Assi, “Demand-side management by regulating charging and discharging of the EV, ESS, and utilizing renewable energy,” IEEE Transactions on Industrial Informatics, vol. 14, no. 1, pp. 117-126, Jan. 2018. [百度学术]

S. Sojoudi and S. H. Low, “Optimal charging of plug-in hybrid electric vehicles in smart grids,” in Proceedings of 2011 IEEE PES General Meeting, Detroit, USA, Jul. 2011, pp. 1-6. [百度学术]

N. Chen, T. Q. S. Quek, and C. W. Tan, “Optimal charging of electric vehicles in smart grid: characterization and valley-filling algorithms,” in Proceedings of 2012 IEEE 3rd International Conference on Smart Grid Communications, Tainan, China, Nov. 2012, pp. 13-18. [百度学术]

S. L. Judd and T. J. Overbye, “An evaluation of PHEV contributions to power system disturbances and economics,” in Proceedings of 2008 40th North American Power Symposium, Calgary, Canada, Sept. 2008, pp. 1-8. [百度学术]

M. G. Vaya and G. Andersson, “Centralized and decentralized approaches to smart charging of plug-in vehicles,” in Proceedings of 2012 IEEE PES General Meeting, San Diego, USA, Jul. 2012 pp. 1-8. [百度学术]

Y. Ota, H. Taniguchi, T. Nakajima et al., “Autonomous distributed V2G (vehicle-to-grid) satisfying scheduled charging,” IEEE Transactions on Smart Grid, vol. 3, pp. 559-564, Mar. 2012. [百度学术]

H. Liu, Z. Hu, Y. Song et al., “Decentralized vehicle-to-grid control for primary frequency regulation considering charging demands,” IEEE Transactions on Power Systems, vol. 28, no.1, pp. 3480-3489, Aug. 2013. [百度学术]

T. Masuta and A. Yokoyama, “Supplementary load frequency control by use of a number of both electric vehicles and heat pump water heaters,” IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1253-1262, Sept. 2012. [百度学术]

S. Gao, K. T. Chau, C. Liu et al., “Integrated energy management of plug-in electric vehicles in power grid with renewables,” IEEE Transactions on Vehicular Technology, vol. 63, no. 7, pp. 3019-3027, Sept. 2014. [百度学术]

S. Hutterer, M. Affenzeller, and F. Auinger, “Evolutionary optimization of multi-agent control strategies for electric vehicle charging,” in Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, New York, USA, Jul. 2012, pp. 3-10. [百度学术]

S. Chandrashekar, Y. Liu, and R. Sioshansi, “Wind-integration benefits of controlled plug-in electric vehicle charging,” Journal of Modern Power Systems and Clean Energy, vol. 5, no. 5, pp. 746-756, Sept. 2017. [百度学术]

M. Ghofrani, A. Arabali, M. Etezadi-Amoli et al., “Smart scheduling and cost-benefit analysis of grid-enabled electric vehicles for wind power integration,” IEEE Transactions on Smart Grid, vol. 5, no. 3, pp. 2306-2313, Sept. 2014. [百度学术]

Y. Li, R. Kaewpuang, P. Wing et al., “An energy efficient solution: integrating plug-in hybrid electric vehicle in smart grid with renewable energy,” in Proceedings of 2012 Proceedings IEEE INFOCOM Workshops, Orlando, USA, Mar. 2012, pp. 73-78. [百度学术]

O. Arslan and O. E. Karasan, “Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks,” Energy, vol. 60, pp. 116-124, Oct. 2013. [百度学术]

M. Vasirani, R. Kota, R. L. G. Cavalcante et al., “An agent-based approach to virtual power plants of wind power generators and electric vehicles,” IEEE Transactions on Smart Grid, vol. 4, no. 3, pp. 1314-1322, Sept. 2013. [百度学术]

K. Shimizu, T. Masuta, Y. Ota, et al., “Load frequency control in power system using vehicle-to-grid system considering the customer convenience of electric vehicles,” in Proceedings of 2010 International Conference on Power System Technology, Hangzhou, China, Oct. 2010, pp. 1-8. [百度学术]

International Energy Agency, Key World Energy Statistics. Paris: IEA Press, 2007. [百度学术]

H. W. Beaty and D. G. Fink, Standard Handbook for Electrical Engineers. New York: McGraw-Hill, 2007. [百度学术]

Y. del Valle, G. K. Venayagamoorthy, S. Mohagheghi et al., “Particle swarm optimization: basic concepts, variants and applications in power systems,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 2, pp. 171-195, Apr. 2008. [百度学术]

W. Kempton and J. Tomic, “Vehicle-to-grid power fundamentals: calculating capacity and net revenue,” Journal of Power Sources, vol. 144, no. 1, pp. 268-279, Jun. 2005. [百度学术]

A. S. Masoum, S. Deilami, P. S. Moses et al., “Smart load management of plug-in electric vehicles in distribution and residential networks with charging stations for peak shaving and loss minimization considering voltage regulation,” IET Generation, Transmission & Distribution, vol. 5, no. 8, pp. 877-888, Aug. 2011. [百度学术]

S. Sawyer, Q. Liming, and L. Fried, Global Wind Report–Annual Market Update 2017. Brussels: Global Wind Energy Council Press, 2018. [百度学术]

A. S. Anees, “Grid integration of renewable energy sources: challenges, issues and possible solutions,” in Proceedings of 2012 IEEE 5th India International Conference on Power Electronics (IICPE), Delhi, India, Dec. 2012, pp. 1-6. [百度学术]

C. Hamon, “On frequency control schemes in power systems with large amounts of wind power,” Ph.D. thesis, School of Electrical Engineering (EES), Electric Power Systems, KTH Royal Institute of Technology, Stockholm, Sweden, 2012. [百度学术]

J. R. Pillai and B. Bak-Jensen, “Integration of vehicle-to-grid in the western Danish power system,” IEEE Transactions on Sustainable Energy, vol. 2, no. 1, pp. 12-19, Jan. 2011. [百度学术]

C. Binding, D. Gantenbein, B. Jansen et al., “Electric vehicle fleet integration in the danish EDISON project–a virtual power plant on the island of Bornholm,” in Proceedings of IEEE PES General Meeting, Providence, USA, Jul. 2010, pp. 1-8. [百度学术]

T. B. Svozil, The Distribution System Operator: A Changing Role. Uppsala: Uppsala University Press, 2013. [百度学术]

J. C. Gomez and M. M. Morcos, “Impact of EV battery chargers on the power quality of distribution systems,” IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 975-981, Jul. 2003. [百度学术]

J. M. Sexauer, K. D. McBee, and K. A. Bloch, “Applications of probability model to analyze the effects of electric vehicle chargers on distribution transformers,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 847-854, May 2013. [百度学术]

J. de Hoog, T. Alpcan, M. Brazil et al., “Optimal charging of electric vehicles taking distribution network constraints into account,” IEEE Transactions on Power Systems, vol. 30, no. 1, pp. 365-375, Jan. 2015. [百度学术]

E. Sortomme, M. M. Hindi, S. D. J. MacPherson et al., “Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses,” IEEE Transactions on Smart Grid, vol. 2, no. 1, pp. 198-205, Mar. 2011. [百度学术]

Z. Yang and X. Yu, “Optimal PHEV charge scheduling for additional power loss ratio and charging cost minimizations,” in Proceedings of 2013 International Conference on Electrical Machines and Systems (ICEMS), Busan, Korea, Oct. 2013, pp. 465-469. [百度学术]

D. Steen, O. Carlson, L. Bertling et al., “Assessment of electric vehicle charging scenarios based on demographical data,” IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1457-1468, Sept. 2012. [百度学术]

A. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich et al., “Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid,” in Proceedings of 2010 Innovative Smart Grid Technologies (ISGT), Gothenburg, Sweden, Jan. 2010, pp. 1-6. [百度学术]

F. Rassaei, W. Soh, and K. Chua, “Demand response for residential electric vehicles with random usage patterns in smart grids,” IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1367-1376, Oct. 2015. [百度学术]

N. Erdogan, F. Erden, and M. Kisacikoglu, “A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system,” Journal of Modern Power Systems and Clean Energy, vol. 6, no. 3, pp. 555-566, May 2018. [百度学术]

N. Jewell, J. Naber, M. McIntyre et al., “A power monitoring and control system to minimize electricity demand costs associated with electric vehicle charging stations,” in Proceedings of 2012 IEEE International Electric Vehicle Conference, Greenville, USA, Mar. 2012, pp. 1-5. [百度学术]

H. K. Nguyen and J. B. Song, “Optimal charging and discharging for multiple PHEVs with demand side management in vehicle-to-building,” Journal of Communications and Networks, vol. 14, no. 6, pp. 662-671, Dec. 2012. [百度学术]

M. Moradzadeh and M. Abdelaziz, “Mitigating the impact of plug-in electric vehicles on distribution systems using demand-side management of electric water heaters,” in Proceedings of 2018 IEEE Canadian Conference on Electrical Computer Engineering (CCECE), Quebec City, Canada, May 2018, pp. 1-5. [百度学术]

S. Shao, M. Pipattanasomporn, and S. Rahman, “Challenges of PHEV penetration to the residential distribution network,” in Proceedings of 2009 IEEE PES General Meeting, Calgary, Canada, Jul. 2009, pp. 1-8. [百度学术]

S. Shao, M. Pipattanasomporn, and S. Rahman, “Demand response as a load shaping tool in an intelligent grid with electric vehicles,” IEEE Transactions on Smart Grid, vol. 2, no. 4, pp. 624-631, Dec. 2011. [百度学术]

S. Shao, M. Pipattanasomporn, and S. Rahman, “Grid integration of electric vehicles and demand response with customer choice,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 543-550, Mar. 2012. [百度学术]

Y. Mou, H. Xing, Z. Lin et al., “Decentralized optimal demand-side management for PHEV charging in a smart grid,” IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 726-736, Mar. 2015. [百度学术]

M. D. Galus and G. Andersson, “Integration of plug-in hybrid electric vehicles into energy networks,” in Proceedings of 2009 IEEE Bucharest PowerTech, Bucharest, Romania, Jun. 2009, pp. 1-8. [百度学术]

P. Richardson, D. Flynn, and A. Keane, “Optimal charging of electric vehicles in low-voltage distribution systems,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 268-279, Feb. 2012. [百度学术]

O. Sundstrom and C. Binding, “Flexible charging optimization for electric vehicles considering distribution grid constraints,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 26-37, Mar. 2012. [百度学术]

J. Hu, S. You, M. Lind et al., “Coordinated charging of electric vehicles for congestion prevention in the distribution grid,” IEEE Transactions on Smart Grid, vol. 5, no. 2, pp. 703-711, Mar. 2014. [百度学术]

M. Vasirani and S. Ossowski, “Lottery-based resource allocation for plug-in electric vehicle charging,” in Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, Richland, USA, Jun. 2012, pp. 1173-1174. [百度学术]

S. Xu, Z. Yan, X. Zhao et al., “Decentralized charging of plug-in electric vehicles using lagrange relaxation method at the residential transformer level,” International Journal of Emerging Electric Power Systems, vol. 17, no. 3, pp. 267-276, Apr. 2016. [百度学术]

M. Ansari, A. T. Al-Awami, E. Sortomme et al., “Coordinated bidding of ancillary services for vehicle-to-grid using fuzzy optimization,” IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 261-270, Jan. 2015. [百度学术]

E. Sortomme, “Combined bidding of regulation and spinning reserves for unidirectional vehicle-to-grid,” in Proceedings of 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington DC, USA, Jan. 2012, pp. 1-7. [百度学术]

E. Sortomme and M. A. El-Sharkawi, “Optimal charging strategies for unidirectional vehicle-to-grid,” IEEE Transactions on Smart Grid, vol. 2, no. 1, pp. 131-138, Mar. 2011. [百度学术]

E. Sortomme and M. A. El-Sharkawi, “Optimal combined bidding of vehicle-to-grid ancillary services,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 70-79, Mar. 2012. [百度学术]

E. Sortomme and M. A. El-Sharkawi, “Optimal scheduling of vehicle-to-grid energy and ancillary services,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 351-359, Mar. 2012. [百度学术]

M. R. Sarker, Y. Dvorkin, and M. A. Ortega-Vazquez, “Optimal participation of an electric vehicle aggregator in day-ahead energy and reserve markets,” IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3506-3515, Sept. 2016. [百度学术]

S. I. Vagropoulos and A. G. Bakirtzis, “Optimal bidding strategy for electric vehicle aggregators in electricity markets,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4031-4041, Nov. 2013. [百度学术]

J. Lin, K.-C. Leung, and V. O. K. Li, “Online scheduling for vehicle-to-grid regulation service,” in Proceedings of 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), Vancouver, Canada, Dec. 2013, pp. 43-48. [百度学术]

J. Lin, K. Leung, and V. O. K. Li, “Optimal scheduling with vehicle-to-grid regulation service,” IEEE Internet of Things Journal, vol. 1, no. 6, pp. 556-569, Dec. 2014. [百度学术]

D. Wu, D. C. Aliprantis, and L. Ying, “Load scheduling and dispatch for aggregators of plug-in electric vehicles,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 368-376, Mar. 2012. [百度学术]

C. Jin, J. Tang, and P. Ghosh, “Optimizing electric vehicle charging with energy storage in the electricity market,” IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 311-320, Mar. 2013. [百度学术]

S. Han, S. Han, and K. Sezaki, “Development of an optimal vehicle-to-grid aggregator for frequency regulation,” IEEE Transactions on Smart Grid, vol. 1, no. 1, pp. 65-72, Jun. 2010. [百度学术]

T. K. Kristoffersen, K. Capion, and P. Meibom, “Optimal charging of electric drive vehicles in a market environment,” Applied Energy, vol. 88, no. 5, pp. 1940-1948, May 2011. [百度学术]

M. G. Vaya and G. Andersson, “Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets under uncertainty,” IEEE Transactions on Power Systems, vol. 30, no. 5, pp. 2375-2385, Sept. 2015. [百度学术]

W. Tushar, C. Yuen, S. Huang et al., “Cost minimization of charging stations with photovoltaics: an approach with EV classification,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 1, pp. 156-169, Jan. 2016. [百度学术]

D. T. Nguyen and L. B. Le, “Joint optimization of electric vehicle and home energy scheduling considering user comfort preference,” IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 188-199, Jan. 2014. [百度学术]

J. Zhao, C. Wan, Z. Xu et al., “Risk-based day-ahead scheduling of electric vehicle aggregator using information gap decision theory,” IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1609-1618, Jul. 2017. [百度学术]

100

C. Jin, J. Tang, and P. Ghosh, “Optimizing electric vehicle charging: a customer’s perspective,” IEEE Transactions on Vehicular Technology, vol. 62, no. 7, pp. 2919-2927, Sept. 2013. [百度学术]

101

A. Sharma, S. Shih, and D. Srinivasan, “A smart scheduling strategy for charging and discharging of electric vehicles,” in Proceedings of 2015 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA), Bangkok, Thailand, Nov. 2015, pp. 1-6. [百度学术]

102

R. Wang, Y. Li, P. Wang et al., “Design of a V2G aggregator to optimize PHEV charging and frequency regulation control,” in Proceedings of 2013 IEEE International Conference on Smart Grid Communications, Vancouver, Canada, Oct. 2013, pp. 127-132. [百度学术]

103

Y. Xu and F. Pan, “Scheduling for charging plug-in hybrid electric vehicles,” in Proceedings of 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), Maui, USA, Dec. 2012, pp. 2495-2501. [百度学术]

104

M. Erol-Kantarci and T. M. Hussein, “Prediction-based charging of PHEVs from the smart grid with dynamic pricing,” in Proceedings of IEEE Local Computer Network Conference, Denver, USA, Oct. 2010, pp. 1032-1039. [百度学术]

105

M. H. Amirioun and A. Kazemi, “A new model based on optimal scheduling of combined energy exchange modes for aggregation of electric vehicles in a residential complex,” Energy, vol. 69, pp. 186-198, May 2014. [百度学术]

106

A. Brooks, E. Lu, D. Reicher et al., “Demand dispatch,” IEEE Power and Energy Magazine, vol. 8, no. 3, pp. 20-29, May-Jun. 2010. [百度学术]

107

M. C. Kisacikoglu, B. Ozpineci, and L. M. Tolbert, “Examination of a PHEV bidirectional charger system for V2G reactive power compensation,” in Proceedings of 2010 25th Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Palm Springs, USA, Feb. 2010, pp. 458-465. [百度学术]

108

K. Knezovic, A. Soroudi, A. Keane et al., “Robust multi-objective PQ scheduling for electric vehicles in flexible unbalanced distribution grids,” IET Generation, Transmission & Distribution, vol. 11, no. 16, pp. 4031-4040, Nov. 2017. [百度学术]

109

M. Kesler, M. C. Kisacikoglu, and L. M. Tolbert, “Vehicle-to-grid reactive power operation using plug-in electric vehicle bidirectional offboard charger,” IEEE Transactions on Industrial Electronics, vol. 61, no. 12, pp. 6778-6784, Dec. 2014. [百度学术]

110

L. Sun, X. Wang, W. Liu et al., “optimization model for power system restoration with support from electric vehicles employing battery swapping,” IET Generation, Transmission & Distribution, vol. 10, no. 3, pp. 771-779, Mar. 2016. [百度学术]

111

A. Sharma, D. Srinivasan, and A. Trivedi, “A decentralized multiagent system approach for service restoration using DG islanding,” IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 2784-2793, Nov. 2015. [百度学术]

112

A. Sharma, D. Srinivasan, and A. Trivedi, “A decentralized multi-agent approach for service restoration in uncertain environment,” IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3394-3405, Jul. 2018. [百度学术]

113

P. Sanchez-Martin, G. Sanchez, and G. Morales-Espana, “Direct load control decision model for aggregated EV charging points,” IEEE Transactions on Power Systems, vol. 27, no. 3, pp. 1577-1584, Aug. 2012. [百度学术]

114

Y. Cao, S. Tang, C. Li et al., “An optimized EV charging model considering TOU price and SOC curve,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 388-393, Mar. 2012. [百度学术]

115

O. Sundström and C. Binding, “Optimization methods to plan the charging of electric vehicle fleets,” in Proceedings of the International Conference on Control, Communication and Power Engineering, Chennai, India, Jul. 2010, pp. 28-29. [百度学术]

116

G. B. Shrestha and S. G. Ang, “A study of electric vehicle battery charging demand in the context of Singapore,” in Proceedings of 2007 International Power Engineering Conference (IPEC 2007), Singapore, Singapore, Dec. 2007, pp. 64-69. [百度学术]

117

S. Bashash, S. J. Moura, and H. K. Fathy, “Charge trajectory optimization of plug-in hybrid electric vehicles for energy cost reduction and battery health enhancement,” in Proceedings of the 2010 American Control Conference, Baltimore, USA, Jun. 2010, pp. 5824-5831. [百度学术]

118

Y. He, B. Venkatesh, and L. Guan, “Optimal scheduling for charging and discharging of electric vehicles,” IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1095-1105, Sept. 2012. [百度学术]

119

M. Kiaee, A. Cruden, and S. Sharkh, “Estimation of cost savings from participation of electric vehicles in vehicle to grid (V2G) schemes,” Journal of Modern Power Systems and Clean Energy, vol. 3, no. 2, pp. 249-258, Jun. 2015. [百度学术]

120

M. Honarmand, A. Zakariazadeh, and S. Jadid, “Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition,” Energy, vol. 65, pp. 572-579, Feb. 2014. [百度学术]

121

Y. Wang and D. Infield, “Optimal demand side response to real time price signal using electric vehicles,” in Proceedings of International Conference on Renewable Power Generation (RPG 2015), Beijing, China, Oct. 2015, pp. 1-6. [百度学术]

122

N. Rotering and M. Ilic, “Optimal charge control of plug-in hybrid electric vehicles in deregulated electricity markets,” IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1021-1029, Aug. 2011. [百度学术]

123

W. Zhang, K. Spence, R. Shao et al., “Optimal scheduling of spinning reserve and user cost in vehicle-to-grid (V2G) systems,” in Proceedings of 2018 IEEE Energy Conversion Congress and Exposition (ECCE), Portland, USA, Sept. 2018, pp. 1058-1064. [百度学术]

124

H. Farzin, M. Fotuhi-Firuzabad, and M. Moeini-Aghtaie, “A practical scheme to involve degradation cost of lithium-ion batteries in vehicle-to-grid applications,” IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1730-1738, Oct. 2016. [百度学术]

Address:No.19 Chengxin Avenue, Jiangning District, Nanjing 211106, China

E-mail: mpce@alljournals.cn

Tel:86-25-81093060

Fax:86-25-81093040

Home

Introduction

Editorial Board

For Author

Call For Papers

APC

Sponsor & Publisher

Grid Integration of Electric Vehicles for Economic Benefits: A Review PDF