The electric vehicle (EV) as a specific electricity load, can be used as a ... Electric Vehicles, Optimal Control Management, Sequential Battery Control, Key Issues.
Proceedings of the 34th Chinese Control Conference July 28-30, 2015, Hangzhou, China
Vehicle to Grid Technology:A Review Yimin Zhou1 , Xiaoyun Li1 1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P. R. China, 518055 E-mail: {ym.zhou, xy.li}@siat.ac.cn Abstract: At present, vehicle to grid (V2G) technology has been received widely attention since a large amount of electric vehicles entering into the market gradually. The electric vehicle (EV) as a specific electricity load, can be used as a mobile storage device to participate load adjusting in the power grid and to provide platform for the renewable energy sources coordination. The management strategies for V2G and the involved key issues are analyzed in details, such as battery weary, bidirectional charger and charging stations via the centralized control, autonomous control and battery management strategies. Besides, the economic benefits from both the power grid & EV owners and research development directions are discussed in the paper. Key Words: Vehicle to Grid, Electric Vehicles, Optimal Control Management, Sequential Battery Control, Key Issues
1
Introduction
The actual operational efficiency of the current power grid is unsatisfying due to high cost and heavy energy waste, which is brought by the daily load demand fluctuations and regulation of voltage and frequency from the power grid [1]. When the energy demand exceeds the base load power plant capacity, the peak load power plant has to be put into operation since the power grid itself has not enough electrical energy storage and sometimes spinning reserves will participate as well. When the power demand is lower than the output of the base load of the power plant, the unused energy will be wasted in vain. Besides, the regulation of the voltage and frequency of power grid will greatly increase the power grid operating costs. The concept of V2G (Vehicle-to-Grid) is proposed to solve the above problems [2], where its core idea is to use a large amount of storage energy of electric vehicles (EVs) as the buffer for power grid and renewable energies, as shown in Fig.1. When the network load demand is too high, the energy stored in EVs can be fed back towards the grid. When the network load demand is low, the unused power in the grid can be stored in the EVs so as to avoid waste. The EV users can thus buy electricity from the grid when the price is low and sell the electricity towards the grid with higher price so that certain benefits can be obtained from this trade behaviour [3]. The plug-in hybrid electric vehicles (PHEVs) and pure electric vehicles (PEVs) are gradually entering into the market [4]. According to the statistical data, there are nearly 20 hours per day for the vehicles in still state, during which period it represent idle asset. If there are enough amount of these vehicles, their total battery capacities can be regarded as a buffer for the power grid and renewable energy systems. However, the electric vehicles can not access to the grid freely and unmanageably. It would cause serious damage to the grid with large amount of charging demand from the EVs if the gird is in peak-load periods. As for the vehicles, in addition to provide ancillary services for the grid, they should satisfy the daily routine driving requirements. Therefore, it is necessary to investigate the V2G technology to coordiThis work is supported by the Shenzhen Science and Technology Innovation Commission Project Grant Ref. JCYJ20140417113430574 and the National Natural Science Foundation of China (Grant No.61271005).
Fig. 1: The schematic diagram of V2G
nate the charging/discharging behaviours between vehicles and grid so that it will not affect the power grid operation and constrain the normal use of automobiles. V2G technology embodies the energy flow among the EVs and grid with mutual, real-time, controllable and high speed characteristics [5]. The remainder of the paper is organized as follows. Section 2 summarizes the methods of V2G technologies, together with the effect of large-scale EVs to the grid. The involved key issues for V2G are discussed in Section 3. Conclusions and future work are given in Section 4.
2
Methods of V2G Technologies
From the grid perspective, it is necessary to disperse the EVs charging load reasonably to avoid the conventional grid peak-load periods so as to reduce their impact on the power grid and other unnecessary construction investment on transmission grid and distribution grid which can ensure the coordinated development of electric vehicles and power grid. Therefore, EVs charging has to be regulated or controlled to achieve peak shaving and valley filling due to their daily consumption demand with the application of effective economic measures or technical measures, which is the concept of ordered charging.
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2.1 The state-of-art of V2G In China, V2G is a relatively new technology, as an important part of smart grid, the research is still in the initial stage [6]. The research on V2G mainly focuses on the feasibility analysis, the overall structure and function of each component. Application problems have been studied in [7] that EVs can participate in power grid peak shaving so V2G modelling is established for further research. Paper [8] describes the EV charging schemes in mobile mode and parking mode. The minimization of delay of charge and peak-toaverage ratios objectives in two different charging modes are discussed, especially for the decrease of the load from grid in parking mode. Paper [9] analyzes the EVs impact on the grid structure in 2020, 2030 in different regions of USA. The results show that clean power plant, fuel generator, and distributed energy power have to be developed to cope with the large-scale EVs charging demand. Besides, demand response of V2G has to be used to guide the EVs charging/discharging behaviours during off-peak periods. The penetration of PHEVs could continuously increase the charging load in distribution grid. Two charging methods with the aid of wind energy are developed to reduce the peak loads via scheduling the daily charging [10]. The heuristic algorithm is used to model and analyze the grid with the objective of least investment and energy consumption[11]. Similarly, primal dual interior point method and particle swarm algorithm [12]-[14] are used to optimize the objectives, i.e., users benefit maximization, minimized grid load fluctuation and peak load, and minimized distribution grid loss and voltage offset for both the power supply and user supply consideration. Furthermore, by the aid of V2G, power can be flowed bidirectionally for intelligent control so as to reduce the grid pressure together with the distributed energy resources [15]. As it can be seen, the research on V2G is still at its starting stage, there are still a lot of basic work to be explored. 2.2 The methods of V2G realization It is feasible to realize V2G by the use of EVs batteries. Due to the diversities, different usages, various power supply modes, there are different methods for the realization of V2G.
2.2.1
Centralized V2G implement methods
The so-called centralized V2G refers to schedule the gathered energy of EVs in certain regions according to the grid demand in order to control the charging/discharging procedure of each EV with specific management strategies. For instance, parking lots are built for the purpose of V2G [16]. The peak load adjusting during EV charging is decided by the grid scheduling system and V2G charging station coordinately, which will be assigned in real-time based on the online grid status. Then V2G is used for the EVs orderly charging and intelligent management inside the charging stations. The assigned power requirement PD from the grid scheduling system is regarded as the basis of the V2G peak adjusting, and battery SOCs (State-of-Charge) of each EV are used as evaluation index in the allowable power range.
V2G is achieved via the optimal power configuration to put EVs working in the appropriate discharging power status and the objective is written as: PD =
N
Pi ;
min(SOCi −
N
SOCi ) N
i=1
i=1
(1)
where Pi denotes the appropriate feed power of each EV; N is the number of EVs participating the V2G. The SOC optimal power strategy based peak shaving feedback can improve the feed power current efficiency of the batteries, which could benefit the duration of batteries and ensure the maximum remainder capacities of each EV as much as possible. The load valley filling purpose can also be achieved via power configuration of EVs and V2G similarly denoting as the minimum power difference between the nominal power of V2G charging station and those of the assembled EVs: ε = min(PD −
N
Pi )
(2)
i=1
One optimal objective for the regulated EVs charging is to minimize the load fluctuation variance: min
T n 1 { ( (Lm,t − μD ))2 } T t=1 m=1
(3)
where μD is the average load at T time interval; Lm,t is the load of node m at T moment. Several constraints for the system grid loss, node voltage, current calculation and the grid load should be considered to adjust the load distribution [11]. As for the centralized V2G, intelligent charger can be built on the ground which would save the investment cost. At the same time, due to the unified scheduling and centralized management, it can achieve the overall optimization. For example, it can calculate the optimal charging strategy of each vehicle via advanced algorithm so as to guarantee the lowest cost and optimal power usage. 2.2.2
Autonomous V2G methods
As for the autonomous V2G, the EVs are often scattered everywhere, which is unable to carry out centralized management [17]. Thus the intelligent charger is adopted on the vehicles, and they can realize V2G automatically based on the reactive power demand, price information issued by the grid or electrical characteristics of power output interface (such as voltage fluctuation) with the combination of automobile own state (such as battery SOC). The power load characteristics of each region are quite different and the EVs number varies as well, where the united scheduling is not appropriated for the dispersed EVs. Through hierarchy divisional control, the control structure of the grid and EVs can be divided into three level: power transmission scheduling, distribution system scheduling and EV control center. V2G is used to optimize energy, load regulation and spinning reserves via an aggregator, which can increase additional system flexibility and peak load shaving but lower charging cost to the customers [18].
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Yutaka Ota and his co-workers proposed a distributed autonomous V2G method to realize the energy intelligent storage [19]. The controllable EVs can be regarded as autonomous distributed smart storage devices for load frequency control. Based on the time-of-use charging prices and the EVs driving patterns, a time division control strategy is proposed in [20]. One day (24 hours) could be divided into red, blue, green such intervals and the charging behaviours of EVs can be encouraged or restrained during these different time intervals for the purpose of peak shaving and valley filling. Another worthy mentioned is the microgrid based V2G realization [21]. A diagram of microgrid based battery power plant is shown in Fig.2. According to the American Electric Reliability Technology Solution Federation, the micro grid is defined as a system composed of load units and common micro powers, which can simultaneously provide electricity and heat. The internal power of the microgrid is mainly controlled by the electronic devices/components responsible for the energy conversion. Compared to the external grid network, micro grid is a single controlled unit to meet the requirement of electricity quality and safety for the end-users.
the direct service object is the micro grid instead of grid network to provide support for the distributed energy sources and power supply for relevant load. The economic benefits from micro-grid including EVs, wind power, micro turbines and other renewable energy resources can be analyzed via its fixed investment and operational cost, where the optimization index of the microgrid is to acquire the least operational cost C: min C = Csys +
Controllable micro energy resouces
Power exchange point
Distribution grid
Load
Storage station
Charging/discharging equipment
Battery packs Power plant
Lack of capacity
Battery distribution station
F charged Fully
Fig. 2: The schematic diagram of microgrid based battery power plant The objective of the optimal scheduling model is to minimize the expectation of the power generation cost:
s.t.
P+
n w i=1
min E(Fall ) ne n mt Pwi + Pei + Pwti = L + D i=1
i=1
Pwi,min < Pwi < Pwi,max Pei,min < Pei < Pei,max Pmti,min < Pmti < Pmti,max
(4)
where Pmti,min and Pmti,max are the minimum and maximum power output of the wind generators, respectively. Pei,min and Pei,max are the lower limit and upper limit of the V2G output power from EVs; L is the system loss, D is the system load and P is the trade electricity capacity of the grid network. PSO(particle swarm optimization) can be used to solve the model optimization [14]. The micro grid based V2G realization can assemble the storage equipment of EVs to the micro grid. The main difference from the previous V2G realization methods is that
(5)
where Csys is the system equipment cost, Ci,f uel is the fuel cost, Ci,buy , Ci,sell are the electricity purchase and sale costs, respectively. Under the constraints of power balance, wind power output, micro turbine output and EVs output are used to solve the minimization. EVs are integrated into the residential power supply network including wind, solar such distributed energy resources and connectd them to the external large grid [22]. It can use EVs to support renewable energy for the power supply to residential and commercial users. 2.2.3
Wind generator
(Ci,f uel + Ci,buy − Ci,sell )
i=0
Microgrid PV
N
V2G realization based on battery pack replacement
The principle of this method is similar to that of the centralized V2G but with different management strategy. Since the battery packs are used for the replacement, certain percentage of the batteries should be kept in full capacity. It combines the advantages of conventional charging and fast charging, which compensates the defects of insufficient mileage of the electric vehicles in certain degree. There are a large amount of batteries in the charging station, and V2G method with battery pack replacement can greatly satisfy the grid scheduling demand plan. However, it is urgent to design the united standards for batteries and charging interfaces [23]. Currently, ‘centralized charging and battery replacement’ mode is adopted by the transit system. To satisfy the battery replacement requirement and practical demand, the EV batteries in the system can be assigned based on certain ratio to the buses. Only batteries stored in the charging station can participate in V2G. In order to improve the efficiency of battery cluster, ‘progressive mode (in order)’ is adopted for the management according to the bus timetable for the battery unloading moment determination [24]. The relationship of cycle number of charing/discharging and the actual capacities of EV batteries is kept in power function [25]: αn = α0 − αnb
(6)
where αn is the capacity-keeping rate (%) after n times; α0 , α and b are unknown parameters decided by the specific battery characteristics. A typical EV power plant model is to minimize the operational cost as part of micro-grid under the condition: PBt = (Uit+ Pit+ − Uit− Pit− ) (7a) i∈Sc
s.t. − Nf0 · e0 ≤
i∈Sc
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Eit ≤ Ne0 · e0
(7b)
(NiT − UiT− = NC )
(7c)
i∈Sc
where the first equation in Eq.7(a) is to define the exchange power between the power plant and the external grid, ‘+’ denoting absorbing power and ‘-’ denoting developed power. Eq.7(b) describes the equivalent electric quantity limits, where Nf0 and Ne0 are the number of full charged battery groups and empty battery groups at initial stage. Eq.7(c) represents that the capacity from battery groups and the market capacity NC should be kept the same in certain cycle. Paper [26] proposed a strategy to solve the PV excess power output via EV connection. Compared with the pumped storage system, the EV power plant will have lower investment cost if there is enough battery replacement capacity.
3 3.1
Key Issues Involved in V2G
V2G intelligent dispatch from the power grid perspective Evidence shows that appropriate power supply strategy of V2G can minimize the electricity generation increment demand and infrastructure investment. How to regulate and schedule the stored energy in EVs is an urgent problem to be solved. The essence of the problem is to schedule each V2G unit and other electric power generation units. The power generation units have different function in the grid: larger capacity units are used to provide basic loads demand with cheaper price but slow response; smaller capacity units are generally used for peak load adjusting with more expensive cost but fast response speed. Therefore, V2G will decrease the reliance of the grid to the expensive generation units as much as possible and reduce the use of reactive power compensation devices based on the grid load state, renewable energy status, available capacities of V2G units and the active and reactive power demand of each V2G unit so that a reasonable electricity price can be given. There are two types of methods to deal with the problem. The first type is that the grid directly regulates each connected EV to the grid and other power generation units unitedly. An intelligent control algorithm is used to control the V2G operation for each EV [16]. However, this method will complicate the situation, and the solution is considered from the grid perspective but without users consideration. The second method is to build an intermediate system, i.e., aggregator between the power grid and EV groups [27]. This aggregator system can organize the EVs connected to the grid in certain region into a whole utility to obey the unified dispatch of power network. So the grid does not need to know the details of each EV status, but send dispatch signals to each aggregator based on the algorithms. The EVs are managed directly by the intermediate systems. Similarly, an aggregated storage strategy of multiple EVs is proposed to reduce impacts of EV charging to the distribution system, together with the combination of renewable energy resources into the power system [28].
3.2
V2G based intelligent charging and discharging from EV users’ perspective The regulation for the EV energy storage power supply is discussed from the grid point of view earlier, where the direct beneficiaries are the power suppliers and service facilities without the interests of EV users consideration. In addition, only the grid and the intermediate systems are discussed, but the involved V2G operation for each EV has not been studied. It is necessary to discuss the intelligent charging/discharging management strategy for each involved EV, where the whole V2G procedure is described as follows. The intermediate system can provide reasonable energy for the charging demand from the EVs and feed the EVs energy back to the grid based on the grid requirement [2][3]. These ancillary service and charging behaviours can not be performed randomly, freely and unlimitedly, where the EVs current and future status (i.e. battery SOC, future driving plan, current location, current electricity bill and connection time) should be considered to guarantee the optimal management on the premise of normal driving demand satisfaction. According to the investigation, EVs have a great potential to satisfy daily driving demands with similar driving behaviour [29]. An optimization of EV charging time is proposed to save electricity cost up to 68%. V2G will affect the EV batteries, however, further research will investigate the energy prices and driving patterns of large amount of EVs with more intelligent charging strategies. Hutson and his co-workers put forward an intelligent method to arrange the available energy storage from PHEVs and EVs [30]. Binary particle swarm optimal algorithm is adopted to calculate the optimal charging/discharging time periods in one day and the price curve from the California ISO database is used to reflect the real price fluctuations. A monitoring and control system for EV operation as well as the business model utilizing EVs are developed in [31]. EV charging can be realized with the aid of renewable energy, and the EV status and nearby charging station such information can be obtained via the system for the convenience of users consideration. Benefits and costs from the use of EVs are analyzed in [32], and the involved factors including fuel expenses, electricity prices and battery costs are also discussed. The V2G electric bus can provide $38 million savings from one local region. In summary, the intelligent EV charging/discharging management strategy mainly involves how to coordinate the charging behaviour for each EV and to designate management strategy to seek for optimal scheme to maximize benefits for EV users, i.e., charging in low-price periods and providing service in high-price periods. However, there is no united strategy for whole V2G management and the developed strategies can only be effective in certain areas, such as frequency regulation or peak load adjusting. On the other hand, the constraints on the EVs has a great influence on the formulation of management strategies, and only battery SOC and capacities are considered in order to simplify the problems. Seldom other constraint factors (especially the user behaviors) are considered.
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3.3 The effect of V2G to the battery As mentioned earlier, the EV owners can benefit from the energy feedback to the grid through V2G. However, this profit is gained at the cost of V2G equipment weary, especially the battery impairment. The duration of batteries is certain and the constant charging/discharing of the batteries could seriously damage its life time and lower their capacities. Hence, the effect of V2G on the battery duration should be studied carefully. The sensitivity of the impact of V2G services on battery degradation is discussed based on battery capacities, charging regimes, and battery depth of discharge [33]. Degradation can be reduced by restraining the time duration connection and the depth of discharge of EVs. An economic analysis of various V2G offerings is presented in [34], where the aggregates power of EVs are modelled based on their driving patterns. Battery wear cost in different V2G operations and profitability are assessed and the cost of EV owners under normal daily usage in long-term duration and V2G services are analyzed as well. Kisacikoglu and his colleagues study the effect on the battery when the EVs are in reactive power compensation mode to the grid [35]. Simulation experiments demonstrate that charging by the Level1 method can realize the reactive power compensation without any power requirement from the batteries and negative effect on the capacities. Currently, there is no perfect model to evaluate the V2G impact on the battery life but only on certain aspect of V2G. Further research should be carried to study how negative effect could be caused on the battery due to higher power level reactive power compensation. 3.4 V2G bidirectional charger To achieve V2G for EVs, bidirectional intelligent chargers are required to equipped between the grid and vehicles. The bidirectional charger possesses the function for EVs battery charging with minimum harmonic current generation but also the ability to feed energy back to the grid. Generally, a bidirectional charger is composed of a filter, a bidirectional DC-DC converter and a bidirectional AC-DC converter. When the charger is in battery charging mode, the alternating current will be filtered to remove the unexpected frequency component first, then the bidirectional AC-DC converter is used to convert AC into DC. A bidirectional DC-DC converter is used for appropriate charging voltage output since the output voltage from the AC-DC converter may not match that of the DC energy storage unit. When the converter is in battery discharge mode, the process runs the opposite. Lixin Tang and Gui-Jia Su propose a low-cost car charger, which use the existing main traction motor and the ancillary motor and related power electronic system to form a charging circuit. The developed charger does not require additional charging circuit, which can significantly reduce the cost, weight and volume [36]. Kramer et al designs a structure of a motor controller and a charger, which can be used as a motor controller but also a bidirectional charger. Jaganathan proposed a battery charging system based on three level ACDC and bidirectional DC-DC converter and designed pulse charging controller for battery charging. Simulation experiments demonstrate that the designed system can greatly re-
duce the harmonic waves [37]. Young-Joo Lee proposed a novel integrated bidirectional AC-DC and DC-DC charger model, where it reduces the number of large current inductance and current sensors and it can provide fault current capacity difference. However, this modified circuit structure system has lower efficiency than the unmodified one [38]. An improved AC-DC control strategy is proposed in [39], compared with the traditional PI controller, which can greatly improve the performance of network controller at the low harmonic output and robustness to background noise restraint. Besides, PWM control scheme is proposed to solve the problem that half-bridge converter is difficult to maintain the high efficiency over a broad battery voltage range. According to the literature, the research for bidirectional charger mainly focus on the topological structure selection & integration and control strategy. The aim is to improve efficiency, reduce costs, reduce the volume and weight, and minimize total harmonic distortion under the condition of ensuring normal function of the charger. The research directions for bidirectional charger includes: 1) Converter structure, including structure selection and integration of bidirectional DC-DC & bidirectional ACDC converter; 2) How to use current traction driven system to accomplish bidirectional charging function; 3) Grid harmonic suppression; 4) How to improve the charger efficiency in broad voltage range.
4
Conclusions and Future Work
V2G can bring noticeable benefits no matter from economical aspect or applicable aspect, where its realization methods can be divided into four categories to adapt to various situations and the involved four key technologies are summarized as: V2G intelligent scheduling technology, intelligent charging and discharging management technology, power electronics technology and battery management technology. Besides, the equipment integration, high efficiency and low cost are important development directions of V2G, which will combine the Smart Grid with the tendency of intelligentization and informationization. At present, China is making great efforts for the development of electric vehicle industry, several suggestion are given for V2G: 1) In the construction of smart grid, designate V2G strategic plan and strengthen the basic research on V2G and consider more equipment and operational conditions. 2) Construction of electric car infrastructure and multi real-time regulation methods with uncertainties, i.e., frequency regulation, voltage adjusting, congestion control. 3) Establish V2G pilot projects in micro grid and its optimal operation, then explore scale application up to the great power grid.
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