A Matlab Simulator for Electric Drive Vehicle toGrid ImplementationYuchao Ma, Andrew Cruden, Member IET, David Infield, Senior Member IEEEDepartment of Electronic & Electrical Engineering, University of Strathclyde, Glasgow, UKyuchao.ma@Abstract- Vehicle to grid (V2G) describes a system in which power can be fed back to the electrical power grid from an electric drive vehicle (EDV) when connected to the grid and notin use. Alternatively, when the vehicle batteries need to becharged, the flow can be reversed and electricity can be drawn from the electrical power grid to charge the battery.Taking into account the specific features of the EDV battery energy storage system, a MATLAB simulator has been developed by the authors to model the scenarios of V2G deployment within a daily power dispatch schedule. Different characteristics of EDV battery energy storage system and testscenarios can be set up by the user. The power networks modeled in the simulator are standard IEEE-30, -57 and -118 bus systems. The simulator provides a tool to investigate the effects of V2G implementation across a wide scope of the economic and technical issues, with respect to the physical power grid operation.Index Terms —Electric Drive Vehicle, Vehicle to Grid, Stateof Charge.I.I NTRODUCTIONElectric drive vehicles (EDV) are traditionally viewed by the electricity utilities simply as a load that needs to be recharged for transportation use. Vehicle to grid (V2G) [1-2]is a concept whereby the electric energy stored in the EDVbattery can be fed back to the power grid when the vehiclesare not in use. Such bidirectional flow characterizes the EDV as a responsive load, even as a generation resource, that canbe called on when required during a day. As an energy storage bank, V2G technology makes it possible to store the excess energy during low-level-demand time periods andsubsequently to release this stored energy during high-level-demand time periods.Initially, a conceptual doubt for V2G technology was associated with the belief that the availability of the vehiclespower would be low because they would be on road. However, over tens of thousands of light vehicles are in usealmost one hour per day. The high availability of the futureEDVs to be a responsive load or even a source of generation is comparable to the base load fossil-fueled power plants [2]. V2G implementation could provide a significant revenue stream to grid-connected EDVs and further encourage their adoptions. So far, the economic analysis of V2G technology applied to the current electricity markets has been widely studied and reported in open literature. In [3] the equations to calculate the capacity for grid power from three types of EDVs are proposed to evaluate revenue and costs for these vehicles to supply electricity to electric markets. In [4] the strategies and business models are proposed as theoretic support on implementation of V2G to stabilize large-scale wind power production. The economic potential of utility-owned fleets of EDVs to provide power for US electricityregulation markets is studied in [5]. The cost analysis ofEDVs providing peak power in Japan is investigated in [6]. In addition, the assessment of V2G technology applied to the UK electricity market is studied in [7].To investigate the effects of V2G deployment on EDVs and the power grid, a Matlab simulator has been developed to model the energy flows between the EDVs and the power grid for a daily power dispatch schedule. The simulator enables the user to model different test scenarios of V2G implementation by specifying the parameters of the EDV battery energy storage system and the characteristics of the power grid. II. B RIEF INTRODUCTION OF ELECTRIC BATTERY CHARACTERISTICS Battery modeling techniques has been widely studied inopen literature [8, 9]. Given a constant discharge current, i d ,the battery usage status can be described by the state of charge (SoC), s , which is expressed as s ( t ) = 1 − ( ∫0 →t i d dt )/C a (i d ) = 1 − i d t /C a (i d ) . (1)Where i d is the discharge current in Amperes and C a (i d ) is the available battery capacity in Ampere-hour (Ah).According to the Peukert equation [10], the available capacity of the battery, C a (i d ), is expressed as C a ( i d ) = i d * C p / ( i d ) k . (2) Where C p is the Peukert capacity of the battery in Ah; k is the Peukert exponent ranging between 1.1~ 1.3. Both C p and k are fixed parameters with respect to a given EDV battery.The Peukert capacity is computed as C p = T *( C N/ T ) k . (3)Where C N is the nominal capacity and T is the rated discharge time in hours; C N /T is the nominal discharging current in Ampere.Equations (1) ~ (3) are used to update the SoC value of the battery discharged or charged at a current of i d after a specified time interval.Another characteristic of the EDV battery is that its open circuit voltage is a dynamic variable. Fig 1 shows the voltage vs. capacity curve of a NiMH battery rated at 240V, 100Ah@5hours, given the discharge current of 2C/5 (40A). In Fig. 1, the available capacity of the battery (87.055Ah) and the delivered capacities when the SoC value is 0.8 and 0.4(17.4110 Ah and 52.233Ah, respectively) are indicated.978-1-4244-5697-0/10/$25.00 ©2010 IEEE 1097Counting from the time point at which the SoC value is 0.8, the delivered capacity is 20Ah (37.412Ah-17.411Ah) after half an hour discharge.Fig. 1 Voltage vs. capacity of battery rated at 240 V, 100Ah@ 5hours, discharge current at 2C/5.In providing V2G service, the SoC value of the battery iscontrolled to remain in the range between s min and s max. The specification of the s min and s max values depends on the using profile of the EDVs. G enerally the s min is set at some values that can provide enough energy for transportation to the nextdestination. Such consideration is based on the fact that only‘excess’ capacity (SoC value lager than s min ) of the EDVbattery is used to provide the V2G service.With respect to the different battery types, a lookup table ofvoltage vs. capacity at different discharge/charge currents anddifferent rated capacities and rated voltages has been built inthis V2G simulator. A partial view of such table is shown intable 1. The first column is named as Category for thereference of the rated voltage and capacity. For example,240100 means the rated voltage and capacity are 240volts and100 Ah, respectively. Voltage values at differentdischarge/charge currents (from 0.1C to 2C, column 3 and 4)and different capacities are included from the fifth column.TABLE I. P ARTIAL VIEW OF THE L OOKUP TABLE OF THE BATTERY VOLTAGE VS .CAPACITYCategory Battery TypeDischarge/Charge Current Capacity (Ah) 0 1.04895 2.0979…(p.u.) (Amp.) Voltage (volt) 240100 Lead Acid 0.1 2 259.38 246.11 246.05…… … … … … … … …240100 Lead Acid 2 40257.1 243.83 243.78…240100 Li-Ion 0.12 279.48 278.340 277.24…… … … … … … … …The database is used to do interpolation to obtain the voltage corresponding to the updated SoC value at the beginning of the current time interval t , s t −_, based on the selected discharge current i d . G iven the value of s t −_, the released capacity till t −, C t − can be obtained by equation (1). The interpolation is then conducted to obtain the voltage value, V t −, based on the value of C t − . With the voltage V t − and the discharge current of i d , the generated power from the EDV is obtained. The value of the SoC at the end of the current time interval, s t +, is updated by the s t − and the value of i d . Fig.2 shows the flowchart of the derivation of the battery power generated during current time interval t .In deriving the generated electric vehicle power (EVP),EVP t , the voltage value at the beginning of time interval, V t − ,is assumed to be constant over that specific time interval. Infact, the voltage of the battery is a dynamic variable as shownin Fig 1. The error created from assuming a constant voltageduring a given time interval, such as half an hour forproviding power to the grid, is negligible. In Fig.1, countingthe start time at which the SoC value is 0.8, the voltagedecreases from 252.2 V to 248.8 V after half an hourdischarge at the 2C/5 rate (40A). The less the dischargecurrent, the less variation of the voltage during a half an hourtime interval. Since the 2C/5 discharge current is set as theupper limit of the developed V2G simulator, the constantvoltage assumption during a half an hour time interval canprovide a fine approximation.In formulating the load flow by taking into account V2G implementation, the dispatchable power from EDVs, via DC-AC power converters, is grouped into an aggregate power injection at the bus to which it is connected. However, such amount power is constrained by the EDV battery state ofcharge. The power flow model isP n , t (v 1, v 2 ,…, v n ,…, v N ) + ∑b EVP b ,n ,t (i b ) *K b = 0 ∀n , t (4) Q n ,t ( v 1,v 2 ,…, v n ,…, v N ) = 0∀n , t (5)Subject tos min ≤ s b , t − + ( t + − t − )*i b /C b (i b ) ≤ s max ∀ b , t (6)Where n , b and t are the indices for power gird bus, individual EDV and time interval, respectively. For instance, EVP b ,n ,t (i b) denotes the battery power from EDV b connectedat bus n during time interval t , given the discharge/charge current of i b ; t +and t − are the beginning and end time point ofthe time interval t ; s b ,t −is the SoC value of EDV b at timepoint t − ; P b ,t (⋅) and Q b ,t(⋅) are real and reactive power balanceat bus n during the current time interval t ; v 1, …, v Nare thevoltage phasor values at each bus; K bis a constant to factor inthe energy loss in converting the DC power output of EDV into the AC power to be injected into the power grid or viceversa; the subscripts of d , denoting the discharge current as i dAmpere-hour (Ah)V o l t a g e (v o l t s )1098in section II, is replaced by the subscript index of b to denote the discharge or charge current of EDV b .III. M ATLAB V2G SIMULATORThe simulator has been developed through the MATLAB and the main interface is shown in Fig.3. The simulator is composed of five parts. Three panels named as EDV configuration , Power system configuration and EDV location & Control signal are placed on the left hand side for data and parameter input. Two areas on the right hand side are used to show the Tabulated results , and Graphic results .Fig. 3. Main interface of the MATLAB V2G simulator.A. EDV configurationThis panel, as shown in Fig.4, is used to trigger the configuration dialog for the EDV banks. Each EDV bank is a group of individual EDVs. The configured data of each bank is applied to all the individual EDVs belonging to that bank. Currently, at most ten banks can be defined according to the value of selection popmenu labeled as How many EDV banks?at the top of the panel.Fig.4. EDV bank configuration panel.The configuration dialog for the EDV bank is shown in the Fig.5. The number of the individual EDVs in this bank is set by the top edit control component named as Enter the EDV number of this bank . Then, the user specifies the battery type, the nominal voltage, the rated capacity and the Peukert exponent values to configure the EDV battery characteristics. Two check control components specify how the initial state of charge of each EDV is defined. The user can choose one of them to set the initial value of the state of charge at thebeginning of the first time interval. The discharge/chargecurrent rate, in p.u. of rated value, is categorised into three levels as Low level , Medium level and High level . To choose which level of the discharge/charge current depends on thestate of charge at the beginning of the current time interval, according to the flowchart shown in Fig.2. In addition, to account for the power loss in converting DC power into AC power injected to the grid, the user can define the value of the Converter efficiency factor used in equation (4) as K b .Fig. 5. EDV characteristics setting dialog.B. Power system configurationIn this panel, Fig.6, the user can choose the power grid simulated for the V2G technology to be implemented. The parameters of the IEEE-30, -39, -57 and -118 test system used in MatPower[11] are available in the simulator or user can manually input real network parameters if available. The demand profile is copied from the Jan. 2009 UK demand data [12] and is effectively scaled by the maximum quantity of the day. The fixed load demand and generator supply at each bus are then shared proportionally over a day, using the scaleddaily demand.Fig.6. Power system configuration panel.Four radio check buttons are listed on the right side of the panel in Fig.6. Each button corresponds to a special simulation scenario where new variables are introduced to study the effects of the V2G implementation. For instance, an economic analysis of the V2G service can be investigated by introducing the spot prices of the electricity of the day whilefirming the wind power integration using the V2G service can1099be studied by enabling the wind power penetration of the day .The other two radio buttons allow the user to introduce two kinds of grid operation constraints, being network overload congestion and reactive power output limits of the traditional generators. Those constraints allow the user to investigate the V2G effects on a more realistic power system basis and to study the possible positive effects of V2G services on mitigating those constraints.C. EDV location & Control signalThis panel is composed of two parts. The grid connection of the EDV banks is on the left hand side whilst the control signal setting is on the right hand side, as shown in Fig.7. The rows of this panel, corresponding to each EDV bank, are enabled for user setting by inputting the number of the EDVbanks, shown in Fig.4.Fig. 7. EDV location & Control signal setting panel.In the column named as Bus in Fig.7, the user can assign which bus the EDV bank is connected to. In addition, user needs to specify the Control signal which calls on the EDV banks at different demand level time intervals that are: peak; valley; non-peak-non-valley load intervals. Four options, discharge , charge , bidirectional and disconnected , are listed in each of the popupmenu of the control signal setting. The bidirectional option means the EDV bank can take both the discharge and charge role randomly while the disconnected option disables the EDV bank from the grid.A reasonable setting strategy for the control signal profile is to let the EDV banks take the discharge role during peak load time intervals and the charge role during valley load timeintervals. Such control signals are designed with the purposeto attempt to level the load. Other strategies for setting the control signal can be devised to achieve specific economic or technical goals, such as to maximize the economic returns by introducing the spot prices of electricity during the day or to minimize the grid network energy flow loss by optimally discharging or charging the EDVs in appropriate time intervals, which are called jointly with one of the four radio buttons listed in Fig.6. D. Results outputThe right part of the simulator shown in Fig. 3 is used forthe results output purpose. The Tabulated results aredisplayed by a Spreadsheet control component. The user canselect which half an hour interval to show the results in theLoad flow results sheet , including the power system operationvariable values and the EDV deployment status, as shown inFig.8. In addition, the user can export the results to an Excelfile by clicking the button labeled as Export to…at the topright hand side of the tabulated results area, Fig. 3.Graphic results are controlled by two buttons labeled as To plot the power systems results and To plot the EDV variables , which navigate the user to two dialogs, as shown in Fig.9, and Fig.12, respectively. In both dialogs, the user can specify the results of interest during specified time intervals that are to be plotted.Fig.8. Partial view of the EDV deployment status, from 07:00-07:30 of a low demand time interval (charging).Fig.9. EDV variables plot configuration dialog.Fig.10. shows the graphics of the SoC and the dispatched power of all the EDVs of EDV bank1 over one day, whilst Fig. 11 shows the information for a specific EDV. From the half hour interval index 33 to 37 (peak load time intervals) the control signal sets the EDV to discharge whilst from time interval 14 to 18 (valley load time intervals) it is to be charged. At other times, the EDVs are disconnected from the grid. Thenegative values of power, Fig. 10 and Fig.11, indicate thebattery discharges whilst the SoC value decrease; whilstpositive values of the power means charging with the SoCvalues increasing. The SoC values of all the EDVs areconstrained in the range between 0.4 and 0.8, s min and s max inequation (6).By clicking the check box labelled as demand quantityduring one day in Fig.12, Fig. 13 shows the peak load shavingeffect from the total contribution of EDV battery energystorage systems. As expected, the peak load demand ispartially offset by the discharging power from the EDVs1100while the valley load demand is increased due to the charging power absorbed by the EDVs from the power grid. By clicking the check box of real and reactive power flow loss and picking the branches of interest, the user can observe the real and reactive power flow losses of the selected branches during one day. In addition, graphic of the bus voltage profiles can be plotted by clicking the check box of Bus voltage magnitude and angle in Fig.12.Fig.10. SoC (upper) and dispatched power (lower) during one day,EDVbank1 (discharge/charge current: 0.5 C-1C-1.5C) connected at bus 3,IEEE30 bus power grid.Fig.11. SoC (upper) and dispatched power (lower) during one day, EDV25 ofbank5 (discharge/charge current: 0.3C-0.9C-1.3C) connected bus 15, IEEE30bus power grid.Fig.12. Power system variables plot configuration dialog.Fig.13. Peak load shaving effect of the V2G implementation.IV.C ONCLUSIONElectric vehicles are expected to significantly increase in numbers over the next few years reflecting their potential environmental advantages. To charge and discharge the EDV battery is equivalent to deploying the EDV as a dispatchable load resource allowing bidirectional power flow between the EDV and the power system. A comprehensive MATLABsimulator has been developed by the authors for modelling a distributed EDV energy storage system implemented as V2G technology. The simulator underpins the ongoing research on economic and technical analysis of the V2G technology at the physical grid operation level.A CKNOWLEDGEMENTSThis work is supported by an E.On International Research Initiative Award 2007.R EFERENCES[1]Kempton W., Tomic J., Letendre S., Brooks A. and Lipman T., “Vehicle-to-Grid power: battery, hybrid, and fuel cell vehicles as resources for distributed electric power in California,” Working Paper Series with number UCD-ITS-RR-01-03, Jun., 2001.[2] Kempton W. and Letendre S., “Electric vehicles as a new source ofpower for electric utilities,” Transportation Research , vol. 2, no. 3, 1997, pp.157-175.[3] Kempton W. and Tomic J., “Vehicle to grid fundamentals: calculatingcapacity and net revenue,” Journal of Power Sources , vol. 144, no.1,Jun. 2005, pp.268-279.[4] Kempton W. and Tomic J., “Vehicle to grid implementation: fromstabilizing the grid to supporting large-scale renewable energy,” Journal of Power Sources , vol. 144, no.1, Jun. 2005, pp.280-294.[5] Tomic J. and Kempton W., “Using fleets of electric-drive vehicles forgrid support,” Journal of Power Sources , vol. 168, no. 2, Jun. 2007, pp. 459-468.[6] Kempton W. and Kubo Toru., “Electric-drive vehicles for peak powerin Japan,” Energy Policy , vol.28, no.1, Jan. 2000, pp. 9-18.[7] Zhong X., Cruden A., Infield D. and Holik P., “Assessment of vehicleto grid power as power system support,” Smart grid & Mobility Europe Conference , 16-17 Jun., 2009.[8] Kroeze, R.C.; Krein, P.T., “Electrical battery model for use in dynamicelectric vehicle simulations,” Power Electronics Specialists Conference , 15-19 Jun. 2008, pp.1336 – 1342[9] Somayajula, D.; Meintz, A.; Ferdowsi, M., “Study on the effects ofbattery capacity on the performance of hybrid electric vehicles,” Vehicle Power and Propulsion Conference , 3-5 Sept. 2008, pp.1 - 5 [10] Larminie J. and Lowry J., Electric vehicle technology explained , John Wiley & sons, 2003.[11] The MATPOWER, /matpower/ .[12] /NR/rdonlyres/8600C184-DFF3-4BB4-A6C8-71E87919E4A6/33735/Demand_Jan_to_Mar2009.xlsSoC Powe in kW Half an hour index SoC Powe in kWHalf an hour index Electricity demand (in p.u. of 100MVA )Half an hour index1101。
