Comprehensive management strategy for plug-in hybrid electric vehicles using national smart metering program in Iran (called FAHAM)

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1 Comprehensve management strategy for plug-n hybrd electrc vehcles usng natonal smart meterng program n Iran (called FAHAM) Masoud Honarmand, Nader Salek Glan and Had Modaghegh Iran Energy Effcency Organzaton (IEEO), Tehran, Iran Masoud.honarmand89@gmal.com, Salek@saba.org.r, Modaghegh@saba.org.r Keywords: Abstract: Bllng solutons, chargng management, plug-n hybrd electrc vehcles, smart meterng. Plug-n hybrd electrc vehcles chargng management as well as bllng solutons may be the most mportant challenges n the comng years. In ths paper, a comprehensve management strategy (CMS) for plug-n hybrd electrc vehcles (PHEV) usng natonal smart meterng program n Iran (called FAHAM) s proposed. The proposed strategy consders PHEVs chargng management and bllng solutons as well. An optmzaton method s appled n order to shft PHEVs chargng load and maxmze load factor. 1. INTRODUCTION Although the wdespread mplementaton of plug-n hybrd electrc vehcles (PHEV) may ntroduce a soluton to the world fossl fuel shortage as well as the ar polluton crss, the antcpaton of connecton of PHEVs nto the power network may brng up many techncal drawbacks that need to be addressed properly. In the near future, a huge number of PHEVs wll add a large-scale energy demand to power systems. An emergng ssue s that a large number of EVs smultaneously wll be connected to the grd that may put at rsk the overall power system qualty and stablty (Sortomme & El- Sharkaw, 2012; Sousa et al., 2012; Plla & Bak- Jensen, 2011). The ntellgent schedulng and control of PHEVs as loads or power sources have great potental for evolvng a sustanable ntegrated electrcty and transportaton nfrastructure (Honarmand et al., 2014; Honarmand et al., 2014; Honarmand et al., 2015; Brahman et al., 2015). Large numbers of EVs have the potental to put at rsk the stablty of the power network. The chargng demand of PHEVs needs to be managed very carefully n order to avod nterrupton when several thousand of them are ntroduced nto the system over a short perod of tme. In ths paper, a comprehensve management strategy (CMS) for PHEVs usng natonal smart meterng program n Iran (called FAHAM) s proposed n order to consder PHEVs chargng management and bllng solutons as well. The proposed strategy s capable of controllng chargng procedure of PHEVs. The rest of ths paper s organzed as follows: Natonal smart meterng project n Iran s presented n Secton II. The comprehensve management strategy for PHEVs s stated n Secton III. The problem formulaton for maxmzng the load factor s presented n Secton IV. Smulaton data and results are presented and dscussed n Secton V. Fnally, the concluson s gven n Secton VI. 2. NATIONAL SMART METERING PROGRAM IN IRAN (CALLED FAHAM) Deployng an Advanced Meterng Infrastructure (AMI) s an essental early step to grd modernzaton. AMI s not a sngle technology but t s an ntegraton of many technologes such as smart meter, communcaton network and management

2 Fgure 1: The communcaton archtecture of FAHAM project (Jadd et al., 2013) system that provdes an ntellgent connecton between consumers and system operators (Jadd et al., 2013; Modaghegh & Zakarazadeh, 2013). AMI gves system operator and consumers nformaton they need to make smart decsons, and also the ablty to execute those decsons that they do not currently able to do. In Iran, lke many other developed countres, Smart Grd mplementaton s regarded as a unque way for encounterng many serous envronmental and economc challenges. FAHAM s the Natonal Smart Meterng Program n Iran. The functonal, techncal, securty, economc, and general requrements of ths project s publshed as a document after a long-tme workgroup of varous stakeholders ncludng representatve of grd operators, meter manufactures, communcaton provders, busness layer software provders, domestc and nternatonal consultants. Iran Energy Effcency Organzaton (IEEO) s responsble for mplementaton and deployment of FAHAM. The IEEO follows promotng energy effcency and load management, mprove system relablty, and reduce operatonal costs by mplementng natonal smart meterng project. The smple structure of communcaton system n FAHAM project s shown n Fg. 1 whch conssts of: Smart meters: These may be sngle phase or three phase smart meters. Electrcty meter provdes the varous nformaton for customers such as amount of consumpton (kwh, KVarh), consumpton parameters (voltage and current), equpment status and last nformaton of water and gas meters. Communcaton nterface: Power Lne communcaton (PLC) and General packet rado servce (GPRS) are two communcaton nterface that connect two dfferent part of FAHAM subsystem together. Data concentrators nstalled n 20kV/400V transformer to manage all smart meters measured data from such nstallatons. Data concentrators ntegrate PLC communcatons that exchange nformaton wth smart meters to communcate wth central meter data management systems. Central Access Systems (CAS): CAS s responsble for the management of all nformaton and data related to smart meterng, as well as the confguraton, control and operaton of all system components. The CAS n order to manage the FAHAM network shall have 2 followng parts: a) AMI Head End (AHE) that has the responsblty to manage the confguraton, WAN and LAN network management system, managng the network equpment, Regstraton of equpment and Operaton & Mantenance of fled equpment n the network. b) MDM/R that manages and archves the acqured data from the AHE. Legacy Systems: These are the exstng commercal or techncal systems that manage the busness processes of the utlty.

3 3. COMPREHENSIVE MANAGEMENT STRATEGY FOR PHEVS As the number of PHEVs ncreases, chargng ponts n both parkng structures and prvate garages wll become more prevalent. These chargng ponts wll be responsble for meetng the requrements of the dstrbuton grd and PHEV owners. These chargng ponts should perform many functons such as supportng the RFID cards, meterng and communcaton. communcates drectly wth a network coordnator and chargng control server. The other mportant capablty of the CMS s to separate chargng bll and the general bll. In ths strategy, the PHEV owner can charge hs/her PHEV by pluggng t nto every possble chargng faclty provded n the electrcal grd and stll has a separated bll. As an example assume that PHEV A whch belongs to home B, s chargng n offcal buldng C. The RFID charger located at C reads the PHEV s ID number and sends t along wth the consumed energy and the charger s ID number to the data management center at the end of the chargng procedure. By subtractng the consumed energy from the meter readng of the man meter located n C and addng t to the chargng bll of A, the separated blls wll be obtaned, and at the specfed perods of tme two separated blls wll be sent for B. 4. PROBLEM FORMULATION An optmzaton s appled n order to shft PHEVs chargng load for achevng the followng objectve functon whch s maxmzng load factor. é 24 N و و t,t PL P ê ھ هç + Ch,PHEV ç t= 1 = 1 è OBJ = ê / MD ç çê 24 êç è ë û ù (1) Fgure 2: Separate bllng usng CMS The proposed CMS supports all possble chargng forms such as chargng n homes, offces, commercal buldngs, chargng statons, etc. To acheve ths goal, usng RFID chargers has been taken nto account. In ths strategy, each PHEV takes smart energy RFID card. These cards cover both aspects of energy sources whch are used by PHEVs, gasolne and electrcty. RFID tags nsde the smart energy cards and chargers equpped wth RFID readers together wth mddleware and a chargng controller to authorze, assgn, and enable chargng s requred. In order to smplfy the chargng authorzaton process and make t more convenent for users, an authentcaton system based on an RFID s proposed. The layered archtecture for managng a varety of automatc dentfcaton hardware, t where P L s the basc load of the feeder,,t P Ch,PHEV s the chargng load of the th charger, and MD s the maxmum demand of the feeder. The maxmzaton of the objectve functon s subjected to the followng constrants: PHEV s charger constrant:,t PCh,PHEV P Ch,max ; " t (2) where P Ch,max s the maxmum chargng power of the th charger. Ths constrant determnes the maxmum chargng rate that the charger can provde. SOC lmts:,t SOC mn SOC SOC max ; " t (3) where SOC max and SOC mn are the maxmum and mnmum SOC of the th PHEV, respectvely. Ths constrant allows the SOC to vary between predefned mnmum and maxmum SOC.

4 Chargng rate lmts:,t DSOC DSOC ; " t (4) max where D SOC max s the maxmum allowable rate for chargng of the th PHEV. The chargng rate of battery are lmted by ths constrant. Battery chargng constrant:,t,t- 1,t PCh,PHEV hg2v Dt RBC Cap ; " t (5) where hg2v s the PHEV s battery chargng,t 1 effcency, RBC - s the remanng battery,t capacty, and Cap s the battery capacty. Ths constrant lmts the chargng power of each PHEV based on the remanng battery capacty n each perod of tme. The proposed model s solved usng mxed nteger lnear programmng (MILP) solver CPLEX under GAMS on a Pentum IV, 2.6 GHz processor wth 4 GB of RAM. Fg. 4 shows the basc load profle of a typcal commercal feeder together wth PHEVs chargng demand n scenaro 1. It s obvous that the uncontrolled chargng demand of PHEVs can cause dffcult stuatons for dstrbuton feeders by overloadng the feeders. Table I: The hourly electrcty prce n the open market Hour Prce ($/kwh) Hour Prce ($/kwh) SIMULATION AND RESULTS The Arrval and departure tmes of PHEVs are assumed as random varables. A commercal feeder s consdered for ths study and the PHEVs penetraton s consdered 30 percent. In ths paper, the dstrbuton system operator manages the chargng procedure of each PHEV consderng ts arrval tme, approxmate duraton of presence n the parkng lot, and open market prcng sgnal. After that the man controller uses the processed data to optmally schedule the chargng procedure of PHEVs. There are several types of electrc vehcles n the market wth varous battery capactes from 8 kwh to 48 kwh (Peltan Fernandez et al., 2011). In ths paper, all electrc vehcles supposed to be Chevy Volt whch t s an average electrc vehcle wth 16.5 kwh battery capacty. The ntal SOC of each EV s consdered as a contnuous unform random number between 0.1 and 0.7. Table I provdes the hourly electrcty prce of the open market. Fg. 3 depcts the basc load of a typcal commercal feeder. In order to analyze the robustness of the CMS, the problem s addressed n two scenaros: Scenaro 1: There s no control on the chargng procedure of the PHEVs. Scenaro 2: The dstrbuton system operator manages the chargng procedure of PHEVs n order to maxmze the load factor and flatten the load profle of the feeder. Power (kw) Basc Load Profle Tme (Hour) Fgure 3: The basc load profle of a typcal commercal feeder

5 Power (kw) Fg. 5 shows the basc load profle of a typcal commercal feeder together wth PHEVs chargng demand n scenaro 2. As llustrated, by applyng controlled chargng through FAHAM nfrastructure and CMS strategy, the load profle of the feeder s flattened. Basc Load Profle Fnal Load Profle Chargng Demand Tme (Hour) Fgure 4: The basc load profle of a typcal commercal feeder together wth the PHEVs chargng demand n scenaro 1 Basc Load Profle Fnal Load Profle Chargng Demand 6. CONCLUSIONS The ntegraton of PHEVs n the power networks makes new challenges; accordngly, there s a growng necessty to address the mplcatons of ths technology on the power network. In ths paper, a comprehensve management strategy based on natonal smart meterng program n Iran s proposed. The proposed strategy helps the system operator to enhance the overall system effcency. The results showed that the chargng was carred out n the hours wth lower loadng, whle n the hours wth hgher loadng, the chargng demand was curtaled. Smulaton results evdenced that the use of FAHAM nfrastructure for managng of the chargng of PHEVs has elmnated the rsk of an electrcty demand growth durng the peak load of the network. REFERENCES Brahman, F., Honarmand, M. & Jadd, S., Optmal electrcal and thermal energy management of a resdental energy hub, ntegratng demand response and energy storage system. Energy and Buldngs, 90, pp Power (kw) Tme (Hour) Fgure 5: The basc load profle of a typcal commercal feeder together wth the PHEVs chargng demand n scenaro 2 The Table II shows the load factor of the feeder n basc load, scenaro 1, and scenaro 2. Usng controlled chargng leads to hgher load factor, and ths can be consdered as a bg opportunty of FAHAM nfrastructure for the system operator to acheve a system wth much more effcency. Table II: Load Factor Basc Load Scenaro 1 Scenaro 2 Load Factor Honarmand, M., Zakarazadeh, A. & Jadd, S., 2014a. Integrated schedulng of renewable generaton and electrc vehcles parkng lot n a smart mcrogrd. Energy Converson and Management, 86, pp Honarmand, M., Zakarazadeh, A. & Jadd, S., 2014b. Optmal schedulng of electrc vehcles n an ntellgent parkng lot consderng vehcle-to-grd concept and battery condton. Energy, 65, pp Honarmand, M., Zakarazadeh, A. & Jadd, S., Self-schedulng of electrc vehcles n an ntellgent parkng lot usng stochastc optmzaton. Journal of the Frankln Insttute, 352(2), pp Jadd, S., Zakarazadeh, A. & Modaghegh, H., Real tme volt/var control usng advance meterng nfrastructure system n faham project. In 22nd Internatonal Conference and Exhbton on Electrcty Dstrbuton (CIRED 2013). pp

6 Modaghegh, H. & Zakarazadeh, A., Feasblty and Cost/Beneft Analyss of Implementng Electrc Vehcle n Iran Smart Dstrbuton System. In Internatonal Smart Grd Conference & Exhbton. Jeju, Korea, pp Peltan Fernandez, L. et al., Assessment of the Impact of Plug-n Electrc Vehcles on Dstrbuton Networks. IEEE Transactons on Power Systems, 26(1), pp Plla, J.R. & Bak-Jensen, B., Integraton of Vehcle-to-Grd n the Western Dansh Power System. Sustanable Energy, IEEE Transactons on, 2(1), pp Sortomme, E. & El-Sharkaw, M.A., Optmal schedulng of vehcle-to-grd energy and ancllary servces. Smart Grd, IEEE Transactons on, 3(1), pp Sousa, T. et al., Intellgent Energy Resource Management Consderng Vehcle-to-Grd: A Smulated Annealng Approach. Smart Grd, IEEE Transactons on, 3(1), pp