Combined Economic and Emission Dispatch Incorporating Renewable Energy Sources and Plug-In Hybrid Electric Vehicles

Transcription

1 Internatonal Journal of Energy Scence (IJES) Volume 4 Issue 2, Aprl 204 do: /jes Combned Economc and Emsson Dspatch Incorporatng Renewable Energy Sources and Plug-In Hybrd Electrc Vehcles Amn Gholam, Mahd Jame 2, Javad Ansar 3 and Arf I. Sarwat 4,3 Iran Unversty of Scence and Technology, Tehran, Iran 2,4 Electrcal and Computer Engneerng of Florda Internatonal Unversty, Mam, USA amn_gholam@elec.ust.ac.r; mjame044@fu.edu; Javad_ansar@elec.ust.ac.r; asarwat@fu.edu Abstract Conventonal transportaton and electrcty ndustres are consdered as two major sources of greenhouse gases (GHGs) emsson. Improvement of vehcle s operatonal effcency can be a partal soluton but t s necessary to employ Plug-In Hybrd Electrc Vehcles (PHEVs) and Renewable Energy Sources (RESs) n the networ to slow the ncreasng rate of the GHGs emsson. However, t s crucal to nvestgate the effectveness of each soluton. In ths paper, a combnaton of generaton cost and GHGs emsson of the two mentoned ndustres, as economc and envronmental aspects of usng PHEVs and RESs wll be analyzed. The effectveness of fve dfferent scenaros of utlzng the mentoned elements s studed on a test system. To have a realstc evaluaton, an extended cost functon model of wnd farm s employed n optmal power dspatch calculatons. Partcle Swarm Optmzaton (PSO) algorthm s appled to the combned economc and emsson dspatch (CEED) non- lnear problem. Keywords Economc Dspatch; Emsson; Plug-In Hybrd Electrc Vehcles (PHEVs); Partcle Swarm Optmzaton (PSO); Smart Grd; Webull; Wnd Farm Introducton In recent years, power ndustry has faced many economc and envronmental ssues. Increasng rate of fossl fuels cost and envronmental laws such as The Kyoto Protocol to the Unted Natons Framewor Conventon on Clmate Change (UNFCCC) and Low Carbon Transton Plan n July 2009,whereby must acheve 80 percent all carbon emssons reducton target by 2050 (DECC), forced governments to go towards wde ncorporatng of renewable energy Sources. The concern about the depleton of fossl fuels along wth ther negatve envronmental mpact are the most crtcal ssues n the feld of energy that encouraged many researchers for development of new technques for control and ln ntegraton of the power system (Farhad, M., et al, 204). Furthermore, the deployment of next-generaton plug-n vehcles on the roads, whch nclude plug-n hybrd electrc vehcles (PHEVs) and EVs wth vehcle to grd (V2G) capablty, both called Grdable Vehcle (GV) n ths paper, seems to be an approprate soluton to our problem. GVs are rapdly developng and penetratng to the fleet transportaton. Snce 2008, more than 6,000 hghway-capable plug-n electrc cars have been sold n the Unted States through June 203 (Voelcer, 203). In 20, based on the U.S. Department of Energy s (DoE) forecasts, Presdent Barac Obama set the goal for the U.S. to become the frst country to have mllon electrc vehcles on the road by 205 (Mtlts, 202).Subsequently, the U.S government has nvested a lot of money on ths secton to accomplsh the ams. For example, t has pledged US$2.4 bllon n federal grants to support the development of next-generaton electrc cars and batteres, and US$5 mllon for the nstallaton of electrc vehcle chargng nfrastructure (Mtlts, 202). Effects of GVs from dfferent aspects are studed n many lteratures. In (Masoum et al, 203; Amn et al, 203), new smart load management (SLM) approach for the coordnaton of multple GV chargers n dstrbuton feeders s proposed. In a report of the Natonal Renewable Energy Laboratory (NREL), emsson s varaton s only consdered n several scenaros for chargng. It has been represented that usng PHEVs wll lead to sgnfcant reducton n CO2 emsson (Pars et al, 2007). Some studes modeled the effect of electrcty prce and chargng mode on electrc vehcles' customer behavour (Amn, M.H. et al, 202). These vehcles have charge/dscharge capablty that can nfluence load profle. In (Kempton and Tomc, 2005), (Kempton and Tomc, 2005), authors surveyed 60

2 Internatonal Journal of Energy Scence (IJES) Volume 4 Issue 2, Aprl the advantage of V2G capablty for PHEVs as a reserve to help load shavng and regulaton. In (Hadley and Tsvetova, 2008), effect of GVs on electrcal load curve grd, generaton capacty and cost has been analyzed. In (Melopoulos et al, 2009), two studes are presented quantfyng the mpact of GVs on the power grd. The frst study quantfes ths mpact n terms of (a) prmary fuel utlzaton shfts, (b) polluton shfts, and (c) total cost for consumers. In the second study, the mpact on dstrbuton transformers s quantfed through a loss of-lfe (LOL) calculaton that s based on the transformers hot-spot temperature. In (Hutson et al, 2008), an ntellgent method has been proposed for schedulng the use of avalable energy storage capacty from GVs. Ths paper presents the best framewor of utlzng GVs to reduce the GHGs emsson and generaton cost. Actually, t s ndcated that employng GVs n an napproprate outlne wthout provdng the necessary nfrastruscture may ncrease the total emsson due to the ncreased load grd caused by connectng GVs and as a result, consumng more fossl fuels by thermal power plants to supply ncreased demand. Ths ncrease n emsson of power plants could be hgher than correspondng decrease by fleet transportaton, thus, the net emsson changng wll be postve.e. emsson wll ncrease. Economcally, engagng GVs n an unsutable scenaro can mpose ncremental costs due to the ncreased load demand. New power plants wll be needed to supply the pea load f t s greatly ncreased whch may be very costly. In ths paper, smart grd framewor has been proposed as the most sutable way to ncorporate GVs because t maes a better use of RESs that can help to solve the problem snce renewable resources use any fossl fuels that mae them cheaper and cleaner than tradtonal types. It s shown that mplementaton of RESs (wnd and solar) wll lead to reduce producton cost and emsson. In order to obtan precse and more realstc results, a new model of Wnd Farms (WFs) s used for optmal power dspatch among unts. Snce the proposed cost functon of WF n ths paper s nonlnear and cannot be solved by analytcal methods, evolutonary algorthms must be appled to solve the problem. Pror research usng the genetc-algorthm (GA) and smulated annealng (SA) technques has provded effectve solutons for mult-objectve optmzaton problem (Moghadas, A.H. et al, 20 and Heydar, H., 20). In ths paper, the partcle swarm optmzaton (PSO) algorthm (Kennedy and Eberhart, 995) s utlzed due to ts hgh ablty n fndng best result. The rest of ths paper s organzed as follows. Secton II descrbes problem formulatons. To avod redundant repetton, concepts related to the economc dspatch(ed) and PSO whch are nown and mentoned n many papers, are brefly explaned n ths secton. In secton III, PSO s appled to the ten unts system n dfferent scenaros to nvestgate the nfluence of GVs and RESs on cost and emsson. Fnally, the concluson s gven n secton IV. Problem Formulaton In ths secton, ED, WF cost functon and PSO formulatons are expressed n bref. Combned Economc Emsson Dspatch (CEED) In ths study, the objectve functon s composed of two terms, generaton cost and emsson, as gven n (). For a specfed power plant, both cost and emsson can be expressed as a polynomal functon separately. The order of these functons depends on the ntended accuracy. In ths paper, a quadratc functon s consdered for cost and emsson functon as descrbed n (2) and (3), respectvely. Ths problem handles power balance equaton (4) and power generaton lmts (5) are consdered as physcal and operatng constrants (Venatesh et al, 2003), (Lu, 20). OF = ω TC + ω2te () TC = TE = P n = n = 2 a + b P + c P 2 (2) α P + β P + γ (3) D = n P = mn max (4) P P (5) where OF, TC and TE are objectve functon, total cost and total emsson, respectvely. ωand ω2 are weght factors.a, b and c are the postve fuel cost coeffcents of unt. α, β and γ are GHGs emsson coeffcents for unt. Wnd Farm (WF) Cost Functon In some lteratures, WF s modeled as a negatve load wthout any cost (Yong et al, 2007), (Farhat and El- Hawary, 200) but t s not compatble wth realty due to the uncertan nature of wnd and output of the WF. Underestmaton and overestmaton of the avalable wnd energy whch may happen as a result of WF s bad modelng can mposes addtonal costs to prvate 6

3 Internatonal Journal of Energy Scence (IJES) Volume 4 Issue 2, Aprl 204 company owner that partcpate n electrcty maret and sell ther power to customer by means of Independent System Operator (ISO). For ths reason, t s necessary to model WF more detaled and accurate. In ths study, authors use a new cost functon n ED formulaton. In ths model, three cost functons form the man wnd cost functon that descrbed n (6) (Jadhav and Roy, 203). C = N = C N N w, ( w ) + C p, w, ( W, av w ) + Cr, w, ( w W, av ) = = where N, w, W,av and wr, are number of wnd farms, scheduled wnd power from the th WF, avalable wnd power from the th WF and rated wnd power from the th WF, respectvely. In (6), the frst term s the cost that system operator must pay to the WF s owner aganst generated power. Ths cost functon s modeled lnearly as ndcated n (7). w, ( w ) dw C = where d s the drect cost coeffcent for th wnd farm. In (Jadhav and Roy, 203), t has been shown that the total cost of wnd generaton s around 57% of that for thermal one. Consequently, ths cost coeffcent can be chosen accordngly n optmal dspatch formulaton. Second and thrd part of (6) s related to the uncertanty nature of wnd power output gven n (8) and (9). (8) s consdered as a penalty cost functon for not usng all the avalable wnd power whch assumed to be lnearly related to the dfference between the W,av and w,. The reserve requrement cost s due to beng W,av less than the w. It s smlar to penalty cost. If the WF s not owned by the system operator, the drect cost coeffcent and penalty cost may be zero. The power Probablty Densty Functon (PDF) of the wnd energy converson system (WECS)output power that ndcated by fw(w) n (0) s obtaned by wellnown two-parameter Webull functon dependent on wnd speed and probablty theory for random varables. More detaled nformaton about the formulaton of wnd cost functon s gven n (Julana and Sauer, 203). Estmatng methods of Webull shape and scale factors ( & c) usng the avalable wnd speed data are gven n (Seguro and Lambert, 2000 ). The PDF of Webull functon are llustrated n Fg. for dfferent and c parameters. (6) (7) r, ( ) = ( ) = ( ) ( ) C W w W w w w f w d p, w,, av p,, av p, w w vr vo + p, w exp exp c c ( ) ( ) ( ) C ( w W ) = w W = w wf wdw rw,, av, r, av, r, w 0 v vo r, w exp exp (9) + + c c ( ) lv ( ) ( ) ( ) + ρl v + ρl v f = w w exp w (0) rc c c where p, and r, are penalty and reserve cost coeffcents due to the underestmaton and overestmaton of wnd power for th WF n $/MW repectvely.v, ρ=w/w and l=(vr-v)/v are wnd speed, rato of wnd power output to rated wnd power and rato of lnear range of wnd speed to cut-n wnd speed repectvely. Probablty densty functon c=5 0.2 c=0 0.5 c=5 c= c=0 c=5 c=5 c= Wnd Speed FIG. PDF OF WEIBULL FUNCTION FOR DIFFERENT K AND C PARAMETERS Case Study To nvestgate the effectveness of the proposed model, a ten-unt system wth 50,000 GVs s smulated usng MATLAB 20a software. The number of vehcles for ths system has been calculated based on an approxmate method whch offered n (Roe and Mesel, 2008). The capacty of solar and WFs s consdered 40 MW and 30 MW respectvely. Calculatons are executed on a 3.2 GHz, Core 5 processor wth 4 GB RAM. The generators parameters and load data are gven n table 2 (Tng et al, 2006). Solar nsolaton and wnd speed data are obtaned from NREL. The WF parameters are gven n Table 2. Other parameters values used n ths paper are as w w (8) 62

4 Internatonal Journal of Energy Scence (IJES) Volume 4 Issue 2, Aprl TABLE GENERATOR SYSTEM OPERATOR DATA Unt Unt 2 Unt 3 Unt 4 Unt 5 Unt 6 Unt 7 Unt 8 Unt 9 Unt 0 Pmax Pmn a($/h) b($/mwh) c($/mwh2) α(ton/h) β(ton/mwh) γ(ton/mwh2) wr vn (m/s) TABLE 2 PARAMETER OF WF vr (m/s) vout (m/s) d ($/MW) p ($/MW) r ($/MW) follows: chargng dschargng frequency= per day; schedulng perod=24 hrs; for PSO, swarm sze=50, teratons=000, and acceleratng parameters are C=.5, C2=2 and fnally Range=0.5. The average dstance drven by each GV n a year and ts needed energy are assumed 2000 mle and 8.22 KWh per day respectvely. Accordng to the average GV s emsson of 445gram/mle, t s concluded that GHGs emsson produced by a GV wll be gram per year. Therefore; the total emsson from 50,000 GVs wll be 267,000ton per year (Seguro and Lambert, 2000 ), (UEPA). In ths paper, to show the effect of GVs and renewable energy sources on the electrcty and transportaton ndustres, fve scenaros are consdered: ) wthout GVs and renewable energy sources, 2) wth GVs consderng load levelng, 3) wth GVs and WF, 4) wth GVs and solar farm 5) wth GVs and renewable sources (solar and wnd smultaneously). Last three scenaros are referred to as smart grd model by the authors. Wthout GVs and Renewable Energy Sources Frst, PSO s appled to the ten-unt system wthout consderng GVs and RESs for 24 hours to fnd optmal power dspatch accordng to the CEED objectve functon. Wth GVs Consderng Load Levellng In ths case, GVs are just charged through conventonal generaton unts usng load-levelng optmzaton and don t have a bdrectonal power flow wth the grd. The purpose of GVs n ths scenaro s to ncrease the load level at off-pea hours n order to mae the load curve flatter. The load profle s shown n Fg.2, before and after load levelng. cost and emsson are calculated consderng the load demand from 50,000 GVs and levelng the extra load. The obtaned results for ths scenaro s gven n Table 3. By comparng scenaro and 2, t can be nferred that daly emsson s ncreased for ton ( ton) wth consderng load levelng. Ths extra emsson ( ton) whch generated by power plants s to supply energy demand of 50,000 GVs durng 24 hrs. Thus, the extra emsson s tons ( ton 365) per year n addton to 267,000 tons from the transportaton sector. Moreover, decreasng system effcency and ncreasng losses caused by added load wll result nto addtonal emsson term that must be added to ths value. Therefore, as t was shown, load levelng by GVs wll not lead to GHGs emsson reducton, and even wll ncrease t. Load demand Load levelng for EVs Orgnal load demand Tme (hour) FIG. 2 EFFECT OF EVS ON THE LOAD PROFILE 63

5 Internatonal Journal of Energy Scence (IJES) Volume 4 Issue 2, Aprl 204 TABLE 3 EMISSION AND COST FROM TEN-UNIT SYSTEM WITH GVS CONSIDERING LOAD LEVELING Tme Demand P P2 P3 P4 P5 P6 P7 P8 P9 P0 Emsson (ton) Fuel Cost ($) Total Smart Grd Model The effect of RESs on producton cost and emsson through three dfferent scenaros s nvestgated n ths secton. ) Wth GVs and WF In ths scenaro, WF s added to conventonal unts and GVs are charged as loads and dscharged nto the grd as sources. PSO successfully employed to analyze the effect of WF on cost and emsson. The total producton cost and emsson wll be $ and tons. Based on the obtaned results, the emsson and generaton cost has been reduced because of utlzng clean and cheap energy of wnd to supply a part of grd s demand. 2) Wth GVs and Solar Farm The WF s replaced wth a solar farm n ths case. The total producton cost and emsson wll be $ and tons. The solar energy s not avalable n all day long (only avalable from 7 AM to 4 PM) whle WF can generate energy n 24hrs. For ths reason, although the rated power of solar PV s greater than that of wnd power, the total generated energy by PV plant s less than wnd type and consequently, producton cost and emsson n ths scenaro wll be hgher than wnd scenaro. 3) Wth GVs and Renewable Sources (Solar and Wnd Smultaneously) Fnally, results from a smart grd model wth wnd, solar and GVs are shown n Table II, where GVs operate as loads and sources. Moreover, uncertantes of wnd and solar sources, load and varable exchanged power between GVs and grd are consdered. Accordng to Table 4, GVs are charged from the grd at off-pea load durng the st 7th, 6th 8th, and 22nd 24th hours. On the other hand, GVs are dscharged nto the grd at pea load durng the 8th 5th and 9th 2st hours. Fg. 3 and Fg. 4 llustrate the comparson of the proposed scenaros wth respect to emsson and cost respectvely. As t s demonstrated n these fgures, ffth scenaro s preferable because of less GHGs emsson and cost caused by supplyng a part of the grd s demand by RESs. 64

6 Internatonal Journal of Energy Scence (IJES) Volume 4 Issue 2, Aprl FIG. 3 COMPARISON OF THE PROPOSED SCENARIOS IN EMISSION FIG. 4 COMPARISON OF THE PROPOSED SCENARIOS IN FUEL COST Tme Demand TABLE 4. DISPATCH OF CONVENTIONAL UNITS AND RESS CONSIDERING GVS AS LOADS AND SOURCES IN SMART GRID V2G/G2V P P2 P3 P4 P5 P6 P7 P8 P9 P0 Wnd Emsson (ton) Fuel Cost ($) Concluson In ths paper, nfluence of GVs and RESs on producton cost and emsson of electrcty and transportaton ndustres s nvestgated comprehensvely. A combned economc emsson objectve functon s used for optmal power dspatch among networ unts. Obtaned results by PSO algorthm prove that usng GVs wthout any sustanable energy resources may ncreases net emsson of both ndustres. However, ths ncrease was just calculated n load levelng framewor whle Total connectng GVs to the grd at off-pea hours for chargng wll even result nto more ncrease than before. Moreover, as t was antcpated, usng RESs n power system reduces cost and emsson. In ths regard, wnd energy has more ablty for ths purpose due to ts more avalablty n same rated power plant. REFERENCES Amn, M.H. et al, Load management usng mult-agent systems n smart dstrbuton networ, IEEE PES General Meetng, Vancouver, BC, Canada,

7 Internatonal Journal of Energy Scence (IJES) Volume 4 Issue 2, Aprl 204 Amn, M.H., Nab, B., Parsa Moghadam, M., Mortazav, S.A., " Evaluatng the Effect of Demand Response Programs and Fuel Cost on PHEV Owners Behavor, A Mathematcal Approach", Second Iranan Conference on Smart Grd, 202. Department of Energy & Clmate Change (DECC), Smarter grds: the opportunty, Avalable onlne at Farhad, M.; Mohammed, O., "Realtme Operaton and Harmonc Analyss of Isolated and Non-Isolated Hybrd DC Mcrogrd," Industry Applcatons, IEEE Transactons on, vol.pp, no.99, pp.,. Farhat, I.A., El-Hawary, M.E., Dynamcadaptve bacteral foragng algorthmfor optmum economc dspatch wthvalve-pont effects and wnd power, IET Gener. Transm. Dstrb., 200, Vol. 4, Iss. 9, pp Hadley, S. W. and Tsvetova,A., Potental mpacts of plugn hybrd electrc vehcles on regonal power generaton, Oa Rdge Natonal Laboratory, Oa Rdge, TN, ORN L/T M-2007/50, Jan Hutson, C., Venayagamoorthy, G.K. and Corzne, K.A., Intellgent Schedulng of Hybrd and Electrc Vehcle Storage Capacty n a Parng Lot for Proft Maxmzaton n Grd Power Transactons, IEEE Energy 2030, Atlanta, GA USA, 7-8 November, 2008 Insttute for Local Self-Relance avalable onlne webste, Jadhav, H.T., Roy, Ranjt, Gbest guded artfcal bee colony algorthm for envronmental/economc dspatch consderng wnd power, Expert Systems wth Applcatons 40 (203), pp Julana, F. C. R., & Sauer, I. L. An assessment of wnd power prospects n thebrazlan hydrothermal system, Renewable and Sustanable Energy Revews, 203, 9, pp Kempton,W. and Tomc, J., Vehcle-to-grd mplementaton: From stablzng the grd to supportng large-scale renewable energy, Power Source Journal, vol. 44, no., pp , Kempton, W. and Tomc, J., Vehcle-to-grd power fundamentals: Calculatng capacty and net revenue, Power Source Journal, vol.44, no., pp , Kennedy, J. and Eberhart, R., Partcle swarm optmazaton, nproc.ieee Int.Conf.Neural Networs (ICNN 95), vol.iv, Perth, Australa, 995, pp Lu, X., Emsson mnmsaton dspatch constraned by costand wnd power, IET Gener. Transm. Dstrb., 20, Vol. 5, Iss. 7, pp Melopoulos, S., Mesel, J., Condes, G., and Overbye, T., Power system level mpacts of plug-n hybrd vehcles, PSerc Project, Fnal Report, Oct [Onlne]. Avalable at: http :// /documents/publcatons /reports/2009 _ reports/ Moghadas, A.H.; Heydar,H.;Farhad,M.,"Pareto Optmalty for the Desgn of SMES Solenod Cols Verfed by Magnetc Feld Analyss," Appled Superconductvty, IEEE Transactons on, vol.2, no., pp.3,20, Feb. 20 Heydar, H.; Moghadas, A.H., "Optmzaton Scheme n Combnatoral UPQC and SFCL Usng Normalzed Smulated Annealng," Power Delvery, IEEE Transactons on, vol.26, no.3, pp.489,498, July 20. Mtlts Jerry, "Rasng the Volt-Age: Is Obama's Goal of Mllon Electrc Vehcles on U.S. Hghways by 205 Realstc?", Scentfc Amercan. Retreved Masoum, A.S. et al, Smart load management of plug-n electrc vehcles ndstrbuton and resdental networs wth chargngstatons for pea shavng and loss mnmsatonconsderng voltage regulaton, IET Gener. Transm. Dstrb., 203, Vol. 7, Iss. 8, pp Natonal Renewable Energy Laboratory (NREL) Solar Radaton Research Laboratory (SRRL), Golden, CO. [Onlne]. Avalable: Pars, K., Denholm, P., and T. Marel, Costs and emssons assocated wth plug-n hybrd electrc vehcle chargng n the Xcel Energy Colorado servce terrtory, Nat. Renewable Energy Lab., Golden, CO, Tech. Rep. NREL/TP , May Renewable Energy Group webst, 66

8 Internatonal Journal of Energy Scence (IJES) Volume 4 Issue 2, Aprl Roe, Curts, Mesel, Jerome, Power System Level Impacts of Plug-In Hybrd Electrc Vehcles Usng Smulaton Data,IEEE Energy 2030, Atlanta, GA USA, 7-8 November, 2008 Seguro, J. V., Lambert, T. W., Modern estmaton of the parameters of the Webull wnd speed dstrbuton for wnd energy analyss, J. Wnd Eng. Ind. Aerodyn., vol. 85, pp. 5 84, Tng, T. O., Rao, M. V. C., and Loo, C. K., A novel approach for unt commtment problem va an effectve hybrd partcle swarm optmzaton, IEEE Trans. Power Syst., vol. 2, no., pp. 4 48, Feb Unted Natons Framewor Conventon on Clmate change avalable onlne webste, Unted state Envromental Protecton Agency, Greenhouse Gas Emssons from a Typcal Passenger Vehcle, Offce of Transportaton and Ar Qualty, EPA-420-F--04, December 20 Venatesh, P., Gnanadass, R.,and Padhy, N. P., Comparson and applcaton of evolutonary programmng technques to combned economc emsson dspatch wth lne flow constrants, IEEE Trans. Power Syst., vol. 8, no. 2, pp , May 2003 Voelcer, John, Electrc-Car Charger Congeston...At Least n Calforna. Green Car Reports. Retreved Wang, xfan, modern power system plannng, McGraw - Hll,994. Yong, Lu, Wuhan Unv., Wuhan, Tao, Shang, Economc dspatch of power system ncorporatng wnd power plant,internatonal Power Engneerng Conference, 2007 Amn Gholam receved the B.Sc. degree n electrcal engneerng from Iran Unversty of Scence and Technology (IUST), Tehran, Iran, n 203. He s currently wth the Department of Electrcal and Computer Engneerng, Unversty of Tehran as MS student. Hs research nterests are ntegratng of renewable energy resources, Wde Area Montorng System (WAMS), Power System Relablty and Power System Restoraton Mahd Jame (S 203) was born n Shahrood, Iran. He receved the B.Sc. degree n Electrcal Engneerng from Iran Unversty of Scence and Technology, Tehran, Iran, n 203. He s currently worng toward the M.Sc. n electrcal engneerng at Florda Internatonal Unversty (FIU), Mam, USA. Hs research nterests nclude cyber securty of smart grd, smart dstrbuton networ, electrc vehcles, power system control. Javad Ansar receved the B.Sc. degree n electrcal engneerng from Iran Unversty of Scence and Technology (IUST), Tehran, Iran, n 202. He s currently wth the Centre of Excellence for Power System Automaton and Operaton, Department of Electrcal Engneerng, Iran Unversty of Scence and Technology (IUST) as MS student. Hs current research nterests nclude ntegraton of renewable generaton nto power systems and control and operaton of smart grds. Arf I. Sarwat receved hs M.S. degree n Electrcal and Computer Engneerng from the Unversty of Florda, Ganesvlle. In 200, he receved hs Ph.D. degree n Electrcal Engneerng from the Unversty of South Florda. Dr. Sarwat wored n the ndustry (SIEMENS) for nne years executng many crtcal projects. Before jonng the Florda Internatonal Unversty, he was Assstant Professor of Electrcal Engneerng at the Unversty at Buffalo, the State Unversty of New Yor (SUNY). He s co-developer of the DOE funded Gateway to Power (G2P) Project along wth FPL/NextEra company. Hs sgnfcant wor n Energy Storage, Mcro-Grd and Demand Sde Management (DSM) s demonstrated by Sustanable Electrc Energy Delvery Systems n Florda. He s also coprncple nvestgator n the study of relablty & predctablty of hghly penetrated renewable resources power plants (DOE funded Sunshne State Solar Grd Intatve (SUNGRIN). Hs research areas are Smart Grds, Hgh Penetraton of Renewable Systems, Power System Relablty, Large Scale Dstrbuted Generaton Integraton, Large Scale Data Analyss, Dstrbuted Power Systems, Demand Sde Management, Communcaton, Power System Optmzaton and Cyber Securty. 67