Peak Load Shifting in the Internet of Energy with Energy Trading among End-users

Transcription

1 1 Peak Load Shftng n the Internet of Energy wth Energy Tradng among End-sers Chn-Cheng Ln, Member, IEEE, Der-Jnn Deng *, Member, IEEE, Wan-Y L, and Lnnan Chen Abstract Recent advances n renewable energy generaton and the Internet of thngs (IoT) has rged energy management to enter the era of the Internet of energy (IoE). The IoE adopts a hge nmber of dstrbted energy-generatng facltes, dstrbted energy storage facltes, and IoT technologes to mplement energy sharng, promote tlzaton of electrcal grds, and mantan safety of electrcal grds. Rapd economc and socal development makes energy shortage tend to be ncreasngly seros. Most cases of energy shortage occr drng the peak energy load, and hence the prevos works focsed on shftng peak load to address energy shortage. However, few of these works took the IoE framework nto accont. Conseqently, ths work ams to consder the IoE framework to nvestgate the peak load shftng problem n whch end-sers n the energy market can adopt ther respectve energy storage facltes to charge and dscharge energy to mnmze the total operatng costs. In sch a problem settng, each end-ser can not only be a demander bt also be a sppler n the energy market, so that operatng costs are concerned; the energes from both conventonal electrcal grds and dstrbted renewable energy sorces can be d n energy storage facltes; real-tme prce of energy wll be appled adeqately to affect energy dstrbton of spply and demand. Smlaton reslts on a case stdy show that the proposed model can obtan the optmal reslt, and acheve peak load shftng. Index Terms Internet of energy, dstrbted energy storage system, peak load shftng I. INTRODUCTION ITH advance n development of renewable energy W generaton and the Internet of thngs (IoT), the world has entered the era of the Internet of energy (IoE) [1]. In the IoE, all energy sorces (ncldng renewable energy sorces) are connected together throgh the Internet, so that energy prodcton, storage, and dstrbton can be controlled smartly. The IoE ams to ncrease the tlzaton rate of energy, remarkably promote the rato of spplyng renewable energy sorces, and ntrodce dversfed dstrbted energy sorces Ths work was spported n part by MOST E MY2, Tawan. (Correspondng athor: Der-Jnn Deng.) C.-C. Ln and L. Chen are wth Department of Indstral Engneerng and Management, Natonal Chao Tng Unversty, Hsnch 3, Tawan. E-mals: ccln321@nct.ed.tw, psppoa@gmal.com. D.-J. Deng s wth Department of Compter Scence and Informaton Engneerng, Natonal Changha Unversty of Edcaton, Changha 5, Tawan. E-mal: djdeng@cc.nce.ed.tw. W.-Y. L s wth Department of Forestry, Natonal Chng Hsng Unversty, Tachng 42, Tawan. E-mal: wyl@nch.ed.tw * D.-J. Deng s the correspondng athor of ths paper. to spply the demand of the whole energy market. Owng to development of IoT technologes, the nformaton of energy spplers and demanders can be obtaned mmedately and precsely to adjst energy allocaton. The framework of the IoE emerges so that the energy market ncreasngly reles on renewable and dstrbted power-generatng facltes n the electrcal grd. End-sers can not only consme the energy from conventonal electrcal grds, bt also swtch to consme other dstrbted renewable energes n the same network, to lessen the power-generatng pressre of orgnal energy spplers. The man reasons of the energy shortage problem drng peak load are as follows: frstly, the total sed energy exceeds the mal energy amont that can be sppled by the energy market, or leads to malfncton of some power-generatng facltes; secondly, shortage of power-generatng fels n conventonal power statons remarkably affects relablty of power spply. Shftng peak load n electrcal grds can effectvely address energy shortage, and hence has attracted a lot of attenton from a varety of felds. In general, peak load shftng s to shft the energy sage demand drng the peak load perod to the off-peak load perod wth low energy demand. For nstance, the work n [2] n 199s appled the energy load management to redce energy consmpton and to arrange approprate power-generatng schedles to acheve the goal of peak load shftng. Note that adjstng load s a poplar strategy to mprove the performance n other felds, e.g., load redcton mltmeda data [3]. Resdental electrc demand-sde management (DSM) has receved mch attenton recently. The work n [4] condcted electrc DSM n hoses wth solar power-generatng facltes to redce grd electrcty power consmpton. The work n [5] ntrodced smart meters n hoses to acqre the electrcty consmpton data of resdental sers, and ntrodced small-scale energy storage facltes for ther own se, to decrease energy transmsson and to encorage sers to manage ther own electrcty sage. The work n [6] appled a control system of electrc DSM n hoses that addtonally consders the neral network controllers to make schedlng plans coordnated for power generaton of home applances of sers n the system. Wth ths system, sers power prodcton and demand can be satsfed, and the tlzaton rate of local energy can be promoted. The work n [7] analyzed electrcty demand of grds sng a mathematcal model for the home energy management system to save the energy consmpton of home applances as well as varos energy storage facltes and to avod any peak load of grds.

2 2 Some works adopted battery energy storage systems (BESS) to shft the peak load of grds. By ncldng BESSs n varos electrc control systems, varos mathematcal programmng models have been establshed to acheve the optmal shftng reslts of peak loads. The work n [8] devsed a BESS n whch the remanng energy that s not sed n the grd can be d drng the off-peak load to spply the later potental energy shortage and to promote the relablty of energy spply of grds. The work n [9] showed that BESS can mprove the peak load, n whch energy are d at a lower market prce and are sold ot at a hgher market prce to mze the beneft. The work n [1] showed the fnctonalty of BESS and proposed a mathematcal model wth BESSs. Then, they adopted nteror ponts to address ths model and obtaned the optmal plan of chargng and dschargng energy. Ther proposed method can smooth the energy load cre, and show the effects of shftng energy load. Smart grd ncldes a hge nmber of smart meters, and provdes a system to atomatcally change energy consmpton of sers accordng to the real-tme prce (RTP) of energy as well as the energy demand of sers [11], [12]. The work n [13] consdered a system that ntegrates smart grds and electrc cars, and makes an energy-chargng plan for electrc cars n smart grds to mnmze the cost of operatng the grds. The work n [14] consdered a BESS n whch energy can be traded wth the grd (rather than tradng among BESS owners), so that BESS owners can charge energy when the energy prce s low, and sell redndant energy when the prce s hgh, to mze the proft and the tlzaton rate of electrcal grds. Note that the work n [14] dd not consder shftng the peak load. Smart grd ncreases smart fnctons n conventonal electrcal grds throgh sng smart measres and technologes; and the IoE transforms centralzed and ndrectonal conventonal electrcal grds nto an electrcal grd and allows more nteracton among end-sers. A lot of prevos works regarded these two technologes as the same technology. However, n realty, smart grds are stll establshed on conventonal electrcal grds throgh sng smart devces to promote the safety and relablty of electrcal grds and the qalty of electrcty spply, and ntrodce new energy sorces. Dfferent from smart grds, the IoE ntrodces the concept of Internet and new energy technologes to acheve the transformaton of energy nfrastrctres, so that t forms a novel network that ntegrates nformaton and energes. Wth the IoE, energes can transmt bdrectonally n the network, and the spply and demand of energy can be balanced dynamcally, whle new energy sorces are ntrodced adaptvely to the mal degree. As for conventonal electrcal grds and smart grds, a lot of prevos works consdered electrc DSM, RTP-based adjstment, and BESS to mprove the peak load shftng of electrcal grds. To mprove conventonal electrcal grds, both the IoE and smart grds ntrodce varos facltes of chargng and dschargng dstrbted renewable energes. However, most smart grds only add smart facltes to conventonal electrcal grds, bt do not provde a tradng platform, so that redndant renewable energy and the energy d n BESSs n smart grds can only be consmed by ther respectve owners. Dfferent from smart grds, the electrcal grds n the IoE allows sers to share the nformaton and nteract wth each other. Lke e-commerce, the IoE provdes a C2C tradng platform. Therefore, throgh the IoE, the tradng for the energy from electrcal grds and dstrbted renewable energy are consdered to shft the peak load. Ths work proposes a peak load shftng problem n the IoE that provdes a C2C energy tradng platform to end-sers to trade the energy d n ther respectve dstrbted energy storage facltes. Ths work establshes a mathematcal programmng model for a schedlng plan of chargng and dschargng the energy from electrcal grds as well as dstrbted renewable energy, to redce the energy consmpton of end-sers as well as the energy waste of grds. Then, the model s solved by an optmzaton solver. Ths model has the followng featres: each end-ser can not only be a demander bt also be a sppler n the energy market, so that operatng costs are concerned; the energes from both conventonal energy grds and dstrbted renewable energy generatons can be charged n energy storage systems; the RTP of energy s appled adeqately to affect dstrbton of energy spply and demand. Fnally, the smlaton on a case stdy s condcted for evalatng performance of the proposed model. The organzaton of ths work s as follows: Secton II ntrodces the related works and prelmnary knowledge of ths work. Secton III descrbes the concerned problem for shftng peak load n the IoE wth energy tradng among end-sers. Secton IV creates a mathematcal programmng model for the problem. Secton V gves the smlaton reslts. Secton IV concldes ths work. II. PRELIMINARIES Ths secton frst ntrodces peak load shftng. Then, we revew the works on the peak load shftng n conventonal and smart grds, and the peak load shftng sng energy storage systems. Fnally, the prelmnary knowledge on the IoE s ntrodced. A. Peak load shftng Peak load shftng s defned as shftng the energy sage demand drng peak load to the off-peak load perod wth low energy demand. Note that even f a strategy of shftng the peak load s appled, the total energy consmpton n ths market s kept nchanged, and only the energy tlzaton tme of sers s redstrbted. In addton, peak load shftng can redce the nflence of energy load changes on power-generatng systems, and redce the energy cost at the same tme [15]. A lot of the related works on peak load shftng appled the dynamc energy prcng to ndrectly affect the energy tlzaton of sers. The dynamc energy prcng system s to determne the energy prce accordng to the real-tme change of the energy prce n the energy market. The work n [16] developed a dynamc energy prcng system to affect the decson of DSM, so that electrc generators and applances n

3 3 the system can make dynamc responses and apply energy-savng strateges. The work n [17] mentoned that n most electrcty markets, end-sers jst prchase electrcty from dstrbted electrcty spplers bt are not nvolved wth the market. And, the market prcng strategy s to determne the prce accordng to the total electrcty consmpton amont of a month or season, or to let sers pay dfferent prces for peak and off-peak load tme perods, respectvely. However, sch a strategy does not allow sers to respond to the market RTP change. Therefore, they created a day-ahead RTP model for electrcty sage schedlng that makes se of characterstcs of RTP and apples smart meters to collect the nformaton of electrcty sage, to optmze the capacty of electrcty prodcton. B. Peak load shftng n conventonal and smart grds In conventonal electrcal grds, f the electrc power generated by power statons s not consmed nor d, the power s wasted. Ths problem s called power loss. The electrc load demand from end-sers changes wth tme change, n whch the peak load cold be mltple tmes of off-peak load. To satsfy the peak load demand n grds, power statons mst generate electrc power accordng to ths peak load, so that the tlzaton effcency of grds s lower drng off-peak loads and there s more power loss. To solve the peak load problem n conventonal electrcal grds, most prevos approaches ndrectly controlled energy consmpton of end-sers by adjstng the electrcty prce accordng to ther energy consmpton amont. The hgher the monthly energy consmpton s, the hgher the energy payment s; contrarly, the lower the monthly energy consmpton s, the lower the energy payment s [2]. However, sch approaches may not solve the problem exactly. Ths approach ndeed shfts the energy demand drng peak load n grds. However, n overall, the total energy consmpton does not change, and the shfted load cold generate another peak load drng the orgnal off-peak load perods [18]. Smart grd s a modernzed electrcal grd, and t apples nformaton and commncatons technologes to collect the nformaton of operatng grds to promote the effcency of generaton, transmsson, and dstrbton of energy [19]. Compared wth conventonal grds, smart grds have the followng featres on energy dstrbton: Smart grds apply advanced energy measrng technologes and facltes (e.g., smart meters) to montor energy consmpton condtons of sers and power generaton of power spplers. Smart grds set a control center between power spplers and end-sers, whch can montor energy demand of the concerned regon n real tme, based on whch the decsons of dstrbtng energy are made Smart grds can adopt energy dstrbton technologes and facltes to establsh an energy dstrbton network. Throgh ths network, the control center can mmedately dstrbte energy reasonably accordng to real-tme energy demand of grds as well as end-sers, to redce any energy waste. However, smart grds only cope wth energy allocaton accordng to energy demand of sers, bt do not address the power loss problem owng to energy demand [11]. To avod power loss, redndant energy needs to be d. Most smart grd frameworks adopt energy storage facltes to redndant energy. Wth advances n related technologes, energy storage facltes become dversfed. Storage facltes n grds nclde not only large-sze electrc storage facltes of power companes, bt also more and more mass and moble storage facltes (e.g., electrc cars) for personal daly se or temporary rgent se. However, becase personal energy storage facltes are not controlled by smart grds, smart grds cannot control ther tme of chargng and dschargng energy, so that these personal energy storage facltes cold charge energy drng peak load to enlarge the energy peak load, and too mch energy chargng cold damage the grd. On the other hand, f storage facltes dscharge energy at mproper tmes, too mch energy cold be dscharged and wasted. C. Peak load shftng sng energy storage systems Most power-generatng statons are located closely to ther connected markets. To avod energy waste, energy power s generated only when t s reqred. To respond to the power shortage drng peak load and to ensre the relablty of energy spply, most energy spplers mst ncrease ther power-generatng scale to satsfy the peak load. However, drng the perod of off-peak load, a large nmber of energy sage devces are not tlzed flly, so that the operatng cost ncreases and the tlzaton effcency decreases. Energy storage systems n grds are n charge of shftng the peak load, and brng a lot of advantages, ncldng ncreasng the tlzaton rate of power-generatng facltes, decreasng the pressre of shftng peak load n grds, and ncreasng relablty of energy spply [1]. The work n [2] expermentally showed that the BESS has the merts of generalty, modlarty, and extensblty when solvng the peak load shftng problem, and the approach of prchasng energy at a low prce can assst sers n savng the cost of sng electrcty. The work n [14] followed the above work to show the practcal feasblty of BESS from the perspectve of cost analyss. The above works have appled the strateges of electrc DSM, RTP control, and BESS to solve peak load shftng problems, as classfed n Table I. Most of recent works have focsed on RTP control and BESS to solve ths problem. Development of RTP and BESS becomes better wth advances n smart grds. Most prevos works appled only one of the above three strateges to address the peak load shftng problem, bt few of them ntegrated two of the three strateges, becase sch ntegraton complcates the orgnal mathematcal model. The works of [11], [13], [16] adopted the strateges of RTP control to plan the schedles of energy sage. Althogh the strategy of RTP control performs well n smlaton, t s hard to attract sers to apply the strategy n practce. Althogh the work n [14] ncorporated two of the three strateges, t was based on mprovng conventonal electrcal grds, bt dd not consder

4 4 other renewable energy sorces except for conventonal electrc energy. Therefore, ther proposed framework s mpractcal n the IoE. TABLE I Classfcaton of prevos works. Ref. Electrc DSM RTP control BESS [4] v [5] v [6] v v [7] v v [8] v [9] v [11] v [13] v [14] v v [16] v D. IoE The IoE s an Internet-based smart grd that ntegrates technologes of new energes and Internet on the bass of the exstng nfrastrctres of energy spply systems and energy dstrbton networks. In the IoE, a large nmber of dstrbted energy-harvestng devces (ncldng home-scale wnd farms, solar energy harvestng, and so on) and energy storage facltes (ncldng personal storage facltes) are nter-connected. Development of the IoE has the followng challenges [14]. Frstly, new types of relatons n the energy market are complcated, and hence t s challengng to nvestgate how members n ths market collaborate. The IoE establshes an energy market of free competton, and breaks the orgnal energy spply chan (.e., power s generated, then dstrbted, and then tlzed). In the IoE, end-sers n the market are not only cstomers bt also spplers, and hence need to face sch a new role change. Secondly, desgn of embedded systems needs to consder the market demand. Both embedded smart meters and energy storage facltes of embedded energy systems need to be redesgned to respond to ser reqrements. Lastly, ntegrated operatons and secrty rsk problems of electrcal grds are concerned. Becase sch a new type of energy market wll connect not only centralzed power-generatng facltes, bt also a large nmber of dstrbted power-generatng facltes as well as dstrbted energy storage facltes (mostly for renewable energes). Therefore, the schemes of ensrng normal operatng of ths grd and the safety problem of electrcty prodcton nformaton are concerned. III. PROBLEM SETTING Ths secton frst ntrodces energy storage systems n the IoE, and then descrbes the concerned problem. A. Energy storage systems n the IoE As llstrated n Fg. 1, the energy storage systems n the IoE concerned n ths work conssts of the followng components: Power generaton: Power generaton ncldes centralzed and dstrbted power-generatng facltes. Centralzed power-generatng facltes are large-scale power-generatng facltes of large power plants, and dstrbted power-generatng facltes nclde renewable energy systems (e.g., photovoltacs (PV) and wnd farms) of end-sers. Control center: Control center s lke the one n smart grds, whch s sed for handlng end-sers energy demand, and when to nform end-sers of proceedng the energy chargng and dschargng operatons of ther own energy storage facltes. Dstrbton grd: Dstrbton grd connects all end-sers n the IoE. Throgh the dstrbton grd, end-sers can se the energy from all power-generatng facltes n the IoE and energy storage facltes of other end-sers. End-ser: Each end-ser has respectve dstrbted power-generatng and power storage facltes, and play the role of electrcty prodcers and cstomers n the IoE. Energy storage: Energy storage ncldes energy storage facltes of both large energy plants and end-sers. Based on the nformaton of energy sage of end-sers provded by the control center, energy storage facltes charge and dscharge energy at approprate tmes. Power generaton Energy storage Control center Store energy Dstrbton grd Sell energy Energy flow Fg. 1. Illstraton of the concerned IoE framework. End-sers Informaton flow The conventonal electrcal grd provdes centralzed power generaton, and s a network wth a ndrectonal electrcty flow (from power generaton to power dstrbton, and then to end-sers). The power generaton spplers adopts centralzed power generaton accordng to the estmated energy demand peak vale of the whole market. Then, the power dstrbton ncldes the control center and dstrbton grds, n whch the control center determnes how to dstrbte energy n the concerned regon, and then the dstrbton grds delver energy to end-sers. End-sers are only cstomers, and consme energy accordng to ther own demands. Dfferent from conventonal electrcal grds, the IoE consders energy storage facltes, as explaned as follows. The IoE ntegrates the concept of the Internet nto the feld of energy,.e., each end-ser n the energy market n the IoE possesses respectve power-generatng and storage facltes, and can not only play the role of an energy prodcer bt also sell the energy dscharged from own storage facltes to other end-sers.

5 5 B. Problem descrpton Ths work nvestgates the problem of peak load shftng n the IoE wth an energy market among end-sers, whch were never consdered before. Ths work plays the role of the whole system (ncldng all power-generatng plants and end-sers) to mnmze the total energy expendtre cost of end-sers. Consder the IoE framework n Fg. 1. In the framework, energy becomes a commodty, and the energy prce changes wth the market demand. The hgher the demand s, the hgher the electrcty prce s. Contrarly, the lower the demand s, the lower the electrcty prce s. Energy demand of end-sers can be realzed by smart meters, and ths nformaton s transmtted to the control center throgh the IoE network. The control center wll base the energy demand to dstrbte energy. If the control center s aware of that the grd s drng the off-peak load perod, t wll transmt the nformaton of chargng energy to end-sers. End-sers can proceed the operaton of chargng energy accordng to the nformaton (as shown n Fg. 2(a)), and choose the lower-cost energy n the market to be d,.e., the energy to be d may be prchased from other power-generatng spplers or be generated by the end-ser self. Off-peak load Power generaton Energy storage Peak load Power generaton Energy storage Control center Store energy (a) Control center Dstrbton grd Dstrbton grd Sell energy End-sers End-sers (b) Fg. 2. (a) Chargng energy drng the off-peak load perod; (b) dschargng energy drng the peak load perod. The system flowchart of dschargng energy s llstrated n Fg. 2(b), whch s smlar to Fg. 2(a). However, dfferent from Fg. 2(a), f the control center s aware of that the grd s drng the peak load perod, t wll transmt the nformaton of dschargng energy to nform end-sers. End-sers can proceed the operaton of dschargng energy accordng to the nformaton, and sell the energy n ther storage facltes to other end-sers. IV. Mathematcal Model Ths secton establshes a mathematcal model for the problem descrbed n the prevos secton. Ths model extends the models n [13], [14] wth the energy-chargng model of renewable energy power-generatng facltes. The notatons sed n ths model are gven n Table II. TABLE II PARAMETER DEFINITION Parameter Defnton Cgrd Total grd energy sage cost of all end-sers. Cm Mantenance cost of energy storage facltes of all end-sers. Cres Cost of all end-sers chargng ther generated renewable energes. B Income of all sers sellng the energy n ther own energy storage facltes. Index of the concerned day. t Index of hor, t {1, 2,, 24}. RTP(t) Real-tme prce at the t-th hor on day. 1 RTP Lowest RTP on the prevos day 1. mn RTP Hghest RTP on the prevos day 1. 1 Δ 1 The mal dfference of RTPs on the prevos day 1. γ A parameter that determnes the rato of peak load and off-peak load perods. E Maxmal capacty of energy storage facltes. P PCS Unt prces of PCS ($/kwh). P Unt prces of energy storage ($/kwh). P BOP Unt prce of BOP ($/kwh). P Energy amont of PCS and BOP (kw). Cwnd Cost of generatng wnd energy. CPV Cost of generatng PV energy. E Total energy amont d n energy storage facltes. μ The chargng and dschargng effcency of energy storage facltes. Ewnd,t The total wnd energy amont that energy storage facltes charge at the t-th hor. EPV The total PV energy amont that energy storage facltes charge. Egrd The total grd energy amont that energy storage facltes charge. MWnd The mantenance cost of a wnd trbne generator per day. MPV The mantenance cost of each nt area of solar power-generatng eqpment per day. E The amont of grd energy sed by end-sers at the t-th hor on L() t day. The mal amont of the energy sed by end-sers horly. E L, Max θ E ES, t Decson varable δgrd,t The percent reserve margn of the grd. The amont of the grd energy sed by end-sers to charge at the t-th hor on day. Defnton A bnary parameter that represents whether end-sers se the grd energy at the t-th hor to charge electrcty Ths work spposes an IoE connected wth storage facltes

6 6 for the energy n exstng grds (called grd energy), wnd energy, and PV energy. The objectve of ths model s to mnmze the energy sage cost of all end-sers n one day, whch s calclated as follows: Mnmze C + C + C B (1) grd m where C grd represents the total grd energy sage cost of all end-sers; C m represents the mantenance cost of energy storage facltes of all end-sers; C res represents the cost of all end-sers chargng ther generated renewable energes; B represents the ncome of all sers sellng the energy n ther own energy storage facltes. Note that the electrcty generated from the wnd energy and PV energy can be ether tlzed drectly by end-sers or d n end-sers respectve energy storage facltes. On constrants n the problem, the constrants sed for comptng the for terms n Objectve (1) are (2), (5), (6), and (9), respectvely. The constrant for comptng the total grd sage cost of all end-sers C grd s as follows: res grd = () L () + () ES, t grd, t t= 1 t= 1 C RTP t E t RTP t E δ On the rght sde of the above eqaton, the frst term s to compte the total cost when end-sers se grd energy on day ; and the second term s to compte the total cost when end-sers se grd energy to charge on day. In ths eqaton, RTP (t) represents the RTP at the t-th hor on day ; E L () t s the amont of the grd energy sed by end-sers at the t-th hor on day ; E E S, t s the amont of the grd energy sed by end-sers to charge at the t-th hor on day ; δ grd, t s a bnary decson varable that decdes whether end-sers se the grd energy at the t-th hor to charge electrcty (.e., δ grd, t = 1 represents power chargng; δ grd, t = represents power dschargng) as follows: δ grd, t (2) -1 1, f RTP( t) RTPmn + γ Δ 1; = (3), otherwse. 1 where R TP 1 and RTP represent the hghest and lowest mn RTPs on the prevos day 1, respectvely; Δ 1 represents the dfference of the two RTPs as calclated as follows: 1 1 Δ 1 = RTP RTP ; (4) mn and γ s a real parameter wthn a predefned range [, 1], sed for determnng the rato of peak load and off-peak load perods. Snce the energy n the IoE s a commodty, the RTP changes wth tme. The hgher the energy demand s, the hgher the RTP s. As shown n Fg. 3, when RTP (t) s greater 1 than RTP + γ Δ, the grd s drng the peak load perod, and mn hence energy storage facltes shold not charge the energy. 1 When RTP (t) s smaller than RTP mn + γ Δ, the grd s drng the off-peak load perod, and hence energy storage facltes can charge the energy. E sed n (2) s compted as follows: ES, t E = ( E E ) (1 θ) (1 δ ) (5) ES, t LMax, L grdt, where E L, Max represents the mal amont of the energy sed by end-sers horly; and θ represents the percent reserve margn of the grd. The prodcton cost of grd energy s hgh, and ts amont s set accordng to the mal grd energy demand. Therefore, f the nsed grd energy s wasted, the energy storage faclty n ths work frst charges energy from the electrcal grd. EL, Max E n the above eqaton s sed to L compte the remanng energy amont of the grd. In addton, to respond to addtonal temporary demand n the grd, t s general to set a percent reserve margn. Ths work apples (1 θ) to compte the rato of the remanng energy that can be sed, and apples the decson varable δ grd,t to decde whether the grd energy s sed to charge at the t-th hor. The constrant for comptng the mantenance cost of energy storage facltes of all end-sers C m s as follows: C = P P + P E + P P (6) m PCS BOP The rght sde of the above eqaton conssts of three terms: power-converson-system (PCS) cost of energy storage facltes, energy storage cost, and balance-of-plants cost (BOP). In ths eqaton, P s the energy amont of PCS and BOP (nt: kw); E s the total energy amont d n energy storage facltes (nt: kwh), whch wll be calclated n later (1); P, PCS P, and P are the nt prces of PCS, BOP energy storage, and BOP, respectvely. Fg. 3. The thresholds for energy chargng and dschargng. The constrant for comptng all end-sers chargng ther generated renewable energes C res s as follows: C res = C wnd + C (7) PV where C wnd s the cost of generatng wnd energy; and C PV s the cost of generatng PV energy. Cost C wnd n (6) s compted as follows: C = M N (8) wnd Charge wnd Dscharge where M wnd s the mantenance cost of a wnd trbne

7 7 generator per day; N s the total nmber of wnd trbne generators. Cost C PV sed n (6) s compted as follows: C P V = M PV S (9) PV where M PV s the mantenance cost of each nt area of solar power-generatng eqpment per day; S pv s the total area of solar power-generatng eqpment. Note that becase wnd and PV power generatons do not need to se addtonal energy, only ther mantenance costs are concerned. The constrant for comptng the ncome of all sers sellng the energy n ther own energy storage facltes B grd s as follows: B grd E μ R = TP (1) where μ s the chargng and dschargng effcency of energy storage facltes; E s the total energy amont d n energy storage facltes, calclated as follows: 24 = grd + t = 1 wnd, t + (11) PV E E E E where E grd s the total grd energy amont that energy storage facltes charge; E wnd,t s the total wnd energy amont that energy storage facltes charge at the t-th hor; E PV s the total PV energy amont that energy storage facltes charge. E wnd,t n (1) s compted as follows: E = f ( v ), for t = 1, 2,..., 24 (12) N wnd, t n = 1 n t where v t s the wnd power at the t-th hor; and f n (v t ) s the amont of the energy generated by the n-th wnd trbne generator. E PV sed n (1) s compted as follows: E = S η p η G (13) PV pv pv f pc t where η pv s the modle reference effcency; p f s the packng factor; η pc s the power condtonng effcency; G t s forecasted horly rradance at the t-th hor. E grd sed n (1) s compted as follows: E grd 24 = E (14) t = 1 ES, t where E s the total grd energy amont that end-sers se ES, t to charge at the t-th hor on day. The total energy amont d n energy storage facltes E n (1) mst be no greater than the mal capacty of energy storage facltes E. That s, the followng constrant mst be satsfed: E E (15) The dfferences of the proposed mathematcal model from the models n prevos works n [13], [14] are as follows: Ths work consders the energy sage cost of all end-sers n one day n (1), whch were not consdered n prevos works. Snce the research goal of [13] was dfferent from ors, the model n [13] dd not consder the cost of generatng the renewable energy. Ths work proposes (9) and (1) to compte the costs of generatng PV and wnd energes. The prevos work n [14] also consdered the energy tradng, and the formla of comptng the mantenance cost of BESSs n [14] s extended as (6) n ths work. However, snce the work n [14] dd not consder the IoE framework, ts mathematcal model lacks the eqatons on generatng renewable energes. Ths work addtonally consders (12) and (13) to compte the total amonts of wnd and PV energes that energy storage facltes charge, and proposes (11) to nclde grd energy and renewable energy. Ths work consders the characterstcs of the energy market n the IoE framework to propose (3) and (4), and adopts the nflence of RTP on dstrbton of electrcty spply and demand. The proposed model adopts parameter γ and the energy market data of the former day to determne the peak load and off-peak load vales of the energy demand of the crrent day, to frther plan the chargng schedlng of energy storage facltes. V. IMPLEMENTATION AND EXPERIMENTAL RESULTS Based on the proposed mathematcal model detaled n the prevos secton, ths secton mplements ths model and condct a comprehensve expermental analyss. We frst show how to generate the expermental data and descrbe the expermental envronment. Then, expermental reslts are analyzed. A. Expermental Data and Envronment The expermental data sed n ths work s generated by extendng the data of the Savona Camps case stdy [13] wth the IoE framework wth energy tradng detaled n the prevos sectons. The case stdy n [13] consdered two energy sorces: smart energy bldng (SEM) and smart polygeneraton mcrogrd (SPM), eqpped wth wnd energy, PV energy, and natonal grd energy sorces. It consders chargng statons of electrc cars, facltes of sng energy n the camps, and two types of energy storage facltes (.e., long-term Na-N and short-term L-Ion). Expermental parameter settngs are detaled n Table III. In the IoE framework, both the facltes of sng and generatng energes transmt n-tme nformaton to the control center to make decsons on energy dstrbton. Energy storage facltes collect the nformaton on energy demand and dstrbted renewable energy generaton n the electrcal

8 8 grd. Therefore, we consder the energy demand of end-sers n 24 hors of one day (Fg. 4), daly renewable energy generatons (Fg. 5), and the RTPs of 24 hors of one day n the energy tradng market (Fg. 6). TABLE III EXPERIMENTAL PARAMETER SETTING Parameter Vale Chargng and dschargng effcency of energy storage facltes μ 85% Unt prce of PCS P ( /kwh) PCS.256 Unt prce of energy storage P ( /kwh).171 Unt prce of BOP P ( /kwh).533 BOP 1 Hghest RTPs on the prevos day R TP ( /mwh) 141 Hghest RTPs on the prevos day 1 RTP ( /mwh) mn 67 Maxmal energy demand amont EL,Max (kw) 1312 Percent reserve margn of the grd θ.3 The objectve fncton and constrants n the concerned problem s a mxed-nteger programmng model. We adopt the mathematcal programmng optmzaton solver Amms 4.3 to solve the model. Fg. 4 shows the energy demand of end-sers n the 24 hors of one day n the experment, n whch the energy demand drng hors 6-17 s obvosly hgher than that drng hors -3 and hors The peak load of the energy demand (1768kwh) s at hor 11, and the off-peak load of the energy demand (392kwh) s at hor 3. Therefore, the orgnal electrcal grd n the concerned area sets the capacty of generatng the energy based on the energy demand at hor 11, so that the mal energy loss of 1376kwh cold be generated. Energy demand (kwe) Hor Fg. 4. The energy demand of end-sers n 24 hors of one day. Fg. 5 shows the daly renewable energy generatons for two energy sorces (.e., SPM and SEM) n the experment. Becase the two energy sorces are from wnd and PV energes, the effcency of generatng the renewable energy changes wth strength of the wnd and lght ntensty n the envronment. The generated renewable energy wll serve as the energy sorce d n energy storage facltes. However, becase the experments n [13] dd not consder the cost of generatng the renewable energy, ths work spposes that the cost of generatng the energy n both SPM and SEB n one day s 164 accordng to the nformaton of the facltes of generatng the renewable energy n [13]. Qantty of renewalbe energy (kwe) SPM SEB Hor Fg. 5. Daly renewable energy generatons for SPM and SEM. Fg. 6 shows the RTPs of 24 hors of one day n the energy tradng market. Becase the energy s a commodty that can be traded among end-sers n the IoE framework, the RTP changes wth the real-tme energy demand and spply n the energy market. In the concerned experment, the RTP acheves the peak vale at hor 11, and the off-peak vale at hor 3. RTP ( /Mkh) Hor Fg. 6. The RTPs of 24 hors of one day n the energy tradng market. B. Expermental reslts The expermental reslts wth dfferent vales for the parameter of determnng the rato of peak load and off-peak load perods (γ) are gven n Table IV. From Table IV, the optmzaton solver can obtan the optmal soltons for all vales of γ; and wth ncrease of γ vale, the optmal reslt decreases. Ths s becase dfferent vales of γ lead to dfferent chargng schedlng plans and energy amont d n energy storage facltes, so as to nflence addtonal energy amont provded n dschargng operatons drng peak load demand. TABLE IV The optmal reslts nder dfferent parameter vales of γ. γ Optmal reslt Fg. 7 shows the chargng plans of energy storage facltes n 24 hors when γ =.65,.7, and.75, respectvely), n whch the vertcal vale represents that the energy storage facltes are dschargng the energy; and the vertcal vale 1

9 9 represents that the energy storage facltes are chargng the energy. From Fg. 7, f parameter γ s larger, the chargng perod s shorter. Remnd that γ s a parameter that determnes the rato of peak load and off-peak load perods. Therefore, f a larger γ vale leads to a smaller range for beng classfed nto the peak load perod, the chargng perod becomes longer, and the dschargng perod becomes shorter. And, end-sers have a longer perod to charge the energy the energy drng off-peak load for later energy demand drng peak load. Fg. 8 shows the effect of peak load shftng of energy storage facltes when γ =.65, n whch the Orgnal load crve represents the orgnal energy load demand; and the Or load crves represents the resltant energy load mproved by the proposed model wth energy tradng of end-sers. From Fg. 7(a), when γ =.65, energy storage facltes dscharge energy drng hors 1-15, and charge energy n the remanng hors. Hence, n Fg. 8, energy storage facltes also dscharge energy drng hors 1-15 so that the peak load changes from 1768 kwh to 146 kwh. From the vsalzaton n Fg. 8, the peak vale of or load crve s shfted to 1687kWh. In addton, becase energy storage facltes charge energy drng off-peak perod, the lowest vale of or load crve s mproved from 392 kwh to 819 kwh. The total energy loss s mproved from 1376 kwh to 868 kwh. Chargng plan Hor Hor (a) γ =.65 (b) γ =.7 Hor (c) γ =.75 Fg. 7. The chargng plans of energy storage facltes n 24 hors when γ =.65,.7, and.75, respectvely. Energy demand (kwe) Chargng plan Hor Fg. 8. The effect of peak load shftng of energy storage facltes when γ =.65. Chargng plan Orgnal Or VI. CONCLUSION Ths work has proposed an IoE framework wth an energy market among end-sers throgh dstrbted storage systems to address the problem of shft peak load. 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