Avability Based Dynamic Demand Response in Smart Grid Environment Mr. Henal P. Bhagatwala 1 Mr. N. G. Mishra 2

Transcription

1 IJSRD - Inernaonal Journal for Scenfc Research & Developmen Vol. 2, Issue 03, 2014 ISSN (onlne): Avably Based Dynamc Demand Response n Smar Grd Envronmen Mr. Henal P. Bhagawala 1 Mr. N. G. Mshra 2 1, 2 Deparmen of Elecrcal Engneerng 1, 2 B.V.M. Engneerng College, V. V. Nagar, Inda Absrac--- The need for a consan balance beween power demand and supply, ogeher wh he lack of cos effecve soluon for sorng elecrc energy, affecs he effcency of power grds and lms he negraon of renewable sources. Dynamc prcng and demand response programs provde mechansms o regulae he power demand accordng o supply condons. Dynamc prcng and demand response programs provde mechansms o regulae he power demand accordng o supply condons. Here we are desgnng an auonomous scheduler for he demand supply. Ths sysem enables he negraon of auomac demand sde managemen funconales. Here we presen an auonomous and dsrbued demand sde energy managemen sysem among he uly company and s users and among users ha akes advanage of a wo-way dgal communcaon sysem whch s he useful n fuure smar grd mplemenaon. Our goal s hrough he mplemenng schedulng algorhms for he supply and users reduce he peak o average rao of he oal energy demand, he oal energy cos as well as ndvdual daly elecrcy charges. Keywords: - Demand sde managemen, Smar grd concep, NOTATIONS Lambda value L Dspachable load a each hour P Toal power D demand P Toal power g generaed by un I mn P G, P max G,, Mn and Max. Power lm for un Fuel cos u Sar me of dspachable load u Run me of dspachable load f fnsh me of SL coeffcens of un f Gen fuel cos TL c Hour SL FP ( g ) Fuel cos funcon of un a P g oal TL oal I. INTRODUCTION dspachable load Shfable load a hour Toal load a hour Toal Shfable load Toal load Demand response programme, ams o manage he power on he demand sde by usng varous economc and echncal measures o reshape he load curve no he arge curve. I bascally opmzes he process/loads o mprove he sysem load facor. The load facor s he rao of he average load o he maxmum load whn a ceran perod. The deal value for he load facor s 1, whch ndcaes ha he average load s equal o he maxmum load. However, n pracce hs s mpossble o acheve and s always less han one (< 1.0). The lower hs load facor, he greaer he flucuaons whn he demand profle. These resuls n ncreased capacy and cos for he operaon of he supply sde. Therefore measures are requred o be mplemened whch mprove he load facor. managemen s a suable way of ncreasng he load facor, whch s he process of schedulng he loads o reduce he elecrcal energy consumpon and/or he peak demand a a gven me. The radonal DSM acves aken by he uly company o aler he load shape can be characerzed no sx caegores based on he sae of he exsng uly sysem [1] The PBDR programs are based on dynamc prcng raes n whch he elecrcy arffs are no fla. To brng down he cusomer blls desgn lower sysem cos n whch hey offerng hgh prce durng expensve hours and lower prces durng nexpensve hours. They are desgned o lower sysem coss for ules and brng down cusomer blls by offerng hgh prce durng expensve hours and lower prces durng nexpensve hours. The man objecve o prce based demand response s o fla demand curve by reducng he peak loads or shfng load from peak o off peak perods. The dfferen Prces raes nclude Tme of Use (TOU), Crcal Peak Prcng (CPP), Exreme Day Prcng (EDP), Exreme Day CPP (ED-CPP), and Real Tme Prcng (RTP) [2]. The elecrcy demand s rapdly ncreases for he las decades. Ths caused o ncrease now no. of end users and also ncreased n elecrcy demand per end-user. Due o he ncrease of elecrcy demand s also affecs he load curve, due o ncreasng demand wll also ncrease he peak load on he nework. Durng peak perods, he elecrcy demand s sgnfcanly hgher han he average elecrcy demand durng off-peak perods. The ncrease of elecrcy demand by he end-users herefore serously reduces he relably and safey of he elecrcy dsrbuon. Elecrcy Demand Sde Managemen (DSM) s consdered o be one of he fundamenal soluons o manage peak load growh and reduce elecrcy consumpon n he me of nsuffcen elecrcy capacy or hgh fuel coss. DSM covers a whole range of echnology and polcy measures desgned o reduce elecrcy consumpon from economc acves. Presenly, he Indan GDP s growng a a rapd speed and, consequenly, he elecrcy demand s sharply elevang a smlar pace. The fas demand growh rae s expeced o connue f no effecve measures are aken o manage he demand. We are facng wo man serous challenges whch are nsuffcen generaon capacy due o hgh demand growh, and hgh fuel coss from nsuffcen fuel producon and polcy changes. To solve he curren elecrcy shorage problems and o seek for susanable resoluons n meeng fuure elecrcy demand, mplemenng DSM sraeges seems o be a bes opon. All rghs reserved by 616

2 Avably Based Dynamc Demand Response n Smar Grd Envronmen In order o undersand he demand paerns and he effecs of varous DSM echnology and polcy sraeges on he demand, and, furher, properly manage he demand, an analycal ool mus be developed o ulze he lmed consumpon nformaon avalable o denfy robus DSM opons. To perform a qualy DSM research, as well as oher relaed elecrcy secor research on boh supply and demand-sdes plannng, he researchers also requre dealed nformaon on he composon and dynamcs of elecrcy demand. Parcularly, basc undersandng on peak loads, as o when and how hey have occurred, her varaons by envronmenal facors, such as emperaure, and how hey may change n he fuure, are among he mos mporan requremens. Already, a number of demand sde parcpaon programs have been mplemened, all of whch am o provde economc ncenves o end-users n order o help balance supply and demand, bu hey have had varyng degrees of success. Ths paper nroduced dynamc prcng and demand response program o regulae power demand accordng o supply condon. Here we are nroduced Auonomous shfng schedulng Algorhm n whch cusomer are nvolved o parcpae whch helpful o reduce he peak on curve. Ths algorhm s useful n smar grd envronmen. II. BLOCK DIAGRAM OF AUTOMATIC DEMAND RESPONSE PROGRAME AND CUSTOMER INVOLMENT MODEL One approach n resdenal load managemen s drec load conrol (DLC). In DLC programs, based on an agreemen beween he uly company and he cusomers, he uly or an aggregaor, whch s managed by he uly, can remoely conrol he operaons and energy consumpon of ceran applances n a household. For example, may conrol lghng, hermal comfor equpmen (.e., heang, venlang, and ar condonng), refrgeraors, and pumps. However, when comes o resdenal load conrol and home auomaon, user s prvacy can be a major concern and even a barrer n mplemenng DLC programs. Now here s an Alernave for DLC s smar prcng, where users are encouraged o ndvdually and volunarly manage her loads, e.g., by reducng her consumpon a peak hours. In hs regard, crcal-peak prcng (CPP), me-of-use prcng (ToUP), and real-me prcng (RTP) are among he popular opons. For example, n RTP arffs, he prce of elecrcy vares a dfferen hours of he day. The prces are usually hgher durng he afernoon, on ho days n he summer, and lower n he cold days n he wner [8]. RTP programs have been adoped n some places n Norh Amerca, e.g., by he Illnos Power Company n Chcago [4]. Ths s also he Par of Drec Conrol. In he Drec Conrol here are some lmaon and draw backs so here we are usng ndrec or auomac demand response model wh cusomer nvolvemen. Usng he auomac demand response model we can reduced he peak demand on he elecrcy grd whou human nervenon. Fg. shows he proposed Auomac demand response model wh cusomer nvolvemen. Fg. 1: Block Dagram of Auomac Demand Response Program The model proposed here allows he cusomer o parcpae n elecrcy marke f he/she s a par of he Smar Grd Smar Meerng Infrasrucure. As of now, f we allow smar meers o ac accordng o he sgnals comng from he Demand Response Conrol (DRC) Cener so as o allow/block he power flow n dfferen load areas accordng o he nsrucons presen n he sgnal, han we can have our shfable loads operae accordng o cusomer preferences. As an npu o he model, cusomer provdes hs me ahead preferences for dfferen loads, conneced o programmable nodes, gvng nformaon abou he run me lms, he acual run me, and he sze of he load conneced o each of he node. Ths can be done hrough a normal web poral based communcaon channel. Ths daa s hen sen as an npu (Fg 4.1) o DR conrol cener wh/whou communcaon channel, and hen processed wh all he daa colleced from all he cusomers, along wh he day ahead load forecased curve, so as o have a new load curve whch s much flaer and beer han he prevous one. DR conrol cener wll han generae he consumpon paern sgnal for he AMI, and also he Generaon schedule for he generang uns as oupus (Fg 4.1). As of now, hs has been esed and mplemened only for 24 hour day ahead scenaro akng 1 hour as he leas quanzaon of me, bu can be mplemened for lesser quana of me as well wh approprae modfcaons. The processng a DR conrol cener nvolves, allocang he loads one by one o dfferen hours accordng o oal margnal cos of producon a each hour and he hours whn he run me lms so as o have he mnmum oal cos of producon requred for allocang ha parcular load. The ncluson of consumer parcpaon s proposed n he workng model, where cusomers segregae her loads no Shfable and Non-Shfable. Fg 3 shows he Shfable and non-shfable loads and her daa requremen o be used n he smulang ADR All rghs reserved by 617

3 Avably Based Dynamc Demand Response n Smar Grd Envronmen Fg. 2: Classfcaon specfed n erms of maxmum and mnmum power ( P, P ) hey can delver once hey are chosen and g,max g,mn swched ON afer he un commmen. These wo daa are hen used o perform he economc dspach for he gven load level P a each hour. g Cos funcon of a generaor s gven by : 2 F( P ) f ( P P ), g c g g such ha Pg,mn Pg Pg,max (4.3) Where, f represens he fuel cos. C. c Consumer Preference daa for he shfabale loads. Each consumer eners he followng deals abou each of he shfable load hey wan o use n day ahead me lne - Value l, Up me u, Down me f and Run me r Fg. 3: Daa needed for Shfable Shfable are he ones whch can be programmed or conrolled by he uly n cusomer preference lms, whle he non-shfable are he ones where cusomers are he only conrollers and hence here s no nerference from he uly sde. The cusomers can now effecvely parcpae by gvng her preference for he Shfable load operaon. Ths daa s aken o demand response conrol cener hrough Smar meerng Infrasrucure,where hs daa, along wh oher daa of sysem consrans s operaed so as o have an opmum load schedule nsrucons o be sen back o Smar meer. III. CONCEPTUAL MODEL For he modelng of he auomac demand response followng model are gven. A. Day ahead load forecased curve. Ideally as of now we jus have oal load forecased curve. Ths model akes he concep of segregang each load no one of he wo caegores, he shfable loads (Programmable/Ones wh flexble me of usage) and he non shfable loads (non-programmable/ones whch are rgd n her me of usage). From he gven oal load forecased curve, calculaon of forecased curve for non-shfale loads has been done assumng he rao of oal shfale load o oal load, a each hour, consan.e. Toal (TL) = Shfable (SL) + Non Shfale (NSL) Fg. 4: Consumer preference daa paen Table represens he way cusomer wll be expeced o ener he daa for each shfable load hey have, where ask represens he load value, cos represens he run me of he load, and he res s up-me and down-me (runme lms). SL = Shfable a hour TL = Toal a hour NSL = Non Shfable a hour SL oal = Toal Shfable for he day B. Generaor fuel cos funcon daa and Generaon lms. Generaor fuel cos funcon s aken o be quadrac, whch wll have hree erms degree 2, degree 1, degree 0 (wh coeffcens,, respecvely). Generaon lms are Fg. 5 Flow char for Shfng Algorhm All rghs reserved by 618

4 Avably Based Dynamc Demand Response n Smar Grd Envronmen D. Algorhm seps : 1. Read n all he gven daa (as n 4.1) vz. Where, lufrl.,,,,,,, L s Non-Shfable a each hour 2. Perform Economc Dspach on L. 3. Calculae Margnal Cos Prce (MCP) a each hour correspondng o load a each. 4. Fnd hour, correspondng o mnmum value of correspondng MCP value. For all where he hour flag = 0, whch mples, all he hours whch sll have some shfable loads assocaed wh hem. 5. Fnd maxmum shfable load l wh load flag 0 correspondng o he hour. flag =0 ndcaes ha he load has no been allocaed before. 6. Check f l Exss? f Yes, go on o nex sep, f No, change he hour flag of hour o 1 and go o sep Calculae k for mn u kr 1, k[0,( f u 1) r 1] u k Ths fnds ou he opmum wndow, whn he run me lms, for allocang he gven load l. The no of such avalable wndows for each load wll be f u r For above correspondng k, updae he oal whn he wndow hours L = L + l [ ukukr, 1] 9. Updae flag o 1, sore value for above menoned load. 10. Check f all he shfables loads has been allocaed. If Yes, hen sop, f No, go o sep 2 Gven day ahead hourly load forecas, generaor prcng daa and oher sysem consrans, schedulng of he dspach able loads has been performed such ha our overall sysem cos s mnmum, keepng he oher consrans of sysem secury whn lms.. The code was wren n MATLAB R2007a, and wndows Xp plaform was used for dong he same. IV. SIMULATION AND RESULTS The Algorhm s successfully mplemened on 6 bus 3 generaor sysems. Fg. 6: smulaon resul usng load shfng algorhm Hour No Inal Fnal Gen 1 Gen Table 1: Fnal and Generaor Schedule Here sandard 6 bus sysem was aken o have daa for generaor fuel coeffcens. Ths sysem had 125 MW load/hour, whch was hen aken as our average load. For he load forecased daa, praccal day ahead daa of MILLWOOD SOUTH, USA (New York ISO) was aken [25], whose average per hour load s MW. Ths forecased daa was hen used o calculae he scalng facor wh base average as MW, and hen hs scalng facor was used for calculang forecased daa for our es sysems on a base average of 125MW. Now, from hs obaned load curve, non-shfable load curve was calculaed, assumng a dsrbuon descrbed n secon 4.1. The oal amoun of energy lef as he shfable one, s same as he oal amoun of energy our shfable loads wll consume, as gven by he consumers n her preference. Toal no. of shfable loads aken n hs case was 5. All rghs reserved by 619

5 Avably Based Dynamc Demand Response n Smar Grd Envronmen Observaon: The shfable loads have flled up he valleys and newload curve (green) seems o be havng less gap beween maxma and mnma. No. Value l Up Tme, u Down Tme, f Duraon r (Hours) Alloed sar Hour algorhm valley flng were obaned o shf maxmum load a mnmum valley s more effcen han oher mehod. Moreover, he proposed sysem could be furher mproved smply by usng more effcen code and subrounes and also can be esed for more no of loads. Ths work allows a sep forward o a Smarer Grd, where elecrcy can be reaed as a commody and can be used/sold/generaed lke any oher commodes. Gen no Fuel cos power Max Power Mn VI. APPENDIX Table 4 Generaor Fuel Cos Funcon Daa For 6 Bus, 3 Generaor Sysem Table 2: The alloed sar hour for varous loads Table 2 was Represens he daa for each shfable load and also fnally provdes he alloed sar hour no. for each load, hs can be sen o Cusomer sde AMI for approprae furher acon Max Hr Mn Avg Sd Devn. facor Hr 6 Bus Old Bus New TABLE 3: Comparson of old and new load curve for 6 bus sysem A. Observaon: Max has decreased, Mn load has ncreased, Average load sll he same, Sandard devaon has gone down by sgnfcan amoun, and load facor has ncreased by almos 1.7 % B. Inference: Shfng of loads has occurred and here are less flucuaons n he sysem Gen no Fuel cos power Max Power Mn C. Concluson: Increase n load facor s a good sgn for effcency of power sysem and narrowng of gap beween maxmum and mnmum levels of load ndcaes he flaenng of he curve. V. CONCLUSION shfng Algorhm wh cusomer nvolvemen can be used for varous shapng objecves lke peak clppng, valley fllng, shfng. Usng he shfng Fg. 7: Nework model for 6 buses, 3 generaor sysem REFERENCES [1] Gomes, A. and marns, A.G. 1995, Elecrc load modelng and smulaon for assenng acon, Cacas, Venezeua, p.p [2] Chars rves assocaes 2005, prmes on demand- sde managemen wh emphass on prce- responsve programs, repor prepased for he World Bank Washngon, PC, CRA no. oo6090, avalable onlne: hp:// [3] Gellng, C.W. and Chamberln,J.H. 1993, Demandsde managemen Concep and mehod, 2 nd edn, Farmon press, USA. [4] S.borensen, T.N.jaske and A. Rosenfed. Dynamc prcng advavce meerng, and demand responss n elecrcy marke Uc beraely, cener for he sudy energy marke,2002. [5] Albad, M.H., and El-saadany, E.F. 2008, A summary of demand response n elecrcy marke, elecrc power sysem research, vol. +8, ssue 11, PP [6] Had Sada, Power Sysem Analyss, Taa McGraw Hll, [7] Peer B. Luh, Lauren D. Mchel, Peer Fredland, Che Guan, Yung Wang, Forecasng and Demand Response, IEEE Power and Energy Socey General Meeng,, 2010, pp [8] L Zhang, Janguo Zhao, Xueshan Han, Ln Nu, Dayahead Generaon Schedulng wh Demand Response, IEEE/PES Transmsson and DsrbuonConference All rghs reserved by 620

6 Avably Based Dynamc Demand Response n Smar Grd Envronmen & Exhbon: Asa and Pacfc Dalan, Chna, 2005, pp [9] Masood Parvana, Mahmud Fouh-Fruzabad, Demand Response Schedulng by Sochasc SCUC, IEEE Transacons on Smar Grd, Vol. 1, No. 1, June 2010, pp [10] Pedro Fara, Za A. Vale, Iude Ferrera, DemS - A Demand Response Smulaor n he conex of nensve use of Dsrbued Generaon, IEEE Inernaonal Conference Sysems Man and Cybernecs (SMC), 2010, pp [11] A. J. Wood, B. F. Wollenberg, Power Generaon Operaon and Conrol, New York: John Wley & Sons, [12] hp:// York, Independen Sysem Operaors All rghs reserved by 621