AN HYBRID TECHNOLOGY IN DISTRIBUTED GENERATION FOR HOUSEHOLD DEMAND WITH STORAGE CAPACITY

Transcription

1 AN HYBRID TECHNOLOGY IN DISTRIBUTED GENERATION FOR HOUSEHOLD DEMAND WITH STORAGE CAPACITY Ms. P. Prya, PG Scholar, Ms. N. Kavthaman PG Scholar, Prof. N. R. Nagaraj Assocate professor, Electrcal and Electroncs Engneerng, RVS College of Engneerng and Technology, Combatore, Tamlnadu, Inda Abstact In Future Decentralzed Power Generaton wll be the most mportant one n the Electrcty Generaton System. Who are prepared to nvest n Generaton-Battery Systems and Employ Energy Management Systems n order to cut down on ther Electrcty Blls. The man objectve of ths paper s to determne the Optmum Capacty of a customer s Dstrbuted-Generaton System and Battery wthn the Framework of a Smart Grd. The proposed approach nvolves Developng an Electrcty Management System based on Stochastc Varables such as Wnd Speed, Electrcty Rates, and Load. In a Resdental Dstrbuton Grd of so that effects of Electrcty Rates as well Wnd and Photo Voltac Generaton and Electrcty Storage Costs on Optmum Capactes of a Battery for a Smart Home. the Hybrd Genetc Algorthm and Partcle Swarm Optmzaton technque to Develop the Electrcty Management System. Keywords Genetc Algorthm, Partcle Swarm Optmzaton. I. INTRODUCTION 1.1 Renewable Energy Renewable energy producton has been steadly ncreasng as nternatonal goals to reduce dependence on fossl fuels have been on the agenda for natons worldwde. Solar photovoltac (PV) power systems are becomng a prevalent renewable energy opton wth the cost of PV cells decreasng and ther solar converson effcency ncreasng. Yearly growth rates over the last fve years were on average more than 40% wth a worldwde producton of 7.3 GW makng photovoltac one of the fastest growng ndustres. 1.2 Power generaton Renewable energy provdes 21.7% of electrcty generaton worldwde as of Renewable power generators are spread across many countres and wnd power alone already provdes a sgnfcant share of electrcty n some areas. 1.3 Power generaton Renewable energy provdes 21.7% of electrcty generaton worldwde as of Renewable power generators are spread across many countres and wnd power alone already provdes a sgnfcant share of electrcty n some areas. 1.4 Wnd power Arflows can be used to run wnd turbnes. Modern utlty scale wnd turbnes range from around 600 kw to 5 MW of rated power although turbnes wth rated output of 1.5 to3 MW have become the most common for commercal use the power avalable from the wnd s a functon of the cube of the wnd speed so as wnd speed ncreases, power output ncreases up to the maxmum output for the partcular turbne. Areas where wnds are stronger and more constant such as offshore and hgh alttude stes are preferred locatons for wnd farms. Typcal capacty factors are 20-40% wth values at the upper end of the range n partcularly favorable stes.globally the long-term techncal potental of wnd energy s beleved to be fve tmes total current global energy producton 40 tmes current electrcty demand assumng all practcal barrers needed were overcome. Ths would requre wnd turbnes to be nstalled over large areas partcularly n areas of hgher wnd resources such as offshore. As offshore wnd speeds average 90% greater than that of land so offshore resources can contrbute substantally more energy than land statoned turbnes. 1.5 Solar Power Solar energy radant lght and heat from the sun s harnessed usng a range of ever evolvng technologes such as solar heatng photovoltac concentrated solar power solar archtecture and artfcal photosynthess. Solar technologes are broadly characterzed as ether passve solar or actve solar dependng on the way they capture convert and dstrbute solar energy. Passve solar technques nclude orentng a buldng Correspondng Author: Ms. N. Prya, RVS College of Engneerng and Technology, Combatore, Tamlnadu, Inda. 757

2 to the Sun selectng materals wth favorable thermal mass or lght dspersng propertes and desgnng spaces that naturally crculate ar.actve solar technologes encompass solar thermal energy usng solar collectors for heatng, and solar power convertng sunlght nto electrcty ether drectly usng photovoltac or ndrectly usng concentrated solar power. II. PROPOSED METHODOLOGY The man objectve of proposed method s to develop an approprate method for determnng the optmum capactes of battery storage and renewable generaton such as a wnd turbne of a smart household wth an electrcty management system that mnmzes the overall electrcty cost of the household. The unscheduled loads are whch may be plugged n wthout any predetermned plan such as har dryers and electrc drlls. Overall consumpton of a house hold s determned n hourly bass. Wnd turbne and PV generates power and s measured n hourly or daly bass. The generated power s stored n a storage battery and dstrbuted to the consumer end accordng to the consumpton of a house hold based on load. Frst f t s nsuffcent the dfference power s purchased from grd whch may be charged on the bass of Electrcty Purchase Rate.The excessve power enerated by the wnd turbne or PV after the utlzaton of the house hold can be sold out on the bass of ESR. By calculatng the electrcty generaton and utlzaton the power consumed by the small house hold system s determned usng hybrd. Fg 1 : block dagram A wnd turbne s a devce that converts knetc energy from the wnd nto electrcal power.the grd may be an electrcty lne whch transmts and dstrbutes the energy to consumer sectors for 24 hrs. Photovoltac power generaton systems use the rays of the sun as a source of electrc power. Accordngly the amount of power generated s strongly nfluenced by the weather condtons that determne how much sunlght gets through. The power output of a photovoltac panel s normally rated based on a solar radaton ntensty of fallng on a panel at a temperature of 25 C. Because the angle of ncdence of sunlght vares dependng on the season and the tme of day the power generated from a panel on a fxed platform typcally starts to ncrease from dawn peaks when the sun reaches ts zenth for the day and then decreases agan toward sunset. Hence maxmum tme duraton s 8hrs.The load s dvded nto three type namely base load shftable load and unscheduled load. Base load conssts of end use devces whose power usage s predetermned and non reschedulable such as refrgerators and most lghtng. The shftable loads are shftable n tme and susceptble to delay Washers dryers and dshwashers are often among the loads whch can be delayed but the task should be accomplshed by a certan deadlne. Ar condtoners and water heaters may be assgned to ether one of the frst two categores accordng to customer preferences and level of comfort desred. 2.1 Optmzaton model As descrbed before obtanng the optmum capacty for the renewable generator and the battery s a plannng problem whch should nclude the behavour of the smart home n the optmzaton process. Operaton of a smart home n the long run s smulated by provdng load generaton and electrcty rates at each hour of the day as nputs to the HEMS.The electrcty cost of the home for the tme nterval of the day can be calculated by Step1: The duraton of each nterval s one hour n ths study.the electrcty cost of the home for the tme nterval of the day can be calculated by the followng equaton. CH( t j )=C G.E G ( t j )+C B.C apb. t j +E Buy ( t j ) EPR( t j )- E Sell ( t j ) ESR( t j ) (1) Where E Buy ( t j ) represent the amount of electrcty bought from the grd durng tme perod t j. E Sell ( t j ) represent the amount of electrcty sold to the grd durng tme perod t j A smple model representng the power curve of the wnd turbne s employed to obtan the output power of the wnd generaton based on wnd speed. Equaton (2) s used to derve.the output energy represented by E G of the wnd turbne for each hour j s calculated by E G ( t j )= C apg (V W ( t j )-V c ) t V c V W ( t j ) V r V r -V c C apg. t V r V W ( t j ) V co 0 otherwse (2) Where V c s cut n speed where wnd turbne starts to generatev co s cut off speed where wnd turbne stops generatng V r s rated speed V w s wnd speed. Correspondng Author: Ms. N. Prya, RVS College of Engneerng and Technology, Combatore, Tamlnadu, Inda. 758

3 There are cost beneft tradeoffs nvolved n optmum capacty calculatons. Hgher capacty generators are costler but contrbute more to supplyng load and reducng dependency on grd power. The surplus generaton can also be sold back to the grd. In the same way payng more for a hgher capacty battery could be compensated for by more surplus energy storage and energy trade capablty. Therefore the objectve functon to be mnmzed s the total electrcty cost of the household calculated by. F N C H j1 ( t j ) (3) The power flow constrant s determned for any duraton s calculated by ( t j )=E Buy ( t j )-E sell ( t j )-E B ( t j ) (4) There are also some nequalty constrants to comply wth the operatonal lmts of the battery as mentoned and power transfer lmts defned by long run t nherently ncorporates the MCS method whle t s searchng for the optmum soluton. The procedure can be expressed by the followng steps. Step2: Determne N ndvdual probablty dstrbuton functons for dfferent varables such as wnd speed load and electrcty rate accordng to hstorcal data. Each functon represents the probablty dstrbuton of a varable for a tme step tj of n the MCS-PSO Step3: ObtanC B,C G and the parameters of the MCS-PSO method such as stop crteron based on maxmum number of teratons or mnmum error and the number of partcles M n the PSO. Intalze each partcle by assgnng two dmensonal poston and velocty vectors accordng to (7), and also ntalze X gbest and the battery charge B nt (K) for the teraton K=1.Intalzaton of each partcle s assgned by X (k)=[c ap G (k) C ap B (k)] V (k)=[v CapG(k) v CapB(k)] (7) 2.2 MCS-PSO Process E Sell ( t j )< E Sell,max E B ( t j )<R C t - E B ( t j )<DOD C apb (5) B( t j )< C apb Snce the electrcty cost of the home depends on HEMS and the nputs to the HEMS are stochastc varables obtaned from ther probablty dstrbutons ths cost can be generally represented by an mplct functon of the followng varables and parameters.the cost can be determned by C H =f(l 1,L 2,L 3,V W,B nt,esr,epr,c G,C B,C apg,c apb ) (6) PSO s used to calculate the optmum CapG and CapB by mnmzng an objectve functon. The objectve functon of the HEMS s to mnmze the total expected electrcty cost of the household calculated by MCS over the duraton of the study. In the PSO method ntal capactes for the generaton and battery are selected; and then a populaton of M partcles s generated to evolve toward the optmum capactes of battery and wnd generaton for the household. Ths method has been demonstrated to be more robust and faster n fndng the global soluton compared wth other heurstc optmzaton methods such as genetc algorthms. To mprove the effcency of the optmzaton process an teratve procedure combnng MCS and PSO methods s proposed. Usng the hybrd MCS-PSO method the nput to each teraton of the PSO s stochastc and orgnates from the varables probablty dstrbuton functons. Therefore n the Step4: For teraton k and every partcle of the populaton gven the current C ap G (k) and C ap B (k). do the followng Calculate the values of the loads, ESP and EPR. Ther N dstnct probablty dstrbuton functons. Step5: Run the HEMS process for duraton of T=N. t j and compute the value of the ftness functon Ftness functon s determned by N F(x (k))= C H (k, t j ) (8) j Step6: If F(x (k)<f(x pbest ) then update the values for the local optmum capactes x pbest =x (k) and f F(x (k))<(x gbest ) hen update the global best capactes x gbest =x (k). X (k+1)=x (k)+v (k+1) v (k+1)=w(k).v (k)+c 1 1 (x pbest-x (k))+c 2 2 (x gbest -x (k)) B nt(k+1)=b (k, t N ) (9) Step7: Determne the optmum capactes assocated wth the mnmum objectve functon. [C ap * G C ap * B ]=x gbest M n {C H (T)}=F(X gbest ) (10) 2.4 Hybrd PSO wth GA The bass behnd ths s that such a hybrd approach s expected to have merts of PSO wth those of GA. One advantage of PSO over GA s ts algorthmc smplcty. Correspondng Author: Ms. N. Prya, RVS College of Engneerng and Technology, Combatore, Tamlnadu, Inda. 759

4 Another clear dfference between PSO and GA s the ablty to control convergence. Crossover and mutaton rates can subtly affect the convergence of GA. but these cannot be analogous to the level of control acheved through manpulatng of the nerta weght. In fact the decrease of nerta weght dramatcally ncreases the swarm s convergence. The man problem wth PSO s that t prematurely converges to stable pont whch s not necessarly maxmum. To prevent the occurrence poston update of the global best partcles s changed. The poston update s done through some hybrd mechansm of GA. The dea behnd GA s due to ts genetc operators crossover and mutaton. By applyng crossover operaton nformaton can be swapped between two partcles to have the ablty to fly to the new search area. The purpose of applyng mutaton to PSO s to ncrease the dversty of the populaton and the ablty to have the PSO to avod the local maxma. Each partcle tres to modfy ts poston usng the followng nformaton The dstance between the current poston and pbest.each partcle knows ts best value so far pbest The dstance between the current poston and gbest each partcle knows the best value so far n the group gbest among pbests.here partcle represents the capacty n kw. III. RESULTS AND DISCUSSION The prevously descrbed MCS-PSO method has been appled to the case study. The total number of smulaton teratons was whch ensured the convergence of the smulaton. The cogntve and socal parameters of the PSO method were selected to be 2.5 and 1.5 respectvely the populaton of the partcles was 20 the problem space was bounded by the maxmum capactes of 15 kw for the wnd generator and 15 kwh for the battery and the maxmum velocty of the partcles was lmted to 20 percent of the maxmum capactes of the wnd generator and the battery. The defnton of the parameters of the PSO method can be found.proper behavor of the proposed method was captured through senstvty analyss to a number of nput parameters. The followng case studes have been defned to demonstrate the results of the proposed method. generaton cost s at ts maxmum value. Smlarly shows the effect of levelzed costs of generaton and battery on optmum capacty of the wnd turbne. The cost of a battery does not have a consderable effect on the generator capacty. On the other hand as the cost of generaton decreases hgher capacty wnd turbnes become more benefcal. In ths graph the generaton cost of about 3.5 cents/kwh acts lke a turnng pont at whch there s a hgh slope toward hgher wnd generaton capactes. Ths s because as mentoned earler the average EPR of ths case study s 3.2cents/kWh and therefore generaton costs less than ths rate become exceedngly appealng. As a result of the optmzaton process the mnmum household electrcty costs are computed and plotted. As expected the electrcty cost of the home s hghest when both and are at ther maxmum values. An nterestng result s acheved by comparng the electrcty cost n ths fgure wth one of a conventonal home wthout a generaton-storage system. In the case of a conventonal home the electrcty cost s 92 cents/day whch s close to the value of the smart home wth a of 5 cents/kwh and a of 0.6 cents/kwh per hour. Therefore we expect that beyond ths operatng pont no addtonal savngs can be acheved by nvestng n a wnd generator and battery, ndcatng the correspondng optmum capacty of the wnd generator and battery should be almost zero. 3.2 Senstvty to EPR In ths case the senstvty of the capactes and electrcty cost of the home for the base case wth dfferent electrcty rates have been studed and the results are plotted based on shape preservng nterpolaton In lab vew. EPR s ncreasng an ncreasng trend toward hgher generaton-battery capactes s observable. In ths case when EPR decreases toward 2.5 cents/kwh, there s less ncentve to nvest n hgh capacty wnd generators and batteres because as depcted n the bottom the mnmzed electrcty costs of the smart home and the conventonal home get closer. It s notable that as the electrcty cost of a conventonal home rases wth a hgher EPR the electrcty cost of the smart home decreases. The dfference between these two costs s more notceable. 3.1 Senstvty To Both R And Esellmax At electrcty rates hgher than the levelzed cost of wnd In ths case, the effect of both R and E Sell max on the optmum generaton where the electrcty cost of the smart home has a capactes of a wnd generator and battery s studed. The hgher rate of decrease. Homes are even able to make a proft optmal surface of the battery capacty wth dfferent storage from sellng ther power to the grd at an average EPR of 5 and wnd generaton costs. As decreases.the optmum pont s cents/ kwh because beyond ths pont the cost of wnd shfted toward hgher battery capactes. In addton as the cost generaton becomes less than the average ESR. Ths s of wnd generaton ncreases larger batteres become relatvely acheved as a result of proper utlzaton of the wnd turbne more effcent than wnd generators It s observed that the and the battery system optmzaton process prefers to choose the hghest battery capacty when the battery cost s at ts mnmum and the wnd Correspondng Author: Ms. N. Prya, RVS College of Engneerng and Technology, Combatore, Tamlnadu, Inda. 760

5 3.3 EPR and ESP [5] C.Marnay, G. Venkatarmanan,M. Stadler, A. S. Sddqu,R. Frestone,and B. Chandran, Optmal technology selecton and operaton of commercal-buldng mcrogrds, IEEE Trans. Power Syst., vol. 23, pp , Aug [6] G. Lalor, A. Mullane, and M. O Malley, Frequency control and wnd turbne technologes, IEEE Trans. Power Syst., vol. 20, no. 4, pp ,Nov [7] S. Grllo, M. Marnell, S. Massucco, and F. Slvestro, Optmal management strategy of a battery-based storage system to mprove renewableenergy ntegraton n dstrbuton networks, IEEE Trans. Smart Grd,vol. 3, no. 2, pp , Jun [8] W. Qao, W. Zhou, J. Aller, and R. Harley, Wnd speed estmaton basedsensorless output maxmzaton control for a wnd turbne drvng a DFIG, IEEE Trans. Power Electron., vol. 23, no. 3, pp , May Fg2 : Output Result IV. CONCLUSION The method descrbed can help resdental customers and small busness owners decde on nvestng n the rght amount of renewable generaton and battery capactes that are optmzed accordng to ther load profle renewable resource avalablty and electrcty rates. Results also ndcate that gven the levelzed costs of a wnd generaton and battery storage system an average electrcty rate may exst at whch nvestng n these systems wll no longer be benefcal. Senstvty analyses were conducted to nvestgate the effects of electrcty rates as well as wnd generaton and electrcty storage costs on optmum capactes of a wnd turbne and battery for a smart home. The results show how the customer could beneft from hgher capactes of wnd generaton and battery as ther assocated costs drop. It was also llustrated that f certan condtons are met n the system the smart grd customer has an opportunty to make proper nvestments and proft from sellng generaton back to the grd as well. [9] Peter Palensky, Senor Member, IEEE, and Detmar Detrch, Senor Member, IEEE Demand Sde Management: Demand Response, Intellgent Energy Systems, and Smart Loads IEEE Transactons On Industral Informatcs, vol. 7, no. 3, August [10] Heydt, Gerald Thomas, et al. "Prcng and control n the next generaton power dstrbuton system." Smart Grd, IEEE Transactons on 3.2 (2012): Reference [1] G.T. Heydt,B. H.Chowdhury,M. L. Crow,D.Haughton, B.D. Kefer, F. Meng, and B. R. Sathyanarayana, Prcng and control n the next generaton power dstrbuton system, IEEE Trans. Smart Grd, vol. 3, no. 2, pp , Jun [2] K. M. Tsu and S. C. Chan, Demand response optmzaton for smart home schedulng under real-tme prcng, IEEE Trans. Smart Grd, vol. 3, no. 4, pp , Dec [3] P. Palensky and D. Detrch, Demand sde management: demand response, ntellgent energy systems, and smart loads, IEEE Trans. Ind. Informat., vol. 7, no. 3, pp , Aug [4] K. Xe and R. Bllnton, Determnaton of the optmum capacty and type of wnd turbne generators n a power system consderng relablty and cost, IEEE Trans. Energy Convers., vol. 26, no. 1, pp , Mar Correspondng Author: Ms. N. Prya, RVS College of Engneerng and Technology, Combatore, Tamlnadu, Inda. 761