No. E-16-AAA-0000 Optimal Resources Planning of Residential Building Energy System

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1 No. E-16-AAA-0000 Optmal Resources lannng of Resdental Buldng Energy System ourya Ahmad,Alreza Lorestan, S. Hossen Hossenan, M. M. Ardehal, Gevork B. Gharehpetan Department of Electrcal Engneerng, Amrkabr nversty of Tenology, Hafez Ave.,.O. Box 15914, Tehran, Iran Abstract Ths paper deals wth optmal resources plannng n a smart resdental buldng energy system, ncludng FC (fuel cell), V (hotovoltac) panels and the battery. A day-ahead energy management system (EMS) based on nvasve weed optmzaton (IWO) algorthm s defned for managng dfferent resources to determne an optmal operaton sedule for the energy resources at ea tme nterval to mnmze the operaton cost of a smart resdental buldng energy system. Moreover, n ths paper the mpacts of the sell to grd and purase from grd are also consdered. All practcal constrants of the ea energy resources and utlty polces are taken nto account. Moreover, senstvty analyses are conducted on electrcty prces and sell to grd factor (SGF) values, n order to mprove understandng the mpact of key parameters on resdental CH systems economy. It s shown that proposed system can meet all electrcal and thermal demands wth economc pont of vew. Also enhancement of electrcty prce leads to substantal growth n utlzaton of proposed CH system. Keywords combned heat and power system (CH); electrcty tarff; energy management system; smart home CBattery, C Gas, C C C ur Grd, Sell Grd, B Gas gas, M B Grd Grd, BBattery NOMENCLATRE Tme nterval Total cost of battery n the th nterval Total cost of purasng gas n the th nterval Total cost of fuel cell n the th nterval Total cost of utlty n the th nterval Total Revenue of utlty n the th nterval Length of tme nterval Cost of purasng natural gas per kw Heat power produced drectly from gas Normalzed prce of electrcty tarff Maxmum value of utlty purasng electrcty cost per kw urased power from utlty by home Operaton and mantenance cost of battery (kw) Output power of battery LR art Load Rato Electrcal power produced by fuel cell Battery, e ή Effcency of fuel cell. Electrcal load demand el, Q Load Thermal load power Q Heat power produced by fuel cell W Mnmum energy lmt of battery mn W Maxmum energy lmt of battery SOC Avalable energy n battery Battery, d, Maxmum lmt of battery dsargng rate Maxmum lmt of battery argng rate Battery,, ή Effcency of battery dsargng. ή d Effcency of battery argng hourly self-dsarge rate pper lmt of ramp rate of fuel cell FC, Lower lmt of ramp rate of fuel cell FC, D mn Mnmum lmt of fuel cell generated power Maxmum lmt of fuel cell generated power ή Effcency of Battery argng r Electrcal to thermal power rato of fuel cell ήd G Effcency of Battery dsargng Reference Irradaton T Envronment Temperature c, SGF Sell to Grd Factor I. INTRODCTION CH (combned heat and power) system s a lucratve alternatve wh offers lower energy costs, hgher effcency, hgher relablty, stablty n the face of uncertan electrcty costs and lower greenhouse gas (GHG) producton. The

2 Optmal Resources lannng of Resdental Mult-Carrer Energy System Based on Invasve Weed Optmzaton Algorthm 31 th ower System Conference Tehran, Iran applcaton of CH systems for resdental loads wll be promoted f optmal operaton of the ntegrated energy system s completely nvestgated. Reference [1] has proposed a mxed nteger nonlnear programmng approa to mnmze annual cost of the system for a gven resdental customer equpped wth the CH plant, combnng wth a back-up boler. Reference [2] has presented optmal szng of RERs (renewable energy resources) ncludng V/WT besde CH unts n a grd connected resdental buldng usng smulated annealng optmzaton (SA). It has been shown n [2] that RERs and CH unt can properly complement ea other. Reference [3] has proposed operatonal strategy for a CHbased grd-ted MG (mcrogrd) ncludng V/FC/WT and MT (mcro-turbne). It has been presented n [3] that an ncrease n the prce of electrcty encourages the utlzaton of MGs based on RERs. Also n order to have a hgher level of relablty, a hgher number of DGs must be employed n MGs. In [4], a hybrd electrcal/ thermal smart home energy system ncludng CH and battery s studed usng hyper-sphercal sear algorthm. It has been shown that the battery and varable electrcty tarffs have substantal effect on system operatng costs. Reference [5] deals wth the home energy system optmal sedulng, ncludng FC and battery usng ICA algorthm. In [5] t has been shown that the effcency of battery has a major effect on the system operatng cost; however, the mpact of sellng electrcal energy to grd s not consdered. In ths paper, an optmal resources plannng n the hybrd thermal/electrcal smart resdental buldng energy system ncludng a V and battery s studed. A day-ahead power sedulng based on nvasve weed optmzaton (IWO) algorthm for managng dfferent resources s developed to generate an effcent look-up table that determnes an optmal operaton sedule for the dstrbuted energy resources at ea tme nterval, n order to mnmze the operaton cost of the system. The mpacts of blateral tradng to grd durng dfferent tarffs are taken nto account and all practcal constrants of the energy resources and utlty are consdered. Also accurate models of ea energy resources ncludng upper and lower lmts of ramp rates of FC and arge and dsarge ramp rates of battery are also appled. Moreover, n order to examne dfferent energy prce polces, senstvty analyss have been conducted on electrcty prces and sell to grd factor (SGF). It should be noted that the model of ths study s formulated n general terms, so t can be easly adapted to varous resdental buldng systems by applyng hourly load data, meteorologcal data, energy prce polces and etc. Ths paper s structured as follows. Next, the system artecture and operaton s gven. Energy management system (EMS) s explaned n secton 3. roblem formulaton s outlned n secton 4. Results and dscussons are nvestgated n secton 5 and Secton 6 draws the concludng remarks. II. SYSTEM CONFIGRATION AND OERATION Fg. 1 shows the confguraton of the proposed hybrd resdental energy system. The system ncludes of V panels as a renewable energy source, a FC as a CH system and battery to store surplus energy and mprove the system relablty. The thermal load can be suppled by ether natural gas or recovered heat from the FC. The electrcal load can be suppled by the V, battery or man grd. Sell to grd s also taken nto account. Fg. 1. Smart resdental buldng energy system A. Modelng the System Components 1) V system The output power of V ( V ) at ea tme (t) can be calculated by [6], [7]: A G (1) V, V,, where G s solar radaton, A s V area and V s overall effcency of solar panel wthout consderng effcency of DC/AC converter. The V n ea tme can be determned usng followng equaton: ή ή [1 T T ] ylog G (2) V, ref ref c, ref 10 2) FC The mum electrcal power of the FC s restrcted by mum capacty of FC gven n Table II. If output power of FC becomes less than a lower threshold, the FC cannot work and t should be swted off. The Effcency of the FC s a functon of part load rato (LR). LR s rato of electrcal generaton to mum FC power ratng. The effcency and rato of the electrcal to thermal energy of FC are also functon of LR [8] and can be obtaned by followng equatons: f LR 0.05 ή r (3) f LR 0.05 ή LR LR LR LR LR r, () t LR LR LR LR FC Therefore, thermal energy provded by FC n ea tme nterval s: (4) (5) Q r (6) 2

3 Optmal Resources lannng of Resdental Mult-Carrer Energy System Based on Invasve Weed Optmzaton Algorthm 31 th ower System Conference Tehran, Iran In ths paper, as an accurate model of the energy resources s consdered, upper and lower lmts of ramp rates of FC are taken nto account. Therefore followng equatons are consdered through smulaton procedures. efc, t efc, t 1 (7) FC, efc, t1 efc, t (8) D 3) Battery The battery bank capacty s presented n Table II. In ths study an accurate model for battery s consdered wh battery and converter effcences, self-dsarge rate and arge and dsarge ramp rates of battery are taken nto account. The arge and dsarge quantty of the battery bank at tme t can be determned by followng equatons, respectvely. SOC SOC 1 (1 ) Battery, ή (9) SOC Battery, SOC 1 (1 ) (10) ήd The followng nequaltes represent lmtatons of dsargng and argng ramp rates for the battery, respectvely: SOC SOC 1, Battery (11) SOC 1 SOC d, Batter y III. ENERGY MANAGEMENT SYSTEM(EMS) The man role of the EMS s to mnmze the operatng cost whle satsfyng all thermal and electrcal demands of smart resdental buldng. In order to use avalable energy resources effectvely, operaton sedulng should be determned one day or longer n advance. It s assumed that the predcted values of heat and electrcty demands are avalable one day ahead n the optmzaton model studed n ths paper. It should be noted that as the objectve of ths study s to mnmze the operatng cost of resdental buldng energy system, the components of system are prevously nstalled and there s no need to consder nstallaton costs. In ths study, the model s formulated n general terms, so t can be easly adapted to varous systems by applyng hourly load data, meteorologcal data tarffs, natural gas prce and etc. A. Cost Functon The objectve of EMS s to mnmze followng cost functon. (12) FC Battery Grd Grd Gas ( ur Sell ),,,,, Mn f x C C C C C ur where C, C, t Battery, t C, Sell Grd, t C and C can be Grd, t Gas, t calculated usng followng relatons: C C e t Gas ή (13) Also, n ths study startup cost and shutdown costs of FC are taken nto account. C B (14) Battery, Battery Battery, C M B (15) ur Grd, Grd Grd, C M B SGF (16) Sell Grd, Grd Grd, C B (17) Gas, Gas gas, B. Constrants Followng constrant should be satsfed. C. ower balance (18) mn SOC (19) mn SOC SOC ma x (20) grd, mn grd, grd, In ths study, the load sheddng s not consdered, so all the electrcal and thermal demands must be suppled. (21) V, Battery, Grd, Lod a, 0 Q Gas, QFC, QLoad, 0 (22) It s assumed that sell electrcal energy to utlty and purase electrcal energy from utlty must not be occurred smultaneously. Namely, at ea t, the smart resdental buldng energy system should only sell energy or purase electrcty from grd. D. Electrcty Tarff In ths paper, three dfferent tarffs are consdered for electrcty prce: peak, ntermedate, and off-peak tarffs. All of the tarffs are normalzed regardng the mum electrcty tarff defned n the peak perod. Normalzed values of these tarffs and ther pertanng tme ntervals are lsted n Table I [9]. In ths study, electrcty prce durng onpeak hours s 0.13($/kWh) [5]. In base scenaro, sell to grd (SGF) factor s assumed to be 0.9 [10]. TABLE I. ELECTRICITY TARIFF [8] erod Tme range Normalzed Electrcty urase rce eak [9,12],[17,22] 1pu Intermedate [13,16] 0.9pu Off-peak [1,8],[23,24] 0.78pu 3

4 Optmal Resources lannng of Resdental Mult-Carrer Energy System Based on Invasve Weed Optmzaton Algorthm 31 th ower System Conference Tehran, Iran IV. A. Terms used INVASIVE WEED OTIMIZATION ALGORITHM Seeds All unts n the optmzaton problem that are assgned a value pertanng to the lmtng condtons. lants Seeds that grow nto plants before beng evaluated. Ftness value A value that determnes how good the plant s,.e. how mu optmzed the soluton s. Feld The feasble area/sear area. The IWO algorthm s nspred from the bologcal growth of weed plants. Ths tenque s based on the colonzng behavor of nvasve weed plants [13]. Weed plants are called nvasve because the growth of weed plants s extensvely nvadng n the growth area. IWO s known to be hghly convergng n nature snce t a dervatve free algorthm. It also converges to the optmal soluton thereby elmnatng any possbltes of sub optmal solutons wth easy codng mplementaton. IWO has been so far mplemented for applcatons lke DNA computng, antenna system desgn [14], optmal arrangement of pezoelectrc actuators on smart structures and unt commtment n power system. In ths algorthm, the number of decson varables are taken n the form of seeds and then randomly dstrbuted n the feasble space [15].These seeds are then permtted to grow nto plants and the ftness of ea ndvdual plant s determned. Dependng upon these ftness values, new seeds are generated by ea plant n accordance wth a normalzed standard devaton. helps algorthm n convergng to the optmum soluton faster as t determnes exactly where to dstrbute the new seeds so that the seeds always approa the optmal soluton. In the next step the combned ftness values of seeds and plants s calculated untl the ftness value converges to an optmal soluton. The objectve functon of ths algorthm depends on the type of applcaton the algorthm s used for. The objectve functon s utlzed as the ftness functon to aeve the optmzed results usng convergence tenque. Smulaton procedure of IWO algorthm has been explaned below. Step 1: The seeds are ntalzed based on the number of selected varables nvolved n the process over the probable sear boundary. The seeds are randomly ntalzed based on feasble space. Step 2: The ftness of the seeds ntalzed s evaluated dependng upon the ftness functon. These seeds then evolve nto weed plants capable of producng new unts. Step 3: The evolved plants are arranged n a defnte order (ncreasng or decreasng) and new seeds are produced by these plants dependng upon ts poston n the sorted lst of plants, startng wth the mum number of seeds produced by the best ft plant. Step 4: The number of seeds to be produced by the plants vares lnearly from N to N wh s obtaned by gven mn formulaton: F Fworst Number of seeds ( N Nmn ) N (23) mn Fbest Fworst Step 5: The generated seeds are dstrbuted normally over the feasble space wth zero mean and a standard devaton that s updated durng ea teraton usng: n ter ter ter ( 0 f ) f ter (24) where n s used to help algorthm to traverse around the feasble space more effcently and s generally assumed to be between 2 and 3. Step 6: The ftness of ea seed generated n the above steps s calculated along wth the parent weeds and by means of compettve excluson, the seed-parent combnatons that are least n ftness are elmnated and the number of weed plants s lmted to the mum of number of weeds allowed. Step 7: The above steps are repeated untl convergence crtera s reaed, so that the plant wth the best ftness value s the optmzed soluton. The smulaton procedure of IWO algorthm s gven n Fg. 2. NO YES Start Intalze Input Data The dstrbuted seeds grow nto weed plants Calculated ftness of ea ndvdual plant Sort the ftness n descendng order and rank the plants Generate new seeds usng spatal dstrbuton based on the rank of the plant Determne the new ftness of the seed plant combnaton Compettve excluson If convergence crtera s satsfed Stop Fg. 2. The smulaton procedure of IWO algorthm 4

5 Optmal Resources lannng of Resdental Mult-Carrer Energy System Based on Invasve Weed Optmzaton Algorthm 31 th ower System Conference Tehran, Iran A. Operaton behavor V. RESLTS AND DISCSSION The smulaton has been conducted for 24 hours n MATLAB software envronment. The profle of thermal and electrcal load demands of the resdental buldng n a typcal day n sprng has been presented n Fg. 3. For ths system, the peak of thermal and electrcty demands are 34 kw and 78 kw, respectvely. The parameters of the system are presented n Table II. Fg.3. Hourly electrcal and thermal demands of resdental buldng [2] TABLE II. SYSTEM ARAMETERS [5, 11,12 ] FC,, pper lmt of ramp rate of fuel cell 0.15, Lower lmt of ramp rate of fuel cell FC, D 18, Maxmum lmt of fuel cell generated power 25, Mnmum lmt of fuel cell generated power 0.05 mn B, Operaton and mantenance cost of battery Battery 0 per kw W, Mnmum energy lmt of battery 0 mn W, Maxmum energy lmt of battery 40 Gas B, Cost of purasng natural gas per kw 0.04 B, Maxmum value of utlty purasng 0.13 Grd electrcty cost per kw ή, Effcency of battery dsargng d ή, Effcency of battery argng 0.921, hourly self-dsarge rate , Intal energy n battery Battery, d, SOC nt, Length of tme nterval 1, Maxmum lmt of battery dsargng 15 rate, Maxmum lmt of battery argng rate -15 Battery,, 25 T, Reference temperature ( C) ref ref ή, Reference effcency of V ref Fg.4 shows the optmal operaton of ea energy resources n a typcal day. Accordng to ths fgure, n early hours of the day, as the prce of electrcty s low, the resdental buldng energy system purases electrcal energy from utlty to supply the load and store n battery. Then, durng the hgh-cost hours, the EMS orders to sell electrcal energy to utlty n order to make the operaton of the system more economc. In addton, ths sold energy contrbutes to power system for supplyng electrcal demand of the system durng on peak hours. Namely, n the frst eght ntervals, as the utlty electrcal cost s at ts lowest level, the battery s arged and the major part of electrcal demand s supples by the utlty. FC does not generate at ts mum capacty and generates the lmted power su that ts cost becomes less than the utlty. In other words, f FC generates more power, ts effcency s reduced and t causes hgher cost compared to the utlty cost. Also as ramp rate of FC s taken nto account, FC needs to ncrease ts power generaton at nterval 6 th and 7 th, so that there wll be no lmt on hgh-cost hours to generate at ts mum capacty. Durng the 13th to 16 th ntervals that the electrcty tarff s n the ntermedate perod, only FC and V supply the electrcal load. Also natural gas contrbutes to recovered thermal energy from FC to meet thermal demand. In the 9th to 12th and 17th to 22th ntervals, the electrcty tarff s n the peak perod. Therefore, the battery delvers all of ts stored energy, FC generates at ts mum power lmt and V panel contrbutes to other energy resources to supply the load and sell electrcal energy to grd. So t can be concluded that proposed resdental energy system sells energy to grd only n hgh-cost perods and purase energy from grd n low-cost ntervals. As a result, proposed resdental system can help to have smoother load profle n power system. The power generatons of ea energy resource are lsted n Tables III. Fg.4. Optmzed resources power generatons. B. Senstvty Analyss Senstvty analyss mproves the understandng of mpact of key parameters on the behavor of proposed resdental CH systems. In ths study, senstvty analyses have been performed on electrcty prces and sell to grd factor parameters. An mperatve parameter of the operaton cost for resdental energy system s electrcty prce, wh also has an mportant effect on the adopton of resdental CH systems. Fg. 5 shows the result of senstvty analyss. It s clear that 5

6 Optmal Resources lannng of Resdental Mult-Carrer Energy System Based on Invasve Weed Optmzaton Algorthm 31 th ower System Conference Tehran, Iran electrcty prce and SGF strongly affect the economy of resdental CH system operaton. TABLE III. SIMLATION RESLT (LOOK- TABLE) nterval bat efc hfc gas pv u Accordng to Fg.5, whle the electrcty prce s low, sell to grd factor has a neglgble effect on operaton cost of resdental CH system, because of low electrcty prce; the EMS provdes the majorty of requred electrcal energy of system from utlty. In fact, purasng electrcty from utlty s more economc than operaton of FC close to ts mum capacty and system. Fg.5 operaton cost senstvty related to dfferent Electrcty prces and SGFs. Wth the ncrease n electrcty prce, the operaton cost of proposed system wll be ncreased. But from one partcular pont on, by ncreasng the electrcty prce, FC generates more power besde V to supply the electrcal load and sell energy to utlty. Therefore after ths pont, by ncreasng the electrcty prce, the operaton of resdental CH system becomes more economc and operaton of FC near ts mum power gradually becomes affordable. Also Fg. 5 demonstrates that by ncreasng the electrcty prce, the mpact of sell to grd to factor on operaton cost of resdental CH system wll be ncreased. Therefore, enhancement of electrcty prce encourages other resdental buldng to nstall proposed CH system. VI. CONCLSION In ths paper, a new confguraton for a resdental buldng energy system has been proposed, ncludng a V and a battery connected to the utlty. An optmzaton model has been establshed based on determnstc predcton of electrcal and thermal power demands and utlty prces. A day-ahead EMS based on IWO algorthm has been defned for managng dfferent resources to determne an optmal operaton sedule for the energy resources at ea nterval to mnmze the operaton cost of the system. The mpacts of the sell to grd and purase from grd have been consdered and all practcal constrants of the ea energy resources and utlty polces have been taken nto account. Senstvty analyses have been conducted on electrcty prces and SGF. It has been shown that proposed system can meet all electrcal and thermal demands wth economc pont of vew. Also t has been proved that wth the ncrease n electrcty prce, the operaton cost of proposed system has been ncreased. But from one partcular pont on, by ncreasng the electrcty prce, the operaton of resdental CH system has been more economc and operaton of FC near ts mum power gradually has become more economc. Also t has been offered that by ncreasng the electrcty prce, the mpact of sell to grd to factor on operaton cost of resdental CH system wll be ncreased. Therefore, enhancement of electrcty prce encourages other resdental buldng to nstall proposed CH system. REFERENCES [1] H. Ren, W. Gao, Y. Ruan, Optmal szng for resdental CH system, Elsever Appled Thermal Engneerng, vol. 28, pp , [2] Y. Yang, W. e, Z. Q, Optmal Szng of Renewable Energy and CH Hybrd Energy Mcrogrd System, n roc IEEE ES Innovatve Smart Grd Te. ASIA Conf., pp [3] M.H. Morad, M. Eskandar, M. Hossenan, "Operaton strategy optmzaton n an optmal szed mcrogrd," IEEE Trans. on Smart Grd, vol. 6, no. 3, May [4] M. J. Sanjar, H. Karam, A. H. Yatm, G. B. Gharehpetan, "Applcaton of Hyper-Sphercal Sear algorthm for optmal energy resources dspat n resdental mcrogrds ", Appl. Soft Comput. J. (2015), Ncole, Ttle of paper wth only frst word captalzed, J. Name Stand. Abbrev., n press. [5] H. Karam, M. J. Sanjar, S. H. Hossenan, G. B. Gharehpetan," An Optmal Dspat Algorthm for Managng Resdental Dstrbuted Energy Resources", IEEE TRANSACTIONS ON SMART GRID,, vol. 5, pp ,

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