ENERGY services are services, processes and commodities

Transcription

1 Improved Energy Servces Provson through the Intellgent Control of Dstrbuted Energy Resources M. A. Pedrasa, Student Member, IEEE, E. D. Spooner, and I. F. MacGll Abstract There s a need to mprove the delvery of energy servces, and utlzng dstrbuted energy resources offers sgnfcant potental. We propose an energy servce modelng technque that would capture temporal varatons of ts demand and value, and dfferentate t from the electrc energy consumed by the end-use equpment. We then use ths technque wth a novel energy servce smulaton platform that ams to maxmze the net beneft derved from energy servces. The smulaton platform creates a strategy for how avalable dstrbuted resources should be operated n order to provde the desred energy servces whle mnmzng the cost of consumpton. The correspondng optmzaton problem s solved usng partcle swarm optmzaton. The smulaton platform proved capable of creatng an operaton schedule that maxmzes net beneft under a range of challengng condtons. Index Terms Energy servces, dstrbuted energy resources, smart home, demand management, partcle swarm optmzaton. I. INTRODUCTION ENERGY servces are servces, processes and commodtes from where energy consumers ultmately derve and apprecate the value of raw energy carrers lke gas and electrcty. Energy servces may be classfed as ether drect or ndrect [1]. Drect energy servces are servces where the raw energy carrers are converted to alternate energy forms that are drectly consumed. Examples are llumnaton, space and water heatng, and moton. Indrect energy servces are servces where the consumer puts value on the processes and products resultng from the utlzaton of energy carrers and raw materals, f needed. Examples are consumer and manufactured goods, nformaton processng, communcatons and entertanment. The value of an energy servce orgnates from the comfort, convenence, products and profts t brngs to the consumer. It s affected by several factors lke tme of the day, weather, socal externaltes, and uncertantes, among others. In a domestc context, for example, the value of ar condtonng s hghest durng summer but has low value at wnter. The value of watchng televson s certanly very hgh f an mportant poltcal or sportng event s n broadcast. The tradtonal method of delverng energy servces s to generate and make avalable electrcty, regardless of the volume consumed, whle ensurng the securty of the power The authors are wth the Centre for Energy and Envronmental Markets and School of Electrcal Engneerng and Telecommuncatons, Unversty of New South Wales, Sydney, Australa (emal: m.pedrasa@student.unsw.edu.au). M. A. Pedrasa acknowledges the scholarshp granted by the Unversty of the Phlppnes (DSF) and the DOST-SEI Engneerng Research and Development for Technology Program. system. Ths approach has become ncreasngly unfavorable due to growng demand and emssons, and worsenng load factors. Wth these problems facng the power system as a whole, there s a need to mprove the effcency of delverng energy servces [1]. The potental of couplng dstrbuted energy resources wth exstng utlty nfrastructure to delver energy servces n a more optmal way has been contnuously ncreasng [2]. Dstrbuted energy resources (DER) are fne-graned equpment and practces, usually co-located wth or near the consumer, that could augment, or even assume the role of the utlty n delverng energy servces. In [3], Lovns dscussed more than 200 benefts that can be derved from DER and lsted a dverse range of ts possble forms. In ths paper, we propose an energy servce modelng technque that works wth a novel energy servce smulaton platform that would maxmze the potental of DER and the utlty n delverng energy servces. The modelng technque would capture and represent the temporal varatons of demand and value of servces, and assgns monetary value to the energy that realzes the energy servce. The smulaton platform ams to mprove the provson of energy servces by maxmzng the net benefts. It would propose a strategy for how avalable DER should be operated so that servces wth dfferent values to the consumer are provded whle the cost of provson s mnmzed. The creaton of the operaton schedule s a challengng optmzaton problem due of the presence of actve and passve storage, and shftable and curtalable demands. We wll llustrate the potental of the modelng technque and the smulaton platform usng a smart home as a case study. We wll consder DER that are most approprate n the context of a smart home: renewable energy generaton, demand resources or flexble demand, and energy storage. The creaton of the strategy s a mathematcal optmzaton problem, and we used a hybrd of partcle swarm optmzaton and ts bnary verson to fnd the soluton. Several smlar works have recently presented technques on how to optmze the operaton of DER. The household energy consumpton s mnmzed n [4] by plannng the operaton of space heaters and shftable loads usng tabu search and mult-agent systems. In [5], electrcty and natural gas, and co-generaton technologes have been used to servce heatng and electrcty end-use loads. The rest of the paper s outlned as follows: The proposed energy servce modelng technque and smulaton platform are dscussed n Sectons II and III. Partcle swarm optmzaton s descrbed n Secton IV. The case study s presented n Secton V. The conclusons are summarzed n Secton VI.

2 2 II. MODELING ENERGY SERVICES The demand for and the value of energy servces change wth tme. In a restaurant, for example, hot water s generally n demand an hour or two before t opens, whle t s open, and an hour or two after t closes. Hot water s generally not needed beyond those hours. Furthermore, the convenence of havng hot water flowng out of the taps s certanly hgher durng those hours, than when no one s usng t. It may be argued that the restaurant owner s wllng to pay more money per 1 unt of thermal energy (MJ or Btu) needed to heat the water when the restaurant s open than when t s closed. In ths llustraton, the value of the hot water energy servce s assgned to the thermal energy content of water, nstead of assgnng t to the energy consumpton of the heater. The workers beneftng from the hot water servce are aware of the convenence of usng hot water, and would not care about the number of kwh of electrcty consumed by the heaters. The value of a servce s the monetary amount that the user s wllng to pay so that the servce s provded. It could also be the amount that the user would lose f the servce s not provded. The monetary value, therefore, s perceved or may be computed by the user. In the proposed modelng technque, the value of the servce s assgned to each unt of energy that realzes the servce. By dong so, we can make a dstncton between that energy and the electrc energy consumed by the equpment that delvers the servce. The energy that realzes the energy servce, whether t s mechancal, thermal or lumnous n nature, could be referred to as the energy equvalent of the energy servce. Puttng the value on the energy equvalent also enables us to dfferentate between dfferent end-use equpment. To llustrate, two dfferent heaters may ntroduce the same amount of thermal energy to water, but consume dfferent amounts of electrc energy. The demand for and the value of hot water servce for the restaurant example, therefore, may be modeled as a par of tme-seres values. The frst seres descrbes the hourly demand for the hot water servce. It may be the hourly consumpton of hot water specfed n lters, or the requred hourly heat content of water (the energy equvalent of the hot water servce, U ES (t)) n kwh. The second seres, λ ES (t), descrbes the hourly varaton of the monetary value that the owner assgns to the energy equvalent of the hot water servce per unt of thermal energy. These temporal varatons can be better vsualzed usng graphs nstead of tme-seres. As an llustraton, the demand and value of the hot water servce may be descrbed by the plots n Fg. 1. The plots show that the demand for hot water durng the openng hours s hgher than outsde of those hours, and the value of hot water s also hgher durng those hours. There could be hours when consumpton s low but value s hgh, and on some hours, the consumpton can be hgh yet the value s low. The amount of U ES (t) depends on the consumpton habts of the user. Its monetary value, λ ES (t), s perceved or may be computed by the user. In some cases, t s not possble to depct the demand for an energy servce as an hourly varaton of some varable Fg. 1. Representaton of energy servce demand and value. or of the energy equvalent, lke what s shown n Fg. 1. Examples are shftable servces, lke cookng or washng, and nterruptble servces, lke pool pumpng. The tmng of delvery of these servces are flexble, that s, the start tmes and duratons may be varable, and they may be nterrupted and the rest of the servce may be postponed to a later tme. In such servces, a narratve descrpton of the demand may be gven, for example, the washng servce requres 1 kw of electrcty over a contnuous two hour perod, and may start anytme between 9 AM and 3 PM. The relatonshp between the energy equvalent of a servce, U ES (t), and the electrc energy consumed by the enduse equpment, P e (t), should be determned for each servce to be provded. The relatonshp heavly depends on the physcal processes occurrng wthn the equpment and the servce tself. The determnaton of the energy equvalent s straghtforward f the end-use equpment nstantaneously converts electrcty to the end-use energy or process. Examples of such equpment are lght bulbs and applances. If the effcency of the equpment n convertng electrcty to the energy servce s η, then, U ES (t) =η P e (t). (1) In some types of energy servces, the converson from electrcty to energy servce s not nstantaneous, that s, there s some form of storage nvolved. Therefore, there s a temporal msmatch between the demand for a servce and the electrcty consumpton of the end-use equpment that delvers the servce. Examples of such servces are space heatng and storage water heatng. To llustrate, n storage-type water heatng, water s heated overnght but t s consumed over the entre day. The relatonshp between the energy equvalent of space heatng servce and the energy consumpton of the heater wll be llustrated n the case study n Secton V. There s no perceptble energy equvalent or t s dffcult to quantfy for ndrect energy servces. In ths case, the actual electrc energy consumpton may be assgned as the energy equvalent of the servce. III. ENERGY SERVICE SIMULATION PLATFORM The energy servce smulaton platform we propose ams to maxmze the net value of the energy servces desred by the

3 3 consumer. The net value s equal to the total benefts derved from the avalablty of the servces less the cost of electrcty consumpton. The smulaton platform takes advantage of the followng ponts: 1) Temporal varaton of the cost of electrcty, e.g. Tme of Use and Real-Tme Tarffs. 2) Flexble delvery of some types of energy servces. 3) Temporal msmatch between energy servce demand and electrcty consumpton of end-use equpment. 4) Avalablty of actve storage DER lke battery banks. The platform would propose a strategy for how avalable DER should be operated. The strategy ams to mnmze the cost of electrcty consumpton whle delverng the requred servces. The strategy wll be n the form of a schedule, or a set of recommended actons at each nterval of the smulaton horzon. The platform wll also quantfy the savngs ncurred by operatng the DER usng the strategy, and ths result may be used for makng nvestment decsons. The smulaton platform s bascally a mathematcal optmzaton problem expressed as (( T S ) ) max λ ES, (t)u ES, (t, x) λ e (t)p e (t, x) (2) t=1 =1 where T number of hours n the smulaton perod, S number of energy servces, λ ES, value of the energy equvalent of the th servce ($/kwh), U ES, demand for the energy equvalent of the th servce (kwh), λ e cost of electrcty ($/kwh), P e total hourly electrcty consumpton (kwh), subject to the operatonal constrants of the DER. The optmzaton problem ams to fnd the schedule of operaton of the DER, x. The frst term n (2) s the total beneft derved from the avalablty of the servces, computed usng the monetary values assgned to the energy equvalent and demand for the servces. The second term s the total cost of electrcty used to delver the servces. The complexty ntroduced by passve storage to the relatonshp between the energy consumpton and energy equvalent, and the presence of complex DER operaton models and actve storage optons lke battery banks suggest that the objectve functon n (2) s non-lnear, non-convex and noncontnuous. Smulaton-based heurstc technques, therefore, offer great potental n fndng the optmal or a near-optmal soluton. We used partcle swarm optmzaton as the optmzaton tool. IV. PARTICLE SWARM OPTIMIZATION Partcle swarm optmzaton (PSO) s a populaton-based search technque that mmcs how a group of smple partcles could acheve complex collectve behavors [6]. Each partcle n a swarm represents a soluton to the optmzaton problem, and the partcles search for the optmal soluton by flyng around the soluton space whle communcatng wth each other. The trajectory of a partcle s affected by the best performng partcle and by the best poston t has vsted. The movement of a partcle s descrbed by ts speed V (v 1,v 2,..., v n ) and poston P (p 1,p 2,..., p n ).The th coordnate of speed and poston are computed by v t+1 = ω v t + c 1 rand () (p t Gbest, p t ) + c 2 rand () (p t Pbest, ) (3) pt, p t+1 = p t + vt+1. (4) In (3), the subscrpts Gbest and Pbest refer to the poston of the best performng partcle (global best) and the best poston that the partcle has vsted (personal best). The frst term s the momentum, whle the last two terms are the weghed pull of the global and personal best postons. rand() s a unform random number generator from 0 to 1. The partcles are placed randomly n the soluton space n the ntalzaton phase. The ntal partcles may be preprocessed to accelerate convergence and ncrease the chances of fndng the optmal soluton. The partcles then fly around the soluton space, as determned by (3) and (4). The global and personal bests are updated f needed, and the selecton s determned by evaluatng the objectve functon. The smulaton stops when a convergence crteron has been satsfed or the maxmum number of teratons has been reached. The global best partcle at the end of the smulaton s taken as the soluton to the problem. The bnary verson of PSO (BPSO) was proposed n [7] to solve bnary-valued optmzaton problems. In BPSO, a coordnate of a partcle poston, p, s ether 0 or 1. The speed s also computed usng (3), however, t s restrcted to be wthn a range, [ V max,v max ]. The poston s computed by mappng the velocty to a probablty usng a sgmod functon (5) and the result s compared to a random number generator. That s, f rand() <S(v t+1 ), thenp t+1 =1,otherwse,p t+1 =0. S(v t+1 )= 1 1+exp( v t+1 ). (5) PSO and BPSO and ther varatons have been shown to be effectve n generatng near-optmal solutons to complex optmzaton problems [8] [10]. V. CASE STUDY A. Energy Servce Provson n a Smart Home The energy servce smulaton platform was used to determne how DER may be controlled n a smart home. The avalable DER are: 1) Heater: for space heatng servce; maxmum heatng power equal to 2.0 kw; resstve type. 2) Pool pump: for pool mantenance; rated 1.0 kw. 3) Battery storage: 2.4 kwh capacty; 400 W maxmum chargng and dschargng rates; 90% chargng and dschargng effcency; 0.1 % coulomb loss per hour; may be dscharged down to 20% of capacty. 4) PV array: 2.0 kwp. All energy servces asde from space heatng and pool pumpng are lumped together nto a must-run aggregate energy servce. The demand for the must-run servce s shown n Fg.

4 4 Fg. 2. Fg. 3. Demand for must-run servce. Demand for space heatng servce. TABLE I DESCRIPTION OF SCENARIOS Case number Descrpton 1 Baselne case. Manual control of DER. No battery storage. 2 DER are scheduled. No battery storage. 3 DER are scheduled. Wth battery storage. 4 DER are scheduled. Wth net feed-n tarff. No battery storage. 5 DER are scheduled. Wth net feed-n tarff and battery storage. 6 DER are scheduled. No battery storage. Value of pool pump servce from 8AM to 10PM s reduced from medum to low. TABLE II ELECTRICITY TARIFF Tarff type Cost, λ e ($/kwh) Tme of Use Peak (2-8 PM) Shoulder (7AM-2PM, 8-10PM) Off-peak (10PM-7AM) Feed-n rate (net) For the space heatng servce, the resdent s comfortable as long as the room temperature s wthn 1 C of the desred value. The desred and outdoor temperatures are shown n Fg. 3. The resdent leaves the house at 9 AM and returns at 5 PM, so there s no ndcated desred value n that perod. The pool pump should be operated at most 6 hours a day. The pump can be run n two shftable 3-hour perods anytme between 8 AMand10PM. The smulaton platform wll generate a strategy for how the frst three DER should be controlled. For each hour of a 24-hour perod, t wll determne (a) the heatng power, (b) whether a 3-hour pool pumpng perod should be started, and (c) the chargng or dschargng rates of the battery. Except for the baselne case, a strategy wll be derved for each of the scenaros summarzed n Table I. In all cases, the house s under Tme-of-Use rates. The electrcty rates are summarzed n Table II. These are the actual rates n Sydney, Australa (ToU) [11] and South Australa (net feed-n) [12] as of July In the baselne case, the resdent manually controls the DER. He rases the thermostat to 21 C at 6 AM so that by 7 AM, the desred temperature s acheved. He also reduces the settng from 21 C to 18 C at 10 PM, an hour before the desred 11 PM reducton. To ensure that the temperature s wthn the desred range when he arrves at 5 PM, he programs the heater to turn on at 3 PM wth the thermostat set to 21 C. He programs the pool pump tmer so t would run from 9 AM to 3 PM, durng the perod when the PV output s hgh. There are no batteres nstalled, and any energy export s compensated by the retaler at ToU rates. To demonstrate the value of schedulng and to determne f the nstallaton of batteres would be a sound nvestment, we used the smulaton platform to create a DER operaton strategy for Cases 2 to 6. In Cases 2 and 4, only the heater and pool pump are controlled, and net feed-n tarff s avalable n Case 4. The value added by the battery storage s determned n Cases 3 and 5. In Case 6, the value of the pool pumpng servce s reduced from medum to low. Ths case wll demonstrate how a dfferent percepton to the value of a servce could affect ts provson. B. Energy Servce Models We must model the temporal varatons of the demand for and value of the three servces to be provded (must-run, space heatng and pool pumpng) before we can use (2) to generate the schedule. For the must-run servce, the actual energy consumpton shown n Fg. 2 s assgned as the energy equvalent of that servce, that s, U ES,must-run (t) =P e,must-run (t). (6) The demand for the space heatng servce s descrbed by the desred hourly temperature shown n Fg. 3. The energy equvalent of the space heatng servce s the thermal energy content of the ndoor ar when t s at the desred temperature. Ths thermal energy s equal to the heatng load of the buldng. The heatng load s equal to the heat losses through the buldng enclosure and external ar nfltraton [13]. Infltraton s gnored, so the heatng load s equal to where Q R θ des θ out Q(t) = 1 R (θ des(t) θ out (t)) = U ES,heat (t) heatng load = energy equvalent of space heatng servce, thermal resstance of the buldng shell =16C /kw, desred temperature ( C), outdoor temperature ( C). We assumed that the heatng servce s delvered f the actual temperature s wthn 1 C from the desred temperature. The (7)

5 5 TABLE III MONETARY VALUE EQUIVALENT OF THE PERCEIVED VALUE OF SERVICES Perceved value of servce Monetary value ($/kwh) Hgh 0.60 Medum 0.20 Low 0.08 No value 0.00 Expense actual ndoor temperature, θ n, may be solved by usng θ n n (7) and combnng the resultng equaton wth (8) to get (9). (8) relates the heat ntroduced to the ar to the change n temperature. C dθ n(t) = P heat (t) Q(t) (8) dt C dθ n(t) = P heat (t) 1 dt R (θ n(t) θ out (t)) (9) In (8) and (9), C s the heat capacty of ndoor ar = kwh/c, and P heat s the heatng power. The dscrete-tme equvalent of (9) usng 1-hour tme-steps s θ n (t +1)=θ n (t)e Δ/τ + RP heat (t)(1 e Δ/τ ) (10) + θ out (t)(1 e Δ/τ ) where Δ = 1 hour and τ = RC. We assumed that the heater energy consumpton s entrely converted to heat so P e,heat (t) =P heat (t). (11) The pool pump should run at most 6 hours a day, anytme from 8 AM to 10 PM and may be run as two shftable 3- hour perods. The actual energy consumpton of the pump s assgned as the energy equvalent of the pool pumpng servce, or U ES,pool (t) =P e,pool (t). (12) Value s assgned to the energy equvalent of all energy servces every hour. The possble values and ther monetary equvalents are lsted n Table III. The monetary values assgned to each kwh of the energy equvalent are chosen arbtrarly but are loosely based on the electrcty tarffs shown n Table II. That s, the value of mportant servces s $0.60 per kwh of the energy equvalent, hgher than the peak cost of electrcty. For medum- and low-valued servces, the monetary values are between the peak and shoulder, and shoulder and off-peak rates respectvely. These assgnments mply that medum-valued servces may not be delvered durng peak perods whle low-valued servces can only be delvered durng the off-peak. The hourly values of the energy servces are shown n Fg. 4. The must-run servce should be delvered so t has a hgh value at all tmes. From 9 AM to 5 PM, the resdent does not care about the temperature nsde the house because he s not present. Therefore, no value s assgned to the energy equvalent of space heatng durng ths perod. He also puts more value to havng the room temperature near the desred value durng the wakng hours than durng the sleepng hours. Fg. 4. Value of the energy equvalent of the servces. The pump could only run from 8 AM to 10 PM so medum value s assgned durng ths perod. In Case 6, the value of the pool pumpng servce durng ths perod s reduced to low. To prevent the pump from runnng from 10 PM to 8 AM, an expense value s assgned to the pumpng servce durng that perod. Ths mples that actually runnng the pump durng the nght s a negatve beneft (or a cost). C. Energy Servce Provson Smulaton The objectve of the smulaton platform s to create a DER strategy x that would maxmze the net value of the mustrun, space heatng and pool pumpng servces. The smulaton platform should fnd x that maxmzes λ ES,must-run (t)u ES,must-run (t) T +λ ES,heat (t)u ES,heat (t, x heat ) (13) +λ t=1 ES,pool (t)u ES,pool (t, x pool ) λ e (t)p e (t, x) where x =[xbattery x heat x pool ], x battery hourly battery chargng/dschargng rate, x heat hourly heatng power of heater, x pool pool pump startng tmes and state =[Starttme 1 Starttme 2 State 1 State 2 ]. The mathematcal optmzaton problem has the followng constrants: 1) The energy stored n the batteres should be wthn 20% to 100% of the capacty. 2) The chargng and dschargng rates should not exceed the maxmum values. 3) The heatng power should be non-negatve and should not exceed the maxmum value. 4) The pumpng perods should not overlap. For the space heatng servce, the room temperature computed usng (10) should be wthn 1 C from the desred temperature. If the room temperature s not wthn that range at tme t, thevalueofλ ES,heat (t) n (13) s set to zero. In the pool pumpng schedule x pool, State = 1 f the th 3-hour pumpng perod wll run startng at Starttme, otherwse, State = 0. The energy consumed by the pool pump P e,pool can be easly derved from x pool. The total electrcty mported from the grd P e (t, x) s computed by addng the battery chargng or dschargng power

6 6 TABLE IV SUMMARY OF RESULTS Case number Battery storage no no yes no yes no Feed-n no no no yes yes no Cost, $ Net mport, kwh Total export, kwh Peak demand, kw Pump hours to the energy requred to delver all servces, less the output power of the PV: P e (t, x) =P e,battery (t, x battery )+P e,must-run (t) + P e,pool (t, x pool )+P e,heat (t, x heat ) (14) P e,p V (t). The DER operaton strategy x s solved usng hybrd PSO. Real-valued PSO s used to solve for the battery chargng/dschargng schedule, heatng power and startng tmes of the pool pumpng perods. The start tmes of the pumpng servce are dscretzed by roundng them to the nearest hour. Bnary PSO s used to determne the state of the pool pump (runnng or not) correspondng to the computed startng tme. The partcles are randomly ntalzed and the constrants are handled usng a repar algorthm [14], that s, the coordnates of partcles volatng the constrants are corrected. For Cases 2 to 6, 10 smulatons were executed and the best soluton was chosen. Each smulaton used 100 partcles and 200 evolutons. The parameters used are ω =0.7 and c 1 = c 2 = 1.4 for PSO, and V max = 5.0,ω = 1.0, and c 1 = c 2 =7.5 for BPSO. Fg. 5. Case 1: Manual control of DER, no battery storage. D. Smulaton Results and Analyss The smulaton results are shown n Fgs and summarzed n Table IV. The average smulaton tme s 7.4 seconds, usng Matlab R2008b, on a 2.0 GHz Intel Pentum Dual Core CPU. In the baselne case (Case 1), the heater and pool pump are manually controlled and t results n a total electrcty cost of $4.40 for the day under study. From 10 AM to 3 PM, the PV output exceeds the demand so the excess s exported to the grd. Snce the total exported energy of 2.0 kwh s compensated only at ToU rates, the credt s not enough to sgnfcantly reduce the total cost. The cost of consumpton s reduced by 18% when the heater and pump are scheduled (Case 2). The operaton strateges are to run the pool pump an hour earler, and preheat the house usng grd and PV energy durng the shoulder perod and PV energy only durng the peak perod. The house s heated up to 24 C and has cooled down to 22 C by the tme the resdent arrves. The consequences of these strateges are the zero energy export over the entre perod and the zero grd mport durng the frst 2 hours of the peak perod. The resdent s comfortable as long as the ndoor temperature s at most 1 C from the desred value, therefore the heater s operated such that the temperature s at the cooler level of Fg. 6. Case 2: DER are scheduled, no battery storage. the comfortable range n some hours. Ths resulted n a lower energy consumpton of the heater. Ths heatng strategy can also be observed n the succeedng cases. The operaton of the heater n Case 3 s almost the same as that n Case 2, causng a smlar temperature profle. The pool pump s also operated from 8 AM to 2 PM. The strategy for the battery s to partally charge t durng the mornng off-peak, and use some of the PV output to charge t fully. The stored energy s then dscharged durng the peak perod to dsplace some grd energy. The result s zero grd mport durng the frst 3 hours of the peak perod. The value added by the battery s not mpressve, however, wth the projected ncrease n retal energy prces due to the mplementaton of emsson reducton schemes, costs assocated wth the achevement of renewable energy targets, and ncreasng network costs [15], the resdent may consder nstallng battery storage after thorough analyss. In Australa, for example, electrcty retal prces are projected to ncrease from $80/MWh n 2010 to $150/MWh n 2020 f

7 7 Fg. 7. Case 3: DER are scheduled, wth battery storage. Fg. 9. Case 5: DER are scheduled, wth battery storage, exported energy s remunerated at hgher rates. Fg. 8. Case 4: DER are scheduled, no battery storage, exported energy s remunerated at hgher rates. the Carbon Polluton Reducton Scheme wll start n 2010 [16]. If net feed-n tarff s avalable (Case 4), the strategy s to maxmze grd export whle delverng only the mportant servces. Energy s exported from 9 AM to 3 PM, and the heater s not operated durng ths perod. The PV output n ths perod s also exported to the grd. For the heatng servce, the room s heated above the desred temperature at 9 AM so that the energy needed to preheat the room from 3 to 5 PM s mnmzed. The pool pump s not operated because the beneft derved from runnng t s smaller compared to the compensaton f the energy that would run t s exported. The 7.8 kwh of exported energy has reduced the electrcty bll to $0.84. If battery storage s nstalled (Case 5), the operaton strategy s to charge t durng the mornng off-peak, and release the stored energy durng the energy export perod. The energy export ncreased to 9.5 kwh and the electrcty bll s reduced to $0.21. In ths case, t s potentally more plausble to nstall battery storage because the value t brngs to the resdent s hgher. The heatng power s dentcal to that n Case 4, and the pool pump s also not operated. In Case 6, the reduced value of the pool pumpng servce has resulted n the reducton of the number of hours of whch Fg. 10. Case 6: DER are scheduled, no battery storage, value of pool pumpng servce s reduced from medum to low. t s run. Only 3 hours of the desred 6 hours of pool pumpng s scheduled. Because of the PV output, the resdent was able to beneft from the pumpng servce although ts value s lower than the prevalng electrcty rate. The case study was able to demonstrate that the net beneft derved from energy servces may be mproved by schedulng the operaton of avalable DER. The smulaton platform was able to reduce the cost of consumpton by takng advantage of the heat storage capablty of ar: the house could be preheated when energy rates are low so the space heatng servce requred at a later tme could be provded. It was able to take advantage of the net feed-n rates by maxmzng the amount of energy export. It was also able to determne f the pump should operate and at what tmes based on the value assgned to the pool pumpng servce and the potental cost of ts provson. The pumpng servce s entrely postponed when feed-n tarff s avalable, and only part of t s delvered when ts value s reduced. In two cases, t was able to quantfy the beneft f battery storage s nstalled. VI. CONCLUSION The tradtonal method of delverng energy servces may be mproved by recognzng that dfferent energy servces have

8 8 dfferent values to the user. The assgnment of dfferent levels of beneft to dfferent energy servces has transformed energy servce provson from cost mnmzaton to net beneft maxmzaton. Ths approach tends to prortze the provson of mportant or hgh-valued servces and opens up the possblty of postponng or cancellaton of low-valued servces. We have demonstrated that by usng ths approach, the provson of servces may be economcally mproved. Ths approach may be adopted when mplementng drect load control demandsde management programs. In ths paper, we demonstrated that the proposed energy servce modelng technque can capture and represent temporal varatons of ts demand and value. By puttng value to the energy that realzes an energy servce, we were able to dfferentate t from the actual energy consumpton of the end-use equpment that delvers the servce. The dstncton between the energy that realzes the servce and the actual energy consumpton s mportant because the presence of passve storage mples that there could be temporal msmatch between them. That s, energy consumpton could occur ahead of the actual servce utlzaton. We used the modelng technque wth a novel energy servce smulaton platform to mprove servces delvery by maxmzng the net beneft due to ther provson. The smulaton platform was able to maxmze the value of the requred servces whle mnmzng the cost of energy consumpton by proposng a strategy, or a schedule, for how avalable DER should be operated. The smulaton platform takes advantage of the temporal msmatch between energy consumpton and the energy that realzes a servce, the avalablty of flexble servces and actve storage optons, and the temporal varaton of the cost of electrcty. In the presented smart home case study, t was able to suggest effectve strateges under dfferent tarff schemes and dfferent values assgned to servces. It was able to schedule the DER to reduce the cost of consumpton, and to maxmze energy export when net feed-n tarff s avalable. It was able to postpone low-valued servces when the cost of provson exceeds the beneft from havng the servce. The creaton of the strategy s a non-lnear, non-convex and non-contnuous mathematcal optmzaton problem. The chosen optmzaton tool, partcle swarm optmzaton, was able to generate effectve strateges wthn short computaton tmes. REFERENCES [1] R. Haas, N. Nakcenovc, A. Ajanovc, T. Faber, L. Kranz, A. Muller and G. Resch, Towards sustanablty of energy systems: A prmer on how to apply the concept of energy servces to dentfy necessary trends and polces, Energy Polcy, vol. 36, no. 11, pp , Nov [2] H. Outhred, Electrcty ndustry restructurng n Australa: underlyng prncples and experence to date, n Proc. 40th Annual Hawa Internatonal Conference on System Scences, 2007, p [3] A. Lovns, E. Datta, T. Feler, K. Rabago, J. Swsher, A. Lehmann and K. Wcker, Small s Proftable: The Hdden Economc Benefts of Makng Electrcal Resources the Rght Sze. Snowmass, CO: Rocky Mountan Insttute, [4] S. Abras, S. Pesty, S. Plox and M. Jacomno, An antcpaton mechansm for power management n a smart home usng mult-agent systems, presented at the 3rd Internatonal Conference on Informaton and Communcaton Technologes: From Theory to Applcatons, Aprl [5] C. Marnay, G. Venkataramanan, M. Stadler, A. Sddqu, R. Frestone and B. Chandran, Optmal technology selecton and operaton of commercal-buldng mcrogrds, IEEE Trans. Power Systems, vol. 23, no. 3, pp , Aug [6] J. Kennedy and R. Eberhart, Partcle swarm optmzaton, n Proc IEEE Internatonal Conference on Neural Networks, vol. 4, pp [7] J. Kennedy and R. Eberhart, A dscrete bnary verson of the partcle swarm algorthm, n Proc IEEE Internatonal Conference on Systems, Man and Cybernetcs, vol. 5, pp [8] K. T. Chaturved, M. Pandt, and L. Srvastava, Self-organzng herarchcal partcle swarm optmzaton for non-convex economc dspatch, IEEE Trans. Power Systems, vol. 23, no. 3, pp , Aug [9] T. O. Tng, M. V. C. Rao, and C. K. Loo, A novel approach for unt commtment va an effectve hybrd partcle swarm optmzaton, IEEE Trans. Power Systems, vol. 21, no. 1, pp , Feb [10] P.-H. Chen, Pumped-storage schedulng usng evolutonary partcle swarm optmzaton, IEEE Trans. Energy Converson, vol. 23, no. 1, pp , Mar [11] Energy Australa NSW [Onlne]. Avalable: energy/ea.nsf/ Content/NSW+home. [12] Tacklng Clmate Change n South Australa, South Australa Solar Feedn Scheme [Onlne]. Avalable: ndex.php?page= feed-n-scheme [13] H. Sauer, R. Howell, and W. Coad, Prncples of Heatng, Ventlatng and Ar Condtonng. Atlanta, GA: ASHRAE, [14] C. A. Coello, Theoretcal and numercal constrant-handlng technques used wth evolutonary algorthms: a survey of the state of the art, n Computer Methods n Appled Mechancs and Engneerng, vol. 191, no , 4 January 2002, pp [15] The Australan Energy Market Commsson, Survey of evdence on the mplcatons of clmate change polces for energy markets. Sydney, Australa, December [16] McLennan Magasank Assocates, Impacts of the carbon polluton reducton scheme on Australa s electrcty markets, Australa Federal Treasury, Canberra, Australa, December Mchael Angelo Pedrasa receved hs B.S. and M.S. Electrcal Engneerng degrees at the Unversty of the Phlppnes and s currently sttng hs Ph.D. at the Unversty of New South Wales n Sydney, Australa. Hs studes at the UNSW are supported by the Unversty of the Phlppnes (UP-DSF) and the Phlppnes Department of Scence and Technology (DOST-ERDT). Hs research nterest are power system optmzaton and ntegraton of dstrbuted energy resources to electrc power systems. Ted Spooner receved hs BE and ME degrees from the Unversty of New South Wales n 1970 and 1973 and has been a senor lecturer at The Unversty of New South Wales n the School of Electrcal Engneerng and Telecommuncatons snce Hs research nterests are n renewable energy applcatons and power electroncs. He was project leader for Australa s renewable energy systems testng laboratory now known as RESLab. He s currently a char of Australan Standards Commttee responsble for renewable energy systems. He s also the Australan representatve on the Internatonal Electrotechncal Commsson s (IEC) techncal commttee TC82 for Photovoltacs workng n the area of systems. Dr. Ian MacGll s a Senor Lecturer n the School of Electrcal Engneerng and Telecommuncatons at the Unversty of New South Wales, and Jont Drector for the Unversty s nterdscplnary Centre for Energy and Envronmental Markets. Ian s teachng and research nterests nclude electrcty ndustry restructurng, sustanable energy technologes wth a partcular focus on dstrbuted resources and energy polcy.