Automated Price and Demand Response Demonstration for Large Customers in New York City using OpenADR

Transcription

1 1 Auomaed Price and Demand Response Demonsraion for Large Cusomers in New York Ciy using OpenADR Joyce Jihyun Kim Research Assisan Lawrence Berkeley Naional Laboraory PhD Suden Universiy of California, Berkeley Berkeley, CA Rongxin Yin Research Assisan Lawrence Berkeley Naional Laboraory PhD Suden Universiy of California, Berkeley Berkeley, CA Sila Kiliccoe Research Scienis Lawrence Berkeley Naional Laboraory Berkeley, CA Absrac Open Auomaed Demand Response (OpenADR), an XML-based informaion exchange model, is used o faciliae coninuous price-responsive operaion and demand response paricipaion for large commercial buildings in New York who are subjec o he defaul day-ahead hourly pricing. We summarize he exising demand response programs in New York and discuss OpenADR communicaion, prioriizaion of demand response signals, and conrol mehods. Building energy simulaion models are developed and field ess are conduced o evaluae coninuous energy managemen and demand response capabiliies of wo commercial buildings in New York Ciy. Preliminary resuls reveal ha providing machine-readable prices o commercial buildings can faciliae boh demand response paricipaion and coninuous energy cos savings. Hence, effors should be made o develop more sophisicaed algorihms for building conrol sysems o minimize cusomer's uiliy bill based on price and reliabiliy informaion from he elecriciy grid. Index Terms Price response, demand response, dynamic pricing, real-ime pricing, auomaed conrol, energy managemen, load managemen, load shedding, load forecasing, dynamic response. I. INTRODUCTION In order o ensure reliable and affordable elecriciy, he flexibiliy of demand-side resources o respond o he grid reliabiliy requess and wholesale marke condiions is required (Borensein e al., 22; Hirs e al., 21). Large cusomers are ofen he immediae arge for demand response (DR) because hey are major conribuors o peak demand for elecriciy and hey are equipped wih cenralized building managemen sysem (BMS) o adjus elecric loads. However, much of DR is sill manual because mos BMS do no have a buil-in capabiliy o suppor DR paricipaion (i.e., preprogrammed DR sraegies). Hence, providing frequen DR is a dauning ask for many cusomers, which undermines he full poenial of demand-side managemen among large cusomers. The cusomer's abiliy o perform DR can be significanly improved by enabling auomaed demand response (Auo-DR) (Piee e al., 25). By eliminaing he human in he loop, Auo-DR eases he operaional burden o provide frequen DR and reduces he cos associaed wih monioring and responding. I has been argued ha Auo-DR and enabling echnologies would play a criical role in creaing price-responsive load (Goldman e al., 22). The applicaion of Auo-DR o dynamic pricing has araced aenion since several saes and uiliies deployed full-scale dynamic pricing programs. To faciliae price and reliabiliy informaion exchange among various sakeholders in he elecric grid, Lawrence Berkeley Naional Laboraory (LBNL) developed Open Auomaed Demand Response (OpenADR) (Piee e al., 29). OpenADR is an open and ineroperable sandard ha uses an XML (exensible Markup Language) based informaion exchange model o send DR requess and pricing signals from a server (i.e., uiliy, sysem operaor, aggregaor) o a clien (i.e., cusomer sie). Ghaikar e al. (21) discussed he use of OpenADR for price response presening sraegies o operaionalize dynamic pricing signals ino load conrol modes. Undersanding Auo-DR poenial in commercial buildings requires examining he capabiliies of exising conrol sysems and communicaion proocols. A cenralized BMS can inegrae individual conrol sysems/devices o provide greaer conrollabiliy and efficiency o building managers. Open communicaion proocols allow ineroperabiliy beween differen vendors sysems/devices. Therefore, as more buildings adop he cenralized BMS and open communicaion proocols, he cos and ime o enable Auo-DR will decrease. According o he Energy Informaion Adminisraion s 23 Commercial Buildings Energy Consumpion Survey (CBECS), 7% of commercial buildings have BMS which represens 31% of he naional floor space (Kiliccoe and Piee, 26). This percenage has probably increased by now since more buildings are buil wih a BMS or rerofied wih i. The recen revisions of building energy efficiency sandards now include DR in heir specificaions. Examples are he Auomaed Demand Response secion in California s Tile and he pilo demand response credi in U.S. Green

2 2 Building Council s LEED (Kiliccoe e al., 212). Sandards like hese may encourage conrol vendors o insall buil-in DR capabiliies in heir BMS. In such case, he effors o cusomize DR sraegies will be significan reduced. II. OBJECTIVES AND SIGNIFICANCE This paper repors on he laes effors o auomae cusomer response o price and reliabiliy signals for large commercial buildings in New York Ciy (NYC). I is significan in wo ways. Firs, he paper raises he awareness o key cos challenges for commercial cusomers who are subjec o he defaul day-ahead hourly pricing in New York Sae (NYS) and provides a pracical soluion ha he faciliy can adop for coninuous energy managemen. Second, i provides a framework o develop and es conrol algorihms ha opimize energy use and cos in large commercial buildings. A noe on erminology: dynamic pricing is referred o energy prices ha are available o cusomers in regular inervals no more han a day in advance. In NYS, wholesale elecriciy prices are se day-ahead, hour-ahead or in real-ime by he New York Independen Sysem Operaor (NYISO) wholesale markes. In his paper, we focus on day-ahead hourly pricing, which is he defaul ariff for large cusomers in NYS. The res of his paper is organized as follows. In Secion II, we summarize he exising demand response programs in NYS. In Secion III, we discuss OpenADR communicaion archiecure, prioriizaion of price and reliabiliy signals, and conrol mehods for large commercial buildings ha paricipaed in our demonsraion projec. In Secion IV, he applicaion of Auo-DR under MHP is explored hrough energy simulaion and field ess of wo demonsraion buildings in NYC. Preliminary findings from he demonsraion projec are discussed in Secion V. Lasly, in Secion VI, we conclude wih suggesions for fuure research direcions. III. DEMAND RESPONSE IN NEW YORK STATE In NYS, DR is mainly promoed hrough reliabiliy-based programs and dynamic pricing. There are a number of reliabiliy-based programs offered o cusomers by NYISO and uiliies, commonly referred o as DR programs. Since he iniial offering in 21, NYISO's DR program regisraion has grown seadily. In 21, here were approximaely 3 paricipans enrolled in reliabiliy-based programs such as Special Case Resource/Emergency Demand Response Program (SCR/EDRP) wih he oal paricipaing load of 75 MW. By 211, NYISO had a oal of 5,87 paricipans for he SCR/EDRP program providing 2,173 MW of curailable load (Paon e al., 212). Mos cusomers in NYS are enrolled in DR programs hrough Curailmen Service Providers (CSPs). CSPs manage a porfolio of DR resources and aggregae demand reducion o maximize DR compensaion. They help cusomers assess he DR poenial and develop load curailmen sraegies. Conracing a CSP ypically means ha cusomers mee he minimum shed requiremens during he DR es/even and receive DR compensaion in reurn. Dynamic pricing is offered o induce price-responsive load, flaening sysem demand by applying high prices during peak periods and low prices during off-peak periods. Pacific Gas and Elecric (PG&E) Criical Peak Pricing and Souhern California Edison's (SCE) Real-Time Pricing are examples of dynamic pricing. In 25, he Sae of New York Public Service Commission ordered uiliies o provide day-ahead hourly pricing as he defaul ariff o non-residenial cusomers whose demand is roughly over 5 kw (NYPSC, 25). This ariff is also known as Mandaory Hourly Pricing (MHP). Alhough uiliies offer MHP as he defaul service o large cusomers, NYS s reail access policy allow cusomers o purchase heir energy from any reail hird pary supplier as an alernaive o he uiliy. Hence, MHP is no sricly mandaory. As of 211, only 15% of he MHP-eligible cusomers were enrolled in MHP and he res (85%) were reail access cusomers (Joskon, 212). The problem of his is ha fla price reail conracs ha hedge agains price flucuaions and herefore do a poor job of reflecing wholesale near-erm marke prices (day-ahead, hour-ahead and real-ime) (Goldman e al., 22). They also end o be expensive due o he inheren risk of offering a less variable rae. When reail prices are no ied o wholesale marke variaions, hey can inefficienly increase he level of peak demand by underpricing elecriciy and can also discourage increased demand during off-peak hours by overpricing i (Joskon e al., 212). Therefore, swiching from MHP o a reail rae can hamper he developmen of price-responsive load. The primary barriers o he adopion of MHP are idenified as he insufficien resources (boh labor and equipmen) o monior hourly prices and inflexible labor schedule (KEMA, 212). This is no surprising since mos cusomers rely on manual approach o provide DR. Providing DR manually is a resource-inensive process. If cusomers are no capable of monioring and responding o hourly price variaions, hey are likely o choose a more convenional rae such as a fixed rae. Moreover, cusomers have no ye found a compelling business case o say wih MHP. Many cusomers presume ha he cos of monioring and auomaion ouweighs he poenial savings. Even if he savings exis under day-ahead hourly prices, hey are no as obvious and repeaable as he DR paymens because he savings are a funcion of he marke and are embedded in he oal elecriciy bill. Therefore, in order o increase he adopion of MHP and dynamic-price reail conracs, we no only need o make he prices broadly available and auomae cusomers price response bu also effecively communicae poenial savings o cusomers and ways o achieve i. In NYC, MHP is billed under Rider M: Day-Ahead Hourly Pricing from Con Edison where he cos of energy is calculaed based on he cusomer's acual hourly energy usage muliplied by NYISO's day-ahead zonal locaional based marginal price (LBMP) (Con Edison). In addiion, cusomers pay demand charge imposed on he maximum demand of each

3 3 NYISO/Uiliy CSP Day-Ahead Hourly Price via web scraping DR Tes/Even Noificaion via Sandard Paricipan Inerface Secure Inerne Operaion Modes via OpenADR Inerval Meer Daa via OpenADR OpenADR Clien BMS OpenADR Server Figure 1. OpenADR communicaion archiecure for he New York Ciy demonsraion projec. Faciliy billing cycle. The demand charge varies depending on he Time-of-Day (TOD) and season (Con Edison). Based on our billing analysis, he demand charge accouns for 19% - 55% of he cusomer's elecric bill depending on ime of use. To reduce he oal elecric bill, cusomers need o conrol heir elecric consumpion according o he hourly price variaions and limi he building's peak demand during expensive hours. IV. APPROACH Since Ocober 211, he Demand Response Research Cener (DRRC) a LBNL and New York Sae Energy Research and Developmen Auhoriy (NYSERDA) have conduced a demonsraion projec enabling auomaed DR and price response in large commercial buildings locaed in NYC using OpenADR. The recruimen effors were focused on large commercial buildings in NYC. Preferences were given o he buildings ha represened he ypical consrucion of commercial buildings in NYC and previously paricipaed in DR programs. Four faciliies were recruied for he demonsraion projec. All of hem previously paricipaed in one or more DR programs hrough CSPs providing manual conrol of HVAC, lighing, and oher sysems during DR evens. Some also provided manual peak load managemen. Bu because DR was manually performed, he buildings did i only on ho days or DR even days. They did no do any price response prior o he demonsraion projec. The cusomer s paricipaion in his projec was driven by he moivaion o auomae he conrol sraegies ha hey used for DR evens. Auomaion allows building operaors o auomaically respond o DR evens wihou having o manually acivae individual conrol sraegies. All faciliies are on a reail rae and are no enrolled in MHP. In his paper, we se ou o invesigae a hypoheical scenario wherein he demonsraion buildings purchase elecriciy under he MHP ariff and herefore have o respond o he variabiliy of day-ahead hourly prices. A. OpenADR Communicaion Model To auomae price and demand response using OpenADR, hree basic echnologies are required: an OpenADR server o receive reliabiliy and price signals; an OpenADR clien a he faciliy o receive he reliabiliy and price signals; and a BMS o program and acivae conrol sraegies (Wikler e al., 28). We used OpenADR version 1. for he demonsraion projec. OpenADR version 2., available currenly, was no released a he ime of he projec implemenaion. Figure 1 shows he OpenADR communicaion archiecure for he demonsraion projec. Day-ahead hourly prices are obained from NYISO's websie and DR es/even noificaions are received from he cusomer's CSP. Based on he price and reliabiliy signals, an operaion mode is deermined for each hour of he following day. Once he signals are processed, he OpenADR server sends weny-four hourly prices and corresponding operaion modes o he faciliy o acivae preprogrammed conrol sraegies for nex day. The OpenADR server also logs he building s 15-minue meer daa via kyz pulses and moniors he elecric demand hroughou he day. All informaion exchange is accomplished hrough a secure Inerne connecion wih 128-bi Secure Sockes Layer (SSL) encrypion. The faciliies can op-ou of Auo-DR a any ime via he OpenADR server s clien inerface accessible over he Inerne. The op-ou can be scheduled in advance for a specified period which can be a few hours or days depending on he faciliy's operaional needs. B. Prioriizaion of DR signals Three ypes of DR signals are issued: 1) reliabiliy, 2) demand limiing, and 3) day-ahead hourly price signals. These signals are prioriized differenly depending on he nex day's DR es/even saus as described in Figure 2. For non-dr es/even days, he faciliies respond o price signals unil he building's elecric demand exceeds a pre-se hreshold, in which case, he OpenADR server would swich he signal ype from price o demand limiing. When a DR es/even is issued, he faciliies only respond o reliabiliy signals during he DR es/even period. If he building s demand exceeds a pre-se hreshold, demand limiing signals would be issued o reduce he demand. We decided o urn off price signals during DR es/even days o preven curailmen aciviies affecing he cusomer baseline. This is applicable o cusomers who use morning adjusmens o calculae heir energy compensaion (i.e., he NYISO's Weaher-Sensiive Cusomer Baseline) (NYISO). The reliabiliy, demand limiing, and price signals are mapped ino four levels of operaion mode ha are ied o preprogrammed DR sraegies via he faciliy s BMS. OpenADR version 1. suppors following operaion modes:

4 4 Normal, Moderae, High, and Special (which we call Criical for he demonsraion projec). Normal indicaes he normal operaion riggered when he energy price is accepable and here is no DR es/even issued. Moderae indicaes he firs level of load shed riggered when he energy price is moderaely expensive. High indicaes he inermediae level of load shed riggered when he energy price is highly expensive. High is also riggered when elecric demand exceeds he pre-se hreshold. Criical indicaes he highes level of load shed riggered when he DR es/even is issued and elecric loads need o be curailed a he maximum reducion level. C. Auo-DR Conrol Logic Using OpenADR, he faciliies can conrol elecriciy usage and cos by responding o boh price and demand limiing signals. The Auo-DR inelligence can reside 1) wihin he faciliy or 2) in he cloud (i.e., he OpenADR server). While he firs opion has he advanage of unresriced building daa rerieval and direc conrol over he building sysems/devices, i requires on-sie developmen and operaion of Auo-DR sofware. Locaing he inelligence in he cloud has he advanage of flexible energy monioring and DR managemen. Cloud compuing also offers remoe daa sorage and processing capabiliies. However, he availabiliy of building conrol and real-ime feedback may be resriced if he building does no wan o open heir nework firewall. Moreover, building managers may be opposed o he idea of heir building being conrolled by remoe inelligence. For our demonsraion projec, we locaed he Auo-DR inelligence wihin he faciliies o obain full access o building daa and avoid poenial hreas o he building nework securiy. If he building daa rerieval and direc conrol over he building sysems/devices are available, he cusomer's energy cos for a given day can be minimized hrough load opimizaion in response o NYISO's day-ahead zonal LBMP ( C ), as expressed in (1). k min C 1 g( u, x, w ) (1) Opimal elecriciy usage (kwh) is deermined by he objecive funcion ( g ) based on following variables: u is he inpu consrains for conrol sraegies; x is he building sysem saes (i.e., HVAC se poins, operaion schedules); and w is he weaher (i.e., ouside air emperaure, relaive Day Signal Type Operaion Mode DR Tes/Even Non DR Tes/Even Reliabiliy Limiing Demand Day-Ahead Hourly Prices Figure 2. OpenADR signal prioriizaion. Criical High High, Moderae humidiy). represens he ime inerval and k indicaes he oal number of ime inervals in a day. The demand charge can be minimized by reducing he building s peak demand during a billing cycle, as expressed in (2). min max h ( ui, xi, wi ) (2) i1,..., N h represens he elecric load (kw) a a given ime inerval ( i ) and N indicaes he oal number of ime inervals in a billing cycle. D. Open-Loop and Closed-Loop Conrol There are wo ypes of conrols ha can be used for Auo- DR: open-loop and closed-loop (Kiliccoe e al., 26). In open-loop conrol, he OpenADR server sends DR signals o he faciliy bu does no use real-ime feedback o rack he performance arge deermined by he objecive funcions in (1) and (2). Closed-loop conrol, on he oher hand, uses he real-ime feedback o reach he performance arge. As such, i is more advanageous if he DR performance has o be guaraneed. However, i requires more granulariy of conrol over he building sysems/devices and real-ime decision making capabiliies. For he demonsraion projec, open-loop conrol is used o respond o price and reliabiliy signals and closed-loop conrol is used o provide demand limiing. The feedback is provided via elecric meer readings o generae demand limiing signals and calculae load predicion. To esimae DR performance under differen operaion mode, we simulaed whole building energy usage using EnergyPlus. EnergyPlus is an energy analysis and hermal load simulaion sofware which allows calculaing heaing and cooling loads based on building geomery, building envelope, inernal loads, HVAC sysems, and weaher (EnergyPlus, DOE). Based on he energy simulaion resuls, we seleced conrol sraegies and inpus for each operaion mode ha would produce he arge load reducion and hermal comfor level. V. APPLICATION Implemening Auo-DR is a muli-sep process. Firs, we need o undersand he building's curren and hisoric elecric use paerns and evaluae building sysems, DR capabiliies, and operaional consrains (Mahieu e al., 211). Then, we idenify DR opporuniies and develop conrol sraegies for each faciliy. Finally, proposed conrol sraegies need o be esed and modified o improve he DR oucome. In his secion, we explain he process of developing conrol sraegies for wo of he paricipaing buildings from our demonsraion projec. A. Sie Descripion The firs building, locaed in NYC, is a 32-sorey office building wih a glass curain-wall exending he full heigh of he building (here in called "office building"). The office building has a oal condiioned floor area of 13, m 2 (1.4 Million f 2 ). The building's HVAC consiss of muliple-zone rehea sysems wih consan air volume and air-handling unis (AHUs) conrolled by variable frequency drive (VFD). There

5 5 Faciliy Peak Load (kw) TABLE I LOAD SUMMARY* Peak Load Inensiy (W/m 2 ) Load Facor Annual Consumpion (kwh) Office Bldg 6, ,612, ,15, *Compued for Sep Aug 212, wih 15-minue inerval daa. are hree 1,35-on cenrifugal chillers wih consan speed and one 9-on cenrifugal chiller wih variable speed ha supplies chilled waer o AHUs. Each zone emperaure is conrolled via direc digial conrol (DDC). Currenly, he office building does no have he Global Temperaure Adjusmen (GTA) capabiliies o change zone emperaure sepoins for he enire faciliy (Moegi e al., 27). The faciliy is heaed via Con Edison seam. The building is equipped wih Honeywell's Enerprise Buildings Inegraor for HVAC conrol. Muli-zone conrol is available for lighing hrough relays bu i is no conneced o he BMS. The faciliy is in operaion from 6am o 6pm during weekdays and closes during weekends. The second building is a 14-sorey universiy building also locaed in NYC (herein called "campus building"). The campus building recenly wen hrough a complee renovaion and sysem upgrades and was recenly occupied in Sepember 211. The newly renovaed building has he oal floor space of 11,33 m 2 (122, f 2 ) conaining classrooms, compuer labs, offices, and conference rooms. There are eleven AHUs, each equipped wih VFDs. The building is equipped wih a 4-on chiller supplying chilled waer o AHUs. Heaing is provided wih seam, which is used for AHU rehea, uni heaers, and sairwell heaing. The campus building has an Auomaed Logic Corporaion s WebCTRL sysem used for HVAC conrol. The indoor space is largely li by T5 fluorescen fixures locaed wihin hallways, offices, and he lobby. Office lighing is on moion sensors. The campus building is equipped wih he NexLigh wo-way digial lighing conrol sysem bu his sysem was no used for DR in he pas. There are hree elevaors in he campus building: wo passenger elevaor and one passenger/freigh elevaor. Previously, one of he hree elevaors was shu off during DR evens. The faciliy is open from 7am o 11pm for seven days a week. B. Load Characerisics Approximaely wo years of 15-minue whole building elecric load daa was made available o he projec eam for he office building and he campus building. Table 1 summarizes he daa over one year period (Sep Aug 212). To characerize he behavior of building energy use, we ploed he load profile agains differen ime scales. Firs, weekly elecric demand and consumpion was ploed from January 211 o Augus 212 in Figure 3. Examining hese plos revealed following findings: 1) boh he office and campus buildings had relaively consan minimum demand hroughou he year; 2) he maximum demand was higher in summer han in winer for boh buildings; and 3) maximum demand (kw) varied more significanly from season o season han elecric consumpion (kwh). Nex, he buildings' inerval load was ploed over a one-week period for summer monhs (May o Aug 212) in Figure 4 and for winer monhs (Nov 211 o Feb 212) in Figure 5. The scaer plos reveal following hings. 1) The office building was in use during weekdays while he campus building was in use for seven days a week, confirming he operaion schedule of he wo buildings provided o he projec eam. 2) In boh faciliies, he spikes shown a he beginning of each weekday during summer monhs indicaed precooling aciviies and he sysem overload. For he office building, precooling ypically sared a midnigh and for he campus building, i sared a 7am. The campus building had a sar-up elecric surge during he firs hour of he building operaion which marked he highes Demand (kw) 6, 4, 2, 1/2/211 1/3/211 2/27/211 3/27/211 4/24/211 5/22/211 6/19/211 7/17/211 8/14/211 9/11/211 Office Bldg 1/9/211 11/6/211 12/4/211 1/1/212 1/29/212 2/26/212 3/25/212 4/22/212 5/2/212 6/17/212 7/15/212 8/12/212 8, 6, 4, 2, Energy (kwh) Demand (kw) /2/211 1/3/211 2/27/211 3/27/211 4/24/211 5/22/211 6/19/211 7/17/211 8/14/211 9/11/211 1/9/211 11/6/211 12/4/211 1/1/212 1/29/212 2/26/212 3/25/212 4/22/212 5/2/212 6/17/212 7/15/212 8/12/212 8, 6, 4, 2, Energy (kwh) Consumpion Min Demand Max Demand Avg Demand Figure 3. Demand usage and elecric consumpion from Jan 211 o Aug 212.

6 6 Of f ice Bldg Mon Tue Wed Thu Fri Sa Sun Mon Tue Wed Thu Fri Sa Sun Figure 4. Scaer plo: ime-of-week from May o Aug 212 excluding holidays (Memorial Day and Independence Day). Of f ice Bldg Mon Tue Wed Thu Fri Sa Sun Mon Tue Wed Thu Fri Sa Sun Figure 5. Scaer plo: ime-of-week from Nov 211 o Feb 212 excluding holidays (Veerans Day, Thanksgiving Day, Chrismas Day, New Year s Day, Birhday of Marin Luher King, Jr., and Washingon s Birhday). demand of he day. In summer, saring precooling a 7am would add more loads o he morning ramp-up and increase he demand even higher. 3) Boh buildings showed a wide range of daily demand during summer monhs versus winer monhs while he base load sayed relaively consan hroughou he year. This was more prevalen in he office building han he campus building. Since boh buildings were heaed wih seam, he difference in summer and winer demand was likely o be influenced by he amoun of cooling loads. To undersand he dependence of he building demand on ouside weaher, we ploed he elecric load for occupied hours during weekdays agains oudoor air emperaure and relaive humidiy as shown in Figure 6. From he Naional Climaic Daa Cener, we acquired hourly oudoor air emperaure daa for each faciliy from he neares weaher saion (NOAA). Some of he missing daa were filled in by linear inerpolaion. As seen in Figure 6, boh he office and campus buildings elecric loads were highly sensiive o he ouside air emperaure. However, some of he peak loads shown in he campus building s scaer plo were more influenced by he classroom schedule han ouside weaher. Boh buildings did no show a significan relaionship beween building load and relaive humidiy. C. Demand Limiing and Price Thresholds In order o deermine operaion mode for each hour of he day, cusomers need o esablish he demand and price hresholds o which he selecion of a paricular operaion mode can be based upon. These hresholds can be updaed as frequenly as required (i.e., weekly, quarerly, or yearly). To help cusomers choose he appropriae demand and price hresholds for heir faciliy, we firs evaluaed he buildings' load duraion curves o look for demand reducion opporuniies. Figure 7 shows he one-year load daa (from Sepember 211 o Augus 212) ploed in descending order over he proporion of ime. For he office building, he weekday load duraion curve descended a a gradual slope and here was no unusual peaks observed in he plo. The weekend/holiday curve was much lower han he weekday's since he office building was no in service during weekend/holidays. However, he weekend/holiday load during he op one percen was "peakier" han he res. This was probably caused by nigh flushing and precooling of hermal mass performed during Sunday evenings in preparaion for he nex business day or occasional use of he faciliy over he weekends. For he campus building, he difference beween Temp (F) Temp (F) Figure 6. Scaer plo of load versus emperaure and humidiy. Daa shown are from May o Aug Of f ice Bldg Percen Time (%) Office Bldg All day Weekday Weekend and holiday Relaive Humidiy (%) Percen Time (%) Figure 7. Load duraion curves. Daa shown are from Sep 211 o Aug Relaive Humidiy (%)

7 7 he weekday and he weekend/holiday load duraion curves was small since he building was in operaion for seven days a week. Boh curves showed a significan increase in load during he op one percen of he ime. This behavior was probably caused by he sysem overload experienced during he firs hour of he building operaion. This issue can be resolved by shifing some loads o earlier imes in he morning or laer during he day and limiing demand below he level corresponding o he op one-percen of he ime. Similarly, price hresholds can be esablished by analyzing hourly price disribuion over ime. Figure 8 displays a price duraion curves over he ime period of Sepember Augus 212. We used NYISO's day-ahead LBMP for Zone J: NYC since boh he office building and he campus building were locaed in NYC (NYISO). Day-ahead LBMP did no vary significanly beween weekdays and weekend/holiday and mos of he ime he price sayed below $1 per MWh. Only significan deviaion was seen during he op one percen of he ime where he price increased up o $363 per MWh. The loads corresponding o he op one percen of he ime are concenraed in summer and winer monhs. When ploed agains he ime of day, i was clear ha he expensive hours were eiher cooling hours (mid-day) or heaing hours (morning and evening). Therefore, limiing he building s demand during he op one percen of he ime via Auo-DR can help cusomers reduce energy cos. D. DR sraegies Boh he office and campus buildings currenly paricipae in NYISO's SCR/EDRP hrough separae CSPs. For he LBMP ($/MWh) Figure 8. Price duraion curves. Daa shown are from Sep 211 o Aug 212. Time of Day (hour) Percen Time (%) All day Weekday Weekend and holiday $3/MWh Zone J: New York Ciy $3/MWh > LBMP $2/MWh $2/MWh > LBMP $1/MWh $1/MWh > LBMP $98/MWh Jan Feb Mar Apr May Jun Jul Aug Sep Oc Nov Dec Figure 9. LBMP disribuion agains monh and ime-of-day during he op one percen of he ime from Sep 211 o Aug 212. Faciliy Office Bldg Campus Bldg Operaion Mode TABLE II DR STRATEGIES AND OPERATION MODES Global emperaure adjusmen Precooling Supply fan speed reducion Exhaus fan quaniy reducion Chilled waer emperaure increase Chilled waer pump speed reducion Shuing off chilled waer pumps Chiller quaniy reducion Condenser waer emperaure increase Shuing off condenser waer pumps Turning off lighing in auxiliary space Slow recovery Sequenial equipmen recovery Exended DR conrol Period Criical x x x x x x x x x x x x x High x x x x x x x x x x x Moderae x x x x x x x x Criical x x x x x x x x High x x x x x x x x Moderae x x x x x x NYISO iniiaed DR es/even, he office building have a minimum shed requiremen of 2, kw. The shed requiremen of he campus building has no ye been esablished. To help he faciliies mee heir DR arges, CSPs developed DR sraegies for heir cliens ha were used for previous DR es/evens. Based on he cusomers' exising DR sraegies, we seleced he ones ha could be auomaed and grouped hem ino Criical, High, and Moderae operaion mode, as shown in Table 2. The projec eam added GTA capabiliies o he office building o enhance DR conrol. Auomaing lighing conrol in auxiliary space such as hallways and lobby was discussed bu was pu on hold due o budge consrains. As for elevaors, we recommended ha he faciliies mainain manual conrol over heir elevaors for boh DR and non-dr days. To minimize he pos-dr rebound effecs, Normal operaion mode reurns slowly wih sequenial equipmen recovery. If here is less han one hour lef unil he end of occupancy period, DR is exended o he end of he occupancy period and hen he building reurns o Normal operaion mode. VI. EVALUATING DR PERFORMANCE In his secion, we show how Auo-DR can be performed on a non-dr even day and on a DR even day hrough fieldes resuls and energy simulaion. Firs, we examined he load daa aken from he acual DR even day on June 2, 212 ha he office building paricipaed, as illusraed in Figure 1. The DR even was called beween 2pm and 6pm, during which he minimum 2, kw reducion was expeced in reference o NYISO's Average Coinciden Load (ACL) baseline (NYISO). 1 The office building achieved he reducion arge only during he las wo hours of he even period by acivaing all DR sraegies lised under Criical operaion mode. I experienced a pos-dr rebound effec wih an average spike of 12% from he baseline load over a one hour period. The maximum 1 NYISO's ACL baseline averages cusomer's 2 highes loads of 4 highes sysem load hours excluding hours in which DR evens were previously acivaed.

8 8 Figure 1. Load and price daa of he sample DR even day. 8, 6, 4, 2, 8, 6, 4, 2, DR even day Weaher regression baseline Hour of day NYISO CBL Zonal LBMP Figure 11. Load and price daa of he sample non-dr even day. rebound was recorded as 19% higher han he baseline load. To avoid he pos-dr rebound effecs, we recommended he developmen of DR recovery sraegies for paricipaing buildings. Nex, we compared he load reducion wih wo differen baselines o evaluae cusomer's DR performance: 1) NYISO's Average Cusomer Baseline (CBL) and 2) he weaher regression baseline developed by LBNL (Coughlin e al., 29). 2 NYISO's CBL has a endency o underesimae or overesimae he building's power usage for he days wih unusual weaher condiions. In general, he weaher regression baseline provides a more accurae predicion of weahersensiive loads han NYISO's CBL. As seen in Figure 1, NYISO's CBL underesimaed he baseline load because he DR even day was warmer han previous days. As such, DR paymens would have been smaller if he compensaion was calculaed based on NYISO's CBL insead of he weaher regression baseline. Figure 11 illusraes he office building's response o price signals on a non-dr even day. The load daa were aken from Augus 9, 212, represening a ypical weekday. The building underwen hree hours of Moderae operaion mode from 2pm o 5pm based on he price hresholds se a LBMP $98 for Moderae operaion mode and LBMP $2 for High operaion mode. We used EnergyPlus simulaion o predic he effecs of DR sraegies for Moderae operaion mode and compared he simulaed load o he acual load which was unaffeced by Auo-DR. According o he simulaion resuls, 15 1 Non-DR even day Simulaed load Zonal LBMP Hour of day LBMP ($/MWh) LBMP ($/MWh) he office building can reduce demand up o 7 kwh by implemening DR sraegies lised under Moderae operaion mode for his day. I is noed ha coninuous energy managemen in response o hourly prices can impac he cusomer's DR baseline, poenially reducing DR paymens due o lowered baseline usage. This can make DR programs less aracive o energy efficien cusomers under he day-ahead hourly pricing. However, DR program evens are called only a few days a year and he incenives colleced from DR programs are likely o be small compared o he uiliy savings achieved under day-ahead hourly pricing due o coninuous energy managemen. Hence, as he commercial buildings move owards more dynamic response o prices, he applicabiliy of baseline-based DR paymens should be re-evaluaed. VII. CONCLUSIONS AND FUTURE STUDIES We presened he process of auomaing coninuous energy managemen wih day-ahead hourly prices and demand response for large commercial buildings in New York who were subjec o he defaul MHP ariff. OpenADR version 1. was used o faciliae he communicaion of price and reliabiliy signals. Based on he preliminary findings from he New York demonsraion projec, we concluded ha: 1) price response o day-ahead hourly pricing can be made easier hrough Auo-DR; 2) undersanding cusomer's financial goals, such as reducion in uiliy bills including demand charges, and curailmen requiremens by CSPs was criical in esablishing Auo-DR goals and performance arges; and 3) price and demand response opporuniies were unique o cusomer's elecric load characerisics, conrol capabiliies, and operaional consrains. Fuure sudies include: 1) creaing dynamic opimizaion capabiliies in buildings given he availabiliy of price and DR signals; 2) monioring and evaluaing he effecs of conrol sraegies on load and occupan comfor during operaions; 3) increasing he cusomer's abiliy o modify and change individual conrol sraegies wihin he faciliy; and 4) evaluaing benefis and drawbacks of having Auo-DR inelligence in he cloud versus inside he faciliy. Finally, we recommend a comparaive sudy on cusomer economics beween MHP and reail raes o be conduced and he role of Auo-DR in cos savings o be furher explored. ACKNOWLEDGMENT The work described in his repor was conduced by he Lawrence Berkeley Naional Laboraory and funded by he New York Sae Energy Research and Developmen Auhoriy under he Agreemen No This work was suppored in par by he California Energy Commission (CEC) under Conrac No and by he U.S. Deparmen of Energy (DOE) under Conrac No. DE-AC2-5CH The auhors give special hanks o Duncan Callaway and Anhony Abae for grea advice and feedback. We also hank Con Edison Company for he elecric load daa. 2 NYISO's CBL averages cusomer's five highes of he previous en weekdays excluding holidays and previous DR even days.

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