IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 49, NO. 1, JANUARY/FEBRUARY 2013 1

An Energy Management System for BuildingStructures Using a Multi-Agent Decision-Making

Control MethodologyPeng Zhao, Student Member, IEEE, Siddharth Suryanarayanan, Senior Member, IEEE, and

Marcelo Godoy Simes, Senior Member, IEEE

AbstractBuilding energy management systems (BEMS) mustconsider energy utilization efficiency improvement, energy costreduction, and renewable energy technology utilization in orderto serve local energy loads in building structures with dispersedresources. The distributed management of building energy systemproposed in this paper utilizes a semi-centralized decision-makingmethodology using multi-agent systems for BEMS for electrical,heating, and cooling energy zones with combined heat and powersystem optimizations aimed at improving energy efficiency andreducing energy costs. A case study is presented to demonstratethe validity of the proposed energy management scheme.

Index TermsBuilding energy management systems (BEMS),cyber-physical systems (CPS), distributed generation, multi-agentsystems (MAS), net-zero energy buildings.

I. INTRODUCTION

THE PATH forward for sustainable energy for society is inthe forefront of public interest, and it is a high priorityfor policy makers. In the US, two energy policy acts havebeen passed in the last decade, in 2005, and 2007, whichinclude programs for conservation and energy development,with Grants and tax incentives for both renewable energy andnon-renewable energy [1], [2]. There have been multiple goalsand provisions for the improvement of energy efficiency byincentives for investments in modernization of the energy in-frastructure based on the fact that the cost of generating energyis greater than the costs for savings. The US National Sci-ence Foundation has been supporting the application of cyber-physical systems (CPS), i.e., a contemporary breed of systems

Manuscript received July 31, 2011; revised February 6, 2012; acceptedMay 1, 2012. Paper 2010-IACC-545.R1, presented at the 2010 Industry Ap-plications Society Annual Meeting, Houston, TX, October 37, and approvedfor publication in the IEEE TRANSACTIONS ON INDUSTRY APPLICATIONSby the Industrial Automation and Control Committee of the IEEE IndustryApplications Society. This work was supported by National Science Foun-dation Award 0931748 CPS Cyber-Enabled Efficient Energy Management ofStructures.

P. Zhao is with the Department of Civil, Environmental, and Architec-tural Engineering, University of Colorado, Boulder, CO 80309 USA (e-mail:Peng.Zhao@Colorado.edu).

S. Suryanarayanan is with the Department of Electrical and ComputerEngineering, Colorado State University, Fort Collins, CO 80513-1373 USA(email: sid@colostate.edu).

M. Godoy Simes is with the Department of Electrical Engineering andComputer Science, Colorado School of Mines, Golden, CO 80401 USA(e-mail: mgs@mines.edu).

Digital Object Identifier 10.1109/TIA.2012.2229682

with integrated computational and physical capabilities that caninteract with humans, expanding system capabilities throughcomputation, communication, and control. The work describedin this paper deals with an application of CPS for buildingenergy management via data fusion and analysis for real-timemonitoring, prediction, and control of energy management sys-tems in modern buildings and cyber integration along the linesof contemporary mandates. The primary objective of this paperis the framework and algorithmic aspects of a CPS for futuregeneration building energy management systems (BEMS) anddoes not include aspects of hardware and controller design inthis study.

The US electricity grid is steadily increasing in supply anddemand growth for the next two decades, while the trend intransmission systems indicates lack of investments and gener-ally restrictive regulatory barriers for new transmission lines[3], [4], [18]. Therefore, management and control of distributedenergy systems at the consumer end present a significant avenuefor investigations and improvements [5]. Due to the limitedcapabilities of centralized computing on large-scale distributedsystems, decentralized or semi-centralized decision-makingprocess is viewed as a suitable option for employment indistributed energy systems. A possible approach to the solutionof managing distributed energy systems is by the utilization ofmulti-agent systems (MAS) [19].

MAS can be considered an aggregation of networked agentsor controllers, for achieving some global objectives by co-ordination and communication among the agents [6]. In thispaper, the applicability of a MAS-based control methodologyfor BEMS, as an example of a distributed energy system, ispresented.

Building structures in the US consume significant levels ofelectrical energy and are responsible for substantial greenhousegas (GHG) emissions [7]. A framework for addressing energymanagement has been proposed in [8] with assumed objectivesof increased energy efficiency, decreased operation cost of en-ergy utilization, decreased dependence on use of fossil fuel forenergy needs, and consequently decreased GHG emissions. Thedefinition of net-zero energy costs for zero energy buildings(ZEB) given in [7] is used to set an optimization goal forthe MAS-based BEMS described in this paper [8]. The mainpurpose of this paper is to describe the framework and the al-gorithmic aspects of a CPS for increasing the energy utilizationefficiency by decision-making control methodology. A detailed

0093-9994/$31.00 2012 IEEE

2 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 49, NO. 1, JANUARY/FEBRUARY 2013

Fig. 1. Physical layer of a building energy management system [8].

description of the physical aspect of CPS-enabled BEMS, theCEBEMS, is available in [8]. The methodology presented inthe paper assumes an integrated data analysis program as in[9]. The rest of the paper is organized as follows: Section IIdescribes the system organization of BEMS; Section IIIpresents the decision-making agents in three energy zone,Section IV presents a case study for achieving minimum energycost, and Section V concludes.

II. SYSTEM STRUCTURES OF BEMS

According to [10], for typical commercial buildings, 50%of the energy is consumed in heating, ventilating, and air-conditioning (HVAC) system; when combined with hot waterneeds, the energy consumption in the heating and cooling en-ergy zones may reach up to 60%. Lighting and office equipmenttake most of the rest of the energy consumption, around 20% ina commercial building [10].

During the last few years, there have been several advancesin the architecture and civil engineering fields related to energy-saving techniques such as passive solar heating, passive cool-ing, natural ventilation, natural day lighting, LED light bulbs,and certified appliances approaching net-ZEB goals [7].

The cyber-enabled BEMS (CEBEMS) based on the con-ceptual framework [8] is discussed in the following sections.The CEBEMS proposed in [8] has particular focus on thephysical aspects, including novel hardware, and cyber aspects,including energy management schemes for heating, cooling,and electrical energy zones of a commercial building.

A. Physical Aspect of CEBEMS

Fig. 1 shows the building energy generation and storage andconsumption units and the energy flow paths. The objective ofsuch a system is to achieve overall high energy efficiency, lowemissions, and economic feasibility, without compromising thepreferences and comfort of consumers.

The proposed local BEMS has three zones of interest: theelectrical zone, the heating zone, and the cooling zone. The

electrical zone may possess some renewable energy sources(RES). In the case study presented in this paper, a photovoltaic(PV) array with grid connection and combined heat and power(CHP) units are used as generation units; the generation sourcesare supported by an electric energy storage unit (e.g., batterybank). Such a configuration can supply the electrical loads inthe building. The heating zone in the building has a solar-thermal heater, recovered heat from CHP units, with a naturalgas furnace and thermal storage as heat generation and stor-age units. The heating loads may be split according to spaceheating and hot water needs. The cooling zone possesses anair-conditioning unit and an absorption chiller that uses therecovered heat from CHP to provide space-cooling needs.

Probably not all the blocks shown in Fig. 1 are eventuallyemployed in a practical CEBEMS. However, the selection andcombination of the appropriate structure will depend on thebuilding size and energy needs. In commercial buildings, on-site electric generator combined with waste heat recovery andabsorption chillers are used for building cooling, heating, andpower (BCHP) system [11], where the waste heat from thegenerator is utilized for both building heating and cooling.Typically, the fuel utilization efficiency BCHP system is around80%, some systems may exceed 90%, [12], which is muchhigher than the maximum efficiency for delivered power ofa central power plant (i.e., 55%60%), when the carbon sav-ings of BCHP system is compared with a traditional boilerand chiller system, the result is significant [12]. Commercialbuildings can also employ solar thermal panels for domestic hotwater (DHW), and solid oxide fuel cell in the CCHP system asclean and RES for the purpose of GHG reduction and fossil fuelindependence.

B. Cyber Aspect of CEBEMS

The CPS of the proposed CEBEMS is achieved by a MASapproach as shown in Fig. 2. Reference [13] lists some ap-plications of MAS in building energy management. In eachzone identified in the previous subsection, the energy conver-sion, storage, and consumption are precisely measured anddispatched by the intelligent agent embedded in that zone.The respective agents are: 1) the E-agent for electricity; 2) theH-agent for heating; and 3) the C-agent for cooling zones,respectively. The three agents communicate with each otherthrough local area network when the energy management taskis beyond the capability of a single agent, or the agents arerequired to work together for a series of tasks. The energymanagement and control methods of three agents and theircommunication are discussed next section.

III. MULTI-AGENT DECISION-MAKING SYSTEM

In a commercial BCHP system, the energy system sizingfor electrical, heating, and cooling zones is very important,because when the on-site generation is working, the recoveredwaste heat should also be utilized simultaneously, otherwise,the overall energy efficiency will go down, and its advantage ofenergy efficiency and cost effectiveness will be lost. The systemsizing has three comparative references: 1) tracking electri-cal load, 2) providing electrical base load, and 3) following

ZHAO et al.: ENERGY MANAGEMENT SYSTEM FOR BUILDING STRUCTURES 3

Fig. 2. Building energy management system via a multi-agent system approach [8].

thermal load. When sizing for tracking electrical load, thebuilding should be considered islanded from the grid, so it canbe energy independent from the service provider. Due to thefuel cost, on-site generator maintenance cost, and time of useelectricity rate, this option 1) is not cost effective for buildingsconnected with the grid. When providing electrical base load asin 2), the system is designed for a grid connected building, sothat the surplus electricity generated during the hours of off-peak grid conditions may be sold back to the grid to offsetthe electricity purchased during on-peak hours. However, thethermal demand is higher during the day and drops to a lowersetpoint at night; this may lead to the thermal energy outputbeing over generated during the night and under-utilized duringthe day. This condition results in surplus thermal energy wastedduring the night, and requirement of supplemental boilers forthe daytime. This results in extra installation costs, operationalbudget, higher energy cost, and eventual loss of BCHP systembenefits. The approach taken in this current study is to sizethe BCHP system based on 3), i.e., to follow thermal load andelectricity becomes a byproduct of the cycle, thus eliminatingthe need for thermal storage. The generator is viewed as anoverall system, composed of a boiler to provide hot water forbuilding heating and also a heat source of absorption chiller forbuilding cooling demand. The electricity generation is viewedas an additional service, which can at least offset some level of

demand, and any surplus is to be sold back to grid for profit.According to [10], the thermal load is higher than electric loadin a typical commercial building, so sizing and scheduling theBCHP system based on comparative reference 3) may providehigh energy utilization efficiency of BEMS, which will beexamined in the case study shown in Section IV whereupon tothe methodology to fully utilize the BCHP system in a BEMSwill be presented.

A. Energy Management in the HeatingZoneDecision-Making Control of the H-Agent

The goal of the CEBEMS is to minimize the energy cost,so the real energy cost in each zone must be examined beforeany optimization technique is implemented for minimizedcost. The proposed BCHP system in the notional commercialbuilding is sized based on the heating load. The H-Agent isresponsible for fully utilizing the recovered heat for heating.However, the space heating demand in each room of the build-ing may not be accurately predicted, so a boiler is needed. Inthe heating zone, the possible energy cost comes from the gen-eration and distribution side of hot water. Hot water recoveredfrom generator is viewed as byproduct of electricity generation,so the fuel cost is already considered in the electrical zone, andthis part of hot water is viewed as free of charge in heating

4 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 49, NO. 1, JANUARY/FEBRUARY 2013

zone. Hot water produced from the supplemental boiler needsnatural gas, so this cost should be counted in the heatingzone. On the distribution side, regardless of the sources of hotwaterbe it powered by natural gas boiler or recovered wasteheat of a generator, pumps need to be working all the time.The electricity consumption is also counted in the electricalzone, so the hot water distribution is viewed as free of chargein the heating zone. Therefore, the minimum energy cost of aday in the heating zone can be shown as (1), where the energyconsumption is computed every 15 min and the sum of the costsin these time intervals will represent the cost for one day

min24t=0

($NG ENGt

)(1)

where $NG is the natural gas price, and ENGt is the hourlynatural gas consumption, and t is the time.

However, (1) does not show the energy-saving process (i.e.the recovered hot water generation, distribution, and naturalgas burning reduction), and has no controllability on the energysavings and natural gas burning minimization.

Therefore, the objective function in the optimization shouldbe changed from minimizing energy cost to maximizing energyutilization efficiency. Due to the intrinsic losses in the hotwater pipeline and other thermal losses and from the hot watergeneration side to the consumption end (i.e., heating coils orradiators), and the water flow speed limit in distribution pipes,the hot water distribution lagging time cannot be neglectedand feedforward control must be used. In [14], a Just-in-Time(JIT) supply chain management method was first introduced fora district heating system; JIT requires the desired amount ofhot water to be dispatched to the desired consumption end atdesired time [14]. This subsection will introduce the hot waterdistribution optimization based on the idea of JIT aiming atincreasing hot water utilization efficiency for space heating.In (2), it is shown to maximize the recovered heat utilizationefficiency, which will result in less natural gas burned in thesupplemental boiler, so the energy cost in heating zone isminimized

max

ni=1

mj=1

24t=0

EjtEi(ttDij)

(2)

where the indices are:

i ith heating supplier (from 1 to n),j jth heating consumer (from 1 to m),t time interval t,

and the variables are:

Ejt amount of hot water delivered to the jth consumerduring t (kg);

Ei(ttDij) amount of hot water sent by the ith supplier during

t tDij (kg),and the parameters are:

Djt hot water demand of the jth consumer during t (kg),tDij hot water distribution time from the ith heating

supplier to the jth heating consumer,

Emaxi(ttD

ij)

maximum amount of hot water can possibly be

provided by the ith supplier during t tDij (kg),Emin

i(ttDij)

minimum amount of hot water that must be provided

by the ith supplier during t tDij (kg),i efficiency of the hot water distribution pipe con-

nected with the ith heating supplier,subject to the constraints:

Ei(ttDij)Djt (3)

Djt Ejt (Emax

i(ttDij) i

)(4)

Emini(ttDij)

Ei(ttDij) Emaxi(ttDij)

. (5)

B. Energy Management in the CoolingZoneDecision-Making Control of the C-Agent

In typical commercial central cooling systems, the cold waterfrom a central chiller system is distributed to cooling coilslocated at handlers, where fans keep blowing when cycled on;then, the cooled air flows into air-conditioned rooms with ven-tilation air. As the BCHP system is sized based on heating load,the absorption chiller might not provide enough chilled waterfor space cooling because of limited recovered heat. Therefore,an electric chiller (i.e., water-compression chiller) must beincluded in the cooling energy zone. Similarly, the chilled waterin BCHP system should be optimized for maximum usage. Theelectric chiller is cycled on when the absorption chiller is notable to provide enough cooling for the demand. In a commercialcentral cooling system, (with only one electric chiller device)the cooling demand is satisfied with only one absorption chillerand one electric chiller. Therefore, the objective function in(6) shows an optimization function embedded in the C-Agent,where the chilled water provided from electric chiller is also in-cluded, because the cooling demand is supposed to be satisfiedby both the absorption chiller and the electric chiller

maxk

q=1

24t=0

EqtEa(ttaDq )

+ Ee(tteDq )

(6)

where the indices are:q qth cooling coil (from 1 to k),t time interval t,

and the variables are:Eqt amount of chilled water delivered to the qth cooling

coil during t (kg),Ea(ttaDq )

amount of chilled water sent by the absorption

chiller during t taDq (kg),Ee(tteDq )

amount of chilled water sent by the electric chiller

during t teDq (kg),and the parameters are:

Dqt chilled water demand of the qthcooling coil during t (kg),

taDq cold water distribution time fromthe absorption chiller to the qthcooling coil,

ZHAO et al.: ENERGY MANAGEMENT SYSTEM FOR BUILDING STRUCTURES 5

TABLE IDEMAND MANAGEMENT OF E-AGENT [15]

teDq cold water distribution time fromthe electric chiller to the qth cool-ing coil,

(Ea(ttaDq )+ Ee(tteDq )

)max maximum amount of chilled wa-ter can possibly be provided bythe two chillers during the intervalthat chilled water is sent for Eqt(kg),

(Ea(ttaDq )+ Ee(tteDq )

)min minimum amount of chilled waterthat must be provided by the twochillers during the interval thatchilled water is sent for Eqt (kg),

q efficiency of the chilled water dis-tribution pipe connected with thecooling coil q,

subject to the constraints:

Dqt Eqt (Ea(ttaDq )

+ Ee(tteDq )

)max q (7)

(Ea(ttaDq )

+ Ee(tteDq )

) Dqt (8)

(Ea(ttaDq )

+ Ee(tteDq )

)min

(Ea(ttaDq )

+ Ee(tteDq )

)

(Ea(ttaDq )

+ Ee(tteDq )

)max. (9)

C. Energy Management in the ElectricalZoneDecision-Making Control of the E-Agent

The energy management in electrical zone has two mainobjectives: 1) demand management, i.e., the reduction of thepeak electric load; and, 2) communication with the utility(service provider/grid) for demand response protocols and real-time prices of electricity and natural gas, trying to engagethe building to participate in demand response, and uploadingthe building energy usage information on to an appropriatedatabase for certain authorized entities to download and use incontrol actions.

CEBEMS allows demand management by reducing lightingset-point levels and also setting back the cooling setpoint duringpeak hours [15]. The demand management has three levels asshown in Table I. The demand management level changes frommoderate to more aggressive. The purpose of load managementis to engage the building in demand response participation withthe utility, so there is a goal of making profits toward net-zerocosts for the building.

The E-Agent continuously communicates with the utilityfor receiving energy prices and demand response information,and attempts to reduce local load demand to participate in

demand response to make a profit. On the other hand, thebuilding users specific levels of comfort vis--vis thermostatsetpoints and lighting levels are also respected. The local energyconsumption and generation data will be collected by meteringdevices installed in the building, and then dispatched throughan apt communication protocol to a database server (e.g., hostserver). The database server stores and archives the data, andbuilding users can access and download appropriate real-timereports using dedicated or generic tools.

D. Interactions Between E-, H-, and C- Agents

The interaction between the three agents is shown in Fig. 3where global information is shared in the computer simula-tion environment. However, the interaction between agents inreal deployments will require specific candidate networkingplatforms.

IV. CASE STUDY: ACHIEVING MINIMUM ENERGYCOST IN EFFICIENT BUILDINGS

In this section, a case study is presented to show the energysaving capabilities of CEBEMS and a typical summer day andwinter day based on local weather data of Golden, CO. A build-ing simulation prototype [16] is used to examine the overallperformance, where the utility energy price is based on rawdata provided by the EnergyPlus software [15]. The followingsubsections describe some of the software applications used inthis case study.

A. Building Energy Simulation Environment

EnergyPlus: EnergyPlus is a building energy analysis andsimulation software, developed based on BLAST and DOE-2[14]. EnergyPlus can calculate the building heating and coolingloads, simulate building HVAC system, and energy generationand consumption, based on users description of the buildingand the associated energy system [14]. Newer versions ofEnergyPlus also include DG and RES (e.g., PV, fuel cell, windturbine and micro-CHP system) for energy efficient buildingapplications.

AMPL: AMPL is a powerful modeling language for bothlinear and nonlinear optimization problems [17].

AMPL does not solve optimization problems directly, butcalls outside solvers, such as CPLEX, SNOPT, MINOS, IPOPT,KNITRO, LANCELOT. Appropriate nonlinear programmingmethods are SQP, based on Lagrange multipliers and IPM.AMPL is a global solver and there is no concern about reachinglocal extrema.

Control Optimization From AMPL to the Building Modelin EnergyPlus: Space heating and cooling water flows areoptimized (as introduced in Section III) by AMPL. The resultsare the amount of water that should be sent from hot or chilledwater suppliers during each time interval (i.e., 15 min in thiscase). Actuators in the water pumps change the water flow rate(i.e., Qijt and Qcqt in heating and cooling zones, respectively)according to the optimized value calculated by H- and C-agents.EnergyPlus supports user-defined schedules of variable flowpumps. Thus, water demanded at the supply side in each time

6 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 49, NO. 1, JANUARY/FEBRUARY 2013

Fig. 3. Complete CEBEMS.

interval is input into the schedule of appropriate pump ascontrol signal from actuator, then the building heating andcooling loads are satisfied by the optimized water flow schedulecalculated by AMPL.

B. Building Test System

The CEBEMS is simulated for a single-floor, rectangular,five-room setting of area 102.19 m2, with details available inEnergyPlus [16]. The building energy and cost saving will becompared among: 1) base case where the building is connectedto the grid and air-conditioned by electric chiller and heatedby heating coils with hot water from a natural gas boiler;2) BCHP model where the building has a micro-turbine com-bined with heat recovery for space heating and absorptionchiller for space cooling. The natural gas burner and electricchiller are also installed as supplemental devices in BCHP, and3) a CEBEMS model, based on the BCHP model but advancedwith the proposed E-, H-, and C-Agents.

C. Results

Tables II and III show the energy consumption at the enduse compared with the base case, BCHP model, and CEBEMSmodel. The energy consumers in the base case are HVACsystem (i.e., cooling, boiler, and fans as shown in Table II),interior and exterior lighting, and interior equipment. TheBCHP and CEBEMS model also have on-site generation withof the hot water loop, the cold water loop, the condensing loop,and the heat recovery loop and their associated water pumpsinstalled as the BCHP system. The interior lighting, exteriorlighting, and interior equipment (appliances and miscellaneousloads) are base loads that are kept in the base load unless loadmanagement from E-Agent is performed. The space heatingand DHW needs are satisfied by boilers in the base case and byrecovered heat in the other two models. Analysis can be madefrom the three energy management system models compared inTables II and III, and the following observations are made:

1) The comparison of base case and BCHP model shows theelectricity usage in BCHP model has been significantlyreduced (particularly in the summer), but the natural gasconsumption has been greatly increased due to the BCHP

TABLE IIBUILDING ENERGY CONSUMPTIONS COMPARED ON A TYPICAL

SUMMER DAY IN GOLDEN, CO

TABLE IIIEND USES OF BUILDING ENERGY CONSUMPTIONS COMPARED

ON A TYPICAL WINTER DAY IN GOLDEN, CO

system installation that has changed the major energyconsumption from electricity to natural gas.

2) The comparison of BCHP model and CEBEMS modelshows that both the electricity and natural gas consump-tion have been reduced in the CEBEMS model, because

ZHAO et al.: ENERGY MANAGEMENT SYSTEM FOR BUILDING STRUCTURES 7

Fig. 4. Comparison of BCHP and CEBEMS models on a typical summer day in Golden, Colorado.

Fig. 5. Comparison of BCHP and CEBEMS models on a typical winter day in Golden, Colorado.

the H- and C- Agents have optimized the water generationand dispatch from on-site generator, so excessive hotwater production is avoided.

3) The end energy uses indicated in Tables II and III didnot specify the energy sources, so on-site generation fromthe BCHP model and CEBEMS model are further investi-gated in Figs. 4 and 5. The electric and thermal demand ofBCHP model are plotted in Figs. 4 and 5 for comparativereference, because if no load management is performedby the E-Agent, the energy demand is the same in thesetwo models. On the other hand, if load management isperformed, the energy demand in CEBEMS model willbe less, so only the energy demand in the BCHP modelwill need to be plotted as a comparative reference.

The following results are observed from Figs. 4 and 5:

1) In the BCHP model, both the thermal and electric gener-ations are greater than the demand; the surplus electricitygeneration is sold back to the grid. Tables II and III alsoshows that natural gas usage in boilers is kept zero inthe BCHP and CEBEMS model, showing that heatingdemand is totally satisfied by recovered heat. Space-cooling electricity usage in the BCHP model is greatlyreduced when compared with the base case. However,the absorption chiller did not totally replace the electricchiller, because the BCHP system is sized based on thespace heating demand, so the absorption chillers chilledwater output is limited by the hot water produced by theon-site generator.

8 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 49, NO. 1, JANUARY/FEBRUARY 2013

2) In the CEBEMS model, the thermal energy output ofBCHP system is optimized by H- and C-Agents, thethermal generation spikes are corrected with the calcu-lated data, which is enough for the end-use requirement;thus, excessive hot water generation is avoided. Noticethat the thermal generation in CEBEMS model can behigher than the thermal generation of BCHP model. Thiscan be used as a preparation for the upcoming higherenergy demand period. As stated in Section III, the BCHPmodel is following the thermal load, but that is high inthe early morning hours (7 AM) and evening (7 PM),so the associated electric generation from the on-sitesources should also ramp up and down concomitantly,which will potentially decrease the energy efficiency ofthe generator. In the CEBEMS model, the thermal outputfrom the generator is relatively smoother than that in theBCHP model, so the associated electric output is alsosmoother, which helps the generator to avoid some fastresponse actions to the sudden rising demand.

The electrical and thermal demand shown in Tables II andIII are the energy usage at the end-user level. The electricaldemand includes interior lighting, exterior lighting, interiorequipment, and fans, pumps, and air-conditioning system of theHVAC system; and the thermal demand is the amount of energycalculated to meet the specific indoor air heating or coolingsetpoints during a certain time. However, when employing theEnergyPlus software for calculating the electrical and thermaldemand values at the end-user level, the following are not takeninto account: any energy consumed in the generation and thedistribution processes, any losses in the processes, and any ofthe efficiencies of the system and equipment.

Tables II and III indicate the respective base loads of thebase case, the BCHP model, and the CEBEMS model forcomparisons between the three energy models, as well as showthe changes in energy usage with the progressive installationsof CHP system in the BCHP model and the cyber aspect inthe CEBEMS model. The electrical and thermal energy gen-eration shown in Figs. 4 and 5, however, considers the energyconsumed in the process, losses, and efficiency of the systemand components.

Since CHP system plays a key role for energy generation inthe BCHP and CEBMES model, these values can be significant.Tables II and III when combined with Figs. 4 and 5 providea qualitative description of the energy generation and demandas follows. In the base case model, the electrical generationexactly matches the demand, since it is assumed that the lossesare supplied by the grid/utility. When the thermal generationfrom the gas boiler is multiplied by the efficiency and lossesare subtracted, it equals the thermal demand. In the BCHPmodel, when the electrical generation from the CHP systemis accounted for the efficiency for converting natural gas toelectric power, any differences between the electric energybought from the utility and the electric energy used insidethe BCHP model and losses should equal the demand. Similaraccounting procedures are handled for balancing the thermalgeneration and the thermal demand. In the CEBEMS model,the physical aspect remains the same as the BCHP model; thus,the same statements are also hold true here; the addition of the

TABLE IVENERGY COSTS COMPARED ON A TYPICAL WINTER DAY

AND SUMMER DAY

cyber aspect helps to increase the efficiency of the system, thus,less energy is consumed in the processes.

Table IV shows the energy cost for the base case, BCHPmodel, and CEBEMS model based on the utility dynamicenergy price provided as raw data in the example model inEnergyPlus [16].

The total utility cost shown in Table IV is calculated bythe following methodology: the sum of energy charges, de-mand charges, and service charges constitutes the basis, whichis added with adjustments and surcharges that constitute thesubtotal, added with taxes (8% in this case). In this case, theadjustments and surcharges are zero, so they are not shown inTable IV. For energy charges, unit cost of electricity and naturalgas price are defined as 10.23 cents/kWh and $9.55/MCF,respectively.

The service charges are determined by the electricity demandof the building from the utility. If the building peak demand isabove 10 kW, then the service charge will be $87.3 per month;if not, then the service charge will be $12.74 per month. Theservices charges applied here are divided by 30 to reflect thecost for one day of the month. The demand charges are employ-ing block rates determined by building service charges type.For detailed block rates, refer to [16]. In this case study, theenergy cost of the BCHP model has a significant cost savingscompared to the base case, because the BCHP system is energyefficient in nature and the CEBEMS model has more energycost savings compared with the BCHP model due to the cyber-enabled MAS decision-making control system working towardoptimizing overall energy utilization. For real implementation,the real cost savings will be dependent of the prescribed utilitytariffs.

Fig. 6 compares the performance of BCHP and CEBEMSenergy management approaches, showing that in a typical 24 htime span, there are significant reductions of thermal powergeneration, and at the same time the electrical power generationis met without constraints, showing the improved performanceachieved by the present control methodology.

V. CONCLUSION

This paper investigated an application of MAS for cyber-enabled energy management of building structures known asCEBEMS. The efficient energy management system is achievedby tapping both physical and cyber aspects of the building,such that the BCHP building model provided an applicable

ZHAO et al.: ENERGY MANAGEMENT SYSTEM FOR BUILDING STRUCTURES 9

Fig. 6. Comparison of electrical and thermal energy consumption for BCHPand CEBEMS models.

energy management computational structure for energy effi-cient buildings. The CEBEMS model advances a BCHP modeland optimizes the energy generation and distribution. The testbuilding chosen in the example shown in this paper is for atypical food service center, whose thermal demand is relativelyconstant compared with common commercial office building;it is expected that the control methodology studied in this workcan be applied for office building as well.

ACKNOWLEDGMENT

The authors acknowledge the Center for Advanced Controlof Energy and Power Systems at Colorado School of Mines andare grateful to the help of Dr. A. Newman at Colorado Schoolof Mines and Dr. S. Leyffer at Argonne National Laboratoryon AMPL.

REFERENCES

[1] 109th Congress of the United States of America, Energy Policy Act 2005(EPACT05), Aug. 2005.

[2] 110th Congress of the United State of America, Energy Independenceand Security Act of 2007 (EISA07), Dec. 2007.

[3] GRID 2030: A National Vision for Electricitys Second 100 Years, UnitedStates Department of Energy, Washington, DC, Jul. 2003.

[4] B. L. Dorgan, The case for a 21st century electricity transmission sys-tem, Democrat. Policy Committee, Washington, DC, Mar. 2009.

[5] H. E. Brown, S. Suryanarayanan, and G. T. Heydt, Some characteristicsof emerging distribution systems under the smart grid initiative, ElsevierElect. J., vol. 23, no. 5, pp. 6475, Jun. 2010.

[6] M. Wooldridge, Intelligence agents, in Multiagent SystemsA ModernApproach to Distributed Artificial Intelligence. Cambridge, MA: MITPress, 1999, pp. 2777.

[7] P. Torcellini, S. Pless, and M. Deru, Zero energy buildings: A critical lookat the definition, presented at the ACEEE Summer Study, Pacific Grove,CA, Aug. 2006. [Online]. Available: http://www.nrel.gov/docs/fy06osti/39833.pdf

[8] R. Carnieletto, D. I. Brando, S. Suryanarayanan, F. Farret, and M. GodoySimes, Smart grid initiative, IEEE Ind. Appl. Mag., vol. 17, no. 5,pp. 2735, Sep./Oct. 2011.

[9] P. C. Peng, L. Jie, Y. Chongchong, and T. Li, Design and implementationof renewable energy and building integrated data analysis platform, inProc. APPEEC, Mar. 2010, pp. 15.

[10] B. L. Capehart, W. C. Turner, and W. J. Kennedy, Guide to EnergyManagement, 6th ed. Atlanta, GA: Fairmont, 2008.

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[12] D. W. Wu and R. Z. Wang, Combined cooling, heating and power:A review, Prog. Energy Combust. Sci., vol. 32, no. 56, pp. 459495,Sep.Nov. 2006. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0360128506000244

[13] L. Klein, J.-Y. Kwak, G. Kavulya, F. Jazizadeh, B. Becerik-Gerber,P. Varakantham, and M. Tambe, Coordinating occupant behavior forbuilding energy comfort management using multi-agent systems, Autom.Construct., vol. 22, pp. 525536, Mar. 2012.

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guage for Mathematical Programming, 2nd ed. Pacific Grove, CA:Brooks/Cole, 2003.

[18] M. G. Simes, R. Roche, E. Kyriakides, S. Suryanarayanan,B. Blunier, K. D. McBee, P. H. Nguyen, P. F. Ribeiro, and A. Miraoui,A comparison of smart grid technologies and progresses in Europeand the U.S., IEEE Trans. Ind. Appl., vol. 48, no. 4, pp. 11541162,Jul./Aug. 2012.

[19] X. Y. Yu, C. Cecati, T. Dillon, and M. G. Simes, The new frontierof smart grids, IEEE Ind. Electron. Mag., vol. 5, no. 3, pp. 4963,Sep. 2011.

Peng Zhao (S09) received the M.S. degree in engi-neering from the Colorado School of Mines, Golden,in 2010. He is currently working toward the Ph.D.degree in the Department of Civil, Environmental,and Architectural Engineering, University of Col-orado, Boulder.

His research interests are commercial building andelectric grid systems integration, intelligent controlsystems for buildings, and building energy efficiency.

Siddharth Suryanarayanan (S00M04SM10)received the Ph.D. degree in electrical engineeringfrom Arizona State University, Tempe, in 2004.

He is an Assistant Professor in the Departmentof Electrical and Computer Engineering at ColoradoState University, Fort Collins, where he serves asthe Site Director of the Center for Research andEducation in Wind, and as a 20112012 ResidentFaculty Fellow of the School of Global Environ-mental Sustainability. His research interests are inthe design, operation, and economics of finite-inertia

systems and integration of renewable energy systems to electric grids.

Marcelo Godoy Simes (S89M95SM98) re-ceived the B.S. and M.S. degrees from the Universityof So Paulo, Brazil, in 1985 and 1990, the Ph.D.degree from The University of Tennessee, Knoxvillein 1995, and the D.Sc. degree (Livre-Docncia) fromthe University of So Paulo in 1998.

He is an Associate Professor with the ColoradoSchool of Mines (CSM), Golden, where he has beenestablishing research and education activities in thedevelopment of intelligent control for high-power-electronics applications in renewable and distributed

energy systems, and where he currently serves as the Director of the Center forthe Advanced Control of Energy and Power Systems. He has been involved inactivities related to the control and management of smartgrid applications sincejoining CSM. In 2002, he received an NSF CAREER Award Intelligent-BasedPerformance Enhancement Control of Micropower Energy Systems. Currently,he is the Chair for the IEEE IES Smart Grid Committee.

http://www.nrel.gov/docs/fy06osti/39833.pdfhttp://www.nrel.gov/docs/fy06osti/39833.pdfhttp://files.harc.edu/Sites/GulfCoastCHP/Publications/BuildingIntegratedCooling.pdfhttp://files.harc.edu/Sites/GulfCoastCHP/Publications/BuildingIntegratedCooling.pdfhttp://www.sciencedirect.com/science/article/pii/S0360128506000244http://www.sciencedirect.com/science/article/pii/S0360128506000244

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