IEEE TRANSACTIONS ON SMART GRID, VOL. 1, … · IEEE TRANSACTIONS ON SMART GRID, VOL. 1, NO. 3, DECEMBER 2010 243 Energy-Efﬁcient Buildings Facilitated by Microgrid Xiaohong Guan,

IEEE TRANSACTIONS ON SMART GRID, VOL. 1, NO. 3, DECEMBER 2010 243

Energy-Efficient Buildings Facilitated by MicrogridXiaohong Guan, Fellow, IEEE, Zhanbo Xu, and Qing-Shan Jia, Member, IEEE

Abstract—Recent research shows that 20%–30% of buildingenergy consumption can be saved through optimized operationand management without changing the building structure and thehardware configuration of the energy supply system. Therefore,there is a huge potential for building energy savings throughefficient operation. Microgrid technology provides an opportunityand a desirable infrastructure for improving the efficiency ofenergy consumption in buildings. The key to improve buildingenergy efficiency in operation is to coordinate and optimize theoperation of various energy sources and loads. In this paper, thescheduling problem of building energy supplies is considered withthe practical background of a low energy building. The objectivefunction is to minimize the overall cost of electricity and naturalgas for a building operation over a time horizon while satisfyingthe energy balance and complicated operating constraints of in-dividual energy supply equipment and devices. The uncertaintiesare captured and their impact is analyzed by the scenario treemethod. Numerical testing is performed with the data of the pilotlow energy building. The testing results show that significantenergy cost savings can be achieved through integrated schedulingand control of various building energy supply sources. It is veryimportant to fully utilize solar energy and optimize the operationof electrical storage. It is also shown that precooling is a simpleway to achieve energy savings.

Index Terms—Energy saving, integrated building control, mixedinteger programming.

I. INTRODUCTION

E NERGY CRISIS worldwide is one of the most seriouschallenges facing the sustainable development of human

society in the 21st century. Among the various ways to resolveor alleviate energy crisis, improving efficiency in industrial,commercial and residential energy consumption is the mosteffective and the least controversial one. According to the U.S.Department of Energy, about 40% of total energy is consumed

Manuscript received September 12, 2010; accepted September 14, 2010. Dateof current version November 19, 2010. This work was supported in part by theTsinghua-UTC Research Institute for Integrated Building Energy, Safety andControl System, and the United Technologies Research Center, the NationalNatural Science Foundation of China under Grant 60921003, Grant 60736027,Grant 90924001, and Grant 60704008, in part by the National High TechnologyResearch and Development Program of China under Grant 2008AA01Z415, inpart by the Program of Introducing Talents of Disciplines to Universities (theNational 111 International Collaboration Project, B06002), and in part by theSpecialized Research Fund for the Doctoral Program of Higher Education uderGrant 20070003110. Paper no. TSG-00128-2010.

X. Guan is with the SKLMS Lab and MOE KLINNS Lab of Xi’an JiaotongUniversity, Xi’an 710049, China, and also with the Center for Intelligent andNetworked Systems (CFINS), TNLIST, Tsinghua University, Beijing 100084,China (e-mail: [email protected]).

Z. Xu is with the SKLMS Lab and MOE KLINNS Lab of Xi’an JiaotongUniversity, Xi’an 710049, China (e-mail: [email protected]).

Q.-S. Jia is with the Center for Intelligent and Networked Systems (CFINS),TNLIST, Tsinghua University, Beijing 100084, China (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TSG.2010.2083705

Fig. 1. Energy system of buildings.

in buildings in industrialized countries, among which 68% iselectricity [1], [2]. Recent research shows that 20%–30% ofbuilding energy consumption can be saved through optimizedoperation and management without changing the structure andhardware configuration of the building energy supply system.Therefore, there is a huge potential for building energy savingsthrough efficient operation and management.

Many efforts have been made to improve building energy ef-ficiency. One of the focuses is on improving building materialsand structures to save energy consumption [8]. Increasing effi-ciency of HVAC systems by natural ventilation with the infor-mation of outdoor environment is investigated in [1], [5]. Energyefficiency of the lighting system can be improved by increasingthe utilization ratio of sunlight [6]. Mooney et al. demonstratedthe potential energy savings by utilizing renewable energy re-sources and distributed storage devices [2], [7]. Sun and Luhshowed that the integrated terminal control and HVAC systemcan save energy significantly while satisfying occupant comfortrequirements [9].

Microgrid technology provides an opportunity and a de-sirable infrastructure for improving the efficiency of energyconsumption in buildings [3], [4], [18], [19]. As shown inFig. 1, a typical microgrid for buildings integrates the operationof electrical and thermal energy supply and demand. Thesupply may include energy sources from distribution grid,autonomous power generators such as fuel cells, combined heatand power (CHP) systems, renewable energy resources suchas PV solar cells and wind powers, and energy storage devicessuch as batteries and water tanks. Dictated by the requirementsof occupant comfort, data processing, etc., the typical electricaldemands or loads include those for HVAC, lighting, elevators,and IT data centers.

The key to improve operation efficiency of building energyconsumption is to coordinate and optimize the operation of var-ious energy sources and loads. However, there are many chal-lenges and difficulties that need to be addressed. First, the oper-ation of different equipment and the devices of different typesof energy supplies, electrical and thermal, such as CHP and re-newable generating units, are coupled and need to be well coor-

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244 IEEE TRANSACTIONS ON SMART GRID, VOL. 1, NO. 3, DECEMBER 2010

dinated. The operation of energy supply and controllable loadsare also coupled in time and need to coordinate along time due toelectrical and thermal storage devices. Second, it is well knownthat renewable energy resources such as PV panels and windpower are subject to large random fluctuations. The high uncer-tainties in both supply and demand may cause major difficultiesin scheduling and control. Furthermore, the lighting demand andcomfort requirements related to dynamic occupant movementsand human behaviors are also subject to significant uncertain-ties. The aforementioned two difficulties make the integratedbuilding operation problem very challenging.

In this paper, the scheduling problem of building energy sup-plies is considered with the practical background of a low energybuilding. The objective function is to minimize the overall costof electricity and natural gas for building operation over a timehorizon while satisfying the energy balance and complicated op-erating constraints of individual energy supply equipment anddevices. The electrical and thermal demand profiles are assumedto be determined from the related work [9] and forecasted fromthe historical data. The uncertainties in demand and renewableenergy sources are also considered.

The above scheduling problem is formulated with power grid,solar PV panels, one CHP unit, and one electrical battery as en-ergy sources and HVAC and lighting as demands, and solved asa mixed integer programming problem. The scheduling problemis then solved by the CPLEX solver. The uncertainties and theirimpact are captured and analyzed by the scenario tree method.Numerical testing is performed with the data of the pilot lowenergy building. The testing results show that significant energycost savings can be achieved through integrated scheduling andcontrol of various building energy supply sources. It is very im-portant to fully utilize the solar energy and optimize the opera-tion of electrical storage. It is also shown that precooling oper-ation during the time periods with low electrical energy pricesis a simple way to achieve energy savings.

The rest of this paper is organized as follows. In Section II, themathematical problem formulation is presented. In Section III,the scheduling problem is approximated by a mixed integer pro-gramming and scenario tree method. In Section IV, the perfor-mance of the mixed integer programming is demonstrated ona typical summer day in Beijing. The value of accurate infor-mation on the demand and renewable energy sources are alsodiscussed. Some discussions are included in Section V. A briefconclusion is shown in Section VI.

II. PROBLEM FORMULATION

The focus of this paper is on the integrated scheduling of themultiple energy supply sources to meet the demand. The energysupply sources include power grid, solar PV panels, battery, andCHP unit. The scheduling horizon is with discretized timeperiods. The objective is to minimize the overall energy costof electricity and natural gas over the entire scheduling horizonwith the time-of-use (TOU) electricity prices. The electrical andthermal load profiles are assumed to be known by the controlstrategy of the building terminal units in the related work [9] andforecasts from the historical data. To simplify presentation, theproblem is first formulated as deterministic and the uncertaintiesare discussed in Section III.

To facilitate the problem formulation, the following notationis introduced.

Time period index,.

Period span (in hour).

Power from the grid at (in kW).

Solar generation at (in kW).

Battery charge or discharge at(in kW).

CHP generation at (in kW).

Cooling quantity supplied byCHP unit at (in kWh).

Volume of natural gas used byCHP unit at (in m ).

State of charge (SOC) of batteryat .

The lower bound of SOC.

Electrical load rate of CHP unitat , (defined as the ratio betweenpower supply and the capacity).

Minimal electrical load rate ofCHP unit.

Maximal electrical load rate ofCHP unit.

Electrical energy supply price at(in RMB/kWh).

Electrical energy price fed intopower grid at (in RMB/kWh).

Total cost of electrical energy at(in RMB).

Natural gas price at (inRMB/m ).

Total cost of natural gas at (inRMB).

Electrical energy consumption ofHVAC system at (in kWh).

Electrical energy consumption oflighting at (in kWh).

Electrical energy consumption offresh air unit (FAU) and fan coilunit (FCU) at (in kWh).

Total cooling demand at (inkWh).

Quantity of grid energy supply at(in kWh).

Quantity of solar energy at (inkWh).

GUAN et al.: ENERGY-EFFICIENT BUILDINGS FACILITATED BY MICROGRID 245

Quantity of battery energy charge(positive) or discharge (negative)at (in kWh).

Quantity of CHP energy deliveryat (in kWh).

Capacity of battery (in kWh).

Electrical energy consumption ofcritical loads (in kWh).

Maximal battery charge (in kW).

Maximal battery discharge (inkW).

Minimal battery charge (in kW).

Minimal battery discharge (inkW).

Electrical capacity of CHP unit(in kW).

Rated natural gas flow rate ofCHP unit (26.9 kJ/Nm ).

The ratio of the consumed naturalgas flow rate to the rated flowrate of CHP unit at .

The cooling power supplied byCHP unit at (in kW).

COP Coefficient of performance.

The scheduling problem is to determine the supply quanti-ties of various energy sources and the operating status of dif-ferent energy supply equipment and devices over the sched-uling horizon such that the total cost of electricity and naturalgas is minimized. It is formulated as the following optimizationproblem:

(1)

subject to the following constraints.a) System constraints

1) power balance:

(2)

2) cooling balance:

(3)

3) the energy supplied in a period:

(4)

b) Cost of electricity and natural gas1) cost of electricity:

(5)

where the positive and negative value of repre-sents the power supplied to the terminal loads and fedinto the grid respectively.

2) cost of natural gas:

(6)

c) Constraint of battery control1) input and output power capacities:

(7)

when , battery is charging; when ,battery is discharging; when , battery isnonoperating.

2) constraint of SOC:

(8)

3) SOC dynamics:

(9)

4) initial SOC:

(10)

d) Constraint of CHP unit operation [10]–[13]1) constraint of operation of CHP unit:

(11)

2) output power of CHP unit:

(12)

3) output cooling of CHP unit:

(13)

4) consumed rate of natural gas:

(14)

e) Operation of PV panels [15], [16]1) electric power output of PV panels:

A PV module is composed of PV cells in series, andmultiple modules in series-parallel connection canform the PV array. The output power of entire PVpanel is below

(15)

where is the series number of PV cell in a PVmodule, is the series number of PV modules, and

is the parallel number of PV module strands.


2) I-V equation of PV cell in PV panels

(16)

The differential of PV cell power to PV cell voltageis zero at the maximum power point (MPP), and thevoltage and current at the MPP can bedetermined by the nonlinear equation below

(17)

where

output current of PV cell at (in A);

output voltage of PV cell at (in V);

current generated by the incident light at (in A);

reverse saturation or leakage current of the diodeat (in A);

equivalent series resistance of the cell (in þ);

equivalent parallel resistance of the cell (in þ);

modified ideality factor.

All of the aforementioned parameters depend on solar irradi-ance, cell surface, temperature, and the optical air mass, we canobtain the computational formula of these parameters in [16].Through (15)–(17), we can obtain the output power generatedby PV panels.

III. SOLUTION METHODOLOGY

A. Deterministic Problem Solved by General Mixed IntegerProgramming Method

Assume the electrical and thermal demands or loads are de-termined from the given demand profile in our related work [9]and the energy-efficient schedule can then be obtained throughsolving the optimization problem for the energy supply system.By analyzing the structure of the model, the nonlinear objec-tive function and constraints can be linearized by introducingthe following variables.

Discrete variable, “1” for power supplied toterminal loads at , “0” otherwise.

Discrete variable, “1” for power fed into grid at ,“0” otherwise.

Discrete variable, “1” for battery charging at ,“0” otherwise.

Discrete variable, “ ” for battery dischargingat , “0” otherwise.

Discrete variable, “1” for CHP unit startup at ,“0” for shutdown.

Power supplied to the terminal loads at (in kW).

Power fed into grid at (in kW).

With the linearization and above definitions, the nonlinearobjective function and constraints of the optimization problemformulated in Section II is further expressed as below.

a) Objective function

(18)

b) Power balance

(19)

c) Operating state of power grid

(20)

d) Electrical energy cost

(21)

e) Input and output power capacities

(22)

f) Constraint of load rate of CHP unit

(23)

g) Constraint of output power of CHP unit

(24)

h) Constraint of output cooling of CHP unit

(25)

where and are parameters of CHP unit, they are de-pended on rated power of CHP unit and .

i) Consumed rate of natural gas

(26)

where and are parameters of CHP unit, they are de-pended on rated power of CHP unit and .

Clearly the above scheduling problem is a mixed integer pro-gramming problem and can be solved by the CPLEX solver.


B. Stochastic Problem Solved by Scenario Tree Method

Uncertainties are associated with many aspects in the abovescheduling problem. First of all, the demand profile contains sig-nificant uncertainties since the electrical and thermal demandsof building occupants such as cooling, lighting, etc., are uncer-tain. The supply of renewable energy sources such as the powergeneration of PV panels is closely related to the environmentand weather condition, and is thus uncertain. Therefore, to find aschedule with the best expected cost in practice, the uncertaintyin both the demand and the supply sides should be consideredduring the decision making.

The scenario tree method [20], [21] is applied to handle theuncertainties in this paper. A scenario is basically a specificdemand curve and weather condition, which is either randomlygenerated according to the demand profile and environmentalstatistics or manually constructed according to heuristics orstructure information of the problem. In order to obtain a goodschedule for various supply resources on average, we needto generate a set of different representative scenarios. TheCPLEX solver is applied to solve the stochastic optimizationproblem with the average cost of all scenarios. The schedulewith the forecasted loads or demands and solar generation isalso obtained and is compared with the results of solving thestochastic problem. The procedure for solving an example ofthe stochastic problem by the scenario tree method will bepresented in the next Section.

If the demands and the weather condition are known perfectlybeforehand, the optimal schedule can be obtained by the deter-ministic model for a known scenario. Comparing the schedulefor a particular scenario with the one corresponding to the fore-casted demand and solar generation, the value of accurate infor-mation on demand and weather can be quantified.

The cost function for the scheduling problem with the uncer-tainties is formulated as follows.

Objective function

(27)

where

scenario index of demand profile, ;

scenario index of solar output power,;

probability of scenario of demand profile;

probability of scenario of solar output power;

subject to all constraints of (19)–(26) and linear constraintsin Section II. This formulation is necessary for evaluating aschedule by the scenario tree method.

TABLE ITOU ELECTRIC PRICE IN BEIJING (SUMMER)

IV. NUMERICAL TESTING RESULTS

The problem based on a pilot low energy office building atthe Tsinghua University campus with area of 3000 m is testedand the results are presented in this section. The energy supplysystem of this building consists of distribution power grid, solarPV panels (two 3-parallel modules, 54 cells in series), one bat-tery unit (capacity of 50 kWh), and one CHP unit (rated powerof 50 kW). The electrical loads include those from the HVACsystem, fan coil unit (FCU), fresh air unit (FAU), and lighting,and are related to the terminal thermal loads that can be changedby blinds and window positions. The test is performed for a typ-ical summer day (2 August 2009), hot and humid in Beijing, andthe weather data is from [17]. Assume that occupants work inthe building from 7:00 to 23:00.

The test is first conducted for the deterministic formulationin several steps. First of all, the electrical and thermal demandprofiles are obtained by solving the terminal control problemaccording to the occupants comfort requirements [9]. Withthe forecasted demand and solar output power, the schedulingproblem is then solved by the CPLEX solver. For the problemwith uncertainties, the demand and solar generation scenariosare generated randomly around the forecasted demand and solaroutput power with the prior knowledge of their distributions.The schedules are generated by solving the problem with theexpected cost in (27) of all scenarios jointly.

A. Electrical and Thermal Demand Profiles

In this case the price of natural gas is 2.05 RMB/m , and theprice of electricity fed into grid is 0.457 RMB/kWh. The TOUprice (RMB/kWh) of electricity supplied to terminal loads isshown in Table I.

For the given comfort level (indoor temperature: [22 C,26 C]; indoor humidity: [50%, 60%]; concentration of CO :[0, 1300 ppm]; luminance lx), the electrical and coolingdemand curves are obtained by solving the terminal controlproblem by dynamic programming [9], and are shown in Fig. 2.

Fig. 2(a) shows the electric energy consumption of lighting,FAU, and FCU and Fig. 2(b) shows the thermal demand pro-file for cooling. The electric energy consumption of the HVACsystem depends on the cooling demand profile and real-timeelectric prices. The electric peak-loads are during 7:00 to 11:00,and 19:00 to 23:00. From 13:00 to 18:00, the outdoor tempera-ture is very high and the cooling demand is high but the lighting


Fig. 2. (a) Electrical demand profile. (b) Thermal demand profile.

demand is low since solar luminance is good. To reduce the en-ergy cost, the HVAC system performs precooling at 6:00 whenthe energy price is low.

B. Solution by the Mixed Integer Programming Method

Assume the demand profile as the forecasted values. TheILOG CPLEX is applied to solve the problem in (18)–(26) andlinear constraints in Section II with the error gap set as 0.0015.The composition of electrical and thermal energy supplies areshown in Fig. 3.

In Fig. 3(a), the electrical energy consumption comes fromthe lighting, FAU, FCU, battery charging, HVAC system, andpower fed to grid and is supplied by the power grid, PV panels,battery, and CHP unit. It is seen that the PV panels, battery andCHP unit supply a significant amount of electrical energy inpeak-price periods (7:00–11:00, 19:00–23:00) with significantreduction in total energy cost compared with the case of the purepower grid supply. The cooling demand is mostly supplied bythe HVAC system with a small supplementary amount providedby CHP unit as seen in Fig. 3(b).

In Fig. 4, positive value of the battery control signal is forcharging, negative value for discharging and 0 for idle (a), cor-responding to positive, negative, and 0 output of the battery(b). As expected the battery is charging in lower price periods,discharging in peak-price periods, and is fully charged beforepeak-price periods. It discharges at very low demand periods tofeed the electrical energy into the grid. Meanwhile, in order tosupply the building in emergency, the SOC should be kept at arequired level.

The relationship between battery capacity and overall costis tested and shown in Fig. 5(a). When a battery with largercapacity is installed, more cost savings can be obtained withhigher investment. Clearly a trade-off needs to be made. Therelationship between battery capacity and usage frequency is

Fig. 3. (a) Composition of electrical energy supplies. (b) Composition ofcooling energy supplies.

Fig. 4. (a) Battery control strategy. (b) Output power of battery.

also investigated as shown In Fig. 5(b). It is observed that alarger battery would result in less frequent usage with possiblylonger life, and subsequently higher investment.


Fig. 5. (a) Relationship between capacity and overall cost. (b) Relationshipbetween capacity and usage frequency.

TABLE IIENERGY COSTS WITH AND WITHOUT BATTERY

Table II shows the total costs with and without a battery indifferent weather conditions. It can be seen that electric energystorage has significant impact on building energy efficiency es-pecially when peak shaving of solar energy is not significant.

The energy generation and the control strategy of the CHPunit are shown in Fig. 6. The CHP control signal is similarlydefined as the battery control signal. It is observed in the Fig. 6that the CHP unit operates during peak-price periods since nat-ural gas is cheaper than electric power for the same amount ofcooling energy, and is shutdown at 10:00 due to availability ofsolar energy. It is not often used in summer based on economicconsiderations.

The energy supply by the power grid is shown in Fig. 7. Thenegative value means power fed into the grid in the periods whenthe solar illumination is high and the solar energy collected ismore than the terminal loads, and when it is economic to dis-charge the battery more than the need of the low terminal loads.

C. Sensitivity of the Schedule

In practice, both the demand and the weather dependent solargeneration are uncertain. It is interesting to test the sensitivity of

Fig. 6. Control strategy and output level of CHP unit.

Fig. 7. Output of power grid.

a schedule under different scenarios. Assume the demand andsolar energy follow independent normal distribution with thestandard deviation of 5% the forecasts. Ten demand curves andten output power curves of PV panels are generated as shown inFig. 8 with totally 100 scenarios. The costs for all scenarios areshown in Fig. 9 and their standard deviation is 0.41% around theone with the forecasted demand and solar energy. This meansthat the performance of the schedule obtained with the fore-casted demand profile and weather condition are relatively ro-bust or insensitive to the uncertainties in demands and weatherconditions.

D. Value of Accurate Information on Demand

Obtaining accurate demand and solar energy forecasts re-quires real-time update and is thus costly. It is of practicalinterest to quantify the value of such accurate information forplanning and operation. If the benefit due to accurate forecastsdoes not match the extra effort or investment, one may notbother to frequently update the forecasts and reschedule.

To analyze the value of the accurate information, the scheduleobtained with the forecasted demand and solar generation inSection IV-B is applied to the 100 scenarios generated inSection IV-C and compared with those obtained with the actualdemands and solar generation. The cost differences are shownin Fig. 10. It is observed that the differences are insignificantin terms of the mean (0.4763 RMB) and standard deviation(0.3857 RMB), and the accurate information does not bringsignificant benefit or extra energy savings. A good schedule can


Fig. 8. (a) Electrical demand generated of all scenarios. (b) Cooling demandgenerated of all scenarios. (c) Solar generation of all scenarios.

Fig. 9. Costs for all scenarios based on the control strategy obtained in Sec-tion IV-B.

be obtained with the forecasted demand profile and weathercondition.

E. Stochastic Problem Solved by the Scenario Tree Method

The objective for solving the optimization problem with un-certainties or stochastic optimization should be in the form of“average.” In the scenario tree method, a schedule should have

Fig. 10. The value of actual demand and solar generation.

Fig. 11. (a) Electrical demands of all scenarios. (b) Cooling demands of allscenarios. (c) Solar generation of all scenarios.

good average performance over all scenarios. To capture themajor uncertainty of solar generation for the problem consid-ered in this paper, a new set of 100 scenarios are randomly gen-erated with the forecasted demand and solar generation profilesas the means, where the distributions of the uncertainties are as-sumed i.i.d. and Gaussian. The scenarios with 5% demand vari-ation (standard deviation to the mean) and 20% solar generationvariation are shown in Fig. 11.


Fig. 12. (a) The optimal average cost and the cost of each scenario. (b) Thecontrol strategy of battery and CHP unit.

TABLE IIITHE PERFORMANCE COMPARISON OF DETERMINISTIC METHOD AND SCENARIO

TREE METHOD

Since the scale of the problem considered in this paperis small, the CPLEX solve is applied to solve the stochasticoptimization problem with the average cost of all scenarios.The computational time is less than 10 s on Windows PC with3.2 GHz and 2 GB memory (the solution time is less than0.2 s if there is only one scenario). The optimal average cost(521 RMB) and that for each scenarios are shown in Fig. 12(a),and the control strategies of battery and CHP unit are shown inFig. 12(b).

The performance of the scenarios tree method is comparedwith the deterministic method with the forecasted demand andsolar generation. The results are summarized in Table III. It canbe seen that the stochastic method does perform better in var-ious scenarios. It is also observed that the schedule obtained by

Fig. 13. The integrated optimization of demand & supply.

the deterministic method with the forecasted demands and solargeneration is quite good and only underperform less than 2% incomparison with the scenario tree method, and is thus a goodmethod for this problem.

V. DISCUSSIONS

The energy consumption in buildings is affected by both thesupply and the demand. A framework of the integrated controland optimization is proposed as shown in Fig. 13 to solve thischallenging problem iteratively. With this framework, the ter-minal control problem is solved for the given comfort levelsrequired by the occupants in the building and the time-of-useprices of electricity, and the demand profile on electrical andthermal loads (cooling and heating) are obtained. The terminalcontrol and optimization within this framework are discussedin Sun et al. [9]. Then with the demand profiles and solar gen-eration given, the supply scheduling problem is solved to allo-cate and schedule energy supply resources as discussed in thispaper. The research work on the integrated optimization is underdevelopment.

VI. CONCLUSION

Optimized control and operation are extremely importantfor building energy efficiency. Microgrid technology providesa way to connect various energy supply resources and thedemands, electrical and thermal, and to facilitate efficientbuilding operation. With electrical and thermal demand profilesobtained in terminal control, a practical model is formulated asa mixed integer optimization problem. The CPLEX solver isapplied to solve the problem with the forecasted demands andsolar generation. The problem with uncertainties is solved bythe scenario tree method. Numerical testing results demonstratethat methods proposed in the paper are efficient and effectivein finding good solutions for building energy supply allocationand scheduling. The sensitivity of the control strategy and thevalue of the accurate demand and solar generation are alsotested. It is found that by comparing the deterministic andstochastic model, scheduling with the forecasted demand andsolar generation under deterministic formation performs quitewell for this problem, and is thus an efficient approximationeven with significant uncertainties.


ACKNOWLEDGMENT

The authors would like to thank Dr. Stella M. Oggianu andDr. Craig Walker at United Technologies Research Center fortheir constructive comments, and Prof. Peter B. Luh, Mr. BiaoSun, Mr. Jianxiang Shen, and Mr. Shupeng Sun of TsinghuaUniversity for their valuable advices, suggestions, and testingwork.

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Xiaohong Guan (M’93–SM’95–F’07) received theB.S. and M.S. degrees in control engineering fromTsinghua University, Beijing, China, in 1982 and1985, respectively, and the Ph.D. degree in electricalengineering from the University of Connecticut,Storrs, in 1993.

He was a Senior Consulting Engineer with PG&Efrom 1993 to 1995. He visited the Division of En-gineering and Applied Science, Harvard Universityfrom January 1999 to February 2000. Since 1995he has been with the Systems Engineering Institute,

Xian Jiaotong University, Xian, China, and appointed as the Cheung KongProfessor of Systems Engineering since 1999, and as the Dean of Schoolof Electronic and Information Engineering since 2008. Since 2001 he hasalso been the Director of the Center for Intelligent and Networked Systems,Tsinghua University, Beijing, China, and served the Head of Departmentof Automation from 2003 to 2008. His research interests include complexnetworked systems including smart power grid, planning and scheduling ofelectrical power and manufacturing systems, and electric power markets.

Zhanbo Xu received the B.E. degree in electricalengineering and automation from Harbin Instituteof Technology University, China, in 2008. He iscurrently working toward the Ph.D. degree at theSystems Engineering Institute, Xi’an JiaotongUniversity, China.

His research interests include smart power grid,buildings energy management and automation, andoptimization of large-scale systems.

Qing-Shan Jia (S’02–M’06) received the B.E. de-gree in automation and the Ph.D. degree in controlscience and engineering from Tsinghua University,Beijing, China, in 2002 and 2006, respectively.

He is currently a Lecturer at the Center for Intelli-gent and Networked Systems (CFINS), Departmentof Automation, Tsinghua University, Beijing. Hewas a Visiting Scholar at Harvard University in 2006,and a Visiting Scholar at the Hong Kong Universityof Science and Technology in 2010. His researchinterests include theories and applications of discrete

event dynamic systems (DEDSs) and simulation-based performance evaluationand optimization of complex systems.

Documents

IEEE TRANSACTIONS ON SMART GRID, VOL. 1, … · IEEE TRANSACTIONS ON SMART GRID, VOL. 1, NO. 3, DECEMBER 2010 243 Energy-Efﬁcient Buildings Facilitated by Microgrid Xiaohong Guan,