Energy Conversion and Management 87 (2014) 1010–1026

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Energy Conversion and Management

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Estimation of the building energy loads and LNG demand for acogeneration-based community energy system: A case study in Korea

http://dx.doi.org/10.1016/j.enconman.2014.07.0590196-8904/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.

Mo Chung a, Hwa-Choon Park b,⇑, Carlos F.M. Coimbra c

a School of Mechanical Engineering, Yeungnam University, 214-1 Dae-dong, Kyungsan, Kyungbuk 712-749, Republic of Koreab Korea Institute of Energy Research, 102 Gajeong-ro, Yuseong-gu, Daejeon 305-343, Republic of Koreac Department of Mechanical and Aerospace Engineering, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093, USA

a r t i c l e i n f o

Article history:Received 26 November 2013Accepted 23 July 2014Available online 29 August 2014

Keywords:LNG demand forecastingCommunity energy systemCogenerationDecision support systemSimulationHeuristics

a b s t r a c t

We analyzed energy consumption by a newly constructed part of a city in Korea to forecast the LNGdemand for 14 years. The electricity, heating, cooling, and hot-water demands for a cogeneration-basedCES (Community Energy System) accommodating 86,000 people in 29,000 houses are estimated usingload models developed through direct measurements and statistical surveys. Based on publishedoccupancy rates and forecasts of the rate of increase in energy consumption by third parties throughindependent study, the energy demands were driven in the form of 8760-h time series for each of the14 years. Next, we simulate the demand–supply matching processes of a specifically chosen cogenerationengine for the CES to forecast the LNG consumption and the electricity trade for each year. We simulatedthe demand–supply matching processes with an automation tool specifically developed for this study.The methodology we established in this study can be applied to similar problems which may arise any-where in the world.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Knowing fuel demand for a new construction project is animportant part of initial planning especially when a large numberof buildings are involved. A good modern technology that allowsefficient energy utilization without environmental burden is a sus-tainable CES (Community Energy System). Using district heatingsystem, we can supply a local community with its energy require-ments from renewable energy or high-efficiency co-generationenergy sources. As an industrialized country Korea consumes alarge amount of energy with exceptionally high dependence rateon overseas oil (Korea imports more than 97% of oil from foreigncountries). Judicious management of energy usage is crucial tonation’s economy. Building energy sector in particular has a greatpotential for saving as residential and commercial buildings areorganized in a way that allows easy deployment of cogeneration-based CES technologies. Ten to thirty story high-rise apartmentbuildings accommodating tens to hundreds of houses in each com-prise a large complex of residences along with schools, hospitals,hotels, and stores. Centralized energy-supplying devices with cleanand efficient treatment of harmful wastes can be economicallyapplied. The Korean government strongly supports the adoption

of distributed cogeneration with diverse incentive programs andfinancial assistance.

The key factors at the early stage of decision-making of CES pro-jects include the energy demand and fuel requirement forecasts forthe entire complex. Gas and electricity (or utility) companies needto know the projected fuel consumption and the amounts of elec-tricity trade to prepare their business portfolio to cope with theprogress of CES construction and moving-in plans. In this study,we will forecast the energy and LNG demand for a cogeneration-based CES located in the suburb of Busan. An aerial view of the siteis shown in Fig. 1 with the dotted line indicating the CES boundary.This complex has been under construction starting from Year 1(2009) and will be completely occupied by Year 14 (2022) by fillingthe buildings summarized in Table 1.

1.1. Simulation and forecasting of cogeneration based building energysystem

A simulation based heuristic approach will be adopted in thisstudy for energy demand and LNG consumption forecasting. Abroad range of topics is relevant to our research. Technologically,our study is most related to the modeling of cogeneration systemsand we can find numerous examples of previous studies in thejournal. Chicco and Mancarella [1] developed a unified model forenergy and environmental performance of natural gas-fueled

Fig. 1. Aerial view of the project site.

Table 1Project overview.

Item Description

Site Jung Gwan District (suburb of Busan, Korea)Land area 4,161,000 m2

Households 28,668 (740 houses and 27,928 apartments)Population 86,004

Room area(m2)

Composition(%)

Households Population

Apartmentcomposition

Below 59 41.7 11,633 34,89959–84 30.7 8576 25,728Above 84 27.6 7719 23,157Total 100.0 27,928 83,784

Schools Elementary (8), middle (5), high (4)Facilities Parks (11), playgrounds (18), hospitals (1), post offices (1), police

stations (2), fire stations (1), government offices (1), powersubstations (1)

M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026 1011

poly-generation systems to quantify the typical benefits that poly-generation systems can bring. Rosato et al. conducted dynamicperformance assessment of a residential building-integratedcogeneration system with natural gas-fueled internal combustionengine under different boundary conditions in their two-part stud-ies [2,3]. They compared the proposed system with a conventionalsystem composed of a natural gas-fired boiler from both energysaving, and economic and environmental perspectives. Many coun-tries in the world are interested in cogeneration as an importantmodern energy utilization technology as manifested by continuouspublications in the related areas of smart grid [4], sustainability of

energy [5], CO2 reduction [6]. For example, Inan et al. [7] investi-gated the change in national exchange rate model depending onthe economic parameters of a natural gas cogeneration systemfor Turkey and Tolmasquim et al. [8] assessed economic potentialof LNG fired cogeneration plants at a mall in Brazil. In Italy, Panta-leo et al. [9] performed thermo-economic assessment for a micro-gas turbine fired by a mixture of natural gas and biomass. Otherpopular topics in this area include cogeneration system optimiza-tion [10,11], innovative modeling techniques [12,13], hybrid tech-nologies of organic Rankine cycle [14] and condensing boiler [15].

Forecasting energy demand in general is one of the most popu-lar subjects in predictive science and diverse techniques areemployed by many authors. As electricity, a modern form ofenergy, receives the most attention in forecasting studies in manycountries. Even though we use a simple time-series analysis, wecan spot examples of modern techniques applied to energy fore-casting: time series [16–18], wavelet transform [19], demand–sup-ply interaction [20], and simulation [21]. Ideas such as rollingmechanisms [22], weather ensembles [23], and short-run forecasts[24] also have been tried. Combined Heat and Power (CHP) plan-ning is multi-disciplinary in nature with certain degree of uncer-tainties [25–27].

As fuel consumption is directly connected to the selection andoperation of the cogeneration system, performance assessment[28–29] and management strategy [30] based on efficient controland optimization [31] are integral parts of system design. Featuressuch as effect of cycle coupling [32] and optimal sizing underuncertain energy demand condition [33] are also relevant to ourstudy. Another important aspect of system design is the economicmerits. A techno-economic assessment involving actual costing

Fig. 3. The combined gas turbine–steam turbine cycle.

1012 M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026

under real market condition [34,35] is highly recommended forrealistic estimations. We can also find many instances of LNGdemand forecasts in many countries with diverse analysis meth-ods. Examples include but are not limited to: Reister [36], Sarakand Satman [37], Lise and Hobbs [38], and Forouzanfar et al.[39]. These studies are generic in nature and did not solve specificproblems using, for example, forecasting techniques. Modern soft-ware package such as spreadsheet was applied to generate a distri-bution of forecasts for electric power demand [40]. We are usingrelational database software in this study.

1.2. System selection and operation

Fig. 2 shows an energy traffic diagram for a typical cogenera-tion-based building energy supply. Regardless of the energy supplymethods, the member buildings of a CES require electricity, heat-ing, cooling, and hot water that independently vary with time. Asmentioned earlier, predicting the demands is one of the essentialparts of this study. On the supply side, the cogeneration systemproduces both electricity and thermal energy and supplies themto the CES member buildings. In many real world scenarios, themaximum capacity of the plant is fixed and it is difficult to makethe supply meet the demand always, and backup plans are neces-sary. If electricity is short supplied, it should be purchased fromoutside (the utility company); if heat supply is short, it must besupplemented either from outside (district heating) or supplemen-tary boilers are necessary. Fuels are required in two places: thecogeneration engines and the supplementary boilers.

After a sufficient market survey and technological investiga-tions, a gas turbine-steam turbine combined cogeneration systemhas been selected as the main energy-supplying device. Theselected type of cogeneration engine for the project is schemati-cally shown in Fig. 3. It combines two power cycles such that theenergy discharged from the gas turbine is used either partially ortotally in a special boiler called HRSG (Heat Recovery Steam Gen-erator). The combined engine has a higher efficiency than that ofeither cycle individually and is increasingly being used worldwidefor cogeneration [41]. Note that fuel (LNG in this study) is suppliedonly to the gas-turbine combustor for the combined cycle, and the

Fig. 2. Demand–supply matching

steam turbine is driven by the recovered heat from the HRSG. Thisis the feature that allows high efficiency, i.e., multiple effects with asingle input.

It is important to remember that most cogeneration systemssimultaneously produce thermal energy and electricity at a nearlyfixed ratio (35–40% electricity plus 60–65% heat). The ratio can beadjusted but is fairly constrained. On the other hand, electricityand thermal energy demands change with time depending onmany factors such as building type and location. As a result, ifwe operate the engines to meet the electricity demand, mismatchin supply and demand of thermal energy is inevitable and viceversa. We need to choose which one to match first, electricity orheat? A general rule of thumb for minimizing energy waste is to

for cogeneration-based CES.

M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026 1013

match the electricity load first when the electricity demand is lar-ger, and the thermal load first when the combined thermal loads ofheating, cooling, and hot water are larger. We call the formermethod electricity tracking, and the latter heat tracking. In the elec-tricity-tracking mode, the recovered heat is passively determined.If heat recovery exceeds the combined thermal loads it must bestored or dumped. The supplementary boiler should be turned onin the opposite cases. In the heat-tracking mode, electricity is gen-erated passively, and we need to sell the excess or buy the deficitfrom a utility company. The process of matching load demandswith energy supplies in cogeneration systems is called an opera-tional simulation and it is an integral part of a successful LNGdemand forecast.

1.3. Consolidation of related information

As is the case with most of the forecasting problems, our prob-lem is messy and complex, entailing considerable uncertainty. Onepossible approach we could take to forecast LNG consumption is toprocess the data obtained from phenomenological observations ora benchmarking survey. Unfortunately, we do not have any operat-ing instances of plants that can provide useful data. Practically, nocompiled information is available for the kind of technology we arestudying. On the other hand, our problem is very specific and welldefined in the sense that the CES boundary and future expansionplans for construction of buildings and power plants are firmlydecided. We can make a good forecast if we take advantage of thisdefiniteness and find a logical approach to the whole problembased on sound engineering practices. Instead of using traditionalforecasting techniques or tools, we will take a heuristic approachbased on ad hoc methods. Starting from solid forecasts for theoccupancy rate and energy consumption increase provided bythird parties, we will try to deduce forecasts for the effects byinvestigating the associated cause-and-effect mechanism. In thiscontext, the causes are the occupancy rate and the rate of increasein the energy consumption of the complex, and the effects are theLNG consumption and electricity trade. Our task is to fill the gapsin the chain connecting the causes and effects.

First, we need to know the initial states of the energy demandsto which the increased rates apply. More specifically, the electric-ity, heating, and hot-water demands for the starting year should beestimated through a reasonably reliable method. We will use apackage our team developed for this purpose. Next, we need toselect suitable devices that will supply the energy demanded bythe buildings. The most appropriate equipment and devices avail-able in the market should be investigated by considering bothtechnological and economic aspects. Once the equipment anddevices are in place, we can estimate how much LNG will berequired to meet the load demands through a process known asoperational simulation. We developed an operational simulatorspecifically for this computation.

Nevertheless, we still need forecasts at a more fundamentallevel to properly account for the population growth in the complexand the increase in the per capita energy consumption over time.The occupancy rate and the rates of increase in electricity and ther-mal energy in the complex represent these effects. We will not tryto forecast these two factors ourselves. As a matter of fact, theoccupancy rate is such an integral part of the whole project thatthree independent organizations had already performed rigorousforecasting studies at the onset of the project. As the three fore-casts were not in good agreement, we will take them as differentscenarios for the resident-occupancy rate in our analysis. We willalso directly adopt a forecast for the rate of increase in the electric-ity consumption through a reliable government agency. However,we will use our previous experiences to forecast the rate ofincrease in thermal energy [42,43].

The main contribution of our research is to organize a widerange of related data and develop a handy software package basedon knowledge and experience of an expert group. The procedurewe established while developing the operation simulator isexpected to serve as a model case for similar research or projectsthroughout the world.

2. Material and methods

2.1. Initial energy demand and rate of increase in energy consumption

For each year of the project span, the electricity, heating, hotwater, and cooling demands for the CES are calculated for thewhole group of member buildings. One of the advantages of theCES is that the load profiles can be substantially flattened out ifgrouping is made judiciously. The reason is that when a set of timeseries that has the peak values at different time is added, theresulting series tend to spread out. For example, religious servicebuildings have more demand on weekends while schools havemore demand during weekdays. Adding the two types with peaksat different time will result in a profile that has less fluctuation. It iswell known that the flatter is the load profile the less stress isimposed on the power grid. We need load profiles in the form oftime series (preferably with an hourly resolution) to perform theoperational simulation appropriately.

Recently, we developed a set of load models for a CES consistingof up to 17 different types of building [43]. In the model, Hourlytime series for the four load types were derived for a span of a yearusing basic information on the member buildings of the CES. Ourbuilding energy demand model consists of the followingcomponents.

– The daily loads per unit area for four types of load (electricity,heating, hot water, and cooling) for 17 building types (PublicBuilding, Apartment, Theater, School, Broadcasting Company,Shopping Complex, Hospital, Office, Residence-Office Combo,Church, Residence-Store Combo, and Hotel).

– The 24-hourly distribution of the loads for each month for eachbuilding type and each load type.

– Correction factors that take the effects of major variables intoaccount. Factors include region, insulation, building direction,building aspect ratio, etc., for each building type and each loadtype.

The combined energy demand profiles of electricity, heating,cooling and hot water supply for each hour over a time span of ayear can be constructed as follows. For the d-th day of the h-thhour, electricity (i = 1), heating load (i = 2), hot water load (i = 3),and cooling load (i = 4) demands of the building b of building typej is expressed as.

Libðd; hÞ ¼ Abai;j

1 ai;j2 ai;j

3 ai;j4 ai;j

5 ai;j6 f i;j

m;hljd: ð1Þ

For each building type j, models for the hourly distribution (f i;jm;h)

for an hour h for a month m, the daily unit load (ljd) for a day d along

with the correction factors (ai;jk ) are published in our previous stud-

ies [42,43] for each type of load (i is the index for load type). Thehourly distribution is subject to the constraint of

P24h¼1f i;j

m;h ¼ 1 forany load i for load type j for a month m, due to the normalization.We provided a user interface shown Fig. 4 to aid users to input thebuilding floor area for a building b (Ab) and the type of the building(j). Typical users would be project managers or supporting staffsand no expert knowledge is required to run our tool. The correctionfactors for load estimation are listed in Table 2.

Relational database technology is particularly convenient whenprocessing many pieces of related data. The contents of Table 2, the

Fig. 4. Building profile input form.

Table 2Correction factors building energy demands.

Factorindex

Representing effect Possible values for user choice

a1 Geographical location Names of 23 major cities in Koreaa2 Insulation Good, fair, poora3 Building facing N, NE, E, SE, S, SW,W, NWa4 Building aspect ratio 1:1, 1:1.75, 1:3a5 Grade(hotel only) 5 Star, 4 star, 3 star, 2 star, othera6 Heat exchange

methodAir to water, water to water, air to air

1014 M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026

load components (ljd, f i;j

m;h), and the correction factors ðai;jk Þ of Eq. (1)

can readily be converted to database tables. Users provided relatedinformation for component buildings through the interface (seeFig. 4) and they are stored in separate tables in the database. Theload calculations are achieved by a series of database queries byrelating associated tables and applying appropriate mathematicalfunctions. Fig. 5 shows an example of such relations among tablesdefined in a query. The whole data processing calculation is

Fig. 5. Relation among building profile

computerized in the form of a database application based on theplatform of Microsoft Access� (Litwin et al. [44]), as explained inour recent publication [42].

Unfortunately, our package does not have a long-term forecast-ing feature. It only calculates the loads for a typical year, and theresults are applicable only for the present time. We have addressedthis limitation by incorporating the forecast rate of increase inenergy consumption from the Korean government. A Korean gov-ernment agency named KPX (Korea Power Exchange) has pub-lished a long-term electricity demand trend forecast (KPX, 2011)up to Year 14 (2022), as shown in Fig. 6. As our project containsmixtures of buildings of different types, we feel it safe (on the aver-age) to apply the KPX forecast to our estimated loads to predict thefuture behavior of the CES. It is known that KPX used a privatelydeveloped load forecasting expert system named LoFy2005. Theofficial document in local language claims that they used combinedmethod of neural network, knowledge base and regression in theirforecasting. The data in English is posted in the official web site ofthe company [45] or downloadable from the Downloads link on thesame page.

parameters and correction factors.

Fig. 6. Electricity consumption forecast by KPX.

M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026 1015

For the variations in the thermal (heating, hot water, and cool-ing) load, we heuristically applied the following rules:

– 5% Reduction in thermal loads due to improved insulation andconstruction technology applied to the CES, and other intangi-ble factors such as sensible building management and globalwarming.

– 1% Annual increase in thermal loads due to the degradation ofinsulation materials and equipment.

2.2. Rate of building occupancy

As observed earlier, the project started in Year 1 (2009), and thecomplex will be continuously built over a span of five-year con-struction periods. In the meantime, residents will start to movein starting from Year 2. As the occupancy rate is one of the mostimportant aspects of project success, the construction company[46] and sponsoring bank (KDB, Korea Development Bank [47] ofthe project, besides a third-party government agency (KIV, KoreaInstitute of Valuation, [48]), have independently undertaken fore-casting studies. They willingly provided their results for this studywithout disclosing the detailed procedures they followed to obtainthe data. As shown in Fig. 7, each organization provided separateforecasts for apartment buildings, commercial buildings, andhouses (as noted earlier in this paper, the respective forecasts werenot in good agreement).

It is interesting to observe that among the three organizations,the construction company (HEC) made the most pessimistic fore-cast, while the bank (KDB) made the most optimistic forecast ofthe occupancy rate. According to KDB’s forecasts, the CES is fullyoccupied by Year 12 even though construction is completed in fiveyears. The actual residents’ moving in takes place by 2 years (viz.,by Year 3) after the project’s initiation due to the required time forconstruction. In this study, we consider the three forecasts as sce-narios and will conduct forecasting studies with reference to eachof them. We will call them the HEC occupancy rate scenario, theKDB occupancy rate scenario, and the KIV occupancy rate scenario,respectively.

3. Computation

3.1. Building loads

The key parameters of the member buildings of the projects andtheir floor areas are summarized in Table 3. These numbers areparts of the input parameters that users provide using the interfaceshown in Fig. 4.

Other parameters for each member building of the CES suchas the building type, heating method, whether or not each typeof load is provided through cogeneration, and building facingdirection should be specified. As the project span is 14 yearsand long-term variation is accounted for, we should repeat theload forecast for each year. To account for the effects of theoccupancy rate and the increase in electricity and thermalenergy, we have augmented the floor areas of the buildings bythose factors. We call the modified floor areas the effective floorareas. Including all the correction factors, the effective area canbe expressed as

�Ai;jb ¼ Ab ai;j

1 ai;j2 ai;j

3 ai;j4 ai;j

5 ai;j6

� �Oi;j

b Ei;j ð2Þ

where Oi;jb is the augmentation factor for occupancy rate and Ei;j is

for energy increase rate. Notice that we are applying different val-ues of the occupancy rate for different building types (see Fig. 7)while using different values for different loads for the energyincrease rate. For a total number of N buildings, the combined loadfor load i at hour h on the d-th day of the m-th month is calculatedfrom

Liðd;hÞ ¼XN

b¼1

Libðd;hÞ ¼

XN

b¼1

�Ai;jb f j

m;h ljd ð3Þ

Then, we analyze a series of projects corresponding to each yearwith different effective floor areas starting from Year 3 (when mov-ing-in starts) to Year 14 (when moving-in completes). Conceptu-ally, we treat each of them as a separate CES project and viewthe whole program as consisting of a series of such projects.

3.2. Device selection and operational simulation

Knowing how much energy we need is not sufficient to forecastthe LNG demand. We need to figure out specifically how we supplythe energy to meet the demands. Using the gas turbine-steam tur-bine combined cogeneration system, we try to achieve the goal ofenergy self-sufficiency. Our energy supply strategy is summarizedbelow.

– The generator capacity will be bigger than the annual electricitypeak load of each year so that self-sufficient on-site generationis possible.

– The system will run in the heat-tracking mode during themonths of high thermal energy (November to April) and inthe electricity-tracking mode otherwise (May to October) tomaximize the system performance.

– Absorption refrigeration will be used to best utilize the summerwaste heat recovery.

– Storage tanks will be adopted for both hot and cold energy stor-age for smoother operation.

There are still additional details to be worked out. The load esti-mator mentioned earlier produces four sets of time series for eachtype of load. The annual maximum values (or peak loads) of eachtype of load are used for device selection. For example, the maxi-mum of the electricity load is used to choose the cogeneration sys-tem capacity. The maximum heating load is used in conjunctionwith the maximum recoverable heat to select the capacity of theboiler that supplements the heat when the demand exceeds theheat recovery from the cogeneration system. In this study, weselected devices for each project year according to the followinggood-practice rules.

– A set of single-effect absorption chillers that cover the entiremaximum cooling load.

(a) Apartments.

(b) Commercial buildings.

(c) Houses.

Fig. 7. Forecasts of the occupancy rate.

1016 M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026

– A thermal energy storage tank that can hold 6 h of peak thermalenergy demand.

– A cold storage tank that can hold 4 h of peak cold energydemand.

Once all the necessary devices are selected, the operational sim-ulation is ready to run. For each project year, our load model pro-duces 8760-h data for each type of load: electricity, heating, hotwater, and cooling. The power generation and the utilization of the

Table 3Buildings included in the CES.

Building type Land area (m2) Floor area ratio (%) Floor area (m2)

Residential Houses 229,156 94.72 217,067Apartments 1,501,622 180.65 2,712,614Facilities 17,673 250.00 44,183Sub-total 1,748,451 2,973,864

Commercial Officetels 38,406 250.00 96,015Ground stores 115,095 384.23 442,225Basement stores 77,751Sub-total 153,501 615,991

Public Schools 242,079 60.00 145,247Hospitals 9,905 200.00 19,810Offices 16,811 60.00 10,087Religious services 3,995 60.00 2,397Sub-total 272,790 177,541

Total 2,174,742 3,767,396

M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026 1017

recovered heat can be calculated by simulating the operation of thedevices according to the operational schemes described below. Theoperational simulation matches the demand with supply of energyand the overall processes are symbolically presented in Fig. 8.

The following criterion is considered.

– Which tracking mode will be used? The electricity-trackingmode is generally preferred in most cases. In this mode thenumber of cogeneration systems that should be turned on isdetermined in such a way that the electricity demand is satis-fied first. The amount of heat recovery is passively determined.In the heat tracking mode, the combined thermal energy of theheating and hot-water loads determines the number of cogene-ration systems to be in operation.

– Where will the by-product be utilized first and then next? Thebyproduct is heat for the electricity-tracking mode and electric-ity for the heat-tracking mode. Generally, the byproduct is usedfirst for the operation of the device that entails the highest oper-ating cost.

After careful examination of the load and device characteristics,we decided to follow a strategy outlined below.

– During the heat-tracking months of November to April, electric-ity will be either over-produced or under-produced. We willbuy the deficit from a utility company and call this quantityEbuy. We will sell the surplus to the company and call this quan-tity Esell.

– During the electricity-tracking months of May to October, therecovered heat from the cogeneration plant is either sufficientor deficient to cover the combined thermal loads of heating,

Fig. 8. Schematic representatio

cooling, and hot water. If it is sufficient, we will dump the heatand call this quantity Qdump. Otherwise, we need auxiliary heatfrom the supplementary boilers and call this quantity Qaux.

For every 8760 h of each project year, the following steps areexecuted for the simulation of the system operation.

1. The number of engines that should be turned on is determinedduring the electricity-tracking months. As the total number ofengines is already decided in such a way that the total capacityof the cogeneration engines is larger than the annual peak of theelectricity demand (self-sufficiency), not all engines are in oper-ation most of the time. The required number of engines isdecided by dividing the load by the capacity of a single engine.Then, the selected sets of devices optimally share the requiredload. Two modes of load sharing are possible here: even-sharingand uneven-sharing. In the even-sharing mode, the requiredelectricity load is produced evenly by all the engines that areturned on, and every engine runs on the same part load condi-tion. In the uneven-sharing mode, all the engines run at theirfull capacity except the last one. Only the last engine ends uprunning at a partial-load condition in this case. We adoptedthe even sharing in this study.

2. During the heat-tracking months, the number of enginesturned on should be determined by comparing required totalthermal energy and heat recovery capacity of the engine. Asa by-product electricity is consumed locally as much as theelectricity load requires and the surplus is sold to a utilitycompany.

3. Once the number of engines that should be turned on is deter-mined, the part load fraction can be decided by:

n of simulation processes.

Fig. 9. Efficiency curves for the cogeneration engine adopted in this study.

1018 M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026

v ¼ PPmax

;

where P is the power production and Pmax is the power productionat full-load capacity.4. The reason the part load fraction is important is that device effi-

ciencies primarily are a function of v. Engines perform differ-ently, and the efficiency varies from engines to engines. In

this study, a pre-selected engine is considered for simplicity.The thermodynamic efficiency (gt) and the heat recovery effi-ciency (gR) of a cogeneration engine can be determined by thedevice-specific efficiency curves that the manufacturers pro-vide. The efficiency curves for the engine adopted in this studyare shown in Fig. 9. Note that the numbers in the legend of Fig. 9represent the ambient temperature in degrees Celsius for the

Fig. 10. Time series for loads (Year 14).

Fig. 11. Annual peak loads for Year 14.

M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026 1019

line. The individual capacity of the engine is 22.57 MW. The effi-ciencies are also functions of the ambient temperature for com-bined gas turbine-steam turbine cogeneration engines. Ingeneral, the efficiencies of devices other than engines are alsofunctions of the part load fraction (v) for the devices, and thefunctional relation is usually specified by the manufacturer.

For the temperature required in estimating the efficiencies pre-sented in Fig. 9, the hourly ambient temperatures averaged overthe most recent 20 years are used. The weather data is providedby Korea Meteorological Administration. As the curves in the figureare given for discrete values, interpolation calculation is adoptedduring the simulation. As the main focus of this study lies on theconsumptions of LNG, parasitic electrical power required runningthe system components such as pumps, heat exchangers, or cool-ing towers are neglected. Perfect insulation of the distributing sys-tem is also assumed to avoid heat loss calculation from the pipes.

In this study, we automated all the above processes by incorpo-rating both device selection and operational simulation into a sin-gle application based on Microsoft Access�. Regarding by-products,the operational simulation produces many pieces of detailed tech-nical information associated with power production and heat utili-zation, for example, how many devices are turned on, how muchenergy is produced, and how much energy should be supple-

mented from external sources. However, the main quantities forthis study are the LNG consumption and the associated amountsof electricity trade (Esell and Ebuy).

4. Results and discussion

4.1. Results of load estimation

We estimated 8760-h load demands for each project year fromYear 3 to Year 14 for electricity, heating, hot water, and cooling.Fig. 10 shows the results for Year 14 when the CES is fully occu-pied. Significant short-term and long-term variations are observedin all types of load.

When we originally developed the load models for apartments,which are the main type of building in this study, the cooling loadwas merged into the electricity demand. We did so because prac-tically all apartments in Korea, electricity-driven turbo refrigera-tors serve the cooling load. From a computational point of view,it does not make any difference because the cooling load servedby turbo refrigerators is converted into electricity, whilst properlytaking device performance into account. The maximum values ofthe loads for Year 14 are plotted in Fig. 11. Two quantities arecompared side-by-side in the figure: the sum of the individual

(a) Electricity.

(b) Heating plus hot water and cooling.

Fig. 12. Variation of the annual peak loads.

1020 M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026

maximum values for each type of building and the maximum ofthe loads for the combined buildings. The two quantities are notthe same because the time-points when the peak loads occur foreach type of building do not coincide in general; hence, the maxi-mum of the combined loads is equal or smaller than the sum of theindividual maxima. This means that in general, combining build-ings reduces the peak loads, and this is considered one of the mainadvantages of using CES.

Fig. 12 shows how the annual peak loads vary as the projectprogresses. The primary factor that determines the values is theoccupation rate as hinted by the resemblance of the curves withthose in Fig. 7. Other factors such as the energy consumption ratevary much less significantly compared with the occupancy rate.The annual peak values for electricity are used as reference valuesfor choosing the minimally required number of engines for eachproject year. In Fig. 12(a), the solid step line at the upper left cornerrepresents the total engine capacity required to cover the peakloads. As each engine has a fixed capacity of 22.57 MW, the num-ber of engines should increase to accommodate the increase inthe peak electricity load. This gives rise to the step change shownin the figure. The maximum values for the electricity, combinedheating and hot water, and cooling loads for Year 14 (the year of

full occupation) are 55.8 MW, 118.8 MW, and 56.7 MW,respectively.

Fig. 13 shows the annual sums of the loads. Here again, we canclearly see that the occupancy rate is the main mover. The annualsums for the electricity, combined heating and hot water, and cool-ing loads can grow up to 994 TJ, 1013 TJ, and 199 TJ, respectively, inthe last year of the project (Year 14).

4.2. Results of operational simulation

We selected devices and simulated the operation of the cogen-eration system for all 12 project years spanning from Year 3 (whenresidents start to move in) to Year 14 (when the complex is fullyoccupied). Fig. 14 shows the monthly LNG demands for Years 5,7, 10, and 14 for the three different scenarios in terms of the occu-pancy-rate forecasts. In the early project years, the scenario-to-sce-nario variation of the LNG demand is significant. As the projectprogresses and the occupancy rates converge, so do the LNGdemands. LNG is in most demand during the months of Decemberand January and is roughly twice as much as that of the minimumvalue in April.

(a) Electricity.

(b) Heating and hot water.

(c) Cooling.

Fig. 13. Annual cumulated load demand.

M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026 1021

Fig. 14. Monthly trends in LNG demand.

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The forecast for the annual consumption of LNG for the entirespan of project years is presented in Fig. 15. As expected, theLNG demand profile roughly resembles the annual electricitydemand plotted in Fig. 13(a) even if we mentioned earlier thatthey are not directly proportional to each other. By Year 14(2022), 69,000 tons/year of LNG is estimated to be required bythe CES.

We may expect that the LNG consumption for the year wouldbe directly proportional to the total electricity demand. This isnot true though. First of all, in cogeneration systems, LNG is used

for both electricity generation and auxiliary heat supply. Thismeans that both the electricity and heating demands will affectthe LNG consumption. As noted earlier, the engine efficiency is afunction of the part load fraction, and the part load fraction isnot uniform through the year as the demands change indepen-dently. Furthermore, as both electricity tracking and heat trackingare employed, the effects of the electricity and heating loads arereflected in the LNG consumption in a mixed manner. We can con-clude that the most accurate way to calculate the LNG consump-tion is through an hour-by-hour operational simulation.

Fig. 14 (continued)

Fig. 15. Annual LNG demand forecast.

Fig. 16. Annual total electricity generation.

M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026 1023

Electricity generation also is a very important quantity for theproject. Fig. 16 shows how the annual total electricity generationvaries as the project progresses. Note that each year, nearly 30%

more electricity is generated than demanded [shown in Fig. 13(a)].This is because electricity is expected to be over-produced duringthe heat-tracking months of November to April every year.

Fig. 17. Electricity trade profile for Year 14.

Fig. 18. Heat utilization for heating (Year 14).

1024 M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026

Fig. 17 shows the monthly electricity generation and trade pro-files. It is evident from the figure that electricity is over-producedduring the heat tracking months. Note that there still are electricitytrades during the electricity-tracking months of May to October. Bydefinition, no electricity should be sold or bought during the elec-tricity-tracking period. However, the cogeneration engine is notallowed to be turned on when the part load fraction is less than0.3 (Fig. 9) to prevent running at too low an efficiency. This invitesa temporary electricity shortage. We counted this deficit as Ebuy

and compensated it by over-producing the same amount in the nexttime-step. Then, we counted the over-produced amount as Esell forthe corresponding time-step. That is why we have about the sameEsell and Ebuy in Fig. 17 during the electricity-tracking months.

The validity of the device selection rule (what we called good-practice rules in Section 3.2) and tracking strategy (heat trackingfor November to April, electricity tracking for May to October)should be checked through the thermal energy utilization profiles.Fig. 18 shows how the recovered heat is utilized for the combinedthermal loads of heating and hot water. For limited spans of hours,the recovered heat is not sufficient to supply the required heat.This is because the engine capacity has been chosen to sufficientlycover only the maximum electricity. The maximum amount ofrecoverable heat happened to be slightly short of what was neededto cover the maximum combined thermal load due to the loadstructure of this particular project. Nonetheless, we can observe

in the figure that the auxiliary heat supply from the supplementaryboiler remains at a minimal level, and the recovered heat satisfac-torily covers the thermal load with negligible waste during most ofthe heating months of November to April.

The utilization of the recovered heat during the cooling monthsis presented in Fig. 18. The COP (coefficient of performance) of theabsorption refrigerator that utilizes the recovered heat is about 0.7.This means that the amount of the required heat recovery is about1.4 (1/0.7) times the cooling load, as indicated by the line for Qabs inthe figure. We can also observe that parts of the recovered heatshould be dumped especially during the early and late cooling sea-sons when neither the heating nor the cooling load is large. Gener-ally, we can conclude that the device selection rule and thestrategy for operational simulation are technologically acceptablebased on assessments of Figs. 18 and 19.

Even though we only presented partial results for Year 14, tech-nological details for other years are also calculated. The completeresults include a range of detailed technological data such as thenumber devices turned on, part load fraction distribution, and heatstorage activities.

5. Conclusions

Our calculation reveals that about 994 TJ of electricity alongwith 1013 TJ of heating and 199 TJ of cooling energy a year will

Fig. 19. Heat utilization for cooling (Year 14).

M. Chung et al. / Energy Conversion and Management 87 (2014) 1010–1026 1025

be necessary when the CES is fully occupied in Year 14 (2022); fur-ther, it will require 69,000 tons of LNG in that year. Obviously, theLNG consumption can be changed if any arrangement or scheme isaltered from the setups that our calculation is based on. We needto recap those important assumptions that are applied to obtainthe results of this study.

– The boundary of the CES is fixed as to include only the buildingmembers specified in Table 2.

– The occupancy rate is growing according to the three scenariosforecasted by: HEC, KDB, and KIV (Fig. 7).

– The cogeneration engine is the 22 MW gas turbine-steam tur-bine combined engine with the efficiency described in Fig. 9.

– Device selection is heuristically made in a good-practice man-ner (Section 3.2).

– Electricity demand grows according to Fig. 6 with an annual 5%reduction in the thermal energy loss due to good constructionpractices, and a 1% increase due to material degradation.

Any modification that makes the device selection far off fromthe optimal combinations or the system operate away from thegood strategy will cause energy to be wasted and cause an unnec-essary increase in LNG consumption. In this sense, our LNG con-sumption forecast should be interpreted as being the minimumLNG requirement based on sound technology that is well-designed and implemented. Trying to forecast load demandsand LNG consumption as far as 14 years seems to be unrealisticbecause changes in plans, sizes and technology can occur. Thereason we need such a long term forecast was that the life timeof cogeneration systems generally spans about 20–30 years, anddecision makers wanted to find the economic feasibility. We needto base our calculation on using 8760 hourly (24 by 365) to makesure that integration over time is meaningful. We have to inte-grate over a span of time (a year) to find the annual sum ofLNG consumption. Obviously, integration is path dependent anddetailed function behavior must be specified for a properevaluation.

Cogeneration based CES has only a short history in Korea, andwe have to wait for a while to compile field data. By that time,the validity of our approach is expected to be fully assessed.Recently several CHP plants started in cities like Seoul and Busanand Daejon and reported positive responses from the residents.The future of CHP is bright, especially in the light of emerging tech-nology named Smart Grid.

Acknowledgements

This publication was supported by the Korea Institute of EnergyResearch through the foreign exchange program while the corre-sponding author was spending his Sabbatical Leave at the Univer-sity of California, San Diego.

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