carvalho 2011 geographic evaluation of trigeneration systems in the tertiary sector

7/30/2019 Carvalho 2011 Geographic Evaluation of Trigeneration Systems in the Tertiary Sector

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Geographic evaluation of trigeneration systems in the tertiary sector. Effect ofclimatic and electricity supply conditions

Monica Carvalho, Luis M. Serra*, Miguel A. LozanoDepartment of Mechanical Engineering Group of Thermal Engineering and Energy Systems (GITSE), Aragon Institute of Engineering Research (I3A), University of Zaragoza,

Zaragoza, Spain

a r t i c l e i n f o

Article history:

Received 15 September 2009Received in revised form19 February 2010Accepted 22 February 2010Available online 31 March 2010

Keywords:

TrigenerationMixed integer linear programming (MILP)OptimizationEnvironmental optimization

a b s t r a c t

The development of trigeneration systems is especially important in the buildings sector, where thethermal loads are imposed by the needs of heating, domestic hot water, and cooling. A strong seasonalcharacter is indicated, since the demands depend totally on local climatic conditions and vary consid-erably throughout the year. Geographic locations were chosen so as to represent the climatic variety inSpain: Canary Islands, Mediterranean Coast, Atlantic Coast, and different locations in the interior of theIberian Peninsula. The solution of a mixed integer linear programming model (MILP) that incorporatedlocal economic/environmental conditions determined the optimal configuration of the different energysupply plants as well as the optimal operation modes throughout an entire representative year. From aneconomic point of view, the optimal configuration for all localities included cogeneration modules. Froman environmental point of view, the optimal solution was strongly dependent on the origin of theelectricity supplied by the grid.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

Trigeneration is the simultaneous production of a threefoldenergy vector (electricity, heating, and cooling) from the samesource of energy (natural gas, for example). Trigeneration systemsallow greater operational flexibility at sites with a variable demandfor energy in the form of heating and cooling. At present, thedevelopment of trigenerationsystems is especially important in thebuildings sector, where thermal loads are imposed by the needs ofheating, domestic hot water, and cooling. A strong seasonal char-acter is indicated, since the demands depend totally on localclimatic conditions and vary considerably throughout the year. Thisis particularly relevant in buildings located in Southern Europe andMediterranean countries where the need for heating is restricted to

a few winter months. In summer, the absorption chillers make useof the cogenerated heat to produce chilled water, avoiding wasteheat discharge.

It is well known that the tertiary sector (considered by Eurostat,the Official European Union Statistical Information Service, as theFinal Energy Consumption Sector e Households and Services) isa major energy consumer, especially in the Mediterranean area,

where a substantial cooling load exists during the summer period,and consequently the sector can benefit from the use of trigenera-tion [1]. Advantages of trigeneration applications in tertiary build-ingswereclearlydemonstratedwithimportanttechnical,economic,and environmental impacts, contributing to the competitiveness,environmental protection, and security of supply [2e4].

In 1993 CouncilDirective 93/76/EEC [5] (regardingthe limitationof CO2 emissions through the improvement of energy efficiency inbuildings) recognized explicitly the importance of buildings in theemission of CO2. According to Directive COM 2002/91/EC [6] (on theenergy performance of buildings), the tertiary sector is responsiblefor more than 40% of the final energy consumption in the EuropeanCommunity. This directive imposed that for new buildings witha total usable space area over 1000 m2, thetechnical, environmental,

and economic feasibility of alternative energy systems, such ascogeneration, must be considered and taken into account beforeconstruction starts.

As mitigation in the tertiary sector includes measures aimed atelectricity savings, it is useful to compare the mitigation potentialwith carbon dioxide emissions, including those through the use ofelectricity. When including the emissions from electricity use,energy-related carbon dioxide emissions from the buildings sectorwere 8.6 Gt/yr, or almost a quarter of the global total carbon dioxideemissions [7]. By transforming the built environment into a moreenergy-efficient environment, the tertiary sector also can playa major role in reducing the threat of climate change.

* Corresponding author. Tel.: 34 976 761883; fax: 34 976 762616.E-mail addresses: [email protected] (M. Carvalho), [email protected]

(L.M. Serra), [email protected] (M.A. Lozano).

Contents lists available at ScienceDirect

Energy

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e n e r g y

0360-5442/$ e see front matter 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.energy.2010.02.036

Energy 36 (2011) 1931e1939
mailto:[email protected]:[email protected]:[email protected]://www.sciencedirect.com/science/journal/03605442http://www.elsevier.com/locate/energyhttp://www.elsevier.com/locate/energyhttp://www.sciencedirect.com/science/journal/03605442mailto:[email protected]:[email protected]:[email protected]


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Climate change represents one of the greatest environmental,social, and economic threats facing the planet. For different sectorsof human activities, a number of key technologies and practices arecurrently commercially available that could contribute to climatechange mitigation. The need to address sustainability in the builtenvironment is being accelerated by external pressures such asenvironmental and resource concerns, rising energy prices, indoorenvironmental quality, global warming, and energy security. Whileeconomies transition from carbon-based to other forms of moresustainable energy, engineers will be challenged to meet an ever-increasing tide of regulation and demand [8].

Less intensive technologies (when compared to coal or oil, forexample) that generate electricity from renewable or less pollutingenergies, such as natural gas, increase their weight in the techno-logical mix of the electricity sector in Spain, and both governmentand electricity sector predict that its production quota will increasein the next few years. Climate change mitigation strategies includethe correct selection of available primary energy as well as theimprovement of the efficiency of the technologies employed inheating and cooling: co- and tri-generation technologies arementioned in the Climate Change Mitigation Report as options tomitigate greenhouse gas emissions in buildings [7].

As environmental awareness increases, industries and busin-esses are assessing how their activities affect the environment.According to the Environmental Protection Agency of USA (EPA) [9],the environmental impacts of products and processes have becomea key issue, analyzed using pollution prevention strategies andenvironmental management systems to improve environmentalperformance. One such tool is Life Cycle Assessment (LCA). LCAenables the estimation of the cumulative environmental impactsresulting from all stages in the product life cycle, often includingimpacts not considered in more traditional analyses (e.g., rawmaterial extraction, material transportation, ultimate productdisposal, etc.). By including the impacts throughout the product lifecycle, LCA provides a comprehensive view of the environmentalaspects of the product or process and a more accurate picture of the

true environmental trade-offs in product and process selection [10].A hospital of medium size was selected to reflect the demands of

heat (Sanitary Hot Water (SHW), and heating), electricity, andcooling. As thermalloads (heating and cooling) are highly influencedby climate, an analysis was carried out to verify the effects of geog-raphy and origin of electricity on the optimal design of an energysupply plant, from economic and environmental points of view.

ThegeographiclocationswerechosensoastorepresenttheclimaticvarietyinSpain(Fig.1):SantaCruzdeTenerife(CanaryIslands),Almera(southern Mediterranean coast), Valencia (eastern Mediterraneancoast), Lugo (northwestern Spain), and Huesca, Zaragoza, and Teruel(northeastern Spain, from north to south, respectively). The followingclimatic datawere obtainedfromtheState Meteorological Agency [11].

Tenerife,a Spanish island, is thelargestof theseven Canary Islands

in the Atlantic Ocean off the coast of Africa. The island, being ona latitude of the Sahara Desert, presents warm, allyear roundclimatewith an average of 19.0 C in winter and 24.0 C in summer, withannual average precipitation of 214 mm and sunshine all year round.

Almera is the capital of the province of Almera, Spain. It islocated in southeastern Spain on the Mediterranean Sea. Almera isthe driest region in Europe (annual average precipitation of196 mm) as well as one of the warmest. It presents an averageannual temperature of 13.8 C in winter and 24.7 C in summer,with 330 days of sun per year on average.

Valencia is thecapital of the autonomouscommunityof Valenciaand presents a very changeableMediterranean climate, with hotdrysummers, mild humid winters, and stormy autumns and springs,with an annual averagetemperatureof 13.0 C inwinterand 24.0 C

in summer, and annual average precipitation of 454 mm.

Zaragoza is the capital city of the autonomous community ofAragn. Zaragoza has a Mediterranean continental desert climateas it is surrounded by mountains, with an annual averagetemperature of 8.0 C in winter and 22.7 C in summer, and annualaverage precipitation of 318 mm.

Huescais located in Aragn, and capital of theSpanishprovince ofthe same name. Huesca presents an annual average temperature of6.6 C in winter and 21.5 C in summer, with an annual precipitationof 535 mm. The climatic characteristics of the zone were formedunder the influence of the Atlantic Ocean and Mediterranean Sea,corresponding to a dry Mediterranean climate, with cold winters.

Teruel is a city in Aragn, the capital of Teruel Province, witha remote and mountainous location (915 m above sea level), and anannual average temperature of 5.2 C in winter and 19.6 C in

summer, with an annual precipitation of 373 mm. In summer thetemperaturesaremild,althoughwithgreat thermaloscillation, andinwinter, coldenough to support afixedperiodofsnowcovereachyear.

Lugo is a city in northwestern Spain, in the autonomouscommunity of Galicia. It is the capital of the province of Lugo,presenting an annual average temperature of 7.1 C in winter and16.7 C in summer, and annual precipitation of 1084 mm. Theclimate of Lugo is Oceanic, in general mild and humid (due to theAtlantic influence), but highly variable throughout the year.

Temperature and precipitation data for all geographical loca-tions considered are resumed in Table 1.

This paper accomplishes the synthesis of trigeneration systemsto be installed in hospitals throughout Spain from an environ-mental viewpoint. Considered were the global environmentalimpact and the total amount of CO

2released into the atmosphere

during the complete life cycle of the system. The global environ-mental impact was evaluated by applying LCA techniques (utilizingthe Eco-indicator 99 method [12]). The objective function waschanged to optimize the annual total cost (V/yr) and verify thechanges implied. Many feasible configurations with differentoperation modes were involved in an analysis, resulting ina complex and difficult issue to solve.

2. Trigeneration system and economic scenario

Thetrigenerationenergysupplysystem analyzed in thispaper hasthefollowingfeatures:i)itproduceselectricity,cooling,andheat;ii)itcoverspreciselythedemandofheat(heatingandSHW)andcoolingof

the energy consumption system; iii) the surplus electricity produced,

Fig. 1. Chosen locations in Spain.

M. Carvalho et al. / Energy 36 (2011) 1931e19391932


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which is not self-consumed internally in the energy supply system,can be sold to the electric grid; iv) it fulfills all legal requirements toinstall and operate a cogeneration plant in Spain.

Fig. 2 shows the superstructure of the trigeneration energysupply system. This reducible structure contains all feasibleconfigurations (structures) for the energy supply system. Theobjective is to find the optimum feasible configuration by applyingmathematical optimization techniques.

The superstructure of the trigeneration system counts on thepossibilityof installingtechnologies suchas: TGVA(gasturbine heatrecovery boiler, producing vapor and hotwater), CGVA (steam boiler),MGWH (gas engine hot water heat recovery system), ICVA(vaporehot water heat exchanger), CGWH (hot water boiler), ICWH(hot waterecooling water heat exchanger), FAVA (double effect

absorption chiller, driven by steam), FAWH (single effect absorptionchiller, driven by hot water), FMWR (mechanical chiller, driven byelectricityand cooled by water), andICWR(cooling tower, to evacuateheat from the cooling water).

The available utilities are CG (natural gas), VA (high temperaturesteam,180 C), WH (hot water, 90 C), WR (cooling water, t0 5 C),AA (ambient air, t0), WC (cold water, 5 C), and EE (electricity).

2.1. Energy demand

The annual demand was expressed considering 24 represen-tative days throughout the year (two representative days for eachmonth of the year: one working day and one holiday/weekendday) divided into 24 periods of 1 h. This means that the demand

of the energy consumption system was characterized by 576periods. Representative energy demand patterns for each repre-sentative day were calculated according to the procedure

described by Snchez [13], which estimated monthly, daily, andhourly profiles of the different energy demands based on the sizeof the hospital and its geographical location in Spain. Climaticdata for each location were obtained from the State Meteoro-logical Agency [11]. Table 2 shows the annual demands for thedifferent hospital locations.

2.2. Equipment

Table 3 presents the selected equipment and technicalproduction coefficients for the superstructure. The rows indicatethe potentially installable technologies and the columns indicatethe utilities. The production coefficient with a highlighted 1 showsthe flow that defines the capacity of the equipment. Positivecoefficients indicate that the utility is produced, and a negativecoefficient indicates the consumption of such utility.

The equipments considered in the optimization were selectedamong commercially available pieces of equipment. Technical andcost data were obtained from equipment catalogs and fromconsultation with manufacturers. CIi is the investment cost of theselected equipment of technology i, obtained from the catalog priceand multiplied by a simple module factor which took into accounttransportation, installation, connection, insulation, etc. The totalplant cost was obtained by adding indirect costs, including engi-neering and supervision expenses, legal expenses, contractor's feesand contingencies, assumed to be equal to 15% of the equipmentinvestment costs (complete analysis in Lozano et al. [14]).

The capital recovery factor, fcr, multiplied by the total plant cost

gives the cost of servicing the required capital [15]. Assuming thatthe interest rate iyr and the equipment lifetime nyr are the same forall types of equipment, the capital recovery factor is given by:

Table 1

Annual average precipitation and temperatures for the geographical locations considered.

Sta. Cruz Tenerife Almera Valencia Zaragoza Huesca Teruel Lugo

Precipitation (mm) 214 196 454 318 535 373 1 084Temperature in summer (C) 24.0 24.7 24.0 22.7 21.5 19.6 16.7Temperature in winter (C) 19.0 13.8 13.0 8.0 6.6 5.2 7.1

Fig. 2. Superstructure of the energy supply system.

M. Carvalho et al. / Energy 36 (2011) 1931e1939 1933


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fcr iyr$1 iyrnyr

1 iyrn 1(1)

Considering the lifetime of the plant to be 15 years with an interestrate of 0.10 yr1 (reasonable for the actual economic circumstancesin Spain), an annual capital recovery factor of 0.13 yr1 wasobtained. Annual maintenance and operating costs, different fromenergy costs, were considered as equal to 7% of the total plant cost(fmo 0.07 yr1). The factor fam took into account both mainte-nance and capital recovery factors:

fam fmo fcr (2)

2.3. Gas and electricity rates

Since 2003, when gas and electricity markets in Spain wereliberalized, consumers can choose a supplier and leave the regu-lated-rate system or remain connected to the regulated market ifthey prefer not to partake in the liberalized market. Here theregulated-rate system was considered.

In the case of natural gas in Spain, the consumer chooses themost adequate rate in function of consumption volume and supplypressure. For the purchase of natural gas, tariff 2.4 from the reso-lution on regulated natural gas rates [16] was chosen consideringthe amount of natural gas consumed. This investigation considereda constant cost of Cg 0.025 V/kWh for natural gas, after addingtaxes and distributing the fixed costs throughout the estimatedannual consumption.

For electricity purchase, tariff 1.1 was chosen, considering theamount of electricity consumed, from the resolution on regulatedelectricity rates [17]. Considering other costs such as taxes, andapproximating the distribution offixed costs, an electricity price of0.095 V/kWh was utilized throughout the year. However, there isa supplement that discriminates the price of electricity by hour ofuse. Hourly discrimination #2 was chosen from reference [17]. Theday was divided into two periods: 4 on-peak hours with a 37%increase in price, and the 20 remaining hours with no increase ordiscount in price. The final electricity price, Cep, was 0.095 V/kWhfor off-peak hours, and 0.130 V/kWh for on-peak hours.

For the sale of surplus autogenerated electricity, reference [18]established the tariff and premium, based on the power output andfuel utilized by the plant. Considering the energy demand for thehospital and the nominal power of the cogeneration modules,

Subgroup 1.1 (cogeneration installations using natural gas) witha1000e10,000 kW capacitywas utilizedto obtain Ces 0.077V/kWhas the price for sold electricity. The Equivalent Electrical Efficiency(EEE) of theplant must be at least equalto thatfixedin reference[18],which was 59% for gas turbines and 55% for gas engines. EEE iscalculated on an annual basis with the formula

EEE Ec

Fc Qc0:9 (3)

where Ec is the generated electricity, Fc is the consumption ofprimary energy measured by the fuel's Lower Heating Value (LHV),and Qc is the cogenerated useful heat.

3. Environmental evaluation

Two environmental criteria were chosen to carry out theoptimization, the EI-99 Single Score (an environmental indicatorthat encompasses several impact categories and therefore givesa more global environmental perspective), and the CO2 emissions.

Substantial reductions in CO2 emissions from energy use in

buildings can be achieved using energy-efficient technologies thatalready exist, such as trigeneration, where significant savings inprimary energy are possible. Design strategies for energy-efficientbuildings should include the selection of systems that make themost use of energy sources and also operate optimally. Also CO2emissions were selected to quantify the environmental loadsbecauseglobal heating and the associated climate change areone ofthe main medium- and long-term identified threats, with greatconsequences on a global scale [7].

SimaPro [19], a specialized LCA tool, was used to calculate theimpactassociatedwiththeproductionandfinaldisposalofeachpieceof equipment portrayed in the superstructure, as it includes severalinventory databases with thousands of processes, plus the mostimportant impact assessment methods. SimaPro was also utilized to

calculate the impact associated with the operation of the system(consumption of natural gas and purchase/sale of electricity).A framework for LCA has been standardized by the International

Organization for Standardization (ISO) in the ISO 14040 series [20,21]. It consists of the following elements: (i) Goal and scopedefinition which defines the goal and intended use of the LCA, andscopes the assessment concerning system boundaries, function andflow, required data quality, technology and assessment parameters;(ii) Life Cycle Inventory analysis (LCI), an activity for collecting dataon inputs and outputs for all processes in the product system; (iii)Life Cycle Impact Assessment (LCIA), the phase of the LCA whereinventory data on inputs and outputs are translated into indicatorsabout the product system's potential impacts on the environment,on human health, and on the availability of natural resources; and(iv) Interpretation, the phase where the results of the LCI and LCIA

Table 3

Selected equipment and matrix of production coefficients.

Technology Selected equipment Utility

Cost CIi (103V) Nominal power Pnom (MW) CG VA WH WR AA WC EE

TGVA 1530 1.21 4.06 1.83 0.53 D1MGWH 435 0.58 2.45 0.96 0.20 D1CGVA 182 0.78 1.20 D1CGWH 30 0.57 1.08 D1ICVA 2.5 0.40 1.00 D1ICWH 6.5 0.40 1.00 D1FAVA 370 1.26 0.83 1.83 D1 0.01FAWH 200 0.49 1.50 2.50 D1 0.01FMWR 175 0.49 1.23 D1 0.23ICWR 25 1.00 1.00 D1 0.02

Table 2

Heat, cooling, and electricity demands for the hospital locations, in MWh/yr.

Sta. CruzTenerife

Almera Valencia Zaragoza Huesca Teruel Lugo

Heat 3511 5189 5675 8059 9431 10,646 10,189Cooling 2500 2170 2077 1265 843 400 0Electricity 3250 3250 3250 3250 3250 3250 3250



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are interpreted according to the goal of the study and wheresensitivity and uncertainty analysis are performed to qualify theresults and the conclusions.

There are different available impact assessment methods that

utilize different environmental criteria and therefore evaluate andassess different environmental aspects. EI-99 [12] was selectedbecause it is widely used in LCA, incorporating relevant environ-mental burdens into different impact categories, which in turnallow the evaluation of damages to human health, ecosystemquality, and resources. Furthermore, the LCA results of EI-99 areaggregated into an easily understandable number, the Single Score,and from a computational point of view, are appropriate to beintegrated into an optimization model.

The EI-99 method (utilized for the LCIA) considers the values ofeleven impact categories, which are added into three damage cate-gories(Fig.3),weighted,andthenaggregatedintoanindex,theSingleScore, which represents theoverall environmental loadin points. Onepoint (pt) can be interpreted as one thousandth of the annual envi-

ronmental load of one average European inhabitant. In order toaccount for the subjectivity of the impact assessment procedure,EI-99 presents three different perspectives, with different impactperceptions, different normalizing factors and weights and thus,leading to different results. The Hierarchist version of the damagemodel was selected for its balanced time perspective.

TheCO2 emissions associated with theproductionof each type oftechnology were calculated utilizing SimaPro, entering data on themanufacturing of the equipment (materials) and selecting the finalinventory for CO2 (utilizing IDEMAT, ETH/ESU, and Ecoinventdatabases [22e24]). The following assumptions have been consid-ered: 100% of the materials was landfilled (worst case scenario, norecycling),anyoilor fluidwasconsideredasanemissionintothesoil,and gases (R134a, for example) were considered to be discharged

into the atmosphere. Transportation of the equipment fulfilledEuropean directive EURO V[25], using a 32t-truck. Average productmanufacturing was considered for each metal [24].

The Single Score was obtained when utilizing the EI-99method to evaluate the overall environmental impact of theinventory stage. Table 4 presents the technologies, the materialcomposition, the CO2 emissions associated with each technology,CO2I, and the Single Score for each technology obtained byapplying EI-99, SSI(i).

The CO2 emissions associated with the consumption of naturalgas in Spain were also obtained by utilizing SimaPro, and calcu-lated as EMg 0.272 kg CO2 per kWh of consumed natural gas(utilizing the related emissions of burning natural gas, from theIDEMAT 2001 database [22], and the total aggregated system

inventory for a natural gas consumer in Spain, from the Ecoinvent

database [24]). The single score obtained when utilizing the EI-99method was SSg 0.0378 pts per kWh consumed.

The system boundaries were defined as seen in Fig. 4, in whichthe autogenerated electricity sold to the grid was evaluated at the

same environmental cost as the electricity purchased from thegrid. The concept of avoided emissions is presented as the emis-sions avoided elsewhere with the production of electricity by thecogeneration module (avoiding the purchase of electricity from thegrid). The avoided emissions were considered as the differencebetween the emissions associated with the generation of electricityand the purchase from the grid.

The CO2 emissions associated with the electricity mix in Spainwere also calculated by SimaPro, considering the proportions(25.8% Coal, 24.4% Natural gas in combined cycle, 19.7% Nuclear,10.4% Others (biomass, cogeneration, minihydraulic), 9.4% Eolic,9.4% Hydraulic, and 0.8% Fuel-gas) to produce the electricityconsumed [26]. The average CO2 emissions associated with elec-tricity in Spain in 2007 were calculated utilizing the Ecoinvent

database [24], as being EMe 0.385 kg CO2 per kWh consumed.The single score obtained when utilizing EI-99 was SSe 0.0226pts per kWh consumed. Special care was taken to correctlydistinguish the electricity mix used in Santa Cruz de Tenerife, asthe Canary Islands present a different mix [27] (66.8% Fuel-gas,30.1% Natural gas in combined cycle, and 3.1% Eolic), based mainlyon fuel-gas (gaseous refinery products, may include coal gas,syngas, ethane, and propane or LPG). The values for Santa Cruz deTenerife were EMe 0.536 kg CO2 per kWh consumed andSSe 0.0699 pts per kWh consumed.

Impact Categories

Carcinogenic effects on humans

Respiratory effects caused by organic substances

Respiratory effects caused by inorganic substances

Damage caused by climate change

Effects caused by ionizing radiation

Effects caused by ozone layer depletion

Quality damage caused by ecotoxic effectsDamage caused by the combined effect of acidification and eutrophication

Damage caused by land occupation and land conversion

Damage caused by extraction of minerals

Damage caused by extraction of fossil fuels

Human

health

Ecosystemquality

Resources

Damage Categories

Fig. 3. Impact and damage categories for Eco-indicator 99.

Table 4

Technologies, material composition, CO2 emissions, and EI-99 Single Score.

Technology Material composition (kg) CO2I (kg CO2) SSI (pts)TGVA 9080 kg steel, 500 kg aluminum 80,500 8700MGWH 5700 kg steel 37,350 4030CGVA 1000 kg cast iron,

1850 kg steel, 50 kg aluminum15,810 1420

CGWH 360 kg stainless steel 3050 205ICVA 850 kg steel, 25 kg aluminum 2350 251ICWH 760 kg stainless steel 5010 532FAVA 3700 kg iron alloy, 10,044 kg steel 98,600 11,100FAWH 9 000 kg steel 58,900 5890FMWR 20 kg aluminum, 2000 kg steel,

500 kg copper, 1000 kghigh-impact PVC,135 kg R134a cooling fluid,360 kg lubricating oil

85,420 3130

ICWR 3500 kg steel , 1605 kghigh-impact PVC

23,530 2990



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The characterization factors used in the EI-99 method explain theSingle Scores obtained for the electricity purchased from the grid(SSe 0.0226 pts per kWh) and natural gas (SSg 0.0378 pts perkWh). The extraction of natural gas is significantly penalized whenevaluating the damage caused by the extraction of fossil fuels(Characterization factors in the Impact Assessment section of [19]),resulting in a high value of damage to resources.

ThefunctionalunitisakeyelementofLCAwhichhastobeclearlydefined. It is a measure of the function of the studied system and

provides a reference to which the inputs and outputs can be related.LCAreportsemissionsonachosenfunctionalunitbasis,enablingthecomparisonof essentially different systems. Thefunctionalunit wasthe production of the demanded energy services during one year ofoperation (8 760 h) of the different alternatives.

Optimization techniques based on Mixed Integer LinearProgramming are useful to determine the best structure for theenergy supply system [28e31]. In order to accomplish this, thedeveloped model implicitlycompares the annual optimal operationof all possible structures. This means that it compares the annualoptimal operation for all feasible combinations of different tech-nologies and the different numbers of installed equipment. Theoptimal solution is the global optimum.

The model considers binary variables (0/1) indicating respec-

tively the absence (0) or presence (1) of the different technologiesin the structure of the energy supply system. In general, thesevariables are free variables during the optimization process, but canalso be set by the analyst. For instance, when determining theoptimum structure of a conventional energy supply systemconsisting of boilers, vapor compression refrigerators and coolingtowers, the binary variables corresponding to cogenerationmodules and absorption refrigerators are set to zero. The modelalso contains integer variables, which represent the number ofequipment for each different technology.

3.1. Objective function

The first environmental objective function was to minimize theEI-99 Single Score evaluation of global environmental impact(considering human health, ecosystem quality, and consumption ofresources), considered as the total annual impact (SStot), whichincluded the annual fixed impact of the equipment (SSfix) and theannual operation impact (SSope), associated with the operation ofthe system.

Min SStot SSfix SSope (4)

Theannualfixedimpactof theequipment (SSfix) wasexpressedby

SSfix fame$X

i

NINi$SSIi (5)

The environmental amortization factor fame represents theapportionment of the global environmental impact throughout the

system's lifetime, and was considered equal to 0.10/yr.

The installed power PIN(i) for each technology i was given by

PINi NINi$Pnomi (6)

NINi YINi$NIN BIGi with YINif0; 1g (7)

Considering that the year was divided into d representative days,which were in turnsubdivided into h hours, (d,h) represented the hthhour of the dth representative day. The annual operation impact

(SSope), associated with theoperation of thesystemwas expressed by

SSope X

d

X

h

SSg$Fgd; h SSe$Epd; h SSe$Esd; h

(8)

SSe$Es(d,h) were considered as the impact avoided elsewherewith the sale of electricity produced by the cogeneration module.

3.2. Operation

Operation was subject to capacity limits, production restrictions,and balance equations.

3.2.1. Capacity limits

For each period (d,h)For each technology i

POPi; d; h$PINi (9)

3.2.2. Production restrictions

For each period (d,h)For cogeneration modules i MGWH or TGVA

POPi; d; h NOPi; d; h$Pnomi withNOPi; d; hf0; 1;.NINig 10

For each technology iFor each utility j

Xi;j; d; h KTUi

;j$POPi; d; h (11)

Restriction (10) imposed that the cogeneration modules oper-ated at full load. This is a common practice to facilitate operation.

3.2.3. Balance equations

For each period (d,h)For each utility j

Prodj; d; h Consj; d; h Pj; d; h Sj; d; h Wj; d; h Dj; d; h 0 12

Prodj;d;h X

i

Xi;j;d;hYTUPi;jwithYTUPi;jf0;1g (13)

Consj;d;h X

i

Xi;j;d;hYTUCi;j withYTUCi;jf0;1g (14)

Pj; d; h YUPj$Consj; d; h Dj; d; h with YUPjf0; 1g(15)

Sj; d; h YUSj$Prodj; d; h with YUSjf0; 1g (16)

Wj; d; h YUWj$Prodj; d; h with YUWjf0; 1g (17)

Dj; d; h YUDj$Prodj; d; h Pj; d; h with YUDjf0; 1g(18)

YTUP(i,j) was 1 when technology i produced utility j. YTUC(i,j)

was 1 when technology i consumed utility j. Production Prod and

MarketPurchased

electricity Ep

Soldelectricity Es

Fuel Fg

EMe

SSe

EMg, SSg

Trigeneration

System

Consumer

center

Electricity

Heat

Cooling

System boundaries

Fig. 4. System boundaries.



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Consumption Cons corresponded to internal utility flows whereasPurchase P, Sale S, Waste W, and Demand D were the interchangesof utilities between the energy supply system and the environment.Binary variables YUP(j), YUS(j), YUW(j) and YUD(j) indicated,respectively, the possibility of such interchanges.

3.3. Other criteria

The second environmental objective function considered wasthe minimization of the total annual emissions (CO2 tot), whichincluded the annual fixed emissions of the equipment (CO2 fix) andthe annual operation emissions (CO2 ope), associated with theoperation of the system. Equations (4), (5) and (8) were changed to

Min CO2 tot CO2 fix CO2 ope (19)

CO2 fix fame$X

i

NINi$CO2Ii (20)

CO2 ope X

d

X

h

EMg$Fgd; h EMe$Epd; h EMe$Esd; h

(21)

Another objective function was introduced into the model, toconsider the economic aspect of the energy supply systeminstalled, in terms of the total annual cost Ctot (in V/yr), whichminimized equipment and fuel costs as well as purchase/sale ofenergy services. Equations (4), (5) and (8) were changed to:

Min Ctot Cfix Cope (22)

Cfix fam$X

i

NINi$CIi (23)

Cope X

d

X

h

Cg$Fgd; h Cep$Epd; h Ces$Esd; h

(24)

3.4. Optimization

The MILP model was implemented in the LINGO [32] modelinglanguage and optimizer, a commercial software package for solvingoptimization problems. LINGO uses the branch and bound solver to

enforce any integer restrictions contained in a model. The advancedcapabilities of LINGO such as cut generation, tree reordering,advanced heuristic and presolve strategies are used as needed. Thebranch and bound solver will, in turn, call the linear solver, whichuses the revised simplex method with product form inverse.

4. Results and discussion

When the scenario defined by the model and conditionspreviously shown was specified, the following results wereobtained. The model was solved by LINGO, freely selecting thetechnologies to be installed and minimizing the different objectivefunctions considered, resulting in three optimal configurations.Table 5 shows the results for the EI-99 and CO2 optimal, and Table 6shows the results for the economic optimal.

The results for the EI-99 optimal and CO2 optimal suggested theinstallation of conventional equipment for the peninsular loca-tions: hot water boilers, vapor compression chillers, and coolingtowers.

When considering the case of Santa Cruz de Tenerife, as the localelectricity supply depends on fuel-gas (with a higher emissionvalue and associated global environmental impact), cogenerationmodules were installed due to the considerable difference betweenthe impacts of the local electricity supplied by the grid and theelectricity produced by cogeneration modules. Different configu-rations were presented for both environmental objective functionsas indicated in Table 5. One absorption chiller was replaced by onemechanical chiller when changing the objective function from CO2emissions to EI-99 Single Score.

The environmental results were not totally unexpected, asaccording to Fumo et al. [33] and Conde et al. [34] trigenerationsystems have a great potential to reduce emissions; but thisreduction depends on the energyconsumption profiles and the fuelmix of the electric grid region. In Canada, for example, Ontario'selectrical utility relies mainly on nuclear and hydraulic energy andfossil fuels (mainly coal); the case study presented by Rosen [35]

suggested that electrical utility-based cogeneration would benefitOntario or a region with similar characteristics in that for the sameservices delivered, cogeneration reduced environmental and healthconsequences.

Table 5

EI-99 and CO2 optimal for the different geographic locations.a

Sta. Cruz Tenerife CO2 Sta. Cruz Tenerife EI-99 Almera Valencia Zaragoza Huesca Teruel Lugo

Composition Number Number Number Number Number Number Number Number

TGVA,CGVA 0 0 0 0 0 0 0 0MGWH 2 2 0 0 0 0 0 0CGWH 1 1 5 5 6 9 10 10ICWH 1 1 0 0 0 0 0 0ICVA, FAVA 0 0 0 0 0 0 0 0FAWH 1 0 0 0 0 0 0 0FMWH 6 7 6 6 4 3 2 0ICWR 4 4 4 4 3 2 1 0Fg 9260 7800 5604 6129 8703 10,185 11,498 11,005Ep 1904 2432 3803 3779 3572 3465 3352 3250Es 1450 1214 0 0 0 0 0 0SSI 4543 4267 3177 3177 2272 1722 1130 205SSg$Fg 350,028 294,846 211,816 231,694 328,984 385,001 434,627 415,971SSe$Ep 133,089 170,012 85,939 85,399 80,730 78,302 75,750 73,451SSe$Es 101,355 84,865 0 0 0 0 0 0Total SS 386,305 384,260 300,931 320,270 411,986 465,025 511,507 489,627CO2I 74,830 77,482 62,189 62,189 43,057 33,077 22,487 3050EMg$Fg 2,518,784 2,121,600 1,524,178 1,667,220 2,367,296 2,770,376 3,127,474 2,993,234EMe$Ep 1,020,288 1,303,552 1,464,003 1,454,812 1,375,264 1,333,911 1,290,431 1,251,263EMe$Es 777,204 650,704 0 0 0 0 0 0Total CO2 2,836,698 2,851,930 3,050,371 3,184,221 3,785,617 4,137,364 4,440,393 4,247,547

a Fg (MWh/yr), Ep (MWh/yr), Es (MWh/yr), SSfix (pts/yr), SSg$Fg (pts/yr), SSe$Ep (pts/yr), SSe$Es (pts/yr), SStot (pts/yr), CO2 fix (kg CO2/yr), EMg$Fg (kg CO2/yr), EMe$Ep (kg CO2/

yr), EMe$

Es (kg CO2/yr), CO2 tot (kg CO2/yr).



8/9

For the economic optimal, cogeneration modules, hot watere

cooling water heat exchangers, and absorption chillers wereinstalled for all locations (see Table 6). All systems took advantageof the lower purchase cost of natural gas and realized profit byselling the autogenerated electricity to the electric grid.

A sensitivity analysis was carried out in Lozano et al. [14], wherethe effects of the financial market conditions and energy prices(electricity and natural gas) on the optimal structure of the trigener-ation system were analyzed. When the amortization and mainte-nance factorfam increased, the number of cogeneration modules andabsorption chillers as well as the sale of electricity decreased; thesame trend was observed when increasing the price of natural gas.

An additional sensitivity analysis is to investigate how thecarbon emissions factor of the electricity purchased from the gridaffects the configuration of the system. For example, in openmarket arrangements, consumers can buy electricity from a rangeof service providers, some offering low carbon and/or renewably-fuelled electricity. The a factor was introduced by Carvalho et al.[36] as being the ratio CO2 emissions associated with theconsumption of natural gas to electricity. For the hospital in Zar-agoza a sensitivity analysis was performed by varying the a factor:when a was close to 1.5, cogeneration modules were installed, andfrom 2.0 onwards, absorption chillers were also installed [36].

5. Conclusion and final remarks

For different representative geographic locations in Spain,a mixed integer linear programming model optimized the configu-ration and operation of a trigeneration system to be installed in

a hospital. Three objective functions were considered: the EI-99Single Score (global environmental perspective), kilograms of CO2emissions, and the total annual cost (in V/yr).

The objective was firstly, to obtain the thermal loads ofa building of the tertiary sector (which are highly dependent on theclimatic conditions), and secondly, to verify whether influence oflocal economic/environmental conditions existed. Also consideredwere the price of energy resources, the price and amortizationpossibilities of the equipment, the options to sell the surpluselectricity to the electric grid, and the possibility that the systemhelped mitigate climate change (avoiding emissions elsewhere).

The optimal results for the objective functions regarding the Eco-indicator 99 Single Score and the CO2 emissions were identical forthe hospitals located in peninsular Spain. The environmental results

suggested the installation of conventionalequipment and a purchase

of electricity from the electric grid to attend the demands of cooling

and electricity. Emissions savings by cogeneration depended highlyon the local electricity supply mix that would be substituted, whichwas why Santa Cruz de Tenerife presented different results from therest of locations, as it is supplied by a different electricity mix (withhigher associated emissions/single score).

The economic optimal results suggested the installation ofcogeneration modules, hot water boilers, and absorption chillersfor all locations except for Lugo, which did not demand cooling andtherefore no cooling equipment was installed. The cogenerationmodules were used to benefit from the lower price of natural gasselling surplus autogenerated electricity to the grid, to minimizethe total annual cost.

Acknowledgments

This work was developed within the framework of researchprojectENE2007-67122, partially funded by theSpanishGovernment(Energy program) and the EuropeanUnion (FEDER program). MonicaCarvalho is supported by the EU Program of High Level Scholarshipsfor Latin America (Alban Scholarship No. E06D100314BR).

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Table 6

Economic optimal for the different geographic locations.a

Sta. Cruz Tenerife Almera Valencia Zaragoza Huesca Teruel Lugo

Composition Number Number Number Number Number Number Number

TGVA,CGVA 0 0 0 0 0 0 0MGWH 1 2 2 3 2 2 2CGWH 2 3 3 3 7 8 8ICWH 2 3 3 4 3 3 3

ICVA, FAVA 0 0 0 0 0 0 0FAWH 1 1 1 1 1 1 0FMWH 6 5 5 3 2 1 0ICWR 4 4 4 3 3 2 2Fg 13,119 25,592 25,922 37,324 29,687 30,199 27,200Ep 296 42 59 29 0 0 0Es 1530 6447 6447 11,389 6695 6453 5467fam$CIi 427,340 495,535 495,535 510,830 396,635 357,535 271,285Cost Fg 327,980 639,792 648,048 933,092 742,179 754,963 680,005Cost Ep 33,294 4558 6659 3207 0 0 0Profit Es 117,796 496,388 496,393 876,960 515,480 496,870 420,992Total cost 670,818 643,497 653,849 570,169 623,333 615,628 530,298

a Fg (MWh/yr), Ep (MWh/yr), Es (MWh/yr), fam$CIi (V/yr), Cost Fg (V/yr), Cost Ep (V/yr), Profit Es (V/yr), Total cost (V/yr).

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NOMENCLATURE

AA: Ambient airCes: Market price of the electricity sold to the grid [V/kWh]

Cep: Market price of the electricity purchased from the grid [V/kWh]Cg: Market price of natural gas [V/kWh]Cfix: Annual cost of the equipment [V/yr]Cope: Annual operation costs [V/yr]Ctot: Total annual cost [V/yr]CG: Natural gasCGVA: Steam boilerCGWH: Hot water boilerCIi: Investment cost of the equipment of technology i [V]CO2 tot: Total annual CO2 emissions [kg CO2/yr]

CO2 fix: Annual fixed CO2 emissions of the equipment [kg CO2/yr]CO2 ope: Annual operation CO2 emissions [kg CO2/yr]Cons(j,d,h): Consumption of utility j in the period (d,h) [MWh]D: DemandD(j,d,h): Demand of utility j in the period (d,h) [MWh]Ec: Generated electricity [MWh/yr]Ed: Electricity demand [MWh/yr]Ep: Purchased electricity [MWh/yr]Es: Sold electricity [MWh/yr]EE: ElectricityEEE: Equivalent electrical efficiency [%]EMe: CO2 emissions associated with electricity [kg CO2/kWh]EMg: CO2 emissions associated with natural gas [kg CO2/kWh]Fc: Consumption of primary energy [MWh]Fg: Consumption of natural gas [MWh/yr]

fam: Amortization and maintenance factor [yr1]fame: Environmental amortization factor [yr1]FAVA: Double effect absorption chillerFAWH: Single effect absorption chiller

fcr: Capital recovery factor [yr1]fmo: Maintenance and operation factor [yr1]FMWR: Mechanical chillerICVA: Vaporehot water heat exchangerICWH: Hot waterecooling water heat exchangerICWR: Cooling toweriyr: Interest rate [yr1]KTU(i,j): Absolute value of the production coefficientMGWH: Gas engine hot water recovery systemNIN(i): Number of pieces of equipment installed for technology iNIN_BIG(i): Maximum limit for the number of equipmentNOP(i,d,h): Number of pieces of equipment of technology i operating

in the period (d,h)nyr: Equipment lifetime [yrs]P: PurchaseP(j,d,h): Purchase of utility j in the period (d,h) [MWh]Pnom(i): Nominal power of the equipment [MW]PIN(i): Installed power of technology i [MW]

POP(i,d,h): Production of technology i in the period (d,h) [MWh]Prod(j,d,h): Production of utility j in the period (d,h) [MWh]Qc: Cogenerated useful heat [MWh]Qd: Heat demand [MWh]Rd: Cooling demand [MWh]S: SaleS(j,d,h): Sale of utility j in the period (d,h) [MWh]SSfix: Annualfixed impact of the equipment, in terms of EI-99Single Score [points/yr]SSope: Annual operation impact, in terms of EI-99 Single Score [points/yr]SStot: Total annual impact, in terms of EI-99 Single Score [points/yr]SSe: EI-99: Single Score for electricity [points/kWh]SSg: EI-99: Single Score for natural gas [points/kWh]SSI: EI-99: Single Score for the production of each piece of equipment i [points]t0: Ambient temperature [C]TGVA: Gas turbine heat recovery boilerVA: High temperature steamW: WasteW(j,d,h): Waste of utility j in the period (d,h) [MWh]WC: Cold water

WH: Hot waterWR: Cooling water

X(i,j,d,h): Energy flow of utility j interchanged with technology i in the period(d,h) [MWh]

YIN(i): Binary variable (0/1) indicating whether technology i was installedYUD(j): Binary variable (0/1) indicating the possibility of demandYUP(j): Binary variable (0/1) indicating the possibility of purchaseYUS(j): Binary variable (0/1) indicating the possibility of saleYUW(j): Binary variable (0/1) indicating the possibility of wasteYTUC(i, j): Binary variable (0/1) indicating the possibility of consumption of utility j

by technology iYTUP(i,j): Binary variable (0/1) indicating the possibility of production of utility j by

technology i

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