sofc cogeneration system for building applications, part 2: system configuration and operating...

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Renewable Energy 34 (2009) 2839–2846

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Renewable Energy

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SOFC cogeneration system for building applications, part 2:System configuration and operating condition design

Kwang Ho Lee a,*, Richard K. Strand b

a University of California at Berkeley, Berkeley, CA, USAb University of Illinois at Urbana-Champaign, Champaign, IL, USA

a r t i c l e i n f o

Article history:Received 25 September 2008Accepted 19 April 2009Available online 22 May 2009

Keywords:SOFCCogenerationSystem configurationControlEnergeticEconomic

* Corresponding author at: 3 Admiral Dr., #F269,Tel.: þ1 217 419 6067.

E-mail address: [email protected] (K.H. Lee).

0960-1481/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.renene.2009.04.012

a b s t r a c t

An SOFC (Solid Oxide Fuel Cell) cogeneration optimization study was carried out for one small-scale andone large-scale building under both hot and cold weather conditions. Several different configurations ofthe SOFC system are operated using a defined set of input parameters to meet the actual heating, coolingand electrical demands on those two buildings The results are discussed and compared from fourdifferent perspectives: electric-only vs. cogeneration, energetic vs. economic, large-scale vs. small-scalebuildings and hot vs. cold weather conditions. The main conclusion of this study is that optimizationresults vary widely depending on different system configurations and loading conditions and thus SOFCsystems should be optimized based on the specific conditions to which they are exposed and not simplyon a single operating condition.

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1. Introduction

Due to higher efficiency for electricity generation and loweremission of greenhouse gases compared to conventional powerplants, fuel cells have been considered to be one of the most prom-ising cogeneration technologies in terms of power generation andthermal energy production in buildings [11]. Among several fuel celltypes, SOFCs are chosen in this study because of their potential asa cogeneration system and their wide system capacity range coveringboth small-scale residential and large-scale commercial buildings. Inaddition, its high operating temperature can provide sufficient heatto activate a highly endothermic fuel reforming process for theproduction of hydrogen gas. The high operating temperature alsoenables SOFCs to use any residual heat for space and water heating,allowing SOFCs to be used as cogeneration systems [11].

In part 1 of the present study [12], a literature review wasprovided, introducing the limitations of the existing SOFC studies [3–10]. One of the significant limitations of cell-level based studies isthat they do not have the potential to specifically evaluatephenomena and processes taking place outside the fuel cell stack andthus balance-of-plant (BOP) components working in conjunctionwith the fuel cell stack itself are not considered. On the other hand,the limitation of existing system-level based studies is that they do

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not provide enough details to select the optimal configuration and todesign the optimal operating conditions under each circumstance,since the simulations and comparisons of each system concept werecarried out only under a single, fixed condition. Based on the iden-tified shortcoming in the literature described above, a system-levelcomputational model was developed and a parametric analysis wascarried out in part 1 of the present study to get a better under-standing of how system parameters and performance are correlatedwith each other [12]. Based on the findings in part 1, this paper (part2) attempts to provide the best control strategy of the overallmethane-fueled SOFC cogeneration system based on the actualelectrical and thermal energy demand of a building under variousdesign conditions. Regarding the optimal operating condition design,not only the conventional cell-level control parameters but also thesystem-level parameters such as cathode gas recirculation rate andfuel pre-reforming rate are considered for this system optimizationstudy [2]. The optimization study is discussed and compared fromfour different perspectives: electric-only vs. cogeneration, energeticvs. economic, large-scale vs. small-scale buildings and hot vs. coldweather conditions. Before actually getting into the optimizationstudy, energy profiles of the two different building types are inves-tigated and briefly described in the next subsection.

2. Energy profile of selected buildings

Two different types of buildings were chosen in this study:a small-scale residential and a large-scale commercial building.

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Fig. 1. Summer day energy demand of residential building (Arizona). Fig. 3. Summer day energy demand of commercial building (Arizona).

K.H. Lee, R.K. Strand / Renewable Energy 34 (2009) 2839–28462840

These two building types are significantly different from each otherin terms of size, occupancy schedule, energy demand profiles andconstruction. In addition, two different weather conditions arechosen as well, i.e. a hot climate (Arizona) and a cold climate(Minnesota), for the comparison of the optimization resultsbetween those two different climates. Therefore, this study iscarried out on four different cases:

� Small-scale residential building under cold weather condition� Small-scale residential building under hot weather condition� Large-scale commercial building under cold weather condition� Large-scale commercial building under hot weather condition

Time-dependent energy demands are calculated using theEnergyPlus building energy simulation program and the buildingenergy simulation results serve as inputs to the stand-alone modelfor the SOFC system. The system inlet methane flow rate iscontrolled to meet the hourly energy demands in the optimizationstudy, and the inlet fresh air flow rate is also controlled to maintainthe air temperature rise of 100 K across the solid structure [11].Hourly variations in thermal and electrical energy demands ofa typical small-scale residential building (240 m2) during summerand winter days are presented in Figs. 1 and 2, respectively. Due tothe hot weather conditions, there was no space heating energydemand during the entire day as shown in Fig. 1. On the other hand,the electrical demand increases mainly due to the need for coolingin the afternoon. The outdoor air temperature reaches 35 �C in theafternoon and results in an increase in the cooling equipment usagewhich runs on electricity. In case of a winter day shown in Fig. 2, theoutdoor air temperature is significantly lower in the early morning

Fig. 2. Winter day energy demand of residential building (Minnesota).

and in the evening, which increases the space heating demand. Dueto the cold weather condition, the electrical demand turned out tobe significantly lower than DHW (Domestic Hot Water) and spaceheating demands as can be confirmed in Fig. 2.

The other type of building chosen for this research is a typicallarge-scale commercial building. Since the energy demand profilesof commercial buildings are definitely different from those ofresidential buildings, they should be addressed separately. Hourlyenergy demand variations of a 50-story commercial building(92,000 m2) during summer and winter days are described in Figs.3 and 4, respectively. In the summer day illustrated in Fig. 3, therewas no space heating energy demand during the entire day due tothe hot weather condition. On the other hand, the electricaldemand for cooling increases from 9 am to 6 pm, since the outdoorair temperature reaches up to 35 �C during that time period. Therapid change in the electrical load around 7 am and around 10 pm isdue to the switchover of the air temperature set-point to and fromthe cooling setback of 28 �C. In the winter day presented in Fig. 4,the space heating demand increases during the occupancy periodfrom 9 am to 6 pm. The reason that the sharp space heating load inthe early morning is due to the switchover of the air temperatureset-point from the heating setback of 13–20 �C during the occu-pancy period. In addition, the heating energy is significantly higherthan cooling energy demand due to the cold weather condition asshown in Fig. 4.

3. Simulation conditions

In addition to the energetic evaluation of the system perfor-mance, the economic performance is also assessed using the

Fig. 4. Winter day energy demand of commercial building (Minnesota).

Table 1Simulation condition.

Location Minneapolis, MinnesotaPhoenix, Arizona

Simulation period Winter day: January 17thSummer day: August 16th

Building type Small-scale residential buildingLarge-scale commercial building

Size of buildings 240 m2 (residential building)92,000 m2 (commercial building)

Annual hourly average electrical loadof buildings

2.11 kW – residential building, Minnesota4.40 kW – residential building, Arizona890 kW – commercial building, Minnesota1,525 kW – commercial building, Arizona

System maintenance cost 0.5 cents/kWhCell and system lifetime 5 years and 20 years, respectively

Table 3Summary of 24 simulation cases.

Configuration type Simulation cases

Basic design 0%, 50% and 100% EMPAnode recycling configuration 0%, 50% and 100% EMPAnode recycling configuration 0% EMP – 3%, 50% and 100% CGR

50% EMP – 3%, 50% and 100% CGR100% EMP – 3%, 50% and 100% CGR

Both anode and cathode recyclingconfiguration

0% EMP – 3%, 50% and 100% CGR50% EMP – 3%, 50% and 100% CGR100% EMP – 3%, 50% and 100% CGR

EMP – extent of methane pre-reforming.CGR – cathode gas recycling rate.

K.H. Lee, R.K. Strand / Renewable Energy 34 (2009) 2839–2846 2841

economic modeling algorithm described below. The concept of Costof Electricity (COE) is introduced for the economic evaluation. TheSOFC system Cost of Electricity (COE) is composed of a systemcapital cost, a maintenance cost and a fuel cost. The interest rate,the system efficiency and the lifetime of cell and other componentsshould also be taken into account to determine the fuel cell systemCOE as follows [11]:

COE ¼ F1RCCcap

CEþ F2CM þ F3

CF

hsys(1)

COE ¼ F1RCCcap

CEþ F2CM þ F3

CF

hsys� F3

�hcog � hsys

�CF

hsyshR(2)

where F1, F2 and F3 are unit conversion factors, Ccap is the unitsystem capital cost, CE is the electric capacity factor, CM is theannual maintenance cost, CF is the natural gas cost, hsys is theelectric efficiency of the fuel cell, hcog is the system cogenerationefficiency, hR is the efficiency of the displaced thermal source and RC

is the capital recovery rate. Eq. (1) shows the electric-only COE,while Eq. (2) shows the cogeneration COE. In this study, the annualoperation and maintenance cost, CM, of 0.005 $/kWh will be used[1] and the cell and plant lifetime will be assumed as 5 years and 20years, respectively. The operating time of the fuel cell will beassumed to be 5000 h annually and an electric capacity factor, CE, of0.8 will be used. These assumptions are similar to those typicallymade in other studies. The capital recovery rate, RC, can becomputed using the following expression [11]:

RC ¼ið1þ iÞn

ð1þ iÞn�1(3)

where i is the annual discount rate and n is the lifetime of SOFCplant system in years. A more detailed description of economicmodeling is provided by Braun [1] and Lee [2].

Table 2Cell and BOP component parameters.

Electro-active area of each cell 120� 120 mm2

Air and fuel inlet temperature 1073 KSteam-to-carbon ratio 2.5Operating pressure 1 barFuel utilization 85%<BOP components> <BOP components>Pressure drops Pre-reformer – 50 mbar

Air pre-heater – 100 mbarWater pre-heater – 15 mbarAfterburner – 20 mbar

Blower efficiencies Fuel compressor – 62.5%Air blower – 70%Water pump – 60%

Simulation conditions for the optimization study are summa-rized in Tables 1 and 2. The electro-active area of each cell is120�120 mm2 with a fuel utilization of 85%. The system mainte-nance cost of 0.5 cents/kWh is used, and the four system configu-rations discussed in part 1 of the present study will be addressed inthis paper [12]. Those four configurations include: basic design,anode recycling, cathode recycling and both anode and cathoderecycling configurations. In each configuration, the recycling rate ofcathode gas (CGR) and the extent of methane pre-reforming (EMP)are varied by three intervals, making a total of 24 simulation casesas summarized in Table 3. There are only three cases for the basicdesign and anode gas recycling configurations, since they do notemploy the cathode recycling concept. The inlet methane flow rateis automatically controlled to meet the hourly electrical demandpresented in Figs. 1–4, and the inlet air flow rate is also automati-cally adjusted to maintain an air temperature rise of 100 K acrossthe solid cell component. Typically, the air temperature rise thatshould be maintained for the prevention of thermal instability ofstack components is 100 K [11]. By comparing the 24 simulationcases summarized in Table 4, the best operating conditions for eachconfiguration can be selected from both an energetic and economicaspects. In addition, the system sizing is determined based on theaverage annual hourly electric load of the building in each location,since it can maximize the electric and thermal capacity factor andminimize the system payback period [1].

4. Results

Averaged hourly efficiencies and COEs for the commercialbuilding during a heating day, for the commercial building duringa cooling day, for the residential building during a heating day andfor the residential building during a cooling day are summarized inTables 4–7, respectively. Not all of the 24 simulation cases areincluded, i.e. only the cases having the highest cogeneration effi-ciency within each configuration type are included in these tables[2]. There was an assumption that all of the electricity generated bythe system were consumed and that only the thermal energy that isactually consumed in the building is considered in the computingcogeneration efficiency. The 0% EMP (Extent of Methane Pre-reforming) case showed the highest electrical efficiency and thelowest COE in the basic design and anode recycling configurationsas shown in these tables since the internal reforming, i.e. 0%methane pre-reforming, reduces the stack operating temperature,the cooling air requirement and the resultant BOP (Balance-of-plant) power consumption as discussed in the parametric study ofpart 1 [12]. Even if the heat recovery is reduced by the internalmethane reforming, the enhanced amount of net power supplyoutweighs the reduced amount of heat recovery, enhancing theoverall cogeneration efficiency. Therefore, 0% EMP, i.e. internalreforming, showed the highest cogeneration efficiency as well inthe basic design and anode recycling configurations as summarized

Table 4Commercial building during heating day (Minnesota).

Electric-only efficiency Cogeneration efficiency

Basic design with 0% EMP 52.6% Basic design with 0% EMP 82.4%Anode recycling with 0% EMP 58.0% Anode recycling with 0% EMP 77.4%Cathode recycling with 0% EMP and 50% CGR 53.1% Cathode recycling with 50% EMP and 50% CGR 86.5%Both recycling with 0% EMP and 50% CGR 59.3% Both recycling with 50% EMP and 50% CGR 84.1%Electric-only COE Cogeneration COEBasic design with 0% EMP 12.4 cents/kWh Basic design with 0% EMP 9.8 cents/kWhAnode recycling with 0% EMP 11.6 cents/kWh Anode recycling with 0% EMP 13.1 cents/kWhCathode recycling with 0% EMP and 50% CGR 12.2 cents/kWh Cathode recycling with 0% EMP and 50% CGR 9.5 cents/kWhBoth recycling with 0% EMP and 50% CGR 11.2 cents/kWh Both recycling with 0% EMP and 50% CGR 9.6 cents/kWh

Table 5Commercial building during cooling day (Arizona).



Table 6Residential building during heating day (Minnesota).



Table 7Residential building during cooling day (Arizona).




in Tables 4–7. Similarly, 0% EMP generally showed the highest andthe lowest COEs in the cathode recycling and both recyclingconfigurations as well, although 50% or 100% EMP showed the bestperformances in some cases as shown in Tables 4–7. In addition,50% CGR (Cathode Gas Recycling Rate) turned out to generallyperform well in both energetic and economic aspects. As can beseen from Tables 4–7, there are significant differences amongdifferent aspects such as energetic vs. economic, hot vs. coldweather, electric-only vs. cogeneration mode and residential vs.commercial building types. Those differences will be discussedlater in a subsequent subsection.

Hourly variations of electrical demand and energy supply bythe SOFC system are illustrated in Figs. 5,7,9 and 11 for thecommercial building during a heating day, for the commercialbuilding during a cooling day, for the residential building duringa heating day and for the residential building during a cooling day,respectively. In addition, thermal loads and energy supplied arepresented in Figs. 6,8,10 and 12 as well. Similar to the previousdiscussion, not all of the 24 simulation cases are included, i.e. onlythe cases having the highest cogeneration performance withineach configuration type are included. Since the system capacity isdetermined based on the average annual hourly electric load as

Fig. 5. Electrical demand and energy supply (commercial building in Minnesota).

Fig. 6. Thermal demand and energy supply (commercial building in Minnesota).

Fig. 8. Thermal demand and energy supply (commercial building in Arizona).

Fig. 9. Electrical demand and energy supply (residential building in Minnesota).


suggested by Braun [1], the electrical supply does not exceed thespecified capacity as can be seen in Figs. 5,7,9 and 11. In addition, itcan be observed from Figs. 6 and 10 that the thermal energy supplyfrom the system is significantly lower than the thermal energydemand due to the fact that the electrical demand is lower thanthermal demand during the heating season and the system iscontrolled to meet the electrical demand. On the other hand, thereare several cases when the hourly thermal energy supply is higherthan the thermal demand during the cooling season as shown inFigs. 8 and 12 owing to the fact that electrical energy demand ishigher than the thermal demand and the system is automaticallycontrolled to meet the hourly electrical load. However, any excess

Fig. 7. Electrical demand and energy supply (commercial building in Arizona).

thermal energy supplied by the SOFC system is not included whencomputing the cogeneration efficiency, i.e. only the thermal energythat is actually consumed is included in the cogeneration efficiencycomputation [2].

5. Discussions

Since a particular configuration or operating condition does notalways perform optimally under every situation, comparisonsamong the different perspectives are provided as a final step of this

Fig. 10. Thermal demand and energy supply (residential building in Minnesota).

Fig. 11. Electrical demand and energy supply (residential building in Arizona).

Fig. 13. COE breakdown of commercial building in Minnesota (both recycling with 0%EMP and 50% CGR).


study. The following subsections provide the comparison of theoptimization results in four different aspects: electric-only vs.cogeneration, residential vs. commercial, energetic vs. economicand hot vs. cold climate conditions.

5.1. Electric-only vs. cogeneration mode

In agreement with common sense, the cogeneration efficien-cies are always higher than the electric-only efficiencies and thecogeneration COEs were always lower than electric-only COEs inall the four configurations as summarized in Tables 4–7, indicatingthat utilizing SOFCs as cogeneration systems is more advantageousthan utilizing them as only electrical power generators. Moreimportantly, the electric-only and cogeneration modes producedifferent optimization results from each other as shown in Tables4–7. For example, the both recycling configuration with 0% EMPand 50% CGR turned out to have the highest electric-only efficiencyin the commercial building located in Minnesota, while thecathode recycling configuration with 50% EMP and 50% CGRshowed the highest cogeneration efficiency. In addition, the bothrecycling configuration with 50% EMP and 100% CGR produced thelowest electric-only COE, while the both recycling configurationwith 0% EMP and 50% CGR turned out to be the most economicalfor cogeneration. This suggests an important point that the oper-ating condition with the highest electric-only efficiency or thelowest COE does not necessarily perform optimally under cogen-eration mode as well [2]. Even if a certain configuration or oper-ating condition performs more effectively than other cases inelectricity generation, other configurations or operating conditionscan have higher thermal outputs, resulting in higher cogeneration

Fig. 12. Thermal demand and energy supply (residential building in Arizona).

efficiency. Therefore, the optimal system configuration and oper-ating condition should be determined separately if the viewpointsfor system optimization, i.e. electric-only or cogeneration, aredifferent.

5.2. Energetic vs. economic aspect

Again, it can be easily observed from Tables 4–7 that the ener-getic and economic aspects show different optimization resultsfrom each other. For instance, the basic design configuration with0% EMP showed the highest cogeneration efficiency, while the bothrecycling configuration with 0% EMP and 50% CGR produced thelowest cogeneration COE for the commercial building located inArizona. Although a particular configuration and operating condi-tion performing optimally in the energetic aspect do not necessarilyshow the best performance in the economic aspect as well (seediscussion above), a tendency for the energy efficiency and costefficiency to have some correlation with each other to some extentcould be observed [2]. In other words, the system configuration andoperating conditions that perform well from both the energetic andthe economic perspective can be easily found. For example, theboth recycling configuration with 0% EMP and 50% CGR showedthe highest electrical efficiency and the lowest electric-only COE atthe same time in commercial building located in Minnesota.

Therefore, it is worth looking into the correlation between theenergetic and economic performances of the SOFC system. In thiscircumstance, the breakdown of the COE for the both recyclingconfiguration with 0% EMP and 50% CGR in the commercial buildinglocated in Minnesota is carried out and illustrated in Fig. 13. Thereason for choosing this case among the 24 simulation casessummarized in Table 3 is that it showed the lowest electric-onlyCOE (Cost of Electricity). This figure shows the relative portion of

Fig. 14. COE breakdown of residential building in Minnesota (both recycling with 0%EMP and 50% CGR).


each factor, i.e. fuel cost, cell stack capital cost, BOP (Balance-of-plant) and O&M (Operations and Maintenance) cost, to the overallCOE. The BOP components include the heat recovery device, the airblower, the air pre-heater, the methane pre-reformer, the fuelcompressor, the water pump, the afterburner, etc. It can beobserved from Fig. 13 that the fuel and cell stack cost account formore than 75% of the total COE. The interesting point is that thosefuel and stack capital costs are directly related to the system energyefficiency. As can be inferred from Eq. (1) and (2), the system effi-ciency is inversely proportional to the portion of the fuel costcontribution to the overall COEs. As the system efficiency isenhanced, the fuel cost contribution is reduced. Regarding therelationship between the cell stack cost and the energy efficiency,the number of cells within a stack, which is proportional to thestack cost, is determined by the actual power supply from a singlecell. Since the system efficiency generates greater power using thesame amount of methane gas, the energy efficiency is also relatedto the stack capital cost. As a result, both the fuel cost and stackcapital cost contributions have a clear correlation with the energyefficiency. Once again, the fuel and cell stack cost take up a largeportion, i.e. more than 75%, of the overall COE as can be observed inFig. 13. Since both the fuel and stack costs are directly related to theenergy efficiency as discussed above, there is typically a closecorrelation between the energetic and economic performance ofthe SOFC system. Therefore, a tendency for the simulation caseshaving high energy efficiencies to show good economical results aswell could be easily discovered from Tables 4–7. However, it shouldalso be taken into account that other cost factors such as the BOPcomponent cost can occasionally affect the overall COE, indicatingthat the highest energy efficiency does not always guarantee themost economical system performance and thus the energetic andeconomic aspects should be considered separately.

5.3. Residential vs. commercial

Residential and commercial buildings are different from eachother in many respects such as the construction, the shape, thesurrounding environment, the size, the thermal and electricalloads, the occupancy time, etc. As a result, they have differentheating, cooling and electrical energy load variations as shown inFigs. 1–4. In addition, the system capacity and the energy supply arenot in the same range due to the different average annual electricalloads of residential and commercial buildings. The system capac-ities for the residential building are set at 2.11 kW and 4.40 kW inMinnesota and Arizona, respectively, while those of the commercialbuilding are set at 890 kW and 1525 kW in Minnesota and Arizona,respectively. As a result, similar to the discussions above, theoptimization results are different between the residential andcommercial buildings, indicating once again that optimizationshould be performed separately when considering different typesof buildings.

In addition, as can be observed from Tables 4–7, the COEs of theresidential building were generally higher than those of thecommercial building. For example, the lowest electric-only COE andcogeneration COE were 17.0 cents/kWh and 15.4 cents/kWh,respectively, in case of the residential building located in Arizona,while the commercial building showed 11.4 cents/kWh and10.7 cents/kWh, respectively. This is because of the fact that theSOFC system size for the residential building is relatively small butstill requires all the necessary fuel and BOP components like thecommercial building in order to operate the whole SOFC system.Another breakdown of the COE for the both recycling configurationcase with 0% EMP and 50% CGR in the residential building located inMinnesota is performed and illustrated in Fig. 14 to show thedifference in the COE composition between the residential and

commercial buildings. This figure can be compared to thecommercial building case presented in Fig. 13. As the building typechanges from commercial (Fig. 13) to small-scale residentialbuilding (Fig. 14), the portion of the BOP components becamesignificantly larger compared to the commercial building, indi-cating that the BOP components have a significant effect on theoverall COE in residential buildings. As one might expect, it is likelyto be unrealistic to place all the expensive BOP components insidea single small-scale residential building.

The unit of COEs is cents/kWh in which the denominator is thenet power supply from the system. Although the fuel and systemcapital costs decrease as the system capacity is reduced, thereduction rate of those costs is lower than those of power outputs.Therefore, COEs having the unit of cents/kWh increase as thesystem capacity is reduced. While all the fuel, cell stack andexpensive BOP components are needed to run the whole SOFCsystem, it can be unrealistic to install all of those expensive systemcomponents inside a single small-scale building as stated in theprevious paragraph, since the low system capacity combined withthe high capital cost can significantly increase COEs compared tothe large-scale commercial buildings. One recommendation tooffset this effect is to have multiple small-scale houses sharesa common SOFC cogeneration system to get the electricity andthermal energy in a more economical way [2].

5.4. Hot vs. cold climate

Since different weather conditions cause different energyprofiles, the system should be optimized in different ways underdifferent climate conditions. As stated earlier, Arizona and Minne-sota were chosen to be representative of hot and cold climates forthis study, respectively. In hot climates such as Arizona, the elec-trical energy for cooling purposes rather than thermal energy isdominant, while the thermal energy is more dominant under coldweather conditions such as Minnesota. Therefore, the two weatherconditions do not show the same optimization results as can beobserved from Tables 4–7. For example, the highest cogenerationefficiency was achieved by the basic design with 0% EMP in thecommercial building located in Arizona, while the cathode recy-cling configuration with 50% EMP and 50% CGR showed the highestcogeneration efficiency in Minnesota. In addition, the both recy-cling configuration and the cathode recycling configurationsresulted in the lowest cogeneration COEs in Arizona and Minnesota,respectively. This indicates that the optimization should beconsidered separately between different climate conditions similarto the previous discussions.

In addition, the system efficiencies for the cold Minnesotaclimate were higher than those in hot Arizona regardless of otherconditions as shown in Tables 4–7. In Minnesota, the highestaverage cogeneration efficiencies in the residential and commercialbuildings were 85.5% and 86.5%, respectively, while they were64.3% and 67.5%, respectively, in Arizona. The higher system effi-ciencies in Minnesota than in Arizona are due to the controlstrategy of the SOFC system adopted in this study. The fuel inletflow rate was automatically controlled to meet the hourly electricalload. In Arizona, the system thermal energy production is higherthan the thermal demand owing to the fact that electrical load ishigher than thermal demand and that the system is controlled tomeet the electrical load. As described earlier, those excess heatproductions were not considered when computing the cogenera-tion efficiency. However, in Minnesota, the system thermal energyproduction is lower than the hourly thermal demand and thus thewhole thermal energy production from the system is utilized andthus included when determining the cogeneration efficiency.


6. Conclusion

In this paper, an optimization study is carried out for bothresidential and commercial buildings under both cold and hotclimate conditions. The system configuration and operatingcondition were controlled based on the actual heating, cooling andelectrical loads of the buildings, which were calculated using theEnergyPlus building energy simulation program. The concept ofCost of Electricity (COE), which considers system capital cost, themaintenance cost, the fuel cost, the interest rate, the system effi-ciency and the system lifetime, was adopted in this study toperform the economic evaluation of the system. As a result of theoptimization study, the following conclusions can be drawn.

Generally, the extent of methane pre-reforming of 0% showedthe highest electrical and cogeneration efficiencies in many cases,since the highly endothermic methane reforming process reducesthe stack temperature, the cooling air flows and the resultant BOPpower consumptions, enhancing the net power output and thecorresponding system efficiency.

There is a close correlation between the energetic and economicperformance of the SOFC system, since the fuel and stack capitalcosts are directly related to energy efficiency and they account fora large portion of the overall COEs. However, other factors besidesthe fuel and stack cost such as the BOP component costs occa-sionally influence the overall COE, indicating that the highestenergetic efficiency does not necessarily indicate the mosteconomical performance.

In addition, the COEs in the residential building were clearlyhigher than those in the commercial building due to many devicesand BOP components needed to operate the SOFC cogenerationsystem in the small-scale building. Therefore, it may be necessaryfor multiple small-scale residences to share a common SOFC systemto get electricity and thermal energy in a more economical way.Finally, the cogeneration efficiencies in cold conditions were higherthan those in hot conditions since the fuel inlet flow rate wascontrolled to meet the hourly electrical load in this study. Under hotclimate conditions, the system thermal energy supplies were largerthan thermal needs of the building and thus not all of those excessamounts of thermal energy could be included when determiningthe cogeneration efficiency.

In summary, the optimization results widely vary depending onfour different viewpoints: electric-only vs. cogeneration, residentialvs. commercial, energetic vs. economic and hot vs. cold weatherconditions. Therefore, the system should be optimized separatelyunder each situation and that the optimal system would attempt toadjust its operational strategy dynamically in reality. The modelpresented in this article is able to account for such differences andthus is able to determine which system will be the most effective inmeeting the building thermal and electrical needs using SOFCs.

Based on this study, future work that should be done includes:an experimental validation of the new model, an environmentalimpact assessment that compares greenhouse gas emissionsbetween SOFC systems and conventional systems, a more realistic

alternative such as using residual heat in absorption chillers anda faster execution speed for a more detailed look at the optimiza-tion process and for the incorporation of the new stand-alonesimulation model into EnergyPlus program.

Nomenclature

Cact actual capital cost ($)Ccap unit system capital cost ($/kWe)CF natural gas cost ($/Mcf¼ $/MMBtu)CM annual maintenance cost ($/kWe)Cref reference cost ($)F1 unit conversion factorF2 unit conversion factorF3 unit conversion factori annual discount rate (%)n system lifetime (years)Pact actual system sizePref reference system sizeRc capital recovery ratehcog system cogeneration efficiencyhsys system electrical efficiency

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sofc cogeneration system for building applications, part 2: system configuration and operating...

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