Techno-economic analysis of PEMFC and SOFC micro-CHP fuel cell systems for the residential sector

Energy and Buildings 103 (2015) 131–146

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Techno-economic analysis of PEMFC and SOFC micro-CHP fuel cell systems for the residential sector R. Napoli, M. Gandiglio ∗ , A. Lanzini, M. Santarelli Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy

a r t i c l e

i n f o

Article history: Received 29 November 2014 Received in revised form 17 June 2015 Accepted 18 June 2015 Available online 22 June 2015 Keywords: Micro-CHP PEM fuel cell SOFC fuel cell Energy storage Load profile Primary energy consumption Economic support

a b s t r a c t This work has the aim to carry on a techno-economic analysis to verify the performance of PEMFC and SOFC based micro combined heat and power systems, analyzing the seasonal performance of the device on the basis of the thermal and electrical 1-minute-averaged user load profiles. A Matlab® program has been written to build the annual profiles and to perform the energy evaluation of the system, considering the fuel cell production and the user demand and including a thermal and electrical storage. Four kinds of operation have been considered in order to evaluate the system behavior with different modulation strategies. Because the fuel cell needs a certain time to adapt its output power to steep load changes, a sensitivity analysis on the ramp rate has been made, in order to consider the impact of the speed of the cell in following the load requests. The calculation has been implemented both for the electrical led and thermal led operations, for single-family buildings. The economic analysis (based on the current prices of the technologies involved) has the aim to analyze different support schemes aimed at facilitating the technology competitiveness in the market, giving a general view of the possible technology applications in the near future. © 2015 Elsevier B.V. All rights reserved.

1. Introduction In the context of the EU 2020 objectives, micro-CHP systems can contribute to a reduction of CO2 emissions in both the residential and commercial sectors and to an increase of the energy performance (efficiency) of existing buildings. A further increase of energy efficiency from locally distributed CHP units comes from to the avoided transmission losses related to a centralized production [1]. The forecast is that energy demand in these sectors will continue to remain high in the next decades and even the renovation of existing buildings will be limited: all these reasons make micro-CHP a key technology to replace, or complement, conventional domestic heating systems in order to obtain primary energy and CO2 emissions savings. In the immediate future, micro-CHP can be seen as a way to foster the transition to an energy system based on renewable sources for mainly two reasons: the first one is the avoidance of the risk

Abbreviations: ASR, area specific resistance; CHP, cogeneration heat and power; DHW, domestic hot water; FC, fuel cell; FU, fuel utilization; IEA, International Energy Agency; NG, natural gas; NPV, net present value; PEM, polymer electrolyte membrane; PEMFC, polymer electrolyte membrane fuel cell; PES, primary energy saving; SOFC, solid oxide fuel cell; TPES, total primary energy saving. ∗ Corresponding author. E-mail address: [email protected] (M. Gandiglio). http://dx.doi.org/10.1016/j.enbuild.2015.06.052 0378-7788/© 2015 Elsevier B.V. All rights reserved.

of “carbon lock-in effect”, a phenomenon linked to the perpetuation of the carbon intensive and inefficient energy system where the traditional power plants with a longer lifetime prevent cleaner technologies from being deployed. The second reason is the balance of supply and demand side in a reliable way: this is mainly due to the almost perfect match between peaking in electricity and thermal demand in households [1]. Some studies and large-scale practical demonstrations have been carried on by grid operators in order to demonstrate the synergy between smart grids and flexible distributed generation technologies such as micro-CHP [2]. The role of micro-CHP in smart grids is linked to the so called Virtual Power Plant concept, that is the aggregation of power production from different grid-connected distributed generation sources in order to harmonize the sources for the customer demand. In addition, a micro-CHP system represents a heat storage capacity because it can be easily integrated with storage tanks to store the heat to supply later: this allows a more flexible operation in the grid. A similar advantage in storage can be applied for the produced electricity, by using batteries [3]. The existing micro-CHP technologies are mainly based on Stirling engine, Organic Rankine Cycle, Internal Combustion Engine. Fuel cells are a new technology to the residential market offering interesting opportunities because they primarily generate electricity (SOFC more than PEMFC) with heat as a by-product and they can be evaluated in the perspective of a future low energy building

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concept. As reported in “The Fuel Cell Industry Review 2013” by FuelCellToday [4], “fuel cell micro-cogeneration systems outsold conventional engine based micro-CHP systems in 2012, with 64% of global sales.” The leading Country in this context is Japan, with the Ene-Farm project, started in 2009 [4]: the total sales hit the 10,000 mark in April 2012 and 20,000 in June 2013, and Tokyo Gas aims to have sold 300,000 by 2020 [5]. The optimization during these last years has given results in terms of efficiency and durability, in addition to lower costs and reduced occupied space. The efficiency has become higher thanks to the enhanced performance of fuel processors, the lowered power consumption of the auxiliary components and the reduction in heat loss. The unit electrical output is between 700 and 750 W and the overall efficiency around 95%, while the financial incentives applied have reduced the cost by more than 25% respect to the original one. After Japan, the second leading country in micro-CHP area is Germany, with the Callux project [6] started in 2008 and scheduled to run until 2015. The German experience has been recently shared with a European-wide micro-CHP demonstration program called Ene.field. In the project there are 27 partners with the aim to install around 1000 fuel cell micro-CHP systems in 12 Member States over the next five years [4]. A PEMFC produces electrical energy and heat: the latter can be recovered to heat up water in a secondary circuit connected to the heating system of a building. This is generally achieved in combination with a traditional heating system equipped with storage tank for domestic hot water [7]. The fuel processing unit has the aim to convert the natural gas entering the system into a gas mixture rich in hydrogen while leaving carbon monoxide below a certain value in ppm (depending on the catalyst). The reformer (a steam reformer with external demi feedwater) requires heat to sustain the endothermic reactions that is derived from the burner. The burner is fed with the exhaust gases from the fuel cell anode, mixed with fresh natural gas from the grid, if necessary [8]. A SOFC is a very versatile fuel cell because of the flexibility in the fuel utilization: the high temperature allows the direct utilization of hydrocarbons (some fuels like CH4 may needs catalyst for the reaction activation). The production of a fuel gas suitable for the anodic reactions is based on the fuel processing: the aim of this processing is to eliminate the components which may react with the catalyst blocking its functioning, to make the conversion of hydrocarbons into gases rich in Hydrogen, to reduce the concentration of CO and water in the final gas product. The catalytic conversion of CH4 into Hydrogen is called steam reforming: this reforming reaction can take place in the anodic section of the cell or in a separate reformer integrated in the cell stack: this ensures higher efficiencies and reduces the system complexity [9]. The heat recovery is based on a water loop which takes the heat from the plant and transfer it to the residential users via heat exchangers. A general scheme of the PEMFC system and the SOFC one with a focus on these heat sources is presented in Fig. 1. This paper concerns the project related to the techno-economic analysis of the system, with the investigation of different scenarios in Italy and Europe, and of the supporting schemes for residential micro-cogeneration with fuel cells. Realistic time-dense users consumption profiles were used, along with the investigation of several operating strategies of the micro-CHP. These are the main innovative features of the presented work coupled with the choice of real load profiles a with real fuel cell performance indexes.

2. User load profiles The determination of the annual electrical and thermal (heating and domestic hot water) consumption profiles is the first step in

the assessment and comparison of the economic and energy performance of residential cogeneration. The consumption pattern is not homogeneous and may vary significantly depending on the geographical location of the house and the building typology. For the purposes of this work, a German reference load profile has been considered: the measured data are collected and reported as part of the guidelines by Verein Deutscher Ingenieure e.V. [10]. The measured data are available as 1-minute averages for the single family houses (detached house and pair of semi-detached house). Starting from the annual measured data, the typical load curves of 10 reference days were determined. The field of application of these data is a CHP system with a fuel input up to 70 kW (referred to the net calorific value of the input fuel), designed for use in residential buildings. It covers single-family houses for up to 12 occupants. According to DIN 4710 [11], Germany is divided into different climatic zones: depending on the chosen city, the number each typical day (sunny, cloudy, rainy, etc.) per year is provided [10]. Starting from the required electrical and thermal load for each typical day and the number of typical days per year, the annual consumption profile for electricity, heating and domestic hot water can be calculated. The calculation of the yearly heating energy demand is made in accordance with DIN EN 832 [12]. The annual electrical energy demand in a single-family house is assumed to be: • 2000 kWh/pers/year for less than 3 persons; • 1750 kWh/pers/year for 3–6 persons; • 1500 kWh/pers/year for more than 6 persons. The evaluation of the yearly DHW energy demand is based on the following criteria: • 500 kWh/pers/year in single-family houses. The calculation of the daily energy demand is done separately for electricity, heating and domestic hot water, for each of the typical days identified. The calculation is made starting from the annual energy demand, the data about building and occupancy conditions and other factors taking into account the climate zone. The following equations are used [10]: QH,d = QH,a · FH,d

(1)

with QH,d daily heating demand [kWh/day], QH,a annual heating energy demand [kWh/year] and FH,d [year/day] factor depending on the climate zone; Wel,d = Wel,a · (1/365 + Npers · Fel,d )

(2)

with Wel,d daily electrical demand [kWh/day], Wel,a annual electrical energy demand [kWh/year], Npers number of persons for single-family case, Fel,d [year/day/pers] factor depending on the climate zone; QDHW,d = QDHW,a · (1/365 + Npers · FDHW,d )

(3)

where QDHW,d is the daily DHW demand [kWh/day], QDHW,a is the annual DHW energy demand [kWh/year], FDHW,d [year/day/pers] is the factor depending on the climate zone. For the daily demand curve calculation, ten different reference load profiles of the normalized energy demand in terms of the three energy forms electrical energy, heating energy and DHW energy are available. The reference load profiles are expressed as ratios of instantaneous energy demand over the daily energy demand. For each time t (1-minute time step for single-family house) the energy value at that time is given by the product of this ratio and the daily energy value calculated before. With this methodology, the daily

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Fig. 1. General configuration of the PEMFC and the SOFC systems.

demand curve is determined for all of the typical days considered. Figs. 2–4 show the electrical, DHW and thermal loads for one of the typical days (1st January). The electrical, thermal and DHW profiles have been determined by the German institute VDI according to the methodology shown in [13]. The thermal profile has a discontinuous trend since the measuring system was not able to measure impulses lower than 100 Wh/min. Concerning the electrical profile, the sensors were measuring the overall electrical consumption including the household consumption and also the electrical consumption of boilers for DHW and thermal coverage. Electric loads thus include all the consumption from the electric appliances in the household including, lighting.

2.1. Influence of the temporal precision on the user load profile To evaluate the peak shaving due to interval averaging of load profiles, an example profile has been considered to show impact of the temporal precision of the time-series used for the calculations. The time-series is a 3-second profile from IEA (International Energy Agency) [14]. Due to the very precise time interval, the profile is provided only for a single day of the year for a large family in the US and it so was not used further except for the calculations shown in this paragraph. Both 1-minute and 15-minute averaging were performed and compared with the 3-second profile. As it shown in Fig. 5, the profile is considerably smoothed through the averaging procedure: passing from the 3-second to 1-minute profile, the

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Fig. 2. Electrical load for 1st January.

Fig. 3. DHW load for 1st January.

Fig. 4. Thermal load for 1st January.

maximum peak of electric power is reduced from 5.4 to 2.4 kW, while from 1-minute to 15-minute the value results further reduced to 1.8 kW. The temporal precision has a therefore significant impact on the technical and economic evaluation of a micro-CHP system, as already stated in [15]. However in [15] the load profiles considered were 1-hour, 1/2-hour, 10-minute and 5-minute average profiles. The results obtained showed relevant differences in term

of both energy and economic performance: moving from the 1-hour profile, frequently used in similar dynamic analysis for residential application, to the 5-minute time precision, more accurate and with higher peaks, the whole plant parameters are strongly changing. The energy indicators as heat from the boiler, electricity to/from the grid showed variations of around 40%; the reduction in CO2 emissions are 40% overestimated and the lifetime cost shows a variation of 8%, leading to a high relative error in the achieved results. These

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Fig. 5. Influence of the time interval in peak shaving.

variations are even stronger when analyzing the optimal installed power of the micro-CHP system. These differences are due to the peak shaving above mentioned that occurs when averaging the data toward a greater time interval. The first evaluation when considering the available reference load profiles in [10] is that electrical energy profiles are the most variable in time, while the thermal ones are smoothed due to the role of thermal storage of the building and the heating system. For these reasons the electrical profiles are the ones which may need an higher temporal precision. Since the absolute error between the 10 and 5-minute profile is lower than the one shown from 1-hour to 30-minutes profile, the authors in [15] concluded that moving to a precision higher than 5 min is not relevant anymore and considered the 10-minute profile a good compromise between accuracy and computational cost. Starting from these evaluations, the choice of 1-minute profile in this work are justified as attaining even more accurate results than those presented in [15].

3. Methodology 3.1. Modulation strategies The fuel cell operation is based on four different modulation strategies in order to capture different dynamic operating strategies and thus limitations in the amount of electricity that is eventually dispatched to the end-user. The modulation strategy defines the fuel cell operation in the different operating points, following different criteria based of the considered load profile of the users (thermal or electrical led mode): this identifies the two operating strategies of thermal led mode and electrical led mode.

3.1.1. Operation without load modulation The fuel cell always works in the same operating point so that the current and power outputs are constant throughout the year, for every single day: this is the simplest operating strategy because the system is designed to operate always at the same power output and no load modulation criteria are introduced. The power output of the fuel cell is the mean value of the annual user load profile (electrical or thermal). The drawback of such an approach is the impossibility to follow the real needs of the user and the necessity to integrate the system with the electrical grid and the auxiliary boiler. This static

case is mostly used as a reference case to benchmark the dynamic operating conditions. 3.1.2. Operation with day–night modulation With this modulation strategy each day of the year is divided into day-operation and night-operation modes: the night-operation mode goes from 00.00 am to 06.00 am and is characterized by the lowest power output (zero in the case of PEMFC, which can be switched off) because the user needs are usually very low during that part of the day. The day-operation mode covers the rest of the day and is based on a power output which is the mean value of the annual user load profile (electrical or thermal) in the hours from 06.00 am to 00.00 am. This kind of modulation may avoid the night operation of the PEMFC (which is generally useless) and the export of electricity to the grid without self-consumption (or the dispersion of the thermal power output). 3.1.3. Operation with segmented modulation The segmented modulation represents an upgrade of the day–night modulation and the basis for the load following. While the day–night modulation divides the day into two intervals, the segmented modulation is based on a number of intervals (more than two) which better follows the load profile. An example is presented in Fig. 6. In each time interval the FC attempts to follow the mean value of the load. The intervals are found by the Matlab program, using an algorithm based on a dichotomic process: the day is divided progressively into two parts until the mean value of each interval respects a condition on the minimum and maximum value of the load profile in that interval. This process can be applied for each day of the year but, in order to avoid a very complex electronic regulation of the system, the interval scheme found for the most repeated day of the year (the most representative) is applied for all the remaining days. This can introduce a large error in case several days with a load profile completely different from the reference one are found. 3.1.4. Operation with load following A daily load following is presented in Fig. 7 for both PEMFC and SOFC based systems. This can be considered the most precise modulation strategy, with the aim to minimize the electricity imported from grid. The electronic regulation of the system is however very complex. Also, because of the limited size of the cell the upper part

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Fig. 6. Fuel cell operation with segmented modulation: FC electrical profile and electrical user load profiles.

Fig. 7. Operation with load following: FC profile and user load electrical profile.

of the user load cannot be followed and, consequently, the integration with the grid is mandatory. In addition some losses in the system are caused by the coverage factor lower than the unity. The electrical ramp rates used for the simulation are justified in [15] and [16]; these documents provide information about the SOFC

electrical ramp rate: the PEMFC ramp rates are evaluated as one order of magnitude larger than the SOFC, coherently as expressed in [15] and [16]. In particular the ramp rate used for SOFC simulation is 1.5 W/s, while for PEMFC the value of 30 W/s has been used because of its lower temperature operation and thermal inertia. The thermal

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led mode has not been considered with this modulation strategy because the concept of thermal ramp rate can be skipped in the simulation (when a thermal storage is considered, the production side and the user side are separated by the thermal inertia of the storage). 3.2. Fuel cell off-design performance In order to calculate the fuel cell and thus overall system performance when power is modulated or extended over the nominal operating point, the off-design performance of the fuel cell at variable load are required. For the PEMFC system, data have been collected from a real stack that was tested in the context of a regional project called RESCOGEN [8]. The performance of the PEM was thus validated on a fuel cell having the same size of the one considered in this work. The system layout adopted in the model (stream connections, flow rates, temperatures) was directly derived from the P&ID of the above-mentioned installation. The four operating points of the stack were directly taken from the PEMFC datasheet [17] and thus are validated values from a commercial product. The SOFC system nominal performance is based on commercial micro CHP generators [18]. The electrical efficiency is representative of anode-supported type cells [19]. The off-design performance was instead calculated taking into account a power modulation between −50% and +25% [20]. A polarization model at variable load has thus been built and is presented below. Input parameters and assumptions to the model are: • The area-specific resistance (ASR) is 0.4 /cm2 , that is representative of planar-type anode-supported SOFC operating at 750 ◦ C, 0.25 A/cm2 current density and 85% fuel utilization; • The FU is not constant with varying current density; as the cell (stack) current density gets reduced, the fuel flow rate cannot be varied accordingly (i.e., linearly) to maintain a constant FU. In fact, a minimum volumetric flow must be always provided to the anode side to prevent air from leaking in (that would cause anode re-oxidation). Following these considerations, for a varying current density below a certain critical value (i.e., the value for which corresponds the minimum allowed anodic flow rate), the FU varies (linearly from the max. value to a minimum) in such way to maintain constant the anode flow rate. For current densities above the nominal value, the FU is instead kept constant to its maximum value (see Fig. 8). The polarization model is able to calculate how the efficiency varies with respect to its nominal value with a varying current density respect to the standard operation value. The current density is related to the power density (ω in Eq. (5), expressed in mW/cm2 ) according to the following formula: ω = j · VSOFC

(5)

where j is the cell current density (A/cm2 ) and VSOFC (V) is fuel cell the operating voltage. The operating voltage is calculated as following: average

¯ − ASR · j VSOFC = VNernst (T, p, y)

(6)

The Nernst potential is given by the reversible thermodynamic potential generated by the electrochemical oxidation of H2 as a function of temperature, pressure and gas composition. Eq. (6) represents a simple SOFC model that is able to incorporate the effects of fuel/oxidant composition by means of an accurate Nernst voltage calculation averaged between the SOFC inlet and outlet diffusion channels. In this way, concentration losses due to mixing between the fresh fuel and the exhaust

137

are accounted for. The ASR is assumed here to be a constant that expresses the cell’s overall equivalent resistance, including ‘ohmic’ + ‘activation’ + ‘contact resistance’ contributions. Ideally, the cell’s (or stack’s) ASR would be provided by the fuel cell manufacturer, measured at thermodynamically and fluid-dynamically relevant conditions (i.e., at the nominal SOFC temperature and high FU operation). The normalized cell efficiency is calculated according to the following equations for the nominal efficiency and the efficiency at a generic current density j, respectively: 0 =



 =

ω0 · ASOFC 0 Gfuel

· LHVfuel

=

Gfuel · LHVfuel

j0 ·ASOFC 2F·FU





ω · ASOFC 

0 (j0 ) · ASOFC j0 · VSOFC

=

· LHVfuel

=

0 (j0 ) VSOFC

2F · FU 0

(7)



VSOFC (j ) 2F · FU



(8)

where ASOFC is the active area of the SOFC stack (cm2 ), Gfuel is the fuel mass flow rate (kg/s) and LHVfuel its lower calorific value (kJ/kg/K), F is the Faraday constant and FU the stack fuel utilization [%]. The “0” superscript is referred to the nominal operating point and the “,” to a generic different operating point. The two previous equations combined together yield the following expression: V (j ) FU 0  · = 0SOFC 0  VSOFC (j0 ) FU 

(9)

In Fig. 9 is represented the curve, calculated from Eq. (9), to be used in conjunction with the SOFC macro-model to evaluate the cell’s efficiency variation when partial load operation is requested. Table 1 summarize the chosen and validated working point for the PEMFC and the SOFC. 3.3. The concept of electrical ramp rate and coverage factor The load-following operation of a fuel cell has technical limitations: it is extremely challenging to follow exactly the load profile because the fuel cell needs a certain time to adapt its output power to the steep load changes (this is linked to fluid-dynamic and thermal regulation limitations). Also, SOFC has generally a slower dynamic response than PEM due to the risk to produce critical thermal gradients across the stack. The electrical ramp rate M can be defined as the maximum FC electrical power variation in a known time interval, and can thus be expressed in [W/s]. The actual ratio between the electrical power variation (increase) in a chosen time interval and the time interval itself can be expressed as: m=

(P2 − P1) (t2 − t1)

(4)

The value m is calculated with Eq. (4) for each chosen interval (the same intervals of load profiles, 1 min for single family house) and is compared to the ramp rate M of the fuel cell (M represents thus the maximum capability of the fuel cell to change/adapt its power output): if the ramp rate M is bigger than m, the fuel cell can exactly follow the load in that particular interval; if the ramp rate M is lower than m, the fuel cell cannot exactly follow the load (because of the limit in power ramp) and its output power at the end of the interval is different than the value of the load. Considering the ramp rate as a constraint for the load following operation mode, its influence in smoothed profiles is not as strong as in more accurate time-dense peaked profiles: the choice of temporal precision is therefore determinant for the evaluation of the losses connected to the ramp rate. In particular the 15-minute average profile is the one easier to follow, also with lower ramp rates, while the 3-second profile is the most difficult to follow and higher

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Fig. 8. FU profile at varying current density.

Fig. 9. Efficiency profile at variable current density.

Table 1 Main system data at different working points. PEMFC

Inlet power (LHV) [W] Fresh NG with recirculation (LHV) [W] Net power [W] Net heat from cogeneration [W]

10 A

20 A

30 A

40 A

1093 760 339 46

1873 1478 650 846

2608 2189 927 1231

3478 2919 1169 1688

SOFC

Inlet power (LHV) [W] Net power [W] Net heat from cogeneration [W]

Point 1

Point 2

Point 3

Point 4

668 500 102

1209 800 288

1667 1000 500

2715 1250 1193

ramp rates are needed. The capability of the system to follow the load profile has been analyzed using the 3-second profile of [14]. Three different ramp rates (0.1, 3 and 100 W/s) have been chosen for the analysis (Fig. 10) representing a fuel cell with different dynamic operability.

The maximum power value in the cell profile is due to the fuel cell size, which is of course limited and usually lower than the load peaks (in this case the maximum electrical power that the cell can supply is about 1.2 kW, while in the 3-second profile the load reaches 5 kW).

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Fig. 10. Load following with different ramp rates for the 3-second load profile.

Table 2 Coverage factors. Ramp rate

3-sec profile

1-min profile

15-min profile

0.1 W/s 3 W/s 100 W/s

0.8686 0.9160 0.9167

0.8709 0.9184 0.9209

0.9205 0.9504 0.9516

In order to evaluate in a quantitative way the concept of load following capability, the coverage factor has been introduced, defined as the ratio between the fuel cell profile (kWh) and the user profile (kWh). The coverage factor values for all the considered cases are reported in Table 2. As can be seen from the calculated values, a higher temporal precision yield lower coverage factors at the given ramp rate; enhanced fuel cell dynamic operation (i.e., higher ramp rates) would be required to achieve the load profile coverage. Anyway, the differences between the results in 3-second and 1-minute profile are limited; therefore, the 1-min profile can be assumed as reference (as already discussed above).

3.4. Technical assumptions 3.4.1. Energy storage The load profiles are variable during the year and they are not necessarily synchronized. The use of energy storage devices can be a solution to reduce the system dependence from the grid, and especially the economic imbalance between kWh exchanged with the grid: for instance, a battery could accumulate electricity when produced in excess by the system and delivered back when the power output of the cell is not sufficient to meet the load. In this way the import of electricity from the grid is reduced. Hence, a battery yield a peak shaving effect resulting in a fuel cell of lower installed power (i.e., closer to the mean value of the load curve) because peaks can be easily followed by the battery – also thanks to its faster dynamic behavior [21]. The thermal storage can allow the combined operation of the FC and a boiler in order to reduce the boiler operation (for example, when the thermal and electrical peaks are not synchronized, the excess of heat produced by the fuel cell can be stored and used in a different period of the day). The profitability of the system is influenced by the storage utilization and its size [22]. The electrical storage operation is based on the following criteria (applied for each time step):

• If the electricity produced by the cell is more than the electricity required by the load, the battery is charged (until its maximum charge level). The excess of electricity which cannot be stored (if present) is exported to the grid. • If the electricity produced by the cell is less than the electricity required by the load, the battery is discharged (until its minimum charge or voltage level, if necessary). If the charge present in the battery is still not sufficient the rest of electricity is imported from the grid. The processes of charge and discharge of the battery are influenced by the charge/discharge efficiencies, that imply an higher energy required for the charge operation and a lower energy available in the discharge phase [23]. The thermal energy storage has been modelled in the Matlab program similarly to the electrical storage (following the same logic) with some simplifications: the storage is adiabatic [22] with respect to the external environment and all the energy stored is considered always available independently on the time in which it will be used (this is a reasonable simplification because the cell system and the load are always in operation and the water is assumed to be stored for few hours). 3.4.2. Primary Energy Saving index: PESFC and TPESSYS The Primary Energy Saving index of the fuel cell device PESFC has been calculated with the following equation [24]: PESFC = 1 −

Efuel Eel el,s

+

Eth th,s

(10)

where Efuel is the fuel cell inlet fuel (natural gas) [kWh], Eel and Eth are respectively the electricity and thermal energy produced with the fuel cell [kWh], el,s is the electrical grid efficiency and th,s is the boiler efficiency. These two efficiencies can be considered as the efficiencies for the separate production of electricity and thermal energy. The total primary energy saving with respect to the ‘zero hypothesis’, TPESSYS , has been calculated with the following equation: TPESSYS = ET − ET,0 [kWh/y]

(11)

where ET [kWh/y] is the actual total primary energy consumption for the user loads and ET,0 [kWh/y] is the total primary energy consumption for the user loads in the ‘zero hypothesis’ (condition in absence of the fuel cell system).

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Table 3 Technical and economic input data.

Technical data Nominal power [kW] Profile time step [min] Electrical ramp rate [15,16] [W/s] Nominal electrical efficiency Nominal thermal efficiency Electric grid efficiency Auxiliary boiler efficiency Performance degradation (based on potential and voltage decay) [27] [%/y]

3.5. Economic assumptions

k=1

(1 + rw )k

1 1 1.5 60% [34] 30% [34]

1.40

3.50

6248 8248

7650 9650

Table 4 Energy price data.

Methane price [30]

[D /m3 ] for heating prod. [D /m3 ] for elc. prod. [D /m3 ] for elc. self-prod.

0.9263 0.8953 0.8949

Electricity price [31]

[D /kWh] 0–1800 kWh/y [D /kWh] 1801–2640 kWh/y [D /kWh] 2641–4440 kWh/y [D /kWh] >4440 kWh/y

0.1687 0.2302 0.3010 0.3525

[D /kWh]

0.0784

Electricity price (for electricity export to the grid) [31] Discount rate

7%

Table 5 Electrical and thermal storage data.

The economic analysis of the system is based on the Net Present Value method [25]. The NPV represents a methodology to define the actual value of a series of cash flows: it compares the present value of money today to the present value of money in the future, taking inflation and returns into account with an appropriate discount rate, which can be selected as the rate that the capital needed for the project could return if invested in an alternative venture (a way to evaluate the risk connected to the investment). The NPV has the following formulation: Ck

1 1 30 34% [33] 46% [33]

10 5% of investment cost 10% of investment cost 427

The total primary energy consumption has been calculated as the sum of the required inlet energy to the system for the coverage of the electrical and thermal profile, sum of chemical energy of the fuel and electrical energy from the grid. The PESFC index is conceptually different from the TPESSYS because while the PESFC index makes a comparison between the energy produced by the fuel cell and the same amount of energy produced using the grid, the TPESSYS uses as term of comparison all the primary energy for the user loads (and not only the amount produced by the fuel cell). Thus, the PESFC index refers only to the fuel cell subsystem, while the TPESSYS considers the entire system (the micro-CHP system plus the auxiliary boiler and the electrical grid).

NPV = −C0 +

SOFC

42% 90%

Economic data Specific cost of the system [28] [D /kW] Cost of the system [D ] including auxiliaries Investment lifetime [y] O & M costs [%/y] Installation cost [%/y] Battery cost (200 Ah, 12 V) [29] [D ]

n 

PEMFC

(12)

where C0 is the initial investment cost, n is the number of years of the investment, Ck is the cash flow of the k-th year (sum of the costs and the incomes of the k-th year) and rw is the discount rate. The cash flows considered are relative and should be intended as the difference between the cash flows in presence of the microCHP system and the corresponding cash flows in the case of the ‘zero hypothesis’ (this last one includes as initial investment cost the only boiler cost). The still higher cost of systems like micro-CHP, respect to the traditional domestic heating solutions is a problem connected to the early market stage in which they are and may represent a great limitations for their spread. This limitations can be skipped using support schemes to promote these technologies at a national level. The support schemes considered and compared in this analysis are [26]: • S1 → the support in D /kWh is given for the electricity in kWh produced for self-consumption by the users; the electricity exported

Electrical storage data [32] Battery voltage [V] Minimum charge level [Ah] Minimum charge level [Wh] Maximum charge level [Ah] Maximum charge level [Wh] Charge efficiency Discharge efficiency

12 42 500 167 2000 95% 95%

Thermal storage data Thermal storage volume [l] Water density [kg/m3 ] Water specific heat [kJ/kg/K] Water T [K]

200 1000 4.186 60

to the grid is not supported, thus only the market price for the energy component is paid to the producer for the electricity dispatched to the grid (this is the business-called “price premium for self-consumption”); • S2 → all the electricity produced with micro-CHP system is supported; consequently the price of electricity exported to grid consists of the market price component – only relative to the energy quota – and the support component in D /kWh (this is the business-called “price premium”); • S3 → a percentage of the initial investment cost is covered with the incentive and does not need to be recovered over the investment time (this is the business-called “capital grants”).

The main technical and economic input data are summarized in Tables 3–5.

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4. Results and discussion

4.1. Energy performance results

The following chapter will present the results of the technoeconomic model. Both energy and economic results are shown for PEMFC and SOFC systems, analyzing electrical and thermal led operation.

Fig. 7 gives the total primary energy consumption of the microCHP system for the electrical led and thermal led modes, both for the PEMFC (Fig. 11a) and SOFC (Fig. 11b) cases. The local energy storage device has always a positive impact because of the reduced

Fig. 11. (a) Total primary energy consumption of PEM system; (b) total primary energy consumption of SOFC system.

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Fig. 12. Electricity to/from grid.

import of electricity from the grid, which implies a higher primary energy consumption. For the PEMFC system, the only case which seems not to be influenced by the presence of storage is the load following modulation: in this case the micro-CHP system is able to follow the load with a very high precision and the dependence on the grid is thus already minimized (for this reason the electrical storage influence is not consistent). The best condition in the PEMFC case is the one with load following operating mode, whereas the segmented modulation scenario is not effective as expected (it implies only a small saving respect to the absence of modulation). It is possible to justify these results considering that the segmented modulation is defined for a typical day (the most repeated during the year) with a load profile that can be completely different from the one of other day types: the choice to repeat the same modulation for all the days in the year eliminates the advantage of using a modulation more detailed than the day–night one. Fig. 11 includes the single quotas from the three sources (boiler, electrical grid and fuel cell). For the PEMFC case, the day–night modulation is the one with the lowest quotas from fuel cell: this is due to the fact that the cell is shut down during the night (even when the user load is not zero) and the electrical energy required is all imported from grid, while the thermal needs are all covered with the boiler. The electrical led mode is more convenient than the thermal led one because of its lower primary energy consumption: it is possible to explain this result considering that the thermal load profile is generally very high to be followed by the cell (which in this case operates at its maximum power), while the electrical load profile can be better followed with the different modulation strategies (only some local peaks cannot be followed because of the reduced size of the micro-CHP system).

The primary energy consumption is always lower in the SOFC case (because the global efficiency is always higher), but the quota are different in percentage: in particular, the quota related to the auxiliary boiler is higher and the quota from the fuel cell is lower (this difference is less evident in the thermal led operation). This result can be explained with the fact that the electrical led operation does not maximize the thermal load coverage: when the SOFC operates far from its maximum output power (due to the modulation), the electrical efficiency is maximized while the thermal efficiency becomes progressively lower, requiring an higher contribution from the auxiliary boiler (in the PEMFC system the situation is the opposite: for lower output power the thermal efficiency is maximized and the electrical efficiency minimized). To conclude it is possible to say that: • from the primary energy consumption point of view, the SOFC system is preferable because of its lower consumption (higher global efficiency); • the electrical led operation is preferable both for the PEMFC and the SOFC system (the primary energy consumption related to the electricity import from grid is very high respect to the primary energy related to the boiler, so following the electrical load is preferable to reduce the dependence on electrical grid); • the best modulation strategy for the PEM system is the load following modulation, while for the SOFC system is the day–night modulation (due to the lower ramp rate of the SOFC which reduces the possibility to exactly follow the users’ requests). Fig. 12 describes the electricity import/export from grid in electrical led mode. Coherently with the primary energy results, in case of PEMFC the day–night modulation is the one with the lowest

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Fig. 13. Comparison between S1 and S2 values.

electricity produced by the fuel cell and the highest electricity import from grid. For the SOFC system, it is possible to notice a lower electricity import from the grid with the day–night modulation (this is in coherence with the presence of a minimum level of production during the night, differently from the PEMFC system which is shut down). In Table 6 the TPESSYS and the PESFC index are presented, for the electrical led mode. The PESFC index only refers to the fuel cell subsystem and does not consider all the auxiliary components of the system and the grid integration as the TPESSYS does. For this reason the PESFC index is always positive (even when the TPESSYS of the whole system is negative) underlining that the operation of the fuel cell to produce the same amount of energy always implies

a lower primary energy consumption (confirmed by the fact that the micro-CHP system has an higher efficiency than the traditional production and distribution system). The lowest PESFC index is the one in load following modulation: this seems a contradiction but it can be linked to the operation of the cell in a condition that does not maximize its efficiency, even if its operation is nearer to the user needs and the TPESSYS is higher because of the reduced dependence on the grid. In the SOFC system TPESSYS and PESFC are higher than in the PEMFC case for all the modulation strategies adopted (this can be explained by with the positive effect of the higher global efficiency of the SOFC). For the load following modulation, the TPESSYS are a bit higher but, because of the negative influence of the SOFC reduced flexibility in load following, this modulation is not much more cost

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Table 6 TPESSYS and PESFC . No modulation Presence of battery:

No

Day–night modulation

Segmented modulation

Load following

Yes

No

Yes

No

Yes

No

Yes

PEMFC TPESSYS [kWh/y] PESFC

−1990.6 0.270

1070.8 0.270

−201.4 0.247

1684.7 0.247

−1146.5 0.257

1810.5 0.257

3061.5 0.243

3060.3 0.243

SOFC TPESSYS [kWh/y] PESFC

2451.2 0.457

5508.3 0.457

4430.0 0.545

7222.5 0.545

1661.7 0.413

4540.1 0.413

3398.1 0.318

4971.5 0.318

effective than the others (as it happens for the PEMFC system): from this point of view the best modulation for the SOFC system is the day–night one. 4.2. Economic results The aim of this part of the analysis is to find the premium that should be given to the electrical kWh either produced by the FC for which the NPV at the fifth year of the investment is zero (the initial investment returns back in five years). The operating support of S2 incentive mechanism applies to all the electricity produced by the micro-CHP system; thus the electricity exported to the grid has a price consisting of two components: the electricity market price and an incentive on the top of it. The S1 incentive applies only to the self-consumed quota of electricity and is strongly dependent on the amount of electricity imported from grid and the electricity produced by the fuel cell that is from the modulation strategy. As we can see in Fig. 13, for the PEMFC system, in presence of S1, the lowest incentive is the one for load following modulation, which is the modulation with the lowest import of electricity from grid. With this result, the advantages of load following modulation for the PEMFC case is confirmed also from the economic point of view and can be explained with the lowest dependence on the grid (the cost of the electricity imported from the grid is four time the price of electricity export to grid: consequently it is not convenient to produce electricity to export, especially in case of support given only for the self-consumed quota). Supporting the self-consumption is in coherence with the effective aim of the incentive: to help the technology development connected with a

lower primary energy consumption and a reduced environmental impact and not to force the producer to increase his production only for a personal economic revenue from the export to grid. As a consequence, the installed power plant is not over-sized but has the correct size for the actual user needs. In Fig. 13 a comparison is made between S3 and S1 for the electrical led mode, for both PEMFC and SOFC systems. The figure shows that S1 is lower, as one can expect being this applied to the whole electricity produced and not only the self-consumed. The higher the percentage of electricity self-consumed, the lower the difference between the two supports: for the load following modulation in fact the incentives are the same. The values of incentive obtained are congruent with the 2012 UK incentive of 12.5 p/kWh (corresponding to 0.15D /kWh) for onsite generation and 4.5 p/kWh (0.054D /kWh) for electricity export to grid [33,34]. Similar are the German incentive values: 0.0541D /kWh for new high efficiency installations below 2 kW [35]. It is interesting to notice that, for the SOFC system, an higher support level is necessary for the load following modulation: this is related to the fact that the load following for a SOFC system is not as flexible as for PEMFC system (for the reasons related to the ramp rates) and also because, due to the behavior of the SOFC efficiency curves, the source utilization is not optimized. The S3 subsidy may represent a good support mechanism for the technology considered because of its immaturity, a support to be adopted in the early stages of its introduction in the market. In Fig. 14 two kinds of cash flows are indicated: one related to the NPV in presence of S3 (40% of the initial investment is financed), the other to the NPV with S1 able to allow a pay back of 5 years as discussed above (0.095D /kWh for the PEMFC system with load following modulation, 0.101D /kWh for the SOFC system

Fig. 14. Comparison between S1 and S3 support (electrical led operation).

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Fig. 15. NPV of the fuel cell system for different initial investment costs (the best scenario for each technology is depicted). The starting value is representative of the today’s cost.

with day–night modulation, which represents the best scenario for the system considered). S3 subsidies are preferable for both the PEMFC and the SOFC cases, because the cash flow reaches the zero at the fourth year instead of the fifth year. It is necessary to remind that in the SOFC case the value of incentive (S1) for which the NPV at the fifth year of the investment is zero is a bit higher than in PEMFC case. The capital grants reduce the first year cash flow, but the S1 incentive leads to an higher cash flow at the end of the investment period. The choice depends of course on the level of risk that it is possible to accept. Considering the that the investment cost plays a crucial role for the micro-CHP technology competitiveness in the residential market, a sensitivity analysis has been carried on assuming a total lack of support schemes available for the micro-CHP FC. Main results are given in Fig. 15. For the PEMFC case it is possible to see that in the load following operating mode a reduction of 30% of the investment cost shifts the break-even point from the year 8 to year 5, while a reduction of 50% shifts the break-even to before the year 4. Similar results are obtained for the SOFC case.

5. Conclusions Starting from the goal of sustainability among the EU 2020 objectives, the results obtained in this work enhance the contribute of micro-CHP systems to the primary energy consumption reduction in the residential sector and to the increase of energy performance of existing buildings. The simulation results show that the PEMFC- and SOFC-based micro-CHP systems always reduce the primary energy consumption for the electrical and thermal need of domestic users. The positive effect is increased when considering an energy storage system, able to reduce the grid dependence. The advantages introduced with these micro-CHP systems are mainly related to their high global efficiency and the possibility of a production in loco, with the avoidance of the transmission losses. The operation with load following modulation increases these positive effects, especially in the PEM system because of its higher flexibility and low temperature operation. The operation with segmented modulation does not introduce the benefits expected due to the fact that this is not a real load following (it is the repeated in the different day types of the year).

From the primary energy consumption point of view, the SOFC system is preferable because of its lower consumption, due to its higher global efficiency; from the economic point of view, the choice should me made taking into account the fact that the SOFC case is the one with an higher investment cost but also with higher yearly savings. The choice to let the system operate following the user electric profile is preferable, both from the energy and economic points of view, because it allows the reduction of the electrical grid dependence: in fact the primary energy associated to the electricity production is still high and even the production cost of electricity is higher than the production cost of thermal energy. In addition, when operating in thermal led mode the system tends to work at its maximum power level, in which its global efficiency is not maximized. From the methodology point of view, it is confirmed that a detailed profile (1-minute averaged) is sufficient to compare the cell profile and the user load profiles and to obtain accurate energy results. One of the main limitations for this technology development is the absence of a homogeneous regulation in the European context about the support schemes. Because of the high investment cost (which is however in reduction) the market competitiveness is still difficult to reach without incentives, but it is demonstrated that a reduction of 30% in the initial investment cost is enough to recover it in a reasonable time (around five years of the investment time). References [1] COGEN Europe Position Paper. Micro-CHP – A cost-effective solution to save energy, reduce GHG emissions and partner with intermittent renewables, 2013. [2] Delta Energy & Environment, http://www.delta-ee.com/ (update 1.7.2014). [3] COGEN Europe Briefing paper on Smart Grid and micro-CHP, 2011. [4] FuelCellToday, The Fuel Cell Industry Review, 2013. [5] TOKIO GAS, http://www.tokyo-gas.co.jp/techno/stp1/00h1 e.html (update 1.7.2014). [6] Callux, Field Test of Residential Fuel Cells. Background & Activities, 19 September 2013. [7] M. Gandiglio, A. Lanzini, M. Santarelli, P. Leone, Design and Optimization of A Proton Exchange Membrane Fuel Cell CHP System for Residential Use, Elsevier, 2014. [8] Environment park – Parco Scientifico Tecnologico per l’Ambiente, http:// www.envipark.com/progetto/res-cogen-polight/ (update 1.7.2014).

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