Last published report of Technical University of Denmark analysed energy systems emphasis [1] in particular the interaction between industrial, domestic and transport consumptions in Denmark. A key issue for the next future was pointed out to be to decrease the use of fossil fuels and replacing it with renewable energies along with a strong improvement in energy efficiency, even though specific strategies must be taken for renewable capacity firming. Other different studies are also reported in the literature on this subject. In [2] a chain analysis of the energetic performance of synthetic natural gas including the upstream, midstream and downstream part was reported. The model was designed to analyse the performance of biomass to synthetic natural gas chain and to estimate the effect of 1% synthetic natural gas in the energy system. In [3] the authors analysed the potential energy benefits and drawbacks coming from the application and deployment of hydrogen technologies for static power and heat production based on the state-of-the-art to face the energy transition. Another study [4] analysed the energy performance of an internal combustion engine fuelled with an environmental friendly fuel, such as Hydrogen-natural gas mixtures (H₂NG), as the bridge technology towards the forthcoming pure hydrogen economy. In [5] the effects of coolant temperature in engines using biofuel were described. The efficiency of combustion process and carbon dioxide emissions were observed to increase with temperature, while carbon monoxide, oxygen and lambda values were observed to decrease with temperature.

Other studies had the main goal to analyse the complete elimination of Carbon dioxide (CO₂) emissions of a city while keeping the power frequency of the grid within prescribed limits, adopting a distributed generation architecture to permit local autonomous operation of the system by the local generation of power [6].

Moreover, electric cars and electric mobility are open topics of research [1] with the aim to decrease environmental impact of transport sector. Different studies show that electrical mobility has an environmental impact that is strictly related to the energy sources used to produce electricity [7]. For example, greenhouse gas emissions can be avoided only if renewable energy sources are used. Electrical mobility has been already studied in relation to the possibility of domestic charging in e.g. [8]. The possibility of using electric cars and their batteries as energy storage systems to stabilize electric systems in scenarios where the majority of the total energy demand is supplied by renewable energy was also analysed (see e.g. [9], [10]). A possible drawback is related to energy storage due to the fact that batteries provide lower energy storage than hydrocarbon fuels and they are heavier (when comparing for example in kWh/kg).

Meanwhile micro-cogeneration energy systems for household application were also proposed to increase efficiency for a better matching between demand and production and also decrease transmission losses [4], [11]. Different micro-cogeneration systems were proposed, analysed and studied. In [12] a dynamic simulation model for the micro-cogeneration unit based on internal combustion engine was built as a new component on TRNSYS, the model was validated by the experiment data during steady and dynamic processes. In [13] a techno-economic evaluation of retrofitting reciprocating internal combustion engine based cogeneration into existing Canadian houses for the purpose of achieving or approaching net-zero energy rating was presented. The simulations based on primary energy and electricity consumption and associated greenhouse gas emissions revealed the economic profitability in different Canadian climates. Also, Stirling engines were analysed: for example, in [14] the authors presented a test results for a μCHP with the Stirling engine for the most frequently used working gases (helium, nitrogen, air, and argon). The influence of the heat source temperature on the performance of the micro-cogeneration system, efficiency, and the effect of maximal power as function of pressure were presented. A similar study was conducted in [15], based on experimental and numerical analysis of a commercial unit generating 8 kW of hot water (up to 15 kW with an auxiliary burner) and 1 kW of electricity burning natural gas. Brandoni et al. and Maghanki et al. [16], [17] analysed a very high range of micro combined heat and power solutions, above all fuel cells, revealed high potential in reducing the greenhouse gas emissions. Investigations on the effect of various parameters (such as condensation and evaporation temperatures, superheating and installing a recuperator) on the performance of a micro-scale tri/co-generation system based on the joint operation of an Organic Rankine Cycle (ORC) and a Vapour Compression Cycle (VCC) were carried out in [18]. A similar study for a single-family located in Italy based on a biomass-fired ORC system was reported in [19]. Thu et al. [20] conveyed a study of a micro gas turbine cogeneration system and analysed the part load and full load operation in terms of both first and second laws. Moreover, a small hybrid cycle consisting of a commercially available micro-steam turbine as bottoming cycle (steam expander) and an automotive turbocharger as toping cycle was simulated through an optimization procedure based on a genetic algorithm in [21]. The results of simulations indicated that such novel typology of hybrid cycles (combined cycles) have the potential to be cost-effective with high level of efficiency. An interesting option are also Photovoltaic (PV) cogeneration modules, where useful heat can be recovered by cooling the PV cells (thus increasing their electrical efficiency) was studied in [22]. An experimental analysis of some kinds of modules demonstrated that the thermal and electrical efficiencies are determined by mass flow of cooling water, the inlet temperature and the solar radiation [23]. Heat pumps were also proposed to achieve high efficiency system for hot water production in [24] and cogeneration systems by integrating the heating systems were also proposed to attain better electricity and heat demands (for the user) in Northern [25] and Mediterranean [26] climates.

In this paper, an integrated system with Solid Oxide Fuel Cell (SOFC), Stirling engine, Ground Source Heat Pump (GSHP) and water tank is proposed for a group of five houses. The effect of different types of fuel as input for the systems is also analysed, wherein SOFC produces electricity with high efficiency, heat pump covers part of the heating demand and Stirling engine is used to increase electricity production when heating request is low and at the same time electricity request is high. Electric mobility is proposed with the aim of simulating a future scenario, when electric cars will have a higher penetration into the transport sector [27]. Energy performance of the system is analysed and primary energy saving with respect to a traditional solution is depicted. Such study is completely new and has not been investigated elsewhere.

The innovative system proposed here is set up by a SOFC system to produce electricity and heat, and a GSHP to cover part of heating demand. A Stirling engine is also used for the cases when electricity produced by the fuel cell is not enough to face the electricity demand and/or heat demand is low. SOFC system is fed with different fuels such as Ammonia, Dimethyl Ether (DME), Ethanol, Methanol and Natural Gas (NG). The system includes a water tank to store heat produced by the system in case of mismatching between production and demand, to prevent heat dissipation and to increase overall efficiency of the system. An auxiliary electric heater is also added to support GSHP to manage peak heat demands. Figure 1 represents the main energy fluxes.

Figure 1.

Schematic of the micro-cogeneration system with energy fluxes

A centralized cogeneration system is supposed to satisfy energy demand from a group of five houses, while each house has been simulated by the program TRNSYS [28] to determine its heating load. It is assumed that in each house there is an Electric Vehicle (EV) whose energy request for charging is covered by the cogeneration system as well.

Solid oxide fuel cell system

SOFC is the main component of the system proposed here. It provides electricity with high efficiency for the user (0.522 is the nominal electrical efficiency), and it supplies the electricity to the GSHP as well electricity for charging the electric vehicle. Heat at high temperature (780 °C) produced by SOFC is recovered to feed the Stirling engine. The fuel cell model is already proposed in [25], [26], [29] with a nominal electric power of 10 kW which is also adapted for the simulation of the system proposed here (Table 1). The model is slightly modified for using in different configurations in order to simulate different types of fuel in input as described in [29]. In this study, ammonia, DME, ethanol, methanol and NG are considered, with the following specifications:

• SOFC fed with ammonia features the simplest system, as only a fuel pre-heater is required (see Figure 2 and Figure 3);

• SOFC fed with DME, ethanol and methanol is designed with a methanator to reform the fuel (as in Figure 4 and Figure 5);

• SOFC fed with natural gas requires a desulfurizer (to avoid sulphur poisoning in SOFC) and Catalytic Partial Oxidation (CPO) reformer to crack down the heavier hydrocarbons (see Figure 6 and Figure 7).

Table 1

Rated data of the system main equipment

SOFC (nominal electric power) [kW]	Stirling engine (nominal electric power) [kW]	GSHP (nominal thermal power) [kW]	GSHP (COP at full load) (W10/W35)	Water tank (capacity) [L]
10	1.3	35	5.1	1,000

Figure 2.

SOFC micro-cogeneration system with Stirling engine fuelled by ammonia [Cathode Pre-heater (CP), Fuel Pre-heater (FP), Heat exchangers for heat recovery (HR1 and HR2) for Domestic Hot Water (DHW) and Space Heating (SH)]

Figure 3.

SOFC micro-cogeneration system (stand-alone) fuelled by ammonia [Cathode Pre-heater (CP), Fuel Pre-heater (FP), Heat exchanger (HR) for heat recovery for Domestic Hot Water (DHW) and Space Heating (SH)]

Figure 4.

SOFC micro-cogeneration system with Stirling engine fuelled by DME, ethanol or methanol [Cathode Pre-heater (CP), Fuel Pre-heater (FP), Heat exchangers (HR1 and HR2) for heat recovery for Domestic Hot Water (DHW) and Space Heating (SH), Air Pre-heater (AP)]

Figure 5.

SOFC micro-cogeneration system (stand-alone) fuelled by DME, ethanol or methanol [Cathode Pre-heater (CP), Fuel Pre-heater (FP), Heat exchanger (HR) for heat recovery for Domestic Hot Water (DHW) and Space Heating (SH), Air Pre-heater (AP)]

Figure 6.

SOFC micro-cogeneration system with Stirling engine fuelled by natural gas [Catalytic Partial Oxidizer (CPO), Cathode Pre-heater (CP), Fuel Pre-heater (FP), Heat exchangers (HR1 and HR2) for heat recovery for Domestic Hot Water (DHW) and Space Heating (SH), Reformer Pre-heater (RP), Air Pre-heater (AP)]

Figure 7.

SOFC micro-cogeneration system (stand-alone) fuelled by natural gas [Catalytic Partial Oxidizer (CPO), Cathode Pre-heater (CP), Fuel Pre-heater (FP), Heat exchanger (HR) for heat recovery for Domestic Hot Water (DHW) and Space Heating (SH), Reformer Pre-heater (RP), Air Pre-heater (AP)]

To recover as much as possible the available chemical energy of fuels, the off-fuel (after the anode side of the SOFC) and off-air (after the cathode side of the fuel cell) are burnt in a catalytic burner. Gases coming out of the burner are then used in a Stirling engine and finally they are used to heat water in heat recover HR2 when the Stirling is switched on, c.f. Figure 2, Figure 4 and Figure 6. In this case, heat can be recovered by HR1 heat exchanger (cold sink of the Stirling engine) as well. Hot water from HR1 and HR2 is used for both Domestic Hot Water (DHW) and Space Heating (SH) as it produced at the common temperature of 45 °C. Otherwise, when the Stirling engine is switched off the gases are used to directly heat the water by means of heat recovery exchanger HR, c.f. Figure 3, Figure 5 and Figure 7. It is also proposed to vary the configuration of the system when the Stirling engine is switched off. In this case, the off-air splitter after cathode side of SOFC is closed and all the off-air from the cathode is used in the Cathode Pre-heater (CP), and successively used into the burner. Again, Figure 2, Figure 4 and Figure 6 represent SOFC system with Stirling operating, with an off-air splitter to distribute off-air for both CP and burner smartly. Figure 3, Figure 5 and Figure 7 represent SOFC system when Stirling engine without using off-air splitter, and all off-air are directed into the burner after cathode CP.

As already mentioned, the system is supposed to supply energy (electricity and heat) for a group of five houses. According to [25], [35], [36], a total energy demand of 14.85 MWh for DHW, 53.63 MWh for SH and 15.14 MWh for electricity is estimated. Figure 8 represents energy demands for each season: winter (December, January and February), spring (March, April and May), summer (June, July and August) and autumn (September, October and November). As seen during the winter heat demand is very high and then it decreases during spring months until it reaches to the lowest level during summer months before rising again during the autumn months. The need for DHW is almost the same year around even though it decreases slightly during the summer months. The need for electricity is more or less the same for different seasons, approximately 4 MWh per season.

Figure 8.

Energy demands (DHW, SH and electricity) divided by season (all data are in MWh)

To complete the data needed for the simulations, a reference year for the daily driving distance of an average Danish car driver is also provided. Five electric vehicles are simulated, one for each house. A mobility model for these cars (daily driving distance) is estimated using a Random function, to define a driving distance reference year for each car. According to [37] and [38] an average annual driving distance of approximately 15,600 km (42.7 km/day) is proposed so that the Random function is set to vary between 21.35 km and 64.05 km (thus the average daily driving is very close to 42.7 km). Table 2 summarizes dataset created and Figure 9 shows the driving distance for the first 15 days simulated.

Figure 9.

Daily driving distance, first 15 days of simulation for each car

In the study fuel efficiency for the electric car is assumed to be 0.15 kWh/km (according to [37]), while for the traditional (gasoline) car the fuel efficiency is assumed to be 20 km/L (according to [39]).

The cogeneration system is composed by different equipment (mainly SOFC, Stirling engine and GSHP), and it produces different types of energy (electricity and heat) for different uses (SH, DHW, electrical energy for electricity user and charging the vehicle). The efficient management of energy fluxes is necessary to achieve high energy efficiency of the system. This can be done by increasing the utilization of the fuel cell as much as possible and by matching the system H/P ratio for the user electrical demand so that the aim is to maximize the grid independency of the system. The following strategies are proposed to operate simultaneously:

• Strategy to Manage Stirling Engine (MSE);

• Electric Load Following (ELF);

• Charging Electric Vehicle (CEV).

Thanks to the availability of hot water produced during night when electric vehicles are charged, it is also proposed to use the Peak Shaving (PS) strategy in order to decrease peak heat demand during daytime.

Charging electric vehicle/Peak shaving strategy

The main scope of charging electric vehicles is to charge the batteries using electricity produced by SOFC-Stirling engine system only, thus avoiding consumption from the grid and maximizing the efficiency of the system. For this reason the charging is scheduled during night, when usually cars are not used and total energy demand is low. The advantages are related to the possibility to store heat recovered from SOFC into the water tank (because during night heat demand is lower than production) in order to use it during daytime (especially when there is peak demands). This results in increasing overall efficiency of the system and also avoiding part-load operation of SOFC during night.

Charging Electric Vehicle (CEV) is the strategy used here to manage charging process. It considers both the nominal power of the SOFC (SOFC_nom,power), energy demands of the user as electricity demand (E_USER), heating demand (H_USER), and DHW demand (DHW_USER) and the electricity request for charging the cars (EC_{charge,demand}). The charging process starts at 10 PM and continues until the battery is fully charged. The duration of the charging process is related to the previous day’s consumption and the EC_charge (electricity available for charging) parameter that varies hour-by-hour:

ECcharge =min[SOFCnom,power −(EUSER+HUSER+DHWUSERCOPavg );ECcharge,.demand ](3)

During the night the electric cars are charging, the electricity requested is mainly covered by the fuel cell (under CEV strategy), and the heat demand (both SH and/or heat for the DHW) is low. So heat recovered from the SOFC is stored (in the tank) and used during the following day to cover the peak demand. Heat is also stored also when charging process ends.

A PS strategy is proposed to be used during morning when there is peak demand both in summer and winter as displayed in Figure 10. It is expected/assumed that the maximum request is between 7 AM to 9 AM, when there is high demand for both SH and DHW. It is also predictable that the PS strategy would decrease the heat request during these hours. The main effect is the reduction of the maximum heat power required by the heat pump with a double advantage, namely a lower investment cost and operation at higher partial load ratio for the heat pump.

Figure 10.

Average hourly heating demand divided by winter and summer

The parameter %Heat_available has been defined in order to use the heat in a way that it aims to shave the peak demand. Here, it is defined that peak hours are between 7 AM-11 AM, and for each hour only a part of the stored heat is available while electric cars are charging. If after 11 AM there is still heat available then it can be used to cover the user heat demand. Table 4 summarises parameters values for each hour of the peak.

Table 4

Values of %Heat_available parameter

Hour (AM)	%Heatavailable
7	24%
8	39%
9	23%
10	11%
11	3%

The micro-cogeneration system proposed here (“innovative system”) is compared to a “traditional system” by means of energy analysis in order to evaluate its potential advantage. The traditional system is a natural gas boiler (for user’s thermal energy demand) and the grid distributor (providing user’s electricity demand). In the traditional system, a gasoline car is also considered. The comparison is based on the total Primary Energy consumption (PE) of each system, and further the Primary Energy Saving (%PES) of the innovative system with respect to the separate energy production is evaluated.

For the traditional system the primary energy consumption PE_trad.sys is estimated as eq. (4):

where H_USER is the user heat demand and η_boiler is the seasonal mean efficiency of a traditional natural gas fired boiler used in the traditional system to cover heating and DHW demands (assumed to be 0.9). E_USER is the user electricity demand, and η_el is the efficiency of electric energy supply from the grid, considering generation with a traditional power plant and grid efficiency (fixed at 43.9%). F_car is the fuel consumption of a traditional car with a lower heating value of 9.7 kWh/L (considered here).

The primary energy consumption for the innovative system PE_inno.sys is calculated as eq. (5):

where F_SOFC is the primary energy (fuel) consumption of the SOFC, and E_GRID is the electricity net consumption from the grid.

%PES (percentage of primary energy saving) is calculated by comparing the primary energy consumption of the traditional system and that of the innovative system. %PES parameter is defined as eq. (6):

According to the simulations, the %PES of the innovative SOCF-Stirling-GSHP system with respect to the traditional one results to be between 46 and 49% depending on the fuel used by the fuel cell (Figure 11). The largest part of the energy saving is due to the electricity self-production. The highest efficiency is obtained by the use of Natural Gas (NG) as fuel, with a %PES of 48.9%. The lowest efficiency is obtained when the micro-cogeneration system uses ammonia as fuel, with a %PES of 46.4%. Nevertheless, the type of fuel seems do not affect strongly on the primary energy saving. Figure 12 through Figure 16 represent energy balances for the innovative system fed by ammonia, DME, ethanol, methanol and natural gas, respectively. Even if energy fluxes change when considering different types of fuel, it is possible to notice the following points:

• Heat demand is mainly covered by GSHP. For example if ammonia is used, GSHP provides approximately 44 MWh on a total heat production of 69 MWh;

• Auxiliary electric heater is used to cover peak demands that happens only for few hours during the year. The maximum contribution to heat demand is 529 kWh (system fuelled by ethanol) which is less than 1% of the total heat request;

• As Stirling engine is used to increase electricity production, heat losses from the water tank are between 800 kWh and 2,146 kWh, which means that they are in the order of few percent of total heating demand (SH + DHW demand). Heat losses are caused by temporal mismatching between heat production and demand, in particular during summer time when electricity demand is similar to the winter time but heating demand is nearly close to zero (Figure 8);

• Electricity use from the grid is low. The largest quantity of electricity from the grid is 1,677 kWh (associated with system fuelled by ethanol) which is lower than 4% of total electricity production. Further, electricity injection to grid is even less. The system could be used as stand-alone if a battery would be installed;

• The main electricity utilizations is from the user demand, GSHP and charge of electric vehicle, with similar weights. For example, in case of natural gas, the user electricity demand is 15,143 kWh, while electric car needs 11,691 kWh and GSHP uses 12,769 kWh of electricity.

Figure 11.

Different contributions to the primary energy consumption for the traditional system and the innovative system according to the different types of fuel and primary energy saving (for the traditional system PE consumed as “fuel input” is not present while there is PE consumed as natural gas by the boiler for heat production)

Figure 12.

Energy fluxes for system fuelled by ammonia in kWh

Figure 13.

Energy fluxes for system fuelled by DME in kWh

Figure 14.

Energy fluxes for system fuelled by ethanol in kWh

Figure 15.

Energy fluxes for system fuelled by methanol in kWh

Figure 16.

Energy fluxes for system fuelled by natural gas in kWh

Another advantage of EV charging during the night time is that the SOFC works more continuously, thus avoiding frequent shut-down and start-up of the stacks with consequent thermal stresses and possible failure and/or breakdowns. Once the SOFC system is started then it will operate nonstop until it is forced to shut-down, e.g. for service, etc.

Figure 12 to Figure 16 show the energy balance of the system for each fuel. It is noticeable that energy input by fuel varies between 73 MWh to 77 MWh depending on the fuel. Electricity produced by SOFC and Stirling engine varies between about 39 MWh to 41 MWh, while electricity demand from the grid varies only from 1.3 to 1.6 MWh. Electricity delivered to the grid varies between 350 kWh to 440 kWh. Heat produced by the heat pump varies between 57% to 67% of the total heat demand.

In this paper an innovative micro-cogeneration system with electric cars is simulated and energy performance is analysed. The system is composed mainly by a SOFC that generates electricity and heat. This heat is used to cover the SH and DHW demands of a group of five residential buildings, and also to run a Stirling engine as a bottoming cycle for the SOFC. Innovative strategies are also analysed with different aims, such as efficiently managing energy production to cover user demands, smartly handling electric car charging by minimizing electrical consumption from the grid, and profitably using the heat available to shave heat demand during peak time. Electric load following strategy relates SOFC electricity production to the user energy requests and increases overall energy saving and profitability of the system. The advantages of considering the charging of the electric cars at nights are related to a less variable utilization factor of the SOFC and to the lower heat peak demand.

A ground source heat pump is proposed to cover heat demand when heat from SOFC is not enough, and by which to manage variation of user’s H/P ratio. Stirling engine is managed with a specific strategy to minimize heat losses when heat production is higher than request and when the tank storage if fully charged. The proposed micro-cogeneration system is analysed with different types of fuel in order to mark its flexibility. Results show high thermodynamic performances: primary energy saving with respect to a traditional system (with petrol car) is in between 46% to 49%. Thus energy advantages of the innovative system are substantially independent on fuel type.