Techno-economic and policy requirements for the market-entry of the fuel cell micro-CHP system in the residential sector

Techno-economic and policy requirements for the market-entry of the fuel cell micro-CHP system in the residential sector

Applied Energy 143 (2015) 370–382 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy Techn...

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Applied Energy 143 (2015) 370–382

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Techno-economic and policy requirements for the market-entry of the fuel cell micro-CHP system in the residential sector Sandro Pellegrino, Andrea Lanzini, Pierluigi Leone ⇑ Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy

h i g h l i g h t s  The impact of different supporting schemes for fuel cell micro-CHP systems.  Policy mechanisms to promote on-site consumption vs. per se generation in micro-CHP generation.  The impact of different supporting schemes on the admissible retail cost of the micro-CHP is calculated.  The effectiveness of different supporting schemes is shown when applied to different countries.  A new support scheme for the micro-CHP FC is proposed to meet grid parity.

a r t i c l e

i n f o

Article history: Received 15 July 2014 Received in revised form 19 November 2014 Accepted 3 January 2015

Keywords: Distributed generation Fuel cells Micro-combined heat and power unit SOFC Support schemes Energy efficiency policy

a b s t r a c t This work analyses the impact of different support schemes on the retail price of micro-combined heat and power units based on solid oxide fuel cells in the residential sector. The implications of each incentive toward the technical implementation of the technology are also analyzed. A detailed techno-economic assessment of the micro-combined heat and power unit is provided considering the best-in-class ceramic fuel cell technology and today’s costs for gas and electricity in the residential sector. The Italian case study was used to evaluate the impact of different supporting schemes. In order to further extend the validity of our research, scenarios for Germany and Denmark are simulated and discussed. For large-family residential users consuming 9000 kWh of electricity yearly, the retail price that yields a five year pay-back-time – without any incentive – is 2500 €. Under the same assumptions, for the average family with a consumption of around 3500 kWh, the required price is 650 €. In both cases, a retail price that is four and seven time higher respectively is achieved with the Feed-in Tariff (FIT) scheme of the type currently in use in the United Kingdom. A modified Feed-in Tariff is finally proposed to support on-site consumption rather than generation with export to the grid. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction 1.1. Status of the fuel cell micro-CHP system technology The residential and commercial sectors accounts for nearly 30% of the overall electricity consumption in Europe [1]. In 2012, the Abbreviations: AC, alternate current; BAU, business as usual; CFCL, Ceramic Fuel Cells Limited; CHP, combined heat and power; DC, direct current; DHW, domestic heating water; DSO, distribution system operator; EU, European Union; FC, Fuel Cell; FIT, Feed-in Tariff; GSE, Gestore dei Servizi Energetici; LHV, lower heating value; NG, natural gas; PEMFC, proton exchange membrane fuel cell; SOFC, solid oxide fuel cell; UK, United Kingdom; US, United States; VAT, Value Added Tax. ⇑ Corresponding author. E-mail address: [email protected] (P. Leone). http://dx.doi.org/10.1016/j.apenergy.2015.01.007 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.

final energy consumption of electricity (EU28 countries) reached 240 Mtoe, of which households contributed for 72 Mtoe. The diffusion of efficient micro-combined heat and power (micro-CHP system) units into the residential market can play a decisive role in reducing the overall energy consumption across Europe, while increasing the final user awareness and engagement – who now also becomes an energy producer – toward energy saving practices. The European Union (EU) directive about the useful and efficient cogeneration of heat and power [2] sets the framework for Member States for the support of technologies able to promote energy savings in the small scale power generation sector. According to the same directive, ‘‘a micro-cogeneration unit shall mean a cogeneration unit with a maximum capacity below 50 kWe’’. Note

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Nomenclature BCost CBattery C Battery EBattery EExp EOnSite F FITExp FITOnSite

G i NGSaved NM

battery cost battery capacity optimal battery capacity quantity of electricity stored in the battery unit and then used by the user quantity of electricity produced by the micro-CHP system and exported to the grid quantity of electricity produced by the micro-CHP system and directly consumed by the user overall revenue (e.g., electricity self-consumption and DHW supply) by the adoption of the micro-CHP system payment per kilowatt-hour of electricity exported to the grid as contemplated by the Feed-in Tariff scheme payment per kilowatt-hour of electricity directly consumed by the user as contemplated by the Feed-in Tariff scheme objective function for the optimization of the battery capacity counter variable representing the time (years). quantity of natural gas for DHW saved by the adoption of the micro-CHP system partial reimbursement as contemplated by the Net Metering scheme

that for households, an installed power output about 1–2 kWe, or even less, might be sufficient. In fact, the electricity consumption of residential dwellings is comprised between 2500 and 5000 kWh/year in European countries, ranging from as low as 1000 kWh/year (single inhabitant in apartment) up to 10,000 kWh/year (single-family detached dwelling). The EU average is around 3500–4000 kWh/year [3]. Micro-CHP system is an option for the small-scale provision of electricity and heat in the residential and commercial sectors. Among the various available technologies, fuel cells represent an interesting opportunity for a future low-energy building concept. As reported in ‘‘The Fuel Cell Industry Review 2013’’ [4] ‘‘fuel cell micro-cogeneration systems outsold conventional engine-based micro-CHP systems in 2012, with 64% of global sales’’. Several demonstration trials are also going to prove the feasibility and technological maturity of the fuel micro-CHP system in real residential or commercial sectors, thus gaining the necessary customer interest. The leading country in this context is Japan, where the EneFarm project is active since 2009. The government is strongly supporting the fuel cell technology by providing a capital grant to the early adopter: the declared aim is that ‘‘fuel cell units eventually become cost-competitive on their own’’ [5]. Overall, demonstrations and customer trials led to the installation of more than 50,000 units operating in Japanese households so far [6]. MicroCHP units mostly employ the Proton Exchange Membrane technology with a nominal power output of 750 W plus the heat recovery from the fuel cell exhaust. Also solid oxide fuel cell (SOFC) units are now being installed. The target is 1.4 million of units deployed by 2020 and 5.3 million by 2030 [7]. Without subsidies, the suggested retail price for the installed system is around ¥ 2 million (approximately € 15,000). After subsidies, however, most consumers end up at or less than ¥ 1.4 million (approximately € 10,000) [8]. The operation with proton exchange membrane fuel cells (PEMFCs) is based on a day-night modulation (the system is switched off during the night, when demand for both hot water and electricity are reduced). In addition, a storage system is used to store the excess of energy and the battery can reduce the integration from the

PeGrid PeSOFC PngGrid PR rEvening r Evening rNight r Night WSOFC W SOFC DNM

gB gT-I s

price of the electricity from the grid price of the electricity generated by the micro-CHP system price of the natural gas for DHW payment per kilowatt-hour of electricity exported to the grid as contemplated by the Purchase & Resale scheme rate of power increase during the evening modulation optimal rate of power increase during the evening modulation rate of power reduction during the night modulation optimal rate of power reduction during the night modulation micro-CHP system capacity optimal micro-CHP system capacity variation of the amount of money paid according with the Net Metering scheme as consequence of the adoption of the battery unit battery efficiency power conditioning unit efficiency discount rate

electrical grid or even allow the off-grid operation. The SOFC technology was introduced in order to achieve higher efficiencies, but the difficulty to operate with start-stop cycles reduces the higher theoretical efficiency of SOFC systems and in actual operation PEMFC and SOFC efficiencies could be comparable similar [9]. Small micro-CHP units (with a nominal power output generally <2.5 kWe) based on solid oxide fuel cell (SOFC) technology are finally approaching the field demonstration stage and are expected to be ready for the market within the next 3–5 years according to manufacturers’ announcements (e.g., the reader could find more details in [10]). The benefits of SOFCs over are widely known and include potential higher system efficiency compared to other fuel cell technologies (e.g., PEMFC) and the capability to run on a variety of fuels (preferred options are grid quality natural gas (NG), biogas from anaerobic digestion and liquefied petroleum gas – that might directly feed the SOFC anode once pre-mixed with either external or anode recirculated steam [11–13]. Following the experience of the German project Callux that started in 2008 and is scheduled to run until 2015 [14], a European-wide micro-CHP system demonstration program called Ene.field has recently begun [15]. Launched in October 2012, the aim of Ene.field trails is to install around 1000 fuel cell microCHP systems in 12 Member States in the next five years, to involve 27 project partners. Elcore (an Ene.field partner and Fuel Cell manufacturer) announced in July 2013 that would be installing 135 of its 300 W systems in due course of the project [16]. Ceramic Fuel Cells Limited (CFCL) together with other manufacturers installed 100 fuel cell micro-CHP systems until 2014 in Germany and in the UK thanks to favorable Feed-in Tariff rates. Also capital based aids were sometimes included. For instance, this is the case of the North Rhine-Westphalia region, whose government began offering 45% of the extra cost of a CFCL BlueGen fuel cell micro-CHP system, compared to conventional technologies [16]. 1.2. Support schemes for micro-cogeneration Both technological and financial risks associated with newly designed products are generally high. This is certainly the case of

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Region

European Union (27 countries) Germany United Kingdom Italy France

2011 ‘average’ potential saving (% electricity price) 30 50 75 25 13

EU ranking # – 1 3 13 18

fuel cell micro-CHP generators that have just started to reach the market. For instance in Japan, starting from November 2011, a well-established oil and gas company commenced the commercialization of SOFC units with a nominal output of 700 W [17,18] for the residential sector. The single unit costs around 25,000–30,000 US $ and is planned to be reduced at 6000 US $ once full commercialization of the product will be established. So far, valuable feedback from field demonstration programs was gained by the fuel cell industry. In order to further stimulate the required technological learning for the manufacturers to reduce production costs, countries could introduce support schemes – similar to those already in use for renewables – to sustain the early commercialization of combined heat and power generation fuel cell systems. The subsidized phase should last inasmuch the required reliability and cost targets are met. Support schemes are divided into two broad categories, namely investment (or capital) grants and operating support schemes. Capital grants are irrespective of the amount of electricity produced as they provide financial aid for the purchase and installation of the generator unit. Operating type support schemes offer instead favorable operating tariffs for the electricity exported to the grid. Also, tax exemptions can apply [19,20]. The operating type subsidy can also foresees a variable – or fixed – premium based on the actual production (e.g., a premium fee for each kWh of electricity produced). Finally, the operating type incentive can further be subdivided between price-based and quantity-based subsidies. In Japan an investment based support scheme in the form of a capital grant reducing by half the initial cost of the generator is currently in use, thus promoting the market entry of SOFC microCHP units [17]. A Feed-in Tariff scheme (price-based) was instead launched in 2010 in United Kingdom (UK) where eligible generators are micro-CHP units with a power output below 2 kWe. 1.3. Gas and electricity prices in Europe for the residential sector The cost of energy is generally widely volatile and thus difficult to predict. In Europe, the rates for gas and electricity are countryspecific and vary according to the specific end-user contract (e.g., based on the amount of energy consumed). Also, the taxation differs from one region to another. In this work, the final prices paid by the end-user (e.g., after taxes and Value Added Tax, VAT) will be used. Clearly, the relative price difference between gas and electricity is an economic driver for the profitability of SOFC-based micro-CHP units running on natural gas. The higher is the difference between the prices paid by the end-user for the electricity coming from the grid compared to the electricity generated by the SOFC, the higher will be the revenues for every unit of electricity avoided from the grid. In Table 1 the potential saving per unit of kWh obtained by the adoption of an SOFC unit in the residential sector is given for different EU countries. Such a specific saving is calculated assuming a net system (electrical based on low heating value, LHV) efficiency of 50% for the SOFC generator; that means 2 energy units (e.g., in GJ, or kWh) of gas are required to produce one

Gas

Electricity

Residential end-user saving by SOFC adoption (%)

0.20 0.18 0.16

37% 35%

34%

40%

37% 34%

35%

31%

0.14

28%

29%

30%

25%

0.12

25%

0.10

20%

0.08

15%

0.06 10% 0.04 0.02

5%

0.00

0%

Percent of unit energy price

Table 1 Potential specific saving on the electricity bill from the adoption of an SOFC unit in the residential sector.

Energy (gas/electricity) price (€/kWh)

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Time (half-yr) Fig. 1. Energy prices for gas and electricity in EU-27 countries; potential saving on the electricity bill (only) from the adoption of an SOFC unit in the residential sector – trend in the EU-27 over the last 5 years.

energy unit of electricity. More quantitative and precise calculations are presented later in this work. In Fig. 1, the energy prices (gas and electricity) for EU-zone countries are given over the period 2007–2011 [21]. In Fig. 1, averaged half-yearly prices that include the country-specific taxation plus VAT are reported; also, prices refer to users with a gas consumption between 20 and 200 GJ/year, and between 2500 and 5000 kWh/year for electricity). The potential saving as percentage the unit electricity price that would derive from the adoption of an SOFC unit is also shown in the same graph (an SOFC electrical efficiency was assumed). 1.4. State of the art Hawkes et al. [22] have developed a model in order to identify the minimum costs required to meet a given energy demand. The optimal installed stack capacity under various decision variables (e.g., stack maximum electrical output capacity, supplementary boiler capacity, stack electrical output, natural gas consumption by the supplementary boiler and electricity import–export) was thus determined. The impact on the dynamic operability of the SOFC was also assessed by varying the maximum allowed current density ramp of the Fuel Cell (FC) system. It was concluded that the inability to respond instantaneously to load changes is a relatively unimportant constraint in term of investment attractiveness. The advantages of an operating strategy able to follow heat and electricity demand is analyzed in [23]. The economic and environmental impacts of each adopted strategy are evaluated. For the SOFC-based micro-CHP system, the mode of operation that minimizes both the given electricity and heat demand profile represents the optimal operating strategy in term of CO2 emissions and operating cost reduction. In this framework, specific patterns for residential heat demand have been developed [24] also looking at heating systems able to complement the micro-CHP system heat provision. Results showed that for low space heating demands the thermal energy storage is less useful, whereas it is better suited to cases where a decoupling of the heat demand and heat supply can result in higher overall system efficiencies. The optimal way to design or control a micro-CHP system has been also analyzed by Hawkes et al. [25], who investigated the impact of a number of constraints (degradation, start-up costs, and minimum up-time) on the performance a micro-FC system. Stack degradation was identified as the most critical aspect in term of system profitability and CO2 reduction potential. The importance of degradation over

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lifetime was assessed by analyzing the impact of target degradation rates against degradation rates of systems commercially viable [26]. Subsidies seem to be fundamental for a large-scale diffusion of micro-CHP FC based systems. The potential for micro-generation was investigated by Watson [27], who applied the concept of energy service co-provision as a strategy for a more active role for consumers who invest in micro-generation. Economic issues including barriers to the adoption of the micro-CHP system and related strategies for overcoming these barriers are analyzed. Considering that payback times are influenced by taxation rules, changes in fiscal rules are fundamental in order to properly incentive the diffusion of these systems, thus also changing the consumers behavior. Hawkes and Leach [28] analyzed different policy approaches regarding the energy use in the residential sector, examining technologies with different heat-to-power ratios and how specific building regulations can affect their thermal consumption. The authors concluded that the energy demand is a fundamental parameter to justify the investment of a micro-CHP system and that policy at the national level should ensure that heat-to-power ration and capacities of the micro-CHP system are adequate for reducing the thermal demand. The interaction of operational strategies and corresponding adopted support schemes have been analyzed for the different country cases (including Denmark, France and Portugal) finding that the interplay of the microCHP units with national energy systems is a critical point [29]. Moreover, the authors suggest that specific support schemes should be introduced soon, in order to lead this technology close to the grid parity in a number of countries. In the present paper, an overall techno-economic analysis of micro-CHP systems has been performed, investigating the impact of load profile, system configuration, support schemes, and energy prices on the producers’ target capacity and the resulting admissible retail price to yield a pay-back time in 5 years. Given that the export of electricity to the grid is never a profitable option, this work is manly focused on the electricity on-site consumption, considering heat recovery as a surplus only for domestic water heating. 1.5. Motivation This work analyzes the impact of different support schemes on the deployment of SOFC-based micro-CHP systems. These effects have been investigated by developing a detailed techno-economic analysis that incorporates the main technical and economic features and constraints of the residential energy market. From an economic point of view, the present analysis takes into account several support schemes including both investment and operating type incentives on the electric energy produced by the generator. Since short term scenarios were investigated, a current currency approach has been adopted considering a nominal discount rate incorporating the effect of inflation. Technical issues have been considered in detail including realistic conversion efficiency of the fuel cell power core and power conditioning units. Moreover, different operating strategies have been accounted including the possibility of power modulation, load following and surplus electricity on-site storage in batteries. The following outcomes will be presented and discussed:  the impact of the supporting schemes on the optimal capacity of the fuel cell micro-CHP system;  the impact of the supporting schemes on the system operating modes and its technical layout (including a local electricity storage device based on batteries);  the impact of the supporting schemes on the operation strategies (on-site consumption vs. surplus generation);

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 the impact of the supporting schemes on the admissible retail cost to ensure a profitable business model for the end-users;  a timing for the implementation of different supporting schemes following the technology learning curve. The above-mentioned outcomes represent a comprehensive assessment on the techno-economic performance of high-temperature fuel cell systems adopted in the residential sector going beyond the state-of-art.

2. Methods In this paragraph, the system configurations and simulation criteria to analyze the micro-CHP SOFC system are explained in detail. Six possible system architectures are considered, from the simpler to the most complex one. The degree of flexibility and complexity of the system is functional to meet the demand response of the user and thus to increase the on-site consumption of electricity. Regarding the micro-CHP system, three SOFC operating modes are examined: (i) constant electric power output (e.g., base load operation at the maximum efficiency operating point); (ii) power modulation based on three different levels in the range 50%/+25% of the nominal power; (iii) operation with an instantaneous control of the delivered electric power in a range from 50% to +25% of the nominal power (e.g., load-following capability). Each of the option above can either include or not a battery for local energy storage. The reader should note that the instantaneous load-following capability is an idealized behavior not accounting for constrained ramp rate of the system in term of power and thus current. Thus, the analysis of this configuration represents a reference scenario to evaluate intermediate cases. In Fig. 2, a schematic of the system configuration is reported. An inverter is required to convert direct current (DC) generated from the fuel cell to an alternating current (AC). A transformer is also required to transform the input low voltage into an higher output voltage. The system can also include a battery for a better management of the demand response of users. The SOFC electrical efficiency is assumed to be as high as 60% (based on LHV of natural gas) [30,31]. A very wide range of degradation rates are reported in the literature for the SOFC [25]. Given that high degradation rates would unlikely result in a commercially acceptable product, in this work, the chosen annual rate is assumed equal to 0.1% per 1000 h. An overall efficiency gT-I equal to 80% is assumed for the inverter and transformer units together (Cf. Fig. 2) [32]. These devices are the two core units of the so called power conditioning unit that is responsible to provide a proper

Fig. 2. Schematic of the system configuration including the power processing unit and local electrochemical storage.

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conditioning of the generated power to meet either user or grid specifications. An annual capacity factor equal to 90% is used; this factor takes into account the period in which the system is turned off (e.g., during the summer break). Other than the fuel cell module and its possible operating modes, the micro-CHP system can be designed in different layouts accounting for the possibility of local electricity storage. In this work, local energy storage was taken into account by considering the possibility to include a lead-acid battery characterized by a charge efficiency of 94% and discharge efficiency of 96%. The battery operation is assumed to be ideal, and the charge/discharge phases are considered to take place instantaneously (i.e., no current limitation). In order to preserve the performance of the battery, the minimum level of charge is set at 60% of the overall capacity [33]. The role of the electrochemical local storage is eventually to deliver power more efficiently and reliably through demand response of the user. The optimal capacity (i.e., installed capacity) of the micro-CHP system is computed according to an economic optimum, which foresees the maximum revenue for the end-user with respect to the business-as-usual (BAU) case. BAU case refers to the normal condition of a dwelling where electricity is supplied from the grid, and domestic hot water is produced through a natural gas-fuelled boiler. Hence two sources of revenues result from the installation and operation of the micro-CHP system: the revenue connected to the amount of electricity produced and consumed in loco, and that linked to saving on natural gas otherwise required to produce the amount of domestic hot water recovered by cogeneration. A revenue from the electricity exported to the grid is eventually considered based on the different considered supporting schemes. The recovery of surplus thermal energy available after proving DHW is not considered in this work; generally, this extra amount of energy would be a waste during the warm season and a potential resource instead during the cold months. This issue is commented in detail in the paragraph 4 of this work together with a detailed accounting of the extra heat potentially available. A direct optimization method has been deployed to find the optimal capacity of the SOFC yielding the highest revenue for the end-user in the different analyzed scenarios. The objective function, that is the end-user revenue, is calculated iteratively for several values of the decision variables (e.g., micro-CHP system capacity, battery capacity, modulation range) until an optimal value is found. To calculate the optimal revenue, specific constraints have been defined for each decision variable. For instance, the micro-CHP system capacity has been varied between 0 and 2000 W. The latter value has been chosen according to the cap given by the Feed-in Tariff actually adopted in UK; this constrain aims at the limiting the overall amount of electricity exported to the grid. In the framework of the FC+COGEN-SIM project, actual profiles for three dwellings representative of low, medium and high electric energy consumption users, respectively, were monitored in detail every day throughout the year, with a sampling time of 5min [34]. The FC+COGEN-SIM was a collaborative research project of the International Energy Agency’s Energy Conservation in Buildings and Community Systems Programme (IEA/ECBCS) established in 2003. This project focused on natural-gas-fired cogeneration devices and the working title was ‘‘The Simulation of BuildingIntegrated Fuel cell and Other Cogeneration Systems’’. The quantity of energy needed for domestic heating water (DHW) is calculated as a function of the floor surface of the house considering an input water temperature equal to 15 °C and an output water temperature equal to 40 °C, in agreement with UNI/TS 11300-2 [35]. An average efficiency equal to 85% is assumed for the boiler.

The techno-economic calculations in this study are based on the above-mentioned load profiles where ‘medium’ users consume 3500 kWh/year of electricity and 1000 kWh/year of thermal energy as DHW; ‘high’ users consume instead 9000 kWh/year of electricity and 1500 kWh/year of thermal energy, always as DHW. Electric load profiles have been taken from the FC+COGENSIM project [34]. The original electric load profiles were recorded on dwellings from United Kingdom. However, by the comparison of these data with ones from other European countries, it was found that there was enough similarity to consider those recorded data as representative of the electrical load profiles of many European countries. The overall saving on the energy bill by the micro-CHP system end-user is calculated considering energy prices (both electricity and natural gas) according to the Italian energy market in 2011 (prices are therefore energy prices for the residential sector, including taxes and other levies). The cost of electricity and natural gas depends on the number of units consumed per year, in agreement with the models of graduated tariff [36]. These prices are incremented annually, based on an apparent annual escalation rate of 3% in the discounted cash-flow analysis.

2.1. Operating modes and technical arrangement of the micro-CHP unit This paragraph describes in detail the different operating modes and technical arrangements of the micro-CHP units. The various configurations foresee a higher level of system flexibility and complexity aiming at increasing its demand response and the overall share of user’s auto-consumption of electricity. In Table 2, a resume of technical assumptions is reported. The efficiency of the micro-CHP system was assumed to be constant regardless the installed capacity. This assumption is justified by modularity of fuel cell systems.

2.1.1. Base-load operation First, the optimal value of the CHP capacity leading to the maximum revenue for the user was calculated. The average annual saving on the energy bill was thus calculated taking into account the first five years of operation of the micro-CHP system. The maximum admissible retail price must yield a pay-back time of the investment within five years. A pay-back time of three years is generally projected by energy services companies and utilities when investing in energy saving solutions. However the residential user is certainly a less speculative and aggressive player, so that a payback time of 5 year was deemed a reasonable assumption.

Table 2 Technical assumptions in this study. Technical assumptions Life-time Micro-CHP DC electrical efficiency Electrical efficiency degradation rate Band of SOFC power modulation Power conditioning unit efficiency Annual capacity factor Charge efficiency of battery Discharge efficiency of battery Minimum charge level of battery Electrical energy consumption, medium load Electrical energy consumption, high load DHW requirement, medium load DHW requirement, high load DHW temperature variation NG boiler efficiency

10 60 0.1 50; +25 80 90 94 96 60 3500 9000 110 160 35 85

(yr) (%) (%, yearly) (%) (%) (%) (%) (%) (%) (kWh/year) (kWh/year) (lt/day) (lt/day) (K) (%)

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The optimization routine is thus deployed and a range of possible values of electric power have been computed. Therefore, for each value of the micro-CHP system capacity, the revenue due to the on-site electrical production and recovered thermal energy for domestic water heating were evaluated. 2.1.2. Power modulation The possibility of modulating the electric power of the SOFC has been evaluated. A modulation range from 50% to +25% of the nominal electric power is assumed. The logic of modulation is based on the fragmentation of the day (24 h) in three time slots, following the example of the day-night modulation proposed in the framework of the Ene-Farm project [15]. In the present work, the three time slots identified are day-time (from 0.00 to 6.00), evening-time (from 6.00 to 18.00) and night-time (from 18.00 to midnight). The definition of these time-slots have not been optimized by analyzing the specific electric load profile of each enduser, rather an a priori decision was made based on the average electric load profile of residential users. Future installations could be benefit of smart systems able to analyse on-line the user demand profile pattern and thus provide a proper modulation tuning for local energy generation. Such a logic of operation is useful to reduce the quantity of energy exported to the grid, for example during the night or when the energy demand is at a minimum. At the same time it is functional to increase the quantity of energy auto-consumed (for instance during the evening, when the energy demand is at a maximum). The different values of modulations were evaluated in the above mentioned range in order to obtain the configuration with the highest saving. The average saving is calculated considering the first five years of operation and the optimal modulation band is found from the analysis. 2.1.3. Load following The advantages of a load-following SOFC unit are evaluated. This technical assessment contemplates the opportunity to implement a load following operating mode in a range of electric power from 50% to 25% of the nominal value. This means that the electric power output of the SOFC can be reduced or increased in order to match (follow) the end-user load profile. The analysis is then performed by assuming the instantaneous capability of the micro-CHP system to adapt to the load changes without accounting for any inertial behavior and/or delay. This is obviously an idealization of the reality that is nevertheless revealing of the potential benefits of a fast dynamic advanced operating strategy. 2.1.4. Local electrochemical storage The previous operating strategies are evaluated including the integration of a local electrochemical storage system. The rationale behind the introduction of local energy storage is to evaluate the positive effect of the battery in term of enhanced saving on the energy bill, against its negative contribution to the overall investment cost. For this purpose, the average cost of battery is supposed. This cost is equal to 25 € per 100 W h of accumulation capacity, according to [37]. Moreover, the battery lifetime is assumed equal to 5 years, according to [37]. The optimal capacity of the battery is then evaluated as the one maximizing operating revenues. 2.2. Support schemes for the micro-CHP system Overall, the micro-CHP system technology provides several advantages in terms of primary energy saving avoiding transmission and distribution losses and reducing emissions [38,9]. At the same time, the smaller capacity of fuel cells, as distributed generation devices, entails exposure of reduced capital and investment

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risk, compared to conventional thermal generation units and electricity supply. In order to increase the admissible cost of micro-CHP units, supporting schemes may be implemented. These supporting schemes differ in terms of qualitative and quantitative features. In general, incentives have the potential of influencing system configurations from an engineering design point of view and also affect the microCHP unit in term of the optimal capacity to be installed. Also, additional features such as system configuration and operating modes (e.g., battery capacity and modulation range) may be influenced by applicable supporting schemes. It is thus fundamental to understand such a kind of technological impact by adopted policy measures on new technology in order to be address manufactures on the design the most coherent with a pursued energy policy. For these reasons, the impact of specific supporting schemes on the system layout and operation strategies are investigated in this work. In Fig. 3, the different typologies of support schemes considered in this work are summarized [19,20]). Concerning Capital grants, this support scheme is generally effective for new technologies as these generally have a high rate of risk associate with them. Capital grants provide a capital subsidy to the new-technology adopter that aims to reduce the overall expenditure associated with the purchase of the good. Such a grant is often issued as an uniform time series of payments spread over the lifetime of the good. In this study, Capital grants are modeled assuming that 55% of the admissible cost of the micro-CHP system is covered by financial aid from the government. This is consistent with the energy efficiency policy presently implemented in Italy in the residential sector. The grant is issued in the form of five annuities corresponded over the first five years of operation. The Purchase & Resale support scheme is a simplified modality of energy trading between the end-user and the distribution system operator (DSO). Producers can use this modality for selling the amount of energy not used immediately and, therefore, exported to the grid. The revenue per kWh exported to the grid changes during the day. In this work, three day-periods are considered: day, evening and night. The tariff is 8.865 €cent/kWh, 8.519 €cent/kWh and 6.469 €cent/kWh for day, evening and night respectively according to the tariffs of the Italian GSE (Gestore dei servizi energetici) [39]. Moreover, these prices have to be guaranteed by the authorities of electricity. These tariffs are updated annually, considering an inflation rate of 3%. In order to stimulate a direct consumption of electricity, the Net Metering support scheme offers the opportunity to exchange the amount of surplus electricity generated exported to the grid (as not required consumed on-site), with the same amount of electricity imported from the grid at different times (i.e., when an on-site deficit must be covered). In this way, there is a partial reimbursement of the expenditure for the quantity of energy imported from the grid. This reimbursement is calculated considering the minimum value between the sum of money related to the imported electricity (evaluated considering only the price of electricity – excluding the cost of grid services and taxes) and the sum of money related to the exported electricity (calculated considering the Net Metering tariff). In other words, this reimbursement cannot exceed the hypothetical sum of money paid for the quantity of electricity delivered to the grid by the distributed generator, but, at the same time, it has the effect to support an overall generation of electricity that matches the producer need. According to the price established in Italy by the Regulatory Authority for Electricity and Gas [36], the value of 7.861 €cent per kilowatt-hour delivered to the grid is established. In this work, this tariff is revised each year, considering the inflation rate of 3%. Finally, two typologies of Feed-in Tariffs are considered. Both these incentives include a generation tariff, dispensed per kilowatt-hour of electricity generated and directly (instantaneously)

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Supporting

INVESTMENT

Capital grants

OPERATING

Price-Based Support FIT FIT auto

Net Metering

Purchase & Resale Fig. 3. Support schemes used to subsidy new technologies.

consumed or stored (and later consumed), and an export tariff, paid per kilowatt-hour of electricity exported to the grid. The first model, called FIT, contemplates a payment of 15 €cent per kilowatt-hour of electricity generated and directly consumed or stored and 18.7 €cent per kilowatt-hour of electricity exported to the grid, according to the model of the Feed-in Tariff proposed in UK recently [40]. These figures are constant on the entire useful life of the device. The second model, called FIT auto, includes a payment of 9 €cent per kilowatt-hour of electricity generated and directly consumed or stored by the end-user and 6 €cent per kilowatt-hour of electricity exported to the grid; this is approximately the sum of money paid by the Italian GSE in Purchase & Resale model of energy payment during the night. In this second case, there is a partial coverage of the cost of the electricity generated and exported to the grid. The summary of the economic assumptions, including special tariffs for each of the adopted support schemes are listed in Table 3. In Table 3, the cost of energy (i.e. electricity and natural gas NG) and the value of the tariffs paid for electricity exported to the grid are given. Only for the Feed-in Tariff cases, a price for electricity directly consumed is also established. The value of natural gas price, electricity price, the Net Metering tariff, the Purchase & Resale tariff are scaled yearly through a rate equal to 3%. The discount rate deployed in the analysis is the nominal discount rate that includes inflation and is suitable for a near-term economic evaluation.

Table 3 Economic assumptions in this study. Economic assumptions Battery cost Electricity average price, medium load Electricity average price, high load Natural gas average price, medium load Natural gas average price, high load Purchase & resale day-tariff Purchase & resale evening-tariff Purchase & resale night-tariff Net metering tariff FIT, directly consumed FIT, exported to the grid FIT auto, directly consumed FIT auto, exported to the grid Annual inflation rate Discount rate

250 17 25 74 77 8865 8519 6469 7861 15 18.7 9 6 3 5

(€/kWh) (€cent/kWh) (€cent/kWh) (€cent/S m3) (€cent/S m3) (€cent/kWh) (€cent/kWh) (€cent/kWh) (€cent/kWh) (€cent/kWh) (€cent/kWh) (€cent/kWh) (€cent/kWh) (%) (%)

2.3. Objective functions The optimal capacity of the micro-CHP system has been calculated by maximizing the overall revenue (i.e. electricity self-consumption and DHW supply) for the end-user; the mathematical formulation of the objective functions FðW SOFC Þ used for the different scenarios are given in Table 4. In Table 4, the following terms are listed and commented in detail:  PeGrid is price for electricity purchased from the grid and this a function of the annual electricity demand.  PeSOFC = f(WSOFC is the cost of electricity generated by the microCHP system. It is bounded to the natural gas price that is a function of the overall natural gas consumed by the end-user. This function also depends on capacity, efficiency, and annual degradation of the micro-CHP system and of the modulation parameters rNight and rEvening.  EOnSite = f(WSOFC) is the quantity of electricity produced by the micro-CHP system and directly consumed. It depends on the capacity, the efficiency, and the annual degradation of the micro-CHP system and it is directly influenced by the electric load profile.  EExp = f(WSOFC is the quantity of electricity produced by the micro-CHP system and exported to the grid. It depends on the capacity, the efficiency, and the annual degradation of the micro-CHP system and it is directly influenced by the electric load profile.  PngGrid is the price of the natural gas from the grid for domestic hot water and it is a function of the quantity of natural gas saved thanks to the adoption of the micro-CHP system.  NGSaved is the quantity of natural gas saved thanks to the adoption of the micro-CHP system instead of a conventional boiler. It is a function of the domestic hot water demand and it depends on the capacity, the efficiency, and the annual degradation of the micro-CHP system.  NM is the partial reimbursement for the expenditure of electricity imported from the grid and contemplated by the Net Metering scheme. It is calculated by taking the minimum value between the sum of money related to the imported electricity and the sum of money related to the exported electricity (calculated considering the Net Metering tariff).  PR is the revenue per kilowatt-hour of electricity exported to the grid as foreseen by the Purchase & Resale scheme.

S. Pellegrino et al. / Applied Energy 143 (2015) 370–382

377

Table 4 Objective functions for the optimal SOFC capacity. Supporting scheme

Objective function P FðW SOFC Þ ¼ 5i¼1 ½ðPeGrid;i P FðW SOFC Þ ¼ 5i¼1 ½ðPeGrid;i P FðW SOFC Þ ¼ 5i¼1 ½ðPeGrid;i P FðW SOFC Þ ¼ 5i¼1 ½ðPeGrid;i

Baseline (e.g., no financial aid) Net metering Purchase & resale FIT auto

 PeSOFC;i Þ  EOnSite;i  PeSOFC;i  EExp;i þ PngGrid;i  NGSaved   ð1 þ sÞi1  PeSOFC;i Þ  EOnSite;i  PeSOFC;i  EExp;i þ NMi þ PngGrid;i  NGSaved   ð1 þ sÞi1  PeSOFC;i Þ  EOnSite;i þ ðPRi  PeSOFC;i Þ  EExp;i þ PngGrid;i  NGSaved   ð1 þ sÞi1 þ FITOnSite  PeSOFC;i Þ  EOnSite;i þ ðFITExp  PeSOFC;i Þ  EExp;i þ PngGrid  NGSaved;i   ð1 þ sÞi1

Table 5 Objective functions for the optimal power modulation. Supporting scheme

Objective function

Baseline (e.g., no financial aid)

Fðr Night ; r Evening Þ ¼

Net metering

Fðr Night ; r Evening Þ ¼

P5

i¼1 ½ðPeGrid;i

P5

 PeSOFC;i Þ  EOnSite;i  PeSOFC;i  EExp;i þ PngGrid;i  NGSaved   ð1 þ sÞi1

i1 i¼1 ½ðPeGrid;i  PeSOFC;i Þ  EOnSite;i  PeSOFC;i  EExp;i þ NMi þ PngGrid;i  NGSaved   ð1 þ sÞ P Fðr Night ; r Evening Þ ¼ 5i¼1 ½ðPeGrid;i þ PeSOFC;i Þ  EOnSite;i þ ðPRi  PeSOFC;i Þ  EExp;i þ PngGrid;i  NGSaved   ð1 þ sÞi1 P Fðr Night ; r Evening Þ ¼ 5i¼1 ½ðPeGrid;i þ FITOnSite  PeSOFC;i Þ  EOnSite;i þ ðFITExp  PeSOFC;i Þ  EExp;i þ PngGrid;i  NGSaved   ð1 þ sÞi1

Purchase & resale FIT auto and FIT

Table 6 Objective functions for the optimal battery capacity. Supporting scheme

Objective function

Baseline (e.g., no financial aid)

GðC Battery Þ ¼ BCost þ

Net metering

GðC Battery Þ ¼ BCost þ

Purchase & resale

GðC Battery Þ ¼ BCost þ

FIT auto and FIT

GðC Battery Þ ¼ BCost

 FITOnSite is the payment per kilowatt-hour of electricity directly consumed contemplated by the Feed-in Tariff scheme.  FITExp is the payment per kilowatt-hour of electricity exported to the grid contemplated by the Feed-in Tariff scheme.  s is the discount rate deployed in the analysis.  i is the counter variable representing the time (years). The optimal micro-CHP system power capacity, W SOFC , determines the maximum revenue for each adopted supporting scheme. W SOFC represents the value of micro-CHP system capacity with the maximum revenue calculated on the first five years of operation. At this point, the possibility to modulate the micro-CHP system power generation has been evaluated. The value of the power reduction rNight during the night and the value of the power increase rEvening during the evening have been evaluated by optimizing the objective functions reported in Table 5. In the table, the following terms can be listed and commented in detail:  PeSOFC ¼ f ðW SOFC ; r Night ; rEvening Þ is the price of the electricity generated by the micro-CHP system. It is also a function of the modulations parameters rNight and rEvening.  EOnSite ¼ f ðW SOFC ; rNight ; r Evening Þ is the quantity of electricity produced by the micro-CHP system and consumed on-site. It depends on the micro-CHP system capacity, annual degradation, and modulations and it is directly influenced by the electric load profile.  EExp ¼ f ðW SOFC ; rNight ; r Evening Þ is the quantity of electricity produced by the micro-CHP system and exported to the grid. It depends on the micro-CHP system capacity, annual degradation, and modulations and it is directly influenced by the electric load profile.  PeGrid ; PngGrid ; NGSaved ; NM; PR;FITOnSite ; FITExp ; s, and i are defined as for the optimization of the capacity of the micro-CHP system.

P5

i¼1 PeGrid;i

P5

 EBattery;i  ð1 þ sÞi1

i¼1 ðPeGrid;i

P5

 EBattery;i þ NMi Þ  ð1 þ sÞi1

i1 i¼1 ðPeGrid;i  PRi Þ  EBattery;i  ð1 þ sÞ P þ 5i¼1 ðPeGrid;i þ FITOnSite  FITExp Þ  EBattery;i  ð1 þ sÞi1

The optimal micro-CHP system power modulation generates for each supporting scheme the rEvening and rNight values that maximize the revenue. The rEvening value was varied between 0% and 25% of the nominal power while rNight varied between 0% and 50% of the nominal power. Finally, the work evaluated the economic feasibility to install a battery unit to the system. The optimal capacity of the battery is calculated taking into account that the installation of the battery increases the overall system cost. Therefore, an objective function which considers the opportunity of the battery compared to the extra cost has been evaluated. The objective function G(CBattery) is mathematically formalized in Table 6. In the table, the following terms can be listed and commented in detail:  EBattery = f(CBattery) is the amount of electricity stored in the battery unit and then used by the electric load. It is a function of the capacity of the battery and it is influenced also by the electric load profile, the micro-CHP system capacity, and the microCHP system annual degradation.  BCost = f(CBattery) is the cost of the battery. It is a function of the capacity of the battery.  DNM is the net change in expenditure related with the Net Metering scheme due to the fact that net outflow electricity exchange with the grid is reduced as consequence of the adoption of the battery unit.  PeGrid, PR, FITOnSite, FITExp, s, and i are defined as for the optimization of the capacity of the micro-CHP system. The direct optimization of the extra-revenue generates the optimal battery capacity C Battery for each supporting scheme. The same optimization routine has been implemented for those scenarios in which the SOFC is able to follow the load in the range 50% and +25% of the nominal power.

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The introduction of more flexible operating modes (e.g., power modulation and load following) as well as more complex configurations (e.g., local energy storage in batteries) gives the opportunity to reduce the electricity exported to the grid while increasing the electricity consumed on site. As a direct consequence, the annual savings for the user increase. An example of model results, in the case of load following mode with battery, is shown in Fig. 4 for both the medium and high load profiles. The system works with an instantaneous control of the electric power in a band of modulation integrated with an electrochemical storage. Fig. 4a is referred to operation profile during week days; Fig. 4b is referred to operation profile during public holiday. In Figs. 5 and 6, the impact on the admissible retail cost to ensure a profitable business model for the end-user is summarized for medium and high load profiles. Considerations about costs can be further explained by considering elaborations in Fig. 7 where the specific cost of the system configuration with the best economical results (e.g., system with battery and modulation) are reported.

3. Results The results of this work mainly concern with the analysis of different support schemes for the market entry of SOFC-based micro-CHP systems in the residential sector. In Table 7, the effect of different supporting schemes on the lay-out and optimal capacity of the system components is discussed. In Tables 8 and 9, implications of different supporting schemes in terms of the amount of exported and consumed electricity are summarized. The optimal system configurations have been investigated by considering the end-user energy bill as the objective function to be minimized. The results about the optimal system capacity are shown in Table 7. Tables 8 and 9 summarize the shares of electricity auto-consumed and exported to the grid in the different support scheme scenarios for both the medium and high load residential profiles. The latter tables allow to consider the effect of different supporting schemes on the best suited operating strategies and configurations both for on-site consumption or generation per se.

Table 7 Results from the techno-economic simulations for different policy schemes and system layouts. System configurations parameters Typology of incentive

Baseline (e.g., no financial aid)

FIT

Net metering

Purchase & resale

Load profile

Medium

High

Medium

High

Medium

High

Medium

High

Medium

High

SOFC power output (W) Power modulation on 3 levels

260 50% 0% 100 50% 0% 50

790 40% 5% 900 40% 5% 0

2000 10% 5% 50 10% 5% 0

2000 10% 15% 1900 10% 15% 0

360 35% 0% 200 45% 0% 50

1350 45% 0% 1600 50% 10% 100

320 35% 0% 50 45% 0% 0

1250 20% 10% 150 10% +5% 0

270 30% 0% 50 35% 0% 0

1290 45% 0% 750 50% 0% 50

Battery (W h) Power modulation on 3 levels and Battery Load-following and battery (W h)

FIT auto

Table 8 Demand response of the system and impact of the adopted policy scheme – high load profile. Electricity amount per system configurations (kWh/year) – high load (kWh/year)

a b

Typology of incentive

Baseline (e.g., no financial aid)

FIT

Net metering

Purchase & resale

Level of demand response

Ex.a

Cons.b

Ex.

Cons.

Ex.

FIT auto Cons.

Ex.

Cons.

Ex.

Cons.

SOFC power output Power modulation on 3 levels Battery Power modulation on 3 levels and Battery Load-following Load-following and Battery

1127 847 803 516 233 –

3873 3738 4165 4037 4424 –

6550 6706 5647 5845 1876 –

6109 6274 6923 7051 6688 –

3419 2565 2624 1797 800 727

5125 5021 5843 5883 5659 5678

2959 2757 2772 2663 675 –

4952 5026 5121 5165 5468 –

3142 2343 2589 1687 723 677

5023 4906 5521 5403 5544 5554

Ex.: export. Cons.: consumption.

Table 9 Demand response of the system and impact of the adopted policy scheme – medium load profile. Electricity exchanges (kWh/year) – medium load

a b

Typology of incentive

Baseline (e.g., no financial aid)

FIT

Net metering

Purchase & resale

Level of demand response

Ex.a

Cons.b

Ex.

Cons.

Ex.

Cons.

Ex.

Cons.

Ex.

Cons.

SOFC power output Power modulation on 3 levels Battery Power modulation on 3 levels and battery Load-following Load-following and battery

275 182 216 110 110 18

1370 1259 1424 1324 1324 1565

10,058 9939 10,037 9807 4103 –

2600 2617 2620 2582 2714 –

628 466 526 320 111 61

1650 1614 1742 1692 1804 1805

476 344 425 266 89 –

1550 1504 1595 1527 1728 –

305 226 262 168 63 –

1404 1355 1443 1387 1596 –

Ex.: export. Cons.: consumption.

FIT auto

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S. Pellegrino et al. / Applied Energy 143 (2015) 370–382 Baseline Battery Load-following

Weekdays 6000 12000

Load from the grid to the grid SOFC Battery level

3000 2000 1000

0

6

12

18

24

10000 8000 6000 4000 2000

[hours]

0

Public holiday

no AID

6000

Purchase & Resale

5000

[W]

Load from the grid to the grid SOFC Battery level

3000 2000

FIT auto

FIT

grants

Fig. 6. Admissible retail price of the system to ensure 5-years pay-back time in the case of high loads user.

Overall cost, Medium Specific cost, Medium

1000 6

12

18

Fig. 4. Dispatch model of an SOFC-based micro CHP system. Load profiles during weekdays (a) and public holiday are shown.

Baseline Battery Load-following

Overall cost, High Specific cost, High

12,000

6000

10,000

5000

8,000

4000

6,000

3000

4,000

2000

2,000

1000

24

Modulation Modulation and Battery Load-following and Battery

5000

Overall retail price [€]

0

[hours]

0

0 FIT

4500

System retail price [€]

Capital

Supporting scheme

4000

0

Net Metering

4000

Specific retail price [€/kW]

[W]

4000

System retail price [€]

5000

0

Modulation Modulation and Battery Load-following and Battery

FIT auto

Net Metering Capital grants and Net Metering Purchase & Resale

Supporting scheme

3500

Fig. 7. Impact of the supporting scheme on the overall and specific retail price.

3000 2500 2000 1500 1000 500 0 no AID

Purchase & Resale

Net Metering

Capital

FIT auto

FIT

grants

Supporting scheme Fig. 5. Admissible cost of the system to ensure 5-years pay-back time in the case of medium loads user.

4. Discussion With reference to Tables 7–9 it is possible to notice that in the baseline case, with no supporting schemes, the electricity exported to the grid has a significant impact on the optimal configuration, both in medium and high load profiles, because no remuneration for this energy is considered. The quantity of energy exported to the grid thus always represents a loss: the best value of power output maximizes the saving of money for electricity directly consumed and at the same time it minimizes the loss for the electricity produced and exported to the grid. Such a worst case tariff scenario would lead the system to operate around the baseload of the end-user. Taking into account a Purchase & Resale supporting scheme, the electricity exported still represents a loss, but its impact on the

optimal configuration especially on the high load profiles is lower because there is a partial coverage of the generation cost of the electricity exported to the grid. The effect on medium load profiles optimal capacity is minimum. An analogue situation can be observed for Net Metering supporting scheme. Indeed, even in this case the partial coverage of the generation cost of the electricity exported to the grid reduces its influence on the optimal generator capacity for both medium and high load profiles. In a Feed-in Tariff supporting scheme scenario, even the electricity exported to the grid represents an earning. In this case, according to the model of the Feed-in Tariff recently proposed in the UK [9], a power output of 2000 W is fixed for both medium and high load profiles. Considering the proposed FIT auto support scheme, we can see that the lower extra premium paid for electricity exported to the grid gives the possibility to find an optimal capacity that is comparable with the value found in with Purchase & Resale or Net Metering scenarios. Within the baseline scenario, the modulation of the system is effective toward reducing the electricity exported to the grid. The battery capacity is reduced to a minimum since the system is basically sized for the base-load operation. Load following operation still gives the opportunity to reduce drastically the electricity exported to the grid while increasing the electricity consumed on-site. Modulation and load following operating are beneficial.

S. Pellegrino et al. / Applied Energy 143 (2015) 370–382

generated. However with the medium load, there is more electricity exported to the grid, resulting in lower revenues. A cost between 2000 and 5000 €/kW seems potentially feasible and in line with target expectations from manufactures. The scaling factor for the SOFC generator is still unclear. While the cost of the cell active area can be easily scalable with power output, the cost of other balance of plant components will be hardly scalable, thus resulting in higher production costs for small power generators. In conclusion, two sensitivity analysis have been carried out. On the one hand the effect of a certain policy supporting scheme (e.g., FIT auto scheme) on the optimal capacity of the fuel cell micro-CHP system technology is analyzed. On the other hand the effect of the energy prices (e.g., both electricity and gas) in different countries on the potential specific saving and the system optimal power capacity are analyzed. Fig. 8 describes the variation of the optimal capacity of the system as a function of the extra tariff (premium) paid for on-site consumption within the FIT auto scheme. The higher is the premium provided, the higher will result the SOFC power capacity that maximizes the profitability for the end-user given the economic and financial constraints listed used in this study. Therefore, extreme care should be put on the choice of the premium as its influence on the micro-CHP unit market size is huge. Fig. 9 shows the influence of electricity and natural gas prices in three different countries: Germany, Denmark and Italy. For Germany and Denmark, the energy prices were taken in accordance with data provided by Eurostat [21]. Values are referred to the medium load profile, with load following configuration and Net Metering operation support. Electricity prices are respectively 0.253, 0.291, and 0.173 €/kWh for Germany, Denmark, and Italy; Natural Gas prices are respectively 0.059, 0.116, and 0.074 €/kWh for Germany, Denmark, and Italy. System economic results depend both on the potential specific saving and the natural gas prices. Electricity exported to the grid is in most of the analyzed cases an economic loss (e.g., the production cost is higher than the revenue). For this reason, higher natural gas prices yield enhanced losses for each electricity unit exported to the grid with an impact on the system optimal capacity and the acceptable cost. Fig. 10 shows the potentially available thermal energy exceeding the domestic heating water demand. For medium demand profiles, the exceeding thermal energy is almost negligible and thus it does not provide the opportunity for an extra revenue In scenarios with Feed-in Tariff schemes, the larger SOFC capacity entails the

High

Medium

2500

500

2000

450

1500

400

1000

350

500

300

Optimal power - Medium load [W]

The introduction of modulation is effective in the case of Feed-in Tariff scheme. On the one hand, it increases the electricity consumed on site and on the other hand, it influences indirectly the natural cost prices (depending on the annual consumption of natural gas) with a lower consumption during the night, when the possibility of revenue are lower. The battery capacity is reduced at a minimum for medium load case while it has a moderate influence for high load case. The load following configuration shows a negative economic effect because it reduces the quantity of energy exported to the grid, which represents an earning under the FIT incentive. Concerning the FIT auto scheme, the modulation of the system is cost-effective toward the lower bound of the investigated range, reducing the electricity exported to the grid. The battery capacity has a significant effect only on the economics of the high load profile case. Even in this scenario, the load following capability gives the opportunity to reduce drastically the electricity exported to the grid, at the same time increasing the electricity consumed on site thus making the installation of local energy storage unnecessary in both the medium and high load profiles. For both the Net Metering and the Purchase & Resale support scheme scenarios, the introduction of more flexible operating modes shows comparable effects. The modulation of the system is in general convenient toward the lower bound of the allowable range. Only for the high load profiles, within the Net Metering supporting scheme scenario, even the higher bound of the allowable range is useful. The battery capacity is reduced at a minimum contribution, as a consequence of the fact that the positive effect of the battery in terms of enhanced saving on the energy bill is lower, considering the significant reduction of economic losses for energy exported to the grid. The load following strategy significantly reduces the electricity exported to the grid and at the same time increases the electricity consumed on site. The impact of support schemes as shown in Figs. 5 and 6. The admissible system cost for medium users should be very low in order to meet the business model target of this work (e.g., a payback time within 5 years). In the baseline cases –Purchase & Resale and Net Metering – the admissible costs for the medium energy user are under 1000 €. The reader should consider that this is a retail cost that means that cost for producer should be much less. In the case of Feed-in Tariff, the admissible retail cost increases and could be up to five times higher than the baseline case. This cost level is in line with recent projections of SOFC-CHP manufactures. The results obtained in the high load users are qualitatively comparable with the medium user with an admissible system cost of about 10,000 € for a system capacity of 2000 We in the best case of a Feed-in Tariff scheme. By reducing the strength of applied support mechanisms (e.g., by promoting Net Metering instead a Feed-in Tariff), the admissible system cost reduces to about 3500 € for a system rated 1250 We. Looking at Figs. 5 and 6, the implementation of a more flexible and complex configuration will add some degree of freedom and a high admissible retail price. However, such a system complexity should be evaluated in term of the added production cost for the manufacturers. Further considerations about costs can be done by looking at the graphs in Fig. 7 where the specific cost of the system configuration with the best economic results (i.e., the micro-CHP system with battery and modulation operating strategy) are given. The results clarify the admissible specific system cost as a function of different supporting schemes. For the high load case, the admissible specific cost is 5000 €/kW with the FIT scheme and 2000 €/kW for the Net Metering case. Except for the FIT scenario, the medium load case has admissible specific costs similar to the high load case. For the FIT scenario, in both high and medium cases the SOFC power capacity is the same, and so the amount of electricity

Optimal power - High load [W]

380

250

0 0

5

10

15

20

Premium tariff for on-site consumption [€cent/kWh] Fig. 8. Sensitivity analysis on the optimal power output of the micro-CHP system when varying the premium tariff for electricity directly consumed on-site.

S. Pellegrino et al. / Applied Energy 143 (2015) 370–382 Acceptable system cost [€]

Optimal Power [W]

1,800

500

1,600

450

Retail price [€]

350

1,200

300 1,000 250 800

200

600

Capacity [W]

400

1,400

150

400

100

200

50 0

0 Germany (DE)

Denmark (DK)

Italy (IT)

Fig. 9. Sensitivity analysis over the optimal power output for the micro-CHP system and the calculated retail system price when varying the potential specific saving.

8,000

Medium demand

High demand

Thermal energy [kWh]

7,000 6,000 5,000 4,000 3,000 2,000 1,000 0

Fig. 10. Excess thermal energy potentially available from the micro-CHP system after DHW production.

opportunity for further thermal energy recovery that could reduce the space heating energy demand. However, an extra capital investment would be required for the thermal heat storage unit and for fluidic integration with the existing space heating system. High electricity demand profiles clearly lead to the possibility for extra thermal energy recovery and thus the opportunity to increase the economic revenue for the user. Considering an average energy price for natural gas of 0.05 €/kWh, the potential revenue would range between 40 €/year (baseline scenario, high profile) to about 150 €/year (medium and high demand profiles, Feed-in Tariff case). Note that thermal energy would recovered only for 6 months per year when space heating is involved. 5. Conclusions and policy implications The present work investigates from both a technical and policy stand-point of view the market-entry of the fuel cell micro-CHP system into the residential market. Different architectures and operating modes are evaluated for the SOFC micro-CHP system. Especially, hybrid configurations that foresee the integration of the SOFC with a local energy storage system (i.e., a battery) are also analyzed. The impact of each analyzed support scheme on the potential penetration of the SOFC as well as on resulting technical requirements are systematically addressed. The results show that in the near future, SOFC micro-CHP system manufacturers should address their product toward those residential customers having a high (i.e., above-average) electrical energy consumption. Policy makers should provide support schemes for these kind of end-users. Thermal energy recovery

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could be also an interesting opportunity for high demand users providing, ideally, an extra income of about 750 € in five years In the case of Feed-in Tariff regime. Even with the achievement of the economies of scale, an operating type support scheme (e.g., Net Metering or Purchase & Resale) seems to be the most appropriate one in order to guarantee a realistic system cost, at the same time ensuring an increase of over 30% in the auto-consumption of electricity compared to the baseline scenario. The Net Metering support scheme results in a generation cost of 13 c€/kWh for the micro-CHP system, which is much lower than the average electricity price of 25 c€/kWh. By assuming a lifetime of 10 years, grid parity for the SOFC-CHP is achieved for a system retail price of 4500 €.

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