A Spatially-Explicit Economic and Financial Assessment of Closed-Loop Ground-Source Geothermal Heat Pumps: A Case Study for the Residential Buildings of Valle d’Aosta Region
Abstract
:1. Introduction
1.1. State of the Art and Related Studies
1.2. Scope of This Study
- Scenario 1 with both subsidies and rooftop photovoltaic systems;
- Scenario 2 without subsidies and with rooftop photovoltaic systems;
- Scenario 3 with subsidies and without rooftop photovoltaic systems;
- Scenario 4 without subsidies nor rooftop photovoltaic systems.
2. Study Area and Input Data
2.1. Study Area
2.2. Study Area Input Data and Relevant Assumptions
- Digital Surface Model (DSM) at 2 × 2 and 0.5 × 0.5 m of spatial resolution (source: GeoBrowser of the Region [45]);
- Digital Terrain Model (DTM) at 2 × 2 and 0.5 × 0.5 m of spatial resolution (source: GeoBrowser of the Region [45]);
- Estimated thermal demand of the residential buildings in kWh/m2/a and MWh/a [36];
- Average solar radiation (see Section 3.3) from DSM, DTM, and Albedo and Linke turbidity data derived from SODA [46];
- PV hourly profile derived from the Renewable Ninja project [47] (see Section 3.3);
- A survey was proposed in the Valle d’Aosta Region for the collection of average values for the financial and economic analysis including HPs, oil boilers, gas boilers, ACS costs and maintenance costs; electricity, oil and gas costs; gas and oil lower heating values. The results of the survey are reported in Appendix B.
3. Methods
- Section 3.1 describes the load curves that were implemented for the financial analysis necessary for the dimensioning of the tested technologies (GSHP, gas/oil boiler and ACS plants) for the four considered scenarios;
- Section 3.2 describes the calculation of the It (investment costs) term in Equations (1) (LCOE) and (2) (DPP) for the four scenarios;
- Section 3.4 describes how to calculate Rt − Ext (operation and maintenance costs and net expenditure, respectively) and Itcom (investment at year t that consider the difference between GSHP and other technologies) in Equation (2) for the four scenarios. Moreover, Section 3.4 briefly presents all the inputs for Equations (1) (LCOE) and (2) (DPP).
3.1. Thermal Loads and BHE
3.2. Calculation of the Investment Cost It and of Et
3.3. O&Mt and Ft Calculation for the Four Scenarios
3.4. Inputs of the Analysis
4. Results
5. Discussion
- The problems that might arise due to dense exploitation of the geothermal resource for heating and cooling (e.g., a set of inefficient plants due to reciprocal interference among adjacent systems) are not considered. All the calculations were carried out considering each building separately;
- The presence of hybrid systems (e.g., HP combined with an auxiliary gas boiler) is not considered;
- Many simplifications and assumptions were necessary to perform an analysis on more than 40,000 residential buildings. For instance, in the estimation of GSHP capital costs, installation and design costs were not directly estimated. Indeed, these specific costs were taken into account with a 40% increase in the excavation costs (see Appendix A). A previous study carried out on a Central European geographic context highlighted that the average BHE depth is about 100 m and that installation costs other than the heat pump and the borehole drilling are not negligible and are highly variable [56].
6. Conclusions
- The procedure and the methodology identified in this manuscript can be potentially replicated in other case studies. However, it is possible to conclude that a spatially explicit analysis (such as the one carried out in this manuscript) of the economic/financial feasibility of GSHP needs a robust set of input data;
- In particular, the followings inputs should be collected (or estimated from other sources of information):
- ○
- The geological properties of the study area;
- ○
- The hydraulic characteristics of the ground;
- ○
- The technical features of the considered buildings (i.e., thermal insulation, etc.);
- ○
- The load curve, which is the hourly thermal demand (heating and cooling) of the buildings;
- ○
- The thermal need required by the activities carried out within the buildings (different for residential, hotel, office, industrial processes, etc.);
- ○
- The investment, maintenance, and operative costs of the alternative technological solutions to perform effective economic/financial comparisons;
- ○
- All of the aforementioned information should be collected using redundant and robust methods, against uncertainties, to improve the reliability of the analysis;
- ○
- All of the aforementioned points should be spatially distributed and assessed to create the conditions for fast data integration (e.g., join of information).
- The method is affected by data abundance, data structure, and granularity. Input and easy-to-use data are needed to perform more reliable and frequent estimations at the building level.
- Due to the high variability of the GSHP potential, a spatial-based assessment of their economic and financial feasibility can support decision-makers in exploiting and incentivising this resource in a more effective way to increase its convenience. Subsidies can make a difference in the convenience of GSHP plants. In the author’s opinion, they are important in the transition phases, in which strong subsidies can enhance the spread of cleaner technologies.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
- The whole thermal demand was satisfied with HP plants (i.e., no auxiliary boilers were included in the analysis). Particularly, HP plants for heating, cooling, and domestic hot water were compared to a coupled natural gas boiler and air conditioning systems (ACS) and a coupled heating oil boiler and ACS. This assumption was made since hybrid systems (HP and boiler) are not available in the market for ground source heat pumps, therefore customized systems would be necessary.
- In these comparisons, the main working hypothesis was that HP plants are characterized by higher investment costs and lower annual costs [15], concerning the aforementioned boiler plus ACS.
- To evaluate the borehole heat exchanger (BHE) length, the ASHRAE method was used.
- Raster data on thermal conductivity, thermal capacity, thermal diffusivity, and ground temperature were provided by the GRETA project [54].
- The following parameters and assumptions were applied for the application of the ASHRAE [39] method for BHE:
- Peak hourly ground load, monthly ground load, yearly average ground load necessary for BHE dimensioning were estimated for each building considering the annual thermal demand and the normalized hourly profile according to [8];
- Fluid thermal heat capacity [J.kg−1K−1] = 3930 (propylene glycol 25%, see Ref. [59]);
- Fluid total mass flow rate per kW of peak hourly ground load [kg.s−1.kW−1] = 0.025 (this value was set assuming a temperature difference of 3 °C (see VDI 4640, Ref. [60]);
- Max/min heat pump inlet temperature [C] = -2.0 (the freezing point of PG25% is −10 °C, according to Ref. [61]);
- Borehole radius [m] = 0.075;
- Pipe inner radius [m] = 0.0137 (32 mm HDPE pipes);
- Pipe outer radius [m] = 0.0167 (32 mm HDPE pipes);
- Grout thermal conductivity [W.m−1.K−1] = 2.0 (typical conductivity value for geothermal grouts, see Ref. [61]);
- Pipe thermal conductivity [W.m−1.K−1] = 0.42 (HDPE pipes);
- Center-to-center distance between inlet and outlet pipes inside a BHE [m] = 0.0511;
- Internal convection coefficient [W.m−2.K−1] = 1000 [40];
- In the application of the ASHRAE method, a single borehole was considered, since the aim was only the estimation the cost of the excavation;
- The length of the BHE was increased by 3% to take into account the possible thermal interference among different BHEs, as this is was an intermediate value between 1 (no short-circuit) and 1.05 (strong short-circuit).
- One kW for seven square meters is the conversion factor implemented in Equation (14). Because we consider a 15% [62] average efficiency of a PV panel, and as we consider that a typical peak value is 1000 W/m2 on a terrestrial surface facing the sun on a clear day around solar noon at sea level, to obtain 1 kWp, the installed measures are necessary, 1/0.15 = 6.667 m2, so at least 7 m2. Given the availability of Digital Surface Model (DSM) data for the territory of Valle d’Aosta, including both terrain quotes and building roof ones, the GRASS GIS r.sun module [55] was used to estimate the beam solar irradiation over the whole year in clear-sky conditions. Roofs were considered as covered by a solar photovoltaic (PV) system (1 kW for 7 square meters) if the annual direct irradiation on the roof area was greater than the 75th percentile of the distribution of the beam solar irradiation. In particular, the main input data involved in the r.sun module computation were:
- An elevation raster map;
- An aspect raster map (map with the direction that slopes are facing counterclockwise from East: 90 degrees is North, 180 is West, 270 is South, 360 is East [63]);
- The Linke atmospheric turbidity raster map that was achieved interpolating Linke atmospheric turbidity data from the SoDa Service (http://www.soda-pro.com/—accessed, accessed on 11 November 2021), albedo data calculated by interpolating albedo data distributed by the SoDa Service, and horizon raster maps (step 5 sexagesimal degrees).
- Solar PV systems were implemented within the simulations and their contribution was evaluated by employing an LCOE of €0.09 for each produced kWh [64]. In this way, the solar PV investment cost was taken into account, even if it was not directly considered in the computations. In addition, sun hourly profiles used for the estimation of the produced energy by solar PV plants were gathered from the renewable ninja website (further details in [47]). Capital costs and annual costs of geothermal HP plants were obtained through regressions performed over surveyed data or from available references [37].
- Since the high variability and uncertainty of the analyzed cases in terms of building thermal demand, insulation, plant configuration, etc., (as depicted in [30]) it was not possible to calculate the plant installation cost of each component of the geothermal system. Therefore, to avoid unrealistic low GSHP capital cost estimations, the estimated excavation and HP costs (capital cost) were increased by 40%. This percentage is estimated from Ref. [30] to consider all the other system components that were not directly estimated in capital costs. Indeed, from Ref. [30] the costs of the heat pump and BHE account for about 60% of the cost breakdown of the GHSP system. For this reason, the estimated costs were increased by the value of 40% to obtain capital costs estimation more in line with the reference.
- The subsidies were estimated starting from the national regulation (i.e., Conto Termico in Italy) and directly subtracted from HP plant costs (i.e., they were not spread over the considered time span, since it may vary according to the plant power and the system efficiency).
- The hourly profiles of thermal demand were derived from the simulations performed by POLITO (further details in [37] and in “GRETA Project—Near-surface Geothermal Resources in the Territory of the Alpine Space—Alpine Space”, 2018) and from the estimation of the annual thermal demand for each residential building in the case study [37].
- BHE supposed unitary excavation cost equal to 150 €/m.
Appendix B
Question | Thermal Power [kW] | Cost [EUR] | Annual Maintenance [EUR] |
---|---|---|---|
please provide five kW/cost/anno. Maintenance entries (possibly equally distributed) included in the range of 6–50 kW (heating performance). The cooling performance is supposed to be greater than 70% of the heating performance. | 7.6 | 10,176 | 91 |
13 | 12,305 | 177 | |
28.8 | 19,391 | 372 | |
42.8 | 25,965 | 488 | |
50 | 34,095 | 610 | |
please provide five kW/cost entries (possibly equally distributed) included in the range of 51–250 kKW (heating performance). The cooling performance is supposed to be greater than 70% of the heating performance. | 57.6 | 42,112 | 792 |
85.6 | 51,924 | 1341 | |
134.6 | 45,332 | 1829 | |
173.2 | 53,544 | 2316 | |
222 | 59,699 | 2926 | |
please provide five kW/cost entries (possibly equally distributed) included in the range of 251–600 kW (heating performance). The cooling performance is supposed to be greater than 70% of the heating performance. | 280 | 286,160 | 5320 |
340 | 347,480 | 6460 | |
400 | 408,800 | 7600 | |
490 | 500,780 | 9310 | |
560 | 572,320 | 10,640 |
Question | Thermal Power [kW] | Cost [EUR] | Annual Maintenance [EUR] |
---|---|---|---|
please provide five kW/cost entries (possibly equally distributed) included in the range of 6–50 kW (heating performance). | 17.4 | 1780 | 91 |
23.8 | 1955 | 177 | |
32.1 | 2321 | 372 | |
40 | 7320 | 600 | |
50 | 9150 | 750 | |
please provide five kW/cost entries (possibly equally distributed) included in the range of 51–250 kW (heating performance). | 65 | 11,895 | 975 |
110 | 20,130 | 1650 | |
150 | 27,450 | 2250 | |
190 | 34,770 | 2850 | |
240 | 43,920 | 3600 | |
please provide five kW/cost entries (possibly equally distributed) included in the range of 251–600 kW (heating performance). | 280 | 51,240 | 4200 |
340 | 62,220 | 5100 | |
400 | 73,200 | 6000 | |
490 | 89,670 | 7350 | |
560 | 102,480 | 8400 |
Question | Thermal Power [kW] | Cost [EUR] | Annual Maintenance [EUR] |
---|---|---|---|
please provide five kW/cost entries (possibly equally distributed) included in the range of 6–50 kW (heating performance). | 17.4 | 1780 | 91 |
23.8 | 1955 | 177 | |
32.1 | 2321 | 372 | |
40 | 7320 | 600 | |
50 | 9150 | 750 | |
please provide five kW/cost entries (possibly equally distributed) included in the range of 51–250 kW (heating performance). | 65 | 11,895 | 975 |
110 | 20,130 | 1650 | |
150 | 27,450 | 2250 | |
190 | 34,770 | 2850 | |
240 | 43,920 | 3600 | |
please provide five kW/cost entries (possibly equally distributed) included in the range of 251–600 kW (heating performance). | 280 | 51,240 | 4200 |
340 | 62,220 | 5100 | |
400 | 73,200 | 6000 | |
490 | 89,670 | 7350 | |
560 | 102,480 | 8400 |
Question | Thermal Power [kW] | Cost [EUR] | Annual Maintenance [EUR] |
---|---|---|---|
please provide five kW/cost entries (possibly equally distributed) included in the range of 6–50 kW (heating performance). | 17.4 | 1780 | 91 |
23.8 | 1955 | 177 | |
32.1 | 2321 | 372 | |
40 | 7320 | 600 | |
50 | 9150 | 750 | |
please provide five kW/cost entries (possibly equally distributed) included in the range of 51–250 kW (heating performance). | 65 | 11,895 | 975 |
110 | 20,130 | 1650 | |
150 | 27,450 | 2250 | |
190 | 34,770 | 2850 | |
240 | 43,920 | 3600 | |
please provide five kW/cost entries (possibly equally distributed) included in the range of 251–600 kW (heating performance). | 280 | 51,240 | 4200 |
340 | 62,220 | 5100 | |
400 | 73,200 | 6000 | |
490 | 89,670 | 7350 | |
560 | 102,480 | 8400 |
Technical Feature | Value |
---|---|
Electricity price [EUR/kWh] | 0.21 |
Gas price [EUR/Sm3] | 0.8 |
Heating oil price [EUR/l] | 1.4 |
Gas lower heating value [MJ/Sm3] | 34 |
Oil lower heating value [MJ/kg] | 40 |
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It | O&Mt | Ft | |
---|---|---|---|
Gas Boiler and ACS | |||
Oil Boiler and ACS | |||
GHSP Scenario 1 | |||
GHSP Scenario 2 | |||
GHSP Scenario 3 | |||
GHSP Scenario 4 |
Itcomm | Rt − Et | |
---|---|---|
GHSP Scenario 1 and gas boiler/ACS | ||
GHSP Scenario 2 and gas boiler/ACS | ||
GHSP Scenario 3 and gas boiler/ACS | ||
GHSP Scenario 4 and gas boiler/ACS |
Itcomm | Rt − Et | |
---|---|---|
GHSP Scenario 1 and oil boiler/ACS | ||
GHSP Scenario 2 and oil boiler/ACS | ||
GHSP Scenario 3 and oil boiler/ACS | ||
GHSP Scenario 4 and oil boiler/ACS |
LCOE_gshp | ||||||
---|---|---|---|---|---|---|
Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | LCOE_Oil_Acs | LCOE_Gas_Acs | |
Average | 0.046 | 0.071 | 0.051 | 0.077 | 0.100 | 0.059 |
Std Deviation | 0.003 | 0.008 | 0.003 | 0.008 | 0.001 | 0.001 |
Median | 0.046 | 0.071 | 0.051 | 0.076 | 0.100 | 0.058 |
Skewness | 35.893 | 32.268 | 36.026 | 29.663 | 3.736 | 6.251 |
Kurtosis | 3231.625 | 2800.181 | 3379.630 | 2592.325 | 59.727 | 61.125 |
Mean DPP GSHP vs. Oil_Acs | Mean DPP GSHP vs. Gas_Acs | |
---|---|---|
Scenario 1 | 4.2 | 13.2 |
Scenario 2 | 13.8 | 24.3 |
Scenario 3 | 4.6 | 17.6 |
Scenario 4 | 15.3 | 26.7 |
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Novelli, A.; D’Alonzo, V.; Pezzutto, S.; Poggio, R.A.E.; Casasso, A.; Zambelli, P. A Spatially-Explicit Economic and Financial Assessment of Closed-Loop Ground-Source Geothermal Heat Pumps: A Case Study for the Residential Buildings of Valle d’Aosta Region. Sustainability 2021, 13, 12516. https://doi.org/10.3390/su132212516
Novelli A, D’Alonzo V, Pezzutto S, Poggio RAE, Casasso A, Zambelli P. A Spatially-Explicit Economic and Financial Assessment of Closed-Loop Ground-Source Geothermal Heat Pumps: A Case Study for the Residential Buildings of Valle d’Aosta Region. Sustainability. 2021; 13(22):12516. https://doi.org/10.3390/su132212516
Chicago/Turabian StyleNovelli, Antonio, Valentina D’Alonzo, Simon Pezzutto, Rubén Aarón Estrada Poggio, Alessandro Casasso, and Pietro Zambelli. 2021. "A Spatially-Explicit Economic and Financial Assessment of Closed-Loop Ground-Source Geothermal Heat Pumps: A Case Study for the Residential Buildings of Valle d’Aosta Region" Sustainability 13, no. 22: 12516. https://doi.org/10.3390/su132212516