Report on the test of the integration of NSGE into Energy ... · GRETA – D5.2.1 Report on the...

GRETA – D5.2.1 Report on the test of the integration of NSGE into Energy Plans for the selected Pilot Areas

GRETA is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme. See more about GRETA at www.alpine-space.eu/projects/.

0

Report on the test of the

integration of NSGE into

Energy Plans for the selected

Pilot Areas

Version: Revision 01, 15th of September 2018

This document is the third deliverable of the Work Package 5 (or WPT4 according to the EmS numbering of WPs):

“Report on the test of the integration of NSGE into Energy Plans for the selected Pilot Areas”. EURAC, as a responsible

partner in the WP5, has elaborated this report with contributions from the involved project partners: TUM, EURAC,

ARPA Valle d’Aosta, GeoZS, BRGM, GBA, and the University of Basel.

This deliverable briefly describes the methodology for assessing the main financial figures of the NSGE closed-loop

(Borehole Heat Exchangers, BHEs) and open-loop (Groundwater Heat Pumps, GWHPs) systems (an extensive

explanation of the methodology and procedure used is available in D5.1.1), and it presents the main results achieved

in the three pilot Areas. The methodology is applied in these three pilot areas: Valle d’Aosta (Italy), Sonthofen

(Germany), and Cerkno (Slovenia). The methodology estimates the thermal energy demand at building level, defines

the size and characteristics of the supply system, estimates the main costs, and identifies which buildings can use the

NSGE source effectively.

The current document displays and discusses the main results of the elaboration performed on the three Pilot Areas.

Deliverable D.5.2.1 – Report on the test of the integration of NSGE into Energy Plans for the

selected Pilot Areas

16/01/2017 – 15/09/2018: A report on the results of the test of procedures and tools developed

in the WP to support the integration of NSGE in the selected Pilot Areas into EPs.

http://www.alpine-space.eu/projects/greta



1

Index

Index 1

1. Introduction 2

1.1. The GRETA project and GSHP 2

1.2. Partner’s involvement 3

1.3. Acronyms and definitions referring to NSGE 4

1.4. The context 4

1.5. Content of the deliverable 5

2. Methods applied in the Pilot Areas 6

2.1. Short description of the methodology for the selection of the Pilot Areas 6

2.2. Short description of the methodology for the spatial financial evaluation of NSGE 6

2.3. Common assumptions for the financial spatial-based analysis 8

2.4. Description of the Pilot Areas 12

2.4.1. Valle d’Aosta 13

2.4.2. Sonthofen 15

2.4.3. Cerkno 16

3. Economic assessment 17

3.1. Economic and financial feasibility of NSGE based on simulations 17

3.2. Results of the economic and financial analyses based on simulations 18

3.3. Spatial-based simulations 22

3.4. Results of spatial-based simulations 23

3.4.1. Valle d’Aosta 23

3.4.2. Sonthofen 32

3.4.3. Cerkno 39

4. Discussion 44

5. Conclusions 46

References 48




2

1. Introduction

1.1. The GRETA project and GSHP

The GRETA project is an Interreg Alpine Space project seeking to foster diffusion of the Ground Source

Heat Pump (GSHP) in the alpine area, and promoting its inclusion in energy and strategic planning. The

project started in December 2015 and will be concluded in December 2018. The consortium is composed

of 12 partners from six countries (TUM, Climate Alliance and TripleS GmbH for Germany, GeoZS for

Slovenia, Indura and BRGM for France, Uni Basel for Switzerland, GBA for Austria, POLITO, RL, ARPA VdA,

and EURAC for Italy). The leading partner is TUM.

The GRETA project focuses on GSHP systems. GSHP, based on the soil and groundwater properties, is to

have an almost constant temperature up to 100m below ground surface. This property can be exploited

both for heating and cooling purposes: this means that the ground/groundwater is used as a heat source

or a sink, respectively. The GSHPs can work with either heat-carrier fluids (closed-loop systems) or water

directly withdrawn from the aquifer (open-loop systems). In both methods, the heat-carrier

fluid/groundwater is at nearly constant temperature along the year. Under certain conditions, the cooling

can be performed by bypassing the heat pump and using the cold water directly (or heat-carrier fluid) in

the air conditioning system of the building. This method is known as free cooling (FC).

GSHPs allow for the exploitation of geothermal energy between the surface and 200 meters deep (the so-

called NSGE). On the contrary, deep geothermal energy exploits the high temperature fluids occurring at

greater depths, either to satisfy directly the heat demand or to produce electricity in a turbine system.

Open-loop systems (or GWHPs, Ground-Water Heat Pumps) extract geothermal energy with two or more

wells. These systems generally have one (or more) abstraction well(s), which withdraw the water to be

used by the heat pump. After that, water is reinjected into the aquifer with one (or more) well(s) or,

alternatively, into surface waters if the local legal framework does not allow aquifer reinjection.

Closed-loop systems (or GSHPs, Ground-Source Heat Pumps) exchange thermal energy with either shallow

or deep ground, depending on the geometry of the pipes installed (horizontal or vertical, respectively).

Closed-loop systems use a heat-carrier fluid, which is generally composed of water with anti-freeze fluids

(normally propylene glycol). The heat-carrier fluid flows into a polyethylene pipe network. In the vertical

configuration, the pipes are installed into one or more borehole(s), known as Borehole Heat Exchanger

(BHE).




3

1.2. Partner’s involvement

The partnership, led by TUM, is composed of the following collaborators:

No.

Partner Nation

Contact E-mail

1 Technical University Munich (TUM) München (Germany)

Kai Zosseder Fabian Böttcher

[email protected] [email protected]

2

Regional Environmental Protection Agency of Valle d’Aosta (ARPA VdA)

Aosta (Italy)

Pietro Capodaglio Alessandro Baietto


3 Geological Survey of Austria (GBA) Wien (Austria)

Magdalena Bottig Stefan Hoyer


4 Geological Survey of Slovenia (GeoZS) Ljubljana (Slovenia)

Joerg Prestor Simona Pestotnik


5 Geological Survey of France (BRGM) Villeurbanne (France)

Charles Maragna [email protected]

6

Polytechnic University of Turin (POLITO) Torino (Italy)

Alessandro Casasso Simone Della Valentina Arianna Bucci

[email protected] [email protected] [email protected]

7

Eurac Research of Bolzano (EURAC) Bolzano (Italy)

Pietro Zambelli Roberto Vaccaro Antonio Novelli Simon Pezzutto Valentina D’Alonzo

[email protected] [email protected] [email protected] [email protected] [email protected]

8 Triple S-GmbH (Triple S) München (Germany)

Reiner Wittig [email protected]

9 Rhône-Alpes Sustainable Infrastructures (INDURA)

Villeurbanne (France)

James Gilbert [email protected]

10 Climate Alliance (CA) Frankfurt am Main (Germany)

Andreas Kress Janina Emge


11 University of Basel (Uni Basel) Basel (Switzerland)

Peter Huggenberger [email protected]




4

1.3. Acronyms and definitions referring to NSGE

AC: Air-Conditioner

ACS: Air Conditioning System

AS: Alpine Space

ASHRAE: American Society of Heating, Refrigerating and Air-Conditioning Engineers

AW: Annual Worth

BEP: Break Even Point

BHE: Borehole Heat Exchanger

CDD: Cooling Degree Days

DHW: Domestic Hot Water

DPP: Discounted Payback Period

DSM: Digital Surface Model

DTM: Digital Terrain Model

ERR: External Rate of Return

FLEH (or FLEQ): Full Load Equivalent Hours

GSHP: Ground Source Heat Pump

GWHP: Ground Water Heat Pump

HDD: Heating Degree Days

H&C: Heating and Cooling

HP: Heat Pump

IRR: Internal Rate of Return

LCOE: Levelized Cost Of Energy

LPG: Liquid Petroleum Gas

MARR: Minimum Attractive Rate of Return

NSGE: Near Surface Geothermal Energy

PV: Photovoltaic

PW: Present Worth

RES: Renewable Energy Source

SC: Space Cooling

SH: Space Heating

SPP: Simple Payback Period

1.4. The context




5

The exploitation of NSGE is not particularly widespread. Its limitation is mainly related to factors such as

the scarce knowledge of this technology, complicated and fragmented legislation, and high installation

costs (Müller et al. 2018; Somogyi, Sebestyén, e Nagy 2017).

Nor has NSGE been adequately considered in energy plans and strategic documents. In this case, the

reason relates both to the difficulty of gathering information on this energy source’s potential to cover the

heating and cooling demand in a certain area, as well as to a lack of awareness of its advantages among

policy- and decision-makers. In particular, knowing the potential of a resource is essential information for

integrating it in sound energy planning.

Within the GRETA project, EURAC oversees the activities of WP5 aiming at supporting the process of

integrating NSGE into energy plans. These activities are divided into three main deliverables:

+ 5.1 - Spatially assessing the economic/financial feasibility of NSGE in Pilot Areas.

+ 5.2 - Supporting the integration of NSGE into energy plans for the Pilot Areas.

+ 5.3 - Elaborating guidelines for policy and decision-makers describing the methodology for the

inclusion of NSGE into energy plans and energy strategy documents.

1.5. Content of the deliverable

The deliverable contains a short description of the methodology and presents the main results of the

spatial financial evaluation of NSGE potential at building level. The discussion of the main results of the

analyses is crucial for any aggregated assessment of the capacity of NSGE to cover the thermal energy

needs of a specific area, and therefore to support the integration of NSGE into the development of local

and regional strategies for the transition to low carbon energy.

This information, together with analysis of the local legislative and economic context, are the prerequisites

for supporting inclusion of NSGE, both in strategic planning development and in the elaboration of

sustainable energy plans. An extensive description of the methodology and procedures that we used to

conduct the analyses in the three pilot areas can be found in Deliverable 5.1.1 “A spatially explicit

assessment of the economic and financial feasibility of Near Surface Geothermal Energy”(GRETA D5.1.1

2018), while more general approaches and analyses that are not site specific are available in the

Deliverable 5.3.1 “Guidelines for policy and decision makers” (GRETA D5.3.1 2018).

The deliverable is divided into the following sections:

+ short description of the methodology, with a more detailed explanation for each pilot area, of the

data used and how we estimated missing information (Section 2);

+ description of the financial assessment of NSGE based on simulated data that are not site-specific

(Section 3.1);




6

+ presentation of the site-specific results that are achieved for each pilot area (Sections from 3.4.1

to 3.4.3);

+ discussion of the main limits and assumptions regarding the data used as input and evaluations

performed (Section 4);

+ conclusion (Section 5).

The deliverable is only marginally linked with the use of the presented results for the inclusion of NSGE

into energy plans and strategies. This aspect will be described in detail in the guidelines for decision- and

policy-makers.

2. Methods applied in the Pilot Areas

2.1. Short description of the methodology for the selection of the Pilot Areas

The GRETA project has six case studies within the Alpine Space region, as follows: District of Oberallgäu in

Germany, Parc des Bauges in France, Valle d’Aosta Region in Italy, Davos Municipality in Switzerland,

Saalbach-Leogang tourist area in Austria, and Cerkno Municipality in Slovenia. These six case studies were

used in the GRETA project to identify, verify and test possible regulation issues and operative criteria, and

to map the energy potential of the NSGE. In WP5, activities will focus on three pilot areas. The main reason

for this approach is to contain as much as possible the time and budget of WP5 activities, with the aim to

preserve the transferability of the outputs to the other Alpine regions. The three pilot areas were chosen

according to the following criteria (further details in Deliverable 5.4.1, (GRETA D5.4.1 2016, 1)):

● The priorities foreseen by local authorities for energy and strategic development of the area;

● The presence of a suitable context to test and develop methods and tools for the integration of

NSGE potential in the energy plans and/or strategies;

● To highlight the main differences and similarities among the three pilot areas;

● Data availability;

● Partner commitment;

● Stakeholder interest;

● Resources and transferability.

Particularly, to guarantee a broader transferability of the performed analyses and outputs, the following

aspects were also considered: regulation on NSGE, area, population, economy, geology, land-use,

geomorphology (elevation, slope, etc.), and climatic conditions (temperature, solar radiation, etc.). A

higher diversity and heterogeneity between the three pilot areas was also considered as an added value.

2.2. Short description of the methodology for the spatial financial evaluation of




7

NSGE

The spatialized economic analysis implements the methods described in (GRETA D5.1.1 2018). Thanks to

this procedure, it is possible to assess the financial and economic feasibility of a selected technical solution

in a specified geographic position. Where possible, different variables dependent on position are taken

into account: the lithological characteristics of the subsurface, the legislative and geological constraints,

the solar radiation, the site-specific heating (or cooling) demand, and the position and power of other

existing closed-loop plants.

Due to a lack of complete technical information needed in each considered geographic position, some

design parameters were assumed constant for all pilot areas (e.g. well distance in open-loop systems, BHE

radius for closed-loop systems). Further details are explained in Section 2.3. In the assessment of financial

and economic feasibility of NSGE, we distinguish between two different analyses:

1. based on simulations related to specific building types;

2. the GIS-based analysis set-up on the real residential built-up areas’ distributions.

The first analysis, based almost entirely on simulated data, has been carried out on 36 different NSGE

configurations: 3 building types (detached house, office, hotel), 2 insulation levels (respectively high and

low), and 6 different climatic zones (Table 1, further details in (GRETA D3.1.1 2018)).

Table 1: The six climatic zones and their main features. Source: (Tsikaloudaki, Laskos, e Bikas 2012).

Climatic

Zone

Cooling

degree days

(CDD)

Heating degree days

(HDD)

Typical for

A CDD ≥ 500 HDD < 1500 Mediterranean area

B CDD ≥ 500 1500 ≤ HDD < 3000 Plain areas close to the sea (Po plain, Rhone plain) and of the

Adriatic coast

C CDD < 500 HDD < 1500 Some maritime areas such as Marseille, with mild winter and

summer

D CDD < 500 1500 ≤ HDD < 3000 Most cities in the Alpine Space (central Po plain, Alsace, most

of Slovenia)

E CDD < 500 HDD ≥ 3000 Piedmont (>500 m a.s.l.) and mountainous areas

F CDD = 0 HDD ≥ 3750 Mountainous towns with very high heating and no cooling

needs




8

The technical data produced by POLITO (further details in (GRETA D3.2.1 2017; GRETA D5.1.1 2018)),

related to the building types investigated, has been used as support for the financial and economic

analysis. The spatial-based analysis regards only residential buildings for the Valle d’Aosta region and the

Sonthofen municipality, while it regards public, residential and commercial buildings for the Cerkno

municipality. The technical data, in particular that related to detached houses, has been normalized and

used to create hourly demand (for heating, cooling and DHW) profiles starting from quantification of the

annual heating thermal demand (further details in (GRETA D5.1.1 2018)).

The economic analysis, therefore, requires software tools that allow integrating spatial data into

algorithms that perform the dimensioning and economic evaluation of the plant. In the GRETA project, this

task has been achieved by using the software GRASS GIS (GRASS Development Team 2017; Neteler et al.

2012; Zambelli et al. 2013) integrated in a Python environment («Welcome to Python.Org» 2018). This

developed tool joins a set of similar tools that have been developed in the Interreg project

“recharge.green” (Garegnani et al. 2018; Hastik et al. 2016; Sacchelli et al. 2013, 2016; Zambelli et al. 2012;

Vettorato, Geneletti, e Zambelli 2011). The description of the most important assumptions and data is

provided in subsequent sections.

2.3. Common assumptions for financial spatial-based analysis

The assumptions made in (GRETA D5.1.1 2018) (e.g. the discount rate for financial indicators has been set

to a value of 3% (Energy education 2018)) have also been applied to the financial spatial-based analysis.

However, this analysis requires further hypothesis and assumptions; due to the lack of spatially distributed

information, computational constraints and simplifications needed to be introduced, to address the

complex issues effectively. The most important assumptions are:

1. The whole thermal demand is satisfied with Heat Pump (HP) plants (i.e. no auxiliary boilers were

included in the analysis). Particularly, HP plants for heating, cooling and DHW were compared to

a coupled gas boiler and air conditioning systems (ACS) and a coupled oil boiler and ACS. In these

comparisons, the main working hypothesis is that HP plants are characterized by higher

investment costs and lower annual costs with regard to the aforementioned boiler plus ACS.

2. The considered lifetime was 27 years for HP plants and 20 years for other plants (U.S. Energy

Information Administration 2018; Edenhofer 2012).

3. The open-loop technical feasibility follows the rules introduced in GRETA D4.2.1 2018. Particularly,

for each considered plant only one pumping and one injection well have been considered

(although multiple wells for either abstraction or injection are possible, according to the size of

the system).




9

4. Further assumptions for open-loop systems are derived from the method for open-loop potential

mapping, described in GRETA D4.2.1 2018, as follows:

a. only the technical feasible volume flow at a well distance of 10 m was considered to satisfy

the input thermal demand (this is a conservative condition: the extractable/injectable

discharge gets lower as the well distance decreases);

b. the depth of the wells were supposed to be equal to sum of the water table depth and

one third of the aquifer thickness (to preserve the aquifer by exceedance in water level

drawdown);

c. the ΔT between abstracted and injected groundwater, related to the maximum power of

the HP, was always equal to 5 °C.

5. For the open-loop systems, the price of the submersible groundwater pump, for each entry, is

calculated by means of a machine learning random forest nonparametric regression algorithm

(Breiman 2001; Pedregosa et al. 2011; Buitinck et al. 2013) involving hydraulic power, hydraulic

head and hydraulic flow). The input prices for the regression algorithm were taken from

(https://www.pippohydro.com/index.php).

6. For closed-loop systems, the (ASHRAE 2018) method was used to evaluate BHE length. The thermal

conductivity, thermal capacity, thermal diffusivity and ground temperature raster data provided

from (GRETA D4.2.1 2018) were used. Moreover, the following parameters and assumptions were

applied for each record:

a. peak hourly ground load, monthly ground load, yearly average ground load (estimated

from the annual thermal demand and the normalized hourly profile provided by POLITO,

for each building function and wall insulation level);

b. fluid thermal heat capacity [J.kg-1.K-1] = 4200;

c. fluid total mass flow rate per kW of peak hourly ground load [kg.s-1.kW-1] = 0.050;

d. max/min heat pump inlet temperature [C] = -2.0;

e. borehole radius [m] = 0.075;

f. pipe inner radius [m] = 0.0137;

g. pipe outer radius [m] = 0.0167;

h. grout thermal conductivity [W.m-1.K-1 = 2.0;

i. pipe thermal conductivity [W.m-1.K-1] = 0.42;

j. center-to-center distance between pipes [m] = 0.0511;

k. internal convection coefficient [W.m-2.K-1] = 1000;

l. since the aim was to estimate cost of excavations, the ASHRAE method was applied under

the hypothesis of a single borehole;

m. the length of the BHE was increased by 3% to account for possible thermal interference

among BHEs.


https://www.pippohydro.com/index.php



10

7. In the case of (digital surface model) DSM data availability (i.e. data including not only terrain

quotes but also building roof quotes), the GRASS GIS r.sun module (Hofierka e Suri 2002) was used

to estimate the beam (direct) solar irradiation, in clear-sky conditions, over the whole year. Roof

areas, in which the annual direct irradiation was greater than the 75th percentile of the

distribution of the beam (direct) solar irradiation, were considered covered by a rooftop solar

photovoltaic (PV) system (1 KW for 7 square meters). In particular, the main input data involved

in the r.sun computation are:

a. an elevation raster map;

b. an aspect raster map;

c. the Linke atmospheric turbidity raster achieved interpolating Linke atmospheric turbidity

data from the the SoDa Service («Home - www.soda-pro.com» s.d.);

d. albedo raster data calculated by interpolating albedo data distributed by The SoDa

Service («Home - www.soda-pro.com» s.d.)

e. horizon raster maps (step 5 sexagesimal degrees) (Hofierka 1997).

8. In the case where DSM data and availability of 3D buildings vector data and digital terrain model

DTM data are lacking, roof vertices were extracted from 3D vector data and overlapped their

corresponding DTM locations. In this way, it was possible to obtain a simplified DSM to apply the

previous point.

9. In case of lack of DSM or 3D buildings vector data, rooftop solar PV systems were not included in

the analysis.

10. As rooftop solar PV systems have been implemented, their contribution has been evaluated by

means of an LCOE of 0.09 € (E Vartiainen, G Masson, e C Breyer 2015) for each KWh produced. In

this way, although not directly considered in the computations, solar PV investment costs have

also been taken into account. Lastly, sun hourly profiles, used to estimate energy produced by

rooftop solar PV plants, were taken from the («Renewables.ninja» s.d.) website (further details in

(Stefan Pfenningera e Iain Staffellb 2016).

11. Capital costs and annual costs for each case were obtained through regressions performed over

surveyed data or from available references (GRETA D5.1.1 2018).

12. Due to the impossibility of calculating the cost of NSGE systems and installation costs of the plant

for each building, due to the high variability of the analyzed cases (in terms of thermal demand,

insulation, plant configuration, etc.), according to (Lu et al. 2017), the capital cost estimation for

HP plants also took into account a 40% increase in the estimated excavation and HP costs.

13. When considered, subsidies were estimated according to the national regulation (e.g. “Conto

termico” (Decreto MISE 16/02/2016) in Italy, “Fonds Chaleur” (Le Fonds Chaleur, 2018 ) in France)




11

and directly subtracted from HP plant costs (i.e. they were not applied over the considered time

span, since it may vary based on the power of the plant and the system efficiency).

14. Hourly thermal demand profiles were derived from simulations produced by POLITO (further

details in (GRETA D5.1.1 2018) and in (GRETA D3.2.1 2017) and from the annual thermal demand

estimation for each considered building (GRETA D5.1.1 2018);

15. The assessment of the groundwater flow direction requires extensive field investigations and/or

numerical modelling simulation. To simplify the computation, the groundwater flow direction was

assumed to be the same for the whole region (0° starting from the East). Therefore, it was assumed

that the flow direction is always constant going from West to East.

16. To assess the maximum spatial density of GSHP plants, the thermal plumes area of the closed-loop

systems was evaluated using the TIGER method (Alcaraz, Vives, e Vázquez-Suñé 2017).

17. For the open-loop systems, as highlighted by (Piga et al. 2017), which compared numerical

simulations with simplified methods, the thermal plume length error can range between 100% and

more than 1000% of its numerically simulated length. The results of the TIGER method have been

compared with the results of the FeFlow dynamic simulations published in (Piga et al. 2017). We

normalized the TIGER values to guarantee that the median error is 0% and the mean error

overestimates the thermal plume length of 17.4%. Particularly, the TIGER method features a

maximum underestimation up to 52% and maximum overestimation up to 348% of the total planar

plume length. Therefore, the TIGER method has been applied to the closed loop without correction

and for the open-loop using the correction factors previously mentioned.

18. Areas of the thermal plumes have been identified considering:

a. a temperature disturbance given by values equal to or higher than 0.5 °C compared to the

undisturbed temperature;

b. a solid volumetric heat capacity of 2.6 [MJ/m3/K];

c. a Water Volumetric Heat Capacity of 4.2 [MJ/m3/K];

d. the thermal conductivity of the terrain extracted by the project raster maps produced in

(GRETA D4.2.1 2018, 2) [W/m*K] ;

e. longitudinal and transverse dispersivity in m of 10 and 1 respectively;

f. the thermal load of the building [W/m];

g. for the stationary condition, the total number of years used to determine the plume size

is 50 [years];

h. a number of operation months equal to 6 [months].

19. The spatial analysis of availability for closed/open-loop installations excludes: (i) water protection

areas, (ii) areas closed to water bodies (rivers and lakes), (iii) areas too close to the roads and the

buildings, and (iv) areas interfering with the thermal plume of already-existing plants. If the area




12

of pertinence of the buildings was available, a further constraint is applied: (v) the borehole of the

closed/open-loop must be contained in this area to be valid. The minimum distance of a borehole

from its building is at least 1 m, while the maximum distance from the building is less than 36 m.

For closed-loop systems, a minimum distance among the BHEs of 7 m is assumed, while for open-

loop systems we assume a distance of 10 m. The LCOE values of the closed/open-loop systems

have been used to give priority to buildings where the system can be installed. Buildings with lower

LCOE have higher priority in the use of this technology.

2.4. Description of the Pilot Areas

The three pilot areas (Figure 1) were chosen according to Section 2.1 and GRETA D5.4.1 2016

Figure 1: The three Pilot Areas of the GRETA project. Source: GRETA project.

Cerkno is a typical remote alpine municipality with area of 132 km2. The town is situated in a narrow valley

surrounded by a mountainous area and dispersed mountain hamlets. Alpine tourism is one of the most

important activities. Heating oil is still the main heating source, thus Cerkno Municipality intends to

develop a long-term new Local Energy Concept and new spatial plans adapted to the gradual transition to

carbon-free heating and cooling. Cerkno is the least populated pilot area in the GRETA project, with about

4,000 inhabitants, but it has a strong interest in NSGE. Indeed, a 12 BHEs field is already feeding a small

district heating network for some public buildings and the Municipality aims at expanding this installation,

also combining biomass and geothermal energy.

Valle d’Aosta is the largest case study area of the GRETA project (about 3,000 km2). At the same time, Valle

d’Aosta is the smallest Italian region, featuring on its borders the highest European summits. The main

bottom valley hosts about 120,000 people and some industrial plants, while lateral valleys are well known




13

tourism and ski centers. NSGE is currently used mainly for heating; a census on the existing NSGE plants in

the entire region was performed within a master thesis (POLITO) and delivered to the Regional Energy

Agency (COA Energia) in 2016. The main topic within the GRETA project is assessment and analysis of data

for the entire region. A Regional Energy Plan was implemented in 2014, based on 20-20-20 European

targets.

Oberallgäu is the most inhabited case study area (about 150,000 inhabitants). The district of Oberallgäu is

located in the margin area of the Allgäuer Alps. Many municipalities in the district and the district

government itself are willing to contribute to sustainable energy policy and urban development, through

the rational use of energy and increased use of renewable energy sources. The district and seven

municipalities showed their commitment by participating in the European Energy Award. The district also

decided to apply for a national climate protection programme called “Masterplan 100% Klimaschutz”,

where they aim to reduce greenhouse gas emissions by 95% and energy consumption by 50%, compared

to 1990 values. Within the district, the Municipality of Sonthofen was chosen as pilot area of the GRETA

project for a small-scale assessment of NSGE potential.

The stakeholder analysis was carried out by the project consortium (GRETA D6.2.1 2018) through a

brainstorming/mind-mapping process. From the stakeholder analysis, a list of possible stakeholders to be

involved within the project implementation was defined.

2.4.1. Valle d’Aosta

The current energy objectives of the Valle d’Aosta region are described in the Regional Environmental and

Energy Plan (Regione Valle d’Aosta 2012) and in the last Monitoring Report of this plan (Regione Valle

d’Aosta 2018). The REEP has year 2020 as its target year, and the main objectives are specified as follows:

o Targets for installed power and energy production from renewable energy sources (RES): 14.8% on

total thermal consumption, more diversified electrical production; new targets in the MR: production

from RES +4%, share of RES production on the total consumption: 86.1%.

o Targets for the reduction of energy consumption: 7% thermal, 6.6% electrical; new target in the MR:

total consumption -1.1%.

o Target for the energy renovation of civil buildings: 4% per year.

o Increase of energy efficiency in different sectors.

o Targets for thermal production from heat pumps (in general not only geothermal HP): 1.28 GWht

renewable of 4 GWht total.

During the GRETA project, EURAC participated at several meetings with the main stakeholders of the Valle

d’Aosta pilot area. The meetings were aimed at understanding which analyses have already been

performed at regional scale and what data is available. EURAC participated actively at the workshops




14

organized by the project partner ARPA VdA, to exchange and share ideas on the best way to integrate the

NSGE into the energy strategy and planning process of the Region.

In the following, the list of data used for the spatial assessment of the thermal energy demand of the

residential building stock and for the spatial financial evaluation of NSGE in Valle d’Aosta is shown (further

details on the methodology are available in (GRETA D5.1.1 2018)):

● Digital Surface Model (DSM) at 2x2 and 0.5x0.5 m of spatial resolution;

● Digital Terrain Model (DTM) at 2x2 and 0.5x0.5 m of spatial resolution;

● Shapefile of buildings (polygons with attributes for gross surface and function);

● Shapefile of historical centres (points);

● Period of construction of buildings (estimated through the data from the Italian National Statistics

Institute - ISTAT);

● Energy performance parameters (U-values from the CENED dataset, see (GRETA D5.1.1 2018));

● Average air temperature (from weather stations) and estimated HDD and CDD;

● Average solar radiation (point 7 of Section 2.3 was applied for this pilot area);

● Degree of occupation of the buildings (estimated through the data from National Institute for

Statistics - ISTAT).

Pilot area base assumptions were the following (further details in (GRETA D5.1.1 2018)):

● Existing buildings for “civil” (i.e. not industrial) use are considered with prevailing residential

function;

● These buildings are considered as entirely heated; since the real occupation is not available at

regional scale, it was derived statistically to respect the percentage constraints obtained from the

ISTAT data (ISTAT 2011);

● Buildings with heated surface smaller than 20 m2 have been excluded from the analysis;

● Data on tourist infrastructures as well as service buildings (hospitals, schools, swimming pools,

etc.) are collected and used to exclude the relevant buildings from the analysis;

● Subsidies were considered;

● The analysis of spatial availability for NSGE plants does not consider the area of pertinence of the

buildings because this data was not available at the regional scale, whereas it includes the water

protection areas and buffers around existing buildings and water infrastructures. Furthermore, the

thermal plume of existing NSGE plants has been computed (Di Feo 2017) and all new potential

NSGE systems must not interfere with their thermal plume.

Analyses performed are the following:

● Open-loop analysis;

● Closed-loop analysis;

● PV panels installation (point 7 of Section 2.3 was applied).




15

2.4.2. Sonthofen

The current energy strategies of the Municipality of Sonthofen can be found in several documents, i.e. the

European Energy Award – Bericht Stadt Sonthofen, the Sustainable Energy Action Plan of the city, and the

Action Plan for Climate Protection. The main objectives of these documents are:

o Creating a qualitative energy policy model;

o Establishing a municipal energy management plan;

o Constructing a wood chip local heating network for the municipal buildings and promotion of projects

to generate heat from RES;

o Share of RES production on total consumption by 2022: 70% for electricity consumption, increase in

share for heat consumption;

o Reducing energy demand: doubling the refurbishment rate to 2%, reduction of 3% of the annual

energy consumption for buildings;

o Energy efficiency: increasing implementation of energy efficiency measures and development of

energy efficiency standards for the renovation and new construction of buildings;

o Providing an annual budget for the municipal energy policy.

Figure 2: Technical potential and actual use for heating from RES. Source: TUM for GRETA project.

During the GRETA project, EURAC participated at the workshop held in Sonthofen with the main

stakeholders of the pilot area. Further details on the data and analyses carried out have been discussed

with the municipality of Sonthofen by means of phone conferences and with the project partner TUM.

In the following, the list of data used for the spatial assessment of the thermal energy demand of the

residential building stock and for the spatial financial evaluation of NSGE in Sonthofen:

● Digital Terrain Model (DTM);

● 3D shapefile of buildings (with attributes for surface, height and function);




16

● Period of construction of the buildings, estimated on the base of the historical topographic maps

available in the Bayern Atlas WebGIS tool («BayernAtlas - der Kartenviewer des Freistaates

Bayern» s.d.)

● Energy demand (kWh/m2 year) related to building age (Zensus-Daten bitte

eingeben:(«Zensusdatenbank - Deutschland» s.d.);

● Average air temperature;

● Average solar radiation.

Pilot area base assumptions:

● Only residential buildings were considered;


● The analysis of spatial availability for open-loop systems has as input data the area of pertinence

of the buildings, which is available for the municipality, and the water protection areas.

Analyses performed are the following:

● Only open-loop analysis;

● PV panels installation was considered (point 8 of Section 2.3 was applied).

2.4.3. Cerkno

The current energy objectives of the Municipality of Cerkno are described in the Local Energy Concept

(LEC, 2011) with target year 2020. They can be summarised as follows:

o 10% reduction of specific energy use for residential heating;

o 3% increase in solar and geothermal energy exploitation: the current exploitation of the

geothermal energy in Cerkno is 2.08 GWh/year, so a 3% increase would mean an additional 62

MWh/year (more or less 4-7 private houses by 2020);

o 5% increase of wood biomass utilization;

o Setting up at least one major district heating system on wood biomass;

o 5% reduction in electricity consumption for households;

o Setting up 5 heating systems based on RES in the industry/service sector;

o Construction of 3 new small hydropower plants;

o Installation of a district heating network on wood biomass, which would also include geothermal

heat pumps (for primary school, kindergarten, museum and music school).

The use of NSGE in Cerkno is proposed in combination with wood biomass; other possible objectives for

the development of the NSGE source are illustrated in the following table.




17

Table 2: Possible objectives for the development of NSGE in Cerkno. Source: GeoSZ for GRETA project.

During the GRETA project, EURAC participated at the workshop held in Ljubljana with the main

stakeholders of the pilot area, to collect information and general data about the stakeholders needs.

Further details on the data and analysis results have been discussed with the project partner GeoSZ.

In the following, the list of data used for spatial assessment of the thermal energy demand of the building

stock and for the spatial financial evaluation of NSGE in Cerkno:

● Digital Terrain Model (DTM);

● Shapefile of buildings;

● Energy demand (raster file provided by GeoSZ, MWh per year per cell of 25x25 m).

Pilot area base assumptions:

● Public, residential and commercial buildings were considered;


● The analysis of spatial availability for NSGE plants considers a buffer around the buildings and a

buffer around streets and water bodies, both extracted from («OpenStreetMap» s.d.). The area of

pertinence of the building was not available for this pilot area.

Analyses carried out:

● Closed-loop analysis;

● Point 9 of Section 2.3 (no PV considered).

3. Economic assessment

3.1. Economic and financial feasibility of NSGE based on simulations

As described in (GRETA D5.1.1 2018) various economic/financial methods to evaluate NSGE projects have

been taken into consideration in assessing economic and financial feasibility of such applications within

the Alpine Space (country-by-country). This economic and financial assessment of NSGE is based on




18

simulated data (see below) and provides general information on the economic/financial feasibility of NSGE

under a set of specific conditions.

As described in Section 3.1, we distinguish among 36 different cases in which a NSGE application takes

place: 3 building typologies, 2 insulation levels, and 6 different climatic zones (A-F). Lastly, the economic

and financial analysis has considered the aforementioned simulated data produced by POLITO (GRETA

D3.2.1 2017) as reference. The investigated building types are the following:

● Detached house;

● Office;

● Hotel.

The analysed NSGE applications are assumed to provide space heating (SH), space cooling (SC), and

domestic hot water (DHW). SH and SC are delivered by fan coils. The implemented analysis is related to

the choice of using NSGE in contrast to conventional heating and cooling (H&C) systems – i.e. oil boilers

for SH and DHW, and electrically powered air-conditioners (ACs) for SC.

The implemented approach focuses mainly on evaluation of new NSGE installations’ payback periods (by

calculating the simple and discounted payback period as well as the Levelized Cost of Energy - LCOE) and

on understanding NSGE economic/financial feasibility (indicated by the Present Worth and the Internal

Rate of Return). With regard to the LCOE, the Break Even Point (BEP) has been used to indicate the payback

on investment in years. The calculations have considered subsidies and incentives at national level.

Different types of economic/financial indicators are calculated for this aim:

● BEP derived from Levelized Cost Of Energy (LCOE) and respective sensitivity analysis;

● Simple Payback Period (SPP);

● Discounted Payback Period (DPP);

● Present Worth (PW);

● Internal Rate of Return (IRR).

For further details on the methodology applied and data sources, see (GRETA D5.1.1 2018, 1).

3.2. Results of the economic and financial analyses based on simulations

The main results of the economic and financial analyses, based on simulations, are reported in the

following sections. Table 3 displays the average values of the economic indicators for all climatic zones (A-

F), divided into high and low insulated buildings and building type (detached house, office, or hotel). In

order to obtain Table 3, where no BEP was reached (for LCOE, SPP and DPP calculations), we assigned an

average value of 20 years – according to the average lifetime of conventional back-up boiler equipment

(Watkins 2011). In the case where no BEP was reached for PW and IRR, we assigned values of 0 and 0%,

respectively.




19

Table 3: Mean results of economic/financial analysis. Geothermal HP in comparison with conventional H&C systems – i.e. oil boilers for SH and DHW and electrically powered ACs for SC calculated based on the 36 considered cases. Source: (GRETA D5.1.1 2018).

Average economic/financial indicators’ values for all climatic zones (A-F)

Building type

Average [years] (BEP -

LCOE, SPP, and DPP)

BEP (LCOE) [years]

SPP [years]

DPP [years]

PW [€]

IRR [%]

High insulated buildings

Detached house 7.14 6.17 7.00 8.25 65,704 16.45

Office 12.86 10.17 13.42 15.00 49,564 6.75

Hotel 8.39 6.58 8.83 9.75 654,869 12.99

Low insulated buildings

Detached house 7.11 6.50 7.00 7.83 264,641 15.39

Office 8.39 7.33 8.33 9.50 441,998 12.35

Hotel 7.31 6.92 6.83 8.17 4,708,508 14.85

As displayed in Table 3, the mean outcomes on payback periods provided by LCOE, SPP and DPP

calculations show just minor variations. The standard deviation of indicated outcomes range from a min

0.58 to a max 2.50 years in value.

If the average of payback periods per insulation and building type is calculated, in both cases (high and low

insulation) the detached house is ranked first, followed by hotel and office buildings. Specific values per

insulation and building type follow:

High insulation:

A. Detached house: 7.14 years

B. Office: 12.86 years




20

C. Hotel: 8.39 years

Low insulation:

A. Detached house: 7.11 years

B. Office: 8.39 years

C. Hotel: 7.31 years

However, it has to be highlighted that in the case of low insulation buildings, the difference for mean

payback periods between detached houses and hotel buildings is negligible (0.2 years).

It must be stressed that the calculations carried out consider the same electricity consumption for SC

generated by a traditional ACS (air-to-air HP) as well as for a NSGE application. This is one reason why

office-type buildings have longer payback periods compared to detached house and hotel building types.

Moreover, concerning the LCOE calculations, a sensitivity analysis has been carried out. An attempt has

been made to understand what is the variation in the LCOE’s BEP [years] of NSGE applications, when

varying some of the input data. For further details on the methodology applied and sources utilized, see

(GRETA D5.1.1 2018).

Regarding the sensitivity analysis, the main outcomes are as follows:

● For BEP (LCOE) calculations concerning both high and low insulation, an increase in annual costs

(variable costs) shows a high variation in the BEP result [years];

● The BEP (LCOE) calculations concerning low insulation buildings suffer from a slightly higher

variation of the BEP result [years] than for high insulation buildings, when varying the discount

rate and fixed costs. The main reason is that the fixed costs appear to be significantly higher

compared to variable costs for low insulated buildings;

● That is because low insulated buildings are considered to carry the costs for the new distribution

system (fan coils).

Thus, it can be said that while the LCOE results obtained through varying the discount rate and fixed costs

come out to have a certain consistency, the LCOE outcomes retrieved by changing the variable costs suffer

from inconsistency.

As already mentioned in (GRETA D5.1.1 2018), the PW calculates the equivalent worth of all cash flows

relative to the starting point. If the PW is higher than 0, the NSGE application appears to be feasible. For

all calculated cases, the PW ranges from a min 49,564€ (high insulation, office) to a max 4,708,508€ value

(low insulation, hotel).

In both cases (high and low insulation), the hotel is first ranked. Specific values per insulation and building

type follow:

High insulation:

A. Detached house: 65,704 €




21

B. Office: 49,564 €

C. Hotel: 654,869 €

Low insulation:

A. Detached house: 264,641 €

B. Office: 441,998 €

C. Hotel: 4,708,508 €

The ranking given and the specific results obtained indicate that economic/financial feasibility of NSGE

applications go hand in hand with growing demand for SH, SC and DHW demand.

Finally, the IRR has been calculated. If the IRR is higher than the Minimum Attractive Rate of Return

(MARR), the NSGE application appears to be feasible. The MARR reflects the minimum rate of return

required for the financial operation. Most companies set a 12% MARR hurdle (Ross, Westerfield, e Jaffe

2012; GRETA D5.1.1 2018).

For almost all calculated cases, the mean IRR appears to be greater than 12%. Solely for the high-insulated

office, a value of 6.75% comes out. The low insulated office indicates an IRR value slightly above 12%:

12.35%. In both cases (high and low insulation), the detached house is ranked first, followed by hotel and

office buildings. Specific values per insulation and building type are as follows:

High insulation:

A. Detached house: 16.45 %

B. Office: 6.75 %

C. Hotel: 12.99 %

Low insulation:

A. Detached house: 15.39 %

B. Office: 12.35 %

C. Hotel: 14.85 %

However, it must be highlighted that in the case of low insulated buildings, the difference between the

mean results of detached house and hotel buildings is just 0.54%.

Once again, one reason leading office buildings to show lower IRR values is that the difference of the

efficiency concerning the SC system (air-to-air HPs vs NSGE applications) has not been taken into

consideration.

As an overall result, it can be stated that the range of payback periods (Tables 3) comes out between 7-8

years. As mentioned above, offices are characterized by slightly higher payback time ranges, due to not

taking into account the higher efficiency levels for SC provided by NSGE.




22

3.3. Spatial-based simulations

For evaluating the suitability of an NSGE source to cover the heating and cooling demand in the pilot areas

and for improving awareness of its advantages among policy and decision-makers, several spatially-explicit

analyses were performed for each pilot area. The spatial-based simulations considered residential

buildings in Valle d’Aosta and Sonthofen, while no distinction was made among different building

typologies in Cerkno.

The main results of the spatial-based analyses are as follows:

A. The appraisal of the thermal energy demand of building stock given by the calculation of the thermal

demand of each building;

B. The evaluation of technical and financial suitability of closed and/or open-loop solutions for covering

the energy demand and replacing, as much as possible, fossil energy sources within H&C systems;

C. The evaluation of the maximum density of closed and open-loop systems for avoiding or minimizing

thermal interference among the systems. Within this analysis, all areas which lacked environmental

and technical data were excluded.

In the following subsections, point B will be described by means of the technology-related LCOE and mean

DPP values related to the performed comparisons. Particularly, where possible, the following scenarios

were considered:

1. Closed-loop with subsidies and rooftop PV systems (cl_sub_PV);

2. Closed-loop without subsidies and with rooftop PV systems (cl_PV);

3. Closed-loop with subsidies and without rooftop PV systems (cl_sub);

4. Closed-loop without subsidies and without rooftop PV systems (cl);

5. Open-loop with subsidies and rooftop PV systems (ol_sub_PV);

6. Open-loop without subsidies and with rooftop PV systems (ol_PV);

7. Open-loop with subsidies and without rooftop PV systems (ol_sub);

8. Open-loop without subsidies and without rooftop PV systems (ol).

It is worth mentioning that the following subsidies/grant were applied in the spatial-based analysis:

● The (GSE 2016) has been implemented for subsidies in the Italian pilot area. When applied, 65%

of the whole closed/open-loop system capital cost was subtracted. No other loan grant/subsidies

were considered.

● The (Bundesministerium für Nachhaltigkeit und Tourismus, s.d.) has been implemented for

subsidies in the German pilot area. Particularly, the market incentive programme (MAP) and the

incentive programme for energy efficiency (APEE) were applied as follows:

○ for the MAP, the innovation subsidy was considered (150% of the basic subsidy of 100€/KW

per installed power);

○ for the APEE, only the APEE grant was considered, thus an additional 20% of the basic subsidy;

○ no other loan grant/subsidies were considered.




23

● The rules of the “Eco Fund, Slovenian Environmental Public Fund” have been implemented for

subsidies in the Slovenian pilot area. Due to the impossibility of discerning among the different

building types, only the non-refundable financial incentive was considered and applied for each

occurrence. In particular, the amount of non-refundable financial incentive is up to 40% of

recognized investment costs, but no more than 4,000 € for a water/water-type HP or brine (such

as ground)/water HP. No other loan grant/subsidies were considered.

3.4. Results of spatial-based simulations

3.4.1. Valle d’Aosta

Estimation of thermal demand

Provided the methodology described in (GRETA D5.1.1 2018, 1) and the data and assumptions listed in

Section 2.3, the thermal energy demand of each building with prevailing residential function was estimated

at regional scale. For the considered number of buildings (41,703 records), the total estimated thermal

demand is equal to 1,959,208.4 MWh per year. Looking at the total heated surface of the considered

residential building stock, the average thermal demand is around 140 kWh/m2.

The following Figure 3 represents an extract of this output for the Valle d’Aosta pilot area.




24

Figure 3: Estimated thermal energy demand of residential buildings of Valle d’Aosta region (example from Aymavilles municipality, in the main valley west of Aosta city). Source: Eurac Research for GRETA project.

Spatial financial analysis

For the Valle d’Aosta, all the possible scenarios (1-8) enumerated in Section 3.3 were produced. The results

for closed-loop scenarios are shown in Figures 4 and in Tables 4-5, whereas the results for open-loop

system are shown in Figures 5 and in Tables 6-7. Particularly, Figures 4 and 5 depict the LCOE histograms

of the three considered systems. It is worth noting that the number of open-loop records was one order

of magnitude lower than the number of closed-loop records, due to the reduced extension of open-loop

input data.

Closed-loop systems




25

Figure 4a: scenario including solar rooftop PV systems, excluding subsidies.

Figure 4b: scenario excluding solar rooftop PV systems and subsidies.

Figure 4c: scenario including solar rooftop PV systems and subsidies.

Figure 4d: scenario excluding solar rooftop PV systems, including subsidies.

Figure 4: Valle d’Aosta closed-loop LCOE histograms for gas boiler and ACS (gas_acs), oil boiler and ACS (oil_acs),

and the four closed-loop scenarios: without subsidies and with rooftop PV systems (cl_PV, Figure 3a), closed-loop

without subsidies and without rooftop PV (cl, Figure 3b), closed-loop with subsidies and rooftop PV systems

(cl_sub_PV, Figure 3c), closed-loop with subsidies and without rooftop PV systems (cl_sub, Figure 3d). Source:

Eurac Research for GRETA project

Figure 4 shows that there is a clear influence of subsidies over closed-loop LCOE. Moreover, the

combination of gas boilers and ACS is always more convenient than the oil boiler and ACS. Considering the

same conditions, this result is justified by higher annual costs for the oil boiler and ACS (especially due to

oil cost). This result is also valid for open-loop scenarios (Figure 5). Instead, Figures 4c-d depict the

importance of the Italian subsidies for a financial convenience of closed loop HP plants. Conversely, in case




26

of their lack (Figure 4a and Figure 4b), the combination of gas boilers and ACS is usually associated with

lower LCOE.

Table 4 shows univariate statistics for the LCOE value distributions of closed loop HP plants, whereas it

shows only one set of univariate statistics for the two combinations of gas and oil boilers with ACS. It is

worth mentioning that the number of results involving rooftop PV systems is less than the number of all

closed-loop occurrences. Some roof areas, where it was not possible to include PV panels (according to

point 7 of Section 2.3), were not included in the input data for the spatial-based simulations. Considering

that the economic and financial improvements deriving from the applications of subsidies and rooftop

solar PV systems were only applied to HP plants (i.e. all the prices for gas and oil boiler combinations do

not change, for a considered i-th building, in the four scenarios proposed in Figure 4), Table 4 shows only

one column for the oil boiler combination and one column for the gas boiler combination. Indeed, being

the LCOE values for gas and oil boilers combinations, in case of rooftop PV system, only a subset extracted

from the whole set of solutions, the univariate statistics of the whole number of records were assumed as

representative for the four considered scenarios.

Table 4: Univariate statistics for LCOE data of the Valle d’Aosta region, considering: gas boiler and ACS (gas_acs),

oil boiler and ACS (oil_acs), and the four closed-loop scenarios: without subsidies and with rooftop PV systems

(cl_PV), closed-loop without subsidies and without rooftop PV (cl), closed-loop with subsidies and rooftop PV

systems (cl_sub_PV), closed-loop with subsidies and without rooftop PV systems (cl_sub). Source: Eurac Research

for GRETA project

LCOE_closed_loop

cl_PV cl cl_sub_PV cl_sub LCOE_oil_acs LCOE_gas_acs

Average 0.078 0.083 0.048 0.053 0.100 0.059

Standard Deviation 0.010 0.010 0.004 0.004 0.001 0.001

Median 0.077 0.083 0.048 0.053 0.100 0.058

Skewness 41.316 38.521 45.060 43.505 3.736 6.251

Kurtosis 3919.700 3702.752 4400.027 4367.600 59.727 61.125

Table 4 provides more insights on the LCOE distributions with respect to Figure 4. It possible to see that

the traditional technologies are characterized by a LCOE values distribution very close to the mean and

median value (smaller standard deviation). All distributions feature a positive skewness and are




27

leptokurtic. This means that all the solutions are characterized by higher extreme LCOE values. Despite this

common behaviour, closed-loop HP LCOE values feature a significant number of extreme values (as shown

in Figure 4 also). This result can be justified considering the higher variability in closed-loop HP plants’

investment costs. Indeed, the reader should bear in mind that according to point 6 of Section 2.3,

excavation costs have been separately calculated for each input record (building) and for each scenario.

Considering the dependence among BHE length, thermal demand, and site-specific geological properties,

this result is justified.

Table 5: Discounted Payback Period (DPP) values for the Valle d’Aosta region, considering the following

comparisons between closed-loop HP plants and: (i) gas boiler and ACS (DPP closed-loop vs gas_acs), (ii) oil boiler

and ACS (DPP closed-loop vs oil_acs). The following scenarios were taken into account: closed-loop without

subsidies and with rooftop PV systems (cl_PV), closed-loop without subsidies and without rooftop PV (cl), closed-

loop with subsidies and rooftop PV systems (cl_sub_PV), closed-loop with subsidies and without rooftop PV

systems (cl_sub). Source: Eurac Research for GRETA project

mean DPP closed-loop vs oil_acs (y) mean DPP closed-loop vs gas_acs (y)

cl 15.3 26.7

cl_sub 4.6 17.6

cl_PV 13.8 24.3

cl_sub_PV 4.2 13.2

Table 5 shows mean DPP values for the scenario 1-4 of Section 3.3 and for the comparisons performed

against conventional technologies. Table 5 features the expected results with regards to the displayed

mean values. Indeed, in the comparisons, DPP values for the oil boiler and ACS are lower than the

corresponding gas boiler values. This is expected due to the higher costs of oil fuel with respect to gas fuel.

Moreover, considering a specific comparison (e.g. DPP closed-loop vs oil_acs), we can note that DPP values

follow expected patterns: DPP cl > DPP cl_PV > DPP cl_sub > DPP cl_sub_PV. This result can highlight the

different contributions of rooftop PV systems and Italian subsidies for closed-loop systems. Lastly, Figures

6a-b show the influence of subsidies over closed-loop HP plants.

Open-loop systems

Figure 5 shows again the existence of a clear influence of subsidies over open-loop LCOE values for Valle

d’Aosta. Unlike closed-loop simulations, in this case, it is possible to highlight some occurrences in which




28

open-loop systems feature a major convenience in each considered simulation (scenarios 5-8 according to

Section 2.2). Moreover, Figures 5c-d highlight the importance of Italian subsidies for a financial

convenience of open-loop HP plants. Indeed, in cases where they are lacking (Figure 5a and Figure 5b)

some open-loop LCOE values approach to the combined oil and ACS values (generally, the less convenient

solution).





Figure 5: Valle d’Aosta open-loop LCOE histograms for gas boiler and ACS (gas_acs), oil boiler and ACS (oil_acs),

and the four closed-loop scenarios: without subsidies and with rooftop PV systems (cl_PV, Figure 4a), closed-loop

without subsidies and without rooftop PV (cl, Figure 4b), closed-loop with subsidies and rooftop PV systems

(cl_sub_PV, Figure 4c), closed-loop with subsidies and without rooftop PV systems (cl_sub, Figure 4d). Source:





29

Table 6 shows univariate statistics for the LCOE distributions of open-loop HP plants and one set of

univariate statistics for the two combinations of gas and oil boilers with ACS. Again, it is worth mentioning

that the number of results involving rooftop PV systems is less than the number of all closed-loop

occurrences (according to point 7 of Section 2.3). Strictly speaking, different numbers of LCOE values

should be associated with different univariate statistics. However, for open-loop simulations, the excluded

records are only a minor part of total ones. Since the main focus of the investigation is related to HP plants,

to avoid repetition, only one unique set of univariate statistics is shown for the two boilers (gas and oil)

configurations.

Table 6: Univariate statistics for LCOE data of the Valle d’Aosta region, considering: gas boiler and ACS (gas_acs),

oil boiler and ACS (oil_acs), and the four open-loop scenarios: without subsidies and with rooftop PV systems

(ol_PV), open-loop without subsidies and without rooftop PV (ol), open-loop with subsidies and rooftop PV

systems (ol_sub_PV), open-loop with subsidies and without rooftop PV systems (ol_sub). Source: Eurac Research

for GRETA project

LCOE_open_loop

ol_PV ol ol_sub_PV ol_sub LCOE_oil_acs LCOE_gas_acs

Average 0.059 0.064 0.044 0.048 0.101 0.060


Median 0.059 0.064 0.044 0.048 0.101 0.059

Skewness 1.470 1.361 1.354 1.265 3.343 4.698

Kurtosis 6.575 5.532 5.609 4.782 14.083 23.178

Table 6 provides more insights on LCOE distributions with respect to Figure 5. Considering the smaller

number of records analysed in scenarios 5-8 for the Valle d’Aosta region, it is confirmed the smaller

standard deviation of traditional technologies (characterized by LCOE values distribution very close to the

mean and median values). Apart from the ol scenario, the other scenarios feature a mean and median

LCOE less than the corresponding values of the traditional technologies. Under the assumptions (Section

2.3) these results show a greater convenience of open-loop systems with respect to closed-loop ones. All

distributions feature a positive skewness and are leptokurtic. This means that all the solutions are

characterized more by higher extreme LCOE values, and that results are not normally distributed.




30

Despite this common behaviour, the open-loop HP LCOE values feature extreme values closer to the mean

LCOE values (as shown also in Figure 5) with respect to the other two configurations. This result can be

justified considering the lower variability of open-loop HP plants investment cost. Indeed, it should be

taken in mind that: (i) only a small study area was considered for open-loop scenarios in the Valle d’Aosta

region; (ii) although calculated for each record, excavation costs feature a lower variability (with respect

to closed-loop systems) owing to the absence, in the considered area, of high water-table depth and

aquifer thickness gradients.

Table 7: Discounted Payback Period (DPP) values for the Valle d’Aosta region, considering the following

comparisons between open-loop HP plants and: (i) gas boiler and ACS (DPP open-loop vs gas_acs), (ii) oil boiler

and ACS (DPP open-loop vs oil_acs). The following scenarios were taken into account: open-loop without

subsidies and with rooftop PV systems (ol_PV), open-loop without subsidies and without rooftop PV (ol), open-

loop with subsidies and rooftop PV systems (ol_sub_PV), open-loop with subsidies and without rooftop PV

systems (ol_sub). Source: Eurac Research for GRETA project

mean DPP open-loop vs OIL+ACS (y) mean DPP open-loop vs GAS+ACS (y)

ol 6.8 14.2

ol_sub 2.3 8.4

ol_PV 6.0 15.0

ol_sub_PV 2.0 6.4

Table 7 shows mean DPP values for the scenario 5-8 of Section 3.3 and for comparisons performed against

conventional technologies. Again, Table 5 features expected results with regards to displayed mean values.

Indeed, DPP values for the oil boiler and ACS are lower than the correspondent gas boiler values. This is

expected due to higher costs of oil fuel over gas fuel. Moreover, considering a specific comparison (e.g.

DPP closed-loop vs oil_acs) we can note that DPP values follow expected patterns: DPP ol > DPP ol_PV >

DPP ol_sub > DPP ol_sub_PV. This result again confirms the contribution of rooftop PV systems and Italian

subsidies for closed-loop systems. Lastly, Figures 6c-d show the influence of subsidies over open-loop HP

plants.




31

Figure 6a: scenario including solar PV systems, excluding subsidies.

Figure 6b: scenario including subsidies, excluding solar PV systems.

Figure 6c: scenario excluding solar PV systems and subsidies.

Figure 6d: scenario including subsidies, excluding solar PV systems.

Figure 6: Maps on the comparison among the minimum values of LCOE per building for some of the technology

scenarios in Valle d’Aosta (examples from Aymavilles and Aosta municipalities). Source: Eurac Research for

GRETA project

The result of the analysis on the space availability, for NSGE plants in Valle d’Aosta, is presented in the

Figure 7 according to:

● Section 2.4.1, for input data and assumptions;

● Section 6 of (GRETA D5.1.1 2018, 1), and points 16-19 of Section 2.3, for the methodology.




32

The analysis of the space availability estimates the maximum number of NSGE systems that can occur in

the area under examination, minimizing the risk of reciprocal interference among adjacent systems. The

results show that 83% (886.6 GWh) of the total energy demand can be covered by NSGE, corresponding

to 93% (11.6 Mm²) of heated surface. However, only 38.9% of the energy demand (39.0% of the heated

surface) is supplied by LPG and diesel, with the remaining part supplied by natural gas and biomass (ISTAT

2011).

Figure 7a: buildings with closed-loop systems and

related thermal plumes, calculated considering the

energy demand of the buildings. The colours of the

buildings and of the thermal plumes are the same.

Figure 7b: buildings classified as potentially supplied by

closed-loop system or not (according to point 19 of

Section 2.3). Buildings in red cannot be supplied by a

closed-loop system for one of the following reasons: i)

the BHE is within an area that has been defined as not

adapt for NSGE use; ii) the thermal plume generated by

the plant interferes with the thermal plumes of an

existing (Di Feo 2017) or potential NSGE systems.

Figure 7: Maps of the spatial availability for closed-loop plants in Valle d’Aosta (example of a hamlet in Verrayes

municipality). Source: Eurac Research for GRETA project

3.4.2. Sonthofen


As for Valle d’Aosta, with the methodology described in (GRETA D5.1.1 2018, 1) and with the data and

assumptions listed in Section 2.4.2, the thermal energy demand for the residential building stock of

Sonthofen was estimated. For the considered number of buildings (3,589 records), the total estimated




33

thermal demand is equal to 120,601.8 MWh per year. Looking at the total heated surface of the considered

residential building stock, the average thermal demand is around 167 kWh/m2.

The following Figure 8 represents an extract of this output for the Municipality.

Figure 8: Estimated thermal energy demand of residential buildings of Sonthofen pilot area (extract of a part of the

city). Source: Eurac Research for GRETA project


For the Sonthofen pilot area, scenarios 5-8 according to Section 2.4.2 (open-loop only) were produced.

The results are shown in Figure 9 and in Tables 8-9. Particularly, Figure 9 depicts the LCOE histograms of

the three considered systems. It is worth noting that:

● The number of bins was adapted in each histogram to highlight differences among the three

implemented technologies;

● The abscissa precision (three decimal numbers) was increased for the sake of a better description

of the results;




34

● In each histogram plot, values less than and greater than respectively of the 2.5th and 97.5th

percentiles were excluded. This was done in order to improve their visualization;

● The number of records, for the simulations implementing rooftop solar PV systems, is less than

the original one according to point 7 of Section 2.3.

Open-loop systems





Figure 9: Sonthofen open-loop LCOE histograms for gas boiler and ACS (gas_acs), oil boiler and ACS (oil_acs), and

the four open-loop scenarios: without subsidies and with rooftop PV systems (ol_PV, Figure 7a), open-loop

without subsidies and without rooftop PV (ol, Figure 7b), open-loop with subsidies and rooftop PV systems

(ol_sub_PV, Figure 7c), open-loop with subsidies and without rooftop PV systems (ol_sub, Figure 7d). Source:





35

Figure 9 shows that there is a clear influence of subsidies for LCOE related to open-loop HP plants, while

increased abscissa precision highlights the contribution of rooftop solar PV systems. In Sonthofen, the

combination of gas boilers and ACS is always more convenient than the combined oil boiler and ACS,

although it is possible to highlight some occurrences in which open-loop systems feature a major

convenience in each considered simulation.

Considering the same conditions, this result is justified by the higher annual costs for the oil boiler and ACS

(especially due to oil cost). For open-loop HP plants, Figures 9c-d depict the positive effects related to the

German subsidies. Indeed, in the case of their lack (Figure 9a and Figure 9b), the combination of gas boilers

and ACS is usually associated with lower LCOE, whereas, in the case where rooftop systems are also lacking,

the combined use of gas boiler and ACS becomes even more convenient (due to the increase of HP LCOE

values).

Table 8: Univariate statistics for LCOE data of the Sonthofen municipality, considering: gas boiler and ACS

(gas_acs), oil boiler and ACS (oil_acs), and the four open-loop scenarios: without subsidies and with rooftop PV

systems (ol_PV), open-loop without subsidies and without rooftop PV (ol), open-loop with subsidies and rooftop

PV systems (ol_sub_PV), open-loop with subsidies and without rooftop PV systems (ol_sub). Source: Eurac

Research for GRETA project

LCOE_open_loop

ol_PV ol ol_sub_PV ol_sub LCOE_oil_acs LCOE_gas_acs

Average 0.048 0.053 0.045 0.050 0.045 0.043


Median 0.047 0.053 0.044 0.050 0.045 0.043

Skewness 2.336 2.232 2.315 2.216 4.433 4.433

Kurtosis 12.260 11.221 12.267 11.197 17.366 17.366

Table 8 shows univariate statistics for the LCOE distributions of open-loop HP plants, whereas it shows

only one set of univariate statistics for the two combinations of gas and oil boilers with ACS. Also in this

case, only one unique set of univariate statistics is shown for the two boiler (gas and oil) configurations

(justifications can be found in Section 3.4.1). Table 8 provides more insights on the LCOE distributions with

respect to Figure 9. Considering the small number of records analysed in scenarios 5-8 for the Sonthofen




36

municipality, it possible to see that traditional technologies are characterized by a LCOE values distribution

very close to the mean and median value (smaller standard deviation).

Considering the comparison against gas boiler-ACS combined system, even the coupled use of subsidies

and solar rooftop systems is not able to achieve mean and median LCOE values better than traditional

technology. This specific scenario (the best one from a financial point of view) performs slightly better than

the oil boiler-ACS combination (lower median value). Under the assumptions of Section 2.3, these results

provide a tendency for greater convenience of open-loop systems only in that specific configuration (i.e. a

well doublet, 5 K of temperature difference, etc.). However, the mean and median values for the three

considered technologic configurations are close even considering only the application of rooftop solar PV

systems. All distributions feature a positive skewness and are leptokurtic. This means that all the solutions

are characterized by higher extreme LCOE values, and these results are not normally distributed.

Despite this common behaviour, the open-loop HP LCOE values feature a more uniform distribution of

extreme values (as shown also in Figure 9) with respect to the other two configurations. This result,

coupled with a greater standard deviation encountered for HP plants, confirms that the convenience for

this installation should be evaluated case by case. The lack of a clear pattern for the Sonthofen municipality

can be justified considering the small number of records analysed, and the different rules and entity of

German subsidies.

Table 9: Discounted Payback Period (DPP) values for the Sonthofen municipality, considering the following

comparisons between open-loop HP plants and: (i) gas boiler and ACS (DPP open-loop vs gas_acs), (ii) oil boiler

and ACS (DPP open-loop vs oil_acs). The following scenarios were taken into account: open-loop without

subsidies and with rooftop PV systems (ol_PV), open-loop without subsidies and without rooftop PV (ol), open-

loop with subsidies and rooftop PV systems (ol_sub_PV), open-loop with subsidies and without rooftop PV

systems (ol_sub). Source: Eurac Research for GRETA project

mean DPP open-loop vs OIL+ACS (y) mean DPP open-loop vs GAS+ACS (y)

ol - -

ol_sub - -

ol_PV 25.7 25.3

ol_sub_PV 21.6 22.0

Table 9 shows the mean DPP values for the scenario 5-8 of Section 3.3 and for the comparisons performed

against the conventional technologies in Sonthofen. Table 9 features expected results with regards to the




37

combined application of rooftop PV solar systems and subsidies. However, due to the similar LCOE values

for the three considered technologies, Table 9 shows also that the investments cannot be recovered (over

the whole HP plant lifetime) for the open-loop scenario without subsidies and without rooftop PV and for

the open-loop scenario with subsidies and without rooftop PV systems. The same occurs even for many

buildings of the other two considered scenarios. This behaviour can be justified considering the lower

entity of German subsidies (e.g. compared to the Italian ones).

Lastly, Figure 10 shows the four considered scenarios for the same spatial subset of the Sonthofen pilot

area, confirming the lack of a scenario in which open-loop HP plants can be recognized as the most

convenient ones without subsidies or PV solar systems.

Figure 10a: scenario excluding solar PV systems and subsidies.

Figure 10b: scenario excluding solar PV systems, including subsidies.

Figure 10c: scenario including solar PV systems, Figure 10d: scenario including solar PV systems and




38

excluding subsidies. subsidies.

Figure 10: Maps on the comparison among the minimum values of LCOE per building for the four technology scenarios in Sonthofen (extract of a part of the city). Source: Eurac Research for GRETA project

The result of the analysis on the space availability, for NSGE plants in Sonthofen, is presented in Figure 11

according to:


● Section 6 of (GRETA D5.1.1 2018), and points 16-19 of Section 2.3, for the methodology.

The results shown in Figure 11 include in the analysis the spatial-based constraints to avoid thermal plume

interference among different open-loop systems. The analysis features a percentage of surface potentially

heated using open-loop systems equal to 19.6% (60,074 m²) of the total surface. This corresponds to 20.6%

(10.2 GWh/year) for the total energy demand of the considered buildings. The reader should bear in mind

that the analysis has been carried out at single building level and, in case of close buildings, only the most

thermally demanding was covered by open-loop system, according to the methodology described (GRETA

D5.1.1 2018, 1).

Figure 11a: buildings with open-loops systems and related thermal plumes, calculated considering the energy demand of the buildings. The colours of the buildings and of the thermal plumes are the same.

Figure 11b: buildings classified as potentially supplied

by open-loops systems or not (according to point 19 of


open-loop system for one of the following reasons: i)

the borehole is not contained in a valid area (e.g. the

area of pertinence of the building); ii) the thermal

plume generated by the open-loop system interferes




39

with a thermal plume of other potential open-loop

plants.

Figure 11: Maps of the spatial availability for GSWP plants in Sonthofen (extract of a part of the city). Source:


3.4.3. Cerkno


Also for Cerkno pilot area, by employing the methodology described in (GRETA D5.1.1 2018, 1) and with

the data and assumptions listed in Section 2.4.3, the thermal energy demand of the building stock was

estimated. For the considered number of buildings (1,888 records), the total estimated thermal demand

is equal to 49,922.3 MWh per year. It is worth recalling that for Cerkno the building stock also includes

commercial and public buildings, in addition to the residential ones.

The following Figure 12 represents an extract of this output for the Municipality.




40

Figure 12: Estimated thermal energy demand of residential, public and commercial buildings of Cerkno Pilot Area

(extract of a part of the town). Source: Eurac Research for GRETA project


For the Cerkno pilot area, scenarios 3-4 according to Section 3.3 (closed-loop only) were produced. The

results are shown in Figure 13 and in Tables 10-11. Particularly, Figure 13 depicts the LCOE histograms of

the three considered systems. It is worth noting that:

● The number of bins was adapted in each histogram to highlight differences among the three

implemented technologies;

● The abscissa precision (three decimal numbers) was increased for the sake of a better description

of the results;

● In each histogram plot, values less than and greater than respectively of the 2.5th and 97.5th

percentiles were excluded. This was done in order to improve their visualization;

● It was not possible to implement the presence of rooftop PV solar systems according to point 9 of

Section 2.3.

Closed-loop systems

Figure 13a: scenario excluding solar rooftop PV systems and subsidies.

Figure 13b: scenario excluding solar rooftop PV systems, including subsidies.

Figure 13: Cerkno Pilot Area closed-loop LCOE histograms for gas boiler and ACS (gas_acs), oil boiler and ACS

(oil_acs), and the two closed-loop scenarios: without subsidies and without rooftop PV (cl, Figure 10a), with

subsidies and without rooftop PV systems (cl_sub, Figure 3b). Source: Eurac Research for GRETA project




41

Figure 13 shows the differences related to the application of the Slovenian subsidies for the two considered

closed-loop scenarios. In this case, subsidies are correlated to a slight decrease of LCOE values for closed-

loop HP plants. In Cerkno, the combination of gas boilers and ACS is always more convenient than the

combined oil boiler and ACS. Considering the same conditions, this result is justified by the higher annual

costs for the oil boiler and ACS (especially due to oil cost). In both scenarios, it is possible to highlight

occurrences in which closed-loop systems feature a major convenience (low LCOE values) in each

considered simulation.

Table 10: Univariate statistics for LCOE data of the Cerkno pilot area, considering: gas boiler and ACS (gas_acs),

oil boiler and ACS (oil_acs), and the two closed-loop scenarios: without subsidies and without rooftop PV (cl),

with subsidies and without rooftop PV systems (cl_sub). Source: Eurac Research for GRETA project

LCOE_closed_loop

cl cl_sub LCOE_oil_acs LCOE_gas_acs

Average 0.059 0.052 0.064 0.056

Standard Deviation 0.005 0.013 0.001 0.001

Median 0.059 0.054 0.064 0.056

Skewness -0.100 -29.306 3.987 4.041

Kurtosis 0.865 1101.811 23.156 22.724

Table 10 shows univariate statistics for the LCOE value distributions of closed-loop HP plants, while it

shows only one set of univariate statistics for the two combinations of gas and oil boilers with ACS. Also in

this case, only one unique set of univariate statistics is shown for the two boilers (gas and oil)

configurations (justifications can be found in Section 3.2). Table 10 provides more insights on the LCOE

distributions with respect to Figure 13. Considering the small number of records analysed in scenarios 3-4

for the Cerkno Pilot Area, it is possible to see that the traditional technologies are characterized by LCOE

values distribution very close to the mean and median value (smaller standard deviation).

Considering the comparison against gas boiler-ACS combined system, only through the use of subsidies a

closed-loop plant is able to achieve mean and median LCOE values better than the traditional technology.

Under the assumptions of Section 2.3, these results provide a tendency for a greater convenience only in

that specific configuration. In the case of oil boiler-ACS combined systems, the tendency provided by

univariate statistics features better mean and median LCOE values for closed-loop HP plants. However, the

mean and median values for the three considered technologic configurations are close to each other even




42

considering the application of subsidies. The distributions related to boiler combinations feature a positive

skewness and are leptokurtic. This shows that their solutions are characterized by higher extremes LCOE

values and that the results are not normally distributed. The same result does not occur for LCOE values

related to HP plants. They are characterized by negative skewness and are leptokurtic in the case of the

use of subsidies, whereas they feature a platykurtic shape. This behaviour indicates the application of

subsidies that are responsible for the presence of a greater number of HP plant LCOE values less than their

mean values (if compared with the other scenario). Despite this behaviour, one should bear in mind that

the experienced variation in LCOE values is quite small. This result can be justified considering the low

entity of Slovenian subsidies.

Table 11: Discounted Payback Period (DPP) values for the Cerkno pilot area, considering the following

comparisons between closed-loop HP plants and: (i) gas boiler and ACS (DPP closed-loop vs gas_acs), (ii) oil boiler

and ACS (DPP closed-loop vs oil_acs). The following scenarios were taken into account: closed-loop without

subsidies and without rooftop PV (cl), closed-loop with subsidies and without rooftop PV systems (cl_sub).

Source: Eurac Research for GRETA project

mean DPP closed-loop vs OIL+ACS (y) mean DPP closed-loop vs GAS+ACS (y)

cl 22.7 24.5

cl_sub 19.5 20.4

Table 11 shows mean DPP values for the scenario 5-8 of Section 3.3 and for comparisons performed against

conventional technologies. Table 11 again features expected results with regards to the displayed mean

values, i.e. the implementation of subsidies is correlated to a reduction of DPPs. Moreover, DPP values for

the oil boiler and ACS are lower than the correspondent gas boiler values. This is expected given the higher

costs of oil fuel with respect to gas fuel. Considering the differences in DPP values, Table 11 again highlights

the fact that Slovenian subsidies can produce positive effects on return times, although their

implementation is not able to produce high reductions in DPP values.

Lastly, Figure 14 shows for the same spatial subset of the Cerkno pilot area the two considered scenarios,

displaying the effects of Slovenian subsidies.




43

Figure 14a: scenario excluding solar PV systems and subsidies.

Figure 14b: scenario excluding solar PV systems, including subsidies.

Figure 14: Maps on the comparison among the minimum values of LCOE per building for the two technology

scenarios in Cerkno (extract of a part of the town). Source: Eurac Research for GRETA project

The result of the analysis on the space availability, for NSGE plants in Cerkno, is presented in the Figure 15

according to:


● Section 6 of(GRETA D5.1.1 2018), and points 16-19 of Section 2.3, for the methodology.

For Cerkno, 88.4% (44.1 MWh/year) of the energy demand can be covered by closed-loop systems without

interfering with the thermal plume of other potential NSGE plants.




44

Figure 15a: buildings with closed-loop systems and

related thermal plumes, calculated considering the

energy demand of the buildings. The colours of the

buildings and of the thermal plumes are the same.

Figure 15b: buildings classified as potentially supplied

by closed-loop systems or not (according to point 19 of


closed-loop system for one of the following reasons: i)

the BHE is within an area that has been defined as not

adapt for NSGE use; ii) the thermal plume generated by

the plant interferes with a thermal plume of other

potential plants.

Figure 15: Maps of the spatial availability for closed-loop plants in Cerkno (extract of a part of the town). Source: Eurac Research for GRETA project

4. Discussion

The deliverable describes the main results and outputs, in the framework of WP5, concerning the spatial

evaluation of the thermal energy demand and of the financial feasibility of NSGE in the Alpine Space.

For this purpose, the following outputs are presented:

● The evaluation, on simulated data (not spatial), of the financial feasibility of the interventions at

building level, under different climate conditions and for different levels of building insulation. This

Issue is addressed in Sections 3.1 and 3.2;

● The capacity of NSGE to cover the thermal energy demand of the three pilot areas (the “techno-

economical” potential), addressed in Sections 3.3 and 3.4.

In the case of simulated data, the financial convenience of NSGE application is evaluated with respect to a

traditional system composed by an oil boiler and AC, considering three typologies of building (detached

house, office, hotel). The results show a substantial convenience of NGSE technology. However, the reader

should bear in mind that (i) the results refer only to the oil boiler-plus-AC combination (i.e. no gas boiler

was considered in Sections 3.1 and 3.2) and (ii) the calculations carried out consider the same electricity

consumption for SC generated by a traditional AC (air-to-air HP) as well as NSGE application.

In Section 3.4, several outputs are presented for the three pilot areas although different input data, with

different levels of detail, were available. Considering the assumptions formulated in Section 2.3, the

analyses carried out do not consider the presence of hybrid systems (e.g. HP combined with an auxiliary

gas boiler) and make simplifications necessary to perform the analysis even in quite big pilot areas, such

as the Valle d’Aosta region. In the data processing, costs not considered (e.g. installation, design costs) are

taken into account by increasing the excavation costs of 40% (according to point 12 of Section 2.3).

Considering the major excavation length associated with closed-loop systems with respect to open-loop




45

systems ((Sanner et al. 2003) report that a typical application of a BHE/HP system in a Central European

house has a BHE average length of 100m), this assumption could lead to an overestimation and an

underestimation of HP capital costs for closed-loop and open-loop systems, respectively.

However, despite the uncertainties introduced from some of the assumptions and the impossibility of

validating case-by-case the thermal demand estimate because of data unavailability, this level of

uncertainty in capital costs estimation is acceptable. From a general point of view, the high variable

excavations costs are the main responsible of the higher spreading of LCOE values related to HP plants.

This is clearly depicted in the three pilot areas, in which LCOE values related to conventional technologies

are always characterized by lower variances. Concerning the spatial-based LCOE and DPP calculated values,

the reader should bear in mind that the analysis was carried out considering each building separately. This

means that neither LCOE nor DPP consider the possibility of creating small district heating networks with

a spatial aggregation of the thermal demand. District heating solutions have not been considered in the

present work, however this aspect can be further explored in future developments of this work.

In general, one of the main constraints in performing the analyses is limited data availability: some data is

often not available or its accessibility is limited by the data providers. This represents a relevant limit for

the analysis itself, as well as for the accuracy of the results. Another major issue is the inhomogeneity in

the detail level of data. For instance, for the same administrative area (region, province or municipality)

some data is accessible at the building level, while other data is available at the census or district level.

However, the analyses here described are able to estimate, for each building, the values needed for the

data processing, allowing to reach a compromise between the number of input data and the level of detail

often required by policy-makers.

In the Valle d’Aosta pilot area, the spatial-based approach has proven to be useful for assessing all 8

scenarios enumerated in Section 3.3. In this pilot area we can find a clear convenience of the gas-boiler

and ACS with respect to the oil-boiler and ACS. This is due to the high difference in fuel costs in Italian

context. The combined use of HP plants with rooftop solar PV systems is certainly able to positively

influence the DPP and LCOE values of geothermal HP systems. However, the real discriminant factor, in

the economic and financial analysis, is constituted by the application of subsidies. It is worth noting that in

Italy the subsidies can be equal to 65% of the whole investment. Thanks to this, there are relatively low

and medium return times for the oil boiler and gas boiler combinations, respectively. Although this is true

for both closed and open-loop systems, in Valle d’Aosta the open-loop HP plants show a greater

convenience. This is evident also without the application of subsidies. However, the reader should bear in

mind that the input data, for open-loop economic and financial analysis, are able to cover only one tenth

of data analysed for the closed-loop systems, and this can reduce the robustness of the result for the whole

pilot area. Furthermore, the application of the constraints to limit the NSGE systems only into the areas

defined as valid (see Section 2.3) and to avoid interferences among the thermal plumes of NSGE plants,

restricts the thermal demand that can be supplied by NSGE systems by 83% of the total. Imagining to




46

replace only LPG and diesel boilers, which is where the NSGE has greatest environmental and financial

advantage, NSGE systems can cover up to 39.4% of the total energy demand of the buildings.

With regard to the Sonthofen pilot area, it is worth noting that the thermal demand is estimated starting

from the age of the building visually assigned by comparison of a stack of topographic maps available in

the Bayern Atlas WebGIS tool (see Section 2.4.2). Considering that quite old topographic maps are

analyzed, the reader should bear in mind that generally the cartographic restitution process (e.g. from an

aerophotogrammetric survey) can take several years. This is true especially for old maps and this can be a

source of biases in the age estimation process. Despite this limitation, the presented approach is able to

provide meaningful data for the Sonthofen municipality. Indeed, Section 3.4.2 clearly depict a geographic

context in which HP open-loop solutions are characterized by comparable or slightly higher mean LCOE

values. In this case, the combined use of rooftop solar PV system is able to decrease the mean and median

LCOE values for HP plants, even more than subsidies. However, neither PV nor subsidies are enough to

make the NSGE solutions really competitive, since only a very little amount of analyzed buildings are

associated to DPP values less than the supposed HP plant lifetime and, even in this case, the featured DPP

are quite high. Furthermore, to respect the spatial and interference constraints the energy demand that

can be covered by open-loop systems correspond to 20.5% of the total energy demand. To reduce the

issue of the interference among thermal plumes and to increase the share of energy demand covered by

NSGE, a possible solution could be the creation of district heating networks supplied by open-loop HP.

Lastly, in the Cerkno pilot area a spatialized estimated energy demand constituted by an interpolated

raster file, which includes the thermal demand of residential, public and commercial buildings, was

obtained. Given the available data, in this pilot area was only possible to assess the presence (or not) of

subsidies for closed-loop HP plants. Due to their limited amount, their influence is limited. Indeed, the

estimated average return times (DPP values) are very high and do not suggest a great economic and

financial convenience for HP plants. Respecting the spatial constraints of valid areas and avoiding

interferences among NSGE thermal plumes reduce the thermal energy demand that can be covered by

NSGE by 11.6%.

Considering the results for the three pilot areas and the contribution of both rooftop solar PV systems and

subsidies, the subsides represent a real discriminant factor for the economic convenience of HP plants only

in the Italian pilot area. Considering the assumptions, rooftop solar PV systems are only able to produce a

limited reduction of both LCOE and DPP values.

5. Conclusions

The NSGE technical potential has been investigated and mapped within the WP4 activities (GRETA D4.2.1

2018, 1), while the spatial evaluation of the thermal demand and of the main financial figures, which

combine the technical potential with the energy demand (the “techno-economical” potential), are carried




47

out as one major activity of WP5. The methodology is described and discussed in the (GRETA D5.1.1 2018,

1) and the main results within the three pilot Areas are presented and discussed in the current document.

In particular, this deliverable presents the results of the spatial evaluation of the thermal energy demand

and of the financial and economic analyses for the three pilot areas. The focus of the study is to provide

knowledge and insights for decision-makers on how to integrate NSGE into energy plans and strategies.

Although in the performed comparisons the environmental benefits related to a massive implementation

of NSGE plants were not assessed (e.g. in (Rivoire et al. 2018) it was estimated in Italy an energy demand

reduction ranging from 33% to 75% and a carbon dioxide reduction ranging from 27% to 56%), the main

findings are:

● The procedure and the methodology identified within the GRETA project can be potentially

replicated in other case studies and not only in the Alpine regions. However, from the analysis

carried out, we can conclude that an effective spatial investigation of the technical and

economic/financial feasibility of NSGE systems should be addressed considering several factors.

E.g.:

○ the physical properties of the ground;

○ 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 (H&C) 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

in order to perform effective economic/financial comparisons;

○ all the aforementioned information should be collected by means of redundant and robust,

against uncertainties, methods in order to improve the reliability of the analysis;

○ all the aforementioned points should be spatially distributed and assessed in order to create

the conditions for fast data integration (e.g. join of information).

● The main limiting factor is the lack of data, or the lack of its fast usability, to perform a more

reliable estimation of the thermal energy demand at building level.

● The economic and financial analysis performed over the simulated data shows that the range of

payback periods for NSGE plants comes out to be between 7 and 8 years. Offices are characterized

by slightly higher time ranges due to the exclusion of higher efficiency levels for SC provided by

NSGE. The reader should bear in mind that payback time calculations were produced considering

the difference between HP plant and conventional plant capital costs and not only the HP.

● Due to the high variability of the NSGE potential, a spatial-based assessment of the economic and

financial feasibility of NSGE plants can support decision-makers to exploit and incentivize this




48

resource in a more effective way in order to increase its convenience. The amount of subsidies can

make the difference in the convenience of the NSGE plants. Particularly, they are important in

transition phases, in which a high amount of subsidies can enhance the spreading of cleaner

technologies.

More specifically for the three pilot Areas, the following considerations emerge:

● In Valle d’Aosta, there is the potential to cover around 40% of the thermal energy demand of the

residential sector substituting the existing LPG and diesel heating systems with a NSGE system.

The switch can have a relevant impact on the total consumption of fossil fuels at regional level,

reducing also CO2 emissions and improving air quality. The comparison with the Levelized Cost of

Energy - LCOE and the Discounted Payback Period - DPP, between the NSGE systems and the LPG

and diesel systems, is always convenient for buildings that are used throughout the year.

● In Sonthofen, the constraints to avoid thermal plume interference between NSGE systems limits

the use of NSGE systems to around 20% of the thermal energy demand of the residential sector.

A higher percentage of the energy demand could be covered by NSGE if integrated within district

heating networks. Due to the lower price of diesel (1.4 €/l in Italy vs. 0.67 €/l in Germany) and to

a different schema of subsidies, the result of the economic and financial analysis of the NSGE

systems is not always an interesting option (from a purely financial point of view), with a DPP that

is over 20 years old. The NSGE option is interesting if, in addition to this factor, we consider other

aspects such as the energy resilience to the variability of the fossil fuel price or the possibility of

using energy generated within the local area (i.e. hydro-power, photovoltaic systems and wind).

● In Cerkno, the thermal energy demand that can be potentially supplied by the NSGE is very high

(more than 88%). However, as for Sonthofen, DPP values are in average around or above 20 years.

Therefore, the NSGE systems can be promoted only if other criteria are taken into account, such

as energy self-sufficiency, CO2 and air pollutant emissions.

References

Alcaraz, Mar, Luis Vives, e Enric Vázquez-Suñé. 2017. «The T-I-GER method: A graphical alternative to

support the design and management of shallow geothermal energy exploitations at the metropolitan scale». Renewable Energy 109 (agosto): 213–21. https://doi.org/10.1016/j.renene.2017.03.022.

ASHRAE. 2018. «Geothermal Heating and Cooling: Design of Ground-Source Heat Pump Systems». 27 giugno 2018. https://www.ashrae.org/technical-resources/bookstore/geothermal-heating-and-cooling-design-of-ground-source-heat-pump-systems.




49

«BayernAtlas - der Kartenviewer des Freistaates Bayern». s.d. Consultato 28 settembre 2018. https://geoportal.bayern.de/bayernatlas/?lang=de&topic=ba&bgLayer=atkis&catalogNodes=11,122.

Buitinck, Lars, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, et al. 2013. «API design for machine learning software: experiences from the scikit-learn project». In ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 108–122.

Bundesministerium für Nachhaltigkeit und Tourismus. s.d. Klima und Energies Fonds, KLI.EN-Fonds. 2018. Di Feo, Andrea Filippo. 2017. «Survey on ground source heat pumps in Aosta Valley». Politecnico di

Torin. E Vartiainen, G Masson, e C Breyer. 2015. «PV LCOE in Europe 2014-30». European Photovoltaic

Technology Platform: Munich, Germany. Edenhofer, O. 2012. Renewable Energy Sources and Climate Change Mitigation.

http://www.ipcc.ch/pdf/special-reports/srren/SRREN_Full_Report.pdf. Energy education. 2018. Discounting. http://energyeducation.ca/encyclopedia/Discounting. Garegnani, Giulia, Sandro Sacchelli, Jessica Balest, e Pietro Zambelli. 2018. «GIS-based approach for

assessing the energy potential and the financial feasibility of run-off-river hydro-power in Alpine valleys». Applied Energy 216 (aprile): 709–23. https://doi.org/10.1016/j.apenergy.2018.02.043.

GRASS Development Team. 2017. Geographic Resources Analysis Support System (GRASS GIS) Software, Version 7.2. Open Source Geospatial Foundation. http://grass.osgeo.org.

GRETA D3.1.1. 2018. «D.3.1.1 Catalogue of techniques and best practices for the utilization of Near-Surface Geothermal Energy». http://www.alpine-space.eu/projects/greta/deliverables/d3.1.1_catalogueoftechniques_update_i.pdf.

GRETA D3.2.1. 2017. «D3.2.1 Catalogue of operational criteria and constraints for shallow geothermal systems in the Alpine environment». http://www.alpine-space.eu/projects/greta/deliverables/d3.2.1_catalogue_operational_criteria.pdf.

GRETA D4.2.1. 2018. «D4.2.1 Local-scale maps of the NSGE potential in the Case Study areas». http://www.alpine-space.eu/projects/greta/deliverables/d.4.2.1_local-scale-maps-of-the-nsge-potential-in-the-case-study-areas.pdf.

GRETA D5.1.1. 2018. «D5.1.1 A spatial explicit assessment of the economic and financial feasibility of Near Surface Geothermal Energy». EURAC. http://www.alpine-space.eu/projects/greta/deliverables/d5.4_selection_of_pilot_areas_latest.pdf.

GRETA D5.3.1. 2018. «D5.3.1 Guidelines for policy and decision makers». EURAC. http://www.alpine-space.eu/projects/greta/deliverables/d5.4_selection_of_pilot_areas_latest.pdf.

GRETA D5.4.1. 2016. «D5.4.1 Selection of three pilot areas among the six case studies». http://www.alpine-space.eu/projects/greta/deliverables/d5.4_selection_of_pilot_areas_latest.pdf.

GRETA D6.2.1. 2018. «D6.2.1 Assessment of Stakeholders’ needs and opinions». 2018. http://www.alpine-space.eu/projects/greta/deliverables/d6.2.1_a-methodology-for-the-identification-of-the-stakeholders-needs-in-the-field-of-nsge.pdf.

GSE. 2016. Incentivazione della produzione di energia termica da impianti a fonti rinnovabili ed interventi di efficienza energetica di piccole dimensioni. https://www.gse.it/documenti_site/Documenti%20GSE/Servizi%20per%20te/CONTO%20TERMICO/REGOLE%20APPLICATIVE/REGOLE_APPLICATIVE_CT.pdf.




50

Hastik, Richard, Chris Walzer, Christin Haida, Giulia Garegnani, Simon Pezzutto, Bruno Abegg, e Clemens Geitner. 2016. «Using the “Footprint” Approach to Examine the Potentials and Impacts of Renewable Energy Sources in the European Alps». Mountain Research and Development 36 (2): 130–40. https://doi.org/10.1659/MRD-JOURNAL-D-15-00071.1.

Hofierka, J. 1997. «Direct solar radiation modelling within an open GIS environment». Proceedings of JEC-GI’97 conference. Vienna.

Hofierka, J, e M Suri. 2002. «The solar radiation model for Open source GIS: implementation and applications». presentato al International GRASS users, Trento, Italy, settembre.

«Home - www.soda-pro.com». s.d. Consultato 28 settembre 2018. http://www.soda-pro.com/home. ISTAT. 2011. «15° Censimento della popolazione e delle abitazioni 2011». 2011.

https://www.istat.it/it/censimenti-permanenti/censimenti-precedenti/popolazione-e-abitazioni/popolazione-2011.

Leo Breiman. 2001. «Random Forests». Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.

Lu, Qi, Guillermo A. Narsilio, Gregorius Riyan Aditya, e Ian W. Johnston. 2017. «Economic analysis of vertical ground source heat pump systems in Melbourne». Energy 125: 107–17. https://doi.org/10.1016/j.energy.2017.02.082.

Müller, Johannes, Antonio Galgaro, Giorgia Dalla Santa, Matteo Cultrera, Constantine Karytsas, Dimitrios Mendrinos, Sebastian Pera, et al. 2018. «Generalized Pan-European Geological Database for Shallow Geothermal Installations». Geosciences 8 (1): 32. https://doi.org/10.3390/geosciences8010032.

Neteler, M., M.H. Bowman, M. Landa, e M. Metz. 2012. «GRASS GIS: a multi-purpose Open Source GIS». Environmental Modelling & Software 31: 124–130. https://doi.org/10.1016/j.envsoft.2011.11.014.

«OpenStreetMap». s.d. OpenStreetMap. Consultato 28 settembre 2018. https://www.openstreetmap.org/.

Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. «Scikit-learn: Machine Learning in Python». Journal of Machine Learning Research 12: 2825–2830.

Piga, Bruno, Alessandro Casasso, Francesca Pace, Alberto Godio, Rajandrea Sethi, Bruno Piga, Alessandro Casasso, Francesca Pace, Alberto Godio, e Rajandrea Sethi. 2017. «Thermal Impact Assessment of Groundwater Heat Pumps (GWHPs): Rigorous vs. Simplified Models». Energies 10 (9): 1385. https://doi.org/10.3390/en10091385.

Regione Valle d’Aosta. 2012. «Piano Energetico Ambientale Regionale (PEAR)». Regione Valle d’Aosta. http://gestionewww.regione.vda.it/energia/pianificazione_energetica_regionale/piano_energetico_ambientale_regionale-pear_i.aspx.

Val d'Aosta. 2018. «Monitoraggio del PEAR». Regione Valle d’Aosta. http://gestionewww.regione.vda.it/energia/pianificazione_energetica_regionale/monitoraggio_pear_i.aspx.

«Renewables.ninja». s.d. Consultato 28 settembre 2018. https://www.renewables.ninja/. Rivoire, Matteo, Alessandro Casasso, Bruno Piga, Rajandrea Sethi, Matteo Rivoire, Alessandro Casasso,

Bruno Piga, e Rajandrea Sethi. 2018. «Assessment of Energetic, Economic and Environmental Performance of Ground-Coupled Heat Pumps». Energies 11 (8): 1941. https://doi.org/10.3390/en11081941.




51

Ross, S.A., R.W. Westerfield, e J. Jaffe. 2012. Corporate Finance. Sacchelli, S., G. Garegnani, F. Geri, G. Grilli, A. Paletto, P. Zambelli, M. Ciolli, e D. Vettorato. 2016. «Trade-

off between photovoltaic systems installation and agricultural practices on arable lands: An environmental and socio-economic impact analysis for Italy». Land Use Policy 56: 90–99. https://doi.org/10.1016/j.landusepol.2016.04.024.

Sacchelli, S, P Zambelli, P Zatelli, e M Ciolli. 2013. «Biomasfor: An Open-Source Holistic Model for the Assessment of Sustainable Forest Bioenergy». IForest - Biogeosciences and Forestry 6 (4): 285–93. https://doi.org/10.3832/ifor0897-006.

Sanner, Burkhard, Constantine Karytsas, Dimitrios Mendrinos, e Ladislaus Rybach. 2003. «Current status of ground source heat pumps and underground thermal energy storage in Europe». Geothermics, 579–588.

Somogyi, Viola, Viktor Sebestyén, e Georgina Nagy. 2017. «Scientific achievements and regulation of shallow geothermal systems in six European countries – A review». Renewable and Sustainable Energy Reviews 68 (febbraio): 934–52. https://doi.org/10.1016/j.rser.2016.02.014.

Stefan Pfenningera, e Iain Staffellb. 2016. «Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data - ScienceDirect». Energy, 1 novembre 2016.

Tsikaloudaki, Katerina, Kostas Laskos, e Dimitrios Bikas. 2012. «On the establishment of climatic zones in Europe with regard to the energy performance of buildings». Energies 5 (1): 32–44. https://doi.org/10.3390/en5010032.

U.S. Energy Information Administration. 2018. «Updated Buildings Sector Appliance and Equipment Costs and Efficiencies». https://www.eia.gov/analysis/studies/buildings/equipcosts/pdf/full.pdf.

Vettorato, Daniele, Davide Geneletti, e Pietro Zambelli. 2011. «Spatial comparison of renewable energy supply and energy demand for low-carbon settlements». Cities, Low Carbon Cities (45th ISOCARP World Congress Porto, Portugal 18-22 October 2009), 28 (6): 557–66. https://doi.org/10.1016/j.cities.2011.07.004.

Watkins, David E. 2011. Heating Services in Buildings. John Wiley & Sons. «Welcome to Python.Org». 2018. Python.Org. 27 giugno 2018. https://www.python.org/. Zambelli, Pietro, Sören Gebbert, Marco Ciolli, Pietro Zambelli, Sören Gebbert, e Marco Ciolli. 2013.

«Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS)». ISPRS International Journal of Geo-Information 2 (1): 201–19. https://doi.org/10.3390/ijgi2010201.

Zambelli, Pietro, Chiara Lora, Raffaele Spinelli, Clara Tattoni, Alfonso Vitti, Paolo Zatelli, e Marco Ciolli. 2012. «A GIS decision support system for regional forest management to assess biomass availability for renewable energy production». Environmental Modelling and Software 38: 203–13. https://doi.org/10.1016/j.envsoft.2012.05.016.

«Zensusdatenbank - Deutschland». s.d. Consultato 28 settembre 2018. https://ergebnisse.zensus2011.de/#StaticContent:00,,,.


Report on the test of the integration of NSGE into Energy ... · GRETA – D5.2.1 Report on the...

Documents

Transcript of Report on the test of the integration of NSGE into Energy ... · GRETA – D5.2.1 Report on the...