The role of district heating in achieving sustainable cities: comparative analysis of different heat scenarios for Geneva

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Energy Procedia Procedia 00 116(2017) (2017)000–000 78–90 Energy

The 15th International Symposium on District Heating andwww.elsevier.com/locate/procedia Cooling The 15th International Symposium on District Heating and Cooling

The role of district heating in achieving sustainable cities: comparative analysisheating of different heat scenarios for Geneva The role of district in achieving sustainable cities: The 15th International Symposium on District Heating and Cooling comparative analysis of different heat scenarios for Geneva Loïc Quiquereza, *, Bernard Lachala, Michel Monnardb, Jérôme Faesslera a Assessing thea,University feasibility of using the heat bdemand-outdoor of Geneva, 66 bvd Carl-Vogt, 1211Monnard Geneva, Switzerland Loïc Quiquerez *, Bernard Lachal , Michel , Jérôme Faesslera Industrial Services of Geneva, 2 ch. du Château-Bloch, 1219 Le Lignon, Switzerland temperature function for a long-term district heat demand forecast University of Geneva, 66 bvd Carl-Vogt, 1211 Geneva, Switzerland a

b

a

b

Industrial Services of Geneva, 2 ch. du Château-Bloch, 1219 Le Lignon, Switzerland

Abstract a

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

b Abstract Veolia Innovation, 291 Avenue Dreyfous Daniel, 78520 Franceboilers, contributing to an In many European cities, the heatRecherche demand & remains mostly supplied by individual fossilLimay, fuel-fired c Département Systèmesenergy Énergétiques et Environnement - IMT Atlantique, 4 ruefuel Alfredconsumption Kastler, 44300inNantes, France sector, an inefficient and non-sustainable system. In order to decrease the fossil the heating In many European cities, the heat and demand remains supplied individual fossil fuel-fired boilers, contributing to an improvement of energy efficiency an increase of mostly renewable energybyuse must be achieved. This study assesses and compares inefficient non-sustainable energyheat system. In order to decrease the fossil consumption the heating the impactsand of implementing different strategies regarding both demand and fuel supply side of the in heating system, sector, throughana improvement and an increase of renewable energyfor use2035 mustwere be achieved. This study and compares case-study in of theenergy city ofefficiency Geneva, Switzerland. Different heat scenarios developed, based onassesses an input/output hourly Abstract the impacts of model implementing different strategies regarding both demand and supply sideThis of the heating system,with through a energy system which ensures theheat matching between heat demand and energy resources. model is coupled spatial case-study in the to cityidentify of Geneva, Switzerland. Different heat scenarios 2035 were developed, based an input/output hourly data that enable the areas where district heating could be for developed. The results show theonimpacts of the different Districtsystem heating networks are commonly in theheat literature asand one of theresources. most effective solutions fordemonstrate decreasing the energy model which ensures the matching between demand energy This The model is coupled with spatial strategies regarding the energy supply, theaddressed CO 2 emissions and the related socio-economic costs. findings the greenhouse emissions from the building sector. These systems require highheat investments which are through the due heat data that enable to identify thenetworks, areas where district could be developed. The resultsthat show the returned impacts different importance ofgas district heating which offer heating the possibility to use local sources otherwise wouldof bethe unused sales. Due tospatial the changed climate conditions and building policies, heat demand in the future couldsavings decrease, strategies regarding the supply, the CO 2 Compared emissions and the relatedessentially socio-economic costs. findings demonstrate the to technical, or energy economic constraints. to arenovation scenario focused on The very high energy in prolonging the investment return period. importance district heating networks, which offer the possibility to use local sourcesreduction that otherwise be unused buildings, aofmore flexible scenario, combining district heating expansion andheat a smaller in heatwould demand, enablesdue to The main of this to assess the feasibility of using the heat demand – outdoor temperature function for heat demand to technical, spatial or paper economic constraints. Compared to a2 emissions, scenario essentially focused on very high energy savings in achieve thescope same reductions inisfossil fuel consumption and CO but with lower socio-economic costs. forecast. The district of Alvalade, located in Lisbon (Portugal), was used case study. The indistrict is consisted of 665 buildings, a more flexible scenario, combining district heating expansion andasa asmaller reduction heat demand, enables to buildings that vary in bothinconstruction period and typology. Three weather scenarios (low, medium, high) achieve reductions fuel consumption and CO 2 emissions, but with lower socio-economic costs. and three district © 2017 the Thesame Authors. Publishedfossil by Elsevier Ltd. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and ©compared 2017 The Authors. Published by Elsevier results from a dynamic heatLtd. demand model, previously developed and validated by the authors. © 2017 Thewith Authors. Published by Elsevier Ltd. Cooling. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium District Heating and Cooling. The results under showed that when only weather change is considered, of error could be on acceptable some applications Peer-review responsibility of the Scientific Committee of The the 15thmargin International Symposium on DistrictforHeating and (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Cooling. Keywords: District heating; renewable energy; energy modeling; heat scenarios scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The valueDistrict of slope coefficient on average within the range of 3.8% up to 8% per decade, that corresponds to the Keywords: heating; renewableincreased energy; energy modeling; heat scenarios decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

Corresponding author.Published Tel.: +41 22 06 62. Ltd. ©* 2017 The Authors. by379 Elsevier E-mail address: Peer-review [email protected] responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +41 22 379 06 62. Cooling.

E-mail address: [email protected] 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 10.1016/j.egypro.2017.05.057

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1. Introduction In accordance with the IPCC recommendations [1], the core of the Swiss government sustainability strategy is based on the concept of 1 ton of emitted CO 2 per inhabitant by the end of the 21st century [2]. In Geneva, 482,500 inhabitants in 2014, the present CO 2 emissions related to the energy sector represents 4.2 tCO 2 per capita, of which 2.2 emitted by the heating sector, 1.1 by the transport sector (not including the airport) and 0.8 by the electricity sector (considering the CO 2 content of the Swiss electricity consumption mix in 2014: 139 gCO 2 /kWh) [3-4]. Consequently the main CO 2 emissions reduction potential lies in the heating sector, which represents about half of the final energy consumption in the city. In 2014, the energy consumed by the heating sector in Geneva amounts to 5,444 GWh or 40.6 GJ/capita [3] and it is mainly based on fossil fuels (Fig. 1). The energy targets of the state of Geneva for 2035 are to reduce this consumption to 29.0 GJ/capita, from which only 19 GJ/capita would be supplied by fossil fuels [5]. Considering an expected population of 557,000 inhabitants in 2035 [6], the use of renewable energy in the heating sector should increase from the current 362 GWh to 1,543 GWh, while the use of fossil fuels should decrease from 5,072 to 2,945 GWh.

Fig. 1. Energy flow chart of the heating sector in 2014, unit: GWh/y.

Although it has now been demonstrated that district heating (DH) could play an essential role in order to decarbonise the European energy system [7-8], its share in the heat market is still marginal in Switzerland (4-5%) [9] and in the city of Geneva (9-10%) [3]. In this context, fundamental questions were addressed through the project REMUER [10]: What is the role of district heating in order to achieve the energy targets? Is there a synergy or a competition between the development of DH on the one hand and the investments in buildings’ energy renovation on the other hand? How could be designed the heating system in 2035? How could it fit into the overall energy system?

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2. Renewable energy availability and district heating expansion potential Local renewable energy resources available for the heating sector have been estimated in Geneva and would represent about 5,500 GWh/y [11] (not including the heat from the outdoor air which is difficult to quantify). The main resources identified are geothermal energy (1,000 GWh/y) and the thermal energy of the lake (4,000 GWh/y). However, some of these local resources can’t be used without district heating for two main reasons: (i) the spatial distance between heat source and heat demand; (ii) the need to share investment costs on a significant number of consumers. In Geneva, the potential for DH expansion is huge, since today it represents only 10% of the heat market while most of the heat demand is located in dense urban areas (Fig. 2). A spatial analysis of the heat demand density indicates that 70% and 80% of the heat consumption is located in urban areas where the heat density is respectively greater than 250 and 500 MWh/hectare. In such areas, the distribution cost of DH should remain affordable to guarantee its competitiveness when supplied by low cost resources [12].

Fig. 2. Heat demand density in 2014.

3. Methodology In order to assess the role of district heating in the future energy system, different scenarios were developed in collaboration with the local energy utility company. Different variables were identified in order to make projections for 2035 regarding the heat demand evolution and the implementation of different energy technologies. In order to compare the scenarios, an input-output model designed for the modeling of energy system at the city scale was developed, inspired from the EnergyPLAN model [13]. The model is based on an hourly time-step which ensures the matching between resources availability, production capacities and the fluctuating heat demand. The load curve of the main DH system is used to characterize the heat demand dynamic [14]. This load curve is then adapted to take

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into account energy savings (reductions applied solely on space heating demand). Different types of DH systems can be modeled. The main model outputs are energy balance, costs and CO 2 emissions. 3.1. Designing the scenarios for 2035 Four scenarios were developed. The business-as-usual scenario (BAU) extends the trends observed in the recent period. The EE&RES scenario is based on both the enhancement of energy savings into buildings and the integration of more renewable energy through the development of individual heat pumps, individual solar installations and district heating systems. Compared to the EE&RES scenario, the EE+ scenario is more focused on heat demand reduction and less on renewable energy integration, whereas this is the opposite logic in the RES+ scenario (Fig. 3).

Fig. 3. Conceptual positioning framework of the different scenarios.

3.2. Future heat demand In each scenario, two important variables that partly determine the future heat demand are identical: (i) the population growth: 557,000 inhabitants in 2035; (ii) and the decreasing heating degree-days (-13 HDD per year) as observed since 1960 [15]. Considering new buildings, it is assumed that 71m2 of heated floor area (HFA) will be constructed per additional inhabitant, with a specific heat demand of 180 MJ/ m2/y. The estimation of the heat demand reduction in existing buildings is carried out from a buildings database which contains the current annual heat consumption as well as the heated floor area of about 70% of the total heated building stock [16], completed by extrapolations for the remaining buildings [17-18]. On the basis of these data, gross energy savings potentials are estimated, assuming that the specific heat demand of all buildings consuming more than 300 MJ/m2/y is reduced to a target value defined differently in the four scenarios (Fig. 4 and Table 1). The effective amount of saved energy subsequently depends on the refurbishment rate, which is defined here as the percentage of the annual gross energy savings potential to be achieved each year.

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Heat demand per m2 of heated floor area [MJ/m2/y]

1000

Heat demand after retrofit

900

5

Current heat demand

800 700 600 500 400

Energy savings potential

300 200 100 0 0

10

20 30 Heated floor area [million m 2 ]

40

Fig. 4. Gross heat savings potential, scenario EE&RES. Table 1. Renovation rate, specific heat demand after renovation and heat savings in 2035 compared to 2014. Scenarios

Renovation rate

Specific heat demand after renovation

Heat savings (2035 vs 2014)

2035 BAU

1.2%

350 MJ/m2/y

205 GWh/y

2

2035 EE&RES

2%

250 MJ/m /y

663 GWh/y

2035 EE+

2.5%

200 MJ/m2/y

1,072 GWh/y

2035 RE+

2

1.5%

300 MJ/m /y

351 GWh/y

In the EE&RES scenario, the heat demand reduction through energy efficiency measures in the existing buildings amounts to 663 GWh/y compared to 2014 (Table 1). This saved energy represents a 17% decrease in the total heat consumption of the existing buildings that currently have a specific heat demand greater than 300 MJ/m2/y. This reduction is more important in the EE+ scenario (-28%), and lower in the BAU and RES+ scenarios (respectively -5 and -9%). 3.3. Future energy infrastructures The buildings heat supply in the four scenarios is described in Table 2. The energy system in the BAU scenario doesn’t differ much from the current system which is mainly based on individual fossil fuel fired-boilers. In the EE&RES and RES+ scenarios, district heating is extended, supplying respectively 30 and 40% of the heat demand. The market share of individual heat pumps (HP) is also respectively increased to 20 and 25%. In the EE+ scenario, renewable energy technologies are slightly less developed. Table 2. Buildings heat supply in each scenario (share of the total heated floor area). District heating

Individual heat pumps

Individual fossilfired boilers

Individual biomassfired boilers

2014

10%

1%

88%

1%

2035 BAU

14%

5%

80%

1%

2035 EE&RES

30%

20%

48%

2%

2035 EE+

16%

15%

68%

1%

2035 RE+

40%

25%

33%

2%

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It is obvious that the development of district heating is relevant when it allows recovering renewable and waste heat. The DH production, transport and storage infrastructures are described in the Table 3. In scenarios with DH expansion, the main share of base loads is supplied by waste heat, medium/deep geothermal heat and large scale heat pumps. As an important development of heat pumps (individual and centralized) requires a certain amount of electricity, especially in winter, gas-fired cogeneration plants (CHP) are also implemented in the EE&RES and RES+ scenarios. Eventually, large scale seasonal storage capacities are also integrated in those scenarios with the aim to shift a part of remaining excess heat wasted in summer. It should be noticed that gas-fired boilers are sized for peak-loads and backup. The total network lengths in the different scenarios are estimated by using a simplified method [19] which is based on the ratio between the current DH length and the road network length in the current DH areas. In average, this ratio is 0.9. By knowing the total road network length, this value is used for extrapolations. Individual solar energy production is considered in every scenario. Hourly distributions of solar PV and solar thermal production for domestic hot water come from actual measurements [20-21]. The PV potential identified in Geneva represents 550-650 MW [22-23]. In each scenario, the PV capacities installed are 400 MW. The hourly comparison between PV production and electricity consumption of HP enables to determine the share of the PV production that can effectively be used in the heating sector. The remaining PV production is logically not considered as an energy input for the heating sector. Regarding solar thermal, only systems supplying domestic hot water are considered in this study. The potential identified represents around 200 GWh/y [22-23]. In the BAU scenario, the total solar thermal panels’ area is multiplied by 5 compared to 2014 and represents 0.35 m2 per inhabitants. This increase is doubled in the other scenarios (0.7 m2/capita). The energy efficiencies assumed for the different energy infrastructures mentioned are indicated in the Table 4. Regarding the heat transport, a small increase of distribution losses is taken into account, which is explained by a decreasing linear heat density (ratio between the heat delivered and the length of pipes) compared to 2014. Table 3. District heating infrastructures (production, storage and transport) in each scenarios. Waste heat (MW th )

Geothermal (MW th )

Heat pumps (MW th )

Gas-fired CHP plant (MW th )

Seasonal heat storage (GWh)

Biomassfired boilers (MW th )

Gas-fired boilers (MW th )

Network length (km)

2014

45

-

0.3

-

-

3

170

62

2035 BAU

45

10

18

-

-

8

200

84

2035 EE&RES

45

48

55

40

25

8

390

180

2035 EE+

45

10

20

-

-

8

180

97

2035 RE+

55

68

67

65

25

8

550

246

Table 4. Annual production, storage and transport efficiencies. Units

2014

2035

Gas-fired boilers

η%

90

92

Oil-fired boilers

η%

83

85

Biomass-fired boilers

η%

80

85

Gas-fired CHP plant, heat recovery

η%

-

45

Gas-fired CHP plant, electricity production

η%

-

40

Heat pumps

COP

3.3

3.5

Seasonal heat storage

η%

-

80

Heat transport in DH

η%

90

88

2

Solar thermal productivity

kWh/m /y

500

500

Solar PV productivity

kWh/m2/y

160

200

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3.4. Quantifying the total economic costs Data about investment costs related to production, storage and transport infrastructures were gathered from the main energy utility company. They are compiled in the Table 5 [24]. The specific costs are related to average size infrastructures. In order to estimate the cost of buildings’ energy renovation, a cost curve was developed based on data from [25]. This curve enables to take into account the fact that energy efficiency measures become more expensive as larger savings are achieved (Fig. 5). All the investment costs were annualized with an assumed 2.5% interest rate. Of course, predicting the future energy prices is very difficult. Based on some hypothesis elaborated in collaboration with the main utility company, prices in 2035 have been defined. They are presented in the Table 6 [24]. Table 5. Investments and O&M costs assumed in 2035 [24]. Investments (USD million per unit)

Life time (year)

O&M (% of investments)

Medium-Deep geothermal

MW th

2

30

3.5%

DH fossil-fired boilers

MW th

0.25

25

1.5%

DH biomass-fired boilers

MW th

0.5

25

1.8%

DH heat pumps

MW th

2

30

1.0%

DH seasonal heat storage

GWh

4

30

0.1%

DH network

km

2.5

40

0.1%

DH substations

MW th

0.2

30

0.2%

CHP plants

MW el

2

15

5.0%

Individual PV

MW el

1

25

1.0%

Individual solar thermal

m2∙103 (panels’ area)

1.2

20

1.5%

Individual fossil-fired boilers

MW th

0.3

20

2.5%

Individual biomass-fired boilers

MW th

0.7

20

2.5%

Individual heat pumps

MW th

2.5

20

1.0%

Buildings’ energy renovation

m2 (heated floor area)

0.2 (BAU); 0.4 (EE&RES); 0.6 (EE+); 0.27 (RES+)

40

0%

Renovation cost [USD/m2HFA]

Units

1000 800 600 400 200 0 0%

10%

20%

30%

40%

Energy savings [%]

50%

60%

Fig. 5. Cost curve of buildings’ energy renovation based on [25].

70%

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Table 6. Energy and CO 2 prices assumed in 2035 [24]. Units

USD per unit

Gas

MWh

110

Fuel oil

MWh

110

Electricity

MWh

200

Biomass

MWh

90

Waste heat (from waste)

MWh

35

CO 2 tax

tons

160

4. Results 4.1. Energy balance and CO 2 emissions

Buildings heat supply [GWh/y]

The main differences in the four scenarios are the level of energy efficiency measures and the level of renewable energy integration. As it is shown in the Fig. 6, the total heat demand in the four scenarios decreases between 6% (BAU) and 24% (EE+) compared to 2014, although the population is more important (+15%). As district heating is extended in the EE&RES and RES+ scenarios, more renewable energy can be integrated, especially through geothermal plants and large scale heat pumps (Fig. 7 and 8).

5000

Solar thermal District heating

Biomass boilers Oil boilers

Heat pumps Gas boilers

4000 3000 2000 1000 0

2014

2035 BAU 2035 EE&RES

2035 EE+

2035 RES+

Fig. 6. Buildings heat supply (useful energy).

District heating supply [GWh/y]

2000

Waste heat (HT)

Geothermal (HT)

CHP gas units

Heat pumps

Biomass boilers

Gas boilers

1500 1000 500 0

2014

2035 BAU

2035 EE&RES

2035 EE+

Fig. 7. Annual district heating heat supply.

2035 RES+

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District heating supply [MW]

86

Gas boilers CHP gas units

450 400 350 300 250 200 150 100 50 0 1

1001

2001

Biomass boilers Geothermal (HT)

3001

4001

Hours

5001

9

Heat pumps Waste heat (HT)

6001

7001

8001

Fig. 8. District heating load curve (scenario EE&RES).

Electricity production (CHP&PV) and consumption (HP) [GWh/y]

In the EE&RES and RES+ scenarios, the electricity consumed by heat pumps is partly offset by the production of CHP units, which simultaneously provide heat to the DH system. Although the annual PV production is relatively high (380 GWh), only a small share of this renewable electricity production (8-13%) can effectively be used for heat pumps due to the temporal mismatch between heat demand and solar resource (Fig. 9). This is the reason why the additional electricity required in the scenario EE+ is more important, especially in winter, even though the electricity consumed by heat pumps is lower.

800

PV unused by HP PV used by HP Electricity consumed by HP

Additional electricity Electricity from CHP used by HP

700 600 500 400 300 200 100 0

2014

2035 BAU

2035 EE&RES

2035 EE+

2035 RES+

Fig. 9. Electricity balance between PV, CHP and HP.

The Fig. 10 illustrates the annual energy flows in the modeled heating system (scenario EE&RES). Aside from the drop in heat demand and the increase in use of renewable energy, such a system is characterized by more interactions between the different urban energy networks (gas, electricity and district heating) in comparison to the current heating system (Fig. 1).

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10

87

Fig. 10. Energy flow chart of the heating sector in 2035 (scenario EE&RES), unit: GWh/y.

6000

Additional electricity Waste heat (LT) Geothermal (HT) Biomass

PV used by HP Waste heat (HT) Air Gas (for CHP)

Solar thermal Geothermal (LT) Lake&River Gas

1200

5000

1000

4000

800

3000

600

2000

400

1000

200

0

0

2014

2035 BAU 2035 EE&RES

2035 EE+

2035 RES+

Fig. 11. Energy inputs consumed by the heating sector and CO 2 emissions.

CO2 emissions [kTon/y]

Energy inputs consumed by the heating sector [GWh/y]

In terms of fossil fuel consumption and CO 2 emissions, the three scenarios EE&RES, EE+ and RES+ are quite similar. As it is displayed in the Fig. 11, the total fossil fuel consumption (gas+oil) in these scenarios decreases between -45% (EE+) and -49% (RES+) compared to 2014. Such reductions enable achieving the fossil fuel reduction target for 2035. By contrast, the BAU scenario does not allow it (Fig. 12).

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Additional electricity Fossil fuels Obj2035: Total energy

50

Renewable energy CO2 Obj2035: Fossil fuels

11

2.5

40

2.0

30

1.5

20

1.0

10

0.5 0.0

0

2035 BAU 2035 EE&RES

2014

2035 EE+

CO2 emissions [Ton/capita/y]

Energy inputs consumed by the heating sector [GJ/capita/y]

88

2035 RES+

Fig. 12. Energy inputs consumed by the heating sector and CO 2 emissions per capita (population: 482’000 in 2014 and 557’000 in 2035).

The total energy consumption reduction target in the heating sector is only achieved in the EE&RES and EE+ scenarios. However, the BAU and RES+ scenarios are close to it: respectively +2.9 and +1.6 GJ/capita compared to the target value. It should be noticed that the additional electricity required (total electricity consumed by HP minus the PV and CHP production used by HP) is not converted into primary energy in this analysis, but that its CO 2 content is integrated (the CO 2 content of the Swiss electricity consumption mix in 2014 is used [4]). In the EE+, RES+ and EE&RES scenarios, CO 2 emissions in the heating sector are lowered from the current 2.2 tCO 2 /capita to about 1 tCO 2 /capita (Fig. 12). 4.2. Socio-economic costs On the basis of the assumptions made regarding investment costs and energy prices, the results indicates that the annual costs in the EE+ scenario are higher than those in both the RES+ and EE&RES scenarios (respectively +31% and +18%) (Fig. 13). The three scenarios have relatively similar CO 2 and fuels costs, but the investment costs in the EE+ scenario are more important due to the deep retrofitting of a significant share of the buildings stock.

Total annual costs [USD millions / year]

1200

O&M CO2 Fuels Investments in heat and power production (CHP&PV) Investments in district heating (production, transport and storage) Investments in individual heat production Investments in building retrofits

1000 800 600 400 200 0

2035 BAU

2035 EE&RES

2035 EE+

Fig. 13. Total annual costs for the heating sector in 2035.

2035 RES+

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The annual costs in the RES+ and EE&RES scenarios are quite similar to the BAU scenario ones. The main difference is their structure. As district heating and renewable production units are more developed in the RES+ and EE&RES scenarios, the fixed costs of the heating system in these scenarios are more important. This increase in investments costs, however, is offset by the fuel and CO 2 cost reduction. Such a cost structure offers a double socioeconomic benefit: reducing capital flight on the one hand, and creating local jobs on the other hand. Of course, the annual cost difference between the BAU scenario and the other three is closely linked to the energy and CO 2 prices assumed. 5. Discussion The results show the importance of the CO 2 emission savings potential in the heating sector. By saving 1.2-1.3 tCO 2 /capita (scenarios EE+, RES+ and EE&RES), the current CO 2 emissions of the overall energy sector (4.2 tCO 2 /capita, airport not included) could be decreased by 30% in 2035. With expected improvements in the other sectors (electricity and transport), higher savings should be reached. Regarding the economic analysis, it should be noticed that the costs of the buildings’ energy renovation are difficult to estimate. In this study, they only refer to measures that increase the energy buildings’ efficiency. However, they involve some co-benefits that are difficult to quantify, such as additional comfort, increased building’s value or indirect contribution to unavoidable building maintenance. Although these co-benefits are not taken into account, it should be mentioned that the costs used in this study are relatively low with respect to a benchmark performed on a sample of energy renovated buildings in Geneva [18]. Besides being more expensive, the EE+ scenario will probably be more difficult to implement. First, deep energy renovations require the buildings’ owners to have high investment capacities. Secondly, from a technical point of view, some studies demonstrated that the actual energy savings are often significantly lower than the energy standards and the expected calculated values [18]. Last but not least, from a certain point, deep renovations may become a problem regarding the conservation of the architectural heritage [26]. This issue could also increase the costs of the buildings’ energy renovation. In contrast, the RES+ and EE&RES scenarios, besides being cheaper than the EE+ scenario, seem easier to implement. In comparison with the EE+ scenario, which is very dependent on the implementation of significant heat savings, the RES+ and EE&RES scenarios offer more flexibility by leaving more choices to each particular situation. In addition, the fact that renewable energy integration in the heating sector will partly be based on the development of heat pumps means the use of a certain amount of electricity, especially in winter. To ensure that heat pumps help to reduce CO 2 emissions at the global level, the development of CHP units seems necessary. In this regard, it should be kept in mind that in Europe, the current marginal electricity production in winter is still essentially based on fossil fuels thermal plants. The development of CHP is all the more important because the Swiss parliament decided to ban nuclear energy. This development is closely linked to the extension of DH. Moreover, DH allows to link CHP and HP with thermal storage facilities. This integration increase the flexibility of the overall energy system, which will be a key point for the massive integration of fluctuating renewable electricity such as wind power (not considered in this study because its regional potential is low) [27]. Another important point in the scenarios developed is the fact that, even if its use will be lowered by 2035, natural gas will still play an important role in the heating system. However, whereas it is currently essentially used as fuel for individual boilers, it will be used in a more efficient and valuable way by supplying further both CHP and peak-load boilers. Lastly, it should be noted that part of the low temperature waste heat used by heat pumps (individual or centralized) in the different scenarios may come from district cooling systems. In this study, the energetic impacts on the cooling sector (reduction of the electricity consumption) have not been taken into account, as well as the cost related to district cooling networks.

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6. Conclusions The aim of this study was to assess different strategies regarding the future heating system and its integration into the overall energy system. The findings demonstrate the importance of district heating networks, which offer the possibility to use local heat sources that otherwise would be unused due to technical, spatial or economic constraints. Compared to a scenario essentially focused on very high energy savings in buildings, a more flexible scenario, combining district heating expansion and a smaller reduction in heat demand, enables to achieve the same reductions in fossil fuel consumption and CO 2 emissions, but with lower socio-economic costs. Therefore the key messages are: (i) energy strategies should consider both demand and supply side of the energy system, (ii) district heating should be considered as an essential infrastructure for achieving sustainable cities. References [1] IPCC. Climate change 2007: Synthesis Report. Geneva: IPCC; 2007. [2] OFEV (Office fédéral de l’environnement). Ordonnance sur la réduction des émissions de CO2, rapport explicatif. Berne; 2012. [3] OCSTAT (Office cantonal de la statistique). Statistiques du canton de Genève. Genève; 2015. [4] OFCL (Office fédéral des constructions et de la logistique). Données des écobilans dans la construction. Berne; 2014. [5] Etat de Genève. Conception générale de l’énergie. Genève; 2013. [6] OCSTAT (Office cantonal de la statistique). Projections démographiques pour le canton de Genève de 2010 à 2040. Genève; 2011. [7] Lund H, Möller B, Mathiesen BV, Dyrelund A. The role of district heating in future renewable energy systems. Energy 2010; 35: 1381-1390. [8] Connolly D, Lund H, Mathiesen BV, Werner S, Möller B, Persson U, Boermans T, Trier D, Østergaard PA, Nielsen S. Heat Road Map Europe: Combining district heating with heat savings to decarbonize the EU energy system. Energy policy 2014; 65: 475-489. [9] OFEN (Office fédéral de l’énergie). Statistiques suisses de l’énergie. Berne; 2015. [10] Faessler J, Hollmuller P, Lachal B, Quiquerez L, Monnard M, Garazi G, Cabrera D, Fraga C, Khoury J, Mermoud F. Projet REMUER : Réseaux thermiques multi-ressources, efficients et renouvelables. Genève; 2012. [11] Faessler J. Valorisation intensive des énergies renouvelables dans l’agglomération franco-valdo-genevoise dans une perspective de société 2000W [PhD thesis]. Genève; 2012. [12] Persson U, Werner S. Heat distribution and the future competitiveness of district heating. Applied Energy 2010; 88: 568-576. [13] Lund H. EnergyPLAN: advanced energy systems analysis computer model. Aalborg; 2008. [14] Quiquerez L, Faessler J, Lachal B. Réseaux thermiques multi-ressources, efficients et renouvelables : Etude de cas de la connexion des réseaux thermiques Cadiom et Cadsig à Genève. Genève: collection Terre&Environnement; 2015. [15] MétéoSuisse (Office fédéral de météorologie et de climatologie). Indicateurs de climat. Payerne; 2015. [16] SITG (Système d’information du territoire à Genève. Geodata portail. Genève; 2015. [17] Schneider S, Khoury J, Lachal B, Hollmuller P. Geo-dependent heat demand model of the swiss building stock. Sustainable built environment regional conference, Zurich; 2016. [18] Khoury J. Rénovation énergétique des bâtiments résidentiels collectifs : Etat des lieux, retour d’expérience et potentiels du parc genevois [PhD thesis]. Genève; 2014. [19] CEREMA. Réseaux de chaleur et territoires, relation en linéaire de réseaux de chaleur et linéaire de voirie. Website. URL: http://reseauxchaleur.cerema.fr/relation-entre-lineaire-de-reseau-de-chaleur-et-lineaire-de-voirie. Accessed on 28th January 2015. [20] Ineichen P. Solar radiation resource in Geneva, measurements, modeling, data quality control, format and accessibility. Genève; 2013. [21] Mermoud F, Khoury J, Lachal B. Suivi énergétique du bâtiment 40-42 de l’avenue du Gros-Chêne à Onex (GE), rénové selon le standard MINERGIE. Genève: collection Terre&Environnement; 2012. [22] Quiquerez L, Faessler J, Lachal B, Mermoud F, Hollmuller P. GIS methodology and case study regarding the assessment of the solar potential at territorial level: PV or thermal? IJSEPM 2015; 6: 3-16. [23] Desthieux G, Gallinelli P, Camponovo R. Cadastre solaire du canton de Genève: analyse du potentiel de production énergétique par les panneaux solaires thermiques et PV. Genève; 2014. [24] Quiquerez L, Lachal B, Monnard M, Faessler J. Evaluation quantitative de scenarios de développement du marché de la chaleur à Genève à l’horizon 2035 : quel rôle pour les réseaux de chaleur ? Genève; 2016. [25] Banfi S, Farsi M, Jakob M. An analysis of investment decisions for energy-efficient renovation of multi-family buildings. Zurich; 2012. [26] Tschopp J. Economies d’énergie et conservation du patrimoine, deux intérêts divergents ? Etude de cas de la cité du Lignon [master thesis]. Genève; 2012. [27] Lund H. Large-scale integration of wind power into different energy systems. Energy 2004; 30: 2402-2412.