Article
Reference
Simulation and comparative assessment of heating systems with tank thermal energy storage – A Swiss case study
NARULA, Kapil, et al.
Abstract
Heating consumes almost 50% of the global final energy consumption but only 10% of the heat supply is from renewable energy sources (RES). Rooftop solar collectors generate low temperature heat, which can be stored as sensible heat over many months. This can lower the use of oil boilers in winters, thereby playing an important part in decarbonisation. This paper examines the feasibility of using tank thermal energy storage (TTES) for decarbonising heating. It simulates hourly energy flows and compares different heating systems in 50 and 200 dwellings in multi-family households at Geneva, Switzerland. Hourly energy flows for four different heating systems configrations, viz. oil boiler, solar collector with TTES, solar collector with TTES & heat pump, and a system having only a centralised air-water heat pump are simulated. Various performance indicators such as levelised cost of heat, percentage share of RES, peak electricity load and cost of decarbonisation are evaluated. Sensitivity of perfomance indicators to head demand, heat supply and heating network temperature is examined. Results show that a heating system having [...]
NARULA, Kapil, et al . Simulation and comparative assessment of heating systems with tank thermal energy storage – A Swiss case study. Journal of Energy Storage , 2020, vol. 32, no.
101810
DOI : 10.1016/j.est.2020.101810
Available at:
http://archive-ouverte.unige.ch/unige:143276
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Contents lists available at ScienceDirect
Journal of Energy Storage
journal homepage: www.elsevier.com/locate/est
Simulation and comparative assessment of heating systems with tank thermal energy storage – A Swiss case study
Kapil Narula
a,c,⁎, Fleury De Oliveira Filho
b,c, Jonathan Chambers
a,c, Martin K. Patel
a,ca Energy Efficiency Group
b Energy Systems Group
c Department F.A. Forel for Environmental and Aquatic Sciences (DEFSE), Institute for Environmental Sciences (ISE), Faculty of Science, University of Geneva, Switzerland
A R T I C L E I N F O Keywords:
Heating systems
Tank thermal energy storage (TTES) Decarbonisation
Simulation Switzerland
A B S T R A C T
Heating consumes almost 50% of the global final energy consumption but only 10% of the heat supply is from renewable energy sources (RES). Rooftop solar collectors generate low temperature heat, which can be stored as sensible heat over many months. This can lower the use of oil boilers in winters, thereby playing an important part in decarbonisation. This paper examines the feasibility of using tank thermal energy storage (TTES) for decarbonising heating. It simulates hourly energy flows and compares different heating systems in 50 and 200 dwellings in multi-family households at Geneva, Switzerland. Hourly energy flows for four different heating systems configrations, viz. oil boiler, solar collector with TTES, solar collector with TTES & heat pump, and a system having only a centralised air-water heat pump are simulated. Various performance indicators such as levelised cost of heat, percentage share of RES, peak electricity load and cost of decarbonisation are evaluated.
Sensitivity of perfomance indicators to head demand, heat supply and heating network temperature is examined.
Results show that a heating system having only a centralised air-water heat pump is the best option, but CO2
emissions cannot be eliminated. On the other hand, in case of low heat demand (due to building renovation and lower heating network temperature in future), use of TTES with solar collectors could lead to a higher share of RES. Further, the peak electricity load is much lower and heating can be completely decarbonised. Thus, TTES has an important role to play in decarbonisation of heating, albeit at a higher cost.
1. Introduction
Globally, almost 50% of final energy was consumed as heat in in- dustry (process heat, hot water and drying) and buildings (space heating (SH), domestic hot water (DHW) and cooking). The source of heat was predominantly fossil fuels and in 2017, only 10% of the heat was supplied by renewable energy sources (RES) [1]. As heating is largely dependent on fossil fuels, it leads to large greenhouse gas (GHG) emissions. It is estimated that globally heating (and cooling) con- tributes to about 40 percent of the energy related CO2 emissions [2].
The case of Switzerland is similar and in 2017, SH demand from buildings was 240 PJ (31% of the final energy demand), DHW demand was 46 PJ (6%) and process heat demand was 95 PJ (12%) [3]. 40% of
heat was supplied by oil boilers; 29% by gas boilers; district heating (DH) and heat pump (HP) together supplied 22%; and 9% heat was supplied by electric heaters and other RES [4]. It is estimated that heat consumption in buildings contributed to about 17 million tonnes CO2 in 2016 but if the Swiss nationally determined contribution (NDC) and the goals of the Swiss energy strategy 2050 have to be met, the country's aggregate CO2 emissions should be 8–16 million tonnes in 2050 [5].
These targets imply that the Swiss heating supply needs to be rapidly decarbonised.
The intermittency of solar heat and the daily/seasonal mismatch between heat supply and heat demand is a challenge for increasing the share of RES in heating. Thermal energy storage (TES) can be used to store heat, which lowers the use of boilers for heating and decreases
https://doi.org/10.1016/j.est.2020.101810
Received 20 May 2020; Received in revised form 10 July 2020; Accepted 22 August 2020
Abbreviations: ATES, Aquifer thermal energy storage; BTES, Borehole thermal energy storage; CCHP, Combined cooling, heating and power; CHP, Combined heat and power; CECB, Cantonal Energy Certificate for Buildings; CRF, Capital recovery factor; DH, District heating; DHW, Domestic hot water; EF, Emission factor; ETC, Evacuated tube collector; GHG, Greenhouse gas; HE, Heat exchanger; HP, Heat pump; IEA, International Energy Agency; MFH, Multi-family household; NDC, Nationally determined contribution; O&M, Operation and maintenance; PCM, Phase change material; PES, Primary energy supply; PTES, Pit thermal energy storage;
PV, Photovoltaic; RES, Renewable energy source; SC, Solar collector; SCOP, Seasonal coefficient of performance; SH, Space heating; TES, Thermal energy storage;
TTES, Tank thermal energy storage; WGTES, Water gravel thermal energy storage; WE, Water equivalent
⁎Corresponding author.
E-mail address: [email protected] (K. Narula).
2352-152X/ © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
T
Nomenclature
ACdec Annualised cost of decarbonisation [CHF/ton CO2] ACel.HP Annual cost of electricity for HP [CHF/yr]
ACoil Annual cost of oil [CHF/yr]
ACsys Total annualised cost of heating system [CHF/yr]
CA Area of SC [m2]
CCeqpt Specific capital cost of equipment [CHF/unit]
Co&m O&M cost factor [%]
Cy Annual SC yield [Wh/m2]
dt, ds,db Thickness of insulation of TTES (top, side and bottom, respectively) [m]
EFel Emission factor of electricity [g CO2-eq/kWh]
EFoil Emission factor of oil [kg CO2/kg]
Ein.boil Primary energy input to oil boiler [Wh]
Ein.el Primary energy input to HP [Wh]
EMsys Total CO2 emissions from heating system [tons CO2] H Height of TTES [m]
LCOH: Levelised cost of heat [CHF/MWh]
n Lifetime of equipment [yr]
N Number of dwellings [nos.]
O&Meqpt Annual fixed operation and maintenance cost of equip- ment [CHF/yr]
Pel.type Price of specific type of electricity [CHF/kWh]
Poil Average price of extra light fuel oil [CHF/kWh]
Qamb Heat extracted from ambient air [Wh]
Qboil Heat supplied by boiler [Wh]
Qdem Total annual heating demand [Wh]
QDH.loss Heat loss in DH system [Wh]
Qdir Heat supplied directly by SC to DH system [Wh]
Qel Heat supplied by electricity [Wh]
Qexc Excess heat supply from SC [Wh]
QHE Heat extracted from TTES by HE [Wh]
QHP Total heat supplied by HP [Wh]
Qin.HP Heat extracted from TTES by HP [Wh]
Qin Heat into TTES [Wh]
Qinitial Heat stored in the TTES at the start of year [Wh]
Qloss Heat loss from TTES [Wh]
Q̇max.boil Maximum capacity of boiler [W]
Q̇max.HP Maximum capacity of heat pump [W]
Qmax Maximum heat storage capacity of TTES [Wh]
Qmin Minimum heat storage capacity of TTES [Wh]
Qspec Annual specific heat demand of dwelling in MFH [kWh/
m2]
Qsto Heat stored in TTES [Wh]
Qsum Heat supplied to DH system [Wh]
Qsup Heat generated by SC [Wh]
QTTES Heat supplied by TTES [Wh]
Qw Heat wasted from SC (due to inadequate storage capacity) [Wh]
r Discount rate [%]
R Radius of TTES [m]
RA.max Maximum available rooftop area for installation of SC [m2]
RESsh Percentage share of RES in total heat supply [%]
ShRE.el Percentage share of RES in PES for producing electricity Sr [%] Global solar irradiance on a vertical plane [Wh/m2] Tamb Ambient temperature of air [°C]
Tavg.soil Average (yearly) temperature of soil (around the TTES) [°C]
TDH Average (yearly) supply temperature of DH system [°C]
Tmax.sto Maximum temperature in TTES [°C]
Tmin.sto Minimum temperature in TTES [°C]
Trtn.DH Average (yearly) return temperature of DH system [°C]
Tsto Average temperature of TTES [°C]
Ttop.sto Estimated temperature at top of TTES [°C]
V Volume of TTES [m3]
ηboil Thermal efficiency of oil boiler [%]
ηcol SC efficiency [%]
ηel Efficiency of conversion of PES to electricity [%]
ηHP HP efficiency [%]
ηsys SC system efficiency [%]
λavg.soil Average thermal conductivity of soil over the year [W/
m∙K]
λt, λs, λb Thermal conductivity of insulation of TTES (top, side and bottom, respectively) [W/m∙K]
Fig. 1. Relationship between storage volume per unit of heat stored and size for different types of TES (Source: Author's compilation from published reports).
emissions. In systems where heating demand is partly met by elec- tricity, TES can help lower the peak electricity demand, thereby playing an important role in demand side management.
There are three different principles of heat storage: sensible heat storage has a volumetric heat capacity of 10–50 kWh/tonne, latent heat storage (50–150 kWh/tonne), and thermo-chemical storage (120–250 kWh/tonne) [6]. Sensible heat storage uses a non-corrosive and cheap medium like water to store thermal energy and the tem- perature of water changes with addition or removal of heat. Latent heat storage use Phase Change Materials (PCMs) such as paraffin com- pounds, salt hydrates, fatty acids etc., which release or absorb energy with a change in their physical state. Heat is stored in the phase-change process, which is an isothermal process. PCMs have high-energy storage density and hence latent heat storage has higher volumetric heat ca- pacity. Thermo-chemical storage use thermo-chemical materials such as metal oxides to store and release heat by a reversible endothermic/
exothermic reaction process. They have the highest volumetric heat capacity and can store heat at a relatively higher temperature [6].
Ref [7] assesses integrated solar absorption cooling and heating systems to meet the heating and cooling demand of a building in Italy.
An energy and economic comparison of heat storage tanks using water to store sensible heat and PCM based thermal storage tanks concludes that PCM in solar heating/cooling plants perform better than sensible heat storage only during periods when the mean temperature of the storage is around the melting temperature of the PCM. The reader can further refer to [8–12] for PCM based storage and its suitability for solar heat storage.
Sensible heat storage can store heat over a period of many months with an efficiency of 50–90%. It is commercially available on a large scale and with a levelized cost of 0.1–10 Euro/kWh, is cheaper than latent heat and thermo-chemical heat storage [6].
Five types of sensible TES are prevalent: Borehole Thermal Energy Storage (BTES), Aquifer Thermal Energy Storage (ATES), Water Gravel Thermal Energy Storage (WGTES), Pit Thermal Energy Storage (PTES) and Tank Thermal Energy Storage (TTES) [13]. WGTES, PTES and TTES have been used for centralised heat storage while TTES (<1000 m3) have been integrated in buildings [14]. BTES has low costs but has high losses. ATES require the presence of an aquifer and are used for pro- viding both heating and cooling. Large heat storages are of the PTES type, with the largest installation at Vojens, Denmark having a water storage capacity of 2,000,000 m3 with ground mounted solar thermal collectors. PTES has the lowest specific investment cost (20–40 Euro/
m3) due to economies of scale [15].
Various authors have reviewed TES systems and have highlighted the key characteristics and differences between them [16–20]. The re- lationship between storage volume per MWh of heat stored and storage volume for different types of TES is shown in Fig. 1 (volume is ex- pressed in water equivalent (W.E)). It is observed that TTES, WGTES and PTES have a low storage volume per MWh of stored heat and are hence attractive for seasonal storage of heat.
Annex 30, 32 and 33 of the International Energy Agency (IEA) technology collaboration programme on energy storage examines dif- ferent types of TES for cost-effective energy management and CO2 mi- tigation; develop models of energy storage for simulation and optimi- sation of energy systems; and discusses materials and components for development of TES, respectively [21]. Details and guidelines for design of TES systems are presented in [22,23].
A comprehensive review of different types of TES and their appli- cation in DH systems has been undertaken in [24–26,27] presents de- sign and construction aspects of large-scale hot water TES. [28] eval- uates the use cooling and TES tanks for optimization of combined cooling, heating and power (CCHP) generation system while [29]
modelled a combined heat and power (CHP) plant with TES and ex- amined its performance. [30] examined the use of heat storage in a DH system fed by biomass fired CHP plant and evaluates economic, en- vironmental and energetic performances of the system while [31]
undertakes an assessment of the commercial viability of solar DH with underground TES in North America.
Modelling and simulation of multi-energy systems (using solar thermal, HP, solar PV, oil boilers etc.) with TES has been undertaken to examine energy flows, solar fraction, CO2 emissions etc. in various studies [32–44]. Certain studies undertake heating system optimisation [45–47]. [48] develops an analytical model for a hybrid heating system, containing solar collector, air-source HP and TES and optimises system operation using inverse problem optimisation but does not undertake an economic evaluation. [49] undertakes an environmental and eco- nomic life cycle assessment of BTES in DH systems and develop an integrated assessment tool using multi-objective optimisation to mini- mize cost of heat and emissions. [50] undertakes a techno-economic analysis for a residential district to examine the role of TES in in- creasing the share of RES and optimises the operation of the system using multi integer linear programming. [51] examines the impact of integration of a small TES (120–300 Litres) with a HP using an opti- misation model and concludes that such systems are cost competitive with conventional heating systems in the UK.
While these studies examine certain aspects of TES and their in- tegration into DH systems, there are some limitations. Many studies use proprietary software such as TRNSYS (Transient system simulation tool), which may not be freely accessible. Many studies assess only a part of the heating system and only a few studies compare different configurations, but energy flows are not shown. Other limitations in- clude poor time resolution (daily and monthly, instead of hourly) and a focus on simulation of energy flows without comparing the relative cost of decarbonisation of different heating systems. Further, it is ac- knowledged that studies have to be localised as heat demand, heat supply, CO2 emission intensity, cost of fuel etc. are influenced by local factors. To the best of authors’ knowledge, no study has been under- taken for Switzerland, which enables an easy comparison of the cost of decarbonisation for heating systems with integrated TES.
This leads to the following research questions:
•
What size of heating systems and TES is required for decarbonising heating in the residential sector in Switzerland?•
How do different heating systems compare on various performance indictors?•
What is the cost of decarbonisation of heat for different system configurations?•
Which parameters influence the choice of heating systems and which heating system is suitable under which condition?In order to address the above research questions, this paper aims to undertake a comparative assessment of different heating systems with TES. This study simulates energy flows in a heating system and evalu- ates various economic and environmental performance indicators for replacing existing oil boilers by different heating technologies in the city of Geneva, Switzerland. This study is highly relevant and timely in the context of growing importance of decarbonisation of heating in a net zero emissions world. Section 2 describes the method and material for the study. Section 3 presents the simulation results followed by discussions and sensitivity analysis and Section 4 concludes the paper.
2. Method and material
This paper builds on the simulation method for assessing hourly energy flows in DH system with seasonal thermal energy storage where four configrations of heating systems were modelled [52]. Ref [46] also compared the result of the simulated energy flows in two systems, Friedrichshafen and Marstal, with monitored values reported in litera- ture. The reported and simulated results were within +/- 10% and hence the simulation tool and method were considered to be validated.
This study applies the model and assessment tool to compare different heating system configurations for 50 and 200 dwellings in multifamily
households connected by a centralised heating system at Geneva, Switzerland. The assessment tool is implemented on a spreadsheet.
2.1. Representation of heating system configurations
Fig. 2 shows the simplified line diagram of different heating system configurations and associated energy flows. Different heat sources are shown as modular components and can be included in the DH loop. The DH loop is coupled to the building loop to provide centralised heating to various buildings. The supply temperature of the DH system is TDH
and the return temperature is Trtn.DH. The total (SH and DHW) annual heating demand is Qdem and the total heat supplied to the DH system is Qsum, of which QDH.loss is wasted as heat loss. The building has a common DHW tank and hot water is provided to radiators for SH in all dwellings. The oil boiler supplies heat (Qboil) to the DH loop via a heat exchanger (HE) in the boiler loop. The solar collector (SC) generates heat (Qsup) and provides the heat directly (Qdir) to the DH loop via SC loop 1 when the sun is shining. When there is excess heat supply (Qexc), e.g. in summer months, SC loop 2 is used to supply Qin to the TTES. QHE
from the TTES is delivered by the HE via TTES discharge loop 1 when the temperature in the top of the TTES (Ttop.sto) is higher than TDH. If this temperature is inadequate, a water-water HP is used to extract heat (Qin.HP) and TTES discharge loop 2 is used to deliver heat (QHP) to the DH loop. The HE & HP are operated continuously at the rated capacity if requisite conditions are fulfilled. The TTES has a designed maximum and minimum temperature (Tmax.sto, Tmin.sto) and the corresponding heat content in the TTES is Qmax and Qmin. Heat stored in TTES is Qsto
corresponding to the average temperature (Tsto) and the heat loss is Qloss. It is assumed that the TTES has a heat content of Qinitial at the start of the year. The HP only loop is used exclusively without the DH loop.
An electrically operated air-water HP extracts heat (Qamb) from ambient air at temperature Tamb and along with heat from electricity (Qel), supplies QHP to the building loop. The technical efficiency of the oil
boiler, SC, SC system, HP and conversion efficiency of primary energy supply (PES) to electricity is ηboil, ηcol, ηsys, ηHP and ηel respectively. The primary energy supplied by the boiler and electricity are Ein.boil and Ein.el respectively. All loops are coupled to DH system but bypass valves can be used to eliminate selected loops for forming different heating system configurations. HEs are used between different loops (100%
efficiency is assumed for simplicity). Four different configurations of heating systems as shown in Table 1 have been simulated and ex- amined. The oil boiler in configuration 2&3 are provided to meet the peak heat demand.
2.2. Method for assessment
The assessment is performed as shown in Fig. 3. Economic, en- vironmental, energy and technical parameters are shown in different colours. The core of the assessment tool are four energy models which are used to simulate hourly energy flows. Different inputs such as heat supply and demand profile, costs, emission factors etc. are exogen- eously provided to the model and are explained ahead. The focus of this study are small systems and two cases corresponding to 50 and 200 dwellings (N) in multi-family households (MFH) connected to a cen- tralised heating system. The first simulation (design run) is for sizing of the equipment. After the equipment size is determined, the simulation is run for a duration of one year. The output of the simulations are hourly energy flows for different heating system configurations. Hourly energy flows are aggregated to provide annual numbers and various performance indicators such as annual CO2 emissions, annual fuel cost, peak electricity load (in a year) etc. are calculated. Comparative as- sessment of different configurations is then undertaken. The results of the model are deterministic but the sensitivity of performance in- dicators to selected input parameters is undertaken.
Fig. 2. Simplified line diagram and energy flows in different heating system configurations.
2.3. Selecting size of equipment
The size of the equipment depends on system design. Heuristic measures are used to determine the size of the equipment but sizes have not been optimised. The impact of varying the size of the equipment is briefly considered in Section 3.6.
2.3.1. Solar collector
This study assumes that SC are only deployed on rooftops. It is as- sumed that there are 5 floors per building, 2 dwellings per floor and the building footprint is 200 m2. The maximum available rooftop area for installation of SC (RA.max) is 120 m2 (assuming only 60% of the rooftop area is available). The rooftop area available per dwelling is therefore 12 m2. It is assumed that one fourth of RA.max will be facing North and this area is not suitable for installation of SC as the annual solar irra- diation is low. Table 2 shows the selected area of the SC (CA) which is restricted by suitable rooftop area.
2.3.2. TTES
For a small heating system, which supplies 50–200 dwellings, a cylindrical TTES can be used as a heat store. The TTES for 50 dwellings is placed above ground (smaller volume) and for 200 dwellings is placed underground (by design). For minimizing the heat losses in a cylindrical TTES, its height (H) and the radius (R) should be equal [53].
The thickness (dt, ds, db), and thermal conductivity of the insulation (λt,
λs, λb) is shown in Fig. 4 for the top, side and bottom of the TTES. The front view of the cylindrical TTES is shown along with other dimen- sions. Hourly heat losses are calculated from the ambient conditions and physical characteristics of the TTES. Hourly values are used for ambient temperature of air (Tamb) but the average (yearly) temperature of soil around the TTES (Tavg.soil) is assumed to be constant due to leakage of heat. It is assumed that the TTES is stratified as modelled in [52].
Qsum is known from the heat demand profile of 50 and 200 dwell- ings (See Section 2.4.1). Once the size of the SC is determined, Qsup can be calculated (See Section 2.4.2). The (expected) Qexc available during the year can thereafter be calculated using Eq. (1).
=
=
Qexc Max (Q (h) Q (h), 0)
h 1 8760
sup sum
(1) The maximum heat storage capacity (Qmax) of the TTES is selected by system designers based on Qexc. For this study, Qmax is selected as 70% of Qexc as a design criterion. The required volume (V) of the TTES Table 1
Configurations of heating systems.
Config. Nomenclature Equipment Heating loops included
1 Boiler Oil boiler only Boiler; DH; Building
2 SC/TTES/B SC, TTES, oil boiler SC 1,2; TTES heating; TTES discharge 1; Boiler; DH; Building
3 SC/TTES/HP/B SC, TTES with HP, oil boiler SC 1,2; TTES heating; TTES discharge 1,2; Boiler; DH; Building
4 HP HP only HP only; Building
Fig. 3. Method for assessment.
Table 2
Rooftop area and area of SC.
MFH (nos.) (a) RA.max (m2) (b) = 12 ∙ (a) CA (m2) (c) = (b) ∙ 3/4
50 600 450
200 2400 1800
depends on Qmax and can be calculated using Eq. (2).
=
V Q · 3.6 · 10
4180 · (T T )
max 6
max.sto min.sto (2)
In configuration 2, Tmin.sto is determined by Trtn.DH. However, in configuration 3, as the HP can extract heat from the TTES beyond Trtn.DH, a temperature of 10 °C can be reached for Tmin.sto. Hence a larger volume of water is required in configuration 2, as compared to configuration 3. The pre-selected design parameters for TTES are shown in Table 3.
2.3.3. Heat pump and boiler capacity
The maximum capacity of the HP (Q̇max.HP) is a design criterion. For configuration 3, it is selected as one-third of the maximum hourly heat demand (Qdem(h)) while for configuration 4 it is Qdem(h) as the HP has to supply the entire heat demand. The maximum capacity of the boiler (Q̇max.boil) is determined based on the heat demand to be supplied by the boiler.
2.4. Model inputs 2.4.1. Heat demand
Hourly heat demand is simulated for 50 and 200 dwellings in MFH and is provided exogenously. Ref [54] developed a library of heat de- mand load curves (including SH and DHW demand) based on measured data from various buildings in Switzerland. A model approximating hourly heat demand for each building in the Swiss national building register was previously developed from this data. Briefly, each building was linked to a normalized load curve randomly chosen from the li- brary of load curves for that building type. A random noise term was added to account for the variability of the specific heat demand per building (driven by differences in occupant behaviour etc.). The load curve for that building was scaled according to the total yearly energy demand for that building as determined in [55]. It is not claimed that this model is accurate to the level of individual dwellings; instead it has been shown to provide a good approximation of the measured total consumption curves when aggregated over a larger number of build- ings.
As the load curves were associated with whole buildings, residential MFH buildings (for which the number of dwellings is known) were randomly sampled from the building registry sequentially, until the total number of dwellings in the selected buildings were 50 and 200.
The heat demand load curves for those MFH were then retrieved from the database and aggregated into two different heat demand profiles.
2.4.2. Heat supply
Hourly global solar irradiance (Sr) on a 35° tilted plane (South, East and West orientation) at Battelle monitoring station located at Geneva, Switzerland (Latitude: 46.17 N, Longitude: 6.14 E, altitude: 432 m) in 2017 was used. The annual SC yield (Cy) for different orientations was calculated using Eq. (3) where ηCol for evacuated tube collectors (ETC) was assumed as 63% [56] and ηsys was assumed as 95%.
=
Cy S ·r col· sys (3)
The hourly heat supply from SC facing in different directions is calculated using Eq. (4). The value of CA as determined in Table 2 is used and one-third of CA is installed facing South, East and West di- rection.
=
=
Qsup ( C (d) · C (d) )
d South, East, West
y A
(4) The hourly values are aggregated and calculated annual values are shown in Table 4.
The hourly heat supply and demand profile provided exogenously to the model (for 50 dwellings) is shown in Fig. 5 which clearly highlights the mismatch between heat supply and demand and the need for TES.
2.4.3. Other technical, financial and environmental inputs Other inputs used for the assessment are listed in Table 5.
2.4.4. Cost
There are three main components of costs: Energy cost (oil and electricity), capital cost of equipment and, operation and maintenance (O&M) cost. Energy cost depends on the amount of consumption and the price of the commodity. The annual cost of oil (ACoil) is calculated using Eq. (5).
=
ACoil P ·Eoil in.boil (5)
The annual cost of electricity used in the HP (ACel.HP) is calculated using Eq. (6) (as there are high/low tariffs depending on the hour of the day) and is summed over the entire year. Electricity tariffs (Pel.type) for different hours and types are shown in Table A.1 (Appendix). Electricity consumption in other auxiliary equipment is neglected.
=
=
ACel.HP ( Q (h) · P (h) )
h 1 8760
el el.type
(6) Specific capital cost of main equipment (CCeqpt), O&M cost factor (Co&m) and lifetime of the main equipment (n) are shown in Table 6.
The total annualised cost of the heating system (ACsys) is calculated using Eq. (7). The capital recovery factor (CRF) depends on the discount rate (r) and the life of the equipment (n) and the annual fixed operation and maintenance cost of equipment (O&Meqpt) is a fraction of the Fig. 4. Dimensions and location of TTES (a) Above ground (b) Underground.
Table 3
Design parameters for TTES.
Parameter Calculation 50 dwellings 200 dwellings Location of TTES Pre-determined Above ground Underground
Qexc (MWh/year) Eq. (1) 244 982
Qmax (MWh) 70% of Qexc 171 687
Config. 2 Config. 3 Config. 2 Config. 3
Tmin.sto (°C) Design 51 10 51 10
Tmax.sto - Tmin.sto
(°C) Design 39 80 39 80
V (m3) Eq. (2) 3772 1839 15,180 7400
R, H (m) (V/π) (1/3) 10.63 8.37 16.91 13.31
capital cost of the equipment (CCeqpt).
= +
=
ACsys ( CC · CRF (r, n) O&M )
eqpt 1 4
eqpt eqpt
where
= +
+ r CRF(r, n) r(1r )
(1 ) 1
n n
and
=
O&Meqpt Co&m·CCeqpt (7)
2.5. Performance indicators
The economic performance indicator is levelised cost of heat (LCOH) which is calculated using Eq. (8).
= + +
LCOH (ACsys ACel.HP AC ) / Qoil sum (8) The environmental indicator is total CO2 emissions (EMsys) from the heating system which is calculated using Eq. (9) where EFel and EFoil are emission factors (EF) of electricity and oil respectively.
= +
EMsys Q ·EFel el Ein.boil·EFoil (9) Percentage share of RES in total PES (RESsh) is another environ- mental indicator and is calculated as shown in Eq. (10). The percentage share of RES in PES for generating electricity (ShRE.el) is assumed as 43.1% [58].
= + + + +
RESsh (Qamb Qsup Q ·Shel RE.el)/(Ein.boil E .in el Q )sup (10) Peak load (assumed equal to maximum electricity demand in the heating system) on the electricity grid is compared for different con- figurations along with the installed capacity of the boiler and the HP.
3. Simulation results and discussions 3.1. Energy flows
Final energy supplied to the DH system for different configurations is shown for 50 dwellings in Fig. 6. The profiles for 200 dwellings are similar and are shown at Fig. A.1 (Appendix). Heat supplied over the year by different energy sources is shown on the primary y-axis and heat content in the TTES is shown as Qsto (red line) on the secondary y- axis. 8760 h over the year are shown on x-axis commencing from 0000h to 0100 h on 01 January. In configuration 2 and 3 heat is provided directly by the SC whenever the sun is shining. When heat from SC is not available, heat from TTES is supplied by the HE in configuration 2 and 3 (provided conditions are met) followed by the HP (only in Table 4
Annual calculated values for heat supply.
South East West
Sr [MWh/m2-yr] 1.57 1.23 1.26
Cy [MWh/m2-yr] 0.94 0.73 0.75
Directional Qsup [MWh] 141.26 110.04 112.68
Total Qsup [MWh] 50 Dw.: 364 200 Dw.: 1456
Fig. 5. Hourly heat supply and demand profile (50 dwellings).
Table 5
Technical and financial inputs for the assessment.
Parameter Value Data source
Technical (Heating system)
ηboil [%] 87 [57]
ηel [%] 56.9 [58]
ηHP [%] 50 Assumed
QDH.loss [%] 5 Assumed based on [59]
Tamb [°C] Hourly values Ambient air temperature at Battelle (Geneva) 2017 [60]
Tavg.soil [°C] 10 Assumed [52]
TDH [°C] 74 Assumed [52]
Tmax.sto [°C] 90 Design criterion
Tmin.sto [°C] 10 Design criterion
Trtn.DH [°C] 51 Assumed [52]
Technical (TTES)
λt, λs, λb [W/m∙K] 0.04, 0.09, 0.07: for TTES above ground 0.6, 0.06, 0.06: for TTES under ground
Assumed [52]
dt, ds,db [m] 0.3, 0.4, 0.55: for TTES above ground 0.3, 0.2, 0.3: for TTES under ground
Assumed [52]
λsoil [W/m∙K] 2.0 Assumed [52]
Qinitial [MWh] 0.6 ∙ Qmax Assumed for simulation
Financial
r [%] 3 Assumed [61]
Pel.type [CHF/kWh] Various Table A.1 (Appendix)
Poil [CHF/ton] 940.41 Average price of extra light fuel oil Environmental
EFel [kg CO2/kWh] 0.149 [62]
EFoil [ton CO2/ton] 3.16 [62]
configuration 3). Heat discharge from the TTES is stopped (in the si- mulation) when Qsto reaches the same value as Qinitial, to avoid over discharge of the TTES. The balance heat in configuration 2 and 3 is provided by the boiler.
3.2. Comparison of performance indicators
Fig. 7 shows the selected performance indicators for 50 dwellings (See Fig. A.2 in Appendix for 200 dwellings). LCOH shows a range for HP configuration as three different electricity prices are used for elec- tricity. LCOH for the boiler configuration is the lowest followed by HP only configuration which has 15–50% higher values. The LCOH for configurations with TTES are 81–87% higher than the HP only con- figuration.
PES from different sources is shown on the left hand y-axis and the share of RES in PES is shown on the right hand y-axis. It is observed that in configuration 4, about 70% of the heat is from RES while the share is about 62% and 55% in configuration 3 and 2, respectively.
In all cases emissions are the highest in configuration 1 as oil has a higher CO2 emission factor. In configuration 2 and 3, the reduction in total emissions is about 50 and 65 percent, respectively, due to lower use of boiler. Total emissions are about 80 percent lower in HP only configuration as compared to boiler configuration.
The comparison of installed capacity reveals that a smaller boiler is
required for configuration 3 as a part of the peak heat demand is met by the HP. HP capacity in configuration 4 is the same as the boiler capacity in configuration 1 as HP has to supply the entire heating demand. The capacity of the HP in configuration 3 is constrained to one-third of the peak demand by design. Peak electricity load for configuration 4 is about 70 kW for 50 dwellings which is three times higher than con- figuration 3.
The seasonal coefficient of performance (SCOP) of the water-water HP in configuration 3 is calculated as 4.2 while that of air-water HP in configuration 4 is 2.6, as the temperature of ambient air is much lower than that of the water stored in the TTES. The performance indicators for 50 and 200 dwellings were similar compared on per dwelling basis with the minor differences being attributed to the impact of lower costs due to economy of scale. Hence, the feasibility of different heating systems remains unchanged.
3.3. Cost of decarbonisation
For decarbonisation of heat, existing oil boilers have to be replaced with other heating sources and systems configurations. Although these configurations will result in lower CO2 emissions, they will be more expensive. The annual cost of decarbonisation of heat shows the addi- tional costs required to reduce 1 ton of CO2 emissions per year. It can be calculated using Eq. (11), where Δ refers to the difference between the Table 6
Cost of main equipment.
Equipment CCeqpt (CHF/unit) Co&m (as a% of CCeqpt) N (yr) Data source
TTES (Non-pressurised) (500 m³ – 15,000 m³): Cost (Eur/m3) = 11,680 ∙ V(−0.5545) +130 1.25% 40 [63, 64, 65, 66]
SC (ETC) Cost (CHF/m2) = 3801 ∙ CA (−0.173) 1% 25 [56, 63]
HP Air source HP (5–50 kW):
Cost (CHF/kW) = 14,677 ∙ Q̇max.HP (−0.683)
Water source HP (20–250 kW):
Cost (CHF/kW) = 11,182 ∙ Q̇max.HP (−0.247)
1.1% 20 [67, 66]
Oil boiler 820,000 (Eur/MWth)
40% cost is added for installation 5% 20 [57, 66]
DH system Not included assuming a pre-existing network – – NA
Fig. 6. Final energy supplied for different configurations (50 dwellings).
existing boiler (configuration 1) and the considered configuration (2, 3 or 4).
=
ACdec AC / EMsys sys (11) Table 7 shows the annual cost of decarbonisation of heat for dif- ferent dwellings to replace an oil boiler. The cost of decarbonisation is the lowest for HP only configuration and is about seven times higher for configuration 3 (50 dwellings). The cost of decarbonisation decreases with an increase in number of dwellings due to economies of scale. In configuration 2, as the heat stored in the TTES cannot be fully dis- charged, the TTES is of a larger volume, which increases the cost.
Hence, configuration 2 without a HP is a sub-optimal configuration and should not be considered.
Configuration 4 (HP only) has low LCOH, highest share of RE, lowest emissions, and least cost of decarbonisation (with the considered assumptions) and hence is the best choice for decarbonisation of 50, 200 dwellings in MFH. As there is a high upfront investment cost for a HP, appropriate financial incentives such as subsidies may be con- sidered to encourage MFH to replace oil boilers with HPs.
3.4. Use of TTES for controlling peak electricity load
Heating systems with TTES (config. 3) have an advantage over configuration 4 systems and they can play an important role in con- trolling the peak electricity load.
Fig. 8 compares the hourly electricity load from HP in configuration 4 (Config 4 Qel) with electricity load from HP in configuration 3 (Config 3 Qel). The contribution of the TTES to the heat demand is also shown (Config 3 QTTES). An analysis reveals that the peak electricity load re- duces by 3.8 times in configuration 3 and hence TTES contributes to peak electricity load shaving. Although, as shown in Fig. 6, peak heat demand in configuration 3 is met by the boiler (occasionally by SC), TTES plays an important role in lowering the peak electricity load of the HP especially during winters. Further, even if the boiler is removed
from the heating system (modified version of configuration 3), the peak electricity load from the HP in configuration 3 will still be lower as the SCOP of the water-water HP will be higher than the SCOP of air-water HP in configuration 4. Hence, apart from storing thermal heat over a long period of time, the TTES also contributes to lowering the peak electricity load.
3.5. Sensitivity to parameters
We examine the sensitivity of the performance indicators to selected parameters. One parameter is changed at a time and all simulations are reported for 50 dwellings.
3.5.1. Heat demand
The average annual specific energy demand (Qspec) derived from the heat demand profile for 50 dwellings in Section 2.4.1 was 104 kWh/m2- yr. Reduction of specific heat demand in residential buildings was identified as one of the important strategies for decarbonising the Swiss heating system [5]. To examine the impact of reduced heat demand we assume that Qspec of each dwelling decreases from the existing 104 kWh/m2-yr to 84.5 and 37.1 kWh/m2-yr due to renovation of the building. This corresponds to the median heat demand of buildings with energy label C and A respectively in the Swiss Cantonal Energy Certi- ficate for Buildings (CECB) database [68]. The existing heat demand profile shown in Fig. 5 is divided by a constant factor of 1.23 and 2.8, respectively, to derive two new heat demand profiles corresponding to Fig. 7. Performance indicators for different configurations (50 dwellings).
Table 7
Annual cost of decarbonisation of heat to replace oil boiler.
Annual cost of decarbonisation (CHF/ton CO2)
N (nos.) Config. 2 (SC/TTES/B) Config. 3 (SC/TTES/HP/B) Config. 4 (HP)
50 1409 597 85
200 1182 322 31
buildings having energy label C and A. The simulation is then under- taken with the new heat demand profiles and the observed variation in performance indicators is shown in Fig. 9.
The percentage share of RES in PES is unchanged in configuration 4 with lower heat demand as the percentage of RES in electricity is constant. However, in configuration 3, the share of RES increases from 62% to 97% if the existing buildings are renovated to energy label A.
Decrease in annual heat demand lead to an increase in excess heat which can be stored in the TTES, to be supplied at a later time. Thus, a higher share of RES can be provided by using a TTES in the case of lower heat demand. There is a decrease in total CO2 emissions per year, due to lower heat demand in all configurations. However, it is observed that deep decarbonisation can be achieved with configuration 3 as use of the boiler can almost be eliminated when heat demand is low. In contrast, CO2 emissions in configuration 4, cannot be completely eliminated and will continue to depend on the EF of electricity. Hence, TTES may be preferable, if the goal is complete decarbonisation.
As seen in Fig. 10, the annualised cost per unit heated area reduces for all configurations and is the lowest at about 12 CHF/m2-yr for the HP only configuration (35% reduction if existing heat demand profile is used). Thus, HP only configuration is preferable when heat demand from buildings is low. Configuration 3, is about 30% more expensive than configuration 4 with the existing buildings, but are about 45%
more expensive when all buildings are certified with label A. Hence in terms of cost, configuration 4 is preferable in the future when most of
the buildings are likely to have low heat demand. There is a decrease in peak electricity load from 68 kW to 25 kW in configuration 4 due to lower heat demand. Similarly, in configuration 3, it reduces from 20 kW to 10 kW. Thus in case of lower heat demand, HP only configuration adds about 0.5 kW (average) peak load per dwelling which is quite manageable. To conclude, if the goal is complete decarbonisation, a TTES may be a better option in case of lower heating demand, albeit at a higher cost than HP only configuration.
3.5.2. Heat supply
To examine the impact of lower heat supply on configuration 3 we increase the number of floors in the building and the calculated para- meters are shown in Table 8. Other assumptions (2 dwellings per floor, RA.max for each building 120 m2) are unchanged.
Increase in number of floors restricts the roof area available for installation of SC (RA.max). As CA is limited by RA.max, Qsup decreases with increase in number of floors.
Fig. 11 shows the change in different parameters as number of floors in a building are increased in configuration 3. It is observed that as the number of floors in a building increase, annual heat supply (Qsup) de- creases from 364 MWh to 152 MWh and hence the percentage share of RES in PES decreases from 62% to 27%. Increasing the number of floors from 5 to 12 also leads to doubling of total CO2 emissions. As there is no impact of increase of number of floors on configuration 4, it can be concluded that configuration 3 should be preferred in low rise buildings Fig. 8. Comparison of peak electricity load.
Fig. 9. Sensitivity of (a) share of RES in PES and (b) Total CO2 emissions per year, to heat demand.
which has higher rooftop area, while configuration 4 can be im- plemented in dense city areas where there is restricted rooftop area.
3.5.3. DH supply temperature
Fig. 12 shows the changes in different parameters for configuration 3 and 4 when TDH is lowered from 74 to 50 °C. Lower TDH is feasible in renovated buildings and for this simulation, we assume that all build- ings have C energy label (corresponds to lower heat demand). The SCOP of the water-water HP coupled with TTES is consistently higher than the air-water source HP in configuration 4. The percentage share of RES in PES is also higher in configuration 3 as TDH is lowered. CO2
emissions in both configurations decrease and are comparable when TDH is 50 °C. It can therefore be concluded that in the future with re- duced heat demand from buildings, there would be suitable opportu- nities to lower TDH and configuration 3 could become a viable alter- native for decarbonisation.
3.6. Impact of varying the size of equipment
As shown in Fig. 3, the first simulation run is a design run and is used to determine the size of the equipment. Section 2.3 explains how
the size of four equipment, viz. SC, TTES, HP and B is selected. While selection of the equipment size is a design problem which is undertaken using specialised simulation software, we highlight some impacts of varying the size of the equipment using the case of 50 dwellings for configuration 3.
3.6.1. Solar collector size
The size of the SC is constrained by the available area on rooftop and certain assumptions are used to calculate size of the SC in sub- Section 2.3.1. We vary the size of SC in sub-Section 3.5.2 and have examined the impact of different sizes of SC on the results.
3.6.2. TTES size
As mentioned in sub-Section 2.3.2, for this study, Qmax is selected as 70% of Qexc as a design criterion. In order to assess the impact choosing a different size of TTES, we vary the volume of TTES by selecting Qmax
to have other shares of Qexc. Assuming that the SC covers the available area on the roof, the heat supply and the heat demand are fixed. The hourly energy flow simulation is then undertaken.
Fig. 13 shows the volume and the capital cost of the TTES on pri- mary y-axis and the percentage utilisation of the TTES (ratio of the maximum heat stored during the year to the heat storage capacity, Max [Qsto (h)]/Qmax) and the percentage of heat which is wasted (Qw/Qsup) on secondary y-axis. Case A, B and C correspond to Qmax being equal to 40%, 70% and 100% of Qexc respectively.
If Qmax of the TTES is designed to be a higher percentage of Qexc, (Case C, Qmax =100% Qexc), the percentage utilisation of the TTES decreases to 75%, as the TTES is oversized and is not utilised effec- tively. Moreover, the capital cost of the TTES increases due to a larger volume. On the other hand, if a smaller volume of TTES is selected (Case A, Qmax =40% Qexc), the percentage utilisation of the TTES in- creases to 100% but 11% of the solar heat which is generated will be Fig. 10. Sensitivity of (a) Annualised cost per unit heated area and (b) peak electricity load, to heat demand.
Table 8
Impact of increasing number of floors on heat supply.
Floor
(nos.)(a) Dw./bldg.
(nos.) (b) = 2 ∙ (a)
RA.max /dw. (m2/
dw.) (c) = 120/(b) CA (m2) (d) = (c) ∙ N ∙ 3/4
Qsup (MWh) (e) = As per (d)
5 10 12 450.0 364
6 12 10 375.0 303
8 16 7.5 281.3 227
10 20 6 225.0 182
12 24 5 187.5 152
Fig. 11. Sensitivity of parameters to varying heat supply (configuration 3).
wasted, because the TTES cannot absorb heat as it is already saturated (undersized TTES). Thus, sizing of the TTES is an optimisation problem and the ideal size of the TTES is one where there is no heat wastage and the volume of the TTES is minimised. The variation in heat demand, solar radiation and design safety factors also have to be considered while selecting the optimal size of the TTES. For the specific case of 50 dwellings shown above, the optimal volume of TTES will be between 1051 and 1839 m3.
3.6.3. HP size
As mentioned in sub-Section 2.3.3, for configuration 3, Q̇max.HP is a design criterion and is selected as one-third of the maximum hourly heat demand (Qdem(h)). Selecting a higher capacity HP (e.g. three- fourth of Qdem(h)), will lead to a faster discharge of the TTES and the heat store will not be able to meet the demand during the entire winter.
This will also result in a higher capital cost of the HP and the boiler will
have to be used earlier in the winter as the TTES would has exhausted its stored heat. On the other hand, if the size of the HP is decreased (e.g.
one-fourth of Qdem(h)), the TTES can meet the demand over a longer time period, but the boiler will have to be used to meet the peak heat demand leading to higher GHG emissions. Further, a higher amount of heat may be lost to the ground or the TTES may not be fully discharged, limiting its effective utilisation. Hence, Q̇max.HP also needs to be opti- mised based on the heating system characteristics and the strategy of operation of the heating system.
3.6.4. Boiler size
The boiler size in configuration 1 cannot be varied as is designed to meet the peak demand. In configuration 3&4, the boiler is sized to provide the balance heat and its capacity will be decided by the selected HP size.
Fig. 12. Sensitivity to varying DH supply temperature.
Fig. 13. Impact of variation in the size of the TTES.
Table 9
Policy objectives, specific cases and preferred heating system.
Policy objective Specific case Preferred heating system
Minimise LCOH All B
Minimise CO2 emissions/ Maximise RE share High specific heat demand (old buildings) HP
Low specific heat demand (renovated buildings) SC/TTES/HP/B
High rise buildings HP
Low rise buildings SC/TTES/HP/B
Minimise electricity peak load High specific heat demand (old buildings) SC/TTES/HP/B
Low specific heat demand (renovated buildings) HP
Minimise cost of heat decarbonisation All HP
Deep decarbonisation Low DH supply temperature (possible only with renovated buildings) SC/TTES/HP/B
High DH supply temperature (old buildings) HP
Fig. A.1. Final energy supplied for different configurations (200 dw.).
Fig. A.2. Performance indicators for different configurations (200 dw.).
3.7. Other factors affecting results
Various costs and technical parameters have been assumed and a variation in these parameters may yield different results. Selection of equipment size is a design criterion and performance indicators may change as different sizes are used. Cost of land for TTES is not included as it is assumed that it will be available from community resources. It can be reasonably expected that prices of electricity and oil will in- crease over time leading to an increase in LCOH in case of configuration 1 and 4. A social discount rate of 3% has been considered for the fi- nancial assessment and a change in this value may alter the financial performance indicators. Heat demand profiles may be different for other locations in Switzerland. There may be changes in the share of DHW and the share of SH demand especially as more efficient buildings are constructed which will impact the aggregate heat demand profile.
The heat supply profile is a function of the ambient solar radiation and results may vary depending on the specific location and solar insola- tion. While minor variation in the above assumptions will have an impact on the absolute numbers, it is unlikely to change the results and the preferred heating system.
The assessment shows that HP only configuration has the lowest cost of decarbonisation but CO2 emissions cannot be completely eliminated and will continue to be dependent on the share of electricity in total PES and its emission factor. This may be an issue of concern in countries where electricity has a high EF. With the reduction in heating demand, use of TTES with SC would result in higher share of RES and lower CO2 emissions. Hence, if the goal is complete decarbonisation, use of TTES is preferable.
3.8. Policy implications
The study has significant policy implications for Switzerland, which is targeting net zero emissions by 2050. Heat decarbonisation will play a key role in such a transition. Table 9 shows different policy objectives, specific cases and preferred heating system that are recommended for each case, based on the assessment in the paper.
Increase in CO2 levy, higher fiscal and financial incentives to in- crease the share of RES in buildings, increase in capital subsidies for HPs and lower electricity prices for HPs are some of the economic in- struments, which could be considered. Certain regulatory measures such as ban on oil boilers, stringent building regulations and codes and stricter minimum energy performance standards for buildings could help in enabling a faster transition. Policies to encourage building re- novation and shifting to lower DH supply temperatures would enable integration of HPs and solar thermal as heat sources. Lastly, specific policies need to be designed for different sectors, and a bouquet of policies/policy packages can be effectively used to influence users to replace boilers with heat pumps/solar thermal coupled with TTES.
4. Conclusion
This paper examined the feasibility of thermal storage for dec- arbonising heating using the case study of Geneva, Switzerland. Energy flows were simulated in four different heating system configurations.
An hourly simulation of energy flows brings a much higher temporal resolution, thereby giving greater insights into the operation of the heating system. A preliminary assessment of the design, costing and size of the heating system has been undertaken. The paper presents the
comparison of various performance indicators for different configura- tions which enables the stakeholder to take an informed decision. The method and the model is simple, reproducible and transparent and does not use proprietry software like TRNSYS.
Results show that a heating system having only a centralised air- water HP has low LCOH, highest share of RES, lowest emissions, and least cost of decarbonisation for 50 (and 200) dwellings in MFH at Geneva, Switzerland (with considered assumptions). However, its large- scale implementation may result in high peak load on the electricity grid and CO2 emissions cannot be eliminated.
For 50 and 200 dwellings in MFH, a TTES having a volume of about 2000 m3 and 7500 m3, respectively, would be required. The cost of decarbonisation of heat (50 dwellings) for a system using only an air source HP will be around 85 CHF/ton CO2 but will be much higher at approximately 600 CHF/ton CO2 for a heating system with a SC, TTES, HP and oil boiler.
Sensitivity analysis shows that if buildings are renovated and have lower heat demand in future, use of TTES with SC could result in higher share of RES and lower CO2 emissions as compared to HP only con- figuration. Further, the peak electricity load is much lower and the heating system can be completely decarbonised. Thus, TTES may be feasible for installation in low-rise buildings and when low DH supply temperatures are used, albeit at a higher cost.
It can therefore be concluded that thermal storage has an important role to play in decarbonisation of heating and can be implemented without a significant impact on the electricity grid. However, it has a higher cost of decarbonisation. A heating system with a solar collector, thermal storage, and a heat pump has distinct advantages over a heat pump only system and hence, the choice of the future heating system in Switzerland should be carefully evaluated.
CRediT authorship contribution statement
Kapil Narula: Conceptualization, Methodology, Software, Validation, Writing - original draft, Visualization. Fleury De Oliveira Filho: Resources, Methodology, Formal analysis, Writing - review &
editing. Jonathan Chambers: Resources, Data curation, Writing - re- view & editing. Martin K. Patel: Supervision, Project administration, Investigation, Funding acquisition, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influ- ence the work reported in this paper
Acknowledgements
This research was conducted in the context of the Swiss Competence Center for Research in Energy, Society and Transition (SCCER-CREST/
Contract no. 1155002547), Swiss Competence Center for Energy Research on Future Energy Efficient Buildings & Districts (SCCER- FEEB&D/Contract no. 1155002539) and Swiss Competence Center for Energy Research on Heat and Electricity Storage (SCCER-HaE/Contract no. 1155002545). All competence centers are financially supported by the Swiss Innovation Agency Innosuisse. This research was also co- funded by Services Industriels de Genève (SIG/Contract no. S19161).
Appendix
Consumers can choose from three different types of electricity supplied by SIG, the local electricity provider in Geneva. Different electricity tariffs are applicable for a HP used for residential buildings depending on the type of electricity (Pel.type, type = 1,2,3). The tariffs for high (full/peak) hours and low (soft) hours are shown in Table A.1.
