Potential of waste heat and waste cold energy recovery in Singapore for
district cooling applications: impacts on energy system
Dominik Franjo Dominkovića, Alessandro Romagnolib, Tim Foxc, Allan Schrøder Pedersena
a
Department of Energy Conversion and Storage, Technical University of Denmark (DTU), 4000 Roskilde, Denmark, [email protected]
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore, [email protected]
c
Energy consultant and MechE Process Industries Division Board, Falmouth, UK, [email protected]
a
Department of Energy Conversion and Storage, Technical University of Denmark (DTU), 4000 Roskilde, Denmark, [email protected]
b
Abstract
Currently, most of the cooling energy demand of Singapore is met by electrically driven devices, such as airto-air heat pumps (split units) or electric chillers. A significant amount of waste heat and waste cold is
available in Singapore, as a part of power generation in gas power plants, waste incineration plants and the
cold released during the LNG regasification process. Waste heat could be utilized via absorption chillers to
convert it in cooling energy and distribute it to the consumers via district cooling grid, increasing energy
efficiency and reducing power consumption; while LNG cold released during gasification process could be
directly utilized. In order to assess this potential, smart energy system was modelled in energy balance
hourly simulation tool in order to investigate the impact of district cooling on the energy system of
Singapore. Business-As-Usual (BAU) and District Cooling (DC) scenarios were developed for the year 2030
and 2050. Results showed that CO2 emissions could be reduced by 11.5% and 9.9% in years 2030 and 2050
compared to the BAU scenario. Primary energy demand could be reduced by 12.2% and 10.2%, while total
socio-economic costs could be 4.6% and 3.8% lower. Thus, it was concluded that although infrastructure
investment in district cooling would be significant in the initial phase, benefits in terms of energy savings
and reduced gas imports are more significant, resulting in the lower socio-economic costs of the energy
system than in the case without district cooling being implemented. Finally, potential for the district cooling
from waste heat was estimated for the South-east Asia’s energy system anticipated in the year 2040.
Keywords: District cooling, energy systems, waste heat recovery, LNG cold energy
Introduction
During the COP21 conference in Paris [1], and more recent COP22 conference in Marrakech [2], an
unprecedented level of commitment to climate change mitigation was shown by the majority of the world’s
nations. The Paris climate agreement resulting from COP 21 is now legally binding and aims to limit the
mean temperature increase in the Earth's atmosphere to below 2oC relative to pre-industrial levels. Failing
to limit temperature rise will result in climate change that will impact everyone, although not at the same
degree. Areas around the equator, as well as coastal locations, are more prone to the harmful effects of
climate change. In order to deal with the consequences, as well as to prevent even more severe effects,
climate change action needs to be initiated and measures implemented in both developed and developing
countries.
There are many differences between developed and developing countries. Developed countries have
already established economies, relatively high human development index, with good living conditions,
mature ageing infrastructure, low infant mortality, more equal distribution of income and high life
expectancy. Fertility rate of these countries is usually much lower than in developing countries.
On the other hand, developing countries have lower per capita gross domestic product (GDP), new
infrastructure and infrastructure deficits, lower education and healthcare standards, higher unemployment
and poverty rates, higher infant mortality rate and higher fertility rate. Due to their high fertility rates, the
population of these countries usually grows rapidly. The latter impacts the infrastructure and energy needs,
causing problems for city planners and other stakeholders to meet the increasing demands. Higher rates of
economic development in these countries also cause substantial growth rates in the number of people
migrating into urban landscapes. The International Energy Agency (IEA) notes that the current global share
of population living in cities (50%) will increase significantly by the year 2050 (to 66%), with almost all the
increase in population being added in cities [3] most of which will be located in developing economies. This
means that the consequences of the infrastructure and energy decisions made today in these countries will
have a significant and lasting impact on future populations and the environment they live in, it is thus of
utmost importance to avoid negative lock-in effects from bad decisions making today.
South East Asia is an area composed of mostly developing countries which are experiencing substantial
growth in economic activity, population and life quality and this is expected to continue into the
foreseeable future. For example, IEA estimates that Malaysia’s energy demand will grow by a factor of 2 by
the year 2040 with fossil fuels making up 90% of supply growth in a business-as-usual scenario [4].
Furthermore, in their report [4], IEA identified four key issues in regards to SE Asia’s future energy systems:
greater integration of energy networks could deliver strong benefits across the region; attracting sufficient
investment in energy is vital in ensuring continued growth; energy sustainability and security; and slow
progress in improving the access to clean cooking facilities and phasing out of subsidies to fossil fuels.
Finally, polluting cooking facilities and dirty energy sources result in an increased burden for society in
terms of air pollution in SE Asia. IEA estimates that globally 6.5 million deaths yearly are attributed to air
pollution, a rate much greater than that due to AIDS, tuberculosis and road injuries combined [5].
Hence, it is important to plan sustainably all aspects of the rapid economic transitions taking place so as to
avoid bad decisions impacting these populations in the decades ahead. On the positive side, different
opportunities can arise from building a completely new infrastructure in many developing cities and
countries, making it possible to directly implement the latest technologies and designs without having to
learn from past mistakes and replace ageing outdated unsustainable infrastructure. One such example is
the deployment of the mobile network in Africa that was implemented directly, without the transition from
landlines, avoiding the large infrastructure costs in telephone landlines.
It is expected from mature developed economies that they will invest more in research, development,
demonstration and deployment (R&DDD) in order to improve existing infrastructure or develop new
technologies. These can subsequently be implemented in currently developing countries, often without the
need to replace existing incumbents. Singapore has a unique opportunity in this regard to become an
R&DDD hub for SE Asia, surrounded as it is largely by developing countries. Successful implementation of
different technologies in Singapore could lead neighboring countries to follow its example, resulting in the
export of technology and knowledge, creating win-win situations for both Singapore and its neighbors.
Energy efficiency represents a crucial measure in helping to meet the Paris agreement climate goals.
Efficient energy conversion, avoiding emissions in the first place, and utilizing cleaner energy sources and
sustainable infrastructure options, is especially important in fast growing developing countries.
Electricity only power stations are currently able to utilize little more than 50% of energy input, in the case
of gas power plants, or less than 50%, if using coal for generation. Thus, a significant part of primary energy
consumption is rejected to the environment in the form of waste heat. As energy demand for heating, and
indeed cooling in areas around the equator, is growing significantly in developing countries, a successful
utilization of the waste heat could provide significant energy savings, increased energy security and more
integrated energy system.
In the climate experienced in Singapore, a large and constant cooling energy demand occurs both diurnally
and seasonally as a consequence of high temperature and humidity during days and nights, all year long. As
Singapore is already a developed economy, its energy consumption per capita is relatively high [6,7], being
a precursor of its neighboring countries in terms of energy consumption. Currently, Singapore produces
more than 95% of its electricity from gas power plants, while 25% of the total gas import is in the form of
liquefied natural gas (LNG) [6]. Furthermore, most of the remaining share of the electricity generation is
met by waste incineration plants [6]. Regasification of LNG from cryogenic temperatures might provide a
source of cooling to help meet a proportion of Singapore’s energy demand for cooling, while waste heat
from waste incineration and gas driven power plants could be utilized via absorption chillers to produce
cooling energy. In order to match the cooling demand of different buildings in the city/country of Singapore
and the cooling energy available from a range of energy dense sources, a district cooling grid may be
usefully established.
This paper deals with matching the cooling energy demand of Singapore with a range of as yet untapped
potential energy sources within the city with a view to improved sustainability. The paper is structured in
six different sections. The introduction section is followed by Methods section, in which the modelling tool
used and infrastructure design assumptions are presented. In the Case study section, a description of the
Singapore’s energy system and its main features are shown. Results and discussion sections present the
results of the simulation in terms of different environmental and economic indicators. Finally, the main
conclusions are summarized in the conclusions section.
Methods
In order to simulate the energy system of Singapore, as well as to simulate the system with the introduction
of a plausible district cooling grid, the EnergyPLAN modelling tool was used [8]. The EnergyPLAN modelling
tool is freeware software, especially suitable for modelling integrated energy systems with increased shares
of intermittent generation. It has been used for modelling of energy systems in the European Union [9],
regions such as South-east Europe [10], different countries [11–15], cities and islands [8].
EnergyPLAN is an deterministic input-output model, having available capacities and infrastructure as inputs,
and providing energy balance, costs and CO2 emissions as the output [16]. It can be used to model the
whole energy system, including power, heat/cool, gas and mobility sectors. Its main intention is to make it
possible to detect the synergies between different energy sectors, utilize them and provide the cheapest
energy system option while simultaneously meeting targets in terms of energy consumption and/or
emissions of CO2 [8]. The latter concept is described as a Smart Energy System. The overview of the energy
system modelling tool EnergyPLAN, as well as the interactions between different energy sectors, is
presented in Figure 1.
Figure 1. The EnergyPLAN model version 12.3 [8]
The major advantages of the model are the ability to capture the interplay between different energy
sectors and the possibility to model whole energy systems, not only certain sectors. Furthermore, due to its
hourly time-step, it captures well the issue of intermittency of renewable energy sources, as well as the
possible mismatch between energy demand and generation. On the other side, a major disadvantage is
that the aggregated approach used for its heating/cooling and power grids models are not able to
represent possible local congestions. Finally, as the model is a simulation and not the optimization tool, it
cannot optimize new installation capacities directly; iterations must rather be carried out manually. Hence,
the modeler should be experienced with analysis of energy systems in order to successfully detect the
optimal energy source portfolio. A more detailed description of the pluses and minuses of the model can be
found in [10], while the comparison of the EnergyPLAN and common optimization tool used for modelling
energy systems (TIMES/MARKAL) can be found in [17].
There are many different indicators used for assessment of energy systems in terms of economic and/or
environmental merit. In this paper, we choose total socio-economic costs of the energy system as the
economic indicator, while total primary energy supply and CO2 emissions are used as energy efficiency and
environmental indicators respectively. The total socio-economic costs incorporated levelized investment
costs for different technologies over their lifetime, fixed and variable operating and maintenance costs and
fuel costs. Furthermore, CO2 tax, currently being proposed in Singapore, was also taken into account.
When reporting total socio-economic costs, taxes are usually excluded as they represent the internal
redistributions of the wealth. However, CO2 tax in this paper was considered as an internalized negative
externality of climate change effects and thus, it was included in the socio-economic costs of the energy
system. In this work, fuels consumed in Singapore were taken into account in the form of primary energy
supply indicator. Furthermore, CO2 emissions were reported only for the energy sector (that included
mobility). Consequently, oil that was imported, refined and exported again was not taken into account
when reporting the primary energy supply of Singapore.
Air pollution and job creation potential are some of the further indicators that can be taken into account
when reporting socio-economic costs of the energy system. Air pollution is the 4th largest threat to human
health according to the International Energy Agency, causing around 6.5 million deaths yearly around the
globe [5], and presents a significant negative burden for society in terms of health costs, as well as in terms
of reduced economic output. On the plus side, job creation potential is a significant indicator that can allow
policy makers to steer towards favourable solution if several solutions with similar economic expectations
exist. However, the latter two indicators were left outside of the scope of this study, although they are
suggested to be included in further studies on this matter.
Case study
EnergyPLAN was used to develop different scenarios for the years 2030 and 2050, along with a reference
scenario created to validate the model by comparing it with the already available energy statistics for
Singapore. Two scenarios for each of the years 2030 and 2050 were developed; i.e. one with and one
without district cooling infrastructure (i.e. DC 2030 and no-DC 2030 and DC 2050 and no-DC 2050).
However, the no-DC scenario included expected measures in terms of energy efficiency in industry and
buildings, as well as stable increases in PV penetration and electric mobility. Furthermore, it included
expected declines in the energy intensity of the country by extrapolating the current trend up to the year
2050 [18]. The two scenarios including district cooling (DC 2030 and DC 2050) added that infrastructure
component on top of the measures already implemented in no-DC 2030 and no-DC 2050 scenarios.
The first step for DC scenarios was to map the locations of potential sources of cooling energy, and/or
waste heat energy that could be utilized via absorption chillers to provide cooling, and calculate the
amount of cooling energy potential. Following the estimation of the potential cooling energy that could be
distributed, infrastructure costs were calculated using the data available in reference [19], where the
reported estimated costs for grid infrastructure were 72 million EUR/TWh of yearly energy delivered.
Finally, it was assumed that the new district cooling network will replace the operation of electricity driven
air-conditioning units with the real average yearly COP of 3.5. Average losses in district cooling grid were
set to 4%. Discount rate for all the technologies in the energy system was set to 4%.
Current energy statistics for Singapore were obtained from national energy statistics [6] and IEA’s website
[20]. Future fuel prices were extracted from the US Energy Information Administration [21] and they are
reported in the Year 2050
The following costs were included only in the DC 2050 scenario:
Appendix 2 – Fuel prices. Technology costs used, as well as the operating and
maintenance costs, were obtained from [8] and [22] and they are reported in
Appendix 1 – Technology costs.
The proposed CO2 tax for the year 2019 is between 10 and 20 SGD [23]. The value adopted in this work was
the mean, i.e. 15 SGD. In order to forecast the increase in the CO2 tax until years 2030 and 2050, the
relative increase in forecasted CO2 allowances inside the European Union was used as a reference [24].
Thus, the adopted values for CO2 tax in Singapore were 35 SGD (24.85 USD) for the year 2030 and 90 SGD
(63.9 USD) for the year 2050.
The overview of the adopted and implemented changes of the energy system of Singapore in years 2030
and 2050 can be seen in Table 1.
Table 1. Different implemented changes implementing the results in years 2030 and 2050
2030
PV capacity [GW]
3
Electric vehicles (% of 15%
the total)
Number of vehicles
984,000
Population
6,418,000
GDP PPP (USD2011)
438.3
Energy
intensity
(MJ/USD2011)
2050
5
40%
Reference
[22]
[25]
984,000
6,680,000
526.1
[25]
[26]
[27]
[18]
Results
Sources for cold energy
The main, currently untapped, sources of energy for cooling taken into account in this study were the LNG
gasification terminal and waste heat from gas power plants and waste incineration plants. Waste heat from
gas power plants and waste incineration plants can be recuperated at suitable temperature levels, i.e. 90
o
C, and utilized via single effect absorption chillers to produced cooling energy that can be fed into the grid.
There were four gas power plants, four operating waste incineration plants and one LNG gasification
located in Singapore that were used for calculations of total cooling energy potential. All four gas power
plants are located on Jurong Island, as well as the LNG gasification terminal. Three out of four operating
waste incineration plants are located on Tuas, while the fourth operating waste incineration plant is located
in Woodlands, at the northern part of the country. Calculated cooling energy potential for years 2030 and
2050 can be seen in Table 2. Waste heat availability from gas power plants and waste incineration plants
were calculated using the typical values for efficiencies of cogeneration plants [28] and obtaining the
amount of natural gas and waste consumption available for energy conversion (in this case for electricity
generation) [6]. The amount of cold energy available from the LNG gasification terminal was calculated
using the Singapore’s statistics on LNG imports [6] and the fact that approximately 0.23 kWh of cold energy
is available from the regasification of one kilogram of liquefied natural gas [29]. Furthermore, it was taken
into account that the annual average growth rate between 2016-2025 is expected to be 10.39% [30], as
well as that the total capacity of gasification terminal is expected to be 9 Million tons per annum [31]. In
order to calculate the cold potential from gasification for the year 2030, the same average yearly growth
rate was extrapolated until the year 2030. For the year 2050, it was assumed that all the natural gas
consumed will be imported in the form of LNG.
Table 2. Sources for district cold
GWh waste
heat
Gas PPs
Waste incineration plants
LNG gasification
Total
52,031
8,154
COP
absorption
chillers
0.7
0.7
Cold
2014
[GWh]
36,422
5,708
463
42,593
Cold
2030
[GWh]
36,877
6,406
2,251
45,534
Cold 2050
[GWh]
24,286
4,927
1,104
30,317
Calculated infrastructure costs for district cooling grid (absorption chillers are not included in these costs)
are presented in Table 3.
Table 3. Estimated infrastructure costs for district cooling grid in years 2030 and 2050
2030
District cooling grid cost
[USD]
Technical lifetime [USD]
Fixed O&M [USD/year]
2050
3,374,570,514 2,281,317,997
40
42,182,131
40
28,516,475
Although they were left outside of the scope of this study, data centers present a significant opportunity for
the waste heat utilization, due to the large share of energy being consumed for cooling purposes. However,
their relatively low heat discharge temperature of around 40 oC is not high enough for direct utilization in
absorption chillers. Hence, heat recuperation from the data centers should be coupled with heat pumps in
order to achieve suitable temperature levels for utilization in absorption chillers. Based on the available
literature, power usage efficiency (PUE) of Singapore’s data centers is relatively low and thus, large amount
of energy could be recovered. According to one industrial survey carried out in 2013, average PUE of
Singapore’s data centers is 2.61 [32], meaning that the data centers consume on average 2.61 times more
energy than needed just for running the IT equipment. Out of the total energy consumption of the data
centers, 37% is consumed for cooling. In 2015, the energy consumption of the data centers of Singapore
was estimated to be 2,260 GWh [33]. Taking into account that 37% of the energy consumption goes for
cooling, and assuming that the electricity was used to drive electric chillers with the average COP=5, the
estimated waste heat discharged to the environment in the year 2015 was 4,181 GWh. Hence, the latter
number presents a significant potential for the energy recovery, in the same time reducing the heat island
effect due to the heat rejection to do environment. In order to successfully utilize the waste heat potential,
a district cooling grid should be available in order to transfer the energy towards the customers. Finally, a
significant growth is expected in the power demand for data centers in Singapore in the future. The total
power capacity over 2016 reached 240 MW and additional 150 MW of additional capacity will be installed
in 2017 [34]. Until the year 2020, expected market growth is 9% per year [34], creating a large additional
electricity demand for cooling in the future.
Scenario results
The total socio-economic costs of different scenarios can be divided into four main categories: annual
investment costs (levelized investments over the lifetime of technologies); variable costs which include
variable operating, maintenance and fuel costs; fixed costs that include fixed operating and maintenance
costs; and finally, CO2 emissions costs that reflect the internalized negative externality in terms of harmful
climate change effects. The resulting socio-economic cost breakdown for different scenarios is presented in
Figure 2.
12
10
[Billion USD]
8
6
4
2
0
Referent
no-DC 2030
DC 2030
no-DC 2050
Variable costs (including fuel)
Annual investment costs
CO2 emissions costs
Fixed costs
DC 2050
Figure 2. Break-down of the total socio-economic costs of different scenarios
Figure 2 clearly shows that the largest share in total socio-economic costs consists of variable costs. One
can also observe that the energy system is cheaper if the district cooling is included in both 2030 and 2050.
Furthermore, it can be observed that the share of variable costs in total costs is lower if district cooling
system exists. On the other hand, annual investment costs are larger in scenarios that include district
cooling system. Finally, costs of CO2 emissions were lower in scenarios that included district cooling
system. The total socio-economic costs in 2030 DC and 2050 DC scenarios were 4.6% and 3.8% lower than
in no-DC 2030 and no-DC 2050 scenarios, respectively.
Except the overall socio-economic costs of the energy system, it is also important to observe the
environmental indicators when evaluating different scenarios. In Figure 3, primary energy supply (PES) and
CO2 emissions are presented for different scenarios.
One can observe from Figure 3 that primary energy supply is significantly lower in scenarios that included
district cooling grid, i.e. it was 12.2% and 10.2% lower in the years 2030 and 2050. Furthermore, CO2
emissions were also reduced in the scenarios that included district cooling systems. For years 2030 and
2050, CO2 emissions were lower 11.5% and 9.9% than in scenarios that did not include district cooling
system.
DC 2050
no-DC 2050
Socio-economic costs
(100 million USD)
DC 2030
PES (TWh)
CO2 (Mt)
no-DC 2030
Referent
0
50
100
150
200
250
Figure 3. Comparison of different economic and environmental scenarios
Peak capacities of absorbers that needed to be installed were 5,062 MWc (7,787
MWh) and 3,270 MWc (5,030 MWh) in the years 2030 and 2050. More detailed
technology
cost
assessment
can
be
seen
in
Appendix 1 – Technology costs
Discussion
Due to the rapid economic development of Singapore in the last 50 years, cooling energy demand and
accompanied power consumption have grown rapidly. Consequently, substantial amounts of waste heat
are currently being rejected to the environment as strategic development of waste heat utilization
infrastructure, such as the district cooling grids considered here, was not high enough on the agenda of the
stakeholders involved in planning of the city’s energy systems. Since they are easier to install and
implement in the short term, electric chillers and split-system units are the dominating cooling machines
used in Singapore today. However, our results from the potential scenarios for years 2030 and 2050
showed that energy systems that include district cooling can potentially be cheaper in socio-economic
terms, as well as more energy efficient in terms of primary energy consumption and less environmental
harmful in terms of CO2 emissions. As the benefits of the district cooling in Singapore are threefold, more
debate among the different interested stakeholders should be facilitated in order to obtain wider
acceptance of this technology.
The main benefit of the district cooling grid is that it can link the energy sources, which are usually highly
concentrated in several geographic locations, often where local demand is limited, with energy demand
across the city. If electricity production from gas power plants and waste incineration plants could be
coupled with heat recovery utilized via absorption chillers, less primary energy supply would be wasted,
less natural gas would need to be imported and far less chiller cooling towers would be evident across the
urban landscape. Furthermore, by avoiding distributed electric chillers and associated cooling towers on
rooftops of buildings more space would be made available for the installation of solar PV capacity.
Moreover, removing the need for many cooling towers inside the densely populated regions would reduce
the heat island effect, that was shown to be an issue that needs to be seriously dealt with in Singapore [35].
One should also note that several sources of uncertainty exist in the results. First, variable costs, with the
majority being fuel costs, had a share between 69-81% in total system costs in different scenarios. Thus,
oscillations in fuel prices can significantly impact the overall economic results. Second, estimation of
technology and infrastructure costs a long way in the future is complicated to carry out so the results of the
investment costs also need to be taken into account. Third, CO2 emission costs represent a share from
9.5% to 12.1% in total system costs. As this is already an important share in costs, estimation of the carbon
tax for the years 2030 and 2050 bring another uncertainty when reporting total system costs. On the other
hand, environmental benefits in terms of CO2 emission reduction and increased energy efficiency are
straightforward in calculation and these results carry less uncertainty than total socio-economic costs do.
Although Singapore can be regarded already as a developed economy, many neighboring countries in South
East Asia are currently undergoing rapid development. SE Asia’s energy demand has grown by 50%
between 2000 and 2013 and IEA estimates that it will further grow by an additional 80% through to the
year 2040 [4]. IEA noted in their projections that the capacity in the SE Asia’s power sector will triple by
2040, increasing generation capacity by 400 GW, the majority of this new capacity being represented by
coal power plants [4]. Clearly this significant additional new capacity that is envisaged in this area will also
create large-scale opportunities for waste heat recovery. In 2040, it is envisaged that in SE Asia 2,837 TWh,
1,081 TWh and 267 TWh of electricity will be generated from coal, gas and bioenergy powerplants,
respectively [4]. If all of the latter energy will be produced in power only plants, more than 2,000 TWh of
waste heat will be rejected to the environment. This tremendous amount of energy can be successfully
utilized for district cooling, significantly improving the energy efficiency of the SE Asia’s energy systems. As
cooling energy demand usually increases in tropical and sub-tropical regions together with the rise in
economy, SE Asia presents an important opportunity for systematically recovering waste heat during
electricity generation and distributing cooling energy via district cooling grids. As such significant
investments in the energy sector are expected in the region until at least the year 2040 and it is therefore
important to consider strategically the most suitable scenarios for the future to avoid sub-optimum
infrastructure decisions being made today. It can be noted from our work that establishing district cooling
grids now, before the further major building expansion in the region occurs, may potentially lower the
infrastructure costs of grids. Finally, as Singapore is already a developed economy it has an opportunity to
become a regional leader in terms of research on district cooling grids, as well as in industrial solutions
connected with the district cooling grids, establishing the possibility to transfer technology and know-how
across the region as it rapidly develops in the future. Thus, a win-win situation for both developed and
developing economies in the SE Asia is possible.
Finally, opportunity for waste heat recovery is present and shown multiple times; however, it is a lack of
clear business model and incentives that is missing to successfully roll-out waste heat recovery on larger
scale. Policy measures in the future should address the problem that the electricity generation companies
face with when they invest in increased energy efficiency measures – consequently, they reduce the energy
sales, curbing their income. Successful policy measures should try to transfer certain benefits from reduced
socio-economic and health costs to the businesses investing in a new technology and/or infrastructure.
Acknowledgements
This work was financed by the EliteForsk grant for young talented researchers awarded by Danish Ministry
of Higher Education and Science. The work was carried out as a part of the CITIES project no DSF130500027B funded by the Danish Strategic Research Council. This research is also supported by the National
Research Foundation, Prime Minister’s Office, Singapore under its Energy NIC grant (NRF Award No.: NRFENIC-SERTD-SMES-NTUJTCI3C-2016.
Conclusions
The main conclusions that can be drawn from this paper are:
 District cooling systems appear to be a viable investment in both targeted years, i.e. in years 2030
and 2050.
 Benefits of integration of district cooling in energy systems are potentially threefold: it lowers total
socio-economic costs, reduces CO2 emissions and increases efficiency of the energy system.
 In the scenario that included district cooling, carried out for the year 2030, CO2 emissions were
lower 11.5%, primary energy supply was 12.2% lower while total socio-economic costs were 4.6%
lower than in scenario that did not include a district cooling system.
 Similarly, in the scenario that included district cooling and carried out for the year 2050, CO2
emissions were 9.9% lower, primary energy supply 10.2% lower and the total socio-economic costs
were 3.8% lower compared to the scenario that did not have district cooling system implemented.
 Finally, additional benefits of district cooling system could be quantified in future research, such as
curbing the heat island effect and recovering additional energy being wasted currently, such as
from large data centers.
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Appendix 1 – Technology costs
Year 2030
The following costs were included only in the DC 2030 scenario:
Year 2050
The following costs were included only in the DC 2050 scenario:
Appendix 2 – Fuel prices
Year 2030 [21]:
Year 2050 [21]: