- TECHNICAL ARTICLE
- Open Access
District cooling network optimization with redundancy constraints in Singapore
- Johannes Dorfner^{1}Email author,
- Patrick Krystallas^{3},
- Magdalena Durst^{2} and
- Tobias Massier^{4}
- Received: 2 September 2016
- Accepted: 7 December 2016
- Published: 3 January 2017
Abstract
This study presents a mathematical optimization model for planning topology and capacity of a district cooling network. The model relies on a mixed-integer linear programming formulation to find the most economic network layout while satisfying redundancy criteria against unavailability of cooling stations. The simplicity of the formulation makes it easy to embed in other models or to extend it to other redundancy cases.
The model is applied to a case study in the central business district of Singapore. Results show that district cooling is a profitable option for Singapore, especially due to its constant high cooling demand that is currently satisfied mainly through decentralized cooling units. This result generalizes to tropical cities world-wide with high cooling demand density.
Keywords
- District cooling
- Mathematical optimization
- Mixed-integer linear programming
- Redundancy
Introduction
District cooling has been established in many countries. Especially in bigger cities, it offers an energy efficient alternative to individual generation of cooling power at the site of customers. Even in a city like Munich, Germany, which is located in a temperate climate zone, several district cooling systems have already been installed successfully and maintained.
In contrast to Munich, in Singapore, warm and humid weather prevails all year long, which results in much higher demand for cooling and dehumidification both in residential and non-residential buildings. However, in most buildings, cooling power is generated by using small chillers that use air for re-cooling which is very inefficient. As a result, much electricity is used to operate the chillers. In this case study, we model a district cooling network for the central business district (CBD) of Singapore. We present several cases and a comparison to conventional individual cooling.
Singapore’s CBD comprises mainly commercial buildings, e.g. shopping malls, hotels and office blocks, often with more than 50 floors. Thus, it stands in contrast to other areas of Singapore which comprise more residential or industrial buildings. Due to the different type of use of commercial buildings with the air conditioning running all day long and amounting to approximately 50% of each building’s energy demand, the introduction of district cooling could lead to tremendous energy savings.
Singapore already has a large underground district cooling system in the Marina Bay. The system contains two plants with a maximum of 330MW of cooling power. [1] According to the Singapore District Cooling Pte Ltd (SDC), a subsidiary of Singapore Power, energy savings amount to 40% which corresponds to the energy demand of 24 000 three-room flats operated by Singapore’s Housing Development Board (HDB).
State of projects
State of research
This is a short summary of existing publications on district cooling in Singapore, district cooling in general, as well as similar or prominent approaches for district heating or more general multi-commodity district energy systems.
Cooling in Singapore
Apart from the aforementioned existing district cooling system [1], a recent study on Singapore [7] focused on comparing surface and subsurface cooling in tropical countries using an EnergyPlus model of a representative building. It concludes that an open-loop groundwater cooling system might be a better option for tropical cities like Singapore compared to conventional systems. The viability of district cooling in the CBD area of Singapore has already been assessed in a working paper [8] by Singapore Power Ltd.
District cooling
A thermal ice storage system and its influence on the cost-optimal design of district cooling distribution network is investigated [9] using non-linear optimization in two exemplary networks. It finds that ice storage can help reduce the required pipe diameters. A TRNSYS model is built to investigate a similar question [10] for a case study in Hong Kong; storage is not found to be an economically attractive option there.
A genetic algorithm is used [11] to assess the optimum shares of five building types to form a cooling load time series that is most suitable for district cooling. This approach was later embedded in an optimization scheme for a development project in South East Kowloon, Hong Kong. Another genetic algorithm was used by Feng and Long [12] to determine and compare piping layouts.
Khir [13] presents a mixed integer linear programming (MILP) model for designing district cooling system. It explicitly models flow rates, temperatures and pressures in a graph of arcs and nodes. Linearization of the physical constraints leads to a large amount of auxiliary variables. Both plant design and operation and network design can be optimized. However, to achieve a tractable problem size, those two subproblems are solved separately. However, this separation requires fixing the supply task. The paper also highlights the relationship with Steiner tree problems.
Södermann [14] presents a similar MILP model for designing district cooling systems in urban areas. It uses a linearized cost function for sizing equipment (plants, pipes, storages). A small set of representative load periods (8 in the example study) approximate the annual cooling load duration curve.
District heating
Due to the basic principle, district cooling and heating are comparable types of supply. In both types, with few central generation plants heat or cold is generated which is mostly transferred to water. This tempered water is transported through pipes to customers buildings. Having transferred the heat or cold to the buildings, the cooled or heated water returns to the generation plants where the process starts from the beginning. The biggest difference between both are the technologies used in generation plants, which is not the focus of this article.
Jamsek [15] presents a linear programming (LP) approach for determining cost-minimal pipe topology and capacity configuration for a given structure and cost data. A more conceptual, but worthwhile discussion of district heating and cooling systems is given by Rezaie [16].
Udomsri [17] presents and compares the different options to combine centralized and decentralized heating and cooling. It points towards solutions that use decentralized, thermally driven chillers to produce cooling on-site. This configuration can have advantages in regions with both heating and cooling demands throughout the year.
Multi-commodity district energy system
A detailed model on combined energy system planning for electric, heating and cooling demand is presented by Chinese from 2008 [18]. It presents a MILP model. A case study in Udine, Italy, is performed with 7 nodes and 41 individually weighted time steps to represent the seasonal demand characteristics of a full year.
A recent paper [19] investigates the case of combined district energy system for electricity, heating and cooling using a mixed integer linear optimization problem. Both spatial (7 nodes) and temporal (2 days in 48 time steps) resolution are low in the presented case study, but the non-linear performance of gas turbines is represented by a set of linearized constraints.
A broader review of computer models for renewable energy system analysis and optimization is provided by Connolly et al. [20]. Weber presents in her thesis [21] a comprehensive methodology to plan cost-optimal polygeneration energy systems.
This work
This paper presents the generalized version of a previously published model developed for planning of district heating distribution networks [22]. That model has then been adapted for optimization of district cooling systems and applied in a small case study for the city center of Munich [23].
Model
This section describes a mathematical optimization model to represent the planning task for a minimum cost district cooling network. Its main input is a connected graph of street segments that represent the discrete possible network parts. Each segment – called edge in the following – has the attributes length and a cooling demand that may (but does not have to) be satisfied by the district cooling network. A subset of the graph’s vertices may provide cooling at fixed specific costs, representing (possible) cooling stations.
The model’s main contribution lies in a simple representation of redundancy constraints by means of artificial time steps (or periods) with pre-determined reduced availability of cooling stations. This technique can easily be embedded into other optimization models and can increase the robustness of obtained solutions against equipment downtime due to outages or maintenance.
Overview
To improve performance for large study regions, the model employs a linearized cost function for sizing the thermal pipe capacity. Depending on the parameterization, one can either cautiously overestimate the real estimated costs or try to fit as closely the observed cost function. In the latter case, one typically slightly underestimates the cost of medium capacities, while overestimating the cost of small and large capacities.
Mathematical description
Sets
A district or city is represented as a graph of vertices and arcs. This graph is derived from the street network. It should be derived in such a way to include all considerable locations for network pipes. The spatial resolution can be chosen as fine as required. Here, it is on the level of building blocks, i.e. a single street segment between two intersections.
Let V be the set of vertices v _{ i }, corresponding to connecting or terminating points of the graph. Set A of arcs then comprises ordered tuples of vertices a _{ ij }=(v _{ i },v _{ j }) with i≠j. A is symmetric, that means either both or none of the pair a _{ ij } and a _{ ji } are elements of A. For readability, the subscript ∘_{ i } is used to denote any parameter or variable that is defined over vertices v _{ i }∈V, while ∘_{ ij } is used to denote a quantity defined over arcs a _{ ij }∈A.
The set V _{0}⊆V defines so-called source vertices, which represent locations of possible cooling sources. Usually, the number of source vertices is low (≤10) compared to the size of the graph.
The neighbor sets N _{ i } of a vertex v _{ i } are defined by the indices of all vertices that are connected to it by an arc: N _{ i }={ k∣a _{ ki }∈A }.
Time is represented by a set T of discrete time steps t, which represent a small number of representative operational situations. The minimum viable number of time steps is two: one for peak demand with a duration of one to several hours, a second for the average annual load with a duration of the remaining year. A more comprehensive choice are three to four time steps: The third can refer to the all-year base load, while the fourth can be used to represent a common intermediate load level. Additional time steps need to be introduced if redundancy requirements are to be stated. Refer to the discussion of the parameter availability below for more details.
Parameters
Optimization model parameters
Name | Unit | Description |
---|---|---|
c _{fix} | S$/m | Fixed investment costs |
c _{var} | S$/(kW m) | Variable investment costs |
c _{om} | S$/(m a) | Operation & maintenance costs |
c _{rev} | S$/kWh | Revenue for cooling |
w ^{fix} | kW/m | Fixed thermal losses |
w ^{var} | kW/(kW m) | Variable thermal losses |
b | — | Concurrence effect |
q | — | Connect quota |
u | 1/a | Annuity factor for investment costs |
\(c^{\text {cool}}_{i}\) | S$/kWh | Cooling costs at source vertices |
\(Q^{\text {max}}_{i}\) | kW | Source vertex capacity |
l _{ ij } | m | Arc length |
d _{ ij } | kW | Arc peak demand |
g _{ ij } | — | Existence of a pipe (1=yes, 0=no) |
\(C^{\text {max}}_{ij}\) | kW | Maximum pipe capacity |
s _{ t } | 1 | scaling factor |
w _{ t } | h | weight/duration |
y _{ it } | — | availability (1=yes, 0=no) |
Vertices have a single parameter: their maximum power capacity \(Q^{\text {max}}_{i}\). It is the thermal output power given in kW for that location. All non-source vertices have this parameter set to 0.
Arcs have three main attributes: their length l _{ ij } (m), the thermal peak demand d _{ ij } (kW) of their adjacent buildings and g _{ ij } (binary), which states whether a pipe already exists in this arc.
As secondary parameter, \(C^{\text {max}}_{ij}\) indicates the maximum thermal power capacity (kW) in an arc, which can be derived from the maximum available pipe diameter. For arcs with existing pipes (g _{ ij }=1), this value should be set according to the diameter of that existing pipe. As both arcs a _{ ij } and a _{ ji } refer to the same street segment, parameter values for both members are always identical.
Global parameters are technical and economic parameters that refer to the district cooling system as a whole.
Economic parameters are all costs and revenues. The investment costs are split into a fixed part c _{fix} and a variable part c _{var}. The fixed part, given in S$/m, captures all costs that are not dependent on the capacity of the pipe to be built, mainly earth works. The variable part, given in S$/(kW m), refers to the capacity-dependent cost component of building a pipe. Both values must be tailored to the study area to compensate for differences in cost structure and availability of different pipe sizes. The same is true for the operation & maintenance (OM) cost parameter c _{om} in S$/m. In contrast to the fixed investment cost term, it is also to be paid for existing pipes. The cost for providing cooling \(c^{\text {cool}}_{v}\) here is dependent on the vertex to allow for representing cheap surface water cooling in contrast to more expensive re-cooling options.
The letter c and a subscript denote economic parameters. These are investment costs for building the pipe network c _{fix} and c _{var}, maintenance c _{om}, costs of providing \(c^{\text {cool}}_{i}\) cooling, and revenue for delivering c _{rev} cooling to consumers. While c _{fix} contains the size-independent costs (mainly earth works), c _{var} contains costs that are dependent on the thermal capacity (diameter) of the pipe.
Time-dependent parameters s _{ t } and w _{ t } represent scaling factor (dimensionless) and weight or duration (h) of a time step t. A value of s _{ t }=1 refers to peak demand, while smaller values correspond to moments of partial load. Together, these two parameters encode a discretized annual load curve.
Redundancy requirements can be stated in the model by setting the binary availability parameter y _{ it } to value 0 in certain time steps for 1,2,… source vertices. If one time step for each foreseen failure configuration is introduced, a full n−1,n−2,… failure safety (against cooling source outages) can be enforced in the produced solution. Clarification: in the conventional time steps discussed above in paragraph time step set, the value y _{ it }=1 is to be set for all source vertices v _{ i }∈V _{0}.
Variables
The main optimization task is to find values for the binary decision variable ξ _{ ij }. If its value is one, a pipe is built in the street segment corresponding to the arc a _{ ij }. For each time step, the actual use of a given pipe is decided by setting the binary pipe usage decision variable ψ _{ ijt }. If its value is one, the pipe in arc a _{ ij } is used in direction from vertex v _{ i } to vertex v _{ j }. Consequently, there must be a power flow \(\pi ^{\text {in}}_{ijt}\) into the pipe. Simultaneously, a value ξ _{ ij }=1 requires that the demand d _{ ij } of this arc has to be satisfied at all times. The power flow variable at the other end of the pipe is called \(\pi ^{\text {out}}_{ijt}\). Its value is determined by the ingoing power flow minus losses and demand.
Optimization model variables
Name | Unit | Description |
---|---|---|
ζ | S$ | Total system cost (Inv, O&M, Rev, Gen) |
ξ _{ ij } | — | Binary decision variable: 1 = build pipe |
ψ _{ ijt } | — | Binary decision variable: 1 = use pipe |
\(\overline {\pi }_{ij}\) | kW | Thermal power flow capacity into arc a _{ ij } |
\(\pi ^{\text {in}}_{ijt}\) | kW | Thermal power flow from v _{ i } into arc a _{ ij } |
\(\pi ^{\text {out}}_{ijt}\) | kW | Thermal power flow out of arc a _{ ij } into v _{ j } |
ρ _{ it } | kW | Cooling in source vertex v _{ i } |
Equations
Equations fall into two categories: The first is the cost function whose value is to be minimized. The second are constraints that codify all the previously discussed rules in mathematical form. By that, they define the region of feasible solutions, under which the solver selects a (close to) cost optimal solution.
This inequality is thus a relaxed version of the law of energy conservation. Relaxed, because it allows for power to vanish at any vertex. As power does not come for free, any solution returned by the solver will satisfy this constraint to equality. For all non-source vertices (i.e. ρ _{ it }=0), the difference between outgoing and incoming power must be smaller than or equal to zero. In source vertices, a positive difference may remain when it is met by an equal amount of input power ρ _{ it } into the network at that vertex.
Equations 2 to (18) together define a linear mixed-integer program, whose optimal solution is the desired vector of pipe capacities \(\boldsymbol {\bar {\pi }}^{\star }\), accompanied by optimal cooling power flows.
Case study
Load profile estimation
Due to lack of access to a load profile from Singapore, a known cooling load from Munich was extrapolated based on weather data in Singapore.
The district cooling system of Munich has mostly non-residential customers like shopping malls, offices, congress centers and smaller retail trades. Its usage type composition is therefore rather similar to Singapore’s CBD. However, the buildings are much smaller and thus have a different surface to volume ratio and surface materials than the much higher buildings in Singapore’s CBD.
To record and to utilize the optimization potential of this expanding system, in a detail data analysis a question amongst others was examined on which factors the customers cooling demand is depending. Furthermore the collected data serve as basis for the evaluation of the cooling demand in other cities like Singapore. Based on a period of two years up to 2015 the influence of temperature, humidity, insolation and the customer behavior itself was analyzed.
The strongest influence on the cooling demand is the temperature. The humidity plays a subordinated role in Munich (but not in Singapore), cause of the temperate climate zone and the previous low dehumidification in the comfort zones. To count in this factor to the further customer and grid extension, the enthalpy of the outside air, as an index for the heat input to the buildings, is used as leading control parameter. The insolation has no considerable influence on the customers’ requirements because of the good insulation of the rather new buildings and the shading situation in the densely built city center.
The second strongest influence factor on the cooling demand of the Munich costumers are the business hours. Due to the simultaneous public traffic in the shopping areas and offices as well as the highly volatile temperature profile of a day, the cooling demand is five times higher during the day than at night or on a holiday. This characteristic day-night cycle are also recorded in other Central European cities ([24], pp. 8, 80).
To reduce the temporal resolution for the optimization model to manageable sizes, this curve is discretized to 8 individually weighted time steps, also shown. One 24 h long time step represents peak demand, while the other 7 steps have durations ranging from 390 h to 2006 h.
Annual cooling demand
Total floor area and cooling peak load by building type
Building type | Peak load | Floor area |
---|---|---|
(W/m^{2}) | (10^{3} m^{2}) | |
Civic & community institution | 112.5 | 529 |
Commercial | 112.5 | 6685 |
Commercial & residential | 100 | 1021 |
Educational institution | 112.5 | 175 |
Health & medical care | 125 | 40 |
Hotel | 112.5 | 1605 |
Open space | 0 | 22 |
Park | 0 | 58 |
Place of worship | 0 | 137 |
Reserve site | 0 | 157 |
Residential | 112.5 | 717 |
Residential/commercial (1st storey) | 112.5 | 357 |
Sports & recreation | 75 | 15 |
Transport facilities | 0 | 29 |
Utility | 0 | 43 |
Input data
The street graph is derived from OpenStreetMap data [26], processed using previously described [22] pre-processing steps using polygon skeletonization for simplifying the dense street network.
Parameter values in case study
Name | Value | Unit |
---|---|---|
c _{fix} | 7000 | S$/m |
c _{var} | 8e-4 | S$/(m kW) |
c _{om} | 80 | S$/(m a) |
c _{rev} | 0.14 | S$/kWh |
w ^{fix} | 0.01 | kW/m |
w ^{var} | 1e-8 | kW/(kW m) |
b | 0.9 | — |
q | 1.0 | — |
u | 0.091 | 1/a |
\(c^{\text {cool}}_{i}\) | 0.03 or 0.07 | S$/kWh |
Scenarios
Base case
The base case is designed to show today’s demand situation. In this scenario, the size and layout of a profitable district cooling network is to be determined.
In the base case, a revenue of 0.14 S$/kWh is assumed. A sensitivity analysis with reduced revenues in steps of 0.02 S$/kWh is also conducted to determine how sensitive the optimal network size is on the price for cooling.
Growing demand
In order to assess how the total cooling costs (generation and distribution) are affected by a possible demand growth in the study area, additional load is introduced in the edge (54, 114) in the south-eastern corner. It’s value is changed from 0MW to 200MW with steps of 50 MW. As this step creates more load than the existing cooling stations could satisfy while satisfying the redundancy constraint, all cooling stations’ capacity is increased by 50%.
Results
Base case
Growing demand
Discussion and future research
District cooling can be an attractive option for cities with constant high cooling load, compared to less efficient distributed cooling. The high demand density of Singapore’s CBD makes it a very well suited candidate. Direct access to surface and seawater provides enough cooling potential to satisfy a significant fraction of the present cooling loads.
As Singapore is planning to establish further dense commercial areas similar to the CBD with many office blocks, e.g. in Jurong, more regions will become suitable candidates for district cooling. Moreover, the potential of district cooling could also be evaluated for densely populated residential areas, e.g. Bukit Panjang or Punggol.
The redundancy requirements on cooling stations could need refinement, possibly by relaxing the full (n-1) capability for the whole system to only certain areas. An extension could introduce the same requirement not only to cooling station, but also to crucial parts of the network.
Declarations
Acknowledgements
We express our gratitude to the Singapore Land Authority for supporting us with geospatial data.
This work was financially supported by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme. This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program.
Availability of data and materials
The optimization model’s implementation is available under the GPL 3.0 license at https://github.com/tum-ens/dhmin. The model input data is available under the CC-BY license via doi:10.6084/m9.figshare.3582456.
Authors’ contributions
JD developed and applied the optimization model to the case study. PK lead his expertise in planning district cooling networks to provide input data and scenario definitions. MD contributed the method for deriving the annual load duration curve. TM provided data for the case study region and vetted model results against local experience. All authors collaborated for writing the article. All authors have read and approved the final submitted manuscript.
Competing interests
The authors declare that they have no competing interests.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
- Singapore District Cooling PTE Ltd (2010) District Cooling as a Utility Service At Marina Bay Business District. http://tinyurl.com/sppg-dc-marina-bay. Accessed 28 Jun 2016.
- Terouanne D (2008) Climespace Cooling Paris, Naturally. http://tinyurl.com/terouanne2010. Accessed 07 Sep 2015.
- Inpal Energie (2010) 70 Km Fernkältenetz in Paris. http://www.inpal.com/Fernk%C3%A4lte-Climespace-Paris-.html. Accessed 26 Jun 2016.
- Kontrollamt der Stadt Wien (2012) Fernwärme Wien Gesellschaft m.b.H., Prüfung des Geschäftsfeldes Fernkälte. Tätigkeitsbericht KA IV - GU 45-4/12, Vienna http://www.stadtrechnungshof.wien.at/berichte/2012/lang/01-15-KA-IV-GU-45-4-12.pdf. Accessed 08 Sep 2015.
- Hill A, Keilaniemi F (2012) District Heating an Cooling in Stockholm: Site Visit to the Ropsten Plant. http://www.geopower-i4c.eu/docs/20120503-Anders_Hill-Fortum.pdf. Acessed 08 Sep 2015.
- Stadtwerke München (2015) M-Fernkälte. https://www.swm.de/english/m-fernwaerme/m-fernkaelte.html. Accessed 13 Mar 2016.
- Liu Y, Qin XS, Chiew YM (2013) Investigation on potential applicability of subsurface cooling in Singapore. Appl Energy 103: 197–206. doi:10.1016/j.apenergy.2012.09.024.View ArticleGoogle Scholar
- Kee TP (2010) District cooling as an energy and economically efficient urban utility – its implementation at marina bay business district in singapore White paper, Singapore District Cooling Pte Ltd.Singapore.Google Scholar
- Augusto GL, Culaba AB, Maglaya AB (2015) Identification of design criteria for district cooling distribution network with ice thermal energy storage system. Energy Procedia 79: 233–238. doi:10.1016/j.egypro.2015.11.470.View ArticleGoogle Scholar
- Chan ALS, Chow TT, Fong SKF, Lin JZ (2006) Performance evaluation of district cooling plant with ice storage. Energy 31(14): 2750–2762. doi:10.1016/j.energy.2005.11.022.View ArticleGoogle Scholar
- Chow TT, Chan ALS, Song CL (2004) Building-mix optimization in district cooling system implementation. Appl Energy 77(1): 1–13. doi:10.1016/s0306-2619(03)00102-8.View ArticleGoogle Scholar
- Feng X, Long W (2008) doi:10.1109/icnc.2008.196 In: Fourth International Conference on Natural Computation.. Institute of Electrical & Electronics Engineers (IEEE), Jinan.
- Khir R, Haouari M (2015) Optimization models for a single-plant district cooling system. Eur J Oper Res 247(2): 648–658. doi:10.1016/j.ejor.2015.05.083.MathSciNetView ArticleMATHGoogle Scholar
- Söderman J (2007) Optimisation of structure and operation of district cooling networks in urban regions. Appl Therm Eng 27(16): 2665–2676. doi:10.1016/j.applthermaleng.2007.05.004.View ArticleGoogle Scholar
- Jamsek M, Dobersek D, Goricanec D, Krope J (2010) Determination of optimal district heating pipe network configuration. WSEAS Trans Fluid Mech 5: 165–174.Google Scholar
- Rezaie B, Rosen MA (2012) District heating and cooling: Review of technology and potential enhancements. Appl Energy 93: 2–10. doi:10.1016/j.apenergy.2011.04.020.View ArticleGoogle Scholar
- Udomsri S, Bales C, Martin AR, Martin V (2012) Decentralized cooling in district heating network: System simulation and parametric study. Appl Energy 92: 175–184. doi:10.1016/j.apenergy.2011.10.009.View ArticleGoogle Scholar
- Chinese D (2008) Optimal size and layout planning for district heating and cooling networks with distributed generation options. Int J Energy Sector Manag 2(3): 385–419. doi:10.1108/17506220810892946.View ArticleGoogle Scholar
- Ameri M, Besharati Z (2016) Optimal design and operation of district heating and cooling networks with cchp systems in a residential complex. Energy Buildings 110: 135–148. doi:10.1016/j.enbuild.2015.10.050.View ArticleGoogle Scholar
- Connolly D, Lund H, Mathiesen BV, Leahy M (2010) A review of computer tools for analysing the integration of renewable energy into various energy systems. Appl Energy 87(4): 1059–1082. doi:10.1016/j.apenergy.2009.09.026.View ArticleGoogle Scholar
- Weber CI (2008) Multi-objective design and optimization of district energy systems including polygeneration energy conversion technologies. PhD thesis, École Polytechnique Fédérale de Lausanne, Bucharest (Romania).Google Scholar
- Dorfner J, Hamacher T (2014) Large-scale district heating network optimization. IEEE Trans Smart Grid 5(4): 1884–1891. doi:10.1109/TSG.2013.2295856.View ArticleGoogle Scholar
- Krystallas P, Dorfner J (2014) Expansion planning for district cooling networks using mathematical optimization: 38–42. EuroHeat Power Int 3:.Google Scholar
- Urbaneck T (2012) Kältespeicher: Grundlagen, Technik, Anwendung. De Gruyter Oldenbourg, Munich.View ArticleGoogle Scholar
- Boehme P, Berger M, Massier T (2015) Estimating the building based energy consumption as an anthropogenic contribution to urban heat islands. Sustain Cities Soc 19: 373–384. doi:10.1016/j.scs.2015.05.006, Accessed 03 Aug 2016.View ArticleGoogle Scholar
- OpenStreetMapLicensed Unter the Open Database License. http://www.openstreetmap.org/. Accessed 3 Aug 2016.
- Vattenfall GmbH (2015) Kälte Für Berlins Mitte. http://www.vattenfall.de/de/berliner-energiewende/kaelte-fuer-berlins-mitte.htm. Accessed 04 Sep 2015.
- Riipinen M (2013) IEA Internationale Energy Agency Joint Workshop. Helsinging Energia. https://www.iea.org/media/workshops/2013/chp/MarkoRiipinen.pdf. Accessed 03 Sep 2015.
- Wallisch A (2012) Fernkälte in Europa. Wien Energie GmbH. https://www.gaswaerme.at/de/pdf/10-1/wallisch.pdf. Accessed 02 Sep 2015.
- Energie W (2014) Wien Energie Energieversorgung Fernkälte. http://www.wienenergie.at/eportal2/ep/channelView.do/pageTypeId/67829/channelId/-48288. Accessed 31 Jul 2016.