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Master of Science Thesis KTH School of Industrial Engineering and Management
Energy Technology TRITA-ITM-EX 2020:252 Division of Applied Thermodynamics and Refrigeration
SE-100 44 STOCKHOLM
Performance assessment in district cooling networks using distributed
cold storages – A case study
Zinar Bilek
i
Master of Science Thesis TRITA-ITM-EX 2020:252
Performance assessment in district cooling networks using distributed cold storages – A
case study
Zinar Bilek
Approved
Examiner
Viktoria Martin
Supervisor
Saman Nimali Gunasekara Commissioner
Norrenergi AB
Contact person
Ted Edén
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Abstract District cooling is a technology that has been gaining traction lately due to increased demand from the commercial and industrial sectors, especially in dense urban areas such as Stockholm. A literature study found that customers such as hospitals, offices, malls and data centres all depend on both comfort cooling and process cooling. District cooling networks operate by producing centralized cooling energy and distributing it to consumers through underground pipes. The produced cold therein is transferred via the district cooling network as chilled water, which is pumped through the heat exchangers located in the consumer facilities which enables maintaining the desired temperatures of the consumers’ intended facilities, by removing the additional heat. The literature review showed that cold storages, which are thermal energy storages, are used to peak shave and to help reduce the output of expensive chillers and heat pumps during peak demand hours.
The aim of this project is to evaluate the possibilities of using distributed cold storages in district cooling network as a means to reduce effects of distribution limitations the bottlenecks and increase distribution capacity. This project defines distribution limitations as areas with specifically low differential pressures. Additionally, the objective is to compare the costs between the scenarios. In this project, Norrenergi AB’s district cooling network is used as a case study. Norrenergi AB is an energy company located in Solna that supplies district heating and cooling to customers, mainly in the Solna and Sundbyberg region. The company delivers roughly 1 000 GWh of district heating and 70 GWh of district cooling annually.
Three scenarios with various configurations of storage size and location are developed and calculated in the network simulation software NetSim, which is a software that allows complex, dynamic simulations of energy networks. According to Norrenergi AB, the criteria for acceptable network operation is that the differential pressure is required to stay between 100-800 kPa. In Scenario 1 & 2, a 15 MW cold storage is implemented in Sundbyberg and Frösunda, respectively. In Scenario 3, two smaller storages with a capacity of 3 MW each are installed in both Sundbyberg and Frösunda. For all scenarios, the energy need to fully charge the storages is calculated along with the charging/discharging profiles of the storages, which are later used as input in NetSim. In all scenarios, the storages charge during the night-time and discharge during peak hours. The main results that can be concluded from this thesis is that all scenarios led to cost savings in terms of daily production cost. The daily cost savings for each of the scenarios were 2.7%, 4.8% and 4.3%, respectively.
In addition to this, the effects of distribution limitations in the network are analysed with regards to the differential pressures. The results indicate that although Scenario 3 displayed only the second lowest production cost, it greatly reduced the effects of distribution limitations in key areas compared to that of Scenario 2 which showed abnormally low differential pressures during peak hours, leading to cooling not being delivered. With these aspects in mind, the deduction is that a combination of the capacity size similar to those of scenarios 1 & 2, combined with the capacity distribution in Scenario 3 should be the optimal setup in the future. Furthermore, cold storages can help reduce the use of chillers and thus help reduce the use of harmful refrigerants in the system. Future iterations of this model should consider the possibilities of including new consumers and optimized charging/discharging profiles of the storages. Variations of the temperature difference should be included as well since an increase/reduction of the temperature difference can directly affect storage capacities.
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Sammanfattning Fjärrkyla är en teknik som på senare år har ökat markant då behoven från de kommersiella och industriella sektorerna har ökat, speciellt i tätbebyggda områden som Stockholm. En litteraturstudie fann att kundbasen omfattas av exempelvis sjukhus, kontor, köpcenter och serverhallar och behovet för både process- och komfortkyla är högt. Fjärrkyla fungerar genom att producera kyla centralt och leverera detta till konsumenter som är kopplade till nätet genom distributionsrör under jorden. Den producerade kylan tar sedan upp överskottsvärme från konsumenter genom värmeväxlare som är belägna i konsumenternas byggnader vilket möjliggör bibehållning av en bekvämlig inomhusmiljö. Litteraturstudien visade också att kyllager är termiska energilager som används för att jämna ut topplaster under dygnet samt för att reducera fjärrkylaproduktion som kommer direkt från dyra kylmaskiner och värmepumpar.
Målet med detta projekt är att undersöka möjligheterna att använda distribuerade kyllager i fjärrkylanätet med syftet att minska effekterna av de distributionsbegränsingar som uppstår vid drift, samt öka leveranskapaciteten. Distributionsbegränsningarna definieras i detta projekt som låga differenstryck. Utöver detta är också målsättningen att jämföra kostnaderna mellan de olika scenariona. I denna uppsats används Norrenergi AB:s fjärrkylanät som fallstudie. Norrenergi AB är ett energibolag som producerar och levererar både fjärrvärme och fjärrkyla till kunder i områden som Solna och Sundbyberg med omnejd. Företaget levererar årligen cirka 1 000 GWh fjärrvärme och 70 GWh fjärrkyla.
Tre scenarion med varierande konfigurationer med hänsyn till storlek och plats har utvecklats och beräknats på i nätberäkningsprogrammet NetSim som används för komplexa och dynamiska beräkningar av olika energinät. Kravet för att nätverket ska kunna säkerställa leveranser, enligt Norrenergi AB, är att differenstrycket håller sig inom intervallet 100–800 kPa. I Scenario 1 & 2 installeras ett 15 MW kyllager i Sundbyberg respektive Frösunda. I Scenario 3 installeras två mindre kyllager á 3 MW i både Sundbyberg och Frösunda. För alla scenarion beräknas den totala energimängd som krävs för att fylla kyllagren och deras laddning- och urladdningsprofiler som sedan används som indata i NetSim. I alla scenarion laddas kyllagren under natten och laddar ur under dagen då behovet är som högst. De viktigaste resultaten som kan sammanställas ur denna uppsats är att alla scenarion leder till kostnadsbesparingar vad gäller den dagliga produktionskostnaden. Dessa kostnadsbesparingar är 2.7%, 4.8% respektive 4.3%.
Dessutom undersöktes flaskhalsarna i fjärrkylanätet där den huvudsakliga parametern var differenstrycket. Resultaten från den analysen påvisar att trots lite högre produktionskostnad än Scenario 1 & 2, hjälper Scenario 3 till att minska de flaskhalsar som uppstår i nyckelområden i jämförelse med exempelvis Scenario 2 som visade extremt låga differenstryck under höglasttimmar, vilket ledde till att en andel av kylan inte levererades till konsumenterna. Givet dessa faktorer är slutsatsen att en kombination av den ungefärliga lagerstorleken från Scenario 1 & 2 samt kapacitetsdistributionen från Scenario 3 bör vara det bästa alternativet i framtiden. Vidare kan kyllager hjälpa till att reducera användandet av kylmaskiner och således minska förbrukningen av skadliga köldmedier i systemet. Framtida arbeten på denna modell bör överväga möjligheten att inkludera nya konsumenter i modellen samt optimerade laddning- och urladdningsprofiler för kyllagren. Temperaturdifferensen i detta projekt har antagits vara konstant men bör ändras för att få med ytterligare variationer då en ökning/minskning av temperaturdifferensen kan direkt påverka kapaciteten i kyllagren.
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Acknowledgments I would like to thank Saman Nimali Gunasekara, my supervisor at KTH, for providing insights, countless helpful comments and for the continuous support throughout the course of the project. Moreover, thank you to Viktoria Martin for supervising this project and for offering invaluable input.
Additionally, I would also like to express my gratitude to Ted Edén at Norrenergi AB for providing important data regarding the production, consumer loads for Norrenergi’s district cooling network as well as for motivating me to keep on working.
Furthermore, thank you to Susanna Videkull at Norrenergi AB for helping me with exporting the calculation model to NetSim, your input was invaluable.
A sincere thank you must also be expressed towards Staffan Stymne for giving me the opportunity of doing my master’s thesis at Norrenergi AB.
This work would not be completed if it were not for the guidance of Johan Dyrlind and Tobias Brändström at Vitec and the numerous hours they spent on troubleshooting my errors in NetSim.
Finally, I would like to thank my friends, family and my partner, Emma, for constantly supporting me no matter the endeavor.
Zinar Bilek
June 2020
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Table of Content Abstract ........................................................................................................................................................................... ii
Sammanfattning ............................................................................................................................................................ iii
Acknowledgments ........................................................................................................................................................ iv
1 Introduction .......................................................................................................................................................... 1
2 Research Questions .............................................................................................................................................. 2
2.1 Purpose ......................................................................................................................................................... 2
2.2 Scope ............................................................................................................................................................. 2
3 Background ........................................................................................................................................................... 3
3.1 Energy in Sweden........................................................................................................................................ 3
3.1.1 History ................................................................................................................................................. 3
3.1.2 Energy Supply ..................................................................................................................................... 3
3.1.3 Energy Use & Losses ........................................................................................................................ 4
3.1.4 Sectors .................................................................................................................................................. 5
3.2 Current Environmental Status .................................................................................................................. 6
3.2.1 Greenhouse Gases ............................................................................................................................. 6
3.2.2 Goals & Targets ................................................................................................................................. 6
3.2.3 Policies ................................................................................................................................................. 7
4 Literature Review ................................................................................................................................................. 8
4.1 District Cooling ........................................................................................................................................... 8
4.1.1 History ................................................................................................................................................. 9
4.1.2 DC Market .......................................................................................................................................... 9
4.1.2.1 Growth ............................................................................................................................................ 9
4.1.2.2 Present & Future .........................................................................................................................10
4.1.2.3 Market Distribution in Sweden .................................................................................................10
4.1.3 Demand .............................................................................................................................................11
4.2 Technology .................................................................................................................................................12
4.2.1 Free Cooling .....................................................................................................................................12
4.2.2 Chillers ...............................................................................................................................................14
4.2.2.1 Vapour-Compression Chillers ...................................................................................................14
4.2.2.2 Absorption Chillers .....................................................................................................................15
4.2.2.3 Adsorption Chillers .....................................................................................................................16
4.2.3 Heat Pumps ......................................................................................................................................17
4.2.4 Cold Storage ......................................................................................................................................17
4.3 Thermal Energy Storage ..........................................................................................................................17
4.3.1 Storage Classification .......................................................................................................................17
4.3.1.1 Sensible Heat Storages ................................................................................................................18
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4.3.1.2 Latent Heat Storages ...................................................................................................................18
4.3.1.3 Thermochemical Storages ..........................................................................................................19
4.3.2 Role in Energy Systems ...................................................................................................................20
4.3.3 Operating Strategies.........................................................................................................................20
5 Methodology .......................................................................................................................................................22
5.1 Norrenergi AB ...........................................................................................................................................22
5.1.1 History ...............................................................................................................................................22
5.1.2 DC Network .....................................................................................................................................22
5.1.3 Future Developments......................................................................................................................24
5.1.4 Price Model .......................................................................................................................................24
5.2 Network Simulation ..................................................................................................................................24
5.3 NetSim ........................................................................................................................................................25
5.3.1 Nodes and Pipes ..............................................................................................................................25
5.3.2 Boundary Conditions ......................................................................................................................26
5.3.3 Time series ........................................................................................................................................27
5.3.4 Core Equations .................................................................................................................................27
5.3.4.1 Differential Pressure ...................................................................................................................27
5.3.4.2 Temperature .................................................................................................................................28
5.3.4.3 Density and Viscosity .................................................................................................................28
5.3.4.4 Further Equations in NetSim ....................................................................................................29
5.4 Base Case Dimensioning ..........................................................................................................................29
5.4.1 Outline ...............................................................................................................................................29
5.4.2 Limitations and Criteria ..................................................................................................................29
5.4.3 Production Load ..............................................................................................................................30
5.4.4 Solnaverket Cold Storage ................................................................................................................33
5.4.5 Order of Merit ..................................................................................................................................36
5.5 Scenario Modelling ...................................................................................................................................37
5.5.1 Scenario 1 ..........................................................................................................................................37
5.5.2 Scenario 2 ..........................................................................................................................................41
5.5.3 Scenario 3 ..........................................................................................................................................42
6 Results & Discussion .........................................................................................................................................45
6.1 Base Case ....................................................................................................................................................45
6.2 Scenario 1 ...................................................................................................................................................51
6.3 Scenario 2 ...................................................................................................................................................57
6.4 Scenario 3 ...................................................................................................................................................62
6.5 Scenario Comparison ...............................................................................................................................67
6.6 Sensitivity Analysis ....................................................................................................................................69
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6.6.1 Sensitivity Analysis - Results & Discussion .................................................................................69
6.7 Sustainability Assessment ........................................................................................................................72
7 Conclusion ...........................................................................................................................................................74
8 Future Work ........................................................................................................................................................76
Bibliography .................................................................................................................................................................77
Appendices ...................................................................................................................................................................81
Appendix A: Capacity factor time series..................................................................................................................82
Appendix B: Pressure loss equations in NetSim ....................................................................................................86
Appendix C: Further thermal equations in NetSim ...............................................................................................87
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Table of Figures
Figure 1: Total energy supply 1970-2016 (TWh) (Swedish Energy Agency, 2018). ........................................... 3 Figure 2: Total energy supplied, Sweden 2016 (TWh) (Swedish Energy Agency, 2018). Adapted by the author. ............................................................................................................................................................................. 4 Figure 3: Energy use and losses by commodity, Sweden 2016 (TWh) (Swedish Energy Agency, 2018). Adapted by the author. ................................................................................................................................................. 4 Figure 4: Annual average temperature in Sweden between 1860-2019 (SMHI, 2019)....................................... 5 Figure 5: Energy consumption by sector, Sweden 2016 (TWh) (Swedish Energy Agency, 2018). Adapted by the author. ....................................................................................................................................................................... 5 Figure 6: Greenhouse gas emissions 1990-2017 (Swedish Environmental Protection Agency, 2018a). Adapted by the author. ................................................................................................................................................. 6 Figure 7: Basic overview of a general DC system with cold storage capacity. Temperature levels are approximate. ................................................................................................................................................................... 8 Figure 8: The increase of DC networks in Sweden between 1996 and 2012 (Energimarknadsinspektionen, 2013). ............................................................................................................................................................................... 9 Figure 9: DC supply (GWh) and network length (km) between 1996-2018 in Sweden (Johannesson, 2019). ........................................................................................................................................................................................10 Figure 10: Share of annual produced DC energy by county in Sweden (% of GWh) (Energimarknadsinspektionen, 2013). ......................................................................................................................11 Figure 11: The development of EHI and ECI (Dalin, Nilsson and Rubenhag, 2006). ...................................12 Figure 12: A conceptual image of the free cooling process in a DC network (Subratty and Riahi, 2016). ..13 Figure 13: A schematic of a basic refrigeration cycle (Energimarknadsinspektionen, 2013). Adapted by the author. ...........................................................................................................................................................................15 Figure 14: A schematic of a single stage absorption chiller (U.S. Department of Energy, 2016). Adapted by the author. .....................................................................................................................................................................16 Figure 15: An overview of different TES's (Socaciu, 2012). Adapted by the author. ......................................18 Figure 16: The heat storage process of a solid-liquid latent heat storage (Vivek and Goswami, 2018). Adapted by the author. ...............................................................................................................................................................19 Figure 17: The working principle of a TCS (Jerz et al., 2015). Adapted by the author. ..................................20 Figure 18: Comparison of different TES operating strategies (Lizana et al., 2018). Adapted by the author ........................................................................................................................................................................................21 Figure 19: Norrenergi's DC network (Norrenergi AB, 2018b). ...........................................................................23 Figure 20: A screenshot of the user interface in NetSim (Vitec, 2019). .............................................................25 Figure 21: Example picture illustrating the symbols of nodes and pipes in NetSim. The circles are nodes while the lines are connected between them. ..........................................................................................................26 Figure 22: Outline of base case in NetSim. The blue and red symbols represent production plants and accumulators.................................................................................................................................................................29 Figure 23: The hourly electricity price for SE3 region, August 2, 2018(Nord Pool, 2020). ............................30 Figure 24: Daily load curve and cut-off production for Norrenergi’s DC network. ........................................31 Figure 25: The charging and discharging profiles of the Solna storage. .............................................................36 Figure 26: The layout of Scenario 1. .........................................................................................................................37 Figure 27: Charging and discharging profile for the 15 MW storage. .................................................................41 Figure 28: The layout of Scenario 2. .........................................................................................................................41 Figure 29: The layout of Scenario 3. .........................................................................................................................42 Figure 30: Charging and discharging profile for a 3 MW storage for Scenario 3. ............................................43 Figure 31: A snapshot of the base case simulation in NetSim at 00:00. .............................................................45 Figure 32: A snapshot of the base case simulation in NetSim at 07:00. .............................................................46 Figure 33: A snapshot of the base case simulation in NetSim at 13:00. .............................................................46
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Figure 34: A snapshot of the base case simulation in NetSim at 19:00. .............................................................47 Figure 35: Comparison in produced cooling between real data and NetSim simulation.................................47 Figure 36: Share of cooling for each technology in the base case. ......................................................................48 Figure 37: Hourly production of cooling for the base case. .................................................................................49 Figure 38: Differential pressure levels for the production plants in the base case. ..........................................49 Figure 39: Differential pressure levels for the consumers in the base case. ......................................................50 Figure 40: The daily production cost for the base case. ........................................................................................51 Figure 41: A snapshot of the Scenario 1 simulation in NetSim at 00:00. ...........................................................51 Figure 42: A snapshot of the Scenario 1 simulation in NetSim at 07:00. ...........................................................52 Figure 43: A snapshot of the Scenario 1 simulation in NetSim at 13:00. ...........................................................52 Figure 44: A snapshot of the Scenario 1 simulation in NetSim at 19:00. ...........................................................53 Figure 45: Share of cooling for each technology in Scenario 1. ...........................................................................54 Figure 46: Hourly production of cooling for Scenario 1. .....................................................................................54 Figure 47: Differential pressure levels for the production plants in Scenario 1. ...............................................55 Figure 48: Differential pressure levels for consumers in Scenario 1. ..................................................................56 Figure 49: The daily production cost for Scenario 1. ............................................................................................56 Figure 50: A snapshot of the Scenario 2 simulation in NetSim at 00:00. ...........................................................57 Figure 51: A snapshot of the Scenario 2 simulation in NetSim at 07:00. ...........................................................57 Figure 52: A snapshot of the Scenario 2 simulation in NetSim at 13:00. ...........................................................58 Figure 53: A snapshot of the Scenario 2 simulation in NetSim at 19:00. ...........................................................58 Figure 54: Share of cooling for each technology in Scenario 2. ...........................................................................59 Figure 55: Hourly production of cooling for Scenario 2. .....................................................................................59 Figure 56: Differential pressure levels for the production plants in Scenario 2. ...............................................60 Figure 57: Differential pressure levels for consumers in Scenario 2. ..................................................................61 Figure 58: The daily production cost for Scenario 2. ............................................................................................61 Figure 59: A snapshot of the Scenario 3 simulation in NetSim at 00:00. ...........................................................62 Figure 60: A snapshot of the Scenario 3 simulation in NetSim at 07:00. ...........................................................63 Figure 61: A snapshot of the Scenario 3 simulation in NetSim at 13:00. ...........................................................63 Figure 62: A snapshot of the Scenario 3 simulation in NetSim at 19:00. ...........................................................64 Figure 63: Share of cooling for each technology in Scenario 3. ...........................................................................65 Figure 64: Hourly production of cooling for Scenario 3. .....................................................................................65 Figure 65: Differential pressure levels for the production plants in Scenario 3. ...............................................66 Figure 66: Differential pressure levels for consumers in Scenario 3. ..................................................................66 Figure 67: The daily production cost for Scenario 3. ............................................................................................67 Figure 68: Case comparison of the average hourly costs. .....................................................................................68 Figure 69: Case comparison of the produced cooling from production units. .................................................68 Figure 70: Share of cooling for each technology, 25% increase in storage size. ...............................................70 Figure 71: Share of cooling for each technology, 25% decrease in storage size. ..............................................70 Figure 72: Differential pressure levels for production plants, 25% increase in storage size. ..........................71 Figure 73: Differential pressure levels for production plants, 25% decrease in storage size. .........................71
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Table of Tables
Table 1: ODP and GWP values of common refrigerants (World Health Organization, 2014). ....................14 Table 2: Installed capacity in Norrenergi's DC network (Edén, 2020). ..............................................................23 Table 3: Constant values for density calculations in NetSim (Vitec, 2013). ......................................................28 Table 4: Constant values for viscosity calculations in NetSim (Vitec, 2013). ....................................................28 Table 5: The hourly differences between the cut-off production and load curve for the base case, charging phase. .............................................................................................................................................................................31 Table 6: The hourly differences between the cut-off production and load curve for the base case, discharging phase. .............................................................................................................................................................................32 Table 7: Given values for the existing cold storage at Solnaverket (Edén, 2020). ............................................33 Table 8: Hourly charging values for the storage in Solna. ....................................................................................34 Table 9: Hourly discharging values for the storage in Solna. ...............................................................................35 Table 10: Given values for the Sundbyberg storage (Edén, 2020). .....................................................................37 Table 11: The hourly differences for 81% cut-off production in Scenario 1. ...................................................38 Table 12: Charging values for the Sundbyberg storage in Scenario 1. ................................................................39 Table 13: Hourly charging and discharging values for the 15 MW storage. ......................................................40 Table 14: Hourly charging and discharging values for the 3 MW storage. ........................................................43 Table 15: Cost comparisons of the different cases.(KPI: Key Performance Indicator) ..................................69 Table 16: The parameters for the sensitivity analysis regarding Scenario 3. ......................................................69 Table 17: Economic variations when increasing and decreasing the storage size by 25%. .............................72 Table 18: Capacity factor time series for the base case. ........................................................................................82 Table 19: Capacity factor time series for Scenario 1. .............................................................................................83 Table 20: Capacity factor time series for Scenario 2. .............................................................................................83 Table 21: Capacity factor time series for Scenario 3. .............................................................................................84
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Nomenclature
CCS Carbon capture & storage
CDD Cooling degree days
COP Coefficient of performance
DC District cooling
DH District heating
DHC District heating & cooling
ECI European cooling index
EHI European heating index
EU European Union
FröAck Frösunda storage discharge
FröAckin Frösunda storage charge
FröFK Frösunda free cooling
FröKM Frösunda chiller
GWP Global warming potential
HVAC Heating, ventilation & air-conditioning
LHS Latent heat storage
MSEK Million Swedish Krona
ODP Ozone depletion potential
PCM Phase-change material
SBG Sundbyberg chiller
SBGAck Sundbyberg storage discharge
SBGAckin Sundbyberg storage charge
SHS Sensible heat storage
SolAck Solna storage discharge
SolAckin Solna storage charge
SolnaKM Solna chiller/Solna chiller + Solna heat pumps
SolnaVP Solna heat pumps
TCS Thermo-chemical storage
TES Thermal energy storage
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1
1 Introduction It is generally agreed that anthropogenic activities are the major driving forces on the issue of climate change in terms of increasing temperatures, pollution and rising sea levels (IPCC, 2019). The coming of the industrialization period has led to major technological advancements and improved the quality of life for many people around the world. At the same time, the economic and social benefits of such advancements have been vital for the progress of mankind. However, the same activities have led to a rapid increase of the effects of climate change. Certain measures need to be taken in order to reduce the environmental impact across the globe.
District heating and cooling (DHC) has been a part of the Swedish energy system for many years, with district cooling (DC) being the newest technology of the two. The aim of a DC system is to produce and provide cooling to consumers connected to the network, much like a district heating (DH) system, albeit with different operating temperatures. Consumers in a DC network include offices, industrial sites, refrigerating rooms, server rooms and many more which actively need either comfort cooling or process cooling to optimize their businesses. Lately, there has been an increase in the demand for both comfort cooling and process cooling (Palm and Gustafsson, 2018).
This increase in demand can lead to certain distribution issues which can complicate the delivery of cooling to consumers due to bottlenecks in the network. The type of bottleneck can vary depending on its definition, however, in this project, bottlenecks are identified as below adequate differential pressures in the network (Brange et al., 2017). It is important to mention that low differential pressures can indicate that distribution limitations in the network exist, but they cannot specify the exact location of these limitations. Different methods can be used to reduce these bottlenecks and facilitate the delivery of cooling to important consumers. One of these methods include installing thermal energy storages (TES) at the weak points (areas with low differential pressure) of the network and thus reducing these bottlenecks (Englund et al., 2017). In this project, the TES’s are cold storages using water as medium to preserve cold water for later use. Research has shown that employing TES’s in energy systems can increase the reliability of the system and lower operational costs (Adeyanju, 2015), in this case the costs of using chillers to produce cooling.
The first DC system in Sweden was established in 1992, in the city of Västerås (Werner, 2017). The largest DC systems in Sweden are also usually found in major cities such as Stockholm, Gothenburg and Uppsala (Werner, 2017). As global warming continues its impact on the climate, the demand for cooling in Sweden is expected to increase to 3 000 GWh by 2030 (Sköldberg, Unger and Göransson, 2013). At the same time, there has been an increase in network length and produced cooling over the years, with a cooling record of 1 156 GWh in 2018 – one of the hottest summers thus far in Sweden (Johannesson, 2019).
This project uses Norrenergi AB’s DC system as a case study to evaluate the potential in using cold storages in DC systems. Norrenergi AB established its DC network in 1995 and delivers cooling to the areas of Solna, Sundbyberg and Frösunda (Norrenergi AB, 2019a). It does so by employing a variety of cooling technologies present in their production plants. Norrenergi AB currently has three main production plants that can produce and deliver cooling through the distribution network. These production plants are located at Solna, Sundbyberg and Frösunda.
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2 Research Questions This thesis investigates the possibilities and limitations of using distributed cold storages in DC networks for the purpose of relieving the distribution during hot summer days when the demand is high. Since the demand increases during peak hours, these distribution issues can further develop into bottlenecks in the network, making it difficult to deliver the cooling to the consumers. The research questions are broken down into several tasks as follows:
• What is the feasibility of using distributed cold storages in DC networks? • Can they replace more expensive chiller/heat pump outputs during peak hours? • Will cold storages help reduce the effects of distribution limitations in the network? • Can the cold storages increase the cost efficiency?
2.1 Purpose The purpose of this thesis is to evaluate new ways towards achieving higher flexibility, steadier supply, grid stability and further sustainability in DC networks, specifically through the use of implementing distributed cold storages. By charging the storages during low-demand hours and using more energy efficient cooling units, environmental and economic benefits can be achieved. This can be performed by discharging the said storages during peak hours and thus replacing expensive chillers and heat pumps during that period when the electricity price is higher.
2.2 Scope The focus of this thesis is limited to Norrenergi AB’s DC network and will therefore consider the network as a case study that can be generalized for Nordic DC systems. Additionally, the cold storages will not be evaluated on a modular level. Instead, the cold storages will only be viewed as additional capacities injected into the network and as regular sensible heat storages but with limited detail on the technology itself. This means that the dynamic calculations performed in NetSim only evaluates the network on a system level. The differential pressure at the consumer and the production plants is used as the main indicator of bottlenecks in the system and will be used for comparison of the different scenarios.
The calculations for both the base case and the subsequent scenarios are based on the load for August 2nd, 2018 which is an especially load-intensive day. In the calculations, hourly timesteps are employed to cover the full range of the day. For the economic calculations, only the electricity price for August 2nd, 2018 is considered which means that there is no focus on capital expenditure, reparation and maintenance costs. Furthermore, the implementation of cold storages results in the need to expand the network pipes in order to connect these to the grid. Therefore, the cost of installation is not considered in this report either. Additional taxes, charges and various fees are also omitted in this study.
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3 Background This section provides context to the research question with regards to Sweden’s past and current energy situation, the energy policies in place and emission sources.
3.1 Energy in Sweden This chapter discusses the current energy possibilities for Sweden and its development towards a more sustainable future. Information regarding the energy mix, the total available supply and how it pertains to certain sectors are presented and discussed in detail.
3.1.1 History
Following the events of World War II, a massive spike occurred in Sweden’s electricity consumption which resulted in the exploitation of one of Sweden’s most abundant natural resources – hydropower. This decision from energy companies led to protests from the public and to satisfy public demands, the power companies resorted to importing petroleum products on a large scale (Cruciani, 2016). Being a country without major fossil fuel reserves, oil quickly became the major product in Sweden’s primary energy mix. Like many nations around the world, Sweden was largely dependent on oil for a long period of time. It was not until the early 1970’s that nuclear energy made itself noticeable in the statistics and changed the energy market in Sweden, as can be seen in Figure 1 below.
Figure 1: Total energy supply 1970-2016 (TWh) (Swedish Energy Agency, 2018).
The oil crisis in 1973 was the first indicator of development in the energy industry and led to a transition towards a more environmentally friendly energy policy. At the time, the pulp and paper industry in Sweden was one of the larger markets and its shift from oil dependency marked an important milestone (Bergquist and Söderholm, 2015). This change can be further observed in Figure 1 above, where crude oil and petroleum products seem to follow a downward trend following the oil crisis. Between 1980 and 1985, nuclear fuel increases its market share even further, while hydropower remains constant up until 2016. At the same time, biofuels steadily increased in popularity to satisfy the demand gap left behind by the oil supply. The Swedish Energy Agency (2018) reports that this is since the heating of residential buildings and other structures through means of oil is no longer trade practice with the increase of other energy carriers.
3.1.2 Energy Supply
In 2016, the total energy supplied to all sectors in Sweden was measured to be 564 TWh, of which roughly 30% was delivered by means of nuclear energy (Swedish Energy Agency, 2018). In regards to electricity generation, the combination of both nuclear and hydropower has allowed Sweden to move towards further
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decarbonisation (International Energy Agency, 2019). The total energy supplied during 2016 can be examined deeper in Figure 2 as shown below.
Figure 2: Total energy supplied, Sweden 2016 (TWh) (Swedish Energy Agency, 2018). Adapted by the author.
As illustrated in Figure 2, biofuels have solidified their place in the energy market, covering almost a quarter of the total supplied energy in 2016. Despite the reduced oil imports over the years, crude oil and various petroleum products are the third largest contributors to the energy share in Sweden and hence still play a significant role towards meeting the energy demand. Sustainability efforts within the transport sector has led to some progress but the sector still remains as largely oil dependant where road traffic is the dominating factor (Swedish Energy Agency, 2018).
3.1.3 Energy Use & Losses
In energy balances, it is considerably rare that the energy supply is equal to the consumption due to losses in the system. For the entirety of 2016, 375 TWh of energy was consumed in various forms between different commodities as can be seen in Figure 3 below.
Figure 3: Energy use and losses by commodity, Sweden 2016 (TWh) (Swedish Energy Agency, 2018). Adapted by the author.
In Figure 3, roughly 184 TWh of the supplied energy is lost in the process. Most of this share, however, is due to the loss of energy that occurs in nuclear power plants. The reason for this is a combination of the large amount of heat released through fission and the fact that nuclear waste is currently viewed as an energy loss (Eriksson, 2017).
Another partial factor that affects this loss of energy can be explained through the term ‘non-energy use’ which refers to the energy that exists within different products such as paraffin waxes and lubricants. This
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energy is not converted to produce electrical or thermal energy but rather infused in raw materials (Eurostat, 2018). The transformation of the energy supply to electric energy is the largest share while the portions of biofuels and petroleum products are roughly the same. Historically, the annual average temperature in Sweden has been low, see Figure 4 below, prompting a heating need in the country which in 2016 held a 9% share of the total energy use as can be seen in Figure 3 above.
Figure 4: Annual average temperature in Sweden between 1860-2019 (SMHI, 2019).
3.1.4 Sectors
The energy consumption in Sweden can be divided into three main categories as shown by Figure 5 below.
Figure 5: Energy consumption by sector, Sweden 2016 (TWh) (Swedish Energy Agency, 2018). Adapted by the author.
In Figure 5, the industrial and residential- and service sectors are approximately equal in their shares of 142 TWh and 146 TWh, respectively. The transport sector accounts for the remaining 23% of the total energy consumption at 87 TWh. Since it is the largest energy consuming sector, there is a major potential and reason in developing more sustainable technologies that can meet the demand for energy in residential buildings. This largest uses within the residential sector includes DH, electricity and biofuels (Swedish Energy Agency, 2018).
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3.2 Current Environmental Status This chapter presents a general evaluation of the current environmental status of Sweden and how climate change affects policy decisions and vice-versa. Statistical data on emissions such as CO2 are illustrated and discussed while the climate targets for both EU and Sweden are mentioned in detail.
3.2.1 Greenhouse Gases
The emissions of greenhouse gases have been greatly reduced since 1990 in most of the sectors in Sweden. As mentioned, the practice of using oil to meet the heating demand for houses and premises has been largely removed and now rely on other energy carriers perform the task. This has in turn reduced the overall CO2-equivalent levels in the sectors such as electricity, DH and industries. The downward trend can be further examined in Figure 6 below, where, the greatest reduction has been recorded in the heating sector between 1990-2014.
Figure 6: Greenhouse gas emissions 1990-2017 (Swedish Environmental Protection Agency, 2018a). Adapted by the author.
Policy decisions from authorities regarding the energy market are one of the main reasons behind this trend which includes a substantial development within the electricity production sector. The shift from oil-based power production to nuclear and hydropower led to significant decreases of greenhouse gas emissions. The DH sector was also subject to similar changes where oil-fired boilers in the production were replaced by boilers using biomass and other waste instead (Swedish Environmental Protection Agency, 2018a).
3.2.2 Goals & Targets
As a member of the European Union (EU), the foundations of Sweden’s climate targets are based on the legislation set forth by EU which include several set goals that need to be achieved. Additionally, Sweden aims to resolve further climate issues by establishing their own goals which address the problems specific to Sweden. The legislation, often called the 20-20-20 targets, was set in motion in 2009 and the climate goals are binding to the nations of EU which exist to solve the issue of the climate crisis (European Commission, 2008). The climate goals of EU include:
• Reduce greenhouse gas emissions by 20% (compared to 1990 levels) • Reduce energy use by 20% through better energy efficiency • The share of renewable energy in the mix shall be at least 20% of the total energy consumption
Moreover, the climate and energy goals set by the Swedish authorities have been decided to help combat climate change on a national level. These goals can be viewed as additional in reference to the EU climate goals and includes the following (Swedish Energy Agency, 2019):
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• Reduce greenhouse gas emissions by 40% (does not pertain to activities outside of EU’s system for trade with emission rights)
• Energy use shall be 20% more efficient compared to 2008 (through decreased energy intensity) • The share of renewable energy shall be at least 50% of the total energy consumption • The share of renewable energy in the transport sector shall be at least 10 %
By achieving these goals, Sweden aims to “combine ecological sustainability with competitiveness and energy supply security” (Swedish Energy Agency, 2019).
In 2017, the parliament of Sweden introduced several goals that needed to be achieved in the future for the long-term planning of the energy transition. By passing a climate law and creating a framework for these goals, the aim is to steadily decrease the net emissions of greenhouse gases. By 2045, Sweden aims to have zero net emissions of greenhouse gases which is interpreted as a reduction of at least 85 % compared to 1990 levels (Swedish Environmental Protection Agency, 2018b).
3.2.3 Policies
As the climate goals of Sweden are based on those of the EU, the policies follow the same principle. Some of the Swedish policies are embedded within the policies of EU and act as guidelines towards meeting the climate goals which were previously introduced. A climate action plan is to be delivered to the parliament every fourth year to determine whether the current political landscape is contributing to the combat against climate change (Swedish Environmental Protection Agency, 2019a).
In order to meet the climate goals, three main challenges are addressed which include (Swedish Environmental Protection Agency, 2019b):
• The revamping of the transport system, specifically an increased efficiency of the system and a transition towards utilization of renewable fuels
• Greenhouse gases emitted from industries need to be close to zero. For this to occur, new technology needs to be implemented while fossil fuels are replaced, while energy efficiency is increased. The EU emissions trading system needs to be further enforced to ensure this development
• Carbon capture and storage (CCS) needs to be commercialized to reduce industry emissions and to enable negative emissions
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4 Literature Review This section of the thesis discusses the current available information on DC, relevant studies on the topic of DC and its comparisons to DH. The market of DC is presented in detail while the technology behind DC is analysed and interpreted. The idea behind TES’ is also introduced and dissected into several areas of application. A presentation of the operating strategies for TES’s and their application is also discussed.
4.1 District Cooling DC is based upon the same core principle as DH systems where thermal energy is produced in a centralized plant and is transferred through underground distribution pipes to customers in residential, industrial and other buildings (Palm and Gustafsson, 2018). The centralized plants use several types of techniques to generate chilled water and deliver it to customers connected to the distribution network to meet the demand. DHC is currently used for various purposes which include space cooling and heating (Fouad, Sayed and Ismail, 2017). As an advantage, the need for individual cooling mechanisms in customer buildings is removed since the entire cooling process is located at the plant, making for an efficient system (Augusto and Culaba, 2019).
The major difference between the two systems is the operating temperature difference. While DH uses a high temperature difference between the supply and return temperature, DC is strictly limited to lower temperature differences. As an example, in a typical DH system, the temperature difference can be between 30-45°C (Luleå Energi, 2020), while in DC systems, this number is around 10°C (Edén, 2020). An overview of an example DC system can be viewed in Figure 7 below.
Figure 7: Basic overview of a general DC system with cold storage capacity. Temperature levels are approximate.
Figure 7 illustrates the use of devices and techniques such as heat pumps, chillers and free cooling, to produce chilled water (5-7°C) in a central production plant which is then distributed through the network’s supply pipes where it is transported to various consumers. There, it absorbs the heat of the building’s internal water through a heat exchanger located either inside or near the building. As a result, the building’s internal water is cooled down while the, now warmer (15-17°C), DC water is transported to the return line for another cycle. During off-peak hours, the produced chilled water can also be used to charge the cold storage for later use. When the demand rises, the central production plant can be operated in conjunction with the cold storage during peak hours.
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4.1.1 History
While DH systems were first introduced in Sweden in the 1940s, it was not until the early 1990s that DC made itself known to the energy market in Sweden. After observations of the American DC market, the first Swedish DC system was launched in the city of Västerås in 1992 (Werner, 2017). In other words, the DC market is a new concept in the context of energy industries compared to DH systems. Since then, several new DC systems have been implemented in cities all over Sweden including Göteborg, Lund and most notably Stockholm.
4.1.2 DC Market
It is worth mentioning that the documentation regarding the Swedish DC market, and DC in general, is not as developed as the market evaluation for DH and hence is limited to a few numbers of reports. The most prominent and extensive report on the DC market in Sweden is “Kartläggning av marknaden för fjärrkyla” by Energimarknadsinspektionen from 2013 which is referred to for this sub-chapter.
4.1.2.1 Growth
Since its introduction to the Swedish energy market, DC has attracted an increasing number of customers while simultaneously multiplying the number of DC networks present in Sweden, which can be seen below in Figure 8.
Figure 8: The increase of DC networks in Sweden between 1996 and 2012 (Energimarknadsinspektionen, 2013).
In Figure 8, the greatest growth period occurred between 1996 and 2003 where the number of DC networks almost tripled which was followed by a stagnating period lasting a couple of years. It is worth emphasizing that the above graph does not represent the expansion rate of each individual network which means that network capacities might have increased despite this. In 2013, it was reported that the Swedish DC market supplied a combined cooling energy of 0.9 TWh per year to approximately 1 000 customers and across 34 companies. At the same time, the total revenue of the DC market was estimated to be 440 MSEK – a fraction of the total revenue of DH which is at approximately 29 BSEK (Energimarknadsinspektionen, 2013).
Even with their similarities in technology, this difference in revenue might be explained through the fact that DH has existed for a longer time in the market and thus has had more time to mature. Werner (2017) suggests that DC has expanded only in recent years due to better insulation among newer Swedish buildings. While better insulation in buildings leads to lower heating demands during the winter, the opposite occurs during the summers since more heat is trapped. Furthermore, the inherent need for DC is not as widespread
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as for DH in Sweden, since most customers with a cooling demand are large corporations rather than individual home owners (Energimarknadsinspektionen, 2013).
Another reason for the difference in market share could be explained by the fact that DC as a system is more expensive to invest in, especially regarding network extensions through piping. Temperature differences are a driving factor in supplying the energy and since DC operates at lower temperature difference, approximately 10°C, this needs to be compensated for through larger pipes (Energimarknadsinspektionen, 2013). In other words, to supply the same amount of energy compared to DH, the pipe diameter needs to be increased to allow for larger volumetric flows, which can be expensive.
4.1.2.2 Present & Future
In 2013, reports suggested that the DC market could expand its total supplied cooling energy to 2 TWh in 2020 and almost 3 TWh in 2030 (Sköldberg, Unger and Göransson, 2013). The total supplied cooling energy in Sweden during 2018 was a record year and measured to be 1 156 GWh according to Figure 9 below.
Figure 9: DC supply (GWh) and network length (km) between 1996-2018 in Sweden (Johannesson, 2019).
As viewed in Figure 9, the supplied cooling energy to customers did not have extreme fluctuations between 2009-2017, with the exception for 2018. Sweden experienced one of the hottest summers ever in 2018 with temperatures reaching above 30°C in many regions and cities (SMHI, 2018). As a result, this temperature deviation led to higher cooling demands than usual and DC companies were forced to supply more cooling energy.
4.1.2.3 Market Distribution in Sweden
There are numerous energy companies that own and operate DC networks, ranging from the southern parts to the northern parts of Sweden. However, the largest market exists within the county of Stockholm where five companies supply customers with cooling (Energimarknadsinspektionen, 2013). The share of produced DC energy per county can be further examined in Figure 10.
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Figure 10: Share of annual produced DC energy by county in Sweden (% of GWh) (Energimarknadsinspektionen, 2013).
The distribution of networks comes down to the cost of implementing district energy networks. Factors such as local conditions and consumer load density, which is very high in Stockholm compared to other counties in Sweden, affect the final cost of the system (Larsson, 2011). Therefore, from a producer’s perspective, it is more profitable to invest in areas with a high density of customers and where local conditions allow easier network extensions. At the same time, a decreased cost for the company would lead to better prices for the consumers.
4.1.3 Demand
One main type of cooling is comfort cooling which is the type of cooling that is used in homes, buildings and offices to achieve thermal comfort indoors. This is important since research shows that the indoor thermal comfort has an impact on the overall quality of life (Mendes et al., 2017). However, there are other applications as well such as process cooling which refers to cooling needed for server rooms, industrial applications and refrigeration rooms for food. Subsequently, DC may be key to solving the issue of increased cooling demands in warmer climates as the quality of life increases generally. Examples of factors that directly increase the cooling demand include sunlight, heat from electrical appliances and the metabolic activities of humans in buildings (Frederiksen and Werner, 2015).
It is important to consider all possible heat sources of a building since they affect the final cooling demand which is used in production planning and dimensioning. For DH systems, heat sources are seen as positive since they lower the total heating demand while for DC systems, the same heat sources lead to increased cooling demands. Humid air is also an issue regarding the topic of cooling since it requires more energy to cool humid air to a certain level than it does for dry air. This stems from the fact that during the cooling process of humid air, the condensed water is removed in an energy intensive procedure, resulting in a higher cooling demand than usual (Frederiksen and Werner, 2015).
Demand can be predicted in several ways and one of the methods frequently used in the US is cooling degree days (CDD) which uses the outdoor air temperature and heat gains to calculate a rough approximate of cooling demand. Since the method itself does not take other influences such as solar heat gains and moisture into consideration, it is limited to countries where the outdoor temperature has a major effect on the overall cooling demand (Frederiksen and Werner, 2015).
There are however more fitting solutions to predicting the cooling demand that include other factors. The European cooling index (ECI) is based on the same methodology as its heating counterpart, European
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heating index (EHI), where the main assumption is that outdoor temperature has a major influence on the demand. The model also differentiates between weather and temperature conditions in urban environments and rural environments where urban environments are usually warmer due to the density of buildings (Dalin, Nilsson and Rubenhag, 2006). The ECI also investigates the effects of effective building insulation, heat recovery from HVAC systems and makes cost assumptions for these. The main reason for this is a negligible difference in specific heat capacity of 2% between the dry and humid air. The development of the EHI and ECI model is shown in Figure 11 below.
Figure 11: The development of EHI and ECI (Dalin, Nilsson and Rubenhag, 2006).
In Figure 11, it can be observed that the designed cooling limit is set at 22°C when the outdoor air temperature is 29°C or higher, meaning that if the outside air temperature rises above 29°C, the indoor temperature will as well, maintaining a 7°C difference. This also helps in restricting the effect of humidity on the system. On the topic of building insulation, the square root of the heating degree-day numbers is used to regulate the internal and solar heat gains in a building with good insulation – such as the newer buildings in Sweden. In order to yield the most accurate results, as much information as possible should be used to determine the cooling demand (Dalin, Nilsson and Rubenhag, 2006).
4.2 Technology This section of the report discusses the different cooling technologies used commonly in DC systems. Cooling is the rejection of heat from a particular medium which results in lower temperatures. There are several ways to achieve this, each one carrying different challenges and implications on the energy system. Moreover, a combination of different cooling techniques can be used for an optimized system with a lower overall cost.
4.2.1 Free Cooling
Free cooling is the concept of utilizing natural cold sources to cool down a medium in an energy system without involving conventional chillers that often require large volumes of power (Johansson, 2012). Natural cold sources include low-temperature air, large cold bodies such as seas or lakes and other options such as snow. For this thesis, the only type of free cooling considered is the cooling involving cold water from the bottom of seas and lakes. It is an efficient technique that is used widely in many DC systems due to its low
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impact on the environment. The advantage lies in the fact that the only power required to drive this process is the pumping power to pump the lake or seawater to the heat exchanging station which gives the technology a higher COP. The basic process of free cooling connected to a DC network can be observed in Figure 12.
Figure 12: A conceptual image of the free cooling process in a DC network (Subratty and Riahi, 2016).
As can be seen in Figure 12, cold water is pumped from the bottom of the lake to a heat exchanger where it interacts with the return water of the DC network. The lake water absorbs the heat from the return DC line which lowers the temperature of the return line and subsequently heating up the lake water. From this point on, the DC water is now sufficiently cooled and can be reused in the supply line, while the now warmer lake water is pumped back to the natural source.
There are several examples of free cooling being used in cooling processes around the world. Purdy’s wharf, a Canadian office complex situated in Halifax, Nova Scotia, is located near the local harbour where the cold seawater is extracted from the bottom and pumped to a heat exchanger. The cold seawater then absorbs the heat from the incoming, warmer return water of the building’s own closed loop in the heat exchanger. Once this is done, the closed loop begins to distribute the now cold water to each section of the building, while fans in the ventilation systems blow warm air over the cold supply pipes. As a result, the warm air is cooled down and its heat is absorbed by the supply water which is then pumped back to the sea through the return line (Newman and Herbert, 2009).
An even larger Canadian example of implementation of a free cooling plant can be found in Toronto where Enwave, a local district energy company, has successfully managed to create synergy between comfort cooling and demand for drinking water. The nearby Lake Ontario is used as the source for free cooling where 5 kilometres of pipes extract cold water from the bottom of the lake, situated at 83 m below sea level (Newman and Herbert, 2009). From there, the cold lake water is pumped to a heat exchanger where it interacts with a closed loop DC system. Like the free cooling system of Purdy’s wharf, the water is then distributed to different buildings to meet the cooling demands. However, the advantage lies in the filter prior to the heat exchanger which treats the water for drinking purposes. Although the water is slightly heated after cooling the buildings, it can still be used as drinking water which has led to lower energy use and minimal water waste (Enwave, 2019).
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4.2.2 Chillers
Chillers are refrigeration machines that provide cooling. They can be installed to co-operate with other processes at plants or be implemented as standalone machines. Either way, chillers use thermal or electrical energy inputs to provide a cooling effect to meet demands.
4.2.2.1 Vapour-Compression Chillers
One of the most widely used type of chillers is the vapour-compression chiller which is also known as the basic refrigeration cycle. Compression chillers have been used extensively ever since the technology was first introduced by Jacob Perkins in America in the 1830’s. The main idea of a vapour-compression chiller is to extract heat from a low-temperature source and transfer it to a heat sink using electrical energy as the main driving force in the system. Vapour compression chillers typically consist of four main parts which are the compressor, the condenser, the expansion valve and the evaporator (Granryd et al., 2011).
Refrigerants are the names of the working fluids within a vapour-compression system and some of the most common refrigerants include R123 and R134a (Islam, 2016). Lately, research has focused on developing more environmentally friendly refrigerants that allow the same cooling abilities while reducing the ozone depletion potential (ODP) and global warming potential (GWP) characteristics of the refrigerant. The ODP and GWP for some common refrigerants can be seen in Table 1 below.
Table 1: ODP and GWP values of common refrigerants (World Health Organization, 2014).
Refrigerant ODP GWP
R123 0.02 77
R134a 0 1430
R22 0.05 1810
R404a 0 3922
R717 0 0
R744 0 1
The aim of the compressor is to transport the refrigerant vapour from the evaporator to the condenser and in doing so it also raises the pressure of the vapour. The condenser’s purpose is to allow heat to be transferred from the refrigerant to the heat sink, thus lowering the temperature of the refrigerant which enters the condenser in the form of hot vapour and leaves as a cooler liquid. After leaving the condenser, the refrigerant is then transferred to the expansion valve which is the device responsible for preserving the pressure difference between the high-pressure side and the low-pressure side. Another necessary function of the expansion valve is to alternate the refrigerant flow to compensate for the changes in heat flux in the heat exchangers. Finally, the refrigerant is transferred to the evaporator. As its name suggests, the evaporator is responsible for evaporating the refrigerant using an external heat input. Additionally, it is worth mentioning that the temperature of the refrigerant remains constant throughout this process, assuming there is not a change of pressure (Granryd et al., 2011).
An overview of the working principle of a vapour-compression chiller can be observed further in Figure 13 below.
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Figure 13: A schematic of a basic refrigeration cycle (Energimarknadsinspektionen, 2013). Adapted by the author.
The same process is applied to industrial compression chillers integrated in a DC network, where the external heat input in the evaporator is the warmer return water of the DC loop. As this return water passes through the evaporator, the heat is absorbed by the refrigerant which is then brought to the compressor. From there, the process of compressing, condensing and rejecting heat takes place, as previously mentioned, until the refrigerant is ready for another refrigeration cycle.
4.2.2.2 Absorption Chillers
Another technology that has been steadily gaining popularity in the cooling market are absorption chillers which use a combination of refrigerants and absorbents to provide cooling. While compression chillers rely on electrical energy input for operation, absorption chillers are driven using thermal energy which can be supplied by either a boiler connected to the chiller or a secondary thermal energy input like waste heat from sources such as hot water or steam (U.S. Department of Energy, 2016). Other thermal inputs such as waste heat from district heating systems and solar heat sources can also be used.
Although the chiller is heat driven, smaller components such as pumps still require a small amount of electricity. However, this electrical energy is considered minimal in comparison to the electrical energy required by conventional vapour-compression chillers, making the absorption technology a very interesting alternative (Saastamoinen and Paiho, 2018). The absorption chiller consists of four integral components which are the generator, condenser, evaporator and the absorber (Wang et al., 2009). Unlike the vapour compression cycle, the absorption cycle does not include a compressor to increase the pressure of the refrigerant. Instead, a pump is installed to pressurize the refrigerant vapours to a desired level (Minnesota State University, 2015). In these systems, the refrigerant is water, while an aqueous solution of a metal halide (a salt), e.g. lithium bromide (LiBr), is typically used as an absorbent. The combination of the pump, generator and absorber is often called the thermal compressor. A schematic of a general absorption chiller cycle can be observer below in Figure 14 below.
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Figure 14: A schematic of a single stage absorption chiller (U.S. Department of Energy, 2016). Adapted by the author.
Water boils at different temperatures which also depends on the pressure. As an example, water boils at 100°C at atmospheric pressure while a lower pressure leads to a reduced boiling point. Phase changes such as water to vapour or vice-versa, require energy in the form of heat to be achieved which for vaporization is the latent heat of vaporization while the same applies to condensation, e.g. latent heat of condensation. The absorption capacity of LiBr-water absorption chillers depends on both the temperature and the concentration of the LiBr-water solution. Furthermore, when the temperature of the solution is increased, the water will be released in the form of vapour, leaving behind LiBr solution in a concentrated form (Thermax, 2008).
First, the thermal compressor receives refrigerant vapor at low pressure and temperature form the evaporator which it then compresses. Unlike a regular compressor, the thermal compressor utilizes the chemical reaction of the absorbent (LiBr) and the refrigerant vapour to compress the solution. This can be explained by the phase change of the solution in which gas is converted to liquid. Once this process is finished in the absorber, the solution is pumped to the generator where the refrigerant is brought to its boiling point using the external heat input. As a result of the boiling process, the refrigerant vapour is then delivered to the condenser where the refrigerant is brought back to its liquid state while the absorbent is transferred back to the absorber for another cycle. The purpose of the cooling tower is to account for the heat rejection (U.S. Department of Energy, 2016). The COP of single-stage absorption coolers is 0.7 while double-stage absorption chillers have a COP of around 1 (U.S. Department of Energy, 2012).
4.2.2.3 Adsorption Chillers
Another sorption technology that has been gaining attention due to its potential in reducing greenhouse gases are adsorption chillers. Like absorption chillers, adsorption refrigeration cycles rely on external thermal inputs rather than electrical inputs. Native to adsorption chillers, they can be successfully driven using low-grade heat sources and eliminate any moving parts in the refrigeration cycle, allowing for lower maintenance costs and longer lifetimes (Elsheniti et al., 2018). Due to its ability to extract heat from lower temperature sources, it has frequently been used in conjunction with solar technology, where solar heat is used as the main heat input to provide cooling.
The main difference between an adsorption chiller and an absorption chiller is that adsorption chillers use adsorbents in the refrigeration cycles such as silica gel and zeolite using water as the liquid sorbent, while
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absorption chillers use mainly water-lithium bromide (LiBr) and ammonia-water, as previously mentioned (Al-Rbaihat et al., 2017).
4.2.3 Heat Pumps
Heat pumps operate according to the vapour-compression cycle (see section 4.2.2.1) and the technologies are similar in terms of the working principle. Like vapour-compression chillers, heat pumps consist of four parts which are the evaporator, the compressor, the condenser and the expansion valve. Thus, the actual cycle of a heat pump can be compared to the previously mentioned Figure 13 where the refrigerant is expanded and compressed in several stages to obtain the desired level of effect. The attraction surrounding heat pumps stems from the fact that they can be used for both heating and cooling which provides flexibility, especially in a combined district heating and cooling plant. Depending on the need, heat pumps can switch from heating mode to cooling mode, by means of heat/cold supply from the evaporator/condenser (Natural Resources Canada, 2017). This can done through the use of a reversing valve which can reverse the cycle to remove or add heat, essentially changing the flow direction of the refrigerant (Salem, 2014).
4.2.4 Cold Storage
In many systems, cold storages are implemented as an extra buffer to fulfil customer demands. Cold storages are usually charged using one of the previously mentioned cooling techniques (in the case of water) and can be considered as an extra capacity injected into the DC network.
4.3 Thermal Energy Storage In conjunction with the transition towards a large renewable share in the energy mix, certain measures have been taken to further improve the situation of reduced fossil fuel dependency. Research has shown that the implementation of TES systems has led to improvements regarding energy efficiency and energy savings (Jeon et al., 2010). The main attribute of TES’s lies in their ability to store thermal energy, either hot or cold, depending on the application. The idea behind the technology is that excess thermal energy can be stored during periods of lower demand and released later when the demand rises again (Sarbu and Sebarchievici, 2018). This often occurs in energy systems where there is a disparity between the produced energy and the consumed energy (Zarma et al., 2017). This also applies to energy industries such as DHC.
Moreover, depending on the type of TES used, thermal energy can be stored for durations ranging from a couple of hours to seasonal storages and offers flexibility in doing so. Additionally, the use of TES’s can help reduce CO2 emissions, peak energy demand and energy consumption (Simbolotti, Tosato and Gielen, 2013). Using TES within an existing energy system can directly increase the reliability of the system and lower the overall operational costs (Adeyanju, 2015). In industrial contexts, these storages are also often referred to as accumulators.
4.3.1 Storage Classification
Generally, TES’s can be divided into three categories based on the storage medium which is the material that is used to store thermal energy in, and these classifications can be seen below in Figure 15. The three types of TES’s are sensible heat storages (SHS), latent heat storages (LHS) and thermochemical storages (TCS’s).
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Figure 15: An overview of different TES's (Socaciu, 2012). Adapted by the author.
In Figure 15, liquid, solid and other storage materials refer to the phase of the medium in question. Each category has their own advantages and drawbacks which is discussed in more detail in the coming subchapters.
4.3.1.1 Sensible Heat Storages
SHS’s refers to TES systems where the temperature of the storage medium is increased or decreased without achieving phase change. It is generally considered to be one of the easier TES systems to implement due to its low cost and its availability on the market (Abdin and Khalilpour, 2019). Storage mediums for these types of systems are cheap and numerous with some of the examples being water, sand, molten salts and rocks. Another reason for its attractiveness is due to the lack of toxic/harmful products associated with it (Sarbu and Sebarchievici, 2018). Water can be used as storage medium for both heating and cooling purposes which offers another advantage in the technology. The energy content of a SHS can be determined by Equation 1 below (Adeyanju, 2015):
𝑄𝑄𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = 𝑚𝑚𝑐𝑐𝑝𝑝Δ𝑇𝑇 = ρVc𝑝𝑝(𝑇𝑇𝑚𝑚𝑠𝑠𝑚𝑚 − 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 ) (1)
where 𝑄𝑄𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 is the measured energy (kJ), 𝑚𝑚 the mass of the water (kg), 𝑐𝑐𝑝𝑝 (kJ/kgK) the specific heat capacity of water, 𝜌𝜌 the density of water (kg/m3), V the volume of the water (m3), 𝑇𝑇𝑚𝑚𝑠𝑠𝑚𝑚 and 𝑇𝑇𝑚𝑚𝑚𝑚𝑚𝑚 the maximum and minimum temperature levels of the water (°C). Seeing as the SHS systems do not utilize phase changes in the storage medium, the capacity of an SHS is often limited by the specific heat capacity (𝑐𝑐𝑝𝑝) of the medium. This results in a need for larger volumes and tanks, which in turn requires more floor space, compared to other TES solutions (Adeyanju, 2015). Although, if height is not an issue, the tank could have an increased height and thus smaller floor area.
4.3.1.2 Latent Heat Storages
LHS’s are storages that utilize phase-change materials (PCM) to store thermal energy for later use. In comparison to SHSs, most of the energy storage in LHSs occurs within the phase-change itself which can be considered an isothermal process due to very small changes in temperature (Sarbu and Sebarchievici, 2018). Likewise, when discharging the thermal energy, e.g. during points of high demand, the working material changes phase. It is recommended that LHSs should fulfil three main characteristics for an efficient working system which are the following (Socaciu, 2012):
• A PCM that operates within the expected temperature interval • A compatible fluid that can carry the thermal energy • A suitable containment device for the PCM
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The main advantage of using LHSs is the ability to store more energy per unit of volume, i.e. PCMs have higher energy densities compared to SHSs. In addition to this, certain PCMs can store 5-14 times more thermal energy than conventional materials used in SHS such as water or different types of rock. At the same time, the PCMs can be divided into subcategories, such as organic and inorganic as well as based on their melting temperatures (Jeon et al., 2010). The latent heat process can be viewed in Figure 16 below.
Figure 16: The heat storage process of a solid-liquid latent heat storage (Vivek and Goswami, 2018). Adapted by the author.
As illustrated by Figure 16, the latent heat storage process consists of three parts, the first being stored heat in the form of sensible heat which takes place at the first temperature level 𝑇𝑇𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠1, followed by the actual phase change of the storage material at 𝑇𝑇𝑚𝑚𝑠𝑠𝑚𝑚𝑠𝑠 which is approximately at a constant temperature level. Thirdly, the latter phase of the storage medium experiences an increase in sensible heat which occurs at the temperature 𝑇𝑇𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠2. This can be further exemplified through Equation 2 below (Vivek and Goswami, 2018):
𝑄𝑄 = 𝑚𝑚�(𝑇𝑇𝑚𝑚𝑠𝑠𝑚𝑚𝑠𝑠 − 𝑇𝑇𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠1)𝐶𝐶𝑝𝑝,𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠1 + Δℎ𝑚𝑚𝑠𝑠𝑚𝑚𝑠𝑠 + (𝑇𝑇𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠2 − 𝑇𝑇𝑚𝑚𝑠𝑠𝑚𝑚𝑠𝑠)𝐶𝐶𝑝𝑝,𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠2� (2)
where 𝐶𝐶𝑝𝑝,𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠1 and 𝐶𝐶𝑝𝑝,𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠2 are the two specific heats related to two different phases in the process. The first refers to the specific heat in the solid phase while the latter represents the specific heat in the liquid phase. Likewise, the temperature levels 𝑇𝑇𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠1 and 𝑇𝑇𝑠𝑠𝑠𝑠𝑚𝑚𝑠𝑠2 are the temperatures for the solid and liquid respectively while 𝑇𝑇𝑚𝑚𝑠𝑠𝑚𝑚𝑠𝑠 is the temperature at which the melting of the storage material occurs. Finally, Δℎ𝑚𝑚𝑠𝑠𝑚𝑚𝑠𝑠 is usually referred to as the latent heat of fusion of the material.
4.3.1.3 Thermochemical Storages
TCS’s are technologies that can store heat or cold by utilizing the chemical reactions between two components in a system. These reactions, also known as bond reactions, are reversible reactions meaning that the heat is stored and can be discharged later by simply reversing the reaction (Socaciu, 2012). This reaction can be described as (Jerz et al., 2015):
𝐴𝐴 + 𝐵𝐵 ⇔ 𝐶𝐶 +𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 (3)
As can be seen from Equation 3 above, adding components 𝐴𝐴 and 𝐵𝐵 together results in a third component 𝐶𝐶 and heat from an exothermic reaction. This is the process of discharging the storage while the charging process can be obtained by reversing the reaction. The working principle of a TCS is further illustrated in Figure 17 below.
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Figure 17: The working principle of a TCS (Jerz et al., 2015). Adapted by the author.
Three main steps are described in Figure 17 above, which are charging, storage and discharging. During the charging process, two components (A and B) are initially merged while thermal energy is being added to separate the two. The entire storage mechanism is based on keeping components 𝐴𝐴 and 𝐵𝐵 separated and the energy loss in this process is close to none which is one of major reasons why TCS are used for long-term storage solutions (Jerz et al., 2015). The discharging process is achieved by the merging of components 𝐴𝐴 and 𝐵𝐵 to obtain component 𝐶𝐶 and thus make use of the heat that is a result of the chemical reaction. There are however very few real applications of this technology as most of the work dedicated to TCSs is in the research phase compared to its availability on the market. Furthermore, the technology is expensive and difficult to implement successfully due to the complexity of the reactor designs (Eames et al., 2014).
4.3.2 Role in Energy Systems
With an increasing urban density and higher energy demands, it has become exceedingly important to meet the demand during the highest peaks of the day. One recurring issue with DHC systems is the time delay that occurs between the production and consumption of the energy due to the large distances between the two in larger networks (Alva, Lin and Fang, 2018). To do this, a mix of increased capacity and flexibility is needed for which TESs are suitable.
4.3.3 Operating Strategies
Load levelling, or load shifting control, is a tool used to reduce the impact of peak demands in energy systems. The effects of implementing load shifting control on a case study for São Miguel Island, Portugal were evaluated and the conclusions were that load shifting is a vital component in reducing the peak demand and allows base load technologies to run reliably (Wimmler et al., 2017). An overview of the most common operation strategies can be further examined in Figure 18 below.
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Figure 18: Comparison of different TES operating strategies (Lizana et al., 2018). Adapted by the author
As can be seen from Figure 18 above, there are mainly three operating modes that can applied given different circumstances. The first mode focuses on a full storage strategy, where the chillers in this case, are running at a higher power output during off-peak hours, allowing the storage to fully charge quickly. By doing this, the storage can fully meet the demand during the peak hours of the day, without the help of the chillers while at the same time ensuring a full use of the storage. This would also require a storage large enough to cover the demand. Another popular strategy is that of the load-levelling mode, in which the chillers are run at a much lower level but do so continuously throughout the day without interruption. An alternative to these two modes is the demand-limiting mode in which the chillers are run continuously throughout the day but lower their capacity during the peak hours, thus allowing the storage to meet the demand partially (Lizana et al., 2018).
The effects of implementing smart operating strategies can lead to several techno-economic benefits including a reduced system capacity due to peak-shaving and higher machine efficiency due to full load operation instead of partial loads. Running a machine on full load also leads to increased life-time and a reduced need for maintenance (Lizana et al., 2018).
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5 Methodology This chapter discusses the methodology used for the thesis regarding the implementation of the distributed cold storages in the DC network, which tools are used to achieve this and the limitations of said tools. Furthermore, an introduction to Norrenergi AB is presented, highlighting their DC system from a technical and economical point of view. Additionally, the studied cases are introduced and analysed.
5.1 Norrenergi AB Norrenergi AB is an energy company situated in the Solna municipality in Stockholm and offers DHC solutions, along with various energy services, mainly to properties and customers in Solna but also to the districts of Sundbyberg, Bromma and Danderyd to some extent. Like many district energy companies, Norrenergi’s main operation is focused on DH where the company delivers approximately 1 000 GWh of heat annually, mostly by burning wood pellets with the occasional use of fuel oil during very cold winters. The share of DC in Norrenergi’s operation is roughly 7% (70 GWh) of the total energy delivered (Norrenergi AB, 2019b). According to Edén (2020), in 2018 the share of fossil free fuels use amounted to 98.8% of the DH energy use at Norrenergi, with the remaining fraction representing fuel oil used for peak heating demands (Norrenergi AB, 2018a).
Norrenergi AB is jointly owned by two municipalities, Solna and Sundbyberg, where Solna owns 66% of the company while Sundbyberg owns the remaining 33%. The annual revenue is approximately 790 MSEK and as of 2019 the company delivers DHC to 1 880 different consumers (Norrenergi AB, 2019b). With Sundbyberg and Solna being two of the most densely populated areas in Sweden (SCB, 2019), there is a major potential in the continuous development of DHC networks.
5.1.1 History
While DH has been a prominent feature of the Solna municipality since the 1960’s, DC was first made part of the Norrenergi’s business in 1995, becoming the first energy company in the Stockholm area to produce and deliver DC. A few years later, the implementation of the cold storage tower, which has a volume of 6 500 m3, was introduced at Solnaverket. By the year 2000, the company started producing chilled water through the use of free cooling by pumping cold water from the bottom of lake Lilla Värtan to a heat exchanger located in Frösunda (Norrenergi AB, 2019a).
5.1.2 DC Network
Roughly 100 properties are connected to Norrenergi’s DC system today with some of the largest consumers being Solna Business Park, Arenastaden and Sundbyberg Centrum. The entire DC network length is 34 km long, contains a total amount of 12 000 m3 of water and spreads across several urban areas such as Solna, Sundbyberg and Frösunda. The network in its entirety can be observed in Figure 19.
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Figure 19: Norrenergi's DC network (Norrenergi AB, 2018b).
As can be seen from Figure 19, there are three main production plants where chilled water is being produced for DC purposes and these are named Solnaverket, Sundbybergsverket and Frösundaverket. The DC network consists of a northern and southern part, whose border is distinguished by the dashed circle near Hagalund according to Figure 19 and this is also the intersection point for the three network branches. The share of cooling load for the northern and southern part is 55% and 45% respectively (Gustafsson, 2019b). Solnaverket provides cooling for the southern part of the network using a combination of heat pumps and compression chillers while Frösunda and Sundbybergsverket supply the northern extents of the network with the help of chillers and free cooling. The installed capacities for all three production plants and their respective COP’s can be viewed in Table 2 below.
Table 2: Installed capacity in Norrenergi's DC network (Edén, 2020).
Production plant Technology Installed capacity [MW]
COP
Solnaverket Heat pumps 18 3
Chillers 10 5
Cold storage 10 -
Total Solnaverket 38
Frösundaverket Free cooling 12.6 10
Chillers 10 5
Total Frösundaverket 22.6
Sundbybergsverket Chillers 12.5 5
Total Sundbybergsverket 12.5
Total capacity DC network 73.1
As observed from Table 2, the total installed capacity for the DC network amounts to 73.1 MW. The network employs a mix of chillers, heat pumps, cold storage and free cooling to satisfy consumer demand.
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The cold storage itself is charged using the one of the various technologies listed in Table 2 above, depending on the daily availability and cost of production (Edén, 2020). This means that the COP of the cold storage also depends on the technology used to charge it.
5.1.3 Future Developments
Aside from the regular minor network extensions, Norrenergi has also planned to implement several changes to the network in terms of supply and demand. One of these changes include the installation of a 15 MW cold storage located in Sundbyberg to account for the peak demand in the northern part of the network. Another project is currently underway which includes extending the DC network from Solnaverket to the area of Bromma and this extension can be seen in Figure 19 above. Estimated time of completion will be during the fall of 2020 and once completed, the added network length will be approximately 2.1 km (Norrenergi AB, 2019c).
5.1.4 Price Model
The price model for DC consumers connected to Norrenergi’s network was updated in January 2020 with the purpose of linearizing the model and consists of three parts (Norrenergi AB, 2020):
• Cooling capacity • Heat recovery • Comfort cooling
o Energy o Flow
The first part refers to the recommended cooling capacity which is discussed and agreed upon between the building owner and Norrenergi. There are different cooling capacity levels with varying prices, however, the general principle of the price model is that an increased need for cooling yields a lower price level. If the building exceeds the agreed level of consumption, an extra fee will be added to the original price level. Heat recovery and comfort cooling are seasonal-based products which apply during certain months of the year. Between October and April, consumers receive financial compensation for consuming cooling energy due to the potential in reusing the warmer return DC water for DH purposes in the winter season. During the heating season, specifically between the months of May and September, the comfort cooling price model is applied which consists of two different components – energy and flow. While the energy component represents the costs of producing DC in the summer, the flow component of this model reflects the costs of maintaining and running the network. The flow price component is based on the consumers hourly flow of DC water (Gustafsson, 2019a).
5.2 Network Simulation Due to the complex nature of the system, which has multiple inputs and outputs such as mass flows, temperature and pressure differences, it is essential to take all factors into consideration. Hence, a suitable simulation software is needed to account for the system operation and the selected software needs to possess the ability to create and calculate simulation models on a network level. To overcome these issues and obtain calculation results, two different software are used jointly. Norrenergi currently uses dpHeating, which is a network information system (NIS) software from Digpro Technologies AB (Digpro, 2020), for documentation purposes. The software is capable of documenting pipe network information such as pumps, valves, pipe lengths and diameters but does not have a working calculation module. Instead, an export function exists within the programme that allows the DC network to be exported and used in conjunction with other network simulation software (Digpro, 2020).
For this thesis, Norrenergi’s DC network was successfully exported from dpHeating and imported to NetSim which is another network software that focuses specifically on calculations and modelling. It is produced and distributed by Vitec Software Group AB (Vitec, 2019). All the calculations and modifications
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to the original DC network were performed in NetSim after importing the network file from dpHeating. Subsequently, all results obtained and presented in this thesis are exported directly from NetSim as a result of the simulation.
5.3 NetSim NetSim is an interactive grid simulation software that offers simulation capabilities such as expansions of the network and identifying existing bottlenecks within the network. Besides being used as a pure simulation software, it can also be used to assess the energy demands of the network by components such as pumps. An overview of the user interface can be seen in Figure 20 below.
Figure 20: A screenshot of the user interface in NetSim (Vitec, 2019).
Some of the functions include sectioning the grid into several smaller parts, merging grids which can be used to predict the performance of network and thus lowering the costs. The simulation can be performed in either a static or dynamic environment which allows flexibility depending on the type of problem. Its main intended purpose is to be used for DHC networks, but it can also be utilized in the case of steam networks. One of the advantages of using NetSim is its cloud access ability, in which the files are also stored. This means that the software is accessible through any computer, given there is an established internet connection. Additional functions for this software are the abilities to optimise the pumping of a DHC network as well as optimising the flow pipe temperature, thus lowering the overall heat losses of the system (Vitec, 2013).
5.3.1 Nodes and Pipes
In NetSim, the network models are comprised of two different elements which are the nodes and the lines. These two elements contain all the necessary numerical information upon which the simulation is based. The nodes can be identified by their circle logo in the interface and are used to store information related to the network, see below in Figure 21. Typically, the nodes contain information such as customer loads or information related to accumulators and production plants. As an example, a production plant can be designed at a node which feeds data related to supply temperature and absolute pressure (Vitec, 2013)
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Figure 21: Example picture illustrating the symbols of nodes and pipes in NetSim. The circles are nodes while the lines are connected
between them.
The lines in the NetSim interface represent the pipes in the DC network. However, one single line represents both the supply and return line of the pipe. This means that for each calculation, both the supply and return values will be evaluated without having to draw both in the software. Lines in NetSim are used to connect the nodes while other components such as pumps and valves can be attached to them for further detail. Several characteristics can be defined for the pipes which include the diameter, heat transfer coefficients, surface roughness’s and pressure losses (Vitec, 2013).
5.3.2 Boundary Conditions
Before the simulation takes place, certain boundary conditions must be met to obtain results from a calculation model. In essence, there are five inputs that need to be specified for a fully functional model which are the following (Vitec, 2013):
• Flow or load must be specified for all nodes except one • Absolute pressure • Reference differential pressure • Nodes with loads needs to include cooling or return temperature • Supply pipe temperature exiting production plant
These are generalized boundary conditions that enable most models to be calculated. However, if valves or pumps are implemented in the network, supplementary conditions may be needed to finalize the model. There is a need to specify the return temperature of the consumers, together with their demand for cooling capacity (Vitec, 2013). Some of the conditions are automatically calculated, such as absolute pressure, as a result of other inputs. Additionally, NetSim requires a reference differential pressure in order to proceed with the calculations. In both the base case and the scenarios, this reference differential pressure is set in a node in western Solna at a value of 250 kPa, which is taken from real data from Norrenergi (Edén, 2020). This node is also chosen due to its representative placement in the existing DC network. The loads at consumer nodes are filled when exporting consumer data from dpHeating to NetSim and their return temperatures are set to 15°C while the supply pipes of the exiting production plants are set to 5°C to maintain a 10°C temperature difference.
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5.3.3 Time series
As part of conducting the dynamic calculations for the scenarios (described further in section 5.5), the function known as time series in NetSim is utilized to achieve daily simulations. Nodes in NetSim can be assigned to up to 10 different node types and every node type can be controlled separately to allow for hourly variations of chosen parameters (Vitec, 2013). Since there is no production-optimization function currently present in NetSim, the cooling output of the production plants need to be manually adjusted hour-by-hour in a way that they do not exceed the production capacity, nor the maximum limit of differential pressure. This project follows this methodology and the parameter subject to time series alteration is the production capacity of each unit. The timespan for the simulations is one day which results in 24 time steps, all of which need to be manually adjusted for each production unit in regard to the capacity factor. NetSim allows values between 0 and 1 for the factors where 0 is 0 % of the maximum output for the production unit and 1 denotes 100 % of the maximum output. To allow for individual control of the cooling outputs, all production units are assigned a unique node type.
Starting with the first time step at 00:00, an initial guess is made for the capacity factor time series upon which the differential pressure is observed for that production plant. If the differential pressure is well below the maximum allowed limit, the deduction is that more cooling can be delivered than the initial guess leading to follow-up guess with increased value. Similarly, if the initial guess leads to an exceedingly high differential pressure, this value is reduced to fulfil the differential pressure criteria. This process continues for all time steps and individually for all production plants. The capacity factor time series for the base case and all subsequent scenarios can be found in Appendix A.
5.3.4 Core Equations
Like many simulative software, the model calculations are based on several mathematical and physical equations. NetSim has its own set of governing equations which it utilizes to obtain results and these equations are specifically related to the fluid conditions such as temperature and pressure. As the input file receives the necessary data from the user, an option to run the calculation becomes available. From there, iterative measures are taken in order to find the hydraulic and thermal solutions for the model. In addition, equations for density, viscosity, valves and pumps are available as well. These are introduced and discussed further below.
5.3.4.1 Differential Pressure
The pressure loss in a NetSim model consists of two parts – one being the pressure loss due to the friction between pipe and water while the second pressure loss is due to individual pressure losses in a pipe. NetSim uses the following formula to calculate the frictional pressure losses in a system (Vitec, 2013):
Δ𝑃𝑃𝑓𝑓𝑠𝑠𝑚𝑚𝑓𝑓𝑠𝑠𝑚𝑚𝑠𝑠𝑚𝑚 = (𝐾𝐾1 + 𝐾𝐾2𝐷𝐷)𝜌𝜌�2𝑣𝑣2𝐿𝐿𝐿𝐿𝐷𝐷
+ 𝑔𝑔(𝑧𝑧𝑑𝑑 + 𝑧𝑧𝑢𝑢)� (4)
where 𝐾𝐾1 (-) and 𝐾𝐾2 (m-1) are calibration factors, 𝐷𝐷 the internal diameter of the pipe (m), 𝜌𝜌 the density of water (kg/m3), 𝑣𝑣 the flow rate of the water (m/s) and 𝐿𝐿 the pipe length (m). Furthermore, λ is the friction factor, 𝑔𝑔 the gravity of Earth while 𝑧𝑧𝑑𝑑 (m) and 𝑧𝑧𝑢𝑢 (m) are the height levels downstream and upstream, respectively.
The individual pressure losses are calculated as follows (Vitec, 2013):
Δ𝑃𝑃𝑚𝑚𝑚𝑚𝑑𝑑𝑚𝑚𝑖𝑖𝑚𝑚𝑑𝑑𝑢𝑢𝑠𝑠𝑚𝑚 = 𝜉𝜉𝜌𝜌𝑣𝑣2
2(5)
where 𝜉𝜉 is defined as the individual pressure loss coefficient. NetSim recognizes the total pressure loss for the system as the sum of the frictional and individual pressure losses, i.e. the sum of Equation 4 and 5.
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The friction factor 𝐿𝐿 in Equation 4 is calculated using the Colebrook-White equation which can be found in Appendix B.
5.3.4.2 Temperature
The temperature levels in the network depend on factors such as consumer load and this is calculated when the flow of water is known in all points in the network. Given a consumer load, the cooling level at a specific node is described by the following equation (Vitec, 2013):
Δ𝑇𝑇 =�̇�𝑄�̇�𝑚𝑐𝑐𝑝𝑝
(6)
where �̇�𝑄 is the heat power (W) and �̇�𝑚 the mass flow (kg/s). Additional equations relating to outlet temperature of a pipe section can be found in Appendix C.
5.3.4.3 Density and Viscosity
The density and viscosity of water is calculated by applying the pressure and temperature conditions of the fluid in NetSim. The density of the water is as follows (Vitec, 2013):
𝜌𝜌 = 𝜌𝜌0𝐻𝐻𝑃𝑃−𝑃𝑃0𝐾𝐾 𝐻𝐻𝛽𝛽(𝑇𝑇−𝑇𝑇0) (7)
where 𝜌𝜌0 is the reference density, 𝑃𝑃 the absolute pressure (N/m2), 𝑃𝑃0 the reference pressure (N/m2) and 𝐾𝐾 the bulk module (N/m2). Furthermore, 𝛽𝛽 is a constant (K-1), 𝑇𝑇 the current temperature (K) and 𝑇𝑇0 the reference temperature (K). The reference values for the constants can be found in Table 3 below.
Table 3: Constant values for density calculations in NetSim (Vitec, 2013).
Constants Unit Value
𝝆𝝆𝟎𝟎 [kg/m3] 988
𝑷𝑷𝟎𝟎 [N/m2] 0.1013×106
𝑲𝑲 [N/m2] 2.1×109
𝜷𝜷 [K-1] -5.47×10-4
𝑻𝑻𝟎𝟎 [K] 323
Similarly, the viscosity of the working fluid is calculated using the following equation (Vitec, 2013):
𝜇𝜇 = Γ𝐻𝐻𝛼𝛼(𝑇𝑇−𝑇𝑇0) (8)
where Γ is the reference viscosity (kg/ms) and 𝛼𝛼 a constant (K-1). The reference values for the constants related to viscosity can be found in Table 4 below.
Table 4: Constant values for viscosity calculations in NetSim (Vitec, 2013).
Constants Unit Value
𝚪𝚪 [kg/ms] 0.00055
𝜶𝜶 [K-1] -0.0139
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The densities and viscosities are then used to in the pressure loss equations, Equation 4 & 5, to determine the bottlenecks, i.e. the differential pressure levels in different regions.
5.3.4.4 Further Equations in NetSim
The friction factor of the pipes is an essential component towards simulating a pipe network and the equations for these can be found in Appendix B. Additionally, NetSim has further equations relating to other components such as pumps and valves. While these components are not used in this thesis, their equations can be found in Appendix B.
5.4 Base Case Dimensioning In this report, the base case is developed in NetSim with the expectation that the simulation results will be similar to that of the real-life case for validation purposes. The base case is a replica of Norrenergi’s network as it is today. All further developed scenarios will be compared to the base case and thus it serves as a reference point for any future adjustments and alterations.
5.4.1 Outline
An overview of the DC network as represented in NetSim can be viewed below in Figure 22.
Figure 22: Outline of base case in NetSim. The blue and red symbols represent production plants and accumulators.
As can be seen in Figure 22, there are three main production plants spread out across the network, with Sundbybergsverket in the north-western part of the network. Additionally, the north-eastern section of the network is covered by the chillers and free cooling located at Frösunda while Solnaverket is located in the south-west. These productions plants operate in an interchanging manner meaning that while they could provide cooling only locally, they could also be used to even out the load in other parts of the network. As an example, the Frösunda production plant could provide cooling to Solna if that helps in evening out the differential pressures.
5.4.2 Limitations and Criteria
Generally, in a distribution network, there are certain limitations that need to be followed in order to guarantee that cooling is always provided to the consumers. From Norrenergi’s point of view, these restrictions are specifically the differential pressure in the network. The lowest allowed differential pressure in Norrenergi’s network is 100 kPa while the maximum is 800 kPa. Differential pressure levels can indicate possible bottlenecks in the network where a lower value means that the consumer demand (MW) is higher
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to the point where it is harder to deliver cooling to consumers. A higher differential pressure indicates that the amount of delivered cooling in the network exceeds the total consumer demand. Additionally, higher differential pressures can be devastating to the components of the pipes since the valves are dimensioned for a maximum of 800 kPa (Edén, 2020).
In reality there are no such things as negative differential pressures, however, they do exist in NetSim. This is due to the fact that the calculation module needs a specified differential pressure in order to converge. Furthermore, the negative differential pressures act as a helpful means when performing calculations on a model since negative values indicate that cooling is not being delivered at all to the consumer (Dyrlind and Brändström, 2020).
Another restriction is the cooling capacity of the production plants, in which the values cannot exceed their maximum capacity. For all the cases, it is assumed that the storage is empty upon starting the simulations. In the base case and every subsequent scenario, the aim is to maximise the use of free cooling since that is the production plant with the lowest cost. Additionally, the production costs are based on the hourly electricity prices for 2nd of August 2018 from Nord Pool (Nord Pool, 2020). The cost of electricity is obtained for region SE3 (Nord Pool, 2020), which is the region that Stockholm is within. The electricity price curve for this specific day can be viewed below in Figure 23.
Figure 23: The hourly electricity price for SE3 region, August 2, 2018(Nord Pool, 2020).
As can be seen in Figure 23, the electricity price is lower during the night-time when the demand is low, while an increase in price is observed as the peak hours approach, due to higher electricity demand at that time.
5.4.3 Production Load
For the purpose of proper dimensioning and design, the simulations of this thesis project are based on observed Norrenergi data for 2018-08-02, a day with high demand. This date was chosen specifically due to its high peak demands which is in line with the overall aim of this thesis, namely, to reduce peak output from expensive cooling technologies and instead cover these with storages. The reasoning is thus that if the network can deliver sufficient cooling while maintaining desirable differential pressure during this specific day, similar days can also be handled properly. Hourly data for each of the days were considered from which the cut-off production was obtained, and this was later used as a basis for the base model. The cut-off production is essentially the mean daily production and its purpose is to calculate the amount of energy
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needed for storage in order to even out the production. The load curve for the simulation day can be observed in Figure 24 below.
Figure 24: Daily load curve and cut-off production for Norrenergi’s DC network.
As can be seen from Figure 24, the lowest load of 21.8 MW (37%) occurs during the night while it increases during the day to reach the peak of 59.0 MW (100%) between at 13:00. Meanwhile, cut-off production is calculated to be 40.2 MW (68%) throughout the day. The energy available for storage is the difference between the cut-off production and the load curve, before and after the points of intersection in the graph. This means that the cold storages can be charged during the time intervals of 00:00-06:00 and 19:00-23:00, while they are discharged during the remaining hours of the day between 08:00-18:00. The cut-off production calculation procedure follows Equation 9 below
𝐶𝐶𝑚𝑚𝑠𝑠𝑠𝑠 = �𝐶𝐶𝑚𝑚𝑠𝑠𝑠𝑠𝑚𝑚 − 𝐶𝐶𝑠𝑠
24
𝑠𝑠=1
= �𝐶𝐶𝑑𝑑𝑚𝑚𝑓𝑓𝑓𝑓
24
𝑠𝑠=1
(9)
where 𝐶𝐶𝑚𝑚𝑠𝑠𝑠𝑠(MWh) is the energy available for storage, 𝐶𝐶𝑚𝑚𝑠𝑠𝑠𝑠𝑚𝑚 (MWh) is the cut-off production, 𝐶𝐶𝑠𝑠 (MWh) the hourly load and 𝐶𝐶𝑑𝑑𝑚𝑚𝑓𝑓𝑓𝑓 (MWh) the difference between the demand curve and the cut-off production. This storage potential is calculated for both periods of charging, that is during the night between 00:00-06:00 and between 19:00-23:00. For the base case, this net energy, or energy available for storage, is shown in Table 5 below.
Table 5: The hourly differences between the cut-off production and load curve for the base case, charging phase.
Time 𝑪𝑪𝒏𝒏𝒏𝒏𝒏𝒏 (MWh)
00:00 18.4
01:00 15.8
02:00 17.2
03:00 17.2
04:00 17.0
05:00 14.3
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06:00 10.7
07:00 0.0
08:00 0.0
09:00 0.0
10:00 0.0
11:00 0.0
12:00 0.0
13:00 0.0
14:00 0.0
15:00 0.0
16:00 0.0
17:00 0.0
18:00 0.0
19:00 0.3
20:00 7.3
21:00 7.5
22:00 12.8
23:00 16.7
TOTAL 155.2
As can be seen in Table 5 above, most of the energy available for storage is present during the nightly hours, while there is some energy available during the evening as well. Cells with values of zero represent the times where the storages are discharging instead. Summing up these values leads to a total of 155.2 MWh of energy being available for storage in the base case, matching with the data provided by Norrenergi AB. The data indicates that there are 12 hours of charging and 12 hours of discharging. The calculations for the discharging phase also follow Equation 9 and the results can be viewed in Table 6 below.
Table 6: The hourly differences between the cut-off production and load curve for the base case, discharging phase.
Time 𝑪𝑪𝒏𝒏𝒏𝒏𝒏𝒏 (MWh)
00:00 0.0
01:00 0.0
02:00 0.0
03:00 0.0
04:00 0.0
05:00 0.0
06:00 0.0
07:00 2.5
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08:00 14.6
09:00 18.2
10:00 17.7
11:00 13.2
12:00 17.1
13:00 18.7
14:00 11.5
15:00 18.3
16:00 8.5
17:00 8.4
18:00 6.3
19:00 0.0
20:00 0.0
21:00 0.0
22:00 0.0
23:00 0.0
TOTAL 155.2
As can be seen, the areas between the curves, in the charging and discharging phase, both amount to 155.2 MWh meaning that energy balance is achieved.
5.4.4 Solnaverket Cold Storage
Calculations for the existing storage located at Solnaverket are based on Equation 1 (see section 4.3.1.1), from which the energy capacity of the existing tank is calculated. The values for the volume, temperature difference and power capacity were provided by Norrenergi and are presented below in Table 7.
Table 7: Given values for the existing cold storage at Solnaverket (Edén, 2020).
Variables Unit Value
Volume, 𝑽𝑽 [m3] 6500
Specific heat capacity, 𝒄𝒄𝒑𝒑 [kJ/kgK] 4.19
Heat capacity, �̇�𝑸 MW 10
Temperature difference, 𝚫𝚫𝑻𝑻 °C 10
The energy capacity of the tank can be described by the following equation:
𝐸𝐸𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 =𝑉𝑉
3600𝜌𝜌𝑐𝑐𝑝𝑝Δ𝑇𝑇1000
(10)
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Where 𝐸𝐸𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 is the energy capacity (MWh). For the existing cold storage in Solna, the energy capacity is calculated to be 75.7 MWh. Since the cold storage is charged during hours of low demand, the ratio of the available energy required by the tank to be charged fully (𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) can be described as
𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 =𝐸𝐸𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝐶𝐶𝑚𝑚𝑠𝑠𝑠𝑠
(11)
which is then used to calculate the amount of energy that could be stored in the tank (𝐶𝐶𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠), given its dimensional limits and this is expressed as
𝐶𝐶𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = �𝐶𝐶𝑑𝑑𝑚𝑚𝑓𝑓𝑓𝑓𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠
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𝑠𝑠=1
= 𝐸𝐸𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (12)
By following the equations above, it is ensured that the storage is fully charged. Multiplying 𝑟𝑟𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 with corresponding time-step data in Table 5 gives the hourly charging values for the storage. As a means to approach the problem more systematically for this storage and the storages in the other scenarios, these values are then averaged out which can be seen in Table 8 below.
Table 8: Hourly charging values for the storage in Solna.
Time Hourly charging [MWh]
00:00 6.30
01:00 6.30
02:00 6.30
03:00 6.30
04:00 6.30
05:00 6.30
06:00 6.30
07:00 0.0
08:00 0.0
09:00 0.0
10:00 0.0
11:00 0.0
12:00 0.0
13:00 0.0
14:00 0.0
15:00 0.0
16:00 0.0
17:00 0.0
18:00 0.0
19:00 6.30
20:00 6.30
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21:00 6.30
22:00 6.30
23:00 6.30
TOTAL 75.7
Similarly, the discharging profile of the existing tank was calculated using the same equations, however for the discharging hours of 07:00-18:00. This can be viewed below in Table 9.
Table 9: Hourly discharging values for the storage in Solna.
Time Hourly charging [MWh]
00:00 0.0
01:00 0.0
02:00 0.0
03:00 0.0
04:00 0.0
05:00 0.0
06:00 0.0
07:00 6.30
08:00 6.30
09:00 6.30
10:00 6.30
11:00 6.30
12:00 6.30
13:00 6.30
14:00 6.30
15:00 6.30
16:00 6.30
17:00 6.30
18:00 6.30
19:00 0.0
20:00 0.0
21:00 0.0
22:00 0.0
23:00 0.0
TOTAL 75.7
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Dividing the values in Table 8 and Table 9 with the heat capacity of the Solna storage results in the ratios (of full capacity corresponding to each time step) which are needed as an input for NetSim with regards to the time series for the storage. As can be seen at the bottom of both Table 8 and Table 9, the total charging and discharging sum both amount to 75.7 MWh, meaning that the storage is fully charged and fully discharged during the day. The overall charging and discharging profile of the Solna storage can be seen in below in Figure 25.
Figure 25: The charging and discharging profiles of the Solna storage.
As expected from Figure 25 above, the charging commences at midnight and continues until 06:00. From there the storage starts to discharge at 07:00 up until 18:00. Once the storage is depleted, the charging phase once again takes over and continues between 19:00 to 23:00.
5.4.5 Order of Merit
Since Norrenergi provides DC to the network using a mix of different production technologies, these can be adjusted and influenced so that the network operation is optimized. There are several different ways to achieve this and in this project the focus is on the COP of the cooling technologies to yield the most cost-efficient performance. In regard to Table 2 (see section 5.1.2), the motivation for this order of merit is specifically the COP of the cooling technologies in which Frösunda free cooling is first to start its production due to the low costs associated with it. The low cost of free cooling stems from the fact that only a pump is essentially needed to transport the cold lake water to the heat exchanger station, in contrast to other cooling technologies which first need to produce the cold and then transport the said cold.
The process of following an order of merit starts with identifying the load at a specific time step. As an example, if the load is equal to or less than the capacity of the cooling technology that is 1st in order, free cooling in this case, it will dispatch the necessary amount of capacity from that technology until the demand is met. If the load is greater than the maximum capacity of that technology, the remaining difference will be dispatched by the technology next in order. This process continues throughout the order of merit until the demand has been fully met for that specific time step, before moving on to the next hourly load where the process repeats itself. If the demand is not met even with all the free cooling, chillers and storages, the heat pumps in Solna will be dispatched as a final measure due to their low COP.
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5.5 Scenario Modelling This section includes descriptions of the different scenarios, their characteristics and how their data is inserted into NetSim. The scenarios are based on the work of Biramo (2019) in which the storage sizes and locations for the scenarios are the same. This is due to the fact that this thesis and Biramo (2019) are offshoots of a larger project between KTH and Norrenergi. While Biramo (2019) investigated the optimization of the DC production at a unit level (Biramo, 2019), this thesis evaluates the DC performance at a network level instead.
5.5.1 Scenario 1
The first scenario includes a 15 MW cold storage in the Sundbyberg area, in addition to the already existing Solna storage of 10 MW. The layout of this scenario can be viewed in Figure 26 below.
Figure 26: The layout of Scenario 1.
Since Norrenergi plans to install a 15 MW cold storage in Sundbyberg in the coming years (Edén, 2020) this scenario is especially important to consider. This combined with the existing storage in Solna leads to a total storage capacity of 25 MW in the network. Like the base case, the first scenario follows the same load curve in Figure 24. However, since an additional storage is used, the total energy need in order to fully charge both storages needs to be calculated. Following the same methodology as in chapter 5.4.3, as was the case with the Solna storage, the energy capacity of the Sundbyberg storage is calculated. The given values for this storage can be viewed in Table 10 below.
Table 10: Given values for the Sundbyberg storage (Edén, 2020).
Variables Unit Value
Volume, 𝑽𝑽 [m3] 15 000
Specific heat capacity, 𝒄𝒄𝒑𝒑 [kJ/kgK] 4.19
Heat capacity, �̇�𝑸 MW 15
Temperature difference, 𝚫𝚫𝑻𝑻 °C 10
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As opposed to the storage in Solna, the Sundbyberg storage has a much larger volume and 50% higher heat capacity. The energy capacity of the storage in Sundbyberg is calculated to be 174.6 MWh. The total energy need to fill both of these storages then becomes
𝐸𝐸𝑆𝑆𝑠𝑠𝑚𝑚𝑚𝑚𝑠𝑠 + 𝐸𝐸𝑆𝑆𝑢𝑢𝑚𝑚𝑑𝑑𝑆𝑆𝑆𝑆𝑆𝑆𝑠𝑠𝑠𝑠𝑠𝑠 = 75.7 𝑀𝑀𝑀𝑀ℎ + 174.6 𝑀𝑀𝑀𝑀ℎ = 250.3 𝑀𝑀𝑀𝑀ℎ (13)
which should also be the area between the cut-off production and the load curve required. Since the base case had a cut-off production of 68%, providing 155.2 MWh of energy for storage, this indicates that there is a need to increase the cut-off production to allow for a more even load shifting. This is done through iterative means, starting with the values in Table 5, where 0.1 MW is added for each iteration. If the sum of the hourly differences is less than the sum of both storage energy capacities, another iteration will be performed until those are equal. This leads to a cut-off production of 47.6 MW compared to the base case, or 81% of the maximum load. The difference between the cut-off production and the load curve for Scenario 2 can be viewed below in Table 11.
Table 11: The hourly differences for 81% cut-off production in Scenario 1.
Time 𝑪𝑪𝒏𝒏𝒏𝒏𝒏𝒏 (MWh)
00:00 25.8
01:00 23.2
02:00 24.6
03:00 24.6
04:00 24.4
05:00 21.7
06:00 18.1
07:00 4.9
08:00 0.0
09:00 0.0
10:00 0.0
11:00 0.0
12:00 0.0
13:00 0.0
14:00 0.0
15:00 0.0
16:00 0.0
17:00 0.0
18:00 1.1
19:00 7.7
20:00 14.7
21:00 14.9
22:00 20.2
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23:00 24.1
TOTAL 250.3
Following Equation 11, the storage ratio 𝑟𝑟 for the 15 MW storage in Sundbyberg is calculated to be approximately 0.70, which means that 70% of the available energy for the storages will be used up by the storage in Sundbyberg, while the remaining 30% will be used to fully charge the Solna storage. Multiplying the 𝑟𝑟 value for the Sundbyberg storage with Table 11 above yields Table 12 below.
Table 12: Charging values for the Sundbyberg storage in Scenario 1.
Time 𝑪𝑪𝒏𝒏𝒏𝒏𝒏𝒏 (MWh)
00:00 18.0
01:00 16.2
02:00 17.2
03:00 17.2
04:00 17.0
05:00 15.1
06:00 12.6
07:00 3.4
08:00 0.0
09:00 0.0
10:00 0.0
11:00 0.0
12:00 0.0
13:00 0.0
14:00 0.0
15:00 0.0
16:00 0.0
17:00 0.0
18:00 0.8
19:00 5.4
20:00 10.3
21:00 10.4
22:00 14.1
23:00 16.8
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TOTAL 174.6
In some instances, such as 00:00-04:00 in Table 12 above, the hourly charging values exceed that of the tank which is 15 MW. In order to avoid this, the same steps are taken here as for the storage in Solna, where the hourly charging and discharging values are averaged and evened out. This allows for the charging and discharging values to fall within the limits of the storage while still maintaining the same total energy capacity that is needed to fully charge the storage. Averaging the values yields the following charging and discharging values viewed below in Table 13.
Table 13: Hourly charging and discharging values for the 15 MW storage.
Time Hourly charging (MWh) Hourly discharging (MWh)
00:00 12.47 0.0
01:00 12.47 0.0
02:00 12.47 0.0
03:00 12.47 0.0
04:00 12.47 0.0
05:00 12.47 0.0
06:00 12.47 0.0
07:00 12.47 14.55
08:00 0.0 14.55
09:00 0.0 14.55
10:00 0.0 14.55
11:00 0.0 14.55
12:00 0.0 14.55
13:00 0.0 14.55
14:00 0.0 14.55
15:00 0.0 14.55
16:00 0.0 14.55
17:00 0.0 14.55
18:00 12.47 14.55
19:00 12.47 0.0
20:00 12.47 0.0
21:00 12.47 0.0
22:00 12.47 0.0
23:00 12.47 0.0
TOTAL 174.6 174.6
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The values in Table 13 above are divided by the heat capacity of the Sundbyberg storage which results in ratios used for the storage time series in NetSim. The charging and discharging profile for this storage can be seen below in Figure 27.
Figure 27: Charging and discharging profile for the 15 MW storage.
5.5.2 Scenario 2
The second scenario includes the same storage calculations as for the first scenario, the difference here lies in the location of storage itself. The layout of the second scenario can be viewed in Figure 28 below.
Figure 28: The layout of Scenario 2.
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In the first scenario, the 15 MW storage is placed in Sundbyberg and in this second scenario, the exact same storage is moved to the network loop located in Frösunda as shown by Figure 28 above. Therefore, the same storage calculations with regards to the charging and discharging profiles are applied here, as it was with Scenario 1, see Table 13. This means that the cut-off production, energy available for storage and charging/discharging profiles are identical to that of the first scenario, see Figure 27. The reason the storage is placed in Frösunda, specifically in the network loop above, is due to the fact that initial results from the base case indicates that there is a deficiency in cooling since very low differential pressures were observed in this area (explained in more detail at section 6.1). The idea is for the storage to act as its own production plant when the discharging phase takes place and help increase the differential pressure to more desirable levels. Another reasoning for the placement of this storage is to evaluate how altering the position of the storage might change the effect it has on the cooling operation in the network.
5.5.3 Scenario 3
The third scenario is slightly different from the first two in the way that it installs two smaller storages of 3 MW each on separate locations. With the addition of these two storages, combined with the already existing storage in Solna, the total storage capacity in the grid is 16 MW. The locations are identical to those of Scenario 1 and Scenario 2, where one 3 MW storage is located in Sundbyberg and another 3 MW storage located in the network loop in Frösunda. Another reason why this particular scenario is interesting is that it employs storages with only one fifth (3 MW) of the heat capacity compared to Scenarios 1 & 2 (15 MW), allowing the evaluation of storage sizing. The layout for this scenario can be found below in Figure 29.
Figure 29: The layout of Scenario 3.
The reasoning for this scenario is due to the ambition that several smaller storages could help balance out the differences in differential pressures and allow for a more stable grid. Following the same storage calculations as before, i.e. Equation 10, the energy capacity for one storage is calculated to be 34.9 MWh. The total energy need to fully charge all storages then becomes
𝐸𝐸𝑆𝑆𝑠𝑠𝑚𝑚𝑚𝑚𝑠𝑠 + 𝐸𝐸𝐹𝐹𝑠𝑠ö𝑠𝑠𝑢𝑢𝑚𝑚𝑑𝑑𝑠𝑠 + 𝐸𝐸𝑆𝑆𝑢𝑢𝑚𝑚𝑑𝑑𝑆𝑆𝑆𝑆𝑆𝑆𝑠𝑠𝑠𝑠𝑠𝑠 = 75.7 𝑀𝑀𝑀𝑀ℎ + 34.9 𝑀𝑀𝑀𝑀ℎ + 34.9 𝑀𝑀𝑀𝑀ℎ = 145.6 𝑀𝑀𝑀𝑀ℎ (14)
which is a lower value when compared to the same for the base case which is 155.2 MWh. Essentially, this means that the total storage size is somewhat lower than what is needed to properly even out the production
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throughout the day. Following the same procedure as for Scenarios 1 and 2, an iterative process takes place in order to find the area under the curve to equal 145.6 MWh which corresponds to a certain cut-off production. However, since the energy need is lower than that of the base case, 0.1 MW is subtracted from each iteration instead. After each iteration, the sum of the hourly differences between the cut-off production and the load curve is calculated and if it is greater than 145.6 MWh, another iteration is performed. With an area of 145.6 MWh, the cut-off production is calculated to be slightly lower at 67%. The charging and discharging profiles for the 3 MW storage in both Frösunda and Sundbyberg are identical and can be viewed below in Figure 30.
Figure 30: Charging and discharging profile for a 3 MW storage for Scenario 3.
The specific charging and discharging values can be found below in Table 14.
Table 14: Hourly charging and discharging values for the 3 MW storage.
Time Hourly charging (MWh) Hourly discharging (MWh)
00:00 2.9 0.0
01:00 2.9 0.0
02:00 2.9 0.0
03:00 2.9 0.0
04:00 2.9 0.0
05:00 2.9 0.0
06:00 2.9 0.0
07:00 0.0 2.9
08:00 0.0 2.9
09:00 0.0 2.9
10:00 0.0 2.9
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11:00 0.0 2.9
12:00 0.0 2.9
13:00 0.0 2.9
14:00 0.0 2.9
15:00 0.0 2.9
16:00 0.0 2.9
17:00 0.0 2.9
18:00 0.0 2.9
19:00 2.9 0.0
20:00 2.9 0.0
21:00 2.9 0.0
22:00 2.9 0.0
23:00 2.9 0.0
TOTAL 34.9 34.9
Similar to the other scenarios, charging commences at 00:00 and goes on until 06:00. Once the time is 07:00 the storages start to discharge with the same amount of cooling capacity up until 18:00. From there, the charging phase is once again initiated between 19:00 and 23:00.
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6 Results & Discussion This section includes results and discussion for the base case and the subsequent scenarios with different storage alternatives. Hourly snapshots of NetSim simulations pertaining to each scenario are presented and discussed as well as results regarding the cost and cooling mix of each scenario. The base case and all scenarios included in the study have a total cooling production of 1 090 MWh for August 2nd, 2018.
6.1 Base Case The dynamic calculation in NetSim simulates on an hourly basis, and a few snapshots highlighting both low- and high demand hours can be viewed below in Figure 31, Figure 32, Figure 33, Figure 34. The abbreviations for the production plants are as follows:
• SBG: Sundbyberg chiller • SolnaKM: Solna chiller • SolnaVP: Solna heat pumps • SolAck: Discharging node of Solna storage • SolAckin: Charging node of Solna storage • FröFK: Frösunda free cooling • FröKM: Frösunda chiller
Figure 31: A snapshot of the base case simulation in NetSim at 00:00.
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Figure 32: A snapshot of the base case simulation in NetSim at 07:00.
Figure 33: A snapshot of the base case simulation in NetSim at 13:00.
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Figure 34: A snapshot of the base case simulation in NetSim at 19:00.
As viewed in Figure 31, Figure 32, Figure 33 and Figure 34, the network is stable in terms of maintaining the differential pressures within the range, with the exception of a few peak hours. The charging of the Solna storage and the demand from consumers is provided by the free cooling unit at Frösunda and a mix of chillers in Sundbyberg and Solna at 00:00 (Figure 31). At 07:00 (Figure 32), the discharging of the storage commences, and the demand starts to increase, which is alleviated by starting the chiller in Frösunda. Peak demand occurs at 13:00 (Figure 33), and from the figure (Figure 33) it is obvious that the distribution limitations are largest within the Frösunda region, specifically in the network loop. However, it cannot be concluded that the bottlenecks are within this region. At this hour, all units except the heat pumps in Solna are running at full capacity. At 19:00 (Figure 34), the use of the heat pumps are reduced to about half as the charging phase begins once again.
The results of the base case simulation in NetSim compared to that of the real provided data from Norrenergi can be observed below in Figure 35.
Figure 35: Comparison in produced cooling between real data and NetSim simulation.
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As can be seen from Figure 35, the load curves are quite similar in their daily profile, with the exception of the nightly hours between 00:00-07:00 and during the evening between 19:00-23:00. This is due to NetSim adding the production needed for charging the storage to the total load in the model. Since this model used averaged values for the charging and discharging profiles, it will differ from the real case. In practice, the charging and discharging profiles can be adjusted to fit the daily estimation of the load and energy availability. However, the NetSim model does include the charging in the total production load, similar to the real data.
The share of cooling for each cooling technology can be observed in Figure 36 below.
Figure 36: Share of cooling for each technology in the base case.
In Figure 36, it can be observed that the free cooling produced in Frösunda provides the largest share in the cooling mix, followed closely by the chiller located in Sundbyberg. The chiller in Solna has a slightly lower cooling output while the heat pumps in Solna and the chiller in Frösunda share almost a similar share. However, the aim of maximizing free cooling in the system has been achieved.
The hourly operation of each production plant can be viewed in Figure 37 below.
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Figure 37: Hourly production of cooling for the base case.
In Figure 37, it is apparent that the free cooling in Frösunda is maximized, which means that it is running at full capacity for the entirety of the simulation. Since this is the cheapest technology, it is important that it is prioritized. The chillers in Sundbyberg and Solna operate at nearly maximum capacity throughout the day, with the exception of a few hours during the charging phase. This is due to the fact that during these hours, the load is not nearly quite as high as it would be during the peak hours. The shape of the hourly production for the heat pumps in Solna resemble that of the load curve for the real data and this is because the heat pumps act as the variable production unit and is the last to dispatch to account for the peak demand.
The pressure difference levels, as mentioned earlier, can indicate the bottlenecks present in the network and the values for the base case are presented in Figure 38 below.
Figure 38: Differential pressure levels for the production plants in the base case.
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The first point noticeable in Figure 38, is the difference between Δ𝑃𝑃 levels between Solna, Frösunda and Sundbyberg. Since the distribution capacity between the northern and southern parts of the network is limited, Frösunda and Sundbyberg experience higher Δ𝑃𝑃 levels than Solna, during the charging hours when the demand is lower. This combined with the fact that both production plants are running at near maximum capacity in the early hours of day, leads to an increased differential pressure when compared to the Solna region. During the peak hours of the day, the Δ𝑃𝑃 levels are drastically reduced in both Sundbyberg and Frösunda which is due to the increase in demand during mid-day hours. This has also to do with the fact that many larger consumers are located in the Frösunda network loop as discussed before (see section 5.1.2).
It is also important to observe the effect on differential pressure for consumers in all areas of the network. A similar graph showing the Δ𝑃𝑃 levels of consumers in all areas of the network can be found in Figure 39 below.
Figure 39: Differential pressure levels for the consumers in the base case.
From Figure 39 , the same behaviour (as in the production plant’s pressure profiles) can be observed in the areas of Frösunda and Sundbyberg, with lower Δ𝑃𝑃 levels being recorded in Frösunda. Since the reference differential pressure is set in the western part of Solna, no change is noticeable in the corresponding curve. The grey line in Figure 39 (termed Central Frösunda) is a consumer node located in the network loop in Frösunda and during the peak hours, this differential pressure dips well below the minimum level and even becomes negative during some hours. This indicates that it is difficult to deliver the needed cooling to this specific area in central Frösunda during peak hours. The negative values during some hours is an indication from NetSim that no cooling is being delivered at all. The total cost of producing the necessary cooling for the entire day in the base case is presented in Figure 40 below.
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Figure 40: The daily production cost for the base case.
In Figure 40, it can be observed that the total cost of production for a single high-demand day is calculated to be 125 600 kr, making this the costliest scenario. The production unit that makes up for the largest share in this cost mix is Sundbyberg chiller, followed by similar shares in cost by the units in Solna. Due to the lower usage of the chiller in Frösunda, combined with the fact that, essentially, only pumping costs are accounted for in the free cooling unit, these two units have the lowest shares in the cost mix.
6.2 Scenario 1 The dynamic calculations with the snapshots of the network operation at 00:00, 07:00, 13:00 and 19:00 can be found below in Figure 41, Figure 42, Figure 43, Figure 44. The additional abbreviations for the Sundbyberg storage are as follows:
• SBGAck: Discharging node of the Sundbyberg storage • SBGAckin: Charging node of the Sundbyberg storage
Figure 41: A snapshot of the Scenario 1 simulation in NetSim at 00:00.
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Figure 42: A snapshot of the Scenario 1 simulation in NetSim at 07:00.
Figure 43: A snapshot of the Scenario 1 simulation in NetSim at 13:00.
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Figure 44: A snapshot of the Scenario 1 simulation in NetSim at 19:00.
At midnight (Figure 41), the production units are running at a higher capacity compared to the same hour in the base case due to presence of a second storage, and subsequently an increase in the cut-off production as mentioned before. At 07:00 (Figure 42), both storages start to discharge simultaneously and thus there is no need to include the heat pumps at that hour. Even though no exceptionally low levels of Δ𝑃𝑃 are recorded for these hours compared to the base case, the distribution limitations between the northern and the southern part of the network are still apparent. In Figure 43, the chiller in Solna and both Frösunda units are at max capacity, while the heat pumps produce more than half of their capacity. Sundbyberg however, is limited to 3.75 MW due to high Δ𝑃𝑃 levels in the Sundbyberg region as a result of the location of the additional storage. As the storage in Sundbyberg discharges, the total production capacity in the northern part of the network increases, resulting in excess production in that region which in turn partly shifts the chiller production from day to night to charge the storage.
In Figure 44, the entirety of the network, with the exceptions of western Solna and northern Frösunda, is experiencing rather low differential pressures. Given the distribution limitations between the northern and southern parts of the network, the drop in Δ𝑃𝑃 levels in these regions is considered normal network operation, i.e. production balancing. At the same time, the production in Sundbyberg is used to charge the Sundbyberg storage. One thing to notice in this figure is the fact that the Solna chiller exceeds its production capacity by 0.9 MW, since it produces 10.9 MW of cooling at that hour. Thus, in theory there is a lack of cooling capacity at this hour to be able to fully charge both storages. However, this is only due to the report-specific model and methodology used to obtain the evened-out charging profiles for the storages. In reality, the production and charging values can be shifted as means to optimize the charging profiles and this would mean that Norrenergi could handle this better in practice.
The production mix of Scenario 1 where a 15 MW storage is installed in Sundbyberg can be viewed in Figure 45 below.
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Figure 45: Share of cooling for each technology in Scenario 1.
From Figure 45, Frösunda free cooling holds the highest share of cooling at 28% which then followed by Solna at 22% and Frösunda chiller accounting for a fifth of the entire cooling production. Compared to the base case, where the chiller in Sundbyberg produces 26% of the total cooling, the cooling production for that unit is only 18% in Scenario 1. This is due to the location of the additional 15 MW storage, which is in this Sundbyberg branch of the network. Since only one main pipeline connects the Sundbyberg region to the rest of the network, it is not possible to increase its output during peak hours when the 15 MW storage is discharging from the same region. A positive side effect of this scenario is that the use of the costlier heat pumps in Solna is limited to 13%, thanks to the storage. With a 15 MW storage in Sundbyberg, the chiller capacity in Sundbyberg is not fully utilized but the production in Frösunda is increased. If network expansions continue in the Sundbyberg region, drawing more consumers, it might be possible to fully utilize the Sundbyberg production plant.
The hourly production for all production units in Scenario 1 can be found in Figure 46 below.
Figure 46: Hourly production of cooling for Scenario 1.
From Figure 46, the free cooling unit is maximized throughout the day and the chillers in Frösunda and Solna are nearly at max capacity. Since the 15 MW storage is located in Sundbyberg, the chiller in that area
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reduces its output during the cold storage discharging hours in order to stay within the differential pressure range. At 07:00 and 19:00, the heat pumps in Solna operate at zero and maximum capacity, respectively. This is due to the charging and discharging profiles of the 15 MW storage and could, in practice, be evened out. Hence, by fine-tuning the charging and discharging profiles of the cold storages from evened out values to real gradual profiles, these abrupt peaks of the heat pumps can be adjusted.
The pressure difference levels for the production plants is presented below in Figure 47.
Figure 47: Differential pressure levels for the production plants in Scenario 1.
In Figure 47, it can be seen that pressure difference levels are within the desired range at all times. An increase in differential pressure can be observed in both the Sundbyberg and Frösunda regions. Since the storage is placed in Sundbyberg and discharges at near maximum capacity, the Δ𝑃𝑃 levels increase. This is also due to the fact that the chiller in Sundbyberg is also producing some cooling during peak hours. Due to the charging profile of the 15 MW storage, the pressure difference levels are greatly reduced for the green curve (Frösunda free cooling) and blue curve (Sundbyberg chiller) during charging hours. The location of the additional storage leads to increased differential pressure at discharging hours in Sundbyberg and since it replaces a substantial amount of cooling production from other units while Solna (grey curve) experiences a lower differential pressure during peak hours. The distribution limitations between the northern and southern part of the network lead to high differential pressures in the Frösunda region. Combined with a high load in Frösunda, this further reduces the differential pressure levels in that region.
The differential pressure levels for the consumers in the network for Scenario 1 can be found below in Figure 48.
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Figure 48: Differential pressure levels for consumers in Scenario 1.
As can be seen from Figure 48, western Solna remain constant, once again due to Norrenergi’s choice to define the reference differential pressure there at 250 kPa. Due to the higher load located at central Frösunda, it experiences a lower differential pressure than the northern section of Frösunda and becomes almost equal to the lower Δ𝑃𝑃 limit at some points during the peak hours. Since the 15 MW storage is located at Sundbyberg in this scenario, the consumers in the same region will experience a relatively high differential pressure, especially during peak hours. The daily production cost for providing cooling in Scenario 1 can be found below in Figure 49.
Figure 49: The daily production cost for Scenario 1.
The first thing noticeable in Figure 49 above is that the heat pumps in Solna have the largest share in the cost mix at 23% together with the chiller in Solna. Since the additional storage of 15 MW has a larger capacity than what is needed to balance the production throughout the day, more production is shifted from day to night. In this case, that shifted production comes from the heat pumps in Solna. Despite this, the total cost is lower than that of the base case, landing at a total of 122 300 kr. Once again, due to pumping costs only, the cheapest technology in this mix is the free cooling unit at Frösunda.
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6.3 Scenario 2 The dynamic calculations with the snapshots of the network operation at 00:00, 07:00, 13:00 and 19:00 can be found below in Figure 50, Figure 51, Figure 52, Figure 53. The additional abbreviations for the Frösunda storage are as follows:
• FröAck: Discharging node of the Frösunda storage • FröAckIn: Charging node of the Frösunda storage
Figure 50: A snapshot of the Scenario 2 simulation in NetSim at 00:00.
Figure 51: A snapshot of the Scenario 2 simulation in NetSim at 07:00.
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Figure 52: A snapshot of the Scenario 2 simulation in NetSim at 13:00.
Figure 53: A snapshot of the Scenario 2 simulation in NetSim at 19:00.
As can be seen from Figure 50 above, Sundbyberg and Frösunda free cooling are operating at maximum capacity while Frösunda chiller is at nearly at full capacity. At 07:00, when the storages start to discharge, the output from Frösunda chiller is reduced to account for the increase in differential pressure, see Figure 51. During the peak hour of 13:00 in Figure 52, the differential pressure becomes too high for both regions of Sundbyberg and Frösunda, which leads to the use of the heat pumps in Solna. Placing the storage in Frösunda solves some of difficulties in delivering cooling to the region, but the distribution limitations between the northern and southern parts of the network still prevents optimization of the cooling production. Once the charging phase takes place once again at 19:00, the consumer loads combined with
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the charging profile of the 15 MW storage exceeds the capacity of the network, which can be seen by the dark blue area in Figure 53.
The production mix for Scenario 2, where the 15 MW storage has be relocated to the network loop in Frösunda, can be found below in Figure 54 below.
Figure 54: Share of cooling for each technology in Scenario 2.
From Figure 54, it can be observed that the highest share of cooling for this scenario is the free cooling unit in Frösunda. Since the storage is located in the network loop in Frösunda, where the charging and discharging takes place in close proximity to many consumers, the free cooling unit can be used at maximum capacity for the entirety of the simulation. The chillers in Solna and Frösunda produce 22% and 14% of the total cooling, respectively. In this scenario, the heat pumps in Solna are used sparingly as they make up for only 10% of the total cooling produced.
The hourly production of cooling for the different units in Scenario 2 can be viewed below in Figure 55.
Figure 55: Hourly production of cooling for Scenario 2.
When comparing Figure 55 to the same graph corresponding to Scenario 1 (Figure 46), the main noticeable difference is the production of cooling using the chiller in Sundbyberg. Since the exact same storage has been relocated to the Frösunda region, close to a cluster of consumers, this allows an increased output from
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the Sundbyberg chiller. The curve for the heat pumps is similar to that of Scenario 1, albeit with a lower peak output due to increased production from Sundbyberg. The heat pumps have a maximum total output of 18 MW which could be more difficult to reach given hotter days in this scenario but since this is affected by the chosen charging profile, the need for the heat pumps would be lower in reality (where e.g. the cold storage charging will start earlier than 19:00). The free cooling unit in Frösunda remains at max capacity while the chiller in the same area significantly reduces its output during peak hours. This is most likely because of the storage location; during discharge hours, the differential pressure becomes too great to run both the free cooling unit and chiller at max capacity.
The differential pressure levels for the production plants in Scenario 2 can be observed below in Figure 56.
Figure 56: Differential pressure levels for the production plants in Scenario 2.
In Figure 56, the differential pressure levels have drastically changed when compared to Scenario 1. The units in Frösunda are close to the upper pressure limit during the entire simulation while Solna experiences a slight increase during peak hours. Sundbyberg also operates at an exceedingly high differential pressure throughout the day due to the restriction of only having one pipeline connected to the rest of the network. One insight when viewing this graph is that the differential pressure related to the 15 MW storage in Frösunda experiences extremely low levels during the charging and discharging hours. Since there are many larger consumers in the same region, the capacity in the network is not sufficient to meet both the charging profile as well as the consumers during those points in time. This could in practice be solved by shifting the production to charge the cold storages to lower load hours and thus increase the rate of charging during those hours for this specific storage. Nonetheless, since NetSim defines negative differential pressure levels as a restriction in cooling, this means that the model has difficulties charging the storage fully.
The differential pressure levels for the consumers in this scenario can be found below in Figure 57.
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Figure 57: Differential pressure levels for consumers in Scenario 2.
From Figure 57, the consumer differential pressures follows the same trend as their respective pressures in Figure 56, with the exception for western Solna (maintained constant due to it being the reference point). Thus, during hours where the Δ𝑃𝑃 is negative, consumers will not receive their contracted cooling capacity. Such is the case for the consumers in central Frösunda at 06:00 and 19:00, and this also applies to consumers in northern Frösunda at 19:00. This indicates a strong bottleneck when placing the storage in Frösunda. Also, these abrupt peaks are also due to the abrupt shift in the cold storages from charging to discharging. By refining the cold storage profile to allow for a more gradual shift, these peaks have the potential to be reduced slightly.
The daily production cost for this scenario can be found below in Figure 58.
Figure 58: The daily production cost for Scenario 2.
As can be seen from Figure 58, the largest share of the cost in this scenario is the chiller in Sundbyberg. Since the storage is located in Frösunda, the bottleneck previously seen in Scenario 1, is reduced which allows for an increased cooling output from the chiller in Sundbyberg. The chiller in Frösunda has 15% share of the total cost and one explanation for this is that due to higher differential pressure levels during peak hours, the chiller is not able to produce as much cooling as in the other scenarios. A fourth of the total cost is covered by the chiller in Solna and 19% is covered by the heat pumps there. Since the charging profile
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of the storage in Frösunda has a high charging rate, more cooling can be produced locally in Frösunda which reduces the need to produce cooling using the heat pumps. This scenario leads to the lowest production cost; however, this cost is only valid under the assumption that cooling can be delivered to all consumers during all times. Without further analysis, it cannot be concluded whether the production capacity of e.g. chillers may be able to provide the additional cooling.
6.4 Scenario 3 The dynamic calculations with the snapshots of the network operation at 00:00, 07:00, 13:00 and 19:00 can be found below in Figure 59, Figure 60, Figure 61 and Figure 62. In this scenario, the heat pumps and chillers in Solna have been coupled to the SolnaKM node, due to restrictions in NetSim regarding node types.
Figure 59: A snapshot of the Scenario 3 simulation in NetSim at 00:00.
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Figure 60: A snapshot of the Scenario 3 simulation in NetSim at 07:00.
Figure 61: A snapshot of the Scenario 3 simulation in NetSim at 13:00.
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Figure 62: A snapshot of the Scenario 3 simulation in NetSim at 19:00.
As seen in Figure 59, during the charging phase, the free cooling unit in Frösunda and the chiller at Sundbyberg are at maximum capacity. As the simulation approaches 07:00 in Figure 60, there is a slight increase of cooling output from the chiller in Frösunda to balance the network while the storages start to discharge. During the peak demand at 13:00 in Figure 61, the previously observed low differential pressures in the Frösunda region in the other scenarios can no longer be observed. Most of the network differential pressure is fairly balanced and no major bottlenecks can be identified. This is also true once the charging phase begins again at 19:00 in Figure 62, where no bottlenecks can be seen. This desirable balance in the network is present through most of the hours in this scenario and the reason for that is due to the use of several, smaller storages in the network as opposed to one that is much larger. For this scenario, the cooling demands are satisfied while maintaining the differential pressure within the acceptable limits. Dividing the storage capacity into two smaller storages in the northern section of the network allows good capacity
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distribution. However, in terms of total storage capacity, which is lower than those of Scenarios 1 & 2, this scenario allows for less load shifting between low demand hours and peak hours which leads to an increased production cost compared to Scenario 2. The optimal storage capacity thus lies somewhere between 6 MW and 15 MW most likely.
The production mix for Scenario 3, which includes two smaller storages at 3 MW each, can be observed below in Figure 63.
Figure 63: Share of cooling for each technology in Scenario 3.
Observing Figure 63, it can be seen that Frösunda free cooling is the largest share in the mix, followed closely by the chiller in Sundbyberg at 27%. The production of cooling in Solna prioritizes the chiller, like all scenarios, which leads to an exceedingly low usage of the heat pumps at only 8%. The free cooling unit is once again maximized which means running at full capacity for the entirety of the day. Since there are two smaller storages in either region, the output from both the chiller in Frösunda and Sundbyberg can be increased drastically.
The hourly production of cooling for Scenario 3 can be seen below in Figure 64.
Figure 64: Hourly production of cooling for Scenario 3.
Like previous scenarios, the free cooling unit in Frösunda is maximized while the chiller in Sundbyberg is running at maximum capacity for the most time, with the exception of a few hours, see Figure 64. Since the sizes of the two additional storages are much smaller than the ones in Scenario 1 & 2, it becomes much
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easier to balance out the cooling production in the network. This can be seen in Figure 64, where the chiller in Solna is at a high capacity. The cooling output from the Frösunda chiller has a lower output during the charging hours, due to sufficient cooling from other units, but it is dispatched to higher outputs during peak hours. The heat pumps in Solna are used to meet the demand during exceptionally load-intensive hours.
The differential pressure levels for the production plants in Scenario 3 can be observed below in Figure 65.
Figure 65: Differential pressure levels for the production plants in Scenario 3.
In Figure 65, the changes in differential pressures are not as drastic when compared to the same figures (Figure 47 and Figure 56) corresponding to Scenario 1 & 2. Throughout the simulation, all production units manage to stay within the allowed range of differential pressure. Since the chillers in Sundbyberg and Frösunda, as well as free cooling, are used to charge all the storages, these have relatively high differential pressures during the charging phases. During peak hours, there are variations in differential pressures for all production plants, except Solna which remains stable. These variations are a result of the shape of load curve for consumers in question.
The differential pressure levels for consumers in the network for Scenario 3 can be observed in Figure 66 below.
Figure 66: Differential pressure levels for consumers in Scenario 3.
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As can be seen from Figure 66 above, the consumers are within the acceptable range of differential pressure at all times which also means that the necessary cooling is provided during the day. The largest drop in differential pressure can once again be identified in the Frösunda region, more specifically the network loop as mentioned before (see section 5.1.2). However, the Δ𝑃𝑃 levels for that region in this scenario are at acceptable levels, going no lower than the reference differential pressure in Solna. The curves for both Sundbyberg and northern Frösunda are roughly the same shape and are relatively high most of the time. This is due to the fact that both production plants are active in both the charging and discharging phase.
The daily production cost for this scenario can be found below in Figure 67.
Figure 67: The daily production cost for Scenario 3.
As shown by Figure 67, the largest share of the costs in this Scenario is the chiller in Sundbyberg since it runs at nearly full capacity due to the existence of a smaller storage located next to it. Since the total additional capacity of the storages are only 6 MW in this scenario, compared to 15 MW in Scenario 1 & 2, the increased cooling demand in the peak hours need to be covered by the heat pumps in Solna, which is why their share is higher in this scenario. Despite the usage of heat pumps during peak hours, the total cost is lower than the base case and Scenario 1, but slightly higher than Scenario 2. The Solna chiller covers 23% of the total costs while the Frösunda chiller is slightly lower at 18%, most likely due to high differential pressures in the Frösunda region once that storage starts to discharge.
6.5 Scenario Comparison The average hourly cost for the different scenarios is presented below in Figure 68.
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Figure 68: Case comparison of the average hourly costs.
As can be seen from Figure 68 above, the base case has the highest average cost of production during the peak hours due to the lack of sufficient storage. During the charging phases however, the scenarios with additional storages have a higher average hourly cost due to an increased energy need to fully charge the storages. An increased energy need leads to more cooling output from the production units during the charging phase which explains the difference. Scenarios 1 & 2, which both have larger storages than Scenario 3, also have a higher average hourly cost than Scenario 3 during these charging hours, since the energy need is greater in the first two scenarios. During the peak hours, Scenarios 1, 2 & 3 have a lower average hourly cost once the discharging phase begins due to the added capacity from the storages.
The produced cooling for the various scenarios can be found below in Figure 69.
Figure 69: Case comparison of the produced cooling from production units.
As presented in Figure 69 above, the lines representing the cooling produced from the production plants, follow the same trend as in Figure 68. Once again, due to only having one storage (Solna storage), the base case has a lower production output during the charging hours, while it increases the cooling output during peak hours to compensate for the lack of storage. Since Scenarios 1 & 2 have the same storage size, their respective cooling output follows the same trend. With Scenario 3 having smaller storages, the cooling produced during this scenario will be in between the base case and Scenario 1 & 2 lines.
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The comparisons for daily production cost and cost savings can be viewed below in Table 15.
Table 15: Cost comparisons of the different cases.(KPI: Key Performance Indicator)
Scenario Base case Scenario 1 Scenario 2 Scenario 3
Daily production cost (SEK) 125 600 122 300 119 900 120 400
Daily cost savings - 2.7% 4.8% 4.3%
KPI (SEK/MWh) 115.2 112.1 110.0 110.3
As can be seen in Table 15 above, Scenario 2 presents the lowest daily production cost of 119 900 kr which leads to a daily cost savings of 4.8%. However, this is under the assumption that all cooling is delivered which is not met in Scenario 2. That is because, in Scenario 2, as shown by the differential pressure graphs (Figure 56 and Figure 57), that there are bottlenecks that congest the network for consumers located in Frösunda. Scenario 1 presents a daily production cost of 122 300 kr which amounts to 2.7% daily cost savings and manages to deliver cooling to consumers during all hours. Scenario 3 has one of the lowest daily production costs at 120 400 kr, which reduces the daily cost by 4.3% when compared to the base case. The key performance indicator (KPI) in the table also displays the cost of cooling per amount of produced cooling energy where Scenario 2 & 3 show the lowest values once again.
6.6 Sensitivity Analysis Since Scenario 3 shows the most positive results of all the scenarios, this section evaluates changes to the production cost when parameters are altered for that scenario. The parameter that is changed is the storage size for Scenario 3 and the changes can be viewed below in Table 16. The sensitivity focuses mostly on the cost results.
Table 16: The parameters for the sensitivity analysis regarding Scenario 3.
Parameter Values
Storage size increase +25 % -25%
Results
New heat capacity [MW] 3.75 2.25
New energy capacity [MWh] 43.6 26.2
By increasing the volume of each of the storages by 25%, the heat capacity is increased to 3.75 MW and the energy capacity increases to 43.6 MWh. Reducing the volume by 25% results in a reduced heat capacity of 2.25 MW and energy capacity then becomes 26.2 MWh. The new energy capacities are calculated using Equation 10. The hourly charging and discharging profiles are then altered using the same approach as in the previous scenarios.
6.6.1 Sensitivity Analysis - Results & Discussion
Following the same steps as before, the first thing decided is the energy need to fully charge all three storages. For an increase in volume by 25%, the energy need in order to fill both 3.75 MW storages and the Solna storage results in 162.9 MWh. This is expected since a larger storage will require more energy to charge them fully. As a result, the cut-off production is increased to 69% (40.9 MW). For the decrease in storage
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size, the total energy need is 128.1 MWh which means a reduction in cut-off production to 64 % (37.8 MW). The production mix for both variations can be found below in Figure 70 and Figure 71.
Figure 70: Share of cooling for each technology, 25% increase in storage size.
Figure 71: Share of cooling for each technology, 25% decrease in storage size.
As can be seen from Figure 70 and Figure 71, the production mixes are quite similar and the only change is a slight increase in the use of heat pumps when decreasing the storage size. This increase most likely is due to more use of heat pumps during peak hours to cover the cooling demand as a result of lower storage capacity.
The differential pressure graphs regarding the production plants for both variations can be seen below in Figure 72 and Figure 73.
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Figure 72: Differential pressure levels for production plants, 25% increase in storage size.
Figure 73: Differential pressure levels for production plants, 25% decrease in storage size.
From Figure 72, it can be observed that increasing the storage sizes by 25% increases the differential pressure further in the network during peak hours. Since the storages discharge a higher amount of cooling, the production plants decrease their output to stay within the desired pressure difference range. In Figure 73, the graph resembles that of the base case. A 25% decrease in storage size results in only 4.5 MW increase in added storage capacity, which is not too far off from the base case. As opposed to an increase in storage size, Figure 73 displays a lower differential pressure during peak hours.
The economic differences of both parameter variations are presented below in Table 17.
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Table 17: Economic variations when increasing and decreasing the storage size by 25%.
Scenario Scenario 3 Scenario 3, +25% storage size
Scenario 3, -25% storage size
Daily production cost (SEK)
120 400 120 500 122 200
Change in cost (%) - 0.08 % 1.5%
KPI (SEK/MWh) 110.3 109.9 111.4
As can be seen from Table 17 above, a 25% increase in storage size does not offer any cost savings compared to Scenario 3. The most probable explanation would be that an increase of this magnitude is not able to show any major changes. One way to control this would be to increase the size by 50% or 75% instead. When decreasing the storage size by 25%, the daily production cost increases by 1.5% to 122 200 kr. Since the storage size is decreased, less cooling energy can be dispatched from storages which increases the need for heat pumps during peak hours. As a result, the cost increases. This could also be due to that a 25% increase in the storage size exerts too much pressure on the system as a whole while a 25% decrease is more expensive. Table 17 also shows the cost per unit of produced cooling with a decrease in storage size being the costliest of the three. The sensitivity analysis results regarding the differential pressures in production and cost of cooling further supports the argument that the optimal storage size for the northern section of the network lies somewhere between 6 MW-15 MW. The insignificant changes in daily production cost can be traced back to the irregular production costs of the heat pumps and free cooling unit.
6.7 Sustainability Assessment The implementation of cold storages can have various effects on the environment. These effects can be linked to the construction and operation of the storages, the cooling technique used to charge them and various electricity and pumping uses relating to environmental effects. Since there is only one free cooling unit, a large portion of the storage charging is done using various chillers in the network. These chillers are operated using refrigerants with a high GWP value, such as R134a (Edén, 2020). During high capacity operation of chillers, there is always a risk of refrigerant leaks, such as during charging hours. If leaked, it could potentially be detrimental to the surrounding environment depending on the refrigerant. However, newer chillers and heat pumps utilize more sustainable refrigerants and there are plans to replace the current refrigerants in existing chillers with refrigerant having better environmental profiles (Edén, 2020).
From a social perspective, there is also the aspect of city architecture when it comes to implementing cold storages in a network. In Scenario 1, where a 15 MW storage is installed in Sundbyberg, this does not pose an issue as the intended storage is actually a decommissioned oil storage underground. For the other scenarios, this could be a potential problem to obtain permits from the municipality of Solna and Sundbyberg. The municipalities of Solna and Sundbyberg have many dense urban areas and depending on where these storages would be installed, the process of obtaining a permit could be long in some cases. The implementation of storages might also be opposed by housing cooperatives if the neighbourhood deems that the structure does not fit in well with the rest of the buildings. Therefore, additional costs might be associated with constructing an attractive façade for the storages in order for them to blend in within the urban environment.
From an economic point of view, the storages have shown that a decrease in daily production cost is possible, according the to the results obtained from NetSim. The key performance indicators have also shown that, through charging storages at hours with lower electricity cost, more cooling energy can be delivered per unit of cost. However, further cost evaluations such as capital expenditures, construction costs and network expansion costs should be included in order to reach a more precise verdict.
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To optimally use the existing DC infrastructure of various chillers, heat pumps etc., cold storages prove suitable, rather than incorporating additional chillers or heat pumps to that DC system, just to cover the peak demands. Thus, cold storages in DC grids have a positive environmental impact when used for peak shaving/load shifting. In the context of this thesis, it can be concluded that cold storages are sustainable in regard to the factors discussed, while still having a degree of uncertainty when it comes to social aspects and costs.
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7 Conclusion This study has evaluated the possibilities of utilizing distributed cold storages in Norrenergi AB’s DC network in Solna and Sundbyberg. Three different scenarios were developed and became subject to a grid-based simulation in the software NetSim. Comparisons between the cases include parameters such as the bottleneck analysis, specifically regarding the differential pressures in the network, the cost of production and the production mix, among others. The comparison between the scenarios has shown that it is feasible to install distributed cold storages in a DC network as means to decrease the effects of distribution issue. Additionally, the effects of the distribution limitation between the northern and southern section of the network are reduced in the scenarios while lowering the daily production cost and thus increasing the cost efficiency.
In regard to the base case, the load curves from NetSim were compared to that of the actual data and aside from differences due to the charging profile of Solna storage, the values for both the simulation and actual data were similar. A considerable use of heat pumps is noticeable during the peak hours since the storage capacity is limited to 10 MW and combined with the electricity prices for that hour, this makes up for a large portion of the daily production cost which is 125 600 kr. Evaluation of the pressure difference levels showed that there are difficulties in delivering cooling to the central part of Frösunda due to distribution limitations between the northern and southern parts of the network.
In Scenario 1, with an additional 15 MW cold storage implemented in Sundbyberg, the Frösunda chiller increases its cooling output compared to the base case, while the chiller in Sundbyberg has a lower output during the peak hours due to the location of the 15 MW storage. Since the differential pressure becomes too high during the peak hours, the chiller operation in Sundbyberg is reduced to allow the storage in that region to discharge cooling. The use of heat pumps is greatly reduced during peak hours as a result of this. Concerning the differential pressures, the production plants display manageable Δ𝑃𝑃 levels, however the bottlenecks (i.e., with Δ𝑃𝑃 levels below the desired minimum) are present for the consumers during some peak hours. The total daily production cost for this scenario is 122 300 kr with the Solna units covering the largest shares.
Scenario 2, with an additional 15 MW cold storage implemented in Frösunda, presented a low use of the heat pumps for the simulated day at only 10% compared to the base case. During the peak hours, the chiller in Frösunda greatly reduces its output due to the discharging of the local storage within that region. The differential pressure levels for both the production plants and consumers in central Frösunda are low at the end of the peak (even below the desired minimum Δ𝑃𝑃 levels) and NetSim records some of these values as negative – indicating that no cooling is being delivered to consumers at that time. However, this is only due to the chosen charging profile of the storage and could be avoided in reality, allowing for a more evened out charging profile. The total daily production cost for this scenario is calculated to be 119 900 kr.
Scenario 3 included two smaller storages of 3 MW each with their locations being spread out in the areas of Sundbyberg and Frösunda. This setup allows for a higher use of the chillers in the network but does require further support from the heat pumps during the peak hours. While the differential pressure levels are high during the charging phase, these values are decreased once the storages start to discharge, all nevertheless within the desired Δ𝑃𝑃 range. However, contrary to the base case, the bottlenecks in Frösunda are greatly reduced to the point where cooling can be delivered even during the high demand hours. With a daily production cost of 120 400 kr, it presents one of the lowest costs of all the scenarios while still being able to maintain a well-balanced network throughout the day in terms of distribution limitations.
Considering the scope of this project and the comments above regarding the results of each scenario, the best setup for implementing distributed cold storages in a DC network is Scenario 3 as it provides good capacity distribution between the southern and northern sections on the network, a low cost and balanced differential pressure levels which remain within the desired range. However, as previously mentioned, the optimal capacity is most likely greater than the 6 MW offered in Scenario 3, yet lower than the 15 MW in the other scenarios. Additionally, since there are other costs associated with implementing cold storages in
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this model, the presented results might not be enough to reach a final verdict. One of the higher costs with expanding a DHC network is the expansion of the pipes since it requires expensive excavation work. In this context, the operational costs related to the electricity price might not be the significant cost. Since the storages are meant to replace expensive investments in chillers and heat pumps, the avoided cost for these could be another important factor which was not included in this thesis.
Furthermore, the model in this thesis assumes that the cold storages are perfectly stratified in which the thermocline is very thin. Due to non-optimized flows entering and exiting the tank, the stratified storage could turn into a mixed capacity in reality, and thus further reducing the capacity of the cold storage. However, historical analysis of the existing storage in Solna has shown that this has not been the case with that specific storage (Edén, 2020). With less storage capacity, the chillers and heat pumps would need to cover the cooling demand even more, resulting in higher operational costs.
In this thesis, it was assumed that the temperature difference between the supply and return pipes was 10°C throughout the simulative day. However, in practice, due to heat gains, the supply temperature might increase due to higher ground temperatures. Likewise, some consumers might increase their consumption of cooling which could increase the return temperature too.
The results and conclusion from this thesis are based on simulations for a single load-intensive day in August 2018 with the motivation that the storages would be able to provide cooling during peak demand. However, to maximize the value of the additional storages, they should be used whenever possible throughout the cooling season. By analysing the entire cooling season, the additional cost savings from cold storage use could be observed in the results.
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8 Future Work A model is only as good as the input it receives from the user and with this in mind, there are certain improvements that can be made if a similar study is carried out in the future. One idea would be to include more costs in the model, specifically costs relating to the construction and installation of the storages. Depending on the size of the storage, the costs can vary, and, with the implementation of the storages, pipe extensions which connect the storages to the rest of the network should be included in future studies as these are expensive in many cases. Capital expenditure concerning both the pipes and the storages themselves could also be a factor to consider in that case. The main difference between the scenarios were the sizes of the storages and their locations. The locations for these storages in this thesis were in Sundbyberg and in the network loop in central Frösunda but other locations should be considered as well. The basis for the choice of locations was first and foremost the bottleneck observed in the base case, however, other locations could have different outcomes on the grid stability.
Another factor to consider would be the environmental impact from leaked refrigerants as a result of using chillers in the network. One common refrigerant, R-134a, is widely used within cooling technologies and it is important to consider the release of CO2-equivalents in a future model as well.
A more thorough analysis of the network could be conducted with regards to the heat transfer coefficients of the pipes. In the NetSim model presented in this report, the heat transfer coefficients are standard values implemented by NetSim. Using actual heat transfer data corresponding to each pipe type would result in more reliable results, especially with regards to the supply and return temperatures. Future market expansions to the Bromma region of Stockholm are planned to be in full effect by the end of 2020 and as a result the consumer load will increase. Therefore, one interesting parameter to analyse would be the effect of these new consumers within this region.
A sensitivity analysis should be conducted to study the effects of different temperature differences since these would alter the cold storage capacity. Another point of interest regarding sensitivity analysis is the cooling capacity of the Frösunda free cooling unit. While assumed that the capacity is constant for that unit, in reality the capacity decreases with increasing outdoor temperatures which is expected as time passes from June to August. As a result of this, the entire cooling season could be evaluated in the future to cover the full spectrum.
Additionally, the operation of the storages are not optimized in this study. Storage charging and discharging profiles can vary from day to day depending on the daily demand and it could be of interest to optimize these profiles to obtain the most efficient charging and discharging profiles for each scenario. Moreover, the supply temperature of the cold storages could be decreased using chillers which would increase the capacity. However, that would need a cost-benefit analysis to evaluate it properly.
As a last remark, there are also plans to connect the main pipeline in Sundbyberg with the network loop in Frösunda to reduce the bottleneck in that region. This would mean that the chiller in Sundbyberg and Frösunda might be able to increase their respective cooling outputs and thus reducing the use of the heat pumps. While not included in this study, it is another important factor to consider in future works.
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Appendices Appendix A: Capacity factor time series
Appendix B: Further thermal equations in NetSim
Appendix C: Further pressure equations in NetSim
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Appendix A: Capacity factor time series Table 18: Capacity factor time series for the base case.
Production units
Time step Solna chiller Solna heat pumps
Sundbyberg chiller Frösunda free cooling
Frösunda chiller
00:00 0.96 0 0.7 1 0
01:00 1.00 0 0.88 1 0
02:00 0.97 0 0.8 1 0
03:00 0.96 0 0.81 1 0
04:00 0.93 0 0.83 1 0
05:00 0.93 0.05 0.86 1 0.15
06:00 1.00 0.15 0.96 1 0.23
07:00 0.87 0 1 1 0.85
08:00 0.99 0.59 1 1 1
09:00 1.00 0.81 1 1 1
10:00 0.99 0.78 1 1 1
11:00 0.99 0.52 1 1 1
12:00 0.99 0.74 1 1 1
13:00 1.00 0.85 1 1 1
14:00 0.99 0.41 1 1 1
15:00 1.00 0.81 1 1 1
16:00 1.00 0.22 1 1 1
17:00 1.00 0.18 1 1 1
18:00 0.91 0.07 1 1 1
19:00 1.00 0.49 1 1 0.77
20:00 1.00 0.25 1 1 0.4
21:00 0.98 0.26 1 1 0.4
22:00 1.00 0.09 0.93 1 0.18
23:00 0.92 0 0.89 1 0
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Table 19: Capacity factor time series for Scenario 1.
Production units
Time step Solna chiller Solna heat pumps
Sundbyberg chiller Frösunda free cooling
Frösunda chiller
00:00 0.99 0.13 1 1 0.6
01:00 0.98 0.2 1 1 0.75
02:00 0.99 0.15 1 1 0.7
03:00 0.99 0.15 1 1 0.7
04:00 0.99 0.15 1 1 0.7
05:00 0.99 0.26 1 1 0.84
06:00 0.99 0.41 1 1 0.96
07:00 0.88 0 0.7 1 1
08:00 0.99 0.35 0.18 1 1
09:00 0.99 0.53 0.25 1 1
10:00 0.98 0.46 0.3 1 1
11:00 1.00 0.29 0.16 1 1
12:00 1.00 0.45 0.25 1 1
13:00 0.99 0.53 0.3 1 1
14:00 0.99 0.21 0.12 1 1
15:00 1.00 0.53 0.24 1 1
16:00 0.99 0.11 0 1 1
17:00 0.99 0.07 0 1 1
18:00 1.00 0.09 0.8 1 1
19:00 1.09 1 1 1 1
20:00 1.00 0.61 1 1 1
21:00 1.00 0.61 1 1 1
22:00 0.98 0.34 1 1 0.9
23:00 0.99 0.17 1 1 0.73
Table 20: Capacity factor time series for Scenario 2.
Production units
Time step Solna chiller Solna heat pumps
Sundbyberg chiller Frösunda free cooling
Frösunda chiller
00:00 0.90 0.00 1.00 1.00 0.93
01:00 1.00 0.05 1.00 1.00 1.00
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02:00 0.99 0.00 1.00 1.00 0.97
03:00 0.99 0.00 1.00 1.00 0.97
04:00 1.00 0.00 1.00 1.00 0.96
05:00 0.99 0.17 1.00 1.00 1.00
06:00 0.99 0.39 1.00 1.00 1.00
07:00 0.81 0.00 1.00 1.00 0.70
08:00 0.99 0.30 0.82 1.00 0.30
09:00 0.99 0.43 0.95 1.00 0.30
10:00 0.99 0.37 1.00 1.00 0.28
11:00 1.00 0.24 0.86 1.00 0.21
12:00 1.00 0.35 0.95 1.00 0.30
13:00 1.00 0.42 0.93 1.00 0.40
14:00 1.00 0.19 0.82 1.00 0.15
15:00 0.99 0.42 0.90 1.00 0.38
16:00 1.00 0.11 0.76 1.00 0.04
17:00 1.00 0.09 0.77 1.00 0.00
18:00 1.00 0.02 1.00 1.00 0.88
19:00 1.09 1.00 1.00 1.00 1.00
20:00 1.00 0.61 1.00 1.00 1.00
21:00 1.00 0.61 1.00 1.00 1.00
22:00 0.99 0.28 1.00 1.00 1.00
23:00 0.99 0.02 1.00 1.00 1.00
Table 21: Capacity factor time series for Scenario 3.
Production units
Time step Solna chiller Solna heat pumps
Sundbyberg chiller Frösunda free cooling
Frösunda chiller
00:00 0.93 0.00 1 1 0.3
01:00 1.00 0.00 1 1 0.44
02:00 0.97 0.00 1 1 0.36
03:00 0.97 0.00 1 1 0.37
04:00 0.96 0.00 1 1 0.37
05:00 1.00 0.00 1 1 0.52
06:00 1.00 0.00 1 1 0.72
85
07:00 0.70 0.00 0.9 1 0.5
08:00 0.30 0.00 1 1 1
09:00 0.30 0.00 1 1 1
10:00 0.28 0.00 1 1 1
11:00 0.21 0.00 1 1 1
12:00 0.30 0.00 1 1 1
13:00 0.40 0.00 1 1 1
14:00 0.15 0.00 0.97 1 0.93
15:00 0.38 0.00 1 1 1
16:00 0.04 0.00 0.92 1 0.85
17:00 0.00 0.00 0.93 1 0.81
18:00 0.88 0.00 0.85 1 0.77
19:00 1.00 0.00 1 1 1
20:00 1.00 0.00 1 1 0.87
21:00 1.00 0.00 1 1 0.83
22:00 1.00 0.00 1 1 0.62
23:00 1.00 0.00 1 1 0.4
86
Appendix B: Pressure loss equations in NetSim Friction factor The friction factor 𝐿𝐿 (-) in Equation 4 is calculated using the Colebrook-White formula which is defined as the following in NetSim (Vitec. 2013):
1𝐿𝐿
= −4 log10 �𝜀𝜀
3.7𝐷𝐷+
1.413𝑅𝑅𝐻𝐻√𝐿𝐿
� (18)
where 𝜀𝜀 is the dimensionless relative pipe roughness (-). 𝐷𝐷 the pipe diameter (m) which is determined through iterative processes. The Colebrook-White formula depends on the Reynolds number (𝑅𝑅𝐻𝐻) which is expressed through (Vitec. 2013):
𝑅𝑅𝐻𝐻 =𝜌𝜌𝐷𝐷𝑣𝑣𝜇𝜇
(19)
This equation is however only relevant for higher Reynolds numbers (>2300). For lower Reynolds numbers the following equation is used to calculate the friction factor (Vitec. 2013):
𝐿𝐿 =𝑅𝑅𝐻𝐻16
(20)
Pressure losses over valves and pumps Depending on the application. valves and pumps may be needed in the network to account for pressure changes. These can be implemented at two places in a network which are either at the upstream or downstream end of a pipe. However. only one of these can be placed per pipe section. Pumps are modelled using the following equation in NetSim (Vitec. 2013):
Δ𝑃𝑃𝑝𝑝𝑢𝑢𝑚𝑚𝑝𝑝 = 𝐴𝐴𝜂𝜂2 + 𝐵𝐵𝜂𝜂�̇�𝑚 (21)
where 𝐴𝐴 and 𝐵𝐵 are constant. These are calculated using relevant setpoint values (Δ�̇�𝑚. �̇�𝑚) in the pump characteristics through the method of interpolation. Furthermore. 𝜂𝜂 is the efficiency of the pump and �̇�𝑚 the mass flow of water in the pipe (kg/s).
The reduction in pressure following the implementation of a valve is expressed through the following equation (Vitec. 2013):
Δ𝑃𝑃𝑖𝑖𝑠𝑠𝑚𝑚𝑖𝑖𝑠𝑠 = −1.296 × 106�̇�𝑉𝐾𝐾𝑖𝑖2
(22)
where �̇�𝑉 is the volume flow (l/s) and 𝐾𝐾𝑖𝑖 is the valve coefficient (m3h-1mwc-0.5*).
*: mwc = Meter Water Column
87
Appendix C: Further thermal equations in NetSim NetSim uses a series of equations to determine the outlet temperature. 𝑇𝑇𝑑𝑑 . of water in a pipe and this is expressed through (Vitec. 2013):
𝑇𝑇𝑑𝑑 =𝑀𝑀𝐾𝐾
+ �𝑇𝑇𝑢𝑢 +𝑀𝑀𝐾𝐾�𝐻𝐻−𝐾𝐾𝐾𝐾 (15)
where 𝑇𝑇𝑢𝑢 is the temperature of fluid upstream of the pipe and 𝐿𝐿 length of the pipe in meters. NetSim identifies the variable 𝑀𝑀 as(Vitec. 2013):
𝑀𝑀 =−𝑔𝑔(𝑧𝑧𝑑𝑑 − 𝑧𝑧𝑢𝑢)𝐾𝐾𝑇𝑇𝑆𝑆
𝐶𝐶𝑝𝑝𝐿𝐿(16)
where 𝑧𝑧𝑑𝑑 and 𝑧𝑧𝑢𝑢 are the pipe height positions downstream and upstream. respectively. Furthermore. 𝑇𝑇𝑆𝑆 is the surface temperature of the water towards the pipe. The variable 𝐾𝐾 is expressed as(Vitec. 2013):
𝐾𝐾 =𝐶𝐶ℎ𝐶𝐶𝑝𝑝�̇�𝑚
(17)
where 𝐶𝐶𝑝𝑝 is the specific heat capacity of water at 4190 J/kg°C and 𝐶𝐶ℎ the total heat transfer coefficient (W/m°C)
