System Simulation of Thermal Energy Storage involved Energy Transfer model in Utilizing Waste heat in District Heating system Application

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Master of Science Thesis

KTH School of Industrial Engineering and Management Energy Technology EGI-2014-102MSC EKV1063

Division of Heat and Power Technology SE-100 44 STOCKHOLM

System Simulation of Thermal Energy Storage involved Energy Transfer model in Utilizing Waste

heat in District Heating system Application

Ludwin Garay Rosas

20 40

60 80

100

5 10 15 20

0 5 10 15

Transport distance [km]

Annual heat demand [GWh]

Number of trucks [-]

2 4 6 8 10 12 14 16 18

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Master of Science Thesis EGI 2014: EGI- 2014-102MSC EKV1063

System simulation of Thermal Energy Storage involved Energy Transfer model in

Utilizing Waste heat in District heating system Application

Ludwin Garay Rosas

Approved Examiner

Dr. Jeevan Jayasuriya

Supervisor

Dr. Jeevan Jayasuriya

Commissioner Contact person

Abstract

Nowadays continuous increase of energy consumption increases the importance of replacing fossil fuels with renewable energy sources so the CO2 emissions can be reduced. To use the energy in a more efficient way is also favorable for this purpose. Thermal Energy Storage (TES) is a technology that can make use of waste heat, which means that it can help energy systems to reduce the CO2 emissions and improve the overall efficiency. In this technology an appropriate material is chosen to store the thermal energy so it can be stored for later use. The energy can be stored as sensible heat and latent heat. To achieve a high energy storage density it is convenient to use latent heat based TES. The materials used in this kind of storage system are called Phase Change Materials (PCM) and it is its ability of absorbing and releasing thermal energy during the phase change process that becomes very useful.

In this thesis a simulation model for a system of thermal energy transportation has been developed. The background comes from district heating systems ability of using surplus heat from industrials and large scale power plants. The idea is to implement transportation of heat by trucks closer to the demand instead of distributing heat through very long pipes. The heat is then charged into containers that are integrated with PCM and heat exchangers.

A mathematical model has been created in Matlab to simulate the system dynamics of the logistics of the thermal energy transport system. The model considers three main parameters: percentage content of PCM in the containers, annual heat demand and transport distance. How the system is affected when these three parameters varies is important to visualize. The simulation model is very useful for investigation of the economic and environmental capability of the proposed thermal energy transportation system.

Simulations for different scenarios show some expected results. But there are also some findings that are more interesting, for instance how the variation of content of PCM gives irregular variation of how many truck the system requires, and its impact on the economic aspect. Results also show that cost for transporting the heat per unit of thermal energy can be much high for a small demands compared to larger demands.

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Abbreviations

PCM Phase Change Material TES Thermal Energy Storage HTF Heat transfer Fluid

TTB Track, Train or Boat

LHTES Latent Heat Thermal Energy Storage SHTES Sensible Heat Thermal Energy Storage

O&M Operation and Maintenance

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Nomenclature

Emax Maximum storage capacity per container [MWh]

LPCM Latent heat of fusion of the PCM [kJ/kg]

PCM Density of the PCM [kg/m³]

container

V Volume of the containers [m³]

mPCM Mass of PCM per container [kg]

ESC Actual storage capacity per container [MWh]

% PCM

f Packing factor of PCM in the containers [%]

charging

t Time for charging a container [h]

dis charging

t Time for discharging a container [h]

travel

t Transport time [h]

d Transport distance [km]

v Mean velocity of the trucks [km/h]

total

t Total time for a transportation cycle of a truck [h]

l oads,possible

N Daily number of possible transportations of heat

loads per truck [-/day]

loads,required

N Daily number of required heat loads [-/day]

demand

E Annual heat demand [GWh/year]

trucks

N Number of trucks needed [-]

consumption

f Fuel consumption factor [l/km]

emission

f Fuel emission factor [kg/l]

CO 2,emission

m Annual CO2 emissions [kg/year]

pricePCM Price of PCM per kg [SEK/kg]

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costPCM Annual cost for the PCM [MSEK/year]

LCPCM Life cycle of the PCM [years]

nPCM Number of times the PCM can be used [-]

truck

price Price of a truck [MSEK]

trucks

cost Annual cost for the trucks [MSEK/year]

truck

LC Life cycle of the trucks [years]

pricefuel Price of fuel per liter [SEK/l]

costfuel Annual fuel cost [MSEK/year]

salary

price Daily salary for a truck driver [SEK/day]

salary

cost Annual cost for the salaries [MSEK/year]

total

cost Total annual cost [MSEK/year]

cost1MWh Cost per unit of transported thermal energy [SEK/MWh]

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Acknowledgements

I would like to express my gratitude to my master thesis supervisor Dr. Jeevan Jayasuriya for his support and guidance during this master thesis. Another person that I would like to thank is Dr. Justin Chiu, who also has provided me with some advices and ideas. I really appreciate all the help I have received throughout this work.

Finally I would also like to thank all the people that in some way have helped me to come to this final stage of my studies, especially teacher and friends from KTH that have helped me to improve and enlarge my technical knowledge.

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1 Introduction 1.1 Background

Sustainable energy utilization is very important today. Due to the threats of global warming, the world has found a greater challenge to find ways to reduce the fossil fuel. New technologies are important in order to make use of the energy in a more efficient way. Thermal Energy Storage (TES) is one of the potential options in reducing thermal energy wastage and hence improve the energy efficiency in industrial applications. Energy systems including TES are very likely to improve the overall efficiency of the system since it can reduce the need for production of thermal energy.

In a cold country like Sweden the heating demand for space heating during the winter can reach extremely high levels. District heating is then suitable for heating houses and buildings in communities that are well populated. Waste heat can be used for district heating, which is also preferable from an environmental perspective. In a district heating system, the heat is supplied in means of hot water, flowing in distributed piping system. Piping system is appropriated when the demand is at a reasonable distance from the supply.

When a certain community (heat demand) is located somewhat far from the available district heating networks, smaller-scale boiler or similar heat generation is normally used to supply heat to the community.

But further investigation within this area has been done to find more feasible solutions. One idea is that available heat from larger waste heat sources or larger-scale CHP plants can be charged in phase change material (PCM) and transported to the smaller communities which are located at a distance from available district heating networks. For this concept it is also appropriate to include a container, which means that a PCM filled container can be used for storing the heat and then transported closer to locations where the heat demand is existent. This is particularly interesting for heat demands which are not significantly large to justify the construction of pipelines. Industrial waste heat has good potential to be used as heat source for this purpose. However, to obtain high storage capacity and good charging rate, the container has to include an appropriate technology. Furthermore the heat can be transported by truck, train or boat, depending on the distance, geographical location and circumstance. Once the small-scale utility has started to receive external energy, the operational time for the boiler will be reduced, thus the operation and maintenance cost for the utility will also be reduced. With help of a water accumulator included in the small-scale utility the heat can be discharged regardless of the heat demand. This makes it possible to have discharges more frequently, which means that more than one container can work in the process. (Hauer, et al., 2010) (Cabeza, 2013)

1.2 Objectives

The main objective of this thesis is to investigate the economic and environmental capability of proposed TES based energy transportation system for delivering heat requirements of communities located at distance from district heating networks. Heat is collected from waste heat resources or from CHP plants and transported on road to demand locations.

A mathematical model is to be developed to simulate and analyze the dynamics of this energy transportation system. The model will take into account variables as annual heat demands, distances to transport, cost of infrastructure and performances of energy storage components to predict the economic value of energy delivered at the user end and environmental benefits of the operation.

The outcome of the analysis is expected to answers to the following:

 Technical – the influence of storage capacity and the charging/discharging rate of the container on the overall operational performance of the system.

 Economical – the influence of the capacity of the demand, distance between supply demand location and the technical performances of thermal energy storage and transport system for determining the unit cost of heat delivered.

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 Environmental – to estimate the environmental benefits of the system operation in the means of avoided CO2 emissions from conventional heat generation systems for the existing heating demands.

1.3 Scope

There are certain parameters in the simulation model that are quite difficult to estimate or find information about and some of them are also unknown. The rates for charging and discharging of the thermal energy are some of them, but also some parameters related to different costs and life cycles are not completely verified. This model will therefore consider and be built on certain assumptions, where some of them probably will be very close to the real values, while some other might be bit more varying.

1.4 Methodology

The energy model in this work was created in MATLAB, due to its advantages when it comes to handle more than one variable with quite wide ranges of study. It was created as a simulation model with input parameters that execute calculations to eventually achieve the requested results as output parameters and graphs.

Estimation of the number of trucks that the transport system requires is a central part, since this parameter has huge impact on the output parameters. The first step is to decide the size of the container and the PCM that will be used for storing heat. Thereafter the ratio between volume of PCM and heat exchanger will be chosen in order to determine storage capacity and charging/discharging rates, these parameters can then decide the charging and discharging time. The storage capacity is also used to determine the number of heat loads that are needed in the transport system, but for this calculation the size of heat demand is also needed. Travel time is another parameter and is simply based on travel distance and mean velocity of the truck.

When all the time parameters and number of heat loads required for meeting the demand are determined, the appropriate number of container used in the system can be found. Obviously the number of truck will be the same, since every single truck is connected to only one container. These steps can be summarized as follows:

PCM



Storage capacity



Charging time

Size of container Charging rate

Ratio PCM/heat

exchanger Discharging rate Discharging time

Energy demand

 Number of heat loads

Storage capacity

Travel distance

 Travel time

Mean velocity

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-10- Number of heat loads

 Number of containers

Travel time Charging time Discharging time

Once the number of trucks needed in the system is determined, calculations for emissions and costs can be started. There will be no emission from the heat itself, since it is waste heat that is already generated from larger CHP plants or surplus heat from industrials. The CO2 emissions will instead come from the transportation and it should also be compared to emissions that come from a small scale CHP plant that is not supported with transportation of waste heat by TES.

There are two different costs in this transportation system, one is the investment cost and the other is operational and maintenance costs (O&M). Cost for trucks, containers, PCM and heat exchangers are included in investment cost. O&M costs include continuous cost, which are fuel for the transport and salaries to the truck drivers. Another analysis within the economic aspect that is of great importance is the transport cost per unit of thermal energy. Transport cost per unit of thermal energy supplied at user end gives the possibility to compare the overview of the economic performance of the transport system with other means of heat generation and transportation systems that provides heat to district heating system.

In order to visualize the techno-economic performance of the energy system some parameters of the system has been considered as variables. The most significant parameters influencing the system performance are the percentage content of PCM in the containers, heat demands and travel distance.

These parameters will vary between certain ranges and provide values that make it possible to plot illustrative graphs.

1.5 Literature review

Before developing the simulation model a literature review has to be done. This will make things more clear, how the technologies works and how parameters are related to each other. The literature study is mostly based on scientific articles about TES and PCM, but also reports from projects within the same areas and course literature about energy systems are included in the review.

1.5.1 Energy storage

Energy storage is an excellent method for using energy in a more efficient way in systems where the production exceeds the demand. It makes it possible to make use of excess energy that otherwise would have been wasted, by storing it for later use. Obviously the gap between demand and supply then decreases, which means that the performance of the energy system improves. Since the use of primary energy source reduces, energy storage is favorable from both an environmental and economic perspective.

(Hauer, et al., 2010)

There are a couple of methods for storing energy, for instance: mechanical energy storage (for instance pumped hydropower storage and flywheel energy storage), electrical storage (batteries), and TES. For storing heat, TES is most suitable since the energy form heat can remain and due to the high flexibility of adapting to the heat demand. Thermal energy can be stored at temperatures between -40˚C and 400 ˚C, which means that also cooling can be stored. TES is normally divided into sensible heat thermal energy storage (SHTES), latent heat thermal energy storage (LHTES) and thermo-chemical storage (TCS).

(Sharma, et al., 2007) (Hauer, et al., 2013)

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SHTES is based on the specific heat and temperature change of a storage liquid or solid, Water is the most common to use as storage medium since it is inexpensive and has high specific heat capacity. By ensuring a high thermal insulation of the storage tank this is a very effective way of storing thermal energy in water. (Hauer, et al., 2013)

LHTES uses the phase change process in a material to store energy, where the most used phase changes are melting and solidification. Phase changes including evaporation and condensation have higher latent heat, but too large volume changes, which make them complex and impractical to implement in a TES system. (Agyenim, et al., 2009)

TCS uses reversible chemical reactions to store and release thermal energy. It is the absorbed and released energy in breaking and reforming molecular bonds that is used in this technology. (Sharma, et al., 2007) SHTES systems are in general cheaper than LHTES and TCS, but on the other hand SHTES requires larger volumes due to its low energy density. However, LHTES and TCS are economically feasible for applications with high number of cycles. SHTES systems are today commercially available while systems based on LHTES and TCS to a large extend are under development. (Hauer, et al., 2013)

1.5.2 Phase Change Material

Phase change materials (PCM) are materials that are used for LHTES. By using PCM, the storage density increases, since a lot of heat can be absorbed in the material during the melting process. When the material later starts to solidify heat will be released and it can then be used for heating purpose. During the phase change process the PCM is also kept within a small temperature span, which is another advantage of these kinds of materials. The PCM technology for TES can be applied in two different manner, one with the heat transfer fluid (HTF) in direct contact with the PCM, and another with submerged heat exchanger. The focus in this work will be on submerged heat exchangers. (Hauer, et al., 2010)

A drawback of PCM is that the conductivity in general is quite low, which leads to low charging and discharging rate of heat to the PCM materials. One way to enhance the charging rate is to increase the surface area of the heat exchanger. But by doing this the storage capacity will decrease, so apparently there is a trade-off between storage capacity and charging power that has to be examined. Adding fins to the heat exchanger is another way to increase the conductivity, but the result will be the same, a decrease of storage capacity. (Cabeza, 2013)

LHTES has been developed a lot in recent years and seems to be an interesting technology for present and future application. PCM materials can also be used in buildings, not precisely as insulation material, instead more as a temperature regulator. (Cabeza, et al., 2009)

A lot of materials that have potential to be used as PCM have been studied, but only a few have been commercialized. There are a couple of companies around the world that offer these materials. Over 50 different examples for PCMs can be found among companies located in Germany, Sweden, France, Australia and Japan, with prices varying between 0.5-10 €/kg. (Mehling, et al., 2007)

When choosing an appropriate PCM there are a couple of properties to consider and they are usually divided as follows:

 Thermal – The phase change temperature has to be around the desired operating temperature range. To achieve high storage density the latent heat of fusion and the volumetric mass density have to be high. It is also favorable if the material has high specific heat capacity so high additional sensible heat can be obtained. Furthermore the thermal conductivity of both solid and liquid phases should be as high as possible so the charging and discharging can be performed faster.

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 Physical - The melting and solidification process should be congruent so the storage capacity can be constant. Small volume change and small vapor pressure during phase change is preferable to avoid containment problem.

 Chemical – The material should have no corrosiveness and low or none subcooling. The cycling stability should be high so the material can maintain its properties after a large numbers of freeze/melt cycles. For environmental and safety reasons the material must be non-toxic, non- flammable and non-explosive.

 Economic – From an economical perspective the material should be abundant and of course it is fine if the price is not too expensive. (Agyenim, et al., 2009)

PCM has to be encapsulated in most of the cases. The reason is simply to avoid the PCM from mixing with the HTF or the environment. The two main principals of encapsulation are microencapsulation and macro encapsulation. In micro encapsulation small spherical or rod-shaped particles are enclosed in a thin, high molecular weight polymeric film, with a diameter smaller than 1mm. The PCM can then be incorporated in any matrix that is compatible with the encapsulated film. Macro encapsulation uses larger packages, usually a larger diameter than 1 cm. (Cabeza, et al., 2010)

PCM is normally divided into organic and inorganic materials. Examples of organic materials are paraffins, fatty acids and sugar alcohols. They cover a small temperature range, from 0˚C to 150˚C, and have a density that is smaller than 1 g/cm3. Inorganic materials cover a wide temperature interval. For example water with melting point at 0˚C, and salts that can have melting points up to around 900˚C. Inorganic materials usually have high densities, higher than 1g/cm3, as well as higher thermal conductivity. (Mehling, et al., 2007)

In previous studies erythritol have been evaluated as possible PCM to be used for storing thermal energy.

This material has a suitable melting point of 120 ˚C as well as high latent heat of fusion, 340 kJ/kg. Other candidates are presented in Table 1, where suitable melting point is assumed to be 90-120˚C. (Setterwall, et al., 2011) (Cabeza, et al., 2010)

PCM Melting point (˚C) Heat of fusion (kJ/kg)

Xylitol 93-94,5 263

(NH4)Al(SO4)·6H2O 95 269

Methyl fumarate 102 242

RT110 112 213

Polyethylene 110-135 200

Acetanilide 118.9 222

Table 1. PCM that could be used in the transport system

The density and thermal conductivity is unknown for most of these materials, only Xylitol has a density that is known, which is 6.7-8.3 kg/m^3.This means that the choice of material can only be based on heat of fusion, which can be unreliable. By comparing the materials with highest heat of fusion, xylitol and (NH4)Al(SO4)·6H2O, it will be a choice between an organic and an inorganic material. Inorganic PCM tends to have higher energy storage capacity and thermal conductivity, which means that (NH4)Al(SO4)·6H2O should be the choice among these materials. But erythritol will remain as the best choice to be used as PCM in a LHTES.

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-13- 1.5.3 Energy System

Energy systems can contain a wide variety of technologies, different energy sources and the most fundamental of everything, the law of conservation of energy. The systems should also include organizational structures and embedded aims. However a lot of systems are complex and contradictory, which can make them hard to understand. Modeling is a very useful tool when studying energy systems and has to take various aspects into account. A model is a formal description of a system that can be done in different ways, depending on the perspective. (Lundqvist, 2010)

There are different definitions of systems. T. J. Kotas wrote a book about this topic and defined it in this way: “A system is an identifiable collection of matter whose behavior is the subject of study”. It is always surrounded by a system boundary that can coincide with real boundary or be purely imaginary. Once the boundary is defined, it will be easier to explore what’s included, and then it will also be easier to study the system. A system can either be opened or closed. In an opened system there is flow of matter across the system boundary, while the flow stays inside the boundary in a closed system. (Kotas, 1995)

A system can also be described more briefly, according to the philosopher C. West Churchman:-”A system is a set of parts coordinated to accomplish a set of goals”. This definition is very general and vague and seems to only be useful for getting a brief overview of the system. (Churchman, 1968)

For the transportation system in this thesis the boundary will enclose supply, where the heat is charged and the demand, not the whole way out the citizens in the community, only to the smaller scale CHP utility. Of course the transportations will be included in the system since it is the link between supply and demand. Since there will be flow across the boundary this system will be assumed as opened.

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2 Model

The relationship between storage capacity and charging times is very fundamental in this model, where the percentage of PCM is the link between these parameters. The simulation model contains different parts, but the structure and calculations are almost the same for all of them. The main parameters are percentage content of PCM, transport distance and annual heat demand, these can be chosen as constants or variables. However the simulation model will never include more than one of these parameters as constant. The most important to find out with help of the simulation models are; how many trucks that should be included in the system, level of CO2 emission, cost for keeping the system working and the generation price for the heat in the unit SEK/MWh.

In the first part all the main parameters are chosen as variables. The model will then for every combination of distance and demand do calculations for all the percentage of PCM that are included in the chosen range. The purpose of varying the content of PCM is to find the best solution for each combination of distance and demand. There can be a lot of different opinions about what “the best solution” is, but at the end it all comes down to priority. In this thesis the first priority is to find the cost per unit of thermal energy and secondary the CO2 emissions. The cost per unit of thermal energy is very important since the whole system has to be feasible from an economical point of view, otherwise it can be hard to find companies interested in this new technology. But of course the emissions cannot totally be ignored for a low cost of thermal energy. With help of plots from the simulations, it will be possible to identify these situations, where cost per unit of thermal energy is low but the emissions are very high.

In the other three parts of the simulation model, one of the main parameters is constant while the other two are variables. The results will first be obtained as 3D-plots, but from these plots it is also possible to generate more common xy-plots with two of the main parameters as constants and only one as variable.

The simulation model starts with calculating how much thermal energy that can be transported in each container as maximum. The following calculation is then needed:

max PCM PCM container

E L 



V ₍₁₎

Where LPCM_and_PCM is the latent heat and the density of the PCM respectively, and Vcontaineris the volume of the container. With the choice of erythritol as PCM and a 20 foot container, the following values will be used in the calculation:

PCM

3 PCM

3 container

336 kJ/ kg 1400 kg/ m

33.1m L

V





(Cabeza, et al., 2010) & (Containerhandel AB)

The maximum load in a 20 foot container is 21.6 tonnes. (Containerhandel AB) This means that the weight of a load of erythritol that completely fills the container has to be verified:

PCM 1400 33.1 46314kg

m   

The mass apparently exceeds the limitations, which means that the maximum load in each container will be 21.6 tonnes in this model. It also has to be mentioned that the heat exchangers that will be made of aluminum will not contribute to a higher weight even though its density is higher, since the tubes will be very thin. With the maximum load of 21.6 tonnes, the maximum storage capacity becomes:

max

7258 MJ

0.336 21600 7258 MJ 2.016 MWh

3600 s/ h

E     

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Thereafter the percentage content of PCM in the containers is chosen, and can be either a specific value or a range. The storage capacity and charging rates can then be determined:

SC max % PCM

E E  f ₍₂₎

The relationship between the charging rates and content of PCM is under development. There has been a study within this area (Hassan, 2014), but still there is a need of more investigation to formulate established equations. For the moment there will instead be a quite simple assumption of the relationship between percentage of PCM and charging times, as follows:

charging

2

6 %PCM

t  f (3)

dis charging

2

4 %PCM

t   f (4)

The next step is then to choose values for the mean velocity of the vehicle, transport distance and annual heat demand, where distance and heat demand could be either fixed values or ranges. With the velocity and distance, the travel time as well as how long time a cycle takes can be calculated. Cycle time is simply how long time it takes from starting charging a container until it’s back at the charging station again, which means that it includes charging, discharging and travel time twice between supply and demand:

travel

t d v

 (5)

total charging dis charging 2travel

t t t  t (6)

The cycle time is then used for calculating how many loads each truck is able to deliver per day:

l oads,possible

cycle

h/ day

N 24

 t (7)

This value has to be rounded downwards, because the trucks are supposed to start and end at the charging station to make it easier for the truck drivers and other staff that work in the energy system to have the same working hours every day. This means that if the cycle time is more than 24 hours, this value will be rounded down to 0 and further calculations will not be possible to make for these kinds of situations. Of course it is also obvious that a cycle time longer than 24 hours will not be possible to implement in the system if the trucks are supposed to start and end at the charging station.

The demand is together with the storage capacity used for calculating how many loads that are required to be transported per day:

demand loads,required

SC 365days/year

N E

E

  (8)

With these two numbers, the number of trucks needed for the system can finally be determined:

loads,required trucks

loads,possible

N N

 N (9)

The calculated number of trucks has to be rounded upwards in order to achieve a number that really will contribute to meet the heat demand. If it’s rounded down it will not be enough heat loads transported.

Once the number of trucks is determined, calculations for costs and emissions can be performed. The CO2-emissions comes from the fuel, which means that consumption and emissions factors are needed.

For transportation by truck the following values are used:

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consumption 0.3l/ km 2.63 kg/ l

emission

f f



(Andersson, 2005) & (Bourelius, 2011)

The distance and number of loads that are required in the system are at this stage known and the total annual emissions of carbon dioxide can then be calculated:

CO 2,emission loads,required consumption emission

days year

2 365

m  d N f f  ₍₁₀₎

There are different costs for the transport system. One of them is the cost for PCM and is calculated according to the following formula:

PCM PCM PCM

PCM trucks

PCM

price cost

LC

f m

  N

  (11)

Where the values for the price for PCM used in this model is:

pricePCM 25SEK / kg

The life cycle for the PCM is dependent on the number of cycles that the material resists and how many cycles that are done annually, defined as follows:

PCM

loads,possible

days/year LC

365 n

N

 

How many times the PCM can be charged and discharged is assumed to be:

10000 nPCM 

The annual cost for trucks is the price for the trucks in the system divided by the life cycle.

truck trucks trucks

truck

price cost

LC

N

 (12)

The price and life cycle for a truck are assumed to be:

truck

price 1MSEK

LC 20 years



In the price of truck, the price of container is also included. It is simpler to have them together as one parameter, since they will work as one unit in the energy system.

The annual fuel cost has to consider transport distance, fuel consumption, fuel price and of course the number of working days per year. The formula used for this calculation is then:

fuel loads,required consumption fuel

days year

cost 2d N  f price 365 ₍₁₃₎

The only new parameter in this formula is the price of fuel:

pricefuel14.5SEK / l (Statoil Fuel and Retail Sverige AB, 2014)

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The annual cost for salary is the salary per day multiplied by the number of trucks and the number of working days per year:

salary salary trucks

days year

cost price N 365 ₍₁₄₎

Where the salary is assumed to be:

salary

price 1000SEK/ day

With all the cost determined, the total cost will of course be the sum of all the cost in the transport system:

total PCM trucks fuel salary

cost cost cost cost cost (15)

The next step is then to decide how much the cost will be for generating 1MWh thermal energy to the district heating system:

total 1MWh

demand

cost cost

 E (16)

The ranges that were chosen for the study of the simulation model, where trucks are assumed to be the transport mode are shown in Table 2. The idea was to not start with too wide ranges, because it might be easier to start with quite small ranges and then look for trends or pattern, and later expand the ranges if it’s necessary.

PCM [%] Distance [km] Demand [GWh]

min 30 10 1

max 99 100 20

Table 2. Ranges of study for the simulation model

The range for PCM volume in the containers could have been chosen from 1-99%, but to low values can make the scales of the graph too difficult to read at some parts. It’s is also more reasonable to fill the containers closer to 99% than 1%, since the storage capacity cannot be too low. Energy systems with distances shorter than 10 km or heat demands lower than 1GWh are not likely to implement this technology. In order to not start with too wide ranges, 100km and 20 GWh were chosen as upper limits for distance and demand.

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3 Results

3.1 All main parameters as variables

Results for the simulation when the main parameters; percentage content of PCM in the containers, annual heat demand and transport distance are all varied, are shown in Figure 1 to Figure 7. Ranges for these parameters were for PCM 30-99%, transport distance 10-100 km and annual heat demand 1-20 GWh. The graphs show results from different perspectives, where every combination of distances and demands is visualized and calculated with the percentage of PCM that gives the lowest cost per MWh.

Calculations where done for all the percentage of PCM, but only the one that gave the most feasible solution for each combination of distance and demand are shown in the figures.

The first graph shows how many trucks that the transport system requires depending on the heat demand and the distance. Thereafter graphs related to the environmental aspect are presented, showing the level of emissions from the system and also the potential of reduction compared to heat generation by oil. Finally costs are presented, both in total and per MWh of thermal energy.

Figure 1. The required number of trucks as function of transport distance and annual heat demand

Figure 1 shows that longer distances and higher heat demands requires more trucks, which is quite logical.

Higher demands will require more heat loads and therefore more trucks will also be needed. Longer distances will require more trucks when the possibility to deliver a certain number of loads is no longer possible.

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Distance [km]

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Figure 2. Annual CO2 emissions as function of annual heat demand and transport distance

The CO2 emissions that come from the fuel consumption during the transportations will obviously increase when the distance is increases. Higher heat demands will also result in higher emissions since higher demands require more loads to be transported. Due to the choice of different percentage contents of PCM, some values between 20 and 60 km have extra high emissions (Figure 2). The chosen values for percentage of PCM give the lowest cost per unit of thermal energy, but apparently some of them give higher emissions.

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Distance [km]

CO2 emission [tonnes]

200 400 600 800 1000 1200 1400

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Figure 3. Annual CO2 reduction compare to a case where the heat is generated from oil.

Figure 3 shows how much CO2 emissions that can be avoided by implementing a transport system of waste heat instead of generating heat from oil. The potential for reduction is higher for short distances with high heat demand. But a change in heat demand will have more impact on the potential for reduction, than a change in transport distance. The reason for this is that the emissions from oil, only come from the combustion, no transportation is assumed for the heat generation from oil.

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0 1000 2000 3000 4000

Distance [km]

500 1000 1500 2000 2500 3000 3500 4000 4500

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Figure 4. Annual total cost as function of annual heat demand and transport distance

The annual total cost for the thermal energy transport system (Figure 4) is higher for longer distances and higher demands, which is reasonable since both investment cost and O&M cost will increase when one of these variables increases. Longer distances require more fuel for transportation while higher demands require more heats loads, which results in more fuel consumption and eventually more trucks and drivers too.

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0 5 10 15

Distance [km]

Cost [MSEK]

2 4 6 8 10 12 14 16

(22)

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Figure 5. The thermal energy cost per MWh as function of annual heat demand and transport distance

If the cost instead is presented per unit of generated thermal energy (Figure 5) it will be different compared to the total cost (Figure 4). The distance will have higher impact and a change in demand will almost have no impact on this cost. Only increases and decreases for demands around the range of 1- 3GWh will show clear changes.

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Distance [km]

heat cost [SEK/MWh]

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Figure 6. The thermal energy cost per MWh as function of transport distance

The curves in Figure 6 come from Figure 5 and shows how the cost per unit of thermal energy changes for some of the annual heat demands. Here it’s clearer that this cost is almost independent of the heat demand, since all the curves except for the one for 1GWh, are more or less following the same curve.

The price for 1MWh of thermal energy in Sweden through district heating was in 2014 in average around 820kr for apartment and 880kr for villas. (Svensk Fjärrvärme, 2014) With these prices in account, 500SEK/MWh might be a maximum acceptable cost for heat generation by a transport system. This means that transportation by truck can only be feasible for a maximum distance of 40km and the demand shouldn’t be too low.

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Distance [km]

heat cost [SEK/MWh]

1 GWh 6 GWh 11 GWh 16 GWh 20 GWh

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Figure 7. The thermal energy cost per MWh as function of annual heat demand for different transport distances

The curves in Figure 7 also come from Figure 5 and shows how the cost per unit of thermal energy varies for some of the transport distances. Except from what was said before regarding the impact from the heat demand, it seems that for longer distances, the cost tends to fluctuate more when the demand varies. This graph also shows that a heat demand of minimum 2GWh can be feasible, since curves for 10km and 31km show costs under 500SEK/MWh for these demands.

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heat cost [SEK/MWh]

10 km 31 km 52 km 73 km 100 km

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3.2 Constant percentage content of PCM

Results for the case where the percentage of PCM is constant and the demand and distance are variables are presented in Figure 8–Figure 16. In this simulation the percentage content of PCM is 70% and the ranges of transport distance and annual heat demand are 10-100 km and 1-20 GWh respectively. The first graph shows how many trucks that the transport system requires. Thereafter graphs related to the environmental aspect are presented, showing the level of emissions from the system and also the potential of reduction compared to heat generation by oil. Finally costs are presented, both in total and per MWh of thermal energy. Results for how the cost per unit of thermal energy is varied are also presented for 80%

PCM.

Figure 8. The required number of trucks as function of transport distance and annual heat demand

Result for this part of the simulation shows that the number of trucks needed in the system is constant at many parts for a certain demand (Figure 8). An increase in distance will only require more trucks when the new distance doesn’t allow the truck to deliver the same amount of heat load as before. In this simulation this happened at around 35km and 95km.

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0 5 10 15 20

Distance [km]

Constant PCM,70%

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Figure 9. Annual CO2 emissions as function of annual heat demand and transport distance

Figure 10. Annual CO2 reduction compare to a case where the heat is generated from oil.

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Results from the emissions don’t show anything remarkable (Figure 9 and Figure 10). Higher demands and distances will require more fuel and therefore the emissions will also increase. The potential to avoid CO2 emissions will be higher for high heat demands since the heat generation from oil will only emit CO2

at the combustion process. Higher distances will for that reason have less impact in this aspect compared to lower heat demands.

Figure 11. Annual total cost as function of annual heat demand and transport distance

The graph for the total cost shows some similarity from the graph of number of trucks (Figure 9). This is reasonable since the number of trucks in the system will impact both investment cost and O&M cost. For instance more trucks will require larger amount of PCM and more truck drivers. But this graph also has similarities from the graph of CO2 emissions (Figure 9), which is related to the fuel cost.

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0 5 10 15 20

Distance [km]

Cost [MSEK]

2 4 6 8 10 12 14 16 18 20

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Figure 12. The thermal energy cost per MWh as function of annual heat demand and transport distance

Figure 12 shows that, apart from the lowest heat demands, the cost per unit of thermal energy is nearly constant for a constant transport distances. This means that total annual cost is almost proportional to the heat demands, which also can be seen in Figure 11.

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Distance [km]

heat cost [SEK/MWh]

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Figure 13. The thermal energy cost per MWh as function of distance for different heat demands, with constant percentage content of PCM of 70%

Figure 14. The thermal energy cost per MWh as function of demand for different transport distances with constant percentage content of PCM of 70%

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Distance [km]

heat cost [SEK/MWh]

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heat cost [SEK/MWh]

10 km 31 km 52 km 73 km 100 km

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Figure 15. The thermal energy cost per MWh as function of distance for different heat demands, with constant percentage content of PCM of 80%

Figure 16. The thermal energy cost per MWh as function of demand for different transport distances with constant percentage content of PCM of 80%

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Distance [km]

heat cost [SEK/MWh]

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300 400 500 600 700 800 900 1000 1100

heat cost [SEK/MWh]

10 km 31 km 52 km 73 km 100 km

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Comparison for the cost per unit of thermal energy between 70% (Figure 13 and Figure 14) and 80%

content of PCM (Figure 15 and Figure 16), shows that a raise from 70% to 80% PCM will result in lower costs for longer distances while the costs for shorter distances will increase. Another difference between these two cases is that there is only at one point where the cost will change drastically for 80% content of PCM. This point can be found at around 45km. For 70% PCM there are two points and these are found at around 35km and 95km. These points are related to the number of trucks that are needed in the system, which change drastically at the same points. For 1GWh the change in price will not be as large as for the other heat demands that are shown in these figures.

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3.3 Constant annual heat demand

Results for the case where the demand is constant and the distance and percentage of PCM are variables are presented in Figure 17Figure 23. In this simulation the annual heat demand is 10GWh while the ranges for percentage of PCM and transport distance are 30-99% and 10-100km respectively. Also results for this simulation are presented in the same order, starting with number of trucks required for the system.

Thereafter graphs related to the environmental aspect are shown and finally the economic aspect.

Figure 17. Required number of trucks as function of percentage of PCM and transport distance

Figure 17 shows that the PCM content in the containers can contribute to more trucks as well as fewer.

Normally a higher content of PCM will contribute to a larger storage capacity so fewer heats loads will be required. But a higher content of PCM also increases the charging and discharging time and at some points the trucks will not be able to deliver the same amount of heat loads, more trucks will then be needed.

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Distance [km]

Constant demand,10GWh

PCM content [%]

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Figure 18. Annual CO2 emissions as function of percentage of PCM and transport distance

An increase in percentage of PCM will to a very large extend contribute to a lower CO2 emissions. Longer distance will of course also result in more emissions. For a lower percentage content of PCM, the

emission level is more sensitive to a change in distance.

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PCM content [%]

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Figure 19. Annual CO2 reduction compare to a case where the heat is generated from oil.

Figure 20. Annual total cost as function of percentage of PCM and transport distance 20

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Distance [km]

PCM content [%]

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PCM content [%]

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Figure 21. The thermal energy cost per MWh as function of percentage of PCM and transport distance

In this simulation, the graph for the total annual cost (Figure 20) has the same shape as the graph for the cost per unit of thermal energy (Figure 21). The reason is simple, since the heat demand is constant the total cost will be divided by the same value all over the graph.

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PCM content [%]

heat cost [SEK/MWh]

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Figure 22. The thermal energy cost per MWh as function of transport distance for different percentages of PCM

Figure 22 show that a low percentage content of PCM is not feasible from an economic perspective. This is quite reasonable since this leads to low storage capacities and therefore more heat load will also be required.

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Distance [km]

heat cost [SEK/MWh]

30 %PCM 48 %PCM 66 %PCM 84 %PCM 99 %PCM

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Figure 23. The thermal energy cost per MWh as function of percentage of PCM for different transport distances

Figure 23 shows that the negative effect from a low percentage content of PCM is more significant for higher transport distances. But this graph also shows that too high percentage content of PCM will have a negative impact for high distances.

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PCM content [%]

heat cost [SEK/MWh]

10 km 31 km 52 km 73 km 100 km

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3.4 Constant transport distance

Results for the case where the distance is constant and the demand and percentage of PCM are variables are presented in Figure 24Figure 30. In this simulation the transport distance is 50km, while the ranges for percentage of PCM and annual heat demand are 30-99% and 1-20GWh respectively. Also results for this simulation are presented in the same order, starting with number of trucks required for the system.

Thereafter graphs related to the environmental aspect are shown and finally the economic aspect.

Compared to previous simulations with different variables, these graphs don’t show so much new discoveries. The PCM content as variable shows the same impact on the results as before. An increase in PCM content can both result in more trucks and higher total cost, as well as fewer trucks and lower total annual cost. The impact from the percentage of PCM will be more consequent when it comes to the emissions, where an increase will result in lower CO2 emissions.

Figure 24. Required number of trucks as function of percentage of PCM and annual heat demand 5

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Constant distance, d=50km

PCM content [%]

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System Simulation of Thermal Energy Storage involved Energy Transfer model in Utilizing Waste heat in District Heating system Application

System Simulation of Thermal Energy Storage involved Energy Transfer model in Utilizing Waste

heat in District Heating system Application

Ludwin Garay Rosas

Abstract

Table of Contents

Abbreviations

Nomenclature

Acknowledgements

1 Introduction 1.1 Background

1.2 Objectives

1.3 Scope

1.4 Methodology

1.5 Literature review

2 Model



3 Results

3.1 All main parameters as variables

3.2 Constant percentage content of PCM

3.3 Constant annual heat demand

3.4 Constant transport distance