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Feasibility of demand response by phase changing materials for cooling application Master Thesis Anders Østergaard Nielsen MEK-TES-EP-2011-01 January 2011

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Feasibility of demand response by phase

changing materials for cooling application

Ma

ste

r T

he

sis

Anders Østergaard Nielsen

MEK-TES-EP-2011-01

January 2011

DTU Mechanical Engineering

Section of Thermal Energy Systems

Technical University of Denmark

Nils Koppels Allé, Bld. 403

DK- 2800 Kgs. Lyngby

Denmark

Phone (+45) 45 25 41 31

Fax (+45) 45 88 43 25

www.mek.dtu.dk

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

1

This report was prepared by:

Anders Østergaard Nielsen Student number: s042181

Supervisors:

Brian Elmegaard, Head of Section, Associate Professor, DTU Mechanical Engineering

Department:

Thermal Energy Systems Department of Mechanical Engineering Technical University of Denmark Nils Koppels Alle Bygning 403 DK-2800 Kgs. Lyngby Danmark www.tes.mek.dtu.dk Tel: (+45) 45 25 41 31 Release date: 17/.01.2011 Version: 1. Edition Remarks: This report is part of the requirements to achieve the (M.Sc.Eng) at the Technical University of Denmark. This report represents 30 ECTS points. Cover: Cover page created from keywords in the report using www.wordle.net

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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Abstract

Purpose

This report covers a feasibility study of electricity demand response for cooling applications by energy

storage through phase changing materials. Three different solution proposals are investigated for

supermarket applications. Furthermore the effect on energy composition in terms of wind energy

percentages and production during off peak hours is investigated.

Method

Storage ability is achieved by three different technologies; ice bank, PCM embedded in display cases and ice

slurry. The three different PCM storage solutions are modeled in EES. The saving potential is found by

comparing yearly running costs for the solution proposals against a reference system. The running costs are

based on electricity prices for West Denmark in 2009, Danish energy taxes in 2010 and a Danish weather

reference year. The optimal operation pattern is determined by a dynamic programming model. The

dynamic programming model is implemented in Matlab. The model optimizes operation pattern based on

both electricity price and operating conditions in terms of condensing temperature.

The performance evaluation of the solution proposals are based primarily on their saving potential and the

thereby given acceptable investment. Wind energy percentages are found for every hour of the year to

determine the average wind percentages for the different solutions.

The best performing solution proposal is tested under three different futuristic scenarios with regards to

wind production and taxation.

Results and conclusions

It is found that the cooling plant COP is of great importance for the feasibility of a demand response

solution. Conclusions on saving potential are based on assuming COP predictions of the solutions to be

correct within 1%. The actual precision of the COP model is expected to be in the range of 15%.

It is found possible to achieve savings on running costs through demand response. The savings are partly

due to reductions in energy use and partly due to lower average electricity price.

The PCM display case solution is found unable to reduce running costs. Both the ice bank solution and the

ice slurry solution are found capable of reducing energy costs. The ice bank solution results in the largest

annual saving (9,600 kr. or 5.2%). The savings are found insufficient to justify the price difference between

the reference solution and the ice bank solution.

The optimal storage size is found to be around 5 hours of max production for all the solution proposals. This

storage level support storage throughout the day, but not from day to day.

All the solutions increase the degree of wind energy used by the cooling system. The increase is highest for

the ice bank solution (5.5%) and smallest for the ice slurry (4.1%). It is found that modifying the electricity

tax system can make the ice bank solution cost-efficient.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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Reading guide The following is practical information which should help in reading the report and understanding the

figures and the diagrams.

In this project heat losses play a large role. Heat losses are defined as unwanted heat transfer and can

therefore be a loss of cold energy. The loss of cold energy is therefore not referred to as cold loss but heat

loss.

References to literature are done by numbers in paragraphs like so: (000). The number refers to the

Bibliography at the end of the report.

In figures, color often indicates temperature where dark blue is colder than light blue and dark red is hotter

than light red. Diagrams are primarily using the following signatures for components.

A list of abbreviations and symbols are presented in appendix I.

All Matlab code and EES scripts are found on the attached CD-rom. A short description on the Matlab

model is found in Appendix H

Compressor

Pump

Expansion valve

Regulating valve

Condenser

Refrigerated display case

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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Table of contents

Abstract _______________________________________________________________________ 2

Purpose ____________________________________________________________________________ 2

Method _____________________________________________________________________________ 2

Results and conclusions ________________________________________________________________ 2

Reading guide __________________________________________________________________ 3

Introduction ____________________________________________________________________ 7

The electricity market _________________________________________________________________ 8

History ______________________________________________________________________________________ 8

Limited trade ________________________________________________________________________________ 8

Price setting _________________________________________________________________________________ 9

Fluctuations in the price _______________________________________________________________________ 10

Future electricity market ______________________________________________________________________ 11

Wind turbines _____________________________________________________________________________ 12

Smart grid ________________________________________________________________________________ 13

Transport ________________________________________________________________________________ 13

Decentralized heating by heat pumps__________________________________________________________ 14

Centralized heating by heat pumps ____________________________________________________________ 14

Extending the network______________________________________________________________________ 14

Stability and taxes _________________________________________________________________________ 14

Taxes ______________________________________________________________________________________ 17

Energy storage ______________________________________________________________________ 18

Sensible heat storage _________________________________________________________________________ 18

Latent heat storage __________________________________________________________________________ 19

Phase changes ____________________________________________________________________________ 19

Supercooling ______________________________________________________________________________ 20

Classification______________________________________________________________________________ 20

PCM systems _____________________________________________________________________________ 23

Phase chancing slurry (PCS) ____________________________________________________________________ 25

Shape-stabilized PCM slurry _________________________________________________________________ 25

Microencapsulated PCM Slurry _______________________________________________________________ 25

Ice slurry _________________________________________________________________________________ 26

Case _________________________________________________________________________ 28

Case possibilities ____________________________________________________________________ 28

Air-conditioning _____________________________________________________________________________ 28

Cold/freeze storage __________________________________________________________________________ 28

Industrial process ____________________________________________________________________________ 28

Supermarket applications _____________________________________________________________________ 29

Case description _____________________________________________________________________________ 29

Supermarket layout ________________________________________________________________________ 29

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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Use pattern _______________________________________________________________________________ 30

Solutions _____________________________________________________________________ 31

Evaluation _________________________________________________________________________ 31

Micro ______________________________________________________________________________________ 32

Macro _____________________________________________________________________________________ 32

Model _____________________________________________________________________________ 34

Refrigeration system configuration model (EES) ____________________________________________________ 35

Operation model (Matlab) _____________________________________________________________________ 36

Time steps _______________________________________________________________________________ 36

Dynamic programming _____________________________________________________________________ 36

State of the art ______________________________________________________________________ 37

Direct transcritical CO2 system without cold storage ________________________________________________ 39

Generation of solutions _______________________________________________________________ 41

Ice bank system _____________________________________________________________________________ 42

Capacity _________________________________________________________________________________ 42

Transport medium _________________________________________________________________________ 43

Storage medium ___________________________________________________________________________ 44

Location and sizing of the Ice banks ___________________________________________________________ 44

Refrigerant _______________________________________________________________________________ 46

Results __________________________________________________________________________________ 47

Conclusions on ice bank _____________________________________________________________________ 51

PCM display cases ____________________________________________________________________________ 51

Storage medium ___________________________________________________________________________ 52

Control __________________________________________________________________________________ 52

Capacity _________________________________________________________________________________ 53

Refrigerant _______________________________________________________________________________ 55

Results __________________________________________________________________________________ 56

Conclusions on PCM display case _____________________________________________________________ 57

Ice slurry ___________________________________________________________________________________ 57

Storage medium ___________________________________________________________________________ 57

Capacity _________________________________________________________________________________ 58

Transport medium _________________________________________________________________________ 60

Results __________________________________________________________________________________ 62

Conclusions on ice slurry ____________________________________________________________________ 64

Conclusions on solution proposals_______________________________________________________________ 65

Future performance _____________________________________________________________ 65

Scenarios __________________________________________________________________________ 65

Scenario 1 (“Elpatronordning”) _________________________________________________________________ 66

Scenario 2 (“Whistle 1”) _______________________________________________________________________ 66

Scenario 3 (“Smooth”) ________________________________________________________________________ 66

Scenario 4 (Reference) ________________________________________________________________________ 67

Results on scenarios _________________________________________________________________ 67

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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Discussion ____________________________________________________________________ 69

Sensibility study ________________________________________________________________ 70

Refrigeration system configuration model (EES) ___________________________________________ 70

Pipe work and pressure drop ___________________________________________________________________ 70

Evaporating temperature and isentropic efficiency _________________________________________________ 71

Operation model (Matlab) ____________________________________________________________ 71

Resolution __________________________________________________________________________________ 71

COP _______________________________________________________________________________________ 72

Use profile __________________________________________________________________________________ 73

Known time _________________________________________________________________________________ 73

Heat loss ___________________________________________________________________________________ 75

Market_____________________________________________________________________________________ 75

Conclusions on sensibility study ________________________________________________________ 76

Conclusion ____________________________________________________________________ 77

Bibliography __________________________________________________________________ 78

Appendix list __________________________________________________________________ 80

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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Introduction Humans need to store energy is an old one, starting with the storing of food for later use often involving

some form of curing. The practice of curing meat was widespread among historical civilizations, as a

safeguard against wasting food and the possibility of a poor harvest. Wood and coal has a long history as

fuels for fires and has been stored in many generations. It was however in the late 1800s, with the

introduction of electricity on large scale, storing became more complicated as electricity is a refined

product not possible to store on a large scale. This leaves two strategies, adjust production to demand or

adjust demand to production.

Historically the production has been regulated to fit the demand rather than demand fitting production.

The regulation has been done by different methods like government regulation or liberal market

mechanisms. In practice the regulation has been done by adding more or less fuel to the boilers, the fuel

being coal, nuclear fuel, gas or oil. This regulation method results in a correlation between expense on fuel

and income on electricity sale. The correlation is nonlinear as it is affected by things as fuel cost, efficiency

of the plant, electricity prices, investment cost and so on, but there is a strong correlation between expense

on fuel and income on electricity. This correlation is almost non-existing for electricity production based on

sun, wind and waves as the fuel is free. The cost is primarily related to the investment. Therefore powering

down does not involve significant savings only lack of income. Regulating sustainable electricity production

is therefore much more unattractive than regulating classic electricity production. If sustainable electricity

production is to take a bigger part of the total production it is therefore necessary to introduce flexible

consumers.

Cooling is a widespread need both in industry and private homes. It is therefore interesting to investigate in

the possibility of doing indirect electricity storage by storing cold energy. Furthermore, there is a tendency

of overproduction of electricity at nighttime, where conditions for producing cold energy are the best due

to lower outdoor temperatures.

Using phase chancing materials (PCM) for energy storage has some important advantages over using single

stable materials. Typically the energy density is much higher using PCM, this result in smaller storage tanks

and less mass flow through heat exchangers and pipes. Furthermore, PCMs are often more or less

temperature stable through the enthalpy changes. This often improves the overall efficiency of the cooling

system.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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The electricity market

Apart from absorption plans almost all refrigeration systems including vapor-compression systems relies on

electricity as energy input. Refrigeration systems typically cost seven to ten times as much to run over their

lifetime as they do to buy. Electricity prices are therefore of great importance for the total lifetime cost for

a refrigeration system (1).

History

In 1996 the European parliament passed new laws forcing an open liberal market on electricity. This was

done in accordance with the ideal of free movement of goods, freedom to provide services and freedom of

establishment. In order to secure free movement the grid must be controlled by independent operator,

called Transmission system operators. In Denmark this operator has since 2005 been energinet.dk, who

also controls facilities to distribute gas.

Since 1999 electricity in western Denmark has been traded on the NordPool exchange. The NordPool

exchange was originally founded to serve only the Norwegian market with day-ahead prices. However

NordPool has gradually extended to serve the market in Norway, Sweden, Finland and Demark.

In 2000 the whole Nordic grid was fully integrated when eastern Denmark joined NordPool as well.

Since 2003 all consumers regardless of size may freely chose electricity retailer. This opens for a large range

of new retailers and products, including for private consumers.

In 2004 eastern Denmark joined Elbas; western Denmark did so in 2008. Elbas is a continuous cross border

intra-day market that covers the Nordic countries, Germany, and Estonia where adjustments to trade is

done in the day-ahead market are made until one hour prior to delivery.

Over the last 15 years electricity has thereby changed from a regulated good into a trading good like gold or

oil being traded hour by hour.

Limited trade

The international trade is still limited by the capacity in the

cables connecting regions. This limitation result in

variations in prices between markets with limited or no

connection. Denmark consists of two markets: western and

eastern Denmark. A new cable opened in 2010 connecting

the two Danish markets, but still price deviations occur as

the capacity in the cable is too small to even out the

differences in the two markets at all time. In Figure 1 it is

seen how Denmark is connected to neighboring countries.

Figure 1. Connections to neighboring countries.

(27)

Feasibility of demand response by

Price setting

The price is set by liberal principles. F

and buy at given rates. A deceleration offer is illustrated in

player is interested in buying if prices are 40 Eur/MWh or below a

Eur/MWh or above. When offers from all commercial plays are given

demand as seen in Figure 2. The market price for the specific hour is given by the intersection of the supply

and demand curve. This market price dictates the amount purchased or sold by all the commerc

The price is therefore determined by supply and demand

This market price is found hour by hour

ahead of the given time. At noon prices are collected from all financial players and the market price is

found and published for all the market areas. In this way just be

hours and just after noon for 36 hours as seen in

Figure 4. Illustration of how the price is released from Nordpool

Often two parts form a financial contract with a hedge price for electricity in a given period of time. A

hedge price contract is normally formed to offset exposure to price fluctuations and thereby to unwanted

Figure 3. Example of bid/offer from a business player

a given hour. (2)

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

ce is set by liberal principles. For every hour retailers and producers declares how much they will sell

and buy at given rates. A deceleration offer is illustrated in Figure 3. As seen in Figure

player is interested in buying if prices are 40 Eur/MWh or below and interested in selling if prices are 50

Eur/MWh or above. When offers from all commercial plays are given, NordPool optimizes

The market price for the specific hour is given by the intersection of the supply

and demand curve. This market price dictates the amount purchased or sold by all the commerc

determined by supply and demand.

hour by hour in blocks of 24 hours. The price is always known

time. At noon prices are collected from all financial players and the market price is

found and published for all the market areas. In this way just before noon the market price is known for 12

r 36 hours as seen in Figure 4(2).

Illustration of how the price is released from Nordpool

Often two parts form a financial contract with a hedge price for electricity in a given period of time. A

price contract is normally formed to offset exposure to price fluctuations and thereby to unwanted

Figure 2. Demand supply relation for electricityExample of bid/offer from a business player for

phase changing materials for cooling application

9

r retailers and producers declares how much they will sell

Figure 3 the commercial

nd interested in selling if prices are 50

Pool optimizes supply and

The market price for the specific hour is given by the intersection of the supply

and demand curve. This market price dictates the amount purchased or sold by all the commercial players.

known minimum 12 hours

time. At noon prices are collected from all financial players and the market price is

fore noon the market price is known for 12

Often two parts form a financial contract with a hedge price for electricity in a given period of time. A

price contract is normally formed to offset exposure to price fluctuations and thereby to unwanted

Demand supply relation for electricity (2)

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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risk. Financial contracts like hedge price contracts extend further than the day-ahead price period and will

often be for much longer time. Trades are also done up to two hours prior to delivery around the clock and

thereby after the day-ahead price is set, this is done on Elbas. Further electricity is sold or bought as

regulating power in order to fine-tune the balance of the system along the way. This regulating power is

administrated by the transmission system operator.

As explained above electricity is traded in different ways and with different time to delivery. Most end-user

customers are charged a fixed price per kWh regardless of time a day or year. But some retailers offer

costumers variable price in order to encourage use in low load periods where the retailer purchases

electricity cheaper than in peak load hours. One of the problems is how to inform the consumer of the

price in an effective and efficient way (2) (3). Often trading electricity is seen more as trading risk than

electricity as contracts defines who benefits or suffers from variations in price in the contract period.

Fluctuations in the price

There are many factors that influence the electricity price as it is controlled by supply and demand. The

supply system is typically influenced by prices on fuel or for renewable energy sources the weather. In

Denmark the main renewable energy source is wind power. As the fuel for the wind turbines is wind it is

out of the operator’s control, though not unpredictable, however there will be times where production is

simply not possible on wind turbines. The power plant can adjust the production to almost any given level,

however not without costs as the efficiency is dependent on the load. Further there are differences in the

efficiency of the different power plants: Some are very expensive to run and are only used when the price is

very high, others run more or less regardless of the electricity price.

There are variations on the demand side as a consequence of difference in the activity level of people from

night to day; people tend to use more electricity when awake. This variation is more prominent on the two

Danish markets then the Swedes and Norwegian1 marked as seen in Figure 5 A. In Germany these

variations are even bigger (4).

Feasibility of demand response by phase changing materials for cooling application

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Figure 5. Two different patterns of electricity consumption on different Scandinavian markets. A: Variations along the day. B:

Variations along the year. Plots based on data from www.nordpoolspot.com

Furthermore there are variations from season to season as seen in Figure 5 B. This variation is not too big in

the Danish markets compared to Sweden and Norway1. One reason for this season variation being less

prominent in Denmark is the limited use of electrical heating compared to in Norway and Sweden. There

are more patterns then the two described above: for example variation along the week and variations with

the weather.

The result of the variations in demand and production costs is the fluctuating electricity price. The price

variation is important when evaluating the business potential in price response. If the fluctuations in

electricity prices increase this will reduce the payback time and increase the rate of interest; but if the

fluctuations decreases it will have the opposite effect.

Future electricity market

The Danish people are interested and concerned about problems concerning environment and climate. 7.3

% of the Danes found environment and climate to be the most critical political problem when asked in

2008. This is more than double fields like immigration policy, healthcare and tax policy (5).

This high interest amongst the public makes environmental and climate politic an important part of the

political agenda in Denmark. In March 2008 the Danish government set up a commission to investigate how

to achieve the European goal of 60-80% reduction in release of greenhouse gasses before 2050. In

September 2010 the commission presented their plan on how to achieve this goal. The plan is ambitious

and involves dramatic changes in the way the energy system runs. The commission aims to replace fossil

fuels primarily with wind energy supported by biomass. Energy sources that delivers less than ¼ of the

energy today is expected to cover the whole sector in 2050, see Figure 6.

Feasibility of demand response by phase changing materials for cooling application

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Figure 6 A: Energy sources used in Denmark in the year 2008 B: The climate commission’s recommendation on use distribution in

the year 2050 (6).

The plan is in its nature a vision for the Danish energy system and thereby one possible future. Even though

the proposals will be implemented, it may not be on the proposed scale. It is of interest to see how the

commission recommends the electricity market to develop. The elements found most interesting in the

commission report is presented and analyzed individually. An account on energy taxation generated for the

Ministry of Tax (4) is also taken into account in relation to energy taxes systems in the future.

Wind turbines

As seen in the Figure 6 the commission recommends wind power to represent almost 50 % of the total

energy consumption. Wind energy is an unstable energy source; it is estimated by the Ministry of Tax that if

wind power is extended by 150% and thereby covering 60% of the yearly consumption approximately 21%

of the time the production will exceed the consumption (4). The variations in wind are often with long time

intervals which means there can be long periods with large production and long periods with almost no

production. A randomly selected wind production date is illustrated in Figure 7; as seen from the Figure

there are around three days with almost no production from around 145 hours to 215.

Feasibility of demand response by phase changing materials for cooling application

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Figure 7. Wind production in February 2010 western Denmark, data from www.nordpoolspot.com

Increasing the energy contribution from such an uncontrollable and fluctuating source will definitely work

towards more fluctuating prices. The increase in wind energy recommended by the climate commission is

so large that it is necessary to find new ways of consuming or exporting under max production to avoid

having to shut down the turbines under these conditions.

Smart grid

There are many different definitions of the concept smart grid and there are many different ways of

implementing it; but the main idea is to incite users to use when it is most optimal for the whole system.

The enticement is normally in the form of a lower price per kWh. The consumer will probably not change

their daily routines dramatically to obtain relative small savings. Therefore the user is not expected to await

low prices for coking and vacuum cleaning, but instead focusing on things that does not influence the daily

routine. One example is intelligent washers where instead of starting the washer and dishwasher when

ready the user asks them to start whenever electricity prices are low. If the user needs the machine to start

regardless of price this is of course also possible. A system like this will work towards less fluctuation in

price as it increases the flexibility of users.

Transport

The transport sector represents around 25 % of the total energy consumption and is today dominated by

fossil fuel (7).

The recommendation from the climate commission is to modify the transport sectors to use electrical

and/or biomass vehicles. This introduces a large energy consumer into the electrical market. Electrical

vehicles will however definitely work primarily as a flexible consumer, charging depending on the current

electricity price known through the smart grid. In other words electrical vehicle will work towards fewer

fluctuations in electricity prices.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

14

Decentralized heating by heat pumps

Decentralized heating by heat pumps are already becoming more and more relevant as an alternative to oil

fired burners, natural gas fired burners or district heating systems for single-family houses. These systems

have some flexibility dependent on the heat capacity of the building materials of the house and the systems

hot water storage capacity.

The decentralized heating by electrical heat pumps will pull in the direction of fewer fluctuations in the

electricity price as it has an even use pattern with the possibility of some flexibility.

Centralized heating by heat pumps

The commission recommends using the existing district heating system by using electrical heat pumps. The

heat pump stations are intended to use overproduction from wind turbines and store as hot water. This

idea is close to the idea of storing cold energy.

Heat losses during transportation are considerable in district heating systems so the efficiency of the

system must be better for it to compete with a decentralized system. The centralized heating systems have

advantages over the decentralized systems when it comes to storage as it benefits from economy of scale

and large storage tanks have better efficiency than smaller ones. The centralized heat pump stations are

without doubt on the market for cheap electricity meaning they will operate when prices are at low level

thereby helping to even out electricity price.

Extending the network

Part of the commission recommendation is to extend the connection between the different markets. This

will level out some variations as the markets can export or import depending on the current situation.

When wind power is high in Denmark it will cover part of the Norwegian market, and when wind power is

low the Norwegian hydro power will cover part of the Danish market. This shifting between wind and hydro

is good as lakes can serve as reservoir for hydro power. On some installations water can even be pumped

backwards up in the reservoir in times of surplus power. It is therefore interesting to establish strong

connection in-between these markets to even out variations.

Stability and taxes

The commission recommends the government to establish long-term boundary condition on energy taxes

to establish a safe reliable fundament for long-term planning of initiatives. Furthermore the commission

recommends a gradually increase in fossil fuel taxes but does not recommend variable taxes on electricity

dependent on use time or degree of renewable energy at the use time (6). The idea of variable energy taxes

has however been found of interest by the government (8).

Energinet.dk finds it interesting to lower the transport charge during peak hours. Energinet.dk finds this of

interest for two reasons. Firstly, transmission losses are smaller during low load, and secondly, spreading

the load more evenly postpones expensive extension of the grid. Ideas like these are of the upmost

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

15

relevance for demand response as taxes and net-charge represent most of the energy price for the end

consumer. Regulating the net-charge and energy taxes is an extremely effective tool for regulating use. The

idea of regulating is however partly against the philosophy of a free market and can easily have unfair and

undesirable consequences for the commercial players. It is therefore of great importance to develop a tax-

system that favors the overall green strategy without disrupting the free market mechanisms in unwanted

ways.

The location of the taxes in the process is of great importance. If taxes are moved from electricity to fuel for

power stations it has the effect of removing taxes from wind turbines as they don’t use any fuel. This

thereby lowers the energy tax in periods with high degree of wind energy which is good but it has the

disadvantage of decreasing the competitiveness towards foreign producers as it increases the price on

Danish produced electricity. It is however difficult to establish tax systems that regulates without any

unwanted consequences as the market is complex in structure and with many different interests and

players.

The government has ordered the tax-ministry to make an account of the possibilities and effects of

chancing the taxes on electricity for better implementation of renewable energy (dynamic tax) (4). The task

force to produce this account is a combination of people representing different interest-parties. Different

tax-systems are simulated and evaluated. Simulations are based on a scenario with an increase in wind

power by 150% in relation to 2009 condition. The commission finds:

• It is found not interesting to vary the taxes according to the time of day. Day/night variation has a

minor effect on the usage of more wind power. This is because wind variations are not only related

to the time of day (4).

• It is found that float tax1 has small effect on implementation of more wind power. The float tax is

low when electricity price is low. Since the price on electricity in general is low when there is a high

degree of renewable energy, this means that the float tax varies along with renewable energy. It is

however found that variation periods of renewable energy are long and unknown making planning

hard for the consumers. Due to the difficulties in planning the use pattern will not changes

significantly and thereby not help implementing more wind power (4).

• In the Whistle model 1 electricity taxes are reduced by 70 % when wind power covers all

consumption. This setup results in long periods with either low or high tax, as wind variation

periods are long. These periods’ length and unpredictability are found to obstruct significant

changes in the use pattern (4).

• In the Whistle model 2 electricity taxes are reduced by 70 % when prices are lower than the

expected cost of production on power stations. This setup is found to have the same drawbacks as

the whistle model 1(4).

• The tax system found to be of most interest is the “Elpatronordning”. The system promotes direct

electrical heating for district heating. The electrical heating system is installed as an alternative

heater to fossil fueled heaters in cogeneration systems. This system is of interest as it introduces a

large flexible consumer to the electrical market and therefore opens for further wind power.

1 Floating tax based on the sails price of the product.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

16

Furthermore the system allows for the cogeneration plants to power down as heat and electricity is

produced by wind (4).

The system is however widely criticized for giving preferential treatment to district heating systems

with relative large energy losses to the ground instead of promoting more efficient decentralized

direct heating combined with oil, gas, or even heat pumps in households.

The “Elpatronordning” solves the problem by introducing a new consumer with high flexibility; this will

probably be sufficient for consumption of even more than 150% (7.875 MWh) more wind power (4). The

climate commission however is recommending a lot more than 150% more wind power (10.000 – 18.500

MWh). The “Elpatronordning” is therefore only a step of the way of more flexible electricity consumption;

inevitably more flexibility must be found.

The idea of the “elpatronordning” is to shut down cogeneration plants when there is overproduction from

wind turbines. Shutting down cogeneration plants is not possible without substituting the heat production;

under the “elpatronordning” it is feasible to shut down cogeneration plants from producing district heating

and electricity and substituting this by direct electrical heating. Thereby the cogeneration plants are not

burning fossil fuel when there is plenty of green wind energy.

The “elpatronordning” works by lowering taxes on electricity from 78 to 20.8 øre/kWh on electricity used

on electrical heating when cogeneration capacity is available. In the report from the Ministry of Tax (4) an

estimation of breakeven points for production with direct heating is done. The breakeven point will differ

dependent on fuel prices and efficiency of the plant. The breakeven point for production by direct heating

is estimated to be in the area of 22 øre/kWh as seen under “Elpatron” in Figure 8. This means that when

prices drop below 22 øre/kWh it is feasible to produce district heating with direct electricity.

Figure 8. Marginal expenses of production of district heating with different technologies. As the fuel price and technological

conditions vary the Figure is only an example of the conditions. (4)

Feasibility of demand response by phase changing materials for cooling application

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Taxes

This project will use tax levels as they are described in the report from the Ministry of Tax (4) after the full

implementation of the spring package 2.0 in 2010. The tax levels are as follows:

Government tax: 78 øre/kWh

PSO: 11 øre/kWh

Transfer tax: 15 øre/kWh

In all 104 øre/kWh

The PSO (Public Service Obligations) is a tax to cover different aspects surrounding the energy market such

as ensuring security of supply, subvention for green energy and R&D in sustainable energy. Subvention for

green energy represents approximately ¾ of the PSO (9).

The energy sector is under dramatic change and the future structure is uncertain both regarding

consumption, production and taxation. It is found impossible to estimate price patterns with a reasonable

accuracy for simulating performance on future markets of demand response. It is however assumed that

the introduction of more renewable energy will in general lead to larger price variations. A conservative

estimation on saving potential is therefore done by assuming the saving potential stays constant.

This uncertainty of price development increases the risk associated with investing in systems benefiting

from price fluctuations. The uncertainty will normally result in investors demanding a short payback time or

a conservative estimation on the future.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

18

Energy storage

In order to evaluate different storage solutions it is necessary to define some important parameters for a

solution. The importance of these parameters will differ from case to case; some may not even be relevant

in all cases. Parameters that are found to be the relevant in almost all storage cases are:

• Cost efficiency

• Temperature ranges

• Volumetric efficiency

• Sustainability

• Lifetime and maintenance costs

• Charge-discharge time

Thermal energy storage can be subcategorized into sensible and latent heat storage. Sensible heat storage

works simply by changing the temperature of the storage medium thereby absorbing or rejecting energy.

Latent heat storage works by changing phase from one state to another with only minor change in

temperature but great chance in energy level.

Sensible heat storage

Sensible heat storage has the advantages of being relatively simple as it only involves chilling a medium and

using it for cooling later one. The medium is normally a fluid (brine) as this allows for the storage medium

to be used also as transport medium. Further many fluids have a relative high heat capacity in relation to

price and volume. One of the technical problems of using brine for heat storage is how to avoid mixing of

return brine with unused brine during the charge and discharging phase. There is however solutions to

these problems, one of which is to use a storage tank with two rooms separated by a movable separator.

One of the main problems with sensible heat storage is that in its nature it depends on a temperature

difference. If you need cooling at a temperature Tc you need to charge the stock by cooling it below Tc the

more you go below Tc the more charged the stock is.

������ = �� − �� ∙ ����� ∙ ��

Equation 1

As seen from Equation 1 storage capacity relies on the mass, specific heat capacity of the brine and the

delta temperature between the storage and the cooling needed. The mass depends on the size of storage

tanks: large storage tanks require a massive investment and have large heat transfer surfaces to the

surroundings. A high delta temperature (� − �) is expensive as it results in decreased efficiency due to

increased temperature difference between evaporator and condenser side, see Equation 2.

������� = �� − �

Equation 2

Feasibility of demand response by phase changing materials for cooling application

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The heat capacity is dependent on the brine but is normally close to that of water. This means that you

either need to accept low efficiency or large heat losses and/or large investments on storage tanks.

Sensible heat storage is however widely used both for heat and cold storage in many different applications.

Latent heat storage

Phase changes

A phase change is interesting for thermal storing purposes because of the enthalpy differences between

two phases. This means large changes in energy stage without significant changes in temperature. This is

illustrated with pure water in Figure 9.

Figure 9. Enthalpy of water as function of temperature. Both red and green marks a 2 K temperature change but the red change

involves significantly larger enthalpy change as it involves a phase change.

The two cases seen in Figure 9(red and green) both contain a change in temperature of 2 Kelvin but there is

a factor of above 40 between the enthalpy differences due to phase change at 0 oC. This is attractive

because it makes it possible to store the same amount of energy in less mass with smaller delta

temperature.

Latent heat storage can be achieved through phase changing solid-solid, solid-liquid, solid-gas and liquid-

gas phase change of a Phase Changing Material (PCM). However the normal phase change used for storage

applications is the solid-liquid change. Liquid-gas phase changes are not practical for use as thermal storage

due to the large volumes or high pressures required to store the materials when in their gas phase. Solid-

solid phase changes are typically very slow and have a rather low heat of transformation (10).

Feasibility of demand response by phase changing materials for cooling application

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Supercooling

A liquid will normally freeze when cooled to its freezing point; however there needs to be a seed crystal or

nucleus for the crystallization to start. Pure water normally freezes at 0 oC but can be supercooled to -42 oC

without freezing under the right conditions. Supercooling can be a problem in a system designed to cool

the PCM only a few Kelvin below the freezing temperature. This temperature will normally cause freezing

but if no nucleus are present the freezing may never starts and latent energy is not removed. The problem

can be solved by adding a nucleating agent or a “cold finger” in the storage material to startup the

crystallization process. However supercooling is something that must be considered when designing a

system based on PCM with a tendency to supercool.

Classification

In principle any material can be used as PCM; it is only a question of the material properties. The material

properties must be in alliance with the application and the system configuration. Things to consider are,

amongst others, phase changing temperatures, latent heat capacity, flammability, toxicity, supercooling,

thermal conductivity and long-term stability. Different PCMs are investigated in order to determine each

PCMs potential for cold storage.

PCM is classified primarily by organic or inorganic and the way they solidify, as seen in Figure 10.

As seen in

As seen in Figure 10 both organic and inorganic PCMs can be in eutectic form. If a PCM is eutectic it has a

specific and defined phase chancing temperature if not the phase changes takes place over a temperature

Materials

Sensible heat Latent heat Chemical energy

Gas - liquid Solid - gas

Solid - liquid Solid - solid

Organics Inorganics

Eutetics Single temperature

Mixtures Temperature interval

Eutetics Single temperature

Mixtures Temperature interval

Figure 10. Classification of heat storage with focus on latent heat storage by solid-liquid phase change (29)

Feasibility of demand response by phase changing materials for cooling application

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interval. Pure water behaves eutectic freezing at 0 oC, when adding salt it becomes a mixture. As salt is

dissolved in water the phase chance temperature decreases. The more salt the lower the phase change

temperature until a certain point where adding salt will increase the temperature, this is the second

eutectic point. At this concentration the solution will again have a well defined freezing point.

When freezing a solution of dissolved salt with a concentration below the eutectic concentration only the

water molecules freezes. By only freezing water molecules the concentration of salt is increased in the

remaining fluid. The increase in concentration lowers the phase change temperature further. This increase

in concentration and lowering of phase change temperature will take place until the eutectic concentration

is achieved in the unfrozen solution. From this point freezing continues at a fixed temperature.

Figure 11. Enthalpy phase diagram for NaCl - H2O (11)

As seen in Figure 11 the eutectic concentration is around 23 % wt with a temperature of 21 oC for sodium

chloride in water. If the concentration is 0.1 the freezing will start -6.5 oC and the temperature will fall while

the ice concentration increases until the eutectic temperature of -21 oC is achieved.

Feasibility of demand response by phase changing materials for cooling application

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Organic

The organic materials melt and freeze without degenerating and with no or little supercooling and are for

the most part non corrosive. Most of them have low thermal conductivity in the solid state.

Paraffin

All paraffins are of the same structure as described by Equation 3. The difference in length alters their

properties.

��� − ������ − ���

Equation 3

More carbon atoms increase melting temperature and heat of fusion, as seen in Table 1.

No. of carbon

atoms

Melting point

[oC]

Heat of fusion

[kJ/kg]

11 -25.5 141

12 -9.5 214

13 5.5 [-]

14 6 228

15 10.0 205

17 21.7 237.1

20 36.7 246

25 49.4 238

30 65.4 251

34 75.9 269 Table 1. Melting point and heat of fusion of paraffins with different length (12).

Often different paraffins are mixed to optimize melting temperature. It is possible to achieve melting

temperatures anywhere between 1.7 oC and 17.9 oC when mixing tetradecane and hexadecane. This is an

interesting area for cooling purposes especially in air conditioning applications. The mixing of paraffins

makes it non eutectic meaning that phase changes will happen over a temperature range (13).

Fatty acids (non-paraffins)

In this group the material properties differ a lot unlike with the paraffins. This group consists of esters, fatty

acids, alcohols and glycols. Aspects to consider for this group are stability at high temperature, toxicity,

flammability and low thermal conductivity. The melting temperature is between 7.8 oC and 127 oC and

latent heat between 247 kJ/kg and 86 kJ/kg (14). Part of this group can have potential in cooling

applications above 0 oC.

Inorganic

The inorganic group is primarily hydrated salts but also contains metallic. The inorganic materials have high

fusion heat per volume and higher thermal conductivity compared to the organic (14).

Feasibility of demand response by phase changing materials for cooling application

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Salt hydrates

Salt hydrates may be regarded as alloys of inorganic salts and water forming a crystalline solid. At the

melting point the hydrate crystals break up into anhydrous salt and water. There is however a difference in

density of the salt and water which can cause separation problems. The hydrated salts are mostly used for

heat storage, for example for storing heat from solar panels (14), (15) and (16). This group is found

uninteresting for cooling applications.

Salt solutions

Salt dissolved in water can be used as a PCM, it is however limited to applications with temperatures below

zero due to the melting point of pure water. Solutions can be either a eutectic concentration or lower

dependent on the application. The latent heat is close to that of water depending on salt and

concentration. This group is found interesting for cooling applications.

PCM systems

PCM-systems can have many different layouts. One of the main characteristic of a system is the contact

between heat load, PCM and cooler. The connection or heat transfer can be done through direct contact or

through a brine, see Figure 12.

When the PCM has direct heat transfer with the heat load on one side and the refrigerant on the other

side, the PCM works like a buffer in between (Figure 12A). The PCM is embedded in the construction where

the cooling is needed, for example in the walls of an office building or on the inside of a cooling display

case.

With ice bank systems2 the storage is done centrally. This is interesting in cases with long distances

between load where small refrigerants fillings are important. This system makes it possible to circulate

brine instead of refrigerant (Figure 12B). Using the brine system will however require more heat exchange

as a carrier fluid is involved. This system allows for better control than the system in Figure 12A, as

circulation of brine can be regulated in order to equal the heat load or change the temperature.

If the heat storage is retrofitted to an existing installation it is possible to do heat transfer between

evaporator and PCM through brine, see Figure 12C. This solution allows for retrofitting to an existing brine

system and is therefore of relevance if heat storage is done in order to extend cooling capacity of an

existing plant. Doing this means compressors may be operated for an extended amount of hours, since

storage is possible.

2 The system is not necessarily based on water/ice; storage can be done in any PCM.

Feasibility of demand response by phase changing materials for cooling application

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In order to avoid mixing of PCM and brine the PCM is normally encapsulated in containers. The brine is

circulated around the containers in order to facilitate heat transfer between them. Often the PCM is

encapsulated in multiple containers in a cluster in order to increase the heat transfer surface area. The

necessary surface area and thereby the individual container size depends on the scenario. The primary

considerations when finding container size are:

• Heat conductivity of the PCM. If the PCM has good heat conductivity, heat is transferred to the

center of the container with high effect. Equation 4 describes heat conductivity in the container

volume; normally the conductivity is lower in the solid phase than the liquid phase.

�̇ =� ∙ � ∙ ∆

Equation 4

Where k is the specific heat conductivity of the PCM, A the surface area of the container, l the

length of the PCM and ∆ the temperature difference of the surface and the phase change

temperature. Often the container design will be so complicated that 3D analyzes is necessary to

determine heat transfer issues.

• The necessary charge-discharge effect. If the time intervals of charging and discharging are long

the energy exchanges between brine and PCM can happen at low effect�� !.

• Delta temperature of the PCMs phase change temperature and the brine. Temperature difference

is necessary for the heat exchange to take place. If the temperature difference is increased the

necessary area is reduced see Equation 4 and Equation 5.

�̇ = ℎ ∙ � ∙ ∆

Equation 5

As described and seen from Equation 4 and Equation 5 there needs to be a temperature difference

between the PCM and the brine to facilitate heat transfer. The brine must be cooler than the phase change

temperature of the PCM during charging in case C of Figure 12. Furthermore the brine can only be cooled

to above the phase change temperature of the PCM doing discharging as seen in Figure 13.

Figure 12. Different system configurations regarding contact between the components. (A) Direct contact between all

components (B) Direct contact between PCM and evaporator, contact to load through brine. (C) Contact between all

components through brine.

Evaporator

PCM

A B

Load

Evaporator

PCM

Load

C E

vaporator

PCM

Load

Feasibility of demand response by phase changing materials for cooling application

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These additional heat changes means having a significantly lower evaporator temperature than the actual

cooling need. As discussed earlier it is important for the efficiency to keep evaporator temperature as high

as possible. It is therefore found that the direct heat transfer system without brine is preferable seen from

an efficiency point of view. It is however necessary to consider the refrigerant also. It may not be possible

to use the most efficient refrigerant in a system with direct heat transfer due to concerns such as toxicity

and flammability. Using a more efficient refrigerant may make up for the added heat exchanges.

Phase chancing slurry (PCS)

Phase chancing slurries combines storage and transport medium. By using phase chancing slurry (PCS)

latent and sensible heat storage are merged together, thereby adopting some of the main advantages from

the two technologies, and leaving some of the disadvantages behind.

Ice slurry is the best known and most used PCS for cooling, as it by far has the best heat capacity. The Ice

slurry is in its nature however restricted to temperatures below 0 oC and therefore not suited for

applications in the higher temperature ranges like HVAC applications. This limitation has lead to

development of other PCS often inspired by PCM for use in the above 0 oC range.

Shape-stabilized PCM slurry

In shape-stabilized PCM slurry a PCM is protected by an open polyethylene structure. The PCM must be

immiscible in the carrier fluid and must be held in the structure even in both solid and liquid state. The

open container results in better heat transfer than with microencapsulated PCM slurry as the heat does not

pass a wall (11).

Microencapsulated PCM Slurry

In a microencapsulated PCM Slurry a PCM is fully encapsulated in a container. The encapsulation gives the

PCM better resistance to stress compared to the shape-stabilized PCM slurry. The stability is of importance

as the PCS is exposed to a large amount of stress in for example valves and pumps. The containers are

normally in the range of 1 to 100 µm in diameter and with capsules as thin as 200 nm. Like shape-stabilized

PCM slurry the microencapsulated PCM slurry is suspended in a carrier fluid. The concentration of the

Evaporator

load

Evaporator

load

Charging Discharging

Figure 13. Charging and discharging of PCM tank with brine.

Feasibility of demand response by phase changing materials for cooling application

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encapsulated PCM can vary as it not only affects the heat capacity but also the viscosity. Often paraffins are

used as PCM as they have properties appropriate for the temperature ranges above 0 oC where ice slurry

cannot be used (11).

Ice slurry

The ice slurry differs from the above described PCSs as it is the carrying fluid that changes phase. In the

process brine is cooled to its freezing point and ice crystals are generated in the brine. The size of the ice

crystals can vary according to production method but is typically in the ranges of 25 # to 1 mm. The

maximum concentration of ice in the slurry can be anywhere from 10 to 60 wt % dependent on application

(11).

Ice slurry is known from other industries where other qualities than the storage potential is wanted. In the

fishing industry ice slurry is sprayed directly onto the fish to ensure a rapid freeze as it has direct contact

with the whole area of the fish. Ice slurry is also used in applications where intensive cooling is required as

heat transfer can be as high as 7000 $%&∙' (11).

Ice slurry solution

Solutions used for ice slurry must be non eutectic. The ice concentration in the ice slurry is defining for the

enthalpy level. The ice concentration is also important for other physical properties of the slurry like heat

conductivity and apparent viscosity. Heat conductivity and apparent viscosity is however not only given by

the ice concentration, the ice crystal structure and size is also important.

The crystal structure and size is strongly dependent on the “history” of the slurry. Things such as storage

method, production method, pumping “history” and so on, influence the structure of the slurry and

therefore its physical properties. Due to these variations it is hard to compute fluid properties without

extensive information about the slurry. Furthermore the slurry changes flow characteristic from behaving

like a Newtonian fluid to behaving more like a Bingham fluid when ice concentration exceeds 20 wt %. In

general ice slurry is pumped with higher pressure drop than the equivalent brine without ice(11).

Production

There are multiple ways of generating ice slurry. The method most suitable for a given application depends

on things such as the working fluid, cooling need, needed reliability and storage size/time.

One of the central problems for most of the production methods are ice sticking to the evaporator surface

as it freezes. To prevent ice buildup different approaches are used depending on the ice generator type.

Some of the main methods are:

• Continuous ice removal where the evaporator surface is mechanically scraped free for ice.

• Special coating preventing the ice from sticking to the surface.

• Fluidized bed where impacts of moving steal balls keep the surface free of ice.

Feasibility of demand response by phase changing materials for cooling application

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• Increasing the pressure lowers the freezing point and thereby delays freezing to later

decompression.

• Defrosting the evaporator by adding heat.

The most commonly used method is the continuous ice

removal as it is relative simple and the method is well

documented. The actual scraping can be done in many

different ways. In Figure 14 a design based on a helical

screw scraper is illustrated.

The scraping evaporator is expensive compared to a

normal evaporator as it has moving parts and is often

custom made (11).

Storage

Due to density differences between unfrozen brine and ice particles, ice particles will have an upwards

buoyant force. The upward buoyant force results in separation of ice and unfrozen brine. There are

different methods of dealing with the separation; the most commonly used methods are continuously

agitation of the slurry. The agitation will differ depending on location in the tank, angel of propeller, speed

and propeller size.

Figure 14. ice slurry generator using a helical screw scraper

(11)

Feasibility of demand response by phase changing materials for cooling application

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Case In order to evaluate the potential in price response different solutions are applied to a chosen case. The

result is evaluated in terms of savings on energy and money in relation to the investment.

Case possibilities

In principle, demand response can be applied to almost any cooling system. It is found of most interest to

investigate in a cooling application that plays a large role on the Danish market. Furthermore it is

preferable to work with a case where the use pattern is relative fixed or at least known some hours ahead.

The potential for savings by demand response are in cases where the load is located primarily in peak load

hours. Different cases are considered and evaluated for their potential.

Air-conditioning

A large consumer of cooling on global scale is HVAC systems as air conditioning is needed in large parts of

the world. Space cooling is however not common in Denmark as it normally only has relevance a few

months of the year. Space cooling is nevertheless used in relation to server rooms or where heat load is

high for other reasons. These cases can normally be satisfied by high temperature cooling. Free cooling is

becoming more and more normal for these applications in Denmark due to the relative cold weather.

Cold/freeze storage

Cold/freeze storage is another large consumer of cold energy found of interest. Most of the stock houses

already do demand response simply by letting the temperature rise in daytime and fall during night time.

This is possible without significant temperature variations as the heat capacity is large compared to the

heat loss from the storage house.

Often the storage houses provide the service of bringing products to the storage temperature for the

costumer; a so called in-freeze. This process is done as soon as the product is ready and freezing is done

rapidly in order to secure a good quality of the product. This process is found of interest as it is energy

intense and pig farming is a large industry in Denmark - an industry which depends on this type of cooling.

After a conversation with engineers at Agri Norcool the case is however found unattractive as the fast

cooling is done by blowing air at -40oC over the meat. The introduction of a storage medium will lower the

temperature even more. Furthermore the cooling need was considered unpredictable by Agri Norcool as

meat quantities vary according to unpredictable meat prices.

Industrial process

There are many different industrial processes requiring cooling both for cooling of machinery and for

processing of products. These applications vary in many ways such as use pattern, energy demand and

temperature range. It may be possible to find multiple different industries where it is possible to apply

demand response on cooling applications. The solution will however be tailored specifically for one plant

and it will be hard to draw general conclusions on saving potential in other facilities and industries.

Feasibility of demand response by phase changing materials for cooling application

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Supermarket applications

Supermarkets are large energy consumers, of which a large part is used on cooling. Supermarkets are

thereby one of the largest consumers of cold energy in Denmark. The consumption on freezing and cooling

in supermarkets are estimated to be around 600 GWh/year, which is equal to more than 1.5 % of the

energy consumption in Denmark (17).

The temperature of freezing cabins are typically around -20 oC and for cooling 0 - 5 oC. These temperatures

are reachable with the PCMs described in the section Latent heat storage. The main part of the

consumption takes place in the opening hours of the supermarket which is where the energy price is the

highest.

Based on these facts, supermarkets are found to be a relevant test case for implementation of demand

response even though there are other relevant cases as well.

A midrange to large supermarket is found of interest as it represents a large part of supermarkets in

Denmark and have energy expenses that makes room for significant savings.

Case description

Supermarket layout

For the case study a supermarket with cooling needs as specified in Table 2 is decided on. The energy

consumption for different equipment and the relation between cooling and freezing is inspired by a report

from Teknologiske institut on the use of natural refrigerants in supermarkets (17). The case is however

scaled up from a small supermarket to a large midrange supermarket.

Utiliser Size Unit Load per unit

[m^2 [W/ freezing (-20 C) cooling (2-5 C)

or m] (m^2 or m)] [W] [W]

Coling room 38 [m^2] 300 11400

Freezing room 28 [m^2] 400 11200

Freezing room ice 20 [m] 400 8000

Cooling island cabins 30 [m] 1440 43200

Cooling display case vertical 30 [m] 380 11400

Freezing display case vertical 30 [m] 590 17700

Sum 29100 73800

Load

Table 2. Cooling needs for the case supermarket. The needs are inspired by case study done by Teknologisk institut(17)

The cooling needs add up to around 75 kW and the freezing to 30 kW as seen in Table 2. These values will

serve as fundament for the case study.

The pipe length from the machine room to the different display cabins and other equipment is estimated to

be 250 meters of which half is return pipe. This estimation is based on shop layout as in appendix A and

with pipes hidden in the sealing and/or floor. The pipe is assumed to have the same diameter all the way

Feasibility of demand response by phase changing materials for cooling application

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from the ice bank to the display cases and containing 2 valves, 15 bends and 6 T-connections. This piping

has a K value of 17.6 in accordance with tables from (18) the flow velocity is assumed to be 0.5 m/s.

The pressure drop in the display case is assumed to be the same as in the 125 meter supply pipe.

The supermarket is assumed to be located in the western Denmark so electricity prices for western

Denmark are used as standard. The performance of the system under different markets is however

investigated in the section “Sensibility study”.

Use pattern

The supermarket requires cooling at all time; however the load varies along the day and week. Variations in

heat loss along the year are small as the temperature in the supermarket is more or less constant during

the year. The use pattern is strongly depending on the display case solution and will therefore differ from

case to case.

If display cases are without doors and are closed down with insulating material at closing hour the

variations from day to night is large. This is because the situation changes from no insulation to relatively

good insulation. On the other hand if the display cases are with doors the variation from day to night is only

due to artificial lights being turned off at night time and doors staying closed.

Three different approaches to estimate the use pattern are considered:

• Imperial data. Rema 1000 do continues measurements on energy use hour by hour in 5 different

shops. The measured energy consumption is available for use in this project through an agreement

with Rema 1000. This data is relevant as it has natural variations; the data however does not

describe cooling load but energy consumption. To transform energy consumption to a cooling load

it is necessary to assume information on COP of the plant at every operating hour.

• Model data. There are different models for use patterns on cooling need. Two different profiles

from the program Pack Calculation is considered. One of the profiles seems to be based on some

random function or empirical data. Another profile is as simple as 100% load in opening hour and

50% when the shop is closed.

• Random generated data. Finally a pattern is generated by adding some random variations to a

pattern of 100 % in the opening hours and 60% when the shop is closed. This is considered a

modified version of one of the Pack Calculator models.

The four different use profiles are plotted together over a week period in Figure 15.

Feasibility of demand response by phase changing materials for cooling application

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Figure 15. Four different use profiles plotted for a week. The data from Rema 1000 is normalized by the max consumption in the

week. Furthermore the data from Rema 1000 is electricity use not actual cooling use; in other words COP is not taken into

consideration.

It is hard to determine which of the profiles in Figure 15 that is the most appropriate to use. The modified

model 2 from Pack Calculator will serve as fundament for the case as it seems to be a middle way of the

other models. The cooling and freezing need are assumed to follow the same pattern in terms of load

distribution. The effect of using the different use pattern models will be investigated as part of the

sensibility study later in the report.

Solutions In order to evaluate saving potential different solutions is generated and evaluated. The solutions are

evaluated primarily on energy expense as this is most important for the cost-efficiency of the system. Issues

surrounding the use pattern in terms of energy use, operating hours and wind share of the energy are also

investigated. Energy consumption and expense will in general be linked; it is however possible for a

solution with high energy consumption to perform well on the expense side if the averages electricity price

is low.

Evaluation

The evaluation can be done from both a macro and micro perspective. Those do not necessarily have

conflicting interests, just different ones. As the interests are different the evaluation will also be split into

two groups.

Feasibility of demand response by phase changing materials for cooling application

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Micro

On the micro scale this project is investigating the feasibility of demand response as part of reducing total

costs. For this part of the evaluation everything is related to costs and savings. The following three

questions are found central when evaluating a solution in the micro aspect.

1. What is the added investment?

2. What is the payback period?

3. Is the publicity/image value positive or negative and of what size?

In order to answer these questions it is necessary to estimate the investment for both the solution

proposals and the reference solution. Calculating prices on refrigeration systems is more than just finding

hardware prices as installing a refrigeration system are labor intense and therefore costly. It is found

impossible to estimate these investments with a satisfying precision without comprehensive work from an

experienced refrigeration contractor.

Instead of computing the payback period another approach is applied by flipping the questions. So instead

of estimating the payback period on the investment we can ask; is the calculated investment worth it,

based on a wanted payback period and the yearly saving.

The payback period is defined as the time the net present value turns zero as described by Equation 6.

()* = 0 ⇔ � ∙ -� − . = 0 ⇔

/ ∙ -� = . ⇔ . = / �1 − �1 + 2�3454�2

Equation 6

The payback period is set to be 10 years and the interest rate is set to 6%.

When the acceptable added investment is found based on these values,

an evaluation on the economy of the project can be done. The added

investment must be less than the price difference of the solution proposal

and the reference solution.

Marketing people often calculate specific values to both bad and good

publicity. The last of the three micro questions concerning publicity/image

value is however considered a discussion question. The refrigeration

industry is aware of publicity effects. A sign like the one in Figure 16 have

an effect on consumers and are thereby something a supermarket can

benefit from. A possible new certification could be “Wind energy

friendly” indicating a use of a high degree of wind energy.

Macro

On a macro scale this project is investigating the opportunities for making cooling systems a flexible energy

consumer as part of implementing more sustainable energy. When evaluating a solution on the macro scale

Figure 16. Sticker stating that non

ozone layer depleting refrigerant is

in the cooling system. No

information on global warming

potential. (28)

Feasibility of demand response by phase changing materials for cooling application

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it is of interest to determine the more general effect of the solution on society. Further it is of interest to

determine the effect on the energy market. In the macro analysis the main focus will be on the

environmental effect of the solution and not so much on the direct macro economical effect. The following

four questions are found central for evaluating a solution on the macro scale:

1. Is the solution able to move energy consumption from peak load hours?

2. Is the solution using energy when there is high wind production?

3. Does the solution increase the overall energy use?

4. Does the solution have negative effect on other aspects of society (environment, economy, safety,

foreign policy, and so on)?

These above questions will serve as the fundament for the macro evaluation of the different solutions.

Regarding question two it is interesting to determine, if the interest is in moving consumption to periods

with high degree of wind energy, or periods with excess capacity; these are not necessarily the same.

Periods with high degree of wind energy will normally be periods with excess capacity resulting in low price.

Periods with excess capacity are however not necessarily periods with high degree of wind energy; it might

be periods with low consumption like for example night time. In other words if the goal is to move

consumption to periods with excess capacity the price is a good indication as it represent the relation

between supply and demand. However if the goal is to move consumption to periods with high wind

energy it is necessary to investigate in the actual wind production and not only the price.

The wind production and the total production are analyzed for western Denmark 2009. The data is

analyzed by investigating tendencies along the year and along the day. The consumption is normalized by

the maximum consumption. Furthermore the resulting average percentages of wind energy are plotted by

month, see Figure 17A and B.

Figure 17. Variations in wind production and total production. A: Along the day. B: Along the year. (Western Denmark 2009) (19)

As seen from Figure 17A the total production and the wind production follows nicely along the day. The

period with high total production is however 4 to 6 hours wider than the period with high wind production.

It is important to note that this plot is based on averages of a whole year; day to day deviations from this

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

34

pattern are larger as illustrated in Figure 7 earlier. Even though the wind production and total production

have a tendency to follow there are. The hours with the highest wind energy percentage are found

between midnight and 5 AM see Figure 17A.

As seen in Figure 17b there are no clear pattern in the wind production along the year. The picture may

however be distorted by the introducing of a large wind turbine farm “Horns Rev 2” in September 2009. In

general spring and fall are the period expected to have the most wind.

In order to quantify the solution proposals potential for moving production to hours with a high degree of

wind power the average percentages of wind energy is found for the different solutions. This is done by

generating a list with the percentages of wind energy for every hour of 2009 called WP. The WP list is used

to find the average wind power (AWP) for the different solutions by Equation 7.

�6)��78�9�� = : �6)�;� ∙ )2<=>�?;<@�;��A�8��

9BC/ : �)2<=>�?;<@�;��

A�8��

9BC

Equation 7

Model

A model is build to establish a base for the evaluation of the solutions performance in terms of economy

and energy. This model is made of two sub-models, one that is related to the refrigeration system

configuration, and one that is related to the operation of the system, see Figure 18. The overall model

simulates one year’s consumption for a given solution proposal. The simulation is based on:

• Solution proposal specifications.

• Electricity prices from 20093.

• Weather data for a Danish reference year.

• Use pattern according to the case description.

The model does not account for difference in maintenance costs and value of occupied space. If a solution

proposal is found to deviate significantly from the reference solution on one or these two points this will be

commented on in the proposal description.

Some model parameters from the solution proposal are given to the EES model and some to the operation

model. See Figure 18 to follow the simulation process.

3 The model uses prices from www.nordpoolspot.com and can be run with prices from the different markets and with

different tax systems.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

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The two model parts are described in more detail in the following two sections.

Refrigeration system configuration model (EES)

The refrigeration system configuration model calculates performance of the refrigeration system under

different system configurations. This part of the model is build in EES and takes parameters like evaporating

temperature, isentropic efficiency, refrigerant, super heat, cooling load, freezing load, pipe configurations,

flow velocities and so on. The model returns a matrix of COPs and matching condensing temperatures.

In order to do all solutions justice the basic model is kept the same for all the different solution so only

parameters are varied depending on the solution. It is however necessary to change the model a bit to

simulate transcritical processes.

Energy demand for fans in the condenser and in the display cases are not considered even though they

often represent a significant part of the energy consumption. This simplification is found reasonable as this

energy use is estimated to be more or less the same for the solutions.

The system modeled is a two stage system with open intercooler and intermediate load. Pressure drop in

suction pipe is found by classic fluid dynamic equations; see Appendix B. Pressure drop in pipes to and from

condenser is neglected. Pressure drop in the condenser is defined as a temperature drop of 1 K. The

isentropic efficiency of the compressors is set to 0.65 for all solutions. An example of the process is

illustrated in a log(P)-h diagram in appendix G.

Thermodynamic properties

• Tevap

• Super heat

• �9�E�� Comp.

• �@= G< <@

EES model

Calculates COP

for different

condensing

temperatures

Operation model

Mapping the

optimal operation

hours in relation

to consumption

System configuration

• Production

capacity

• Storage capacity

• Heat loss

Solution

proposal

Evaluation and analyses

Case

• Cooling need

• Price data

• Weather

Figure 18. Flow chart of information and results for the model. Green arrows are information or model input, red arrows

are model results.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

36

Figure 19. Dynamic programming grid after the problem is made

discrete. The red lines illustrate the limits of the stock. The blue

lines illustrate possible production paths. Resolution=5, Hours =4,

Stock start=50 %

Operation model (Matlab)

The operation model finds the optimal use pattern for the refrigeration system. This operation plan is

based on results from the EES model, use pattern, electricity prices and weather data. The model takes only

three basic parameters; heat loss, installed production and storage capacity. These are the only parameters

that vary from solution to solution for this part of the model.

Time steps

It is unrealistic to assume that the electricity prices and weather are known for the whole year from day

one. Planning must therefore be done based on a continuous amount of steps with known conditions.

Planning should be done as long ahead as possible. Long-term planning however requires known conditions

for a long period of time. It is therefore a question of how long time it is realistic to predict conditions.

• Weather predictions are normally relative precise for up to three days. Actual temperatures are

normally a translation of predictions. Meaning that the curve of the temperature stays intact, but

that the curve is moved either up or down. A translation of temperature will have an almost linear

effect on all production hours. The error in weather prediction will therefore hardly cause

significant error in the production plan. It is found that it is realistic to assume the temperature

known for three days (72 hours)

• Cooling need is very predictable as the opening hours are known and the temperature in the shop

is more or less constant. It is therefore found that it is realistic to assume the cooling load known

for the whole year.

• The electricity price is hard to predict as it is influenced by many thing as described in the section

“Fluctuations in the price”. It is however found realistic to assume that it is possible to buy

electricity at the NordPool market price which is known in between 12 and 36 hours ahead.

The most critical prediction is the price; this therefore defines the limits of the step size. Because of this,

the model will do a simulation of 36 hours every

day at noon when price data is released from

NordPool. The effect of this limitation on

knowledge is investigated in the section Sensibility

study.

Dynamic programming

The optimal operation plan is found by using

dynamic programming on each of the 36 hour time

steps. In order to make dynamic programming

possible it is necessary to make the problem

discrete. The problem is made discrete by fixing

the production to a number of levels from zero to

max production. As the consumption is

independent of stock and production level the

nodes add up, forming a grid of different ways to

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

37

A

B

produce; see the blue lines in Figure 19.

All routes that pass through nodes above or below stock limits must be disregarded as invalid solutions as

they break the limits of the stock. In Figure 19 the problem is made discrete with a resolution of 5 allowing

the model to run the compressor at 0, 25, 50, 75 and 100 % of full load. When the model is run for the

different solutions the resolution is in general set to 11 allowing the model to go from 0 to 100 % in steps of

10 %. In reality load regulation will be done by only running the compressor parts of the present hour as

this allows for the compressor to operate at the load level with the highest efficiency.

COP for a given hour is defined by the outdoor temperature. There is however limitations to how low the

pressure in the condenser can be for the expansion valve to operate properly. The system limit for

condensing pressure is set equal to a pressure corresponding to 20 oC. Assuming a delta temperature of 5 K

between surroundings and condensing pressure the system is found to not benefit from lowering the outer

temperature lower than 15 oC. It is however possible that the system will save energy on fans supplying air

for the condenser under conditions below 15 oC; this effect is not simulated. A plan of the Matlab model is

found in Appendix H.

State of the art

In order to evaluate the performance of the

different solutions a reference system must be

established. The reference system must be a

realistic alternative and represent what is

being installed in supermarkets at the time of

writing.

In Denmark there is tradition for using remote

systems in supermarkets. In a remote system

the compression and heat rejection is done

centrally away from the display furniture itself,

typically outside the shop. The refrigerant is

transported to the different display furniture

in pipes and expanded just before entering the

show furniture, a so called direct expansion

(see Figure 20 A). In some cases brine systems

are however used, in these systems brine is

chilled centrally and then distributed to the

display furniture (see Figure 20 B). Brine

systems are however more unusual in Danish

supermarkets as it normally has lower

efficiency than direct expansion systems under Danish weather conditions (17). The direct expansion

system normally has large gas fillings as distribution pipes can be long and there are multiple evaporators.

Figure 20 A: Direct expansion system with four display furniture with

individual expansion valves. B: Brine system with heat

exchanger, pump and four display furniture.

Feasibility of demand response by phase changing materials for cooling application

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The long distribution pipes can also results in a constant small escape of refrigerant; additionally there is

the danger for a large spill if a pipe is fractured.

Since 1/1-2007 it has been forbidden to use HFC gasses for systems with a fill larger than 10 kilos. This

means that alternatives are needed for widely used HFC gasses like R134a, R404A, R407C and R507A. The

gasses are typically replaced with naturally refrigerants like R600a (Isobutane) R290 (Propane) R744

(Carbon dioxide) and R717 (Ammonia) depending on the cooling needed and case requirements (20). R600a

and R290 are flammable and R717 toxic therefore they require large safety systems to work with. The

safety requirements are considerably especially if the filling is large or/and if the plant is installed in a place

with people such as a supermarket.

R744 (CO2) is non toxic and non flammable, the main problem with CO2 is the low temperature critical

point. This low temperature critical point makes it impossible to do condensation above 31.1 oC like in

classical refrigeration systems. It is however still possible to use CO2 as refrigerant above this point, in

which case energy is removed by cooling the gas rather than condensing it. See Figure 21 B

Figure 21. A: Subcritical refrigeration cycle. B: Transcritical refrigeration cycle. (21)

A system made for working transcritical can be run subcritical when conditions allows for it, thereby

increasing efficiency.

The transcritical CO2 systems are especially interesting for installation in Denmark as the Danish climate is

relatively cold; there are only about 50 hours of the year with more than 25 oC. Therefore the system will

run subcritical with a relative good efficiency most of the year. Only a relative small number of hours the

system will switch into transcritical mode.

Both COOP and Rema 1000 are currently installing transcritical CO2 plants in their facilities in Denmark

when replacing old installations or establishing new shops.

The environment friendliness, suitability for the Danish climate and the fact that these transcritical systems

are popular amongst large supermarket chains makes this the most suitable reference point for other

solutions.

Feasibility of demand response by phase changing materials for cooling application

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39

Direct transcritical CO2 system without cold storage

The direct transcritical CO2 system is modeled in EES in order to find COP values for different condensing

temperatures.

Normally the pressure in the condenser is given by the condensing temperature, this is however not the

case when running the system transcritical as no condensation takes place. The pressure has influence both

on the capacity and the efficiency of the system. In this report only the efficiency issue will be covered.

Three different gas cooler pressures are applied to a system with the same evaporator temperature and gas

cooler exit temperature, this is illustrated by the orange, green and blue color in the log(P)-h diagram in

Figure 22. All the three pressures will work

but they have very different COP.

If the pressure is too high the enthalpy

difference over the compressor [1]

becomes too large compared to the

enthalpy difference over the evaporator

[2] as in the case indicated with an orange

line. If the gas cooler pressure is too low

the enthalpy difference over the gas

cooler [2] becomes too small compared to

the enthalpy difference over the

compressor [1] as in the case indicated

with a blue line. There is an optimal

pressure for the gas cooler. This pressure

is depended on the system configuration

regarding evaporator temperature,

isentropic efficiency of the compressor,

super heat and so on. The optimal pressure

must be found by modeling the system for each system configuration.

The EES model is run in transcritical mode with different pressures and gas cooler exit temperatures by

using a parametric table to map COP. As seen from Figure 23A the COP depends on both pressure in the gas

cooler and the exit temperature of the gas cooler.

In the temperature range from 25 oC to 30 oC the pressure in the gas cooler must be as low as it is

transcritically possible, to achieve the optimal COP as seen from Figure 23. When the gas cooler outlet

temperature is above 30 oC the optimal gas cooler pressure increases with temperature. Furthermore it is

observed that it is more harmful to run with too low pressure than to run with high in the gas cooler for exit

temperature in ranges above 35 oC. It is worth noticing that these temperatures and pressures are not valid

for other system configurations than the one simulated for making Figure 23.

The optimal gas cooler pressure is determined for all temperatures. This is done by analyzing the data

plotted in Figure 23A, the result is plotted in Figure 23B. The optimal pressure rises almost linearly from

about 75 Bar at 30 oC to about 103 Bar at 40 oC. See Figure 23B.

Figure 22. Different compression cycles with the same exit temperature

of the gas cooler but at different pressures in a log(P)-h diagram. [2]

marks the changes in cooling capacity in the three cases. [1] marks the

changes in work done by compressor in the three cases.

P

h

Feasibility of demand response by phase changing materials for cooling application

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40

Figure 23. COP dependency on pressure and exit temperature of gas cooler for a one stage system. Model is run with isentropic

efficiency of 0.68 and evaporator temperature of -10 oC and 5 K ineffective superheat. A: Full plot of COP as function of pressure

and gas cooler exit temperature. B: Optimal pressure and COP as function of exit temperature

From this analysis the optimal COP and appurtenant pressures are known when the system is running

transcritical. The system however needs to switch between subcritical and transcritical at some point. To

find this shift point the sub and transcritical COP data is held op against each other as seen in Figure 24. The

optimal COP is found as the best COP of the subcritical and the transcritical. The intersection of the two

COP curves defines the shift point for the compressor, see Figure 24A and B. In order to avoid the

compressor to shift constantly in situations where the temperature is fluctuating around the shift point,

working intervals are defined.

Figure 24. COP dependency on exit temperature of gas cooler / condenser. Model is run with isentropic efficiency of 0.68 and

evaporator temperature of -10 oC and 5 K ineffective superheat. A: COP of both transcritical and subcritical together. B: The

overall COP as function of temperature of gas cooler/ condenser; the green mark indicates the point of shift between subcritical

and transcritical.

In this way the COP is found for different running conditions regarding system configurations.

Feasibility of demand response by phase changing materials for cooling application

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41

In order to achieve the optimal COP the delta temperature between the surrounding air and the gas cooler

is kept as low as 2 K (21). In the model the gas is set to leave the cooler 3 K higher than the surrounding air.

Other system specifications are set as shown in Table 3 in the EES model.

Parameter Value Unit

Maximum cooling need 75 kW

Maximum freezing need 30 kW

Isentropic efficiency 0.65 [-]

Evaporator temperature coolers -4 [C]

Evaporator temperature freezer -25 [C]

Super heat 5 [K]

Pipe length 250 m

Power for pumping brine 0 (direct expansion) W/kW

Table 3. Model parameters for the reference system. Remote direct transcritical CO2 with no heat storage.

The performance of the reference plant is presented in Table 4.

Total

price

Eletricity

price

AVG.

Price Tax price

Energy

use AWP

[kr] [kr] [øre/kWh] [kr] [MWh] [%]

Reference system 186833 36815 25.5 150018 144.2 21.9

Table 4. Performance of reference system as described in table 3.

These results will serve as reference point for other potential solutions.

Generation of solutions

To outline some of the ways solutions can vary in a structured way, a table of different functions and parts

are created. Along with the functions different possible solutions are presented, see Table 5. The table is

used in the process of generating different solution proposals. Not all combinations are possible and some

more attractive than others. It is for example unattractive to use R717 in a direct expansion system as it

requires large investment in safety systems and a very tight system to avoid obnoxious smell.

Function/part

TransportDirect

expansionBrine Slurry CO2

Refrigerant Les than 10 kg R744 R717 R290 R404 R600a

PCM Organic Inorganic PCS

Storage form PCM bankSlurry in

storage tank

Decentralised

storages

Possible options

Table 5. Different functions or parts and the possible solutions.

Feasibility of demand response by phase changing materials for cooling application

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42

Three solution proposals are found particular interesting as they are based on good combinations through

Table 5. Furthermore the solutions represent variations in interesting ways as they cover:

• Storing centralized and decentralized.

• PCM and PCS.

• Toxic/flammable refrigerants and harmless refrigerants.

The three solutions are described in the following sections. The focus in the study is mostly on the solutions

energy demand and storage potential. The specific technical solutions are not covered in depth as this

investigation mainly serves to document savings on energy.

Ice bank system

An ice bank system with direct contact between PCM and evaporator is found suitable for the case. The

solution is based on the principle seen in Figure 12B in the section “PCM systems” at page 24.

Capacity

The dimensions of the system are defining for the capacity of the solution in two ways. Capacity is related

to production and storage capacity.

Production

The production capacity defines the amount of cooling energy the system can produce in one hour. The

production capacity is therefore determining in how much the system can benefit from hours with cheap

electricity.

The maximum average hourly need is held as a reference point for production capacity. This reference

point is also the capacity of the reference system if no variation along the hour is assumed. The reference

system will however need to deal with local peaks during hours and some excess capacity is needed to

ensure security of supply. It is assumed that the reference system will have capacity to cover 20 % more

than the highest hourly average. This assumption means that a production capacity of 1.2 has the same

installed capacity as the reference system.

Production capacity is as stated important for the capability to produce when prices are low. On the other

hand production capacity requires large investments. The optimal production capacity can be found by

running the model with different capacities and thereby mapping the effect in savings. The added saving

must justify the added investment.

Storage

The ice bank technology allows for almost limitless storage capacity as the ice bank tank size has no upper

limit. Tank size however influences investment cost and heat losses is therefore of interest in determining

the optimal tank size. The tank size can be optimized by running the model with different storage sizes

similar to optimizing production capacity. In actuality it is necessary to optimize the production and storage

capacity together as they interact together on COP.

Feasibility of demand response by phase changing materials for cooling application

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43

Transport medium

It is impossible to do direct expansion from the compression cycle in the display cases as an ice bank is in

between; therefore another transport medium is needed. Slurry is considered too investment intense as

transport medium for this application, the choice is therefore down to brine and CO2.

Pipe dimension, pressure drop and energy consumptions for different transport systems are estimated by

using a separate EES model. To simplify the problem only the transport to the cooling need is considered.

This is considered a reasonable simplification as cooling need is twice the size of freezing need. Pressure

drops are based on flow velocities under max load (75 kW). The pipe system is assumed to be 125 meters

out and the same in return in accordance with the case description. The efficiency of the pumps is assumed

to be 70%. Heat losses from pipes are not considered. The pressure drop in the display cases is assumed to

be the same as in the supply and return pipe together. Three different solutions are simulated based on

calculations as in Appendix B:

1. Brine 1. A brine system with 10 K temperature difference between brine out and brine return. The

relative large temperature difference results in a relative low mass flow. The relative small mass

flow decreases the necessary pipe diameter and the pumping power. The large temperature

difference however requires the temperature to be lower in the ice bank; this decreases the

efficiency of the refrigeration plant.

2. Brine 2. A brine system with only 5 K temp difference between brine out and brine return. This

system runs with larger mass flow and pressure drop than the brine 1 system. The system therefore

uses more energy on the transport. It is however possible to run the system with higher

temperature in the ice bank; thereby increasing the efficiency of the refrigeration plant compared

to brine 1.

3. CO2. A CO2 system based on pressurized CO2 doing phase changes in the ice bank and display case.

This system requires the whole network to work under high pressure. Furthermore a system to deal

with excess gas when the system is not running is needed. Even though the pipe diameter is smaller

for the CO2 system it is found more investment intense compared to the two brine systems, due to

the higher complexity.

The results of the investigation are presented in Table 6. The relative effect in the last colon is defined as

the effect needed to deliver one kW of cooling when the cooling system is running under max load (75 kW).

It is assumed that it is possible to satisfy the cooling needs (2-5 C) with the temperatures of the transport

mediums given in colon 2 and 3 in Table 6.

Table 6. Three different transport solutions.

Medium Mass flow

Volume

flow Velocity

Pipe

diameter

out return Specific Relative

[C] [C] [kg/s] [l/s] [m/s] [mm] [W] [W/kW]

Brine 1 -8 2 1.8 1.8 0.5 67 144 1.92

Brine 2 -4 0 4.4 4.4 0.6 97 282 3.76

CO2 -4 -2 0.31 0.33/3.4 0.5/1 29/66 45 0.6

Temperature Effect

Feasibility of demand response by phase changing materials for cooling application

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44

The CO2 system is by far the most energy efficient and requiring the smallest pipes (see Table 6). The effect

used on pumping brine is however relative small. The saving potential is evaluated to be too small to justify

the expensive CO2 pumping system.

Choosing between brine 1 and 2 is an optimization between pumping and cooling power as described

earlier. The power used for pumping is however found small relative to the cooling power. For this reason

the brine 2 system is found most suitable as it allows for a relative high temperature of the ice. It is possible

to optimize the specific temperature difference of the brine further. This optimizing is however not done

here and the data for the brine 2 system is used.

Regarding the freezing load it is estimated that the brine can have an out temperature of -26 oC and a

return temperature of -22 oC and satisfy the freezing need of -20 oC. The pumping effect for supplying

freezers is assumed to be the same as for coolers.

Storage medium

Water and salt are used in a eutectic solution as storage medium. This medium is found attractive because

of the following reasons:

• Fixed melting temperature of a eutectic solution.

• Low price.

• Low environment impact.

• Good heat transfer property.

Data for a commercial salt solution from the British company PCM Products Limited is used as storage

medium reference. Particularly two solutions are found interesting in the product series. The solutions E-6

and E-29 have the properties shown in Table 7 See Appendix C for the full solution table.

Table 7. Two eutectic solutions from PCM Products Limited found suitable for the ice bank (22).

The E-6 and the E-29 are found to be useful for this application. These solutions are found suitable as phase

change temperatures are 2-3 K below the brine exit temperature thereby allowing heat transfer.

Location and sizing of the Ice banks

The system will need two ice banks as cooling is needed at two different temperatures.

As an initial guess the storage capacity is set to 10 hours of max production and max production is set to 1.2

times max consumption. These values are set in order to estimate size and heat loss from the system. With

these system dimensions the storage capacity is found as:

10 ℎ<>2G ∙ 1.2 ∙ 75 �6 = 900 �6ℎ

Feasibility of demand response by phase changing materials for cooling application

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Equation 8

The mass necessary to store this amount of energy is estimated by:

� = ∙ ∆ℎ ⇔ = �∆ℎ

Equation 9

= 900�6ℎ ∗ 3600�E�A

275�P�Q

= 11,781 �T

Equation 10

This mass is equivalent to a volume of 10.6 m3. Furthermore the containment and flow channels for brine

and refrigerant will require some space. From looking into different commercial solutions it is found that

the space efficiency is around 40 % (see appendix D1 and D2). It is therefore estimated that the ice tank for

cooling is approximately 26.5 m3.

In the same manner as above, calculations are done for the freezing load. The storage capacity is found to

be 360 kWh and the mass of E-29 is 5,837 kg with a volume of 4.1 m3. This results in a container size of 10.2

m3.

The storage containers can be installed in the freezing and cooling rooms. This will minimize heat losses as

the delta temperature is small compared to the surroundings. Furthermore the ice bank will exchange

energy with the storage rooms needing cooling anyway. The installation of the ice banks will however

increase the necessary volume of the storage rooms. The larger storage rooms will increase heat loses.

The cooling needed for the cooling and freezing room is respectively 300 W/m3 and 400 W/m3 according to

the case description. The floor-to-ceiling height is around 2.4 meters in the rooms (17).

����79�Q = 26.5 �

2,4 = 11 � ; � ���79�Q = 11 � ∙ 300 WX& = 3.3 �6

�Y�EEZ9�Q = 10.2 �

2,4 = 4.25 � ; � Y�EEZ9�Q = 4.25 � ∙ 400 WX& = 1.7 �6

This estimation of heat losses is considered to be conservative as there are some scaling benefits associated

with increasing the size of the cold/freezing rooms. Furthermore the heat load includes the door being

opened through the day and articles arriving at higher temperature than the storage temperature. The

storing of the ice bank under these assumptions increases the cooling and freezing need by 3.3 kW (4.4%)

and 1.7 kW (5.6%) respectively. This 5 kW extra consumption is added to the usage when running the

model. The relation between storage size and heat loss is assumed to be linear so the added heat loss is

given as:

� AE�� 7��� = 5 �6 ∙ /<�� ∙ [2<=>�?;<@1.2 ∙ 10 ℎ<>2G

Equation 11

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

46

Where Stock is the number of hours of max production.

In some installations this heat loss will have a negative effect in two ways as it will also require added

heating. The added cost of the increased heat load is disregard in the modeling.

Refrigerant

The ice bank solution is found suitable for all refrigerants as the filling can be small. The small filling is

achieved by locating Ice bank, compressor and condenser close together. With a small filling it is possible to

deal with the risks associated with toxic or flammable refrigerants.

In order to evaluate the performance of different refrigerants the model is run with the ones found of

interest. Only the refrigerant is changed between the model evaluations, different aspects of heat

conductivity are therefore not dealt with.

The evaporator temperature is set to -32 oC and – 9 oC for freezing and cooling respectively. These

temperatures are assumed to be sufficient to freeze the solutions as tendencies of subcooling is small.

The pipe length is set to only 10 meters as it is assumed that the compressor will be located close to the ice

bank. This relative short distance will result in small pressure losses in the refrigeration system itself. Energy

is however used later on for the pumping of brine. The results of running the model with different gasses

are illustrated in Figure 25.

Figure 25. COP as function of condensing temperature for different gasses with load distribution and pipe length as described in

the Ice bank solution.

The refrigerant influence the COP as seen in Figure 25. It is however only R744 (CO2) and R404A that

performs significantly different from the others. There are natural refrigerants among the best performing.

Furthermore it is found possible to deal with the risks associated with flammable and toxic refrigerants in

this setup. R134A is therefore not regarded as interesting for this solution.

Whether the supermarket is best suited for dealing with flammable or toxic refrigerants can vary from

supermarket to supermarket. As it has little influence on the COP if R290, R600A or R717 is used this

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

47

decision is not so important for the result of saving potential. For now COP data for R290 will serve as

fundament for the model. The model is run with parameters set as seen in Table 8.

Parameter Value Unit

Maximum cooling need 78.3 kW

Maximum freezing need 31.7 kW

Isentropic efficiency 0.65 [-]

Evaporator temperature coolers -9 [C]

Evaporator temperature freezer -32 [C]

Super heat 5 [K]

Pipe length 10 m

Power for pumping brine 3.76 W/kW

Table 8. Model parameters for the ice bank solution with 10 hours of stock.

Results

With COP data from the EES model it is now possible to run the Matlab model for the ice bank solution. The

energy demand for pumping the brine is included in the energy consumption. Furthermore the added heat

loss from the storage rooms is added to the use. The use profile is therefore multiplied with 105 kWh plus

the heat loss found by Equation 11.

The model is run with multiple production and storage capacities in order to map the saving potential for

different configurations. See Figure 26A. The ridge of the plot in Figure 26A is found and plotted; this ridge

represents the storage level that results in the highest saving for all production levels see Figure 26B. It is

worth noticing that when the production capacity increases so does the storage capacity in kWh. This

relation between production and storage in kWh is due to storage capacity being defined by max

production.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

48

Figure 26A: Saving as function of storage and production capacity, with storage capacity relative to production capacity with the

ice bank solution. B: optimal storage capacity as function of production capacity. Blue line is hours of max production red dots

are hoers of max use.

In order to visualize the relationship between production capacity and storage capacity in maximum use

hours as opposed to production capacity and storage capacity in maximum production hours. This is

plotted in Figure 27.

Figure 27. Saving as a function of storage and production capacity, with storage capacity relative to maximum use.

A seen from Figure 27 the useable amount of storage is dependent on the production capacity. A large

storage capacity gives almost no saving if there is no production capacity to fill it when production

conditions are good.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

49

As seen from Figure 27 it is possible to increase the saving potential by increasing the production capacity,

this is however also investment intense. Increasing production capacity further than 1.2 is evaluated to be

non cost-effective.

As seen from Figure 26B the optimal storage capacity for a production capacity of 1.2 is 5.6 hours of max

production. The storage level is however set to 5 hours as this has almost the same saving (See Figure 26A)

and this reduces the needed investment on the ice bank. The best configuration of the ice bank solution is

therefore found to be a production capacity of 1.2 and a storage capacity of 5 hours of this production.

Notes that the sock contains 6 hours of max averages hourly consumption�1.2 ∙ 5 = 6�. The system

configuration is analyzed closer along with a setup with more storage capacity and one with less. The

results are presented in Table 9.

Stock

Total

price

Eletricity

price

AVG.

Price Tax price

Energy

use AWP

[-] [kr] [kr] [øre/kWh] [kr] [MWh] [%]

Reference 0 186833 36815 25.5 150018 144.2 21.9

Ice b

ank

solu

tion

2 179881 34006 24.2 145875 140.3 22.5

5 177186 31951 22.9 145235 139.6 23.1

8 177866 31674 22.5 146192 140.6 23.4Ice b

ank

solu

tion

Saving

2 6952 2809 1.3 4143 4.0 -0.7

5 9647 4864 2.6 4783 4.6 -1.2

8 8967 5141 3.0 3826 3.7 -1.5Saving

Table 9. Performance of the ice bank solution.

The solution is evaluated based on the parameters and aspects described in the section Evaluation.

Micro

For the ice bank solution there is a saving in energy of 4.6 MWh (3.2 %) and a saving on electricity of 4.864

kr. (13.2 %). The overall saving on electricity after taxes is however 9.647 kr. (5.2 %). The propane plant has

a significant better average COP than the CO2 reference system. This better COP is partly due to low

condensing temperatures at night and partly due to the use of propane rather than CO2. The relative small

saving in energy compared to the significantly better average COP of the ice bank solution is primarily due

to the heat loss from the ice bank.

The ice bank technology is a relative cheap storage method relative to other storage methods considered.

The added expense must, besides ice banks, cover larger storage rooms and brine circulation system. The

acceptable added investment is found by Equation 6 to be 71,000 kr. The acceptable 71,000 kr. is

considered too little to cover the added expense. The added space requirement for the ice banks may even

cancel out the saving in form of added mortgage payment.

As seen from Table9 the average electricity price can be lowered by increasing storage capacity to 8 hours;

the benefit of this is however canceled out by the added energy consumption.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

50

There can be some positive publicity effect for the supermarket if the system gets a certification of being

friendly to renewable energy. On the other hand there is possible bad publicity in using a toxic or

flammable refrigerant.

The solution is overall found unattractive seen from a macro perspective.

Macro

Is the solution able to move energy consumption from peak

load hours?

The produced and used cool energy are added for all hours

of the day and plotted together with the price including tax.

Note that the model day starts at noon where price data is

released, see Figure 28. As seen from Figure 28 most of the

production takes place in the hours from around 9 PM and 7

AM, these hours covers 61 % of the production and only

33% of the use. The five hours with the highest price

represent 25 % of the use but only 10 % of the production.

From this it is concluded that solution moves consumption

from peak hours to of peak hours.

Is the solution using energy when there is high wind production?

The average wind percentage is slightly higher for the ice bank solution than for the reference solution as

seen in Table 9. The increase in wind energy is highest for the solution with 8 hours of stock at 6.8 %. The

most profitable solution with 5 hours of stock use 5.5 % more wind energy. The tendency to increase the

degree of wind energy is thereby increasing with storage capacity.

The model optimizes the operation pattern based on when production cost is low and not necessarily when

there is a high density of wind energy. The price of production will normally be low in hours with high

degree of wind energy due to low prices. It is however also important that condensing temperature is low

not necessarily when wind energy is high. The change in density of wind energy is relative small and due to

the fact that production in general is favorable doing night time where the density of wind degree is

highest.

Does the solution increase the overall energy use?

As seen from Table 9 the overall energy consumption is 4.6 MWh (3.2%) lower for the ice bank solution

than the reference system.

Does the solution have negative effect on other aspects of society (environment, economy and so on)?

The solution is not found to perform significantly different compared to the reference solution in relation to

this point. The risk of accidents associated with the use of propane or ammonia is found unimportant.

Figure 28. Average price on electricity, Produced and

used energy plotted for every hour of the day for the

ice bank solution proposal. 5 hours of stock and 1.2

times production capacity.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

51

Conclusions on ice bank

The solution is found to pull in the direction of wanting to even out the energy consumption over the day.

The solution is found to have a slightly higher concentration of wind energy than the reference solution.

The solution proposal is found unprofitable because of a small saving relative to the required investment

and the added space requirement.

PCM display cases

In this solution proposal the heat storage is done decentralized close to the actual heat load. No

commercial supplier of cooled display cases with embedded PCM was found. The study will therefore take

off from estimations regarding storage capacity, added heat loss and condensing temperature.

Literature on embedding PCM in structures was found (23), however not for the specific use of

supermarket application. Inspired by studies on embedding PCM in walls of buildings (23) a design is

established as seen in Figure 29. Figure 29 illustrates a solution proposal where the PCM is embedded on

the back side of the display case. Locating the PCM on the back side ensures minimum interference with

the display case design seen by the costumers. It is possible to flip the back panel design so the PCM

container is embed in the bottom or sealing of a display case. Orientation of the PCM unit changes

according to the specific display case design.

As seen in Figure 29 the condenser is embedded in the PCM container resulting in direct contact between

the condenser pipe and the PCM. If found necessary fins can be attached to the evaporator pipe to increase

the heat conductivity to the PCM. Attaching fins will increase the charge capacity.

A B

Fan Insulation

PCM Container

Articles

Pipe

Condenser

Shelving unit

Figure 29. PCM embedded in a display cabin as part of the back panel.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

52

As seen in Figure 29 a fan is located close to the PCM container surface to increase convection from the

surface. It might be necessary to increase the surface area of the PCM container in order to facilitate the

necessary heat transfer. The surface area can be increased by adding fins to the surfaces facing articles.

Storage medium

As with the ice bank solution a eutectic solution is used as storage medium. Again the fixed phase change

temperature, low price, good heat transfer and low environment impact makes this solution attractive.

Solutions from the same company are used for this solution proposal however different solutions are used.

Surface temperature of -4 oC and -25 oC is found sufficient for the PCM in cooling and freezing display cases.

These temperatures are equal to the evaporator temperatures of the reference solution. A solution for -25 oC is however not available from this company so the closest one is used, see Table 10.

It is necessary to lower the evaporator temperature below the phase change temperature to facilitate heat

transfer to the PCM. The actual evaporator temperature is therefore lower than that of the reference

solution.

Table 10. Two eutectic solutions from PCM Products limited found suitable for the PCM display case (22).

Control

Control is seen as one of the main challenges of this solution. Normally the temperature in a display case is

controlled by regulating the surface temperature of the evaporator. The surface temperature of the

evaporator is regulated by altering flow of brine/refrigerant or regulating the evaporator pressure. In this

case the surface temperature will however be close to the phase changing temperature of the PCM

regardless of pressure in the evaporator and storage level.

The regulation is necessary as the heat load is not constant. The control must therefore be able to

compensate for variations in heat load. The heat load for the supermarket is seen in Figure 15 where the

variations are from about 50% till 100%. This variation is however based on hourly averages for the whole

supermarket. The variation for every individual container is assumed to be larger. The variations of heat

load for the individual display case is estimated to be between 30% and 100% of max load. The controller

system must be able to deal with these variations.

The heat transfer coefficient of the PCM container is dependent on the velocity of the air surrounding the

surface. Heat exchanges between the display case and the PCM container can therefore be controlled by

the air flow of the fan seen in Figure 29. If the temperature gets too high in the container the fan velocity

must be regulated up. If the temperature is too low the velocity must go down. It is estimated that the

overall heat transfer coefficient can be changed sufficiently enough to control the system in the ranges of

30% to 100%. This will however not be investigated further.

Controlling and measuring the storage level is hard with this solution primarily for two reasons. First the

storage is done locally and energy cannot be transported from one display case to another. Secondly it is

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

53

hard to measure the ice concentration in the PCM units. It is found necessary to install excess capacity in

order to compensate for imbalance between storage level and uncertainty on storage level measurements.

Capacity

For this solution proposal storage capacity is limited unlike with the ice bank solution. The storage capacity

in the PCM display case solution is limited by the amount of PCM it is possible to physically make room for

in the display cases. It is found reasonable to deviate from standard display cabins as long as the same

amount of capacity is installed and with the same functionality.

Three different display cabin types are investigated for their potential for housing PCM containers. The

three display cabins are evaluated on their storage capacity in relation to their energy consumption. See

Figure 30.

Figure 30. Three different display cabins for supermarket applications. A: An island display case. B: A vertical multi desk

C: A vertical roll-in display case. (24)

Islands

The island display case (see Figure 30A) is normally located away from walls so the cabin is accessible from

all sides. The only possible location of PCM containers in this display case is found to be below the articles

on display.

The cabin measures are assumed to be around 1.4 m high and 3 m wide, the length varies dependent on

shop layout. It is estimated that 50 cm of the height is used for food storage the remaining 90 cm can be

used for PCM units. The island display case from the case description has an energy consumption of 1440

W/m. The amount of storage is estimated in terms of number of hours of max consumption.

As some insulation is needed in the sides of the container the maximum width of the PCM units are found

to be 2.8 m. It is assumed that 7 cm of insulation to the floor is sufficient. This leaves 83 cm for the PCM

unit including the air layer between the PCM container and the articles as seen in Figure 29. It is found that

the PCM containment can have a height of 70 cm. A slice view of the design is seen in Figure 31.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

54

As with the ice bank some of the container space is used for evaporator and some for the containment

material. The PCM is estimated to occupy 40 % of the total space.

2.8 ∙ 0.7 ∙ 0.4 = 0.78X\X

The energy released or absorbed by a phase change in that amount of volume is found by using data from

Table 10:

0.78X\X ∙ 299]P

X\ = 234]PX

The maximum hour of maximum consumption is then found as:

234]PX

1440^X

= 234 _`1440 6 = 1.63 a6 G = 45 ℎ<>2G

The island container type is therefore found to be able to store enough PCM to cover 45 hours of maximum

load without changing the outer dimensions of the container.

Vertical multi desk

The vertical multi desk (see Figure 30B) is investigated in the same manner as the island display case. For

this display case the best location of the PCM units is found to in the bed and on the back panel.

Implementing PCM units in the back panels will however increase the depth of the whole display case.

Increasing depth of the display case results in using more shop space. It is therefore investigated how much

energy it is possible to store in the bed of the display case alone. The bed of the display case is estimated to

have a height of 45 cm and a depth of 90 cm. using the same assumptions regarding insulation thickness it

is possible to install a PCM container that is 25 cm thick and with a depth of 70 cm. Calculations regarding

maximum hours of storage capacity is done as for the island display case. The vertical multi desk has an

energy consumption of 380 W/m and 590 W/m for cooling and freezing respectively according to the case

description (See Case description Table 2). The storage capacity is found to be 13.4 hours for freezing and

19.1 hours for cooling. These storage times are much less than the island, it is however possible to increase

the storage capacity by adding PCM units on the back of the display case.

Insulation

PCM Container

Articles

Pipe

Condenser

Fan

50 cm

13 cm

70 cm

7 cm

1.4 m

Figure 31. Island display case with embedded PCM container.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

55

Vertical roll-in display case

The vertical roll-in display (see Figure 29 C) case is often used for dairy product. The articles sold in large

volumes are rolled in to the display case in trolleys to avoid lifting. In this display case there is no space in

the bed and the PCM units must be located in the back panel of the display case. The storage capacity will

then depend on how much space is allocated for PCM on the back panel.

In some cases the trolleys are rolled in from the back as the display case is in direct contact with the cooling

room. In this case there is no space for PCM units in the back panel as there is not any back panel. The PCM

must be located in the adjacent cooling room. Cold energy can be supplied by fans blowing air from the

cooling room to the display case.

It is found possible to install at least the same amount of capacity for the vertical roll-in display case as for

the vertical multi desk. Installing PCM units in the back of roll-in-display cases operated from the front will

however increase space requirements for the display case.

It is found possible to install at least 12 hours of maximum load in all the investigated display case types. In

most cases PCM can be embedded without changing the lock and dimensions of the display cases. The Heat

loss to the surroundings is expected to be larger than from a traditional evaporator as cold areas are

extended. The relation between storage and heat loss are assumed to follow the same relation as the ice

bank solution. The added cooling need is thereby 500 W per hour of max production with a production

factor of 1.2 as given by Equation 11.

Refrigerant

As the system is based on direct expansion in the PCM units the refrigerant will serve as transport medium

as well as refrigerant. When the refrigerant pipe is in contact with the shop environment and the filling is

relative large it is found necessary to use a non toxic, non flammable and natural refrigerant. R744 is found

best suited for this purpose as it is harmless in case of a leak.

The evaporator temperature is set to -7 oC and -29 oC for cooling and freezing respectively. These

temperatures give a delta temperature of 3 K between phase changes temperature of the PCM and the

refrigerants evaporating temperature. To generate COP data the EES model is run with parameters as seen

in Table 11.

Parameter Value Unit

Maximum cooling need 76.7 kW

Maximum freezing need 33.3 kW

Isentropic efficiency 0.65 [-]

Evaporator temperature coolers -7 [C]

Evaporator temperature freezer -29 [C]

Super heat 5 [K]

Pipe length 250 m

Power for pumping brine 0 (direct expansion) W/kW

Table 11. Model parameter for PCM display case solution.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

56

Results

The added heat losses from the PCM units are added to the use as in the ice bank solution. An investigation

of relation between saving, production and storage capacity is done as for the ice bank solution. The

relation is found to be the same as with the ice bank solution. This similarity is due to the similarities in

terms of heat loss. The COP data is however very different resulting in radical different performance in

saving. The most optimal storage level with a production capacity of 1.2 is found to be 5.4. Again a storage

capacity of 5 is decided on to save on investment.

Energy and price performance for the PCM display case solution is presented in Table 12 with the optimal

storage capacity. Further solutions with larger and smaller storage capacities are presented.

Stock

Total

price

Eletricity

price

AVG.

Price Tax price Energy use AWP

[-] [kr] [kr] [øre/kWh] [kr] [MWh] [%]

Reference 0 186833 36815 25.5 150018 144.2 21.9

PCM

displa

y

case

2 210830 39887 24.3 170943 164.4 22.5

5 205553 37068 22.9 168485 162.0 23.1

8 205578 36588 22.5 168989 162.5 23.4PCM

displa

y

case

Saving

2 -23997 -3072 1.3 -20924 -20.1 -0.6

5 -18719 -253 2.6 -18467 -17.8 -1.2

8 -18744 227 3.0 -18971 -18.2 -1.5Saving

Table 12. Performance of the PCM display case solution.

The solution is evaluated based on the parameters and aspects described in the section Evaluation.

Micro

The PCM display case solution performs significantly worse than the reference solution. The overall yearly

running cost is 18,719 kr. (10%) higher than that of the reference solution.

There are savings on the average electricity price as night production is done. The savings are however not

enough to compensate for the added energy consumption due to heat loss from storage and lower

evaporating temperature.

As with the ice bank solution it is possible to achieve a positive publicity effect. The effect relies on a

certification of being friendly to renewable energy.

The solution is overall found unattractive seen from a macro perspective.

Macro

Is the solution able to move energy consumption from peak load hours?

A plot similar to the one done for the ice bank solution in Figure 28 is made for the PCM display case

solution. The plot illustrates the production distribution over the day. The plot for the ice bank solution and

the PCM display case solution is almost identical as storage and production capacity is the same.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

57

Furthermore the trend of high condensing temperature resulting in lower COP applies for both R290 and

R744.

As the two running patterns are almost identical the conclusions regarding moving production to of peak

hours are the same as for the ice bank solution. The solution proposal has a clear tendency to move

production to off peak hours (see Ice bank system under “Results” for more). The actual plot can be seen in

Appendix E.

Is the solution using energy when there is high wind production?

Again the results from the ice bank solution and the PCM display case are close to identical. The PCM

display case solution proposal has a slightly higher degree of wind energy than the reference solution. See

Ice bank system under “Results” for more information regarding the solutions performance regarding using

more wind energy.

Does the solution increase the overall energy use?

The PCM display case solution uses 17.8 MWh (12.3%) more energy than the reference solution as seen

from Table 12. This added energy consumption cannot be justified by the only slightly higher percentages

of wind energy.

Does the solution have negative effect on other aspects of society (environment, economy and so on)?

The solution is not found to perform significantly different compared to the reference solution in relation to

this point.

Conclusions on PCM display case

The solution proposal is found unprofitable as it will require a large investment and increase the running

costs compared to the reference solution. The solution is found to pull in the direction of evening out the

energy consumption over the day. Overall energy consumption is 12.3 % larger than that of the reference

solution.

Ice slurry

In this solution storage is done by increasing the ice concentration of ice slurry in a storage tank. The ice is

produced in an ice scrape evaporator; this solution is chosen as it is found to be the most suited for the

supermarket application. Furthermore ice scraping evaporators are the best documented production

method and there are commercial players delivering standardized systems based on this technology (11).

Storage medium

In order to facilitate the growth of ice crystals in the right way the brine must have a sub eutectic

concentration. Different solutions are evaluated based on their enthalpy phase diagrams. A solution with a

high enthalpy difference over a small temperature interval is the goal. Furthermore the temperature ranges

must be in accordance with the cooling loads.

It is estimated that the cooling and freezing need can be satisfied by circulating ice slurry at temperatures

of -1 oC and -21 oC respectively. The temperature of the slurry will depend on ice concentration as the brine

is non eutectic. The ice slurry circulation temperatures are temperature at ice concentration equal to zero.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

58

Inspired by the Handbook on ice slurries (11) sodium chloride, ethylene glycol, propylene glycol, ethyl

alcohol, ammonia, potassium formate and calcium chloride is considered as freezing point depressants.

For the cooling application the choice of freezing point depressant has small influence on storage potential.

This small influence is due to the relative small amount of additive needed to lower the freezing point to -1 oC. Sodium chloride is chosen as it is both cheap and easy to work with. Corrosion must however be

considered.

By cooling a 1.5 wt % NaCl solution from -1 oC to -2 oC the ice concentration is increased from 0 to 45 wt%

and the enthalpy difference is 150 kJ/kg (11). (Se Figure 11 for enthalpy phase diagram of sodium chloride).

For the freezing application relative high concentrations of freezing point depressant is needed. It is not

even possible to use sodium chloride as the eutectic freezing temperature is -21C. A delta temperature of 5

K between supply and return slurry is decided on. Increasing this temperature difference will lower the

condensing temperature but also reduce needed storage mass. The delta temperature is therefore an

optimization parameter (11).

The freezing point depressant that gives the highest enthalpy difference with a 5 K temperature difference

is an ammonia water solution. The solution has an enthalpy difference of 60 kJ/kg and an ice concentration

of 14 wt%. The ammonia solution is however found unsuited for the application due to the relative large

amount of toxic and smelly ammonia needed. The second best choice is found to be ethyl alcohol with an

enthalpy difference of 55 kJ/kg and an ice concentration of 16 wt%. Ethyl alcohol is regarded as flammable

however not in water solutions in the ice slurry range (11).

Capacity

As with the two other solution proposals an initial investigation of the relation between savings on costs,

production and storage capacity is conducted.

An initial guess is made on storage capacity of the system. The storage level is set to be 10 hours of max

production where max production is equal to 1.2 times max consumption. This storage capacity represents

900 kWh for cooling as found in Equation 8 on page 45. The necessary mass is found by using Equation 9 on

page 45 and the enthalpy differences found above. The needed mass for cooling is found to be 21,600 kg

and 23,563 kg for freezing. It is noticed that the mass required for covering the freezing need is larger than

that of cooling. This large mass requirement for freezing is due to the relative small enthalpy difference of

the ethyl alcohol.

The storing of ice for the freezing need is evaluated to be uneconomic due to the large storage mass

relative to the small freezing load. Three different approaches on the problem of covering the freezing load

without using slurry ice is considered.

1. Running the freezing system as an individual system with no connection to the ice slurry system

and with no storage on the freezing load. This solution is close to the reference system as it is

possible to use condenser and compressor from the ice slurry system as the high stages in a two

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

59

stages system. In this way the freezing part of the refrigeration plant is kept cost neutral to the

reference system.

2. Using an alternative PCM solution for the freezing load for example ice bank or micro encapsulated

slurry. Micro encapsulated slurry is a possible solution for the low temperature area. Slurry based

on paraffin for example n-Undecane is a possible PCM solution. n-Undecane has 11 carbon atoms

and a melting temperature of -25.5 oC (see Table 1 on page 22 for data on paraffins). Furthermore

it is possible to service the freezing load with an ice bank as described under the solution proposal

ice bank.

3. Running the freezing on demand as a direct expansion system but condensing refrigerant in the ice

slurry tank through a gas to ice slurry heat exchanger (see Figure 32). In this system CO2 is

condensed in the ice slurry tank increasing the load on the cooling system but also increasing the

COP of the freezing system (see Figure 32). Using the ice slurry for condensing the CO2 insures that

the system can stay sub critical. Furthermore part of the freezing load is moved to the cooling

range where storage is possible.

The third solution is evaluated to be the most interesting as it allows for some load shifting on the freezing

system. The solution of using an ice bank for the freezing is evaluated to be more interesting than using

micro encapsulated slurry due to the relative low enthalpy potential in a PCS based paraffin. The use of ice

bank for freezing load is not further investigated as it is partly covered in the ice bank solution proposal.

Using the combined ice slurry and direct expansion system requires the load calculations to be redone. The

freezing circuit is running under constant conditions regarding temperature in evaporator and condenser.

The COP for the freezing circuit is found in CoolPack based on the system settings as seen in Table 13. The

COP is found to be 4.84.

Ice slu

rry

ge

ne

rato

r

Storage tank

En

gin

e

R717

R744

Ice slurry

Figure 32. System configuration of a system based on ice slurry and direct expansion.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

60

Refrigerant T_evap. T_cond. µ_isent. Super heat L_pipe

[name] [C] [C] [-] [K] [m]

R744 -25 0 0.65 5 250

Table 13. Configurations for the freezing circuit in the ice slurry system.

The cooling need is recalculated based on the load from the freezing circuit by using Equation 12. The

added heat load is found to be 36 kW resulting in a total heat load of 111 kW on the cooling circuit (75

kW+36kW).

� � = � � b1 + 1�c)d

Equation 12

The ice slurry storage mass is recalculated based on the new cooling load and 10 hours storage; the mass is

found to be 32,000 kg. The precise volume of slurry will differ depending on ice concentration. It is

estimated that a tank with a volume of 33 m3 is sufficient by assuming an ice slurry density of 970 kg/m3.

The actual volume of the tank is larger as some space is required for condenser for the low stages and

agitating mechanism. The needed volume for the storage tank is found to be 37 m3 by assuming a

volumetric efficiency of 90%.

For storing ice slurry a round tall tank is preferable as it reduces buoyancy separation and eases

agitation(11). For this reasons it is found unattractive to locate the storage tank in the cooling room. An

insulated storage tank with a height to width ratio of 2 is chosen for storing slurry. This results in a storage

tank with a diameter of 2.9 m and a height of 5.7 m. A storage tank with these measures has a surface of 64

m2. The average U value is assumed equal to 0.4 $%&∙' inspired by the article “heat losses from storage

tanks” (25). Using the mean temperature of the reference year 7.8 C, the average heat loss effect is found

to 201 W by using Equation 5.

This heat loss is small compared to that of the ice bank solution, this is partly due to smaller volume of the

stock. Furthermore it is worth noticing that this heat loss is constant compared to the heat loss in the ice

bank solution which varies with the consumption. As the heat loss is small compared to the overall cooling

load it is found acceptable to assume constant heat loss. The heat loss is added to the use profile in the

Matlab model.

Transport medium

In this solution proposal the ice slurry works as transport medium for the cooling load and CO2 is directly

expanded in the freezing circuit. Energy used for transporting CO2 is therefore covered in the EES model as

part of the COP. The energy used for pumping the ice slurry is estimated in a separate EES model. It is

assumed that the ice slurry is circulated slowly enough to melt all the ice before returning to the storage

tank. This results in an enthalpy difference between supply and return of 150 kJ/kg. From this enthalpy

difference and the maximum cooling need the maximum mass flow is found to be 0.5 kg/s equal to a

volume flow of 0.52 E-3 m3/s.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

61

As described in the section “Ice slurry” on page 26 it is difficult to estimate the viscosity for an ice slurry

flow as it depends not only on ice concentration. The viscosity is estimated by using a formula only

considering the ice concentration as this is the only data known. The formula is from the Handbook on ice

slurry (11); the effective viscosity is found to be 1.4 E-2 ef%∙g by Equation 13

hEYYE��9iE = h79j89k�1 + 2.5 ∙ � + 10.05 ∙ �� + 0.00273 ∙ lCm.m∙��

Equation 13 (11)

A mean velocity of 0.4 m/s is found reasonable for the flow to and from the heat exchanger. By using this

velocity and a volume flow of 0.52 E-3 m3/s the pipe diameter is found to be 3.6 cm.

The Reynolds number is found to 1300 by using the diameter, viscosity and velocity described above. With

normal brine this Reynolds number will give a friction factor through a Moody chart, this is however not the

case for the ice slurry. The Reynolds number is instead used along with formulas from the Handbook on ice

slurry to calculate a friction factor. See Equation 14 and Equation 15

nl�78��o = nl���X�7 ∙ b1 + 9.75 ∙ �p d

3C

Equation 14 (11)

q�78��o = 64nl�78��o

-<2 nl�78��o < 2100

q�78��o = 0.316nl�78��os.�t -<2 nl�78��o > 2100

Equation 15 (11) and (18)

The friction factor is found to be 44.2 E-3. This value is used for calculating the pressure drop in the cooling

supply line (see Appendix B for more information on pressure drop calculations). In the return pipe the

brine is considered as being pure water. The effect needed to supply the 75 kW of cooling is 87 W or equal

to 1.16 W/kW delivered. This effect is somewhere in between the CO2 solution and the brine solution

described in the ice bank solution proposal. (See Table 6 on page 22).

The energy used on agitation depends on the ice concentration in the storage tank and the storage volume.

The agitation effect is set to a constant value of 25 W per cubic meter of storage (11). The agitation works

both as a heat load and as a direct energy consumption.

Regarding refrigerant the system is assumed to use ammonia as this is the most common natural

refrigerant for ice slurry generators on the market. The model is run with parameters set as seen in Table

14.

Feasibility of demand response by phase changing materials for cooling application

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Parameter Value Unit

Maximum cooling need 111 kW

Maximum freezing need 30 kW

Isentropic effisiency 0.65 [-]

Evapurator temperature ice generator -5 [C]

Evapurator temperature freezer -25 [C]

Super heat 5 [K]

Pipe length 250 m

Power for pumping brine 1.2/ (direct expansion) W/kW

Table 14. Model parameter for ice slurry solution.

Results

The heat loss from the storage tank is not scaling linear with the storage capacity due to relations between

area and volume of the cylindrical storage tank. The heat loss from the storage tanks are found along with

agitation power using a small Matlab function. Heat losses are added to the use profile. The power used on

agitation is added along with the power used for pumping ice slurry.

The production for cooling load is done when production facilities are optimal. The freezing lode is covered

on demand with condensation in the ice tank. The relationship between running cost, storage and

production capacity are investigated as with the two other solution proposals see Figure 33A and B.

Figure 33. Saving as a function of storage and production capacity, with storage capacity relative to production capacity with the

ice slurry solution. B: Optimal storage capacity as a function of production capacity. Blue line is hours of max production red dots

are hours of max use.

As seen from Figure 33B the optimal storage level is 4.8 hours of max production with a production level of

1.2. This storage level is lower than the storage level of the other two solution proposals (5.4 -5.6 hours).

Feasibility of demand response by phase changing materials for cooling application

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As with the other solutions the storage level is set to just below the optimal to reduce investment. The

system is configured with 4.5 hours of stock and with a production capacity of 1.2. Performance of the

system is further investigated and the results can be seen in Table 15.

The heat loss in the second to last column in Table 15 is the sum of surface heat loss from storage tank and

agitation heat loss. The energy consumption to the agitation is seen in the last column in Table 15. Results

from running the model are presented in Table 15 along with configurations with larger and smaller stock

size.

Stock

Total

price

Eletricity

price

AVG.

Price Tax price

Energy

use AWP

Heat

Loss

Agitation

power

[-] [kr] [kr] [øre/kWh] [kr] [MWh] [%] [W] [W]

Reference 0 186833 36815 25.5 150018 144.2 21.9 0 0

Ice sl

urry 2 181629 34247 24.2 147382 141.7 22.5 306 206

4.5 180541 33056 23.3 147485 141.8 22.8 570 412

7 181490 32934 23.1 148556 142.8 22.9 1075 824Ice sl

urry

Saving

2 5204 2568 1.4 2636 2.5 -0.6

4.5 6292 3759 2.2 2533 2.4 -0.9

7 5343 3881 2.5 1462 1.4 -1.0Saving

Table 15. Performance of the ice slurry solution.

Micro

The solution proposal reduces annual costs of electricity with 3,759 kr. (10.2%). The overall electricity

expense including taxes is reduced by 6,292 kr. (3.3 %). A storage capacity of 7 hours increases the saving

on electricity to 3,881 kr. as it brings the average electricity price further down. The increase in storage

increases expense on added tax due to slightly larger energy consumption. The increase in taxes for the 7

hour configuration brings the overall running cost up higher compared to the 5 hour configuration.

The acceptable investment is found by Equation 6 to be 46,310 kr. This amount of money is considered too

small to cover the added expense on ice slurry unit and storage tank. In the hand book on ice slurry prices

on ice scraper units are given to be in the range from 160 to 600 vw $

�^ ��WE� 9����77Ek (11). The cooling need of

111 kW times the production factor of 1.2 results in a production capacity of 133 kW. Using the low price of

160 US$/kW the scraper unit will cost more than 120,000 kr. alone. The investment on ice slurry production

evaporator thereby represent close to three times the acceptable added investment. The solution proposal

is therefore found unfeasible.

Feasibility of demand response by phase changing materials for cooling application

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Macro

Is the solution able to move energy consumption from peak load hours?

A plot similar to the one done for the ice bank solution

in Figure 28 is made for the ice slurry solution, see

Figure 34. The production pattern for the ice bank

solution and the ice slurry solution follows the same

trend of moving production to night time. The peak

production interval in the night hours is narrower than

that of the ice bank solution. The production doing

daytime is larger than that of the ice bank solution. The

ice slurry solution proposal has less tendency of moving

production to peak hours for two reasons.

1. Only part of the freezing load is flexible as low

stage compression is done on demand.

2. The storage capacity is less, thereby allowing for

fewer hours of max production in row.

Plots of production patterns for all the solution

proposals are seen together in appendix E.

Is the solution using energy when there is high wind production?

The ice slurry solution proposal has a 0.9 % (or increase of 4.1 %) higher degree of wind energy than the

reference model. The solution proposal has less use of wind energy than the ice bank solution. The lower

usage of wind energy is due to the lower degree of night production where the wind part in general is

higher.

Does the solution increase the overall energy use?

As seen from Table 15 the energy consumption is 2.4 MWh (1.7 %) lower for the ice slurry solution than for

the reference solution. The saving in energy is due to better COP. The COP is higher due to ammonia as

refrigerant and lower condensing temperatures at night. Some of the saved energy is however lost on

agitation of ice slurry and heat loss from storage tank.

Does the solution have negative effect on other aspects of society (environment, economy and so on)?

The solution is not found to perform significantly different from the reference solution in relation to this

point.

Conclusions on ice slurry

The ice slurry solution is found to pull in the direction of evening out the energy consumption over the day.

Overall energy consumption is 1.7 % smaller than that of the reference solution. The solution is found to

reduce overall expenses on energy by 3.3%. The solution proposal is found unprofitable as it will require

too large an added investment compared to the reference solution.

Figure 34. Average price on electricity, produced and

used energy plotted for every hour of the day for the ice

slurry solution proposal. 4.5 hours of stock and 1.2 times

normal production capacity.

Feasibility of demand response by phase changing materials for cooling application

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Conclusions on solution proposals

Results from running the model with settings for the different solution proposals indicate potential savings

on both energy and running cost. The ice bank solution is estimated to involve the least added investment

and has the largest annual saving; it is therefore the proposal estimated to be closest to cost-effective.

The ice bank solution with storage capacity of 5 hours has a yearly saving of 9,647 kr. (5.1 %) allowing for an

added investment of 71,000 kr.

The ice bank solution results in a saving in energy of 4.6 MWh representing a value of 5,835 kr. The average

electricity price is lowered by 24.4 kr/MWh(10%), this saving represents a value of 3,812 kr. The saving on

the most profitable storage solution is therefore mostly due to lower energy consumption and secondly

due to lower prices. The benefits on the energy consumption are partly due to the ice bank storage solution

refrigerant and partly the low night time condensing temperature.

The savings on running costs are however not found sufficient to justify the added investment for any of

the solutions. Demand response is therefore fount unattractive seen from a micro perspective

All the solution proposals show potential for moving energy consumption from peak load hours.

Furthermore the solutions have a higher degree of wind energy than the reference system. The ice bank

solution and the ice slurry solution reduce the energy consumption slightly compared to the reference

solution. The PCM display case solution increases the energy consumption compared to the reference

solution. Seen from a macro perspective the ice bank and the ice slurry solution proposals are desired. The

solutions are found desired as they pull in the direction of evening out the energy consumption by acting as

a flexible user; furthermore they reduce the overall energy consumption. Demand response in cooling

applications is therefore found to be of interest when seen in the macro perspective.

As demand response in cooling applications is found non cost-effective and interesting seen from a macro

perspective it is interesting to investigate the necessary measures for making it profitable in micro terms.

Procedures are done in other sectors for motivating the investment in systems that will introduce flexibility

in the energy demand as described in the section “The electricity market”. Results for the different

solutions can be seen together in Appendix F.

Future performance The ice bank solution proposal is not found profitable in micro economical terms; however changes in the

tax system can alter this. To investigate the effect of changing the tax system and the degree of wind

energy the model for the ice bank solution is run with new conditions.

Scenarios

Four different scenarios are set up regarding energy taxation and wind production. The scenarios are based

partly on the Energy commission’s recommendations in their final report (6) and the Ministry of Taxes

report on changing the tax system for implementation of more renewable energy (4).

Regulating the taxes will have consequences on the electricity price as electricity with a low tax is a more

attractive good than electricity with high tax. A drop in taxes will therefore result in an increase in

electricity price; the scale of this price effect is highly dependent on how many users see and benefit from

Feasibility of demand response by phase changing materials for cooling application

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this tax altering. In these scenarios elasticity on both price and use is neglected, these parameters will

therefore stay the same regardless of tax level. 2009 data will serve as fundament in terms of production,

use and price pattern. The scenarios are described below in terms of tax and wind production.

Scenario 1 (“Elpatronordning”)

In this scenario the tax system is modified so taxes drop when the price gets low as this indicates an over

production. Under the “Elpatronordning” direct electrical heating is made feasible when electricity price is

below 22 øre/kWh as described in the section “Stability and taxes”. In this scenario the electricity tax is set

to “Elpatronordning” level when prices drop below 22/øre/kWh. In other words the tax is set to drop when

it is cost-effective for cogeneration plants to shot down turbines and produce district heating by direct

electrical heating.

The tax is regulated according to levels given in the Ministry of Tax’s report (4) for 2010. The level for the

“Elpatronordning” power is set to 26 øre/kWh and 104 øre/kWh for the normal power.

Scenario 2 (“Whistle 1”)

Under this scenario the taxes is reduced when wind production is equal to or larger than the use. In reality

it is hard to know if the production from wind exceeds the use for a future hour. This prediction is hard as

the production is partly unknown and the use is dependent on price. If the taxes drop the use will increase

due to price elasticity; this might result in a tax increase as use increases but wind production stays the

same.

The tax is set to 29 øre/kWh when wind production represents 100 % or more of the electricity use; at all

other times the price is set to 101.8 øre/kWh. These prices are in accordance with the levels stated in the

report from the Ministry of Tax (4) in the “Fløjte 1 model”.

The Climate commission recommends wind to represent almost 50% of the energy in 2050, see Figure 6.

This massive increase in wind energy is expected to cause more hours with 100 % wind production; even

though the commission also recommends other flexible users like electrical cars and so on to be

implemented. The wind production is set to increase 50 % to simulate expected changes. This increase is

small compared to the commission’s recommendation; however no changes are done to the consumption.

This increase in wind energy will increase the number of hours with 100 % wind energy.

Scenario 3 (“Smooth”)

Under this scenario the tax is variable in relation to the degree of wind energy like in Whistle 1. The

variation is however non discrete but given by Equation 16. The tax will vary linear from 130 øre/kWh at

zero wind production to 29 øre/kWh at 100 % wind production; for wind production above 100% the tax is

constant at 29 øre/kWh.

yz�)6� = 130 ø2l�6ℎ − 1.21 ø2l

�6ℎ ∙ 6); -<2 0 < 6) < 100%

yz�)6� = 29 ø2l�6ℎ ; -<2 6) > 100%

Equation 16

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As for the Whistle model the wind production is assumed to be 50 % larger than 2009 and consumption

assumed to be the same. This scenario is not described in the Ministry of Tax’s report (4) but handled for

this simulation only.

Scenario 4 (Reference)

In order to evaluate the changes the tax system of 2010 is used as reference. The fixed level for electricity

for 2010 is set to 104 øre/kWh (4).

The four tax scenarios are applied to the 2009 data and the overall electricity price is plotted for the four

scenarios, see Figure 35. As seen from the figure the four scenarios result in different price patterns. All the

three new scenarios result in a significantly larger variation in the overall price relative to the current 2010

system. This variation is confirmed by calculating the standard deviation on price for the different cases.

The standard deviation is between 20 and 35 øre/kWh for the three new scenarios and only 7.8 øre/kWh

for the current 2010 system. As mentioned earlier the price elasticity is expected to cancel out some of the

variation - this is however not simulated.

Figure 35. Total electricity price for scenario 1, 2, 3 and lastly the current system 2010 taxes. Wind production data, price data

and consumption data from 2009.

Results on scenarios

The Ice bank model is evaluated with the new price information based on the three scenarios but with the

same COP and weather data. As the tax system is fully changed it is necessary to run the reference model

Scenario 1

Scenario 2

Scenario 3

Scenario 4

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again for the three scenarios. The performance of the ice bank solution relative to the reference system is

found for all scenarios.

The production capacity is set to 1.2 times max use and stock level size is optimized to give the best yearly

saving for all four scenarios. The results are plotted for the different scenarios in Figure 36 (Higher is

better).

Figure 36. Performance of the ice bank solution under 4 different tax systems. The ice bank solution performance is held against

the performance of the reference system under the scenarios. Performance increase in terms of Energy saving, Running cost

saving and Wind part are found in percent. The optimal storage level is found for the ice bank solution under the different

scenarios in hours of max production.

As seen in Figure 36 all the three scenarios increase saving on the running cost relative to the 2010 system

(scenario 4). The added saving is due to larger fluctuations in the overall electricity price; large fluctuations

facilitates large savings. The least effective method of increasing saving potential is scenario 2 which has

almost the same saving potential as the 2010 system.

As seen in Figure 36 the saving in energy is lower for all three scenarios. This is because it becomes more

important to use cheap electricity than minimize electricity use. There are however savings on energy

under all scenarios due to better COP at night time and propane refrigerant.

As seen in Figure 36 the utilization of wind energy is higher for all the three scenarios, however it is

significantly higher for the third scenario. This significantly better performance in terms of wind energy use

is due to the tax being directly linked to the degree of wind energy. The feasible storage medium is around

6 hours of max production in all the scenarios except scenario 3 where it is 8 hour.

Feasibility of demand response by phase changing materials for cooling application

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It is found possible to increase the degree of wind energy used by the cooling system by changing the tax

system. The tax system in scenario 3 is found to be the most effective way of moving towards more wind

energy. The demand system uses 23.9 % more wind energy than the reference system under scenario 3.

The corresponding value is 5.8 % for the 2010 scenario. The most profitable stock level under scenario 3 is

found to be 8 hours of max production not considering investment. This stock level allows for energy

storage from day to day but not week to week. The solution will therefore not move production from long

periods with much wind to long periods with no wind. The concentration of wind energy is however

significantly higher.

The running cost savings are 10.6 % for scenario 3 and 5.2% for the 2010 tax system. A 10.6 % saving under

the 2010 tax system represent an annual saving of 19,804 kr. using Equation 6 the acceptable investment is

found to 145,761 kr. With this amount of added investment it is evaluated that it is realistic to construct

the ice bank system with 8 hours of stock.

Demand response for supermarket cooling is evaluated to be cost effective with scenario 3 under the

stated assumptions. The assumptions on no price elasticity in relation to taxes are however naive and other

aspects of the consumer side must be considered.

Discussion The electricity tax law is currently in a form where it taxes all electricity equal. This taxation method has a

positive effect in reducing electricity consumption in general. On the Ministry of Tax homepage it is stated:

"The purpose of green taxes is to affect companies and consumers decisions, so they show consideration

towards the environment. The green tax ideally represents the environmental impact associated with the

production and use of the given article."4

If all use patterns are equally harmful to the environment the current taxing is in harmony with the above

statement. This is however not the case as a use pattern in alliance with wind production is less harmful to

the environment.

A tax system will always have a lack of justice and control. This lack is partly due to the complexity of

electricity market, and partly due to the need for transparency and simplicity. It is important that there is

harmony between tax law and the favorable direction of use pattern. It is favorable to encourages users to

reduce consumption but it might be just as important to encourage appropriate use patterns. Other tax

system has been modified to favor environmental friendly user behavior. For example the owner’s tax on

cars was modified to promote environmentally friendly cars.

4 Formålet med grønne afgifter er at påvirke virksomhedernes og forbrugernes beslutninger, så de tager

mere hensyn til miljøet. Den grønne afgift svarer ideelt set til de miljøomkostninger, der er forbundet med

at fremstille og forbruge varer.

http://www.skm.dk/tal_statistik/skatter_og_afgifter/675.html (1/3-2011)

Feasibility of demand response by phase changing materials for cooling application

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The electricity net will need flexible users in order to implement more renewable energy. Market price

variations may be strong enough to breed these flexible users. If this is not the case, tax regulation is found

to be an effective tool for making cooling systems flexible electricity (see Future performance).

To evaluate the efficiency of tax regulations further studies in other sectors are needed. The Ministry of

Tax(4) concludes that the unpredictability of wind production makes it unattractive to use for taxation. The

model however shows large potential for switching towards wind energy if predictions are only 36 hours

ahead.

Ice banks are an energy efficient storage method compared to pumping water to reservoirs with hydro

power. The taxation however favors the storage in water reservoirs as tax is only paid on delivered

electricity. The equivalent for the ice bank solution would be to refund taxes paid on energy used to cover

heat losses. In order for the cooling industry to rightfully claim this refund of taxes, they must store energy

for time intervals as long as the hydro power plans. Storage time for the investigated systems is significantly

shorter than for hydro power.

Sensibility study In order to determine the sensibility of the model, different aspects of settings are investigated. The

investigation is split into sub categories and the sensibilities of parameters are investigated individually.

Refrigeration system configuration model (EES)

The EES model calculates the operating performance regarding COP under different condensing

temperatures. For this study the build-in uncertainty calculator in EES is used; uncertainty intervals are

defined and the overall uncertainty is found from this. The reference system is used for the investigation;

the effects are expected to be similar for the solution proposals as the same basic model is used.

Pipe work and pressure drop

The pipe work can be done in many ways for a system like this. The effect of changing the settings is

investigated by setting uncertainty intervals as seen in Figure 37. The parameters lG[�9�E, K, L and v are

used in calculating pressure drop from evaporator in accordance with Appendix B.

Figure 37. Uncertainty on COP as consequence of pipe work.

As seen in Figure 37 the important parameter is the pressure drop in the condenser (dTconden).The pressure

drop from the evaporator is of little or no importance. The overall effect on COP due to pressure drop is +/-

0.1 (2%). A change of 2 % seems small, it is however important to remember that the savings are strongly

Feasibility of demand response by phase changing materials for cooling application

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related to the energy consumption. Further savings on energy is in the range of few percent. The effect on

the final saving potential for these variations in COP is investigated in the section “Operation model

(Matlab)” later on.

Evaporating temperature and isentropic efficiency

The evaporating temperature and isentropic efficiency of the system are based on values used in literature

and assumptions regarding heat transfers in heat exchangers. As with the investigation of the pipe work,

uncertainty intervals are set up as seen in Figure 38.

Figure 38. Uncertainty on COP as consequence of temperature settings and compressor

As seen in Figure 38 the intermediary temperature is of great importance along with the isentropic

efficiency of the compressor. The overall variation within the uncertainty intervals are +/- 0.63 (16 %). This

deviation is significantly higher than that of the pipe work and will change the performance of the system

entirely.

Operation model (Matlab)

The Matlab model determines the best operating pattern of the system based on conditions given. The

main uncertainty of the result is associated with the information used by the model and not the model

itself as it is only optimizing the operation pattern.

Resolution

The model uses dynamic programming to determine the optimal operating pattern. In order to use

dynamic programming the operating problem has to be made discrete. The production in a given hour is

thereby not fully flexible. The models resolution is defined as the number of levels production can be on in

every given hour as described in the section “Dynamic programming”. The effect of changing the resolution

is investigated by plotting the saving in running cost with different resolutions. See Figure 39.

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Figure 39. Convergence of saving with resolution.

As seen from Figure 39 the saving reaches an almost constant level when the resolution is above 9. Even at

resolutions below 9 the changes in saving is less than 0.1%. The difference in saving with a resolution of 3

and 25 is 0.035% (0.9 %). In general the model is executed with a resolution of 11 as it seems to be

sufficient and the model has a reasonable runtime at this resolution. For some of the 3D-plots illustrating

relation between saving, production and storage capacity the resolution is however lowered to 5 to reduce

runtime.

COP

As described earlier there are relative large changes in the COP within the uncertainty intervals of the EES

model. Furthermore the COP is found for both the reference system and the solution proposals. This

double calculation of COP opens up for a potential double error. The consequence of having an error on the

COP of 16 % as described earlier in the section “Evaporating temperature and isentropic efficiency” is

investigated. The model is run with increased and decreased COP of the solution proposal as seen in Table

16. The COP of the reference model is kept constant.

Deviation

on COP

Total

price

Eletricity

price

AVG.

Price Tax price

Energy

use AWP

[%] [kr] [kr] [øre/kWh] [kr] [MWh] [%]

Reference 0 186833 36815 25.5 150018 144.2 21.9

Ice

bank

5 hou

rs-16 210316 37915 22.9 172401 165.8 23.1

0 177186 31951 22.9 145235 139.6 23.1

16 153197 27633 22.9 125564 120.7 23.1Ice

bank

5 hou

rs

Saving

-16 -23483 -1100 2.6 -22383 -21.5 -1.2

0 9647 4864 2.6 4783 4.6 -1.2

16 33636 9182 2.6 24455 23.5 -1.2Saving

Table 16. Performance of the ice bank solution with different uncertanty on the COP.

In Table 16 it is seen that a deviation on COP has significant effect on the saving potential of the solution

proposal. Increasing the COP 16 % increases the saving on running costs with 250 %. Lowering the COP with

16 %, result in running costs being higher for the ice bank system, than for the reference system. This

analysis illustrates the importance of having the COP data correct. Even a deviation in COP of 3 % results in

a +/- 50% deviation on annual saving on running costs.

Feasibility of demand response by phase changing materials for cooling application

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The tendency to increase wind energy use and lowering the averages electricity price is unaffected by the

changes in COP. In general assumptions for the reference and solution proposal, EES model inputs are done

in the same way; this will have a tendency to make errors appear equal in both models thereby cancelling

each other out. This effect will however not cancel out all errors. In order to use results for the model it is

therefore necessary to verify the COP data.

Use profile

The model is in general run with a use profile generated based on a Pack Calculation II model for

supermarkets with some added randomness. The effects of using other use profiles are investigated by

running the model with the three other use profiles described in the case description. The effects of

changing the use profile are documented in Table 17.

Use

profile

Total

price

Eletricity

price

AVG.

Price

Tax

price

Energy

use AWP

[Name] [kr] [kr] [øre/kWh] [kr] [MWh] [%]

Reference 186833 36815 25.5 150018 144.2 21.9

Ice bank 5 hours 177186 31951 22.9 145235 139.6 23.1

Saving 9647 4864 2.6 4783 4.6 -1.2

Reference 190901 37952 25.8 152948 147.1 21.8

Ice bank 5 hours 180545 32816 23.1 147729 142.0 23.1

Saving 10356 5136 2.7 5220 5.0 -1.3

Reference 194617 37919 25.2 156698 150.7 22.0

Ice bank 5 hours 186330 33629 22.9 152701 146.8 23.0

Saving 8287 4290 2.3 3997 3.8 -1.0

Reference 162663 31863 25.3 130800 125.8 22.0

Ice bank 5 hours 153412 27041 22.3 126371 121.5 23.5

Saving 9251 4822 3.1 4429 4.3 -1.6

Rema1000

Modified

model 2

model1

model2

Table 17. Performance of the ice bank solution with 5 hours of stock under four different use profiles.

As seen from Table 17 changing the use profile has effect on the energy consumption. Using the Rema 1000

data results in an annual energy consumption of 126 MWh compared to 144 MWh standard profiles. There

are also changes in the degree of wind energy used. The change in wind energy use applies both to the

solution proposal and the reference solution. There is however savings on running costs under all use

profiles in the ranges of 5.1 to 5.6 %, except model 2 where the saving is only 4.3%.

Known time

The model is set to simulate 36 hours every day at noon as this is the time where the price is released from

Nordpool. It is of interest to see how the saving potential is affected by the length of this time interval. In

order to determinate the effect of changing the length of the known interval the model is run with longer

time steps. The model is set to simulate every fourth day and know the price and weather for 144 hours

(4*36 hours). The results are plotted along with the result of the one day a head simulation in Figure 40.

Feasibility of demand response by phase changing materials for cooling application

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Figure 40. Saving in percent on running costs with two different time intervals. A: Simulate every 24 hours with prices known for

36 hours. B: Simulate every fourth day with prices known for 144 hours (4* 36 hours) under 2010 tax system.

As seen in Figure 40 knowing the price and weather longer does not change the saving potential (figure A

and B are almost identical). The most feasible storage capacity is too small to benefit from knowing the

price and weather for more than one day. There may be savings by load shifting with time intervals longer

than one day. The savings are however too small to justify the added heat lost from larger storage tanks.

The same analysis is done for the ice bank solution running under tax scenario 3, see Figure 41. This reveals

a difference. Under these tax conditions it is possible to increase saving by knowing conditions more than

36 hours ahead.

Figure 41. Saving in percent on running costs with two different time intervals. A: Simulate every 24 hours with prices known for

36 hours. B: Simulate every fourth day with prices known for 144 hours (4* 36 hours) under tax scenario 3.

Feasibility of demand response by phase changing materials for cooling application

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Heat loss

The heat loss from storage is one of the defining parameters for the feasibility of the demand response as it

represents an added energy demand. The heat loss is estimated based on two different methods, overall

heat transfer coefficients found in literature and on energy demand per square meter of storage room

found in the case study in(17). The heat loss will depend on the insulation of the system and on the location

of the storage; therefore there may be variations in heat loss from one setup to another. The effect of

running the system with another heat loss is investigated by running the model for the ice bank solution

proposal with +/- 30% heat loss.

Optimal

Stock

Total

price

Eletricity

price

AVG.

Price

Tax

price

Energy

use AWP

[hours] [kr] [kr] [øre/kWh] [kr] [MWh] [%]

Reference 0 186833 36815 25.5 150018 144.2 21.9

Heat loss-30% 6.2 175476 31415 22.7 144061 138.5 34.8

Model heat loss 5.8 176998 31687 22.7 145311 139.7 34.8

Heat losse +30% 5.8 178521 31959 22.7 146561 140.9 34.8

Heat loss-30% 6.2 11357 5400 2.8 5957 5.7 -12.9

Model heat loss 5.8 9835 5128 2.8 4707 4.5 -12.9

Heat losse +30% 5.8 8313 4856 2.8 3457 3.3 -12.9

Resu

lts

Savin

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Table 18 Performance of the ice bank solution with different heat losses.

As seen from Table 18 the solution proposals performance is affected by changes in heat loss. Lowering the

heat loss increases the optimal storage capacity and increases the saving potential. The variations in saving

potential are in the range of +/- 15 % with a variation of +/- 30 % in heat loss.

Market

As described in the section “The electricity market” there is differences in electricity prices on different

markets. To determine the significance of the market the model is run under the East Danish and Swedish

markets as well, see Table 19. Danish tax system are applied to all prices including the Swedish. Average

wind production data is unknown for the Swedish market and the analysis on this field is excluded.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

76

Price

market

Total

price

Eletricity

price

AVG.

Price

Tax

price

Energy

use AWP

[Name] [kr] [kr] [øre/kWh] [kr] [MWh] [%]

Reference 186833 36815 25.5 150018 144.2 21.9

Ice bank 5 hours 177186 31951 22.9 145235 139.6 23.1

Saving 9647 4864 2.6 4783 4.6 -1.2

Reference 194274 44256 30.7 150018 144.2 16.1

Ice bank 5 hours 183107 37888 27.1 145218 139.6 17.0

Saving 11167 6367 3.5 4800 4.6 -0.9

Reference 190691 40673 28.2 150018 144.2

Ice bank 5 hours 181558 36372 26.1 145186 139.6

Saving 9133 4301 2.1 4832 4.6

DK1

(West)

DK2

(East)

SE

(Swedish)

Table 19. Performance of the ice bank solution with 5 hours of stock under three electricity markets.

As seen in Table 19 the saving potential is higher under the East Danish market than the West Danish

market. The overall expense on electricity is higher under the East Danish market. The solution has a higher

tendency to switch to more wind energy under the West Danish market. This is partly due to the general

higher degree of wind energy in western Denmark.

Conclusions on sensibility study

As described above there are many model inputs, some with large influence on the result and some with

small. The variable of most importance is found to be the COP. Even small changes in COP in either the

reference or solution proposal model change the overall result dramatically. Unfortunately many model

parameters have an effect on the COP. The parameter having the largest effect on the COP is the

temperature at the intermediate load. Secondly the isentropic efficiency of the compressors also has a

large impact. Before any safe conclusions can be made, COP data must be confirmed or exchanged with

data with less uncertainty. All conclusions of cost effectiveness are therefore done based on an assumption

of COP data being correct with no deviation.

Parameters as use pattern, electricity market, heat loss and time with known conditions has an effect on

the savings of doing demand response. The expected variations in these fields do however not change the

saving potential dramatically as variations in COP do.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

77

Conclusion The feasibility of demand response was investigated by using a case study of a supermarkets cooling

system. It was found that the feasibility is strongly dependent on the COP performance of the refrigeration

plant. The uncertainty on COP for both reference and solution systems result in large uncertainties of the

feasibility. In order to verify results a precise verification of COP performance is necessary. Conclusions are

done based on the assumption that the COP models are precise within deviations of less than 1%. The

precision of the COP models are expected to be in the range of 15%.

It was found possible to reduce energy consumption and achieve savings on electricity prices through

storage with ice slurry. Ice slurry was found unsuitable as storage medium in the freezing temperature

range (-25 C). The annual savings are in the range of 6,000 kr. and were found insufficient to cover added

investment costs.

Installing containers with eutectic salt solutions was found to be an unattractive storage method. The

solution proposal based on local PCM containers increases the energy consumption by 12.3%. The solution

decreases the average electricity price by 2.6 øre/kWh, this is however not enough to justify the added

energy use.

An ice bank solution was found to be the most efficient and efficient way of achieving demand response

abilities. The ice bank solution achieves annual savings of 9,650 kr. and reduces the energy consumption by

3.2%. The acceptable added investment was found to be 71,000 kr. with a 10 year investment period and 6

% interest rate. The saving potential was evaluated insufficient to justify the added investment, relative to

the reference system.

The optimal storage capacity is around 5 hours of max production for all the solution proposals. This

storage capacity facilitates storages along the day but not from day to day. All solution proposals were

found to pull in the direction of evening out electricity consumption during day. Production is moved from

day time to night time.

Moving consumption to off peak hours, results in increasing the average degree of wind energy. The ice

bank solution with 5 hours stock has a 5.5% higher degree of wind energy, relative to the reference

solution. Increasing storage capacity further than 5 hours increases the degree of wind energy used.

Changing the tax system can significantly alter the saving potential for the ice bank solution. The solution is

found cost-efficient under a tax system with low taxes when cogeneration plants use the

"Elpatronordningen". The ice bank solution is found cost-efficient under a system where taxes are based on

wind energy percentages. The wind energy based tax system has a significantly larger effect on moving

production towards using more wind energy, compared to the 2010 tax system.

The saving potential of demand response was found to be 15.8% higher in Eastern Denmark than Western

Denmark. The effect of moving towards more wind energy is highest in Western Denmark.

It was found that knowledge of production conditions more than 36 hours ahead has little effect, due to

the relative short storage capacities of the solutions.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

78

Bibliography 1. Food & Drink Industry Refrigeration Efficiency Initiative. Dairy UK, British Beer and Pub Association, Cold

Storage and Distribution Federation, Institute of Refrigeration. s.l. : Carbon Trust Networks, 2007.

2. The Nordic Electricity Exchange Nord Pool Spot and the Nordic Model for a Liberalised Electricity Market.

Anders Plejdrup Houmøller. s.l. : Nord Pool Spot, Denmark, 2009.

3. Forbrugerne betaler prisen for liberalisering af elmarkedet. Møllerhøj, Jakob. s.l. : ing.dk at

(http://ing.dk/artikel/109761-forbrugerne-betaler-prisen-for-liberalisering-af-

elmarkedet%20http://www.nordpoolspot.com/about/History/), 2010.

4. Skateministeriet. redegørelse om muligheder for, og virkninger af, ændrede afgifter på elektricitet med

særlig henblik på bedre integration af VE (dynamiske afgifter). s.l. : Skateministeriet, 2010.

5. Finanskrisen fortrænger dårligt integrerede udlændinge, velfærd og klima som det mest akutte problem,

som regeringen – ifølge danskerne – skal håndtere i det nye år. Flores, Philip Egea. s.l. : Dine penge

(http://www.dinepenge.dk/investering/finanskrisen-bekymrer-danskerne-mest), 2009.

6. Grøn energi - vejen mod et dansk energisystem uden fossile brændsler. s.l. : Klimakommissionen, 2010.

ISBN: 978-87-7844-879-8.

7. Kestenbaum), Danuta. Energistatistik_2009. København : Energistyrelsen, 2010. 978-87-7844-872-

9/0906-4699.

8. Nye analyser anbefaler variable elpriser. Wittrup, Sanne. s.l. : ing.dk (http://ing.dk/artikel/110703-nye-

analyser-anbefaler-variable-elpriser), 2010.

9. Sørensen, Rasmus Zink. http://www.ens.dk/da-

dk/undergrundogforsyning/elogvarmeforsyning/elforsyning/elproduktion/udgifter_til_pso/sider/forside.as

px. www.ens.dk. [Online] Energi Styrelsen. [Cited: 1 5, 2011.]

10. Wikipedia. http://en.wikipedia.org/wiki/Phase_change_material. [Online] [Cited: 11 11, 2010.]

11. Michael Kauffeld, Masahiro Kawaji, Peter W. Egolf. Handbook on Ice Slurries. Paris : International

Institute of Refrigeration, 2005. 2-913149-42-1.

12. chemicalland. chemicalland. http://www.chemicalland21.com/petrochemical/n-PARAFFINS.htm.

[Online] [Cited: 1 4, 2011.]

13. Tetradecane and hexadecane binary mixtures as phase change materials (PCMs) for cool storage in

district cooling systems. He Bo *, E. Mari Gustafsson, Fredrik Setterwall. s.l. : Department of Chemical

Engineering and Technology, Transport Phenomena, Royal Institute of Technology,, 1999, Vol. 14.

14. Review on thermal energy storage with phase change materials and applications. Atul Sharma a, *, V.V.

Tyagi b, C.R. Chen a, D. Buddhi b. s.l. : A. Sharma et al. / Renewable and Sustainable Energy Reviews 13

(2009), 2009, Vol. 14.

15. Review on sustainable thermal energy storage trchnologies. Hasnain, S. M. s.l. : Pergamon, 1997.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

79

16. EPS. http://www.epsltd.co.uk/files/product_summary.pdf. epsltd. [Online] [Cited: 12 15, 2010.]

17. Anvendelse af naturlige kølemidler i supermarkeder. Preben Bertelsen, Kim Christensen and Tom

Gøttsch. s.l. : Miljøstyrelsen, Superkøl A/S, Fakta and Teknologisk, 2001, Vol. 57.

18. White, Frank M. Fluid Mechanics. s.l. : Mc Graw Hill, 2008. 978-0-07-128645-9.

19. energinet. energinet. http://www.energinet.dk. [Online] energinet. [Cited: 12 1, 2010.]

20. http://www.hfc-fri.dk/. [Online]

21. Transcritical Refrigeration Systems with Carbon Dioxide (CO2). Marketing/MWA), Danfoss A/S (RA. s.l. :

Danfoss A/S (RA Marketing/MWA), 2008, Vol. 20.

22. products, PCM. www.pcmproducts.net. pcmproducts. [Online] [Cited: 1 5, 2011.]

23. Preliminary Study of Passive Cooling Strategy Using a Combination of PCM and Copper Foam to Increase

Thermal Heat Storage in Building Facade. Mohd Hafizal Mohd Isa, Xudong Zhao and Hiroshi Yoshino. s.l. :

mdpi, 2010. 2071-1050.

24. Arneg. http://www.arneg.it/. [Online] Arneg. [Cited: 12 8, 2010.]

25. HEAT LOSSES FROM STORAGE TANKS: UP TO 5 TIMES HIGHER THAN CALCULATED! Suter, J.-M. Bern

(Switzerland) : Suter Consulting.

26. Perkin, John H. Can we save the world. http://www.canwesavetheworld.com/reduce-electrical-

appliance-energy-use.html. [Online] [Cited: 11 11, 2010.]

27. vestas.com. http://www.vestas.com/en/media/article-display.aspx?action=3&NewsID=1597. [Online]

Vestas. [Cited: 11 11, 2010.]

28. blogs. www.blogs.consumerreports.org. [Online] [Cited: 12 7, 2010.]

29. Review on thermal energy storage with phase change: materials, heat transfer analysis and

applications. Belen Zalba, Jose M, Luisa F. Cabeza, Harald Mehling. Spain : Thermal Engineering 23 (251-

283), 2003.

Feasibility of demand response by phase changing materials for cooling application

Anders Østergaard Nielsen

80

Appendix list

Appendix A

Shop layout of the case supermarket

Appendix B

Fluid calculation method of pressure drop

Appendix C

PCM solutions in the eutectic series form www.pcmproducts.net

Appendix D

A: Ice bank solution from www.thermal-eng.co.uk

B: Ice bank solution from www.htt-ag.com

Appendix E

Production patterns for all three solution proposals together.

Appendix F

Results from all three solution proposals together

Appendix G

Refrigeration process simulated in the EES model plotted in a log(P)-h diagram

Appendix H

Over view of the Matlab model.

Appendix I

List of abbreviation

Appendix A

35 m

45

m

Cash register area

Freezing room

Cooling room

Freezing display cases

Cooling display cases

Machine room Stock

Shop area

Appendix B

Calculation of pressure drop The following calculations are done in EES for both cooling and freezing circuit on suction lines. Further the

pressure drop model is used to calculate pressure drops and energy demand for distributing brine for

systems mot based on direct expansion. Her calculations are done for circulation of brine in a cooling

system based on water.

Given:

• Load (������)

• Mean velocity (�̅) (Assumed 0.5 m/s)

• Supply temperature (Tsupply)

• Return temperature (Treturn)

Mass flow ℎ���� = �� ∙ ����� ℎ����� = �� ∙ ������

������ = �� ∙ �ℎ���� − ℎ������ ⇒

�� = ℎ���� − ℎ�����������

Pipe diameter

������ = ���

�� = � ∙ �̅ = �4 � ∙ � ⇔ �= "4 ∙ ��� ∙ �̅

Friction factor

#$ = �̅ ∙ �%&

1√) = −2.0 log 1 2�3.7 + 2.51#$ ∙ √)7

Pressure drop

∆9 = : ∙ � ∙ �̅ ∙ ;) <� + = >?

Appendix B

Pumping Effect

@ = ∆9 ∙ ��

Shaft power

@A%BC� = @D�E�

WORLD LEADER IN PCM TECHNOLOGIES & PRODUCTSWORLD LEADER IN PCM TECHNOLOGIES & PRODUCTS

Unit 32, Mere View Industrial Estate, Yaxley, Cambridgeshire, PE7 3HS, United KingdomTel: +44-(0)-1733 245511 Fax:+44-(0)-1733 243344 e-mail:[email protected] www.pcmproducts.netwww.pcmproducts.net

PlusICE Range 2009-1

©2009 Phase Change Material Products Ltd.Printed in England;

PHASE CHANGE MATERIAL PRODUCTS LIMITEDPHASE CHANGE MATERIAL PRODUCTS LIMITED

For additional information contact;

Distributor / Installer Stamp

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Heat Transfer Technology AG, Bernastrasse 6, CH-3005 BernTelefon +41 (0)31 310 2400 | Telefax +41 (0)31 310 2415

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ice chill

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Ice bank

NH3–R22–R404a–R134a–R507–Glykol / Glycol

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Anwendungen:

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Diese neue Baureihe basiert auf mehr als fünfzig JahrenErfahrung in der Eisspeichertechnik.

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Rohrschlangensystemen.

This new standard range is based on more than fifty yearsof BUCO experience in the design of customer ice storagewaterchillers.

Ice banks are “trickle charged" energy storage systems. Aswith an electric battery, an ice bank is used to reduce thesize and cost of compressor, its power con-sumption and itsrunning cost.

Advantages:

• Waterchilling down to 0,5 °C.• Constant water temperature up to the end of the

melting period due to constant ice surface.• Excellent heat transfer, because of high turbulence.• Safe operation, becauce no moving parts inside

the tank.• Low space requirement.• All parts in contact with water made of SS 304.• Compact for lorry or container transport.• Optimal oil rectification.• Smaller refrigerant volumes (approx. 40%) compared

to coil in tank systems.

L

2300

2175

6

3 4

1

2

5

Anschlüsse:

1. Kälte ein2. Kälte aus3. Wasser ein4. Wasser aus5. Entleerung6. Überlauf

Connections:

1. Refrig. in2. Refrig. out3. Water in4. Water out5. Drain6. Over flow

Richtwertleistungen - maße - gewichte:

Guidance capacities -dimensions - weights:

Heat Transfer Technology AG, Bernastrasse 6, CH-3005 BernTelefon +41 (0)31 310 2400 | Telefax +41 (0)31 310 2415

www.htt-ag.comE-mail: [email protected]

Eisspeicher / Ice bank

Richtwerttabelle, genaue Abmessungen + Gewichte lt. Angebot Table of guidance, detailed values of dimensions and weights according to the quotation

Type

BIC

50

75

100

150

200

300

400

500

625

750

1000

1250

1500

Speicherung

Capacity

kWh

50

75

100

150

200

300

400

500

625

750

1000

1250

1500

Eismasse 1)

Storage 1)

kg

540

810

1080

1620

2160

3240

4320

5400

6750

8100

10800

13500

16200

Abschmelzung 2)

Capacity 2)

kW

35

50

70

105

140

210

280

350

440

530

705

885

1060

Luftkompressor

Air agitator

kW

0,55

0,55

1,3

1,3

1,3

1,3

1,3

1,7

1,7

1,7

3,0

3,0

4,0

Maße (mm)

Dimensions (mm)

L

530

690

850

1170

1490

2200

2840

3480

4280

5080

6680

8280

9880

Gewicht/Weight (kg)

net

670

780

890

1110

1330

1860

2300

2740

3490

3830

4920

6010

7110

brt

1950

2700

3450

4950

6450

9560

12560

15560

20720

23060

30560

38070

45570

1) bei maximaler Eisdicke

2) bei 2 ∞C

Wassertemperatur

1) with maximum

ice thickness

2) 2 ∞C Water temp.

Appendix E

Averages price on electricity, Produced and used

energy plotted for every hour of the day for ice

bank solution. 5 hours of stock and 1.2 times

normal production capacity.

Averages price on electricity, Produced and used

energy plotted for every hour of the day for PCM

display cases solution. 5 hours of stock and 1.2

times normal production capacity.

Averages price on electricity, Produced and used

energy plotted for every hour of the day for the ice

slurry solution proposal. 4.5 hours of stock and 1.2

times normal production capacity.

Sto

ck

To

tal

pri

ce

ele

tric

ity

pri

ce

AV

G.

Pri

ceT

ax

pri

ce

En

erg

y

use

AW

P

He

at

loss

es

Ag

ita

tio

n

po

we

r

[-]

[kr]

[kr]

[øre

/kW

h]

[kr]

[MW

h]

[%]

[W]

[W]

Re

fere

nce

01

86

83

33

68

15

25

.51

50

01

81

44

.22

1.9

00

Ice b

ank so

lutio

n

21

79

88

13

40

06

24

.21

45

87

51

40

.32

2.5

10

00

0

51

77

18

63

19

51

22

.91

45

23

51

39

.62

3.1

25

00

0

81

77

86

63

16

74

22

.51

46

19

21

40

.62

3.4

40

00

0

Ice b

ank so

lutio

n

Savi

ng

26

95

22

80

91

.34

14

34

.0-0

.65

59

64

74

86

42

.64

78

34

.6-1

.22

88

96

75

14

13

.03

82

63

.7-1

.49

22

10

83

03

98

87

24

.31

70

94

31

64

.42

2.5

10

00

0

52

05

55

33

70

68

22

.91

68

48

51

62

.02

3.1

25

00

0

82

05

57

83

65

88

22

.51

68

98

91

62

.52

3.4

40

00

0

2-2

39

97

-30

72

1.3

-20

92

4-2

0.1

-0.6

3

5-1

87

19

-25

32

.6-1

84

67

-17

.8-1

.17

8-1

87

44

22

73

.0-1

89

71

-18

.2-1

.46

21

81

62

93

42

47

24

.21

47

38

21

41

.72

2.5

30

62

06

4.5

18

05

41

33

05

62

3.3

14

74

85

14

1.8

22

.85

70

41

2

71

81

49

03

29

34

23

.11

48

55

61

42

.82

2.9

10

75

82

4

25

20

42

56

81

.42

63

62

.5-0

.57

4.5

62

92

37

59

2.2

25

33

2.4

-0.9

4

75

34

33

88

12

.51

46

21

.4-1

.03

Ice s

lurr

y

Savi

ng

Savi

ng

PCM dis

play

case

Savi

ng

Ap

pe

nd

ix F

Enth

alp

y [

kJ/

kg]

50

10

01

50

20

02

50

30

03

50

40

04

50

50

05

50

60

06

50

70

07

50

80

08

50

90

09

50

10

00

Pressure [Bar]

0.5

0

0.6

0

0.7

00

.80

0.9

01

.00

1.0

0

2.0

0

3.0

0

4.0

0

5.0

0

6.0

0

7.0

08

.00

9.0

01

0.0

0

20

.00

30

.00

40

.00

50

.00

s=2.30

s=2.40s=2.50

s=2.60

s=2.70

s=2.80

s=2.90

s=3.00

s=3.10 s=3.20 s=3.30 s=3.40 s=3.50 s=3.60 s=3.70

-50

-40

-40-3

0

-20

-20-1

0

0

0

10

20

203

0

40

405

0

60

6070

80

80

90

10

01

20

14

01

60

18

02

00

0.00

40

0.00

50

0.00

60

0.00

700.

008

00.0

090

0.0

10

0.0

15

0.0

20

0.0

30

0.0

40

0.0

50

0.0

60

0.0

70

0.0

80

0.0

90

0.1

0

0.1

5

0.2

0

0.3

0

0.4

0

0.5

0

0.6

0

0.7

0

0.8

0

0.9

0

1.0

1.5

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

70

80

90

x =

0.1

00

.20

0.3

00

.40

0.5

00

.60

0.7

00

.80

0.9

0

s =

0.8

01

.00

1.2

01

.40

1.6

01

.80

2.0

02

.20

2.4

0

v=0.

0060

v=0.

0080

v=0.

010

v=0.

015

v=0.

020

v=0.

030

v=0.

040

v=0.

060

v=0.

080

v=0.

10

v=0.

15

v=0.

20

v=0.3

0

v=

0.4

0

DT

U,

Dep

artm

ent

of

Ener

gy E

ngin

eeri

ng

s in

[kJ/

(kg K

)].

v in [

m^3/k

g].

T in [

ºC]

M.J

. S

ko

vru

p &

H.J

.H K

nudse

n.

11-0

1-0

5

R290

Ref

:W

.C.R

eyno

lds:

Ther

mo

dynam

ic P

roper

ties

in S

I

Appendix H

Matlab model The matlab model contains different scripts and functions. Beyond the model itself there is a number of

service scripts for preprocessing of data and plotting results. The functions and scripts for optimizing the

operation pattern is presented in short her.

The basic model has a structure as seen in the figure below where boxes represent functions

Run_1_4_X

In this script the following things are defined: production capacity, storages capacity, price market, tax

level, lowest condensing temperature, delta temperature between condenser and surroundings, resolution,

step size and known horizon, solution proposal (COP-Data to be used).

The function calls different service functions to generate basis for the optimization. When the necessary

data is ready the function loops the sim36H functions until a whole year is past. The number of loops

depends on step size. After the lopping is don the whole production pattern is known and analyzed.

There are multiple different versions of the run file. The different versions serve different investigations.

Heat_loss

The heat loss function calculates the heat loss and agitation power for the ice slurry solution based on:

production and storages capacity.

gridBuilder

The grid builder function generates a set of matrixes and vectors defining the general structure of the

discrete production pattern. The grid size matches on simulation interval typically 36 hours. The grid is

general and can be modified ladder to match a specific simulation interval. The grid is based on: resolution,

max production and known horizon.

run_1_4_X

gridBuilder

COPbyTemp

sim36H

pathF

Heat_loss

Appendix H

COPbyTemp

The COPbyTemp function transform whether data to COP data. The transformation is done by finding the

corresponding COP to any temperature of the year. The function transforms temperatures based on:

Solution COP, lowest condensing temperature, delta temperature between condenser and surroundings

sim36H

The sim36H function corrects the standard grid in to fit the accrual time step being simulated. The

correction is done by accounting for: use pattern, price, COP

pathF

The pathF function receives the corrected grid from sim36H and doses the accrual path finding. The optimal

Path is found by applying dynamic programming to the grid.

The grid is generated in a manner so there is time dependency on node number. The starting node is

named 1 and nodes are numbered sequential in time. As it is impossible to jump back in time you can only

go to a higher node number. The dynamic programming algorithm is optimized to benefit from the time

dependency of the grid.

Appendix I

Abbreviation

A Area

APW Averages percentages of wind

energy

AVG Averages

c Ice concentration

COP Coefficient of performance

�� Capitalization factor

h Heat transfer coefficient

HVAC Heating, Ventilating, and Air

Conditioning

I Investment

k Thermal conductivity coefficient

l Length

m mass

NPV Net present value

PBP Pay back Period

PCM Phase chancing material

PCS Phase chancing slurry

PW Percent wind energy

Q Energy

S Savings

T Temperature

t Time

V Volume

� Friction factor

Subscripts

C Condenser

c Cooling

E Evaporator

s Stock