DEGREE OF MASTERS OF RENEWABLE ENERGY

ASSESSING THE OPTIMUM OPERATING CONFIGURATION

FOR MULTIPLE CHILLERS AT MURDOCH UNIVERSITY

RESEARCH BY

JOSHUA NGALANDE

JANUARY 2015

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Declaration

I, Joshua Ngalande declare that the work in this paper is my own and that any help or reference

materials that I have used have been acknowledged, either in the bibliography, the appendices and the

reference section.

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Acknowledgements

Trevor Pryor - supervisor

Jonathan Whale – Supervisor

Gary Higgins – GM Assets & Maintenance Murdoch University.

Basil Arrow – Assets & Maintenance Murdoch University

Michael Heywood Schneider Electric consultant

Marti Davi, Harry, Bruce Smith and all other staff that assisted me while working on the chiller units

collecting information.

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Table of Contents

Declaration .................................................................................................................................................... 1

Acknowledgements ....................................................................................................................................... 2

Table of Contents .......................................................................................................................................... 3

1.0 Abstract ............................................................................................................................................... 7

2.0 Introduction ........................................................................................................................................ 8

3.0 Background ....................................................................................................................................... 10

3.1 Chiller Metering Project .................................................................................................................... 12

3.2 Murdoch Chiller Systems Background .............................................................................................. 12

4.0 Methodology ..................................................................................................................................... 15

4.1 Methods of Improving Working Efficiency of Multiple Chillers ........................................................ 15

4.2 Improving Efficiency of an Individual Chiller ..................................................................................... 16

4.3 Improving Energy Efficiency via Energy Storage ............................................................................... 16

4.4 Improving Overall Plant Efficiency using Load as Simulated Storage ............................................... 18

4.5 Modelling .......................................................................................................................................... 21

4.6 Methodology Summary .................................................................................................................... 25

4.7 Methodologies –Algorithm - Steps ................................................................................................... 27

4.7.1 Step 1: Determine the Load ....................................................................................................... 27

4.7.2 Step 2: Use Spreadsheets to Determine Chiller Function .......................................................... 28

4.7.3 Step 3: Use Equations to Derive Energy Results ........................................................................ 30

4.7.4 Step 4: Ranking Results .............................................................................................................. 31

4.7.5 Ideal Flowchart ........................................................................................................................... 32

4.8 Load Parameter Considerations ........................................................................................................ 37

4.9 Load Prediction ................................................................................................................................. 39

4.10 Temperature Setting ....................................................................................................................... 39

5.0 Thermal Comfort Levels .................................................................................................................... 41

6.0 Actual Procedure Results and Analysis ............................................................................................. 44

7.0 Software Analysis .............................................................................................................................. 56

8.0 Problems Encountered ..................................................................................................................... 57

9.0 Conclusion ......................................................................................................................................... 58

10.0 Recommendations for Future Research and Development ........................................................... 59

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10.1 Data Matching Validation and Normalisation ................................................................................ 59

11.0 References ...................................................................................................................................... 61

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Table of Figures

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Table 1……………………………………………………………………………….………………………………………………………30

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Figure 29……………………………………………………………………………………………………………………………………60

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1.0 Abstract

This paper endeavours to answer the research question of how to efficiently operate a system of

multiple chillers in the most effective combination regime. In the process, a successful method of

assessing this has been developed, which has proven that the most effective way to run a multi-

configuration chiller scheme is by following two distinct methods. One method deals with a multi-chiller

setting that has thermal storage, and the other method deals with a multi-chiller systems without

storage. The latter method is discussed more in this paper. The former method is based on other

research papers that argue the easiest and most efficient method of running a multi-chiller system is to

have thermal storage. This allows a staging regime that can operate any number of chillers at their

maximum co-efficiency of performance (COP) or efficiency performance, while either supplying the load

or charging the thermal storage. The fact that the chillers can always operate at their maximum point

makes this method the most efficient available; however, in some cases there is no thermal storage

available and the analysis changes slightly. A detailed analysis of the different methods of staging has

been carried out and it proved that the most efficient way of running a multi-configuration setup chiller

system is to run the target load closest to the next nearest chiller configuration that can supply that

load. An algorithm and system has been developed on the best way to do this, and further work on how

computational resources can be exploited to do this more easily is discussed.

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2.0 Introduction

In large and multi-storey building systems such as hospitals and university campuses, the function of air

conditioning and cooling is usually done using chiller units, which are huge heat pumps normally in the

megawatt range. These systems contribute a large proportion of the electricity usage in such facilities.

The COP of a chiller directly affects its power consumption and the COP is generally at a maximum when

the chiller is operating at near or at full load. (Note that in one of the example curves shown later in this

paper at figure 21, the COP at rated output is not the maximum COP value). Therefore, uncoordinated

switching on and off of individual chillers in a system may lead to those individual chillers not operating

at their optimal running points, thus leading to higher electricity demand and higher energy

consumption.

As energy costs continue to rise, the effective management of energy usage in buildings is becoming

increasingly important and is worthy of detailed analysis.

Figure 1: Building 352-Library North Chiller 2MW, Building 356-Library South Chiller 4MW, Building 265-Vets’

Chiller 2MW, and Building 383-Two Air-cooled Chillers.

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Figure 1 shows a multiple chiller system at Murdoch University where the water chilled distribution

system takes the chilled water from the chiller units to the building loads around the campus. The

chilled water is set at 7 degrees Celsius and the chillers always have to maintain this temperature, either

increasing or decreasing their cooling power (and hence their electricity consumption) as the cooling

load varies. The chillers are currently staged using an intuitive process provided by Schneider Electric

Consultants. However, it has not been compared mathematically to other staging regimes, nor have

other methods of staging been tried to ascertain if they would lead to lower electricity consumption. It is

therefore in the best interests of the Murdoch chiller operating team to know which would be the best

method to run these chiller systems in order to achieve the highest energy savings for the university.

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3.0 Background

The chiller system at Murdoch University consists of five large industrial chillers that cool the

surrounding university buildings. They form an important and integral part of the university ecosystem.

They have a significant impact on the energy usage of the university, as they are intended to provide a

comfortable indoors environment at all times, especially during periods of extreme temperatures, such

as in the midst of the Australian summer.

Achieving the functionality of air-cooling by this chiller system requires a large investment in capital and

maintenance costs, hence the department is obliged to justify a return on investment and validate costs

in terms of energy savings and utility delivery.

Savings can be compared to alternate forms of cooling, in this case the most common being normal

reverse cycle and pure air conditioner systems, as compared to air-cooled or evaporative cooling with

the same cooling power. It is therefore in the interest of the Murdoch’s engineering team to prove that

the chillers that Murdoch University has purchased prove to be financially profitable. In order to do this,

they have to be run correctly and in the most efficient way, minimising costs of maintenance, energy use

and labour.

Murdoch chillers also occupy a large amount of physical space. Considering this huge investment of

time, space and finance, it is important that they reach their full potential in terms of cost returns,

mainly from the view point of energy efficiency. Since Murdoch is operating five chillers that are

meeting one very dynamic, dispersed load consisting of different buildings with different specifications,

it is important to match these integrated chillers to the integrated buildings, in order to operate within a

region of optimum efficiency of electrical energy use. This analysis is complex, as the buildings are

widely separated, as are the chillers. In addition, the buildings all have different dynamics of orientation,

thermal mass, sunlight irradiation and other environmental aspects. The complexity of these differences

may mean changes in energy savings opportunities; however, these have to be understood with analysis

and results.

Over the years, Murdoch chillers have been supplied by TRANE systems. As the cooling demand

increased, Murdoch purchased new chillers and added them to the existing system. Currently, the

chillers are operated in a linear manner of staging by switching on each single chiller one by one, as the

cooling load grows. The purpose of this research is to find out the best way of operating these chillers as

one system, to provide the cooling load to these multiple buildings in the most efficient way.

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It is important to note that the current linear method being used is assumed to be the best approach

and was developed using “back of the envelope” analysis by the chiller suppliers. It is hoped that the

research undertaken for this paper will confirm these assumptions. It is also hoped that the results of

this research will act as a general guide to running multiple chillers in other industries, and that the

methodology of how to assess energy usage due to different chiller staging developed in this work will

act as a guide for other multi-chiller systems.

Considering the fact that all the individual chillers have different efficiency regimes and curves, the

determination of how they can be jointly operated to produce optimum outcome (generally the lowest

electricity consumption) is not a simple exercise. However, it is important in order to achieve both a

smaller carbon footprint as well as to achieve considerable energy savings. The Murdoch University

system provides a challenging and worthwhile site for such a research project.

In this paper, the research question explored is what is the best way to run a multiple chiller system;

alternatively, which is the best way to combine different chillers in order to meet a specific load.

A multi-chiller system often has changing loads. It is necessary to determine how best to combine our

different chillers to meet this dynamic load in the most efficient manner, which is explored in this paper.

In order to do this analysis, we required data from the chiller systems at Murdoch University. Sufficient

data would be in the range of a minimum of six months; a year’s data would be a much better sample to

have. However, the Murdoch chiller metering project did not finish as expected, hence this data was not

available. Thus, the focus of the thesis changed slightly to develop a generic methodology of

determining an optimum combination while running multiple chillers to supply a load. This data would

mainly be useful to observe trends and determine chiller efficiency curves. The methodology used in the

paper simply uses generic curves to do this. Due to the unavailability of data, the final research question

was, “What methodology can be used to determine the optimum chiller operation in a multiple chiller

system, in order to produce the lowest electricity consumption?”

In order to answer this, the following questions had to be addressed:

A. How does the chiller performance vary at part load conditions?

B. How can the performance of different chillers with different sizes and different performance

curves be used to determine the optimum chiller deployment?

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Hence, the lack of detailed information at the beginning and end of this project meant that the key

outcome of the research only demonstrates in principle the basic approach. This should be fine-tuned in

future, as more performance data becomes available.

Thus, the research question, “What methodology can be used to determine the optimum chiller

operation in a multiple chiller system, in order to produce the lowest electricity consumption?” can still

be achieved by following multiple logical and mathematical paths to assess the outcomes.

3.1 Chiller Metering Project

In order to measure the efficiency of a chiller, both the energy output and energy input have to be

measured. The current system at Murdoch successfully measured the load energy supplied by the

chillers but the electricity going into them was not being measured; hence, an important part of the

research depends on these energy meters being installed. The current setup is that the chiller connects

to a substation transformer that provides other electrical loads to the buildings and the chiller at the

same time, making the individual energy that goes to the buildings or the chillers impossible to

determine. At the beginning of the year, it was anticipated that energy meters be installed by mid-

semester, however this project was delayed and is still not complete. Hence this vital data has not been

available and the research had to use other methods in order to achieve the same goal. This was done

by developing a generic methodology of approaching and assessing the chiller staging problem,

therefore most of the work done was on methodology. It is anticipated that once the data is available it

can simply be input into the methodology and the results analysed in the same way as the sample data

in this paper.

3.2 Murdoch Chiller Systems Background

Company history: Schneider Electric is the main contractor that Murdoch currently employs for its chiller

management running controls, programming system automation servers, systems testing and

commissioning. They also install and manage reporting servers, which store operations data and query

the many remote automation servers that independently run every chiller unit. These small automation

servers are small computers running a light version of the Linux operating system. Although Linux is a

completely open source, the hardware used is classified for exclusive use by Schneider Electric. These

automation servers (AS) are the onsite brains of the chiller; they store all the commands and programs

that come from the central server, they also remotely store all the operations information from the

many sensors installed on the chiller. In case there is a loss in link between the chiller and the main

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server, the chiller will continue operating normally using these remote AS until the connection is

restored. A diagram of this configuration is shown in figure 2

Figure 2 showing the configuration of AS in building management systems. At Murdoch, these take care of

the chiller units

The AS constantly monitors water temperature, pressure and flow rate of the chilled water flowing in

and out of every independent pipe in the chiller, cooling tower, loops and secondary loop pipes. Speeds

of the motors are controlled via variable speed drives, and all these parameters quickly scale up to be a

huge load of data. The AS operate at a rate of 1 second with each sensor, however they are capable of

operating at 100 milliseconds. Cheaper controllers operating at a speed lower than 1 second per sensor

are too slow to monitor and control the many sensors and peripherals in a chiller system.

The data server installed in the past was installed by a company called Vista ,which was later bought by

Schneider Electric. This is a common occurrence in the chiller industry, as Schneider Electric has become

a huge monopoly by successfully buying out its competition. Consequently, Schneider bought most of

the technology and is a leading company in chiller interface and software reporting. Nevertheless, they

still have the huge task of consolidating all the different technologies they purchase into an operable,

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harmonious system. This was one cause of the delay of the project, as many of the delays were

attributed to difficulties interfacing the new and the old systems.

The old Vista data server was also unreliable and held data in a somewhat compromised database, with

some parameters stored properly while others were lost in cyberspace. Many data point references

were missing or simply discontinued. Schneider is migrating to a new and modern system that will work

much better and make reports more accessible.

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4.0 Methodology

4.1 Methods of Improving Working Efficiency of Multiple Chillers

In order to achieve optimum efficiency in a multiple chiller configuration, more than one method can be

deployed, each with its own level of effectiveness. It is the hope that most of these different methods

and combinations may lead to significant savings in terms of energy as well as financially.

Notwithstanding, most of these methods need more in-depth research in order to achieve maximum

efficiency in the specific methods.

Sequencing multiple chillers to meet a combined cooling demand can improve the overall COP of the

individual chiller plants; however, most of the time there will be at least one chiller that is operating a

sub-optimal PLR. This is because it is hard for the instantaneous demand load to be distributed in equal

and optimal proportions to all the chillers. Figure 3 shows the overall COP verses the PLR of four equally

sized chillers operating in a combined mode. The blue regions (1-4) show operation at near optimal COP.

The large red regions (A-D) show the points with low COP; these are the regions that ideally should be

avoided. (Behl et al, 2012).

Figure 3 COP curve a single chiller plant. The Blue regions (1-4) are the optimal COP regions and Red

regions (A-D) are the sub-optimal and low COP regions. The white regions are average regions in

transition. Source (Behl et al, 2012).

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4.2 Improving Efficiency of an Individual Chiller

Improving the working efficiency of a single chiller will certainly improve the total efficiency of multiple

chillers. However, there are many papers that have covered mono-chiller operation designs, including

many models that look at the components that make up a chiller. These components cover chiller

compressors, the addition of variable speed drives to motors, primary and secondary pumps to the fluid

controls, evaporator and condenser improvements, correct sizing of control valves, properly controlling

pressure levels in the chiller piping work (mostly done using bypass valves), intelligent cooling tower

controls that exploit sensible and latent heat via monitoring of outside wet bulb temperatures, correct

piping insulation and planning, cooling water supply temperature and flow capacity. These are all

methods and parameters that if managed properly, will improve the performance and energy use of an

individual chiller. Nevertheless, this paper will not concentrate on methods that improve the steady

state efficiency of a single chiller, rather it will focus on improving the operational combination chillers

and try to achieve overall efficiency of multiple chillers by changing and harmonising their operation.

Thus, it concentrates on the operations side of a multiple chiller configuration rather than the

engineering of the manufacturer or installations design.

While the focus will be on harmonising multiple chiller operation, recommendations that involve

engineering design installation will be still be stated if they are found important. However, many chiller

maintenance and operations have limited capability to change how the chiller was designed. Once a

chiller is manufactured and installed, little can be done to improve these aspects further as by then, it is

too late. Any improvements that involve re-installation of a new chiller or re-configuration of

infrastructure are generally too costly and may be outside the realm of an operator’s domain. Therefore,

the focus is on methods of operating the existing chillers to the most energy efficient regime.

4.3 Improving Energy Efficiency via Energy Storage

Running multiple chillers that individually have optimum efficiencies at full load or close to full load

regions can be effectively run by using energy storage. The ideological thinking behind this is that

running a single chiller cannot be very different from running multiple chillers, caution being taken by

not disregarding the effects of economies of scale. However, chillers are most commonly of different

types and technologies and have different COPs—even if they are of the same sizes. It is important to

note that chillers of the same size will have different COPs if one is engineered or even installed better

than an older chiller with poor engineering and commissioning.

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The efficiency graph of a single chiller follows a concave curve that may have a quadratic, bi-quadratic,

cubic or bi-linear function. These functions can be of the third or fourth order; however, no matter the

shape of the curve the combination methodology that can be developed for combining these curves can

be generically the same and development of such a method is what is demonstrated in this paper. Since

the highest efficiency of a single chiller occurs near full load, the provision of storage in a multiple chiller

plant enables chillers to operate always within their optimum efficiency region. When the load reduces

lower than what the chiller can optimally operate on, it can switch off and use stored energy. This can

be called ‘discharging’. When the chiller is operating it will be operating in a ‘charged’ mode, providing

both energy to the building load and the energy storage simultaneously.

Figure 4 below shows this phenomenon.

Figure 4: Example showing benefits of adding thermal energy storage to a chiller plant. In (a), the chiller plant

has no thermal storage and operates at a lower COP while in (b), thermal storage is operating with high

efficiency in discharge mode. Inclusively in (c), the chiller is also operating with high COP in charging mode.

A small addition in building load is allowed to use energy that has been stored in the storage unit,

without compromising the operation region of a running chiller. When there is a small addition in

building load, which can be in the initial/morning hours of a day, the chiller is allowed to operate at full

load even though the actually building heat load does not require this. When the load reduces, the

chiller can be switched off to use the storage energy until it is depleted.

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4.4 Improving Overall Plant Efficiency using Load as Simulated Storage

Another approach to improve energy use and efficiency would be to consider the ability of the load to

hold heat charge. The thermal capacity of the walls, floor and roof of the building can be empirically

assessed if its value is of any significance to the operational usage of chillers. Once the load is calculated,

a decision can be made whether to run the chillers slowly ramping up, or quickly running to full load in

order to operate them at their full load and maximum efficiency.

The load will depend highly upon the building temperature gradient and the work that the chillers have

to do. If the chillers operate according to the concave curve functions discussed above, then the higher

the building target temperature gradient, the higher the chiller efficiency will be.

The temperature gradient will be derived from studies that have been done on human comfort levels.

This seems to be quite a complex exercise, hence only the results from these studies will be used for the

purpose of the thesis and only a superficial analysis may be done into the area. The graph below shows

the temperature gradient that humans can feel comfortable in.

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Figure 5: Source ASHRAE website http://smap.cbe.berkeley.edu/comforttool

A successful reflection of these comfortable temperatures to significant load changes will mean

sufficient energy savings to the way the chillers can be run. Alternatively, an analysis on precooling

technics will be observed if they are sufficient with the value of building thermal mass.

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If the above strategy is successful, then the load can be predicted using previous years’ weather

information and weather forecasting data. The temperature gradient and forecasted weather level can

be used to plan the staging information for the chillers. This can be compared with current staging

sequences and to test their effectiveness and energy savings achieved. The analysis will either validate

the current staging methodology or propose a new one, based on the calculation results and

comparisons of actual data.

The last analysis on staging will be more theoretical, since chillers do not like to be frequently switched

on and off. The other analysis will look at the effectiveness of always predicting the load based on the

above methods, and then deciding to run the smallest chillers first in order to have them operate at

their optimum efficiency. This would allow the particular running chiller to operate at its full load before

it hands off capacity to a bigger load, which can also immediately run at its most optimum load. Hence,

the smaller chillers can be used as intermediary or hand over chillers by swaying through their optimum

point then handing over capacity.

All these methodologies are based on the following assumptions:

a. All chillers operate at their highest efficiency when operating at or near full load.

b. Energy savings of running the chillers in combination at full load will in the long run achieve

more savings than running the chillers at part load for the same amount of time. (Effects of

variable speed drives may affect this analysis with a large impact as these may alter the COP

curve and increase the efficiency band).

c. Load changes due to ambient temperature can produce significant energy savings if chiller

sequencing account for them.

d. Thermal mass of buildings and walls of Murdoch University has a significant heat storage factor.

e. All chillers can be represented as a mathematical function that follows a polynomial order and

can have several bi-quadratic forms.

The best efficiency methods occur when the individual COP curves of all the chillers have been looked at

and analysed. If the data of the chillers can be represented as a mathematical function, then the rest of

the predictive and simulative analysis will be much easier. Representing the chillers as approximate

mathematical functions will also allow us to separate our analysis from the hundreds of internal parts

that improve individual efficiency from the combined functionality of the chillers.

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4.5 Modelling

The load on a chiller unit is continuously changing. This is due to changing weather conditions and the

actual internal cooling load of the building. The cooling load depends on people, equipment and

sunshine irradiance falling into the building. As such, the operation of the unit is also continuously

changing to meet this changing load. Running or operating a multi-chiller plant to the most efficient

mode and to minimise demand is quite a complex system for any given set of load conditions. However,

there are benefits in mathematically modelling a multi-chiller plant so that it can easily

duplicate/simulate the performance of a real life plant, and it can reduce the complex operation of the

hundreds of components into a simple set of equations. This allows us to extrapolate performance of

multiple chillers in different weather and load conditions, some of which may not be immediately

available for external data collection.

The parameters of interest in modelling of a chiller plant can be temperatures, fluid flows, system loads,

kilowatt demanded by system components and heat load removed. These parameters can help us

understand the real life chiller’s performance and will also help us to design other amalgamated values.

A chiller unit simulation model must obey the laws of thermodynamics, just as real systems do. With

certain inputs, it should resolve the performance characteristics of the chiller(Yung Chang, 2007).

Multiple chillers are typically arranged in parallel and seldom in series, each with their dedicated power

systems and accessories such as pumps, compressors cooling towers and measuring equipment.

The load and the cooling water supply temperature on each chiller can be adjusted. For a given cooling

load, the individual chiller load can be controlled using either a different water supply temperature set

point or by adjusting the flow rate for identical set points. A simpler model for a chiller can be developed

by breaking the system down into the chiller, the thermal storage, and the load.

Other industrially common and acceptable models have been developed by:

The Department of American Society of Heating, Refrigerating and Air Conditioning Engineers

(ASHREA). This model was developed by the ASHRAE primary systems toolkit (DOE, 2006)

The American Department of Energy also have a model DOE2 (DOE 1980). There is a DOE2a and

a modified DOE2b model chiller model.

The Gordon and Ng Model utilised also by ASHRAE research (DOE,2006)

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The Lawrence Berkeley National Laboratories in California also developed a regression model of

chillers as part of chiller commissioning. (Berkeley, 2006 ) and (DOE, 2006)

All these chiller models focus primarily on the simulation of individual chiller performance; however, in

this research the simplest and most easily adoptable chiller model equations will be used in order to

achieve chiller augmentation performance values.

The DOE2 model is well documented; formulae used are publicly available and are considered to be

comparatively more accurate than most of the other models across a wide range of chiller

arrangements. (Mark Hydeman, 2002).

The model from ASHRAE can sometimes pose difficulties, as it requires details of the chiller that can are

not easily available to customers or people using the chiller; rather it requires data from the

manufacturers, which can be hard to find.

The DOE2 model requires that the available data be separated into full load and part load conditions.

Another technique used for the least regression method employs a standard least squares linear

regression curve fitting method that can be used to develop the chiller equation and come up with

model coefficients directly from the available data. These curves can then be used to extrapolate the

chiller’s performance beyond the range of the available data sets. These techniques were tested over a

wide range of air-cooled and water-cooled electric chillers in a study by Mark Hydeman (2002). Other

chillers tested with this method were chillers with screws, scrolls, centrifugal compressor and

reciprocators, including chillers with different refrigerants and variable speed drives.

Curve functions of the DOE2 model

The basic format of the DOE2 model is that it is made up of the following three curves:

CAPFT – this curve represents the available capacity as a function of condenser and evaporator

temperatures. (Capacity as a function of temperature).

EIRFPLR – this curve represents chiller efficiency as a function of percentage unloading. (Energy

input ratio to Part load ratio).

EIRFT – this curve represents full load efficiency as a function of condenser and evaporator

temperatures. (Energy input ratio as a function of temperature).

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These functions use the temperatures from the heat exchange fluids as proxies for the refrigerant

operating pressure in the condenser and evaporator. The temperature of the condenser water supply is

used for conditions of all water-cooled electric chillers; the temperature of the evaporator water supply

is similarly used for all electric chiller conditions. (Gillespie, 2002). Together, these functions can be used

to simulate and predict the power over a wide range of operating conditions.

The following functions show the format of the curves:

CAPFT = a1 +

𝑎1 + 𝑏1 𝑥 𝑡𝑐ℎ𝑤𝑠 + 𝑐1𝑥 𝑡𝑐ℎ𝑤𝑠2 2

+ 𝑑1 𝑥 𝑡𝑐𝑤𝑠 + 𝑒1 𝑥 𝑡𝑐𝑤𝑠/𝑜𝑎𝑡2 + 𝑓1𝑥𝑡𝑐ℎ𝑤𝑠 𝑥 𝑡𝑐ℎ𝑤𝑠 𝑥 𝑡𝑐𝑤𝑠/𝑜𝑎𝑡

EQ.1 EIRFT = =

𝑎2 + 𝑏2 𝑥 𝑡𝑐ℎ𝑤𝑠 + 𝑐2𝑥 𝑡𝑐ℎ𝑤𝑠2 + 𝑑2 𝑥 𝑡𝑐𝑤𝑠/𝑜𝑎𝑡 + 𝑒2 𝑥 𝑡𝑐𝑤𝑠/𝑜𝑎𝑡

2 + 𝑓2 𝑥 𝑡𝑐ℎ𝑤𝑠 𝑥 𝑡𝑐ℎ𝑤𝑠 𝑥 𝑡𝑐ℎ𝑤𝑠/𝑜𝑎𝑡

EQ.2 EIRFLPR = a3 + b3 x PLR + c3 x PLR² EQ.3

PLR = 𝑄

𝑄𝑟𝑒𝑓 𝑥 𝐶𝐴𝑃𝐹𝑇(𝑡𝑐ℎ𝑤𝑠,𝑡𝑐ℎ𝑤𝑠/𝑜𝑎𝑡) EQ.4

24

Where:

𝑡𝑐ℎ𝑤𝑠 = temperature of the chilled water supply (°C)

𝑡𝑐𝑤𝑠/𝑜𝑎𝑡 = temperature of the condenser supply water (°C) for equipment that is water-cooled

while for air-cooled equipment it would be outdoor dry-bulb temperature.

𝑄 = the chiller capacity in tonnes.

𝑄𝑟𝑒𝑓 = the capacity in tonnes at the reference condenser and evaporator temperatures where

the curves approach unity.

𝑃𝐿𝑅 = a function that represents the chiller’s part load operating ratio.

𝐶𝐴𝑃𝐹𝑇 = capacity as a function of temperature.

𝐸𝐼𝑅𝐹𝑇 = the Energy input ratio as a function of Temperature. This represents the full load

efficiency as a function of (1/COP) condenser and evaporator temperatures.

𝐸𝐼𝑅𝐹𝑃𝐿𝑅 = This is the energy input ratio as a function of part load ratio. It represents the

efficiency of the part load ratio and in the modified case of the DOE2 model, it also has terms

that account for condenser temperatures.

𝑎𝐾 , 𝑏𝐾 , 𝑐𝐾,𝑑𝐾 , 𝑎𝐾 , 𝑒𝐾 are coefficients of regression.

25

Fig 5b Model of a single chiller plant containing 3 chillers in parallel and a thermal storage system. The

chilled water supply ‘Tchws’ flows towards the load from the chiller. The opening and closing of the

valves allow for charging and discharging functionality.

4.6 Methodology Summary

Considering each of the different models, an attempt will be made to represent all the chillers at

Murdoch via the corresponding mathematical functions. The coefficients will be found using linear-

squares regression and curve fitting techniques and then these functions will be integrated into a

representation of a single chillers performance in terms of results.

The load will be looked at with prediction methods of thermal comfort and data from weather

forecasting—these will be used to predict operation load. The building load thermal mass will also be

estimated as a small load (for what it is worth), and an attempt to calculate the optimum operating

point with these parameters in mind will be considered. Outdoor ambient temperature and humidity

will also have to be considered if the calculations do not prove to be too complex for the project scope.

Successful results in either of the stated methods will result in major energy and economic savings to

26

the operation of chillers at Murdoch University and it is hoped that these results and the simulation

algorithms developed will able to be applied in other multiple chiller plants.

Figure 6: Derived from lecture of F. Kappel, 2008

The normal process of simulation and/or modelling of a system is shown in figure 6 above.

27

When the real world problem is defined as a mathematical problem, then assumptions are made that

act as guidelines to solve the problem, after which equations are formulated with defined accuracies.

These equations are then solved and the data compared and interpreted with real world solutions. This

process is a basic summary of what has been explained and what will be done in this procedure.

4.7 Methodologies –Algorithm - Steps

4.7.1 Step 1: Determine the Load

The first part of the process is to determine the load that is applied onto each of our chillers; however,

in assessing the method we will begin by assessing the load on only one chiller. The load of the chiller

will be incrementally increased on the chiller and this load will be translated as a part load ratio of that

chiller:

PLR = 𝐿𝑜𝑎𝑑

𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝐹𝑢𝑙𝑙 𝐿𝑜𝑎𝑑

This part load ratio will then be used in our formulae to determine the COP or even the efficiency of the

chiller. . The load will have to be incremented with a desired step to fill our entire region of simulation,

and this can be either a single day, a week, or a year. It is important to note that data available from

Murdoch’s chiller system is collected every 15 minutes. However, this data has not yet been

synchronised to a single master clock, hence every element of data analysis in the system is on its own

clock that collects data at every 15 minutes with an individual counter, which causes problems.

However, a virtual master clock can be used that puts all elements that have been collected within the

master clock’s 15 minute window and can be slotted in that region. Thus, the elements that have their

cycle within the next 15 minutes will also be in the next region of the 15 minute cycle of the master

clock.

In the simulation, the load can be simply incremental with a specific step and in future analysis these

performance simulations can be mapped out with improved analysis that can predict the load by either

looking at how the load changes with other external factors such as how many people are in the room,

the ambient temperature outside, and equipment. These further improvements can be implemented on

the base methodology described in this paper.

It is also worth noting that the load can either be expressed as incremental steps or a more extensive

analysis can be done on all possible combinations of chillers for that particular load. In order to do this, a

probability distribution for chiller possibilities can be done. With 5 chillers at Murdoch being switched

28

between their on and off states, we will have (2n), 25= 32 chiller combinations and they can be assessed

to find out which of these 32 paths is the best one to take for a specific load prediction. With a more in-

depth analysis we can also choose an increment of X load and this will be divided with the chiller

capacity in order to find out all the states possible for that load.

Incremental steps = 𝐶ℎ𝑖𝑙𝑙𝑒𝑟 𝐿𝑜𝑎𝑑

𝐷𝑒𝑠𝑖𝑟𝑒𝑑 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡

For example, we can decide to do an analysis with a 100 KW increment, and with a 2MW chiller we will

hence have:

Incremental steps = 2000 𝐾𝑊

100𝐾𝑊 = 20 steps

Thus, if we had 2 chillers of similar capacity we would have 20 steps each and 20 x 20 combinations to

analyse.

4.7.2 Step 2: Use Spreadsheets to Determine Chiller Function

At this stage, the data from the chillers operation will be input into a spreadsheet program and will be

graphed and analysed until a suitable fitting curve with an acceptable regression fitting factor is derived.

This process is supposed to be done for all the chillers available at any multi-chiller unit site. This is the

most critical step of the process of calculation of optimisation, as the curve represents the entire chiller

as a single module. Without having to consider the internal interactions of all the individual elements,

this approach will achieve simplicity of the design and exceptions to internal interactions will only be

taken into account where that particular internal factor has a large impact on the simulation accuracies.

As described by ( Madhur Behl, 2012) the formula for a chillers COP is:

This formula will have to be derived from operational field data; however, a generic curve can be

used for preliminary investigations of the method. An example of sample chiller data for deriving the

above equation is shown in Table 1 below and has been developed in Microsoft Excel.

Table 1

PLR PLR^2

PLR a b c COP

0.5 5 -2

29

0.1 0.5 0.5 -0.02 0.98

0.2 0.5 1 -0.08 1.42

0.3 0.5 1.5 -0.18 1.82

0.4 0.5 2 -0.32 2.18

0.5 0.5 2.5 -0.5 2.5

0.6 0.5 3 -0.72 2.78

0.7 0.5 3.5 -0.98 3.02

0.8 0.5 4 -1.28 3.22

0.9 0.5 4.5 -1.62 3.38

1 0.5 5 -2 3.5

Figure 7: Chiller COP curve

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.2 0.4 0.6 0.8 1 1.2

COP

PLR

Chiller COP Curve

Chiller A

30

It is important to remember that the chiller can also be represented as a function of temperature with

the following equations :

However, the DOE manual stipulates that in order for simplicity of the capacity factor, COP can be

represented as a function of the PLR only, and sufficient modelling can be achieved. However, the other

funcitons can bring in more accuracy in a later analysis.

Secondly, it is important to note that this curve will change as a factor of several other factors, especially

humidity, outside temperature and condition of the chiller system; however, representing the chiller as

a simple quadratic function offers a very good starting point for analysing the chiller optimisation

process, and once the process is mastered the changing equation factor can simply be factored in and

the rest of the procedur remain the same. Hence, changing the equations to corresponding humidity

and other outside factors will only improve the method’s accuracy while the method itself will not be

affected.

4.7.3 Step 3: Use Equations to Derive Energy Results

Every chiller will have its own chiller equation describing its COP, and the corresponding load on each

chiller stipulated in the first stage of the process will be applied on the chiller function to determine how

much energy it will use at that particular loading. The equation will be fed with the PLR of the chiller and

then from the PLR derive this COP, which will then be used to calculate ‘electricity in’.

Electricity in = 𝐿𝑜𝑎𝑑 𝐻𝑒𝑎𝑡(𝐾𝑊)

𝐶𝑂𝑃

These results will then be stored in a sheet that will be used for comparison analysis in the next step.

31

4.7.4 Step 4: Ranking Results

In this step, for every load that we have in our incremental step we will use the results gained in the

previous step in order to assess which one of all the combinations uses the least electricity. This will

simply be done by sorting the results for every specific load.

It is important to note that these results will only be close to ideal, because the best combination of

chillers may not necessarily be the most achievable one, as the difference between the first and second

stage on combinations may theoretically involve switching on and off a single chiller rapidly. In practice,

a chiller is such a huge machine that needs a few minutes of starting time and it cannot be easily

switched on and off in terms of every 15 minutes, as in the case of our data collection.

Murdoch chillers run a minimum of 3 hours at every stage. However, the practical running regime can

be compared with the best operating regime calculated and these can offer a benchmark for the

operating strategy.

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4.7.5 Ideal Flowchart

Figure 8 COP Methodology Flowchart

33

The flowchart above shows the methodology used in the analysis and can be summarised as

follows:

A. First we consider all our inputs for a simplified analysis we only consider a few input

parameters however other parameters that may yield more accurate results are listed

on page 34 of this same section.

Inputs considered are

1. What step size to use in incrementing out load. (i.e 400KW is used in our

analysis)

2. Chiller function in the format of a + bx + cx² and is calculated from the chiller

energy use and heat energy out data.

3. Chiller sizes at the site. (Murdoch has two 1 MW , one 4 MW and another two

2MW chillers )

B. Then we calculate the load increments from zero to the total sites chiller capacity. Using

our input load increment. (400KW)

C. Calculate all possible combinations of chillers that can supply each particular load

calculated in step C.

D. Calculate corresponding electricity usage for all these combinations

E. Rank the best combination for each load.

F. Compare this data for each load with the actual staging regime used on site.

It is worth noting that other parameters that can improve the accuracy of the process in future would

have inputs as follows:

34

1. Input chiller function that changes with external conditions like weather

2. Input predicted weather data, temperature and humidity

3. Input electricity tariffs

4. Calculate optimum chiller operating range (i.e. 50% - 100% ). Most chillers will actually start

operating at 40% capacity; anything below this cold burns out the compressor motors (TRANE

chiller manuals)

5. Calculate corresponding energy load that can be met with the corresponding regime

6. Repeat this for all chillers (Library South, Library North, Vets’, Air-cooled A, Air-cooled 2)

7. Store the results

8. From weather data predict load, or just match it

9. Calculate best combination of chillers to operate to meet specific load. In the case of Murdoch,

5 chillers means 2𝑛 combinations 25= 32 combinations

10. Calculate each sub-set of remaining chillers 2𝑛−1 ( 25, 24, 23, 22, 21 )

11. Choose optimum path depending on optimum list in each subset

12. Plot the results

Other factors to consider while performing the analysis are:

Input parameters

Chiller parameters

Thermal comfort levels, system complexity

Load prediction, load parameters (number of human heat production, number of building load

equipment)

Inputs parameters

In order to explain the workings of the simulation it is important to show explicitly the workings of the

inputs that will be needed.

After the curve fitting procedure we will have the chiller representative function. We will use this curve

as the main input to the simulation process; however, other inputs that will be needed are:

Ambient temperature*

Ambient wind speed*

Number of equipment in the building, (slightly variable/constant)

35

Number of people in the building (variable)

Humidity*

Size of building (constant)

Thermal capacity

Solar sunshine falling into and onto the building

Number of windows (constant)

Seasonal weather changes. (a weather file with parameters )

Date and time of weather files

Note (* parameters are critical or most important)

36

Figure 9: Load inputs

This information may be rather difficult to acquire, so it will be decided empirically to the load

in kilowatts needed for the chiller to remove from the building. Most of these will only be a

summation of this total quantity; however, those with a star will prove to be more critical

parameters for our simulation. Chiller parameters

Chiller type(water or air-cooled)

Chiller number or position in system

Energy input

Energy removed by chiller from load

Calculated values will be:

COP

COP of performance across a wide range of magnitudes

Part load efficiency

Energy used by motors, pumps compressors, and evaporator units

Date and Time of data

Building Load

Solar irradiance

Wind

People heat Dissipation

Equipment heat

dissipation

Humidity

Ambient/ Temperature

Lights

Thermal Mass

37

Time interval of data (whether 30 min or 1 hour intervals)

Figure 10: chiller parameters

Things like variable speed drives on the motor can be individually modelled into the simulation;

however, for our procedure we will simply represent the chiller as the equation derived by the curve

fitting process.

4.8 Load Parameter Considerations

Another important parameter when considering the load is the weather. In fact, this is the most

important parameter, as it directly affects the load on a building. A weather file will have parameters

such as temperature that will translate due to correlation to load. Over time, this may be done manually

and empirically in a spread sheet or if time allows, this feature may be added later on as an extra feature

to the simulation processes.

The load depends on the variables involved, which are mainly energy input and energy removed by the

chiller. The more accurate formula for a chillers load will be Heat emitted (H.E) by:

Load = H.E by number of people + H.E #equipment + Solar radiance + H.E Lights – Heat removed

by wind – Heat removed by rain water evaporation)

Chiller

Electricity in (KW)

Heat removed (Thermal

Tons)

Motors compressors

Variable speed drives

38

Counting the number of students may not be an easy task; however, approximations can be made and

attached to a coefficient that can be related to previous data. Thermal radiation from the human body

mainly occurs in the infrared region-predominantly in the wavelengths of around 12 microns ( Shuk-

ming, 2010, http://www.hko.gov.hk/education/edu02rga/radiation/radiation_02-e.htm).

Treating the human body as a black body radiator has been found to produce the same radiation as that

produced by a black body radiator at the same temperature of 37 degrees Celsius. Studies done by

(Andris Auliciems, 2007) also show that the radiation absorbed or radiated by black skin and white skin

are slightly different, even though the internal body temperature effectively stays the same.

Thermal radiation from the human body itself is a widely varying factor that depends on the body

weight, body size, clothing, skin colour and most importantly, body activity. However, research on

radiation from human skin temperature estimates an average range of 80 watts to 116 watts being

emitted from humans depending on their body activity. (Andris Auliciems, 2007)

This means a single person in a building is an average of having a single 100-watt incandescent tungsten

electric bulb on. Multiplying this by the number of occupants in a place like a university building can

certainly cause complications to the modelling process, although if accounted for it can also introduce

some form of accuracy.

An average computer consumes around 80 to 250 watts (Andris Auliciems, 1997) . These are easier to

calculate, as there are a static number of computers in the university buildings. Nevertheless, not all of

this energy is exactly converted to heat. A desktop computer can dissipate about 100watts as heat,

while a computer monitor can produce around 50watts of heat, coming to an average total average of

150watts of heat per computer. This also depends whether the computer is in standby mode or not.

The lights in the buildings also produce light and heat; even though heat produced by most efficient

lamps is much lower than in previous incandescent bulbs, fluorescent and LED lamps still produce a

significant amount of heat. Again, this can be slightly easier to calculate than people, who are more

variable than static lights.

Fortunately, all these variables can be simplified and represented as a single parameter of load from

empirical values, and it is hoped that the error in simulation of these parameters as a single unit should

be reasonable and accepted for analysis.

39

This empirical method can be considered as load following representation where the parameters of the

parameters discus represent and follow the actual load measured by the chiller’s energy removed from

the building.

4.9 Load Prediction

One function that is required will be to follow the predicted load according to temperature and seasonal

human activity. In order to predict the building load in our simulation, we can calculate the parameters

above. This will be more complicated because of the missing information of variable human presence,

therefore the collected data will simply be mapped and correlated to the previous load of the chiller,

hence the code will be learning from history, while from the given temperature the load will be

predicted. Since weather is easily predicted through meteorological and local monitoring, from this

weather data the load can be mapped with previous data with a small margin for error.

Predicted temperature >> mapped temperature >> mapped load >> predicted load.

Predicted temperature can be done from weather forecasts; however, since this can be more tedious, it

can more easily be done by observing historical records of each day and hoping that this will be similar

to the actual temperature for the next day.

A given temperature with the fairly predictable human attendance to the buildings—also from historical

data—should be fairly accurate to derive.

4.10 Temperature Setting

From predicted load we will go to the next stage, which will be to set the building temperature

according to research done on human comfort levels. Again, this target temperature will translate or

correspond to a particular load target. With this load target in mind, it will now be the purpose of the

simulation to calculate the best size chiller to meet this load at the best efficiency according to that

particular chiller’s part load efficiency graph. As the load rises from morning to midday and mid-

afternoon, the simulation will have to calculate the combination of chillers that would be best for a step-

by-step rise in load of the system. A mathematical attempt will also be attempted to see the effects of

the thermal mass on the chiller operation and if the thermal mass can offer any potential thermal

storage capabilities, which the chillers can use to maximise their operation. This will be done after

analysing the chiller performance data. This set temperature will also have to be compared to the

40

previous year’s temperature of the same day, and the difference has to be within an acceptable range

that takes human comfort into consideration.

41

5.0 Thermal Comfort Levels

Professor Fanger developed a heat balance equation after studying the human thermal energy balance

and the average comfort temperature for human skin, with consideration of optimal sweat exhaustion

rates.

Figure 11: Thermal comfort levels Professor Fanger

Professor Fanger developed an index called the PMV (predicted mean-vote), which exhibits the

sensational thermal index that is produced by a combination of environmental factors. He proposed that

apart from personal variables like clothing and activity, there are four physical variables that are

significant in analysing the effects of thermal comfort. These parameters are air temperature, air

velocity, relative humidity and radiant temperatures. The formulae developed by Fanger for PMV and

PPD were:

𝐏𝐌𝐕 = 𝟑. 𝟏𝟓𝟓[𝟎. 𝟑𝟎𝟑𝒆−𝟎.𝟏𝟏𝟒𝑴 + 𝟎. 𝟎𝟐𝟖]𝑳

𝐏𝐏𝐃 = 𝟏𝟎𝟎 − 𝟗𝟓𝐞−[𝟎.𝟑𝟑𝟓𝟑𝐏𝐌𝐕𝟒 +𝟎.𝟐𝟏𝟕𝟗𝐏𝐌𝐕𝟐]

42

These thermal comfort levels are based on steady state conditions; however, in many situations

transient conditions do prevail. In order to predict comfort under transient conditions, a two-node

model was developed for moderate and low human activity levels, in cool to very hot environments. For

normal conditions, the PMV-PPD indices are sufficient. These can be accounted for together with the

Effective Temperature and Standard Effective Temperature (SET) developed by (Holopainen et al, 2012).

In addition, the PMV-PPD indices are included in the ISO standard 7730 developed by the European

standard EN ISO 27730 and the ET and SET indices were developed by ASHRAE. The quality of the

thermal environment can be expressed by a PPD index, which is then related to the PMV value. For a

PMV = 0, the PPD is equal to 5%, which means 5% of the occupants are not satisfied with the thermal

environment . A PMV value of ±0.5% will correspond to 10% being dissatisfied. The ASHRAE standard 55-

1992 defines thermal comfort as the thermal environment at which 90% of the occupiers are

comfortable. It illustrates a boundary that states an operative temperature at which only 10% of

occupants can be dissatisfied as a boundary level. A PMV of plus minus 5% has been recommended to

satisfy this standard. The ASHRAE standard 55-2004 classifies an acceptable thermal environment when

80% of the occupants are satisfied; this means an environment with a -1 and +1 thermal sensation vote

(TSV) is acceptable. (Chowdury et al, 2007).

Chowdury et al’s research showed that using these models of thermal comfort demonstrated they were

able to have desirable effects in a subtropical climate with temperatures ranging from 20°C to 24°C after

work hours and 22°C to 23°C during working hours. This was achieved with 30% to 70% humidity, while

environmental conditions averaged around 23°C and 55% humidity. This experiment will act as a guide

in the analysis at Murdoch.

43

Figure 12: Thermal comfort levels (Professor Fanger)

The comfort zones according to the graph in figure 12 show that the ranges for PMV for comfort are

within -0.2 to 0.2, -0.5 to 0.5 and -0.7 to 0.7 corresponding to PPD values below 6%, 10% and 15%, The

analysis of figure 12 above allows us to see that because of individual differences between people, even

for a situation which is averagely and universally agreed as thermal neutrality with (PMV=0), the

percentage of people dissatisfied with this agreement are 5%.

44

6.0 Actual Procedure Results and Analysis

In order to calculate the best combination of chillers we exploited the computational resources available

by computers today, to calculate all possible chiller combinations for each possible load, and then

compare these with individual step calculations by the overall, either manual chiller combination or any

other existing combination. An example of such an approach would be if we had two chillers each of size

10MW and we were simulating at a step of 5MW each. We would start by increasing the simulation

from 0 to 5, then simulating all possible chiller combinations to supply this 5 MW load.

In which case we would have:

Load 5MW = Chiller A On (5MW) and chiller B OFF(0MW)

Our next combination would be:

Load 5MW = chiller A OFF(0MW) and chiller B ON (5MW)

We would then increment the load from 5 to 10:

Load = Load + Load increment (5MW)

Load = 5 + 5 = 10MW

At Load 10MW = chiller A ON (5MW) and chiller B ON (5MW)

At Load 10MW = chiller A ON (10MW) and chiller B OFF (0MW)

At Load 10MW = chiller A OFF (0MW) and chiller B ON (10MW)

Thus, at 10MW we would have 3 combinations to simulate. We would continue doing this until we

achieve the maximum possible load of 20MW, since the combination of the two 10MW chillers makes

20MW.

A more detailed example and explanation of this is in the paragraph below. Software was developed in

Microsoft Visual Basic to calculate the combination possibilities and the results then exported to Excel.

45

The software interface is shown below:

Figure 13: Simulation software

The parameters setups for the first simulation are taken to be similar to what is setup at Murdoch

University.

Library South chiller is taken as chiller A and is of the capacity 2MW.

Chiller B is Library North and is the newest chiller at Murdoch, having the capacity of 4MW.

Chiller C is the Vets’ chiller and also has 2MW capacity.

Chiller D and E are the air-cooled chillers, rated at 1 MW each.

A step resolution of 400 KW has been chosen and when the “Matchsim 3” button is clicked, the software

executes and produces the following computer screen as shown in figure 14.

46

Figure 14: Simulation software

The software produces 6321 simulations of load chiller combinations. At the time of writing this paper,

the software was only able to simulate the maximum load minus 1 simulations, which leaves the

maximum load up to 9600 KW and not 10,000. The last simulation is hence calculated manually.

This stage produces all the possible combination of chillers at the load of 400:

Figure 15: Simulation software

In summary, at 400KW we can either have all the chillers off and only the first chiller on, then we can

have all chillers off and only the second chiller on. We can continue like this with every chiller being on,

supplying only 400 MW from chiller 1 all the way to chiller number 5. The software was doing an

47

analysis for 7 chillers, hence the last two zeros were simply ignored. At load 800, we start to have

combinations of two chillers being on at 400KW load supplying the 800KW.

After these calculations are done, we then click on the staging menu as shown in figure 16 below:

Figure 16: Simulation software

This leads us to the next screen of the software, which now calculates the corresponding part load ratios

associated with the load distribution of that chiller. The staging button initiates the calculation:

48

Figure 17: Simulation software

After the part load ratios are calculated, the Export button is clicked in order to export these results to

Excel for further analysis of the procedure. Because of time constraints, it is much quicker to finish off

the analysis manually in Excel than to program the software to do it automatically; however, in future

perhaps more work can be done to make the whole process automated within the tool.

After exporting to Excel, the data for the stages explained above are shown below:

Figure 18: chiller combination results

49

The first columns having the load, i.e. at 400KW and the combination load distribution and the Part load

ratios in the 2nd and 3rd set of columns.

A probability distribution of the combination of chillers is shown below:

Figure 19: chiller combination probability distribution

This shows that at the maximum load of 10,000 KW of all the chillers combined, we only have one

possibility of combinations. Since there is only one possibility, this will automatically be the best

combination. At 9600 KW, there are 2 possible combinations and the next higher combination possibility

is 400KW with 9 possible combinations and so forth.

The chiller COP functions are defined in the Excel sheet as shown in figure 20 below:

Figure 20: chiller curves input in excel

50

Figure 21: chiller curves

At the time of analysis, these functions were generically chosen just to show the methodology

procedure; however, in future work they will have to be refined in order to attain more accurate results.

These functions were taken from a simulation database of chillers done by “THE SIMULATION RESEARCH

GROUP” at Berkeley’s lab (Berkley, 2012) and are functions of real chillers that are very close to the ones

operational at Murdoch University. After collection of data for the Murdoch chillers, which was not

available by the time of writing this paper that data can then be used to fine tune the functions used in

this analysis.

The function curve is then fed with the part load ratio calculated in the Excel sheet and then used to

calculate what the COP of an individual chiller will be at a particular chiller load.

2.4 2.5

2.15

1.1 1.1

0

0.5

1

1.5

2

2.5

3

0 0.2 0.4 0.6 0.8 1 1.2

COP

PLR

Murdoch Chiller COP Functions

Chiller A

Chiller B

Chiller C

Chiller D

Chiller E

51

The COP results are shown below:

Figure 22: COP of performance data

Figure 23: chiller electricity per loading

The last column is the rank column. In this column, every load is ranked from the best to the least best

option. Again, this is done manually and the chiller staging information is compared against these

rankings.

52

Figure 24: chiller combination Ranking

For a load of 9200KW, we find that there are two best chillers ranked 2nd each because in the rank

average function in Excel it is followed by rank number 3 ,4 and 5.

This ends the generation of the analysis template.

The next stage is now to look at the actual staging information for Murdoch University; a summary of

this is as below in figure 25:

Initialise End Stage # Murdoch Staging

0 1650 1 Load LBN LBS vets’ Air1 Air2

1650 1850 1 2000 2 0 0 0 0 2

1850 3600 2 4000 0 4 0 0 0 4

3600 3750 3 6000 2 4 0 0 0 6

3750 5400 3 6

5400 6900 4 8000 2 4 2 0 0 8

6900 7600 4 8

7600 8800 5 9000 2 4 2 1 9

8800 10000 6 10000 2 4 2 1 1 10 Figure 25: chiller electricity per loading

A preliminary assessment of the Murdoch chiller staging methodology is found to be close to the

optimum staging strategy revealed by our ranking template or ranking methodology. Each stage of the

Murdoch regime is compared with the optimum staging calculated with our algorithm and indeed, it

turns out to be the optimum way of running the chillers. At the first analysis of a staging, we have to

53

decide the first chiller to meet the first simulation load, i.e., if we had 4 chillers of different sizes e.g.

2MW, 1MW, 4MW, 3MW and we had to supply a load of say 500KW, then at this point the best chiller

would be the one that has the closest power to the load. In this case, the 1MW chiller would be the one

to run and indeed even by calculation, a 500KW load would give the highest COP for the 1MW chiller

than the rest as it would stand at 50% while the rest of the chillers in this example would be smaller.

What the template ranking data produced by the software tells us is that once this 1Mw chiller is turned

on to supply the load of 0.5MW, it will continue to have the highest COP when compared to the rest of

the chillers until that particular chiller load is exceeded. Depending on the curve shape, it is highly likely

that the higher COP ratio of the load to the closest chiller size would also be the most efficient.

However, this has to be taken cautiously as the chiller performance curves can tip the scales in a rare

case. Notwithstanding, even if the scales of efficiency were indeed turned by the efficiency curve, the

chiller cannot easily by switched on unless that load is going to be supplied for a long time because

chillers do not last long if they will be switched on and off frequently. These two approaches are what

seemed to have intuitively guided the installation team for the chillers at Murdoch University and

indeed our data shows that this simple intuitive approach is a very close regime to run; it produces

results that are very close to the optimum calculations performed by our methodology. (This is assuming

that the efficiency curves behave as we expect them to).

Initialise End Stage #

Murdoch Staging

Template Rank

0 1650 1 Load LBN LBS vets’ Air1 Air2

1650 1850 1 2000 2 0 0 0 0 2 1

1850 3600 2 4000 0 4 0 0 0 4 1

3600 3750 3 6000 2 4 0 0 0 6 1

3750 5400 3 6

5400 6900 4 8000 2 4 2 0 0 8 1

6900 7600 4 8

7600 8800 5 9000 2 4 2 1 9 2

8800 10000 6 10000 2 4 2 1 1 10 1 Figure 26: Murdoch chiller staging regime

The results above show that at every stage of Murdoch staging, the optimum chillers are chosen to be

switched on and operated. However, this staging and the staging of the ranking results only gives us

power usages levels. In order to get energy we would have to compare with a load profile of how long

the chillers will run at a particular load; this can be left for future analysis because the main research

question of how to optimally run our chillers has been answered by this data.

54

Figure 27: Electricity vs Load from simulation

Figure 28: Electricity vs Load from simulation

The electricity in versus load output out graph is shown above for our template and the variations in the

electricity in on every load is the major difference between an efficient staging regime and a sub-optimal

one. Hence, a top line on the variation curve will signify the most efficient strategy and a bottom line

would display the sub-optimal regime. These differences are expressed in KW-power magnitudes and

will increase once they are translated to energy, as they will be multiplied by the time factor in the load

0

2000

4000

6000

8000

10000

120001

19

8

39

5

59

2

78

9

98

6

11

83

13

80

15

77

17

74

19

71

21

68

23

65

25

62

27

59

29

56

31

53

33

50

35

47

37

44

39

41

41

38

43

35

45

32

47

29

49

26

51

23

53

20

55

17

57

14

59

11

61

08

Electricity vs Load

Load Electricity

55

profile, hence it would then also depend on how long the chillers have been running on that sub-optimal

point. Again, this analysis has not been done because it does not directly assist in answering our

research question; rather it only quantifies something which has already been revealed to us by our

staging method.

56

7.0 Software Analysis

The software and algorithm developed for the analysis has proven to be quite powerful in its simulation

analysis as it covers almost all possible load combinations available and automatically analyse which one

of these combinations is the best. By exploiting the computational power available today, the software

can only be improved; however, some things in it were completed in Excel as the time available for its

development was not quite sufficient.

There were a few bugs in the software. One main bug is that it sometimes repeats the same load

combination more than once. Even though this is annoying, it does not necessarily change the results

much as we already have the solution to the simulation only that it is repeated.

57

8.0 Problems Encountered

One of the major problems encountered while writing this paper was the delay of the project to install

energy meters on the Murdoch chillers. The project was scheduled to be completed by the middle of the

first semester, 2014; however, it was still not finished at the end of the second semester and academic

year. The only data available was for one chiller, which is shown in the section 10.1 and can be used to

calibrate the COP function used for that chiller in our analysis. Nevertheless, it will not change much as

the other chiller information would also be required. The data available is also not synchronised to a

master clock and will thus be required to be categorised in slots of time to enable the analysis of all the

points together. When the energy meters for the chiller at the vets’ department was installed, the time

was not set correctly to the current calendar; instead it was logging with a time stamp of 1986; a

significant chunk of data was logged within this period and will need to be validated.

58

9.0 Conclusion

Analysis of the results shows that the current staging regime offers the optimum staging solution. An in-

depth analysis can give the actual electricity run; however, this would require a load profile, which is not

currently available as data from all the chillers is not yet completed. In addition, once a chiller is

switched on it will have to be run from 0% of its load all the way to the maximum load it can handle,

hence if the optimum chiller is chosen to be switched on, the results show that all the smaller

incremental loads that are within the chillers’ operational region will also have the best COP. If the

wrong chiller is chosen to start, then it will similarly have poor performance throughout its loading. This

paper was expecting to have extensive analysis on the data available from the chillers; however, as the

data was not available it skewed more to the development of the methodology that can be improved

upon in future in terms of energy input representation. Notwithstanding, the main objective of the

thesis has been met and a suitable tool to determine or assess the best way to run a multiple chiller

system configuration has been achieved.

59

10.0 Recommendations for Future Research and Development

The methodology used in this research has yielded a good basis for further work on chiller staging

analysis. Moreover, developing the method further would improve the area energy assessment and

assist in decision making for chiller operators. More work is needed to improve on making the method

easier to implement, so that results can be clearly observed and analysed, and more work is also

required in making the method more accurate.

This can be done by obtaining more data on the actual running of the chillers in order to develop more

accurately representing chiller functions. More analysis also needs to be carried out to factor in other

ways the chiller performance changes according to other environmental factors. The DOE has currently

released a modified chiller performance curve function, which factor in temperature differentials. The

chiller function is bound to be affected by other external factors that can change its efficiency. One

factor that can do this on a big scale is humidity, therefore future work on how the curve changes to

humidity can also be factored into the analysis and thus assist in ascertaining the best method of

combining chillers together. If the variation between a chiller’s efficiency and COP is significant, then the

staging results can also largely affected.

10.1 Data Matching Validation and Normalisation

Figure 29: Actual chiller Electricity usage from the vets’ 2MW chiller

Data collected from the vets’ chiller unit is shown above, but does not seem to match with the energy

output in terms of calculation of COP, hence further examination of the data times and the data spikes

0

20

40

60

80

100

120

140

16

71

33

19

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65

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91

51

98

12

04

72

11

3

KW

Re

al p

ow

er

Actual Electricity usage of Vets’ Library chiller

60

needs to be done to derive the coefficient curve. An average COP can be used but it still has to go

through some validation process.

Figure 30: Actual Heat removed from the vets’ 2MW chiller- time matching of the two data sets is required

The heat removed by the vets’ chiller is seen above and requires some further examination before being

used to determine the COP; where one was expecting a normal coefficient of performance of around 3

the spiking can lead the result to infinity. However, after data validation the procedure can be used as

discussed in the paper.

0

500

1000

1500

2000

1

32

63

94

12

5

15

6

18

7

21

8

24

9

28

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31

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55

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59

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62

1

65

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68

3

71

4

74

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77

6

80

7

Co

olin

g K

W

Heat Removed by chiller

Series1

61

11.0 References

Auliciems, Andris., S. V. Szokolay, International PLEA Organisation., University of Queensland.,

and Department of Architecture. 1997. Thermal Comfort. Brisbane, Qld.: PLEA in association

with Dept. of Architecture, University of Queensland.

Auliciems, Andris., S. V. Szokolay, International PLEA Organisation., University of Queensland.,

and Department of Architecture. Revised edition 2007. Thermal Comfort. Brisbane, Qld.: PLEA

in association with Dept. of Architecture, University of Queensland

Behl, Madhur, Truong Nghiem, and Rahul Mangharam. 2012a. “Green Scheduling for Energy-

Efficient Operation of Multiple Chiller Plants.” http://repository.upenn.edu/mlab_papers/47/.

Behl, Madhur, Truong X. Nghiem, and Rahul Mangharam. 2012b. “Green Scheduling for Energy-

Efficient Operation of Multiple Chiller Plants.” In , 195–204. IEEE. doi:10.1109/RTSS.2012.71.

Berkleys lab. 2006. “Berkeley Thermal Comfort.” http://smap.cbe.berkeley.edu/comforttool.

———. 2014. “Berkeley Thermal Comfort.” http://smap.cbe.berkeley.edu/comforttool.

Chang, Yung-Chung. 2007. “Optimal Chiller Loading by Evolution Strategy for Saving Energy.”

Energy and Buildings 39 (4): 437–44. doi:10.1016/j.enbuild.2005.12.009.

“chillermeps_factsheet_march2009(2).pdf.”

Chowdhury, Ashfaque Ahmed, M.G. Rasul, and M.M.K. Khan. 2008. “Thermal-Comfort Analysis

and Simulation for Various Low-Energy Cooling-Technologies Applied to an Office Building in

a Subtropical Climate.” Applied Energy 85 (6): 449–62. doi:10.1016/j.apenergy.2007.10.001.

———. 2009a. “Modelling and Analysis of Air-Cooled Reciprocating Chiller and Demand Energy

Savings Using Passive Cooling.” Applied Thermal Engineering 29 (8-9): 1825–30.

doi:10.1016/j.applthermaleng.2008.09.001.

Chowdhury, Ashfaque Ahmed, M. G. Rasul, and Mohammad Masud Kamal Khan. 2009b.

“Modelling and Analysis of Air-Cooled Reciprocating Chiller and Demand Energy Savings

Using Passive Cooling.” Applied Thermal Engineering 29 (8): 1825–30.

http://www.sciencedirect.com/science/article/pii/S1359431108003694.

DOE. 1980. “Building Energy Use and Cost Analysis Program” 2 (June).

———. 2006a. “Building Energy Use and Cost Analysis Program” 6 (41 - 44).

———. 2006b. “Building Energy Use and Cost Analysis Program Volume 6.” James J.Hirsch &

Associates.

“Fact Sheet - Water Chillers: Minimum Energy Performace Sandards.” 2008. Chiller Meps.

www.eergyrating.gov.au.

F. Kapel. 2008. “Basic Concepts in the Methodology of Mathmaical Modeling.” University of Graz.

Hendrickson, Alan E., and Paul Owen White. 1964. “Promax: A Quick Method for Rotation to

Oblique Simple Structure.” British Journal of Statistical Psychology 17 (1): 65–70.

http://onlinelibrary.wiley.com/doi/10.1111/j.2044-8317.1964.tb00244.x/full.

Holopainen, Riikka, and Valtion teknillinen tutkimuskeskus. 2012. “A human thermal model for

improved thermal comfort.”

Hydeman, Mark, S. T. Taylor, and D. Winiarski. 2001a. “Application of Component Models for

Standards Development.” TRANSACTIONS-AMERICAN SOCIETY OF HEATING

REFRIGERATING AND AIR CONDITIONING ENGINEERS 108 (1): 742–50.

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http://www.taylor-engineering.com/downloads/articles/ASHRAE%20Symposium%20AC-02-9-

2%20Cooling%20Tower%20Model-Hydeman%20%26%20Taylor.pdf.

———. 2001b. “Application of Component Models for Standards Development.” TRANSACTIONS-

AMERICAN SOCIETY OF HEATING REFRIGERATING AND AIR CONDITIONING

ENGINEERS 108 (1): 742–50. http://www.taylor-

engineering.com/downloads/articles/ASHRAE%20Symposium%20AC-02-9-

2%20Cooling%20Tower%20Model-Hydeman%20%26%20Taylor.pdf.

Hydeman, Mark, N. Webb, Priya Sreedharan, and Steve Blanc. 2002. “Development and Testing of a

Reformulated Regression-Based Electric Chiller Model.” ASHRAE Transactions 108 (2): 1118–

27. http://www.taylor-engineering.com/downloads/articles/ASHRAE%20Symposium%20HI-02-

18-2%20Symposium%20Hydeman%20%26%20Webb%20New%20Chiller%20Model.pdf.

INNOVA. 1997. “Thermal Comfort.” Airtech Instruments.

James J, and Hirsch & Associates. 2006. “Building Energy Use and Cost Analysis Program” 6 (41 -

44).

Kim, Young-Sub, and Kang-Soo Kim. 2007. “Simplified Energy Prediction Method Accounting for

Part-Load Performance of Chiller.” Building and Environment 42 (1): 507–15.

doi:10.1016/j.buildenv.2005.09.001.

Lam, Joseph C. 2000. “Energy Analysis of Commercial Buildings in Subtropical Climates.” Building

and Environment 35 (1): 19–26.

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Refrigeration System.” Applied Thermal Engineering 25 (14-15): 2258–71.

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Optimization—in-Building Section.” Energy and Buildings 37 (1): 11–22.

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“Multiple Chiller System Design and Control.” 2001. TRANE.

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Fault Detection.” Lawrence Berkeley National Laboratory.

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in Hong Kong.” Building and Environment 40 (1): 143–51. doi:10.1016/j.buildenv.2004.05.009.

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63

12.0 Appendix

12.1a Additional comments on software and possible enhancements

The software was developed in visual basic and all of its source code is available on the

disk/softcopy submitted together with the thesis. The software was meant to perform all the

steps of the algorithm explained on page 34 of section 4.7.5 however with time it was only

possible to develop the software to calculate the different combinations possible with every

incrementing load. From this the combination distribution and analysis was possible to be

completed in excel and the electricity usage of each load calculated. Future developments may

require eliminating the use of excel as a slight change in the analysis would mean, quite an

endeavour to calculate in excel.

Figure 31 showing the software to perform the chiller analysis.

12.1b Additional functionality that can be added to the software

12.1.1 Input and calculation of Manual chiller staging

Due to time constraints the software was only programmed to calculate the automated holistic

combinations and an option for force sequencing of the chillers was not included, the actual sites chiller

sequencing is performed in excel including the electricity consumptions of each sequence combination.

64

An update to the software that would include this would simplify the analysis and ease the examination

process. Also manual input of a different sequencing regime would allow for analysis If a signal or

multiple chiller was offline due to maintenance. At the moment this can again be performed in excel and

is quite an endeavour to undertake. All these manual calculations can again go through the normal

process to be compared with the holistic results calculated by the software and concluded to which one

would be the best sequencing method to use.

12.1.2 Energy output calculation

Using a load profile of the particular chiller site, the optimum calculated chiller sequence calculated for

that load will give us only the power output, however the load profile will tell us how long a particular

chiller combination will be held and run for, multiplying this chiller combined power by the time will give

us how much energy will be used or saved at that particular sequence and time period. This can be done

with availability of extra site data and extra development of the software however energy analysis

would be very important as at the moment the data available only allows calculations of the optimum

combinations that are expressed and compared in terms of power.

Calculating the energy usage can also help calculate the energy bill if the electricity tariffs are considered

and input in the software.

12.1.3 Curve adaptation

Curve adaptation would be important in order to factor in the humidity and other environmental factors

that affect the COP curve of the chiller. It is anticipated that the chiller curve would change slightly with

environmental factors mostly humidity and temperature as a chillers coefficient of performance changes

with humidity because of the nature of the evaporative cooling. With higher humidity the chillers

efficiency lowers down and again increases with lower humidity as it is easier for the liquid in the

cooling towers to evaporate and dissipate heat. Hence the COP curve is expected to be different with

these changing conditions having the software to factor in these changes and calculate how far up or

own the curve changes would be quite ideal.

12.2 Additional information for Software developed for analysis.

12.2.1 Chiller simulation sizes

The software was developed in order to ease the analysis process and proves to be valuable tool for

this purpose. Other functionalities available for the procedural analysis are the listing of the

simulation sizes as described in figure 32

65

Figure 32 showing the list sim sizes functionality

This part of the functionality was only used in the development process and is not used by the

user rather it gives an overview of what is happening in the background of the software

calculation as listing the sizes of the chillers to simulate in order to calculate the electricity

usage of the sequencing regime. The simulation size option calculates all the chiller sizes to

consider in order to achieve the power load demanded. This is done by increasing every chiller

available with the incremental step resolution that has been input. Figure 32 shows the chiller

sizes for the chillers as follows:

For the first chiller the possible sizes are

1. Chiller 1 : Size 0 KW with a Part load ratio of 0% then

2. Chiller 1 : Size 400KW with a Part load ratio of 0.2 or 20% then

3. Chiller 1 : Size 800 KW with a Part load ratio of 0.4 or 40% then

4. Chiller 1 : Size 1200 KW with a Part load ratio of 0.6 or 60% then

5. Chiller 1 : Size 1600 KW with a Part load ratio of 0.8 or 80% then lastly for this chiller

6. Chiller 1 : Size 2000 KW with a Part load ratio of 1 or 100%

This is continued for all the chillers until the entire chiller size option is exhausted. The

user does not get to use this function much however it is essential for the program as

this is the first stage of calculation that the program does.

66

First it calculates chiller sizes and then it starts combining them, this is the second stage

and is explained in the next section.

12.2.2Matching the combination of simulation sizes.

The second step of the software procedural calculation is to match the different simulation sizes

calculated in the first stage explained in section 12.2.1 and correspond these to an increasing load this is

shown in figure 33 below

Figure 33 shows the results after executing the Match sim sizes button

The Matching simulation size button produces the following results fot our example.

67

For every load it calculates all the possible combinations of chillers available that can supply

that load. Figure 34 shows an example of these results.

A load of 400KW can either be met by all the chillers being equated to zero while increasing

ever chiller by only switching it on to 400KW. Even though this is so it is important to note that

the PLR will be different and this function is added in the 3rd step of the programs procedure.

The fourth button on the software is the Results button which was not fully developed and

apart from showing the increasing load does not offer any further significance at this stage

however it was intended to show the graphical form of the chiller options.

Further staging information

It is important for the program to calculate the PLR of each of these combinations because the

closer the PLR is to 100% the more efficient that particular chiller will run, however it is

important to note that this is considering a basic chiller curve and is not absolutely valid for

every curve for some curves can have their optimum point at 80% or 90% PLR ratio however

this is only stated for intuition purposes however the actual energy will be calculated when the

curve of the chiller is considered.

Figure 35 shows the results of these simulation sizes and their Part load ratios.

68

The software was intended to rank all these combinations however time restrictions meant that

the rest of the analysis was performed in excel which is ok for a once of calculation however if

the procedure changes it would be quite a daunting task. Another further development of the

software would be to continue the analysis all the way to the end, not having to use excel

would prove to be a good advancement to the software functionality.

The tick boxes are also for entering the manual sequencing of a chillers site regime; however

this was also not possible to implement due to time constraints. The source code of the

software is available with the softcopy of the thesis document however a sample of the visual

basic program for the first function explained that just lists the simulation sizes is shown below.

Private Sub Button2_Click(ByVal sender As System.Object, ByVal e As System.EventArgs) Handles Button2.Click 'Chiller Simulation sizes Calculation 'Begins Dim r, p, l, m As Integer Dim nc, nd, ne, nf, ng As Integer j = TxtchillerA.Text / TxtRes.Text + 1 k = TxtChillerB.Text / TxtRes.Text + 1 nc = TxtChillerC.Text / TxtRes.Text + 1 nd = TxtChillerD.Text / TxtRes.Text + 1 ne = TxtChillerE.Text / TxtRes.Text + 1 nf = TxtChillerF.Text / TxtRes.Text + 1 ng = TxtChillerG.Text / TxtRes.Text + 1 ' Dim ChillerArray(j, k) ReDim Preserve xchA(j) ReDim Preserve xchB(k) ReDim Preserve xchC(nc) ReDim Preserve xchD(nd) ReDim Preserve xchE(ne) ReDim Preserve xchF(nf) ReDim Preserve xchG(ng) ReDim Preserve PLRch1(j) ReDim Preserve PLRch2(k) ReDim Preserve PLRch3(nc) ReDim Preserve PLRch4(nd) ReDim Preserve PLRch5(ne) ReDim Preserve PLRch6(nf) ReDim Preserve PLRch7(ng) listarray.Items.Clear() r = 0 p = 0 l = 0 m = 0 'Calculate all sizes for particular chiller. CALC sizes chiller A. 'ReDim Preserve xchTEMP(j) 'ReDim Preserve PLRchTEMP(j) 'p = TxtchillerA.Text 'calcChillerSize(p) 'l = xchTEMP.GetLength(l) 'Array.Copy(xchTEMP, xchA, l) Do While p <= TxtchillerA.Text ' listarray.Items.Add(p)

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' ChillerArray(l, 0) = p xchA(l) = p PLRch1(l) = xchA(l) / TxtchillerA.Text p += TxtRes.Text l += 1 Loop 'Calculate all sizes for particular chiller. 'ReDim Preserve xchTEMP(k) 'ReDim Preserve PLRchTEMP(k) 'p = TxtChillerB.Text 'calcChillerSize(p) 'l = 0 'l = xchTEMP.GetLength(l) 'Array.Copy(xchTEMP, xchB, l) Do While r <= TxtChillerB.Text xchB(m) = r PLRch2(m) = xchB(m) / TxtChillerB.Text r += TxtRes.Text m += 1 Loop ''Calculate all sizes for particular chillerC 'p = TxtChillerC.Text 'calcChillerSize(p) 'l = xchTEMP.GetLength(l) 'Array.Copy(xchTEMP, xchC, l) 'xchA() = xchTEMP() 'listchillersizes(xchA() As integer 'LIST in List Box For p = 0 To (l - 1) 'listarray.Items.Add(ChillerArray(j, k)) listarray.Items.Add("Chiller 1. " & xchA(p) & "KW ..... PLR " & PLRch1(p)) ' p += 1 Next For r = 0 To (m - 1) 'listarray.Items.Add(ChillerArray(j, k)) listarray.Items.Add("Chiller 2. " & xchB(r) & "KW .... PLR " & PLRch2(r)) ' r += 1 Next 'Chiller Simulation sizes Calculation 'Ends 'Combination Calculations begins ' For End Sub Public Sub listchillersizes(ByVal l As Integer, ByVal i() As Integer, ByVal j() As Integer) Dim p = 0 Dim PLRch1() As Integer For p = 0 To (l - 1) 'listarray.Items.Add(ChillerArray(j, k)) listarray.Items.Add("Chiller 1. " & xchA(p) & "KW ..... PLR " & PLRch1(p)) ' p += 1 Next End Sub 'Public Function calcChillerSize(ByVal chsize As Integer, ByVal xarray() As Integer, ByVal xPLR() As Double) As Integer Public Function calcChillerSize(ByVal chsize As Integer) As Integer Dim l, p As Integer

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p = 0 l = 0 Do While p <= chsize ' listarray.Items.Add(p) ' ChillerArray(l, 0) = p xchTEMP(l) = p PLRchTEMP(l) = xchTEMP(l) / chsize p += TxtRes.Text l += 1 Loop Return chsize End Function

12.3 Additional information on the Murdoch chiller system and

information available from BMS

12.3.1 System overview

A typical BMS is made up of multiple Automation Servers (AS) which are brains of the

individual chiller units and they control and monitor every single aspect of these units. The

AS is a small industrial computer that runs on a special version of Linux Operating system

however the hardware is proprietary to Schneider electric, The AS servers communicate

with monitoring equipment on its input and output buses using LON and MODBUS protocols

which are also open source communication protocols and allow the Schneider electric AS to

communicate with old legacy or even newer forms of equipment that may have been

developed by another company.

The AS can theoretically control every gadget, At Murdoch a typical AS would control

classroom clocks, chillers, lights etc. Figure 36 shows a struxuware BMS server that shows

the physical connection of the Automation servers on a standard site. Struxuware is one of

the server systems used at Murdoch university and it has to be integrated with other servers

that also collect data on some of the chiller sections, augmentation and interfacing of all this

data has been one of the reasons for the delay of the Murdoch project and the different

server systems used different database configuration and standards.

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Figure 36 showing a struxuware BMS server and its hardware configuration.

The other server system used at Murdoch University is vista server which is shown in figure

37. It is shown using TAX Xentra controllers which are AS that have been manufactured by a

different company other than Schneider .

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Figure 37 showing another format of a BMS network Architecture

12.3.2 BMS home screen

The figures 38 through to X show the screen shots available from the BMS used Murdoch system.

The system starts by showing a campus map and can be zoomed in to , in order to find the required

data and components available per building or per chiller site. Most of the buildings are coded by

numbers and not name, while so ; when a particular building is zoomed in its name still appears on

the left column of the building heading.

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Figure 38 Building management system home screen starts by showing the campus map. The

buildings can be clicked and zoomed into, analysed and observed.

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Figure 39 showing the building number codes across Murdoch campus

Figure 40 shows the vet chiller system zoomed in all the data available is shown graphically, however it is worth

noting that extracting this data into an excel format is slightly a different procedure , that has to be done more

manually.

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Figure 40 is a Vista screen shot showing the VETS chiller system, with all the associated parameters, the

temperatures of the chilled water going in the condenser and leaving, also the building return and

leaving water temperature the diagram is also showing the pipe pressures and the temperatures of

water going and leaving the evaporation towers.

Figure 41 The screen shot shows the staging information that has been programmed in the server

system, information of when to initiate which chiller can be seen that they are 6 stages that the

Murdoch chillers go through.

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Figure 42 Vista screen shot showing the Library South chiller system, with all the associated parameters,

the temperatures of the chilled water going in the condenser and leaving, also the building return and

leaving water temperature the diagram is also showing the pipe pressures and the temperatures of

water going and leaving the evaporation towers.

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This figure 43 shows the two air chillers and there parameters, with all the associated parameters, the

temperatures of the chilled water going in the condenser and leaving , also the building return and

leaving water temperature the diagram is also showing the pipe pressures and the temperatures of

water going and leaving the air-cooled towers.

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Figure 44 shows the newly installed energy meters interface that measure the heat flow from the chiller

load.

Figure 45 Another building map accessed via the vista screen. Many internal aspects of the buildings can

be controlled from the BMS installed by Schneider electric, even the school classroom clocks are

controlled and synchronized by the main server