Study of Electrical Usage and Demand at the Container Terminal30048431/tran-studyelectrical... ·...
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Study of Electrical Usage and Demand at the Container Terminal
Transcript of Study of Electrical Usage and Demand at the Container Terminal30048431/tran-studyelectrical... ·...
Study of Electrical Usage and Demand at the
Container Terminal
To Tam, Matthew and Chloe
i
Acknowledgements
I am particularly indebted to my principal supervisor Professor Alfred Deakin
Professor Saeid Nahavandi for his constant guidance and support throughout this
PhD. I am very grateful for his insights, assistance, patience and support over the last
few years. This thesis would not be completed without his encouragement and
support.
I also would like to thank my associate supervisor Dr. Doug Creighton for the
valuable guidance and advice he provided me during the course of my PhD.
I would especially like to thank Robert Reid of Robert Reid and Associates, a mentor
and colleague who arranged for permissions to collect data at Melbourne container
terminals for this study. He also provided access to data of overseas container
terminals for validating the results.
I acknowledge Patrick Stevedores, P&O Ports, Hutchison Port Holdings and Maher
Terminal Holding Corp. for their assistance in providing the data.
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ABSTRACT
Modeling and simulation techniques are the tools to be used for optimizing the
operation and fully utilize access of a container terminal for a projected container
throughput. The container terminal operator uses these study results to make
decisions and planning for the redevelopment and/or expansion of the terminal.
Usually, a new terminal layout with new truck traffic and more container handling
machines is required to cope with the projected container throughput. It is then the
electrical engineers’ task to calculate the terminal maximum electrical load demand
and design the electrical infrastructure accordingly.
A container terminal is a specific engineering field and currently there is no standard
or guidance for electrical engineers to accurately calculate the maximum electrical
demand. This study of electrical usage and demand at the container terminal was a
practical approach to:
addressing the problem of how to estimate/calculate the maximum electrical
demand of a container terminal with known number of electrical equipment
and
contributing to the understanding of regenerative energy issue of container
handling cranes at the container terminal.
Operation and electrical data at a Melbourne container terminal were daily collected
for more than two (2) years for this study. Collected operation data was analysed
according to the number of containers, their weights and set temperature for
refrigerated containers (reefers). Container weights were used to calculate the
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electrical demand of the container handling cranes. Collected daily electrical data
was correlated to the number of reefers to determine the electrical demand of these
reefers. Maximum electrical demands of container handling cranes and reefers were
determined by analysing all calculated values over the whole data collection period.
Maximum electrical demand of the container terminal was then calculated by adding
the other loads at the terminal: office, lightings and workshop.
The maximum electrical demands of several container terminals in Australia, USA,
Canada and China were calculated using the results of this study and the other
method (the diversity factor method). These calculated maximum electrical demands
were compared with the actual electrical demands with pleasing results: whilst at
least 34% less than the value calculated using the other method, the electrical
demand calculated using the results of this study was indeed the MAXIMUM
DEMAND and still with ample spare capacity of at least 20% for the safety margin
and future expansion of the terminal.
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Tables of Contents Table of Contents iv
List of Abbreviations viii
List of Figures ix
List of Tables xiii
List of Formula xiv
1. Introduction 1
1.1 Background information 1
1.2. Research aims and objectives 4
1.3. Outline of the thesis 6
2. Literature Review 7
2.1 Overview Papers 7
2.2 Electrical Energy Usage and Demand Papers 9
2.3 Formula for Electrical Power calculation 11
3. Electrical Assets Identification and Set up Data Collection Scheme 19
3.1 Identification of electrical assets at container terminal 20
3.1.1 Processes at container terminal 20
3.1.2 Electrical assets at container terminal 26
3.2 Definition of Electrical Demand 28
3.2.1 Definition from the Utilities 28
3.2.2 Definition from Electricity Bills and measured energy 29
3.2.3 Definition from Digital Power Meters 32
3.3 Focusing study on average electrical demand 34
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3.3.1 Reasons for focusing study on average demand instead of peak
demand 35
3.3.2 Decision of focusing the study on average demand 36
3.4 Set up at Melbourne Container Terminal for collecting data 36
3.5 Conclusions 40
4. Container Handling Cranes 42
4.1 Brief Discussion of container handling cranes 43
4.2 Load Profiles of Quay Crane – Comparison between AC and DC drive
systems 47
4.2.1 AC and DC quay cranes under study 48
4.2.2 Study results 49
4.2.3 Study conclusions 57
4.3 Container Weight Analysis 57
4.3.1 Weight of container – container ship and ISO standard 57
4.3.2 Weight of container at Melbourne Container Terminal 60
4.3.3 Results of analysing data collection 63
4.3.4 Conclusions of weight analysis 63
4.4 Calculate Demand & Energy usage of container handling cranes 67
4.4.1 Quay Crane and Maximum Electrical Demand 67
4.4.2 RMG and ASC and maximum Electrical Demand 70
4.5 Conclusions 72
5. Refrigerated Container 74
5.1 Brief Description of Refrigerated Container 74
5.2 Estimate Electrical Demand of Refrigerated Container 76
5.2.1 Maximum Demand of a Reefer Stack 77
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5.2.1.1 Demand calculation using Australian Standard AS3000 77
5.2.1.2 Demand calculation using diversity factor 80
5.2.1.3 Other demand calculation method 81
5.2.1.4 Reefer demand information from Container Handbook 82
5.2.1.5 Demand calculation based on heat transfer & required cooling 83
5.3 Measure the actual reefer electrical demand 88
5.3.1 Description 88
5.3.2 Data collection and analysing 89
5.3.3 Results of analysing data collection 95
5.4 Comparison of maximum demand calculated by different methods 104
5.5 Conclusions 108
6. Reducing electrical maximum demand and energy usage 109
6.1 Reducing electrical maximum demand 109
6.1.1 Improving power factor to reduce maximum demand 109
6.1.2 Using cranes with DC drive system to reduce maximum
Demand 112
6.2 Reducing electrical energy usage 113
6.2.1 Using cranes with DC drive system to reduce energy usage 113
6.2.2 Utilisation of the regenerative energy to reduce energy usage 114
6.2.3 Reduce energy usage by lighting 123
6.2.4 Energy Storage and Peak Lopping 126
6.3 Conclusions 130
7. Verification of this study results 131
7.1 Calculation of the maximum demand at container terminal 132
7.1.1 Calculation to AS/NZS 3000:2007 133
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7.1.2 Calculation using diversity factors 134
7.1.3 Calculation using findings of this study 135
7.2 Maximum demand at Container Terminals 136
7.3 Comparison of the results 140
7.4 Conclusions 149
8. Conclusions and directions for future research 150
8.1 Conclusions 151
8.2 Directions for future research 153
Appendix
Appendix A Daily Container Report, Code of Excel macro & Results 155
Appendix B Daily Reefer Report, Code of Excel macro & Results 161
Appendix C Specific Heat Capacity of various Products 173
Appendix D Calculated Reefer Electrical Demand using Heat transfer and
Cooling require Method 174
Appendix E Data Volume 183
References 184
viii
List of Abbreviations
AC Alternating Current
AGV Automatic Guided Vehicle
ASC Automatic Stacking Crane
DC Direct Current
ESCAP Social Commission for Asia and the Pacific
EMS Energy Management System
RMG Rail Mounted Gantry
RTG Rubber Tyred Gantry
QC Quay Crane
SC Straddle Carrier
STS Ship to Shore Crane
SWL Safe Working Load
ix
List of Figures
3.1 Stowage plan of a container ship
20
3.2 Quay cranes
21
3.2 Straddle Carrier
21
3.4 Container ship unloading plan
21
3.5 Melbourne Container Terminal storage stack
22
3.6 Straddle Carrier deliver container to truck
23
3.7 Container ship loading
24
3.8 Processes at Container Terminal
25
3.9 Port Botany Terminal – November 2010 Electricity bill
29
3.10 Single Line Diagram with measuring devices locations
38
3.11 Energy Management System Layout
39
4.1 Different forms of quay cranes
44
4.2 Quay Cranes - Type of Lifts
45
4.3 Rail Mounted Gantries
46
4.4 Automatic Stacking Cranes
46
4.5 AC quay crane – Graph of powers vs. time (second)
50
4.6 DC quay crane – Graph of powers vs. time (second)
50
4.7 AC quay crane – Graph of powers vs. time (second) for one loading cycle
51
4.8 DC quay crane – Graph of powers vs. time (second) for one loading cycle
51
4.9 AC quay crane – Graph of power factor vs. time (second) for one loading cycle
54
x
4.10 DC quay crane – Graph of power factor vs. time (second) for one loading cycle
54
4.11 AC quay crane – Graph of THD (%) vs. time (second) for one loading cycle.
56
4.12 DC quay crane – Graph of THD (%) vs. time (second) for one loading cycle.
56
4.13 Drawing showing stacking area at Melbourne Container Terminal
60
4.14 Number of container at Melbourne Container Terminal in 2007 – 2008
66
4.15 Percentage of 40’ container, empty container and heavy container at Melbourne Container Terminal in 2007 – 2008
66
4.16 Average weight of container and TEU at Melbourne Container Terminal in 2007 – 2008
67
4.17 Calculation of average electrical demand of quay crane
69
4.18 Calculation of average electrical demand of RMG/ASC
71
5.1 Refrigeration supply system for porthole container
75
5.2 Clip on unit for transport by road
75
5.3 Portholes at the end of a porthole container
75
5.4 Integral refrigerated containers
76
5.5 Photo showing Reefer location at Melbourne Container Terminal
89
5.6 Drawing showing Reefer location at Melbourne Container Terminal
89
5.7 Electrical Demand per Reefer in 2007
96
5.8 Electrical Demand per TEU in 2007
98
5.9 Electrical Demand per Reefer in 2008
99
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5.10 Electrical Demand per TEU in 2008
100
5.11 Electrical Demand per Reefer in 2009
101
5.12 Electrical Demand per TEU in 2009
102
5.13 Mix Reefer sizes in storage at Melbourne Container Terminal
107
6.1 Reducing electrical demand by improving power factor
112
6.2 Single line diagram of substation D
117
6.3 Energy consumption without utilization of regenerative energy
120
6.4 Energy consumption without utilization of regenerative energy
121
6.5 High mast lighting at container terminal
124
6.6 Container terminal at night
124
6.7 Quay Crane load profile
128
6.8 Proposal from Powercorp using flywheel technology to limit peak demand at 500kW and allow 100kW regenerative energy to be utilized by other load
129
6.9 Proposal from S and C using super capacitor technology to limit peak demand at 400kW and capture all regenerative energy
129
7.1 East Swanson Dock terminal – actual and calculated maximum electrical demands
140
7.2 West Swanson Dock terminal – actual and calculated maximum electrical demands
141
7.3 Swanson Dock terminals – actual and calculated maximum electrical demands
142
7.4 Port Botany terminal – actual and calculated maximum electrical demands
143
7.5 Fisherman Islands terminal – actual and calculated maximum electrical demands
144
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7.6 China – Yantian terminal – actual and calculated maximum
electrical demands
145
7.7 Canada – Fairview terminal – actual and calculated maximum electrical demands
146
7.8 USA – Maher terminal – actual and calculated maximum electrical demands
147
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List of Tables 3.1 Port Botany Terminal – Meter 1 data for November 2010
30
3.2 Port Botany Terminal – Meter 2 data for November 2010
31
3.3 Port Botany Terminal – Summary of Electricity foe November 2010
32
4.1 Main data of Quay cranes under observation
48
4.2 Results of measurement
53
4.3 Container ship capacity and deadweight
58
4.4 Dimension and Payload of container
59
4.5 Sample of Container daily Report
61
4.6 Results of running “CONTAINERS” macro
63
4.7
Weight Analysis of container at Melbourne Container Terminal 65
5.1 Maximum Demand non-domestic Electrical Installation
70
5.2 Cooling capacity of Reefer Power Unit
85
5.3 Calculated Average Electrical Demand of different reefer cargo
88
5.4 Example of Reefer daily Report
91
5.5 Example of Reefer power Report
92
5.6 Results (temperature analysis) of running “REEFERS” macro
94
5.7 Results (weight analysis) of running “REEFERS” macro
95
5.8
Reefer Electrical Average demand 97
5.9 Maximum Demand calculated using different methods
104
6.1 Extract from Yantian 2005 report on QC CONSUMPTION STUDY
115
6.2 Recorded consumed real energies at substation D
119
7.1 Calculated maximum demand at Australian Container Terminal
138
7.2 Calculated maximum demand at Overseas Container Terminal
139
7.3 Comparison of calculated and actual maximum Electrical Demand 148
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List of Formulas 2.1 Basic motion formula – Distance
12
2.2 Basic motion formula – Distance
12
2.3 Hoist Power with Load
12
2.4 Lower Power with Load
12
2.5 Hoist acceleration Power with Load
13
2.6 Hoist deceleration Power with Load
13
2.7 Lower acceleration Power with Load
13
2.8 Lower deceleration Power with Load
13
2.9 Hoist motor acceleration Power with Load
13
2.10
Hoist motor deceleration Power with Load
13
2.11 Lower motor acceleration Power with Load
13
2.12 Lower motor deceleration Power with Load
13
2.13 Hoist total acceleration Power with Load
13
2.14 Hoist total Power with Load
13
2.15 Hoist total deceleration Power with Load
13
2.16 Lower total acceleration Power with Load
13
2.17 Lower total Power with Load
13
2.18 Lower total deceleration Power with Load
13
2.19 Hoist Power without Load
14
2.20 Lower Power without Load
14
2.21 Hoist acceleration Power without Load
14
2.22 Hoist deceleration Power without Load
14
2.23 Lower acceleration Power without Load
14
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2.24 Lower deceleration Power without Load
14
2.25 Hoist motor acceleration Power without Load
14
2.26 Hoist motor deceleration Power without Load
14
2.27 Lower motor acceleration Power without Load
14
2.28 Lower motor deceleration Power without Load
14
2.29 Hoist total acceleration Power without Load
14
2.30 Hoist total Power without Load
14
2.31 Hoist total deceleration Power without Load
14
2.32 Lower total acceleration Power without Load
14
2.33
Lower total Power without Load
14
2.34 Lower total deceleration Power without Load
15
2.35 Friction Load with Load
15
2.36 Wind Load with Load
15
2.37 Main Hoist rope inflexibility with Load
15
2.38 Static Power in Adverse Wind with Load
15
2.39 Static Power in favourable wind with Load
16
2.40 Trolley acceleration Power
16
2.41 Trolley deceleration Power with Load
16
2.42 Trolley motor acceleration Power with Load
16
2.43 Trolley motor deceleration Power with Load
16
2.44 Cross travel total acceleration Power in adverse wind with Load
16
2.45 Cross travel total Power in adverse wind with Load
16
2.46 Cross travel total deceleration Power in adverse wind with Load
16
2.46 Cross travel total acceleration Power in favourable wind with Load
16
2.48 Cross travel total Power in favourable wind with Load
16
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2.49 Cross travel total deceleration Power in favourable wind with Load
16
2.50 Friction Load without Load
16
2.51 Wind Load without Load
17
2.52 Main Hoist rope inflexibility without Load
17
2.53 Static Power in Adverse Wind without Load
17
2.54 Static Power in favourable wind without Load
17
2.55 Trolley acceleration Power without Load
17
2.56 Trolley deceleration Power without Load
17
2.57 Trolley motor acceleration Power without Load
17
2.58 Trolley motor deceleration Power without Load
17
2.59 Cross travel total acceleration Power in adverse wind without Load
17
2.60 Cross travel total Power in adverse wind without Load
17
2.61 Cross travel total deceleration Power in adverse wind without Load
17
2.62 Cross travel total acceleration Power in favourable wind without Load
17
2.63 Cross travel total Power in favourable wind without Load
17
2.64 Cross travel total deceleration Power in favourable wind without Load
18
3.1 Total consumed Energy
31
3.2
Real Demand 31
3.3 Reactive Demand
31
3.4 Apparent Demand
32
3.5 Maximum Electrical Demand
32
5.1 Increase Temperature due to Heat transfer
86
5.2 Refrigerating Capacity for Cooling
86
5.3 Average Electrical Demand
87
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7.1 Maximum Electrical Demand
133
7.2 AS/NZS:3000 calculation method – Reefer Load Demand
133
7.3 AS/NZS:3000 calculation method – Crane Load Demand
133
7.4 Diversity Factor Method – 20’ Reefer Load Demand
134
7.5 Diversity Factor Method – 40’ Reefer Load Demand
134
7.6 Diversity Factor Method – Reefer Load Demand
135
7.7 Diversity Factor Method – Crane Load Demand
135
7.8 Results from this study – Reefer load Demand
136
7.9 Results from this study – Crane load Demand
136
1
CHAPTER ONE
Introduction
1.1 Background information
Containerization is the use of transport containers to unitize cargo for supply,
transportation and storage without the need for intermediate handling of the content.
Since the introduction in 1956 [84], containerization of cargoes is becoming ever
more widespread worldwide and almost all products are now transported by
container.
In the Container Traffic Forecast [65] published by United Nation Economic and
Social Commission for Asia and the Pacific (ESCAP), container traffic has grown
substantially from 28.7 million twenty-foot equivalent units (TEUs) in 1990 to
113.6 million TEUs in 2005. This is corresponding to an average annual compound
growth of 9 percent. The forecast suggest continued trend of increasing of the
container traffic of annual compound of 7.6 percent till 2015 taking into account the
World Economic Crisis 2008/2009. It is expected a traffic of 235.7 million TEUs in
2015.
The growth in the container traffic leads to the growth in the capacity of the
container ship as the shipping lines prefer to use larger container ship to lower the
costs. It is claimed that the transportation cost per container for the sixth generation
container ship (Post-Suezmax) may be about 30% lower than that of a typical
2
5,000-6,000 TEUs container ship. Historical development of container ships [20,
22] is shown below:
1. First generation Small Feeder < 1,000 TEUs
2. Second generation Feeder 1,000 - 2,500 TEUs
3. Third generation Panamax 2,500 - 4,500/5,000 TEUs (draught of 12m)
4. Fourth generation Post-Panamax 4,500/5,000 - 10,000 TEUs (draught
of 13m)
5. Fifth generation Suezmax 10,000 - 12,000 TEUs (draught of 16.4m)
6. Sixth generation Post-Suezmax > 12,000 TEUs (draught of 21m)
With the intended increase of the cross section breadth and depth of the Suez Canal
over the coming ten years, the 18,000 TEUs container ship will also be able to pass
the Suez Canal [50]. On the other hand, a future container ship with a draught of 21
m would require existing ports to be dredged. Today, only the ports of Singapore
and Rotterdam are deep enough.
Given the expected growth of container traffic, most container terminals around the
world have terminal expansion and development projects that are either planned or
currently underway. Deploying more container handling machines, leasing more
land, changing operation mode are examples of such plans. Before spending any
money, all container terminal operators like to optimize their operation and fully
utilize their access (land, machines, labours etc.) to produce the maximum
3
productivity [33, 41, 68, 71, 86]. Modelling and simulation appear to be the best
tool for this optimization task.
A lot of simulations have been done to study and optimize the operation of existing
container terminal [23, 25, 27, 32, 35, 73, 74, 81, 83, 90, 108, 130, 174, 201] or
even design a new one. These simulations are carried out to find the impacts of
terminal layout [151], allocating berthing for ship [129, 131, 132, 152, 153, 154],
predicting number of cranes for certain handling rate [127], rail logistic, truck
logistic and even impact on in land transportation. However, none have been done
for the electricity power demand and consumption or the utilization of electrical
infrastructure of a container terminal.
Planing for a new container terminal or expanding an existing container terminal
must include the power demand at the initial design stage of such development.
Increasing number and size of container handling machines: Quay Cranes (QCs),
Rail Mounted Gantries (RMGs), Automatic Stacking Cranes (ASCs) and
Refrigerated Containers (Reefers) have brought a significant increase in electrical
power demand for container terminals [112]. Accurate assessment of the projected
electrical load is of critical importance as this electrical demand is used for:
- sizing and selection of principal electrical assets, thus impacting on the
capital cost of the electrical infrastructure,
- request an update or new electrical supply from the electrical power supply
company. Capital cost of electrical supply could be very high if the current
electrical network can not provide the requested demand.
4
1.2. Research aims and objectives
As container terminal is a specific engineering field and currently there is no
standard or guidance for electrical engineers to accurately calculate the electrical
demand [6], all are depended on the experiences of those engineers for this
estimation. This would normally lead to an over design of electrical infrastructure
and resulting in a very costly exercise if a new electrical switching station would be
built to supply the projected load demand. For example, in a recent re-development
of a container terminal in Australia, a load demand of 16MVA was stated for this
container terminal with 8 QCs, 5 RMGs and 800 refrigerated containers. A new
electrical switching station was required to supply such demand with a total cost of
around AUD 10 million. A similar size container terminal in Australia has an actual
load demand of only 4MVA.
There are number of private studies of energy consumption and power supply at
several container terminals that concern about their electrical bills [95].
Presentations [18] and information [70, 98, 136] about electrical demand and
energy usage of electrical machines are now a requirement as part of technical
documents to be submitted to electrical supply tenders called by all container
terminal operators.
The main aims of this research are: how to estimate/calculate the maximum
electrical demand of a container terminal with known number of electrical
equipments? What is the likely electrical energy usage for a container terminal with
a known through put? A practical approach is used to find out the answers:
5
With the permission of the container terminal management, installing a
power monitoring system consist of a server and number of digital power
meters for logging electrical data. At the same time, details of containers at
the terminal are provided for every day and monthly electrical invoices are
also obtained for comparison. . The monthly electrical invoices are also
obtained for confirmation of the analysed results. Data have been collected
for over two (2) years.
Learning the spreadsheet simulation technique from simulation conferences
papers [165, 166, 168, 169, 171, 177, 178, 179, 181, 182, 183, 184].
Calculation and spread sheet simulation are performed to estimate the
electrical load of the machine. Examining the working of the smart meter,
how power supply company calculate the demand and analysing the
collected data. Results are used for estimating the total demand of the
terminal.
Electrical energy consumptions at several other container terminals around
the world are also obtained to confirm the study results.
The environment concern of green house emission is also looked at by investigating
how to reduce such electrical demand end energy consumption - the design of
electrical network, the application of the new technical innovations such as
synchronizing operation of multiple machines and using peak lopping device.
This research looks into the gap left by previous researches and studies related to
container terminals. Hopefully, it will clarify some electrical issues contribute to
the knowledge of designing and operation of the container terminals.
6
1.3. Outline of the thesis
This thesis consists of eight chapters. In the next chapter, an introduction to
electrical power demand and energy usage at a container terminal and review of
related literature are presented. Chapter 3 outlines the operating environment of
container terminal, identifies the electrical assets to be studied, investigate how
electrical consumption is measured and charged then describe the set up of data
collection scheme. Chapter 4 looks at the container handling machines group
consists of Quay Cranes (QCs), Rail Mounted Gantries (RMGs) and Automatic
Stacking Cranes (ASCs). A brief discussion and focus on what would be studied
followed by obtaining the quay cranes’ specifications and profiles, discusses the
drive systems (DC and AC) and analysing the weights of containers in stack of
Melbourne Container Terminal from collected data and finally calculate the
electrical demand and energy usage of the container handling machines group.
Chapter 5 investigates the refrigerated containers, methods of calculating the
refrigerated container’s electrical demand, describes another way of calculation.
The actual (more than two years) measurements and calculation results are
tabulated for comparison. Chapter 6 discusses several ways of reducing the
maximum electrical demand and energy usage at container terminal ranging from
the design of electrical network to utilise the regenerative energy, requesting for net
metering scheme, the use of energy storage and peak lopping devices and lighting
level at the container terminal. Chapter 7 verifies the finding of this study by
showing the comparison between the actual electrical demand and the calculated
maximum electrical demand of several container terminals around the world.
Finally, chapter 8 will summarised the thesis, make concluding remarks as well as
recommendations for future research.
7
CHAPTER TWO
Literature Review
To the best of the author’s knowledge from the literature review and long time
working in the port, there was no published academic research into the electrical
energy usage and demand at container terminal. Literatures [49, 51, 52, 118, 195,
196, 197, 200, 205] on the rail/traction area had also been reviewed to find any
applicable information for use. Because of the lack of published research in this
field, the author had to rely on the commercial articles written for magazines
specialised in this field, the presentation at commercial conference as well as the
internal reports of various container terminal operators and electrical supply
companies for review and gather information.
The reviewed papers are grouped into following categories:
container terminal overview papers to provide an understanding of the
operation of container terminal,
electrical energy usage and demand papers to find what have been done in
this field and
formulas for electrical power demand calculation.
2.1 Overview Papers
A detailed literature review on the transhipment of containers at a container
terminal was given by Vis and Koster in 2002 [135]. Different type of material
8
handling as well as planning and control level involving the movement of
containers. The processes at container terminal are discussed next with the detailed
descriptions of each process with reference to relevant information when required.
These pre berth allocation, unload and loading of container ships, transportation of
containers from ship to storage area, stacking these containers and delivering them
to owner directly or by inter-terminal transportation. In the conclusion, they stated
that the majority of published papers only address single type of handling machine
so that the future work shall be concentrate in addressing multiple types of handling
machines for optimising the operation at container terminal.
On the same topic, Stahlbock and Vob [54] provided a comprehensive literature
review of research on optimising methods applied to container terminal operations.
The paper began with an update on the new challenges that the container terminal
operators have to overcome, especially with the requirement of handling new mega
size container ships capable of carrying 10,000 TEU to 12,000 TEU. They then
discussed the container terminal operation system and its sub system such as the
handling equipment, human recourses and supporting system. Research on
optimising methods was discussed in details of few particular subsystems that have
big impact on the operation such as berth allocation and stacking logistics. They
concluded the review with a summary; they also identified and suggested a number
of promising and interesting topics for future research.
9
2.2 Electrical Energy Usage and Demand Papers
It was reported early this year (March 2012) in the Port Technology International
[7] that a simulation model had been developed by Kim Le of AECOM for studying
the electric power of yard cranes. The concern about the increasing of required
electrical demand, especially when a large number of cranes are installed and
connected to the electrical grid at the container terminal, and the lack of suitable
method for calculating this demand was the reason for such study. The most
interesting result from this simulation study is that for 36 yard cranes with 700 KW
demand each totalling of 25,200 kW, the average demand of all 36 machines is only
1,000 kW and for a percentile of 99%, a demand of 3,240 kW is required. However,
the critical information is not provided: yard crane electrical data, container weight,
travel distances etc. for the readers to make use of the results. To an electrical
engineer reader, it appeared to have mixed up between electrical energy
consumption (kWHr) and electrical demand (kW) terminologies.
In the “Efficient use of energy in container cranes” article of the same magazine
Port Technology International, edition 48 [26], Fredrik Johanson of ABB described
the regenerative energy issue of electrical powered cranes and suggested ways for
utilising this energy especially for automatic stacking cranes.
In the “Driving innovation: high handling efficiency, low energy use” article of the
Port Technology International, edition 47 [28], Gottwald Port Technology described
a successful innovation for its mobile crane – using energy storage system to
capture the regenerative energy when the crane lowering and discharge this energy
when the crane hoisting.
10
Another useful information was described in the “Crane life cycle costs” in the Port
Technology International edition 20 [128] by Gerhard Fischer of Siemens that the
average net amount of energy required to move a container was 1.94 kWHr.
At the Terminal Operators Conference in 2005, Robert Reid of Robert Reid and
Associates had present a paper titled “Design, Installation and Electrical
Management of Container Terminals with Large Electrical Demand” [110]. An
overview of the electrical infrastructure of the container terminal and regulatory
requirements in Australia had been discussed. The finance impact as well as
benefits would be achieved by reducing the electrical demand. In discussion of the
electrical demand, the paper raised concern about the lack of accurate method for
calculating the maximum electrical demand. The actual facts were also presented:
average weight of container traffic, the large size of container handling cranes as
well as their characteristic, the affect of number of refrigerated containers in the
terminal, and the actual electrical energy consumption by the container terminal.
The paper concluded by stating that accurately calculating the maximum electrical
demand is really needed for designing a new container terminal or upgrading the
existing one.
In a presentation to DP World the terminal operator at Brisbane Port in 2011 [18]
for an Automatic Stacking Cranes (ASC) project, G Nordman of ABB presented an
Excel spreadsheet simulation for 12 ASCs. With a known operating characteristic of
one ASC, the simulation was performed with various operating conditions such as
fix hoisting delay between machines and assuming operating of multiple ASCs at
the same time would not cause any issue for the electrical supply network.
Following data is of interested:
11
For one ASC Maximum Demand 930 kW
Average Demand 69 kW
For 12 ASCs at hoisting delay of 20 seconds:
Maximum Demand 2,167 kW
Average Demand 822 kW
2.3 Formula for Electrical Power calculation
Part of tender documents submitted for bidding to supply container handling cranes
is that theoretical calculation of electrical power under pre-set operating conditions.
The author had access to the document of successful tenders providing the container
crane to various container terminals in Australia [70, 98, 136]. For this study,
electrical demand calculation would have to be performed and reviewing these
documents for formulas used in electrical power calculation has the advantage of all
needed formulas are available saving time in reviewing a lot of different text books
[121, 207, 210] for needed formulas.
When calculating the maximum electrical demand, boom hoist and long travel
motions can be ignored because:
the boom motion is only used to put the crane in the working position to
start loading/unloading containers and to stow the boom at the end of its
work,
other motions are not available when boom hoist is in use.
the boom’s electrical motor is not as large as the hoist’s electrical motor, the
demand is not the maximum demand
other motions are not available when long travel is in use
12
the long travel’s electrical motor is not as large as the hoist’s electrical
motor, the demand is not the maximum demand
Basic motion formulas:
vts (Eq. 2.1)
tvats 02
21 (Eq. 2.2)
Where v speed in m/sec
v0 initial speed in m/sec
s travel distance in m
t travel time in second
The following naming index conventions are used on all formulas in this section:
Nxy Power (in Watts) with: x = 1 for motion with load, x = 2 for motion without load y = 1, 2,… for different powers Pwxyz Total Power (in Watts) with: w : w = 1 for motion with load and w = 2 for motion without
load x : H for Hoisting, L for Lowering, XT for cross Travel and
LT for Long Travel. y : A for acceleration, D for deceleration & nothing for motion
at constant speed z : W for travel against wind, NW for travel with wind, nothing
for hoist motion
A. Hoist/Lower motion The following formulas are used to calculate the average demand of the hoist motion: With load (lift container)
Hoist Power u
VgLSLLN*60
**)( 111 (Eq. 2.3)
13
Lower Power uV
gLSLLN *60
**)( 312 (Eq. 2.4)
Hoist acceleration Power ut
VLSLLN*
)60/(*)(1
21
13 (Eq. 2.5)
Hoist deceleration Power ut
VLSLLN *)60/(*)(2
21
14 (Eq. 2.6)
Lower acceleration Power ut
VLSLLN *
)60/(*)(
5
23
15 (Eq. 2.7)
Lower deceleration Power ut
VLSLLN
*)60/(
*)(6
23
16 (Eq. 2.8)
Hoist motor accel. Power 1
212
17 *1000)60/**2(*
tnWKN h (Eq. 2.9)
Hoist motor decel. Power 2
212
18 *1000)60/**2(*
tnWKN h (Eq. 2.10)
Lower motor accel. Power
5
21132
19 *1000)60/*)/(**2(
*t
nVVWKN h (Eq. 2.11)
Lower motor decal. Power
6
21132
20 *1000)60/*)/(**2(
*t
nVVWKN h (Eq. 2.12)
Hoist accel. Power (W) 1713111 NNNP HA (Eq. 2.13)
Hoist Power (W) 111 NP H (Eq. 2.14)
Hoist decel. Power (W) 1814111 NNNP HD (Eq. 2.15)
Lower accel. Power (W) 1915121 NNNP LA (Eq. 2.16)
Lower Power (W) 121 NP L (Eq. 2.17)
Lower decel. Power (W) 2016121 NNNP LD (Eq. 2.18)
14
No load - without load
Hoist Power u
VgLSN*60
** 221 (Eq. 2.19)
Lower Power uVgLSN *60
** 422 (Eq. 2.20)
Hoist acceleration Power ut
VLSN*
)60/(*3
22
23 (Eq. 2.21)
Hoist deceleration Power ut
VLSN *)60/(*4
22
24 (Eq. 2.22)
Lower acceleration Power ut
VLSN *)60/(*7
24
25 (Eq. 2.23)
Lower deceleration Power ut
VLSN*
)60/(*8
24
26 (Eq. 2.24)
Hoist motor accel. Power 3
222
27 *1000)60/**2(*
tnWKN (Eq. 2.25)
Hoist motor decel. Power 4
222
28 *1000)60/**2(*
tnWKN (Eq. 2.26)
Lower motor accel. Power
7
2224
229 *1000
)60
*)/(**2(*
t
nVVWKN (Eq.2.27)
Lower motor decel. Power
8
2224
230 *1000
)60
*)/(*14.3*2(*
t
nVVWKN (Eq. 2.28)
Hoist accel. Power (W) 2723212 N N NHAP (Eq. 2.29)
Hoist Power (W) 212 NHP (Eq. 2.30)
Hoist decel. Power (W) 2824212 N N NHDP (Eq. 2.31)
15
Lower accel. Power (W) 2925222 N N NLAP (Eq. 2.32)
Lower Power (W) 222 NLP (Eq. 2.33)
Lower decel. Power (W) 3026222 N N NLDP (Eq. 2.34) Where LL Weight of load (container) in tones LS Weight of spreader & headblock (lifting device) in tones V1 Hoist speed with load in m/min V2 Hoist speed without load in m/min V3 Lower speed with load in m/min V4 Lower speed without load in m/min t1 Hoist acceleration time with load in seconds t2 Hoist deceleration time with load in seconds t3 Hoist acceleration time without load in seconds t4 Hoist deceleration time without load in seconds t5 Lower acceleration time with load in seconds t6 Lower deceleration time with load in seconds t7 Lower acceleration time without load in seconds t8 Lower deceleration time without load in seconds n1 Hoist motor speed with load in rpm n2 Hoist motor speed without load in rpm WKh
2 Total rotational inertia (include gearbox, drum, load) in kgm2 u Overall efficiency g Gravity (9.81m/sec2) Constant Pi = 3.14 N1i Hoist/Lower with load Power in Watts (i = 1,2,3….9) N2i Hoist/Lower without load Power in Watts (i = 1,2,3….9) B. Cross Travel motion The following formulas are used to calculate the average demand of the hoist motion: With load (container) Friction Load cLSLLTLL *)(11 (Eq. 2.35) Wind Load QAL *112 (Eq. 2.36)
Main hoist rope inflexibility load 2
)(*)1(*1000 3
13LSLLvL (Eq. 2.37)
Static power in adverse wind
u
VgLLLN xt
*1000*60**)( 13121111 (Eq. 2.38)
16
Static power in favourable wind
u
VgLLN xt
*1000*60**)( 131112 (Eq. 2.39)
Trolley acceleration power ut
V
LSLLTLNxt
xt
*
)60
(*)(
1
2
13 (Eq. 2.40)
Trolley deceleration power 1
214 *)
60(*
xt
xt
tuV
TLN (Eq. 2.41)
Motor acceleration power 1
2
215 *1000
)60
**2(*
xt
xt
xtt
n
WKN (Eq. 2.42)
Motor deceleration power 1
2
216 *1000
)60
**2(*
xt
xt
xtt
n
WKN (Eq. 2.43)
Cross travel acc. power in adverse wind (W) 1513111 NNNP LXTAW (Eq. 2.44) Cross travel power in adverse wind (W) 111 NP XTLW (Eq. 2.45) Cross travel deceleration power in adverse wind (W)
1614111 NNNP XTDW (Eq. 2.46) Cross travel acc. power in favourable wind (W) 1513121 NNNP XTANW (Eq. 2.47) Cross travel power in favourable wind (W) 121 NP XTNW (Eq. 2.48) Cross travel decal. power in favourable wind (W)
1614121 NNNP XTDNW (Eq. 2.49) Cross travel without load
17
Friction Load cLSTLL *)(21 (Eq. 2.50) Wind Load QAL *222 (Eq. 2.51)
Main hoist rope inflexibility load 2
*)1(*1000 3
23LSvL (Eq. 2.52)
Static power in adverse wind (W)
u
VgLLLN xt
*1000*60**)5.0( 23222121 (Eq. 2.53)
Static power in favourable wind (W)
uV
gLLN xt
*1000*60**)( 232122 (Eq. 2.54)
Trolley acceleration power (W) ut
V
LSTLNxt
xt
*
)60
(*)(
2
2
23 (Eq. 2.55)
Trolley deceleration power (W) 2
224 *)
60(*)(
xt
xt
tuV
LSTLN (Eq. 2.56)
Motor acceleration power (W) 12
2
225 *1000
)60
**2(*
xt
xt
xtt
n
WKN (Eq. 2.57)
Motor deceleration power (W) 2
2
226 *1000
)60
**2(*
xt
xt
xtt
n
WKN (Eq. 2.58)
Cross travel acc. power in adverse wind (W) 2523212 NNNP XTAW (Eq. 2.59) Cross travel power in adverse wind (W) 212 NP XTW (Eq. 2.60) Cross travel deceleration power in adverse wind (W)
2624212 NNNP XTDW (Eq. 2.61) Cross travel acc. power in favourable wind (W)
2523222 NNNP XTANW (Eq. 2.62)
18
Cross travel power in favourable wind (W) 222 NP XTNW (Eq. 2.63) Cross travel decal. power in favourable wind (W) 2624222 NNNP XTDNW (Eq. 2.64) Where LL Weight of load (container) in tones LS Weight of spreader & headblock (lifting device) in tones TL Weight of trolley in tones A1 Wind area with load in m2 A2 Wind area without load in m2 Q Wind pressure in kg/m2 v Sheave efficiency Vxt Trolley speed in m/min txt1 Cross travel acceleration time in seconds txt2 Cross travel deceleration time in seconds nxt Cross travel motor speed in rpm WKxt
2 Total rotational inertia (include gearbox, drum, load) in kgm2 u Overall efficiency g Gravity (9.81m/sec2) c Friction coefficient in kg/t Constant Pi = 3.14 N1i Cross Travel with load Power in Watts (i = 1,2,3….9) N2i Cross Travel without load Power in Watts (i = 1,2,3….9)
19
CHAPTER THREE
Electrical Assets Identification and Set up Data
Collection Scheme
Before any study of electrical usage and demand at the container terminal can be
started, all electric powered assets have to be identified. The term electric powered
asset or electrical asset refers to the asset that actual connects to electrical grid and
consumes electricity not asset that providing electric power. For example, quay
cranes are electrical assets but the high voltage switchgears connecting these cranes
to the electrical grid are not.
Understanding of how energy and demand are defined, measured and charged by
the power supply companies (the Utilities) is also important as it help to focus the
study as well as deciding what and how to collect data for this study.
Three main topics will be described and discussed in this chapter:
- Identification of all electric powered assets at container terminal,
- Electricity bills and measured data supplied by the Utilities – to focus the
study and set up data collection scheme,
- Describe the data collection system at Melbourne Container Terminal.
20
3.1 Identification of electrical assets at container terminal
3.1.1 Processes at container terminal
The container terminal knows in advance the expected arrival time of a container
ship, the number of containers to be exchanged and the ship stowage plan so that a
unloading plan and/or loading plan can be prepared, equipment and labour can be
allocated to work on that container ship. Figure 3.1 shows a typical container ship
stowage plan that is the lay out of the ship and container positions.
When the container ship arrives, QCs as shown in Figure 3.2 working according to
a prepared unloading plan take the import containers off the ship and put on the
wharf. The containers are then transferred to the storage stack be transport vehicles
such as Forklifts or Straddle Carriers (SCs) – Figure 3.3 - that travel between the
QCs and the storage stack.
Figure 3.1 Stowage plan of a container Ship
21
Figure 3.2 Quay Cranes Figure 3.3 Straddle Carrier
Figure 3.4 Container ship unloading plan
22
Equipment, such as straddle carriers (SCs), Rubber Tyred Gantries (RTGs), Rail
Mounted Gantries (RMGs) then put these containers into the storage stack
according to a prepared storage plan. Figure 3.4 shows a typical unloading plan
with container identification and details, position on the ship and unloading
sequence.
The storage stack consists of a number of lanes where containers can be stored for a
certain period. Dry cargo containers and refrigerated containers are stored in
different areas. Containers can be stored several high depend on the equipment used
in this storage stack. Melbourne Container Terminal use mainly SCs for container
transportation and stacking. Its storage stack is shown in Figure 3.5.
After a certain period the containers are retrieved from the stack and transported by
vehicles to transportation modes like trucks or trains to leave the container terminal.
Figure 3.6 shows SC delivers container to the truck.
Figure 3.5 Melbourne Container Terminal storage stack
23
Figure 3.6 Straddle Carrier deliver container to truck
To load export containers onto a ship, these processes are also executed in reverse
order. A typical loading plan is shown in Figure 3.7 and Figure 3.8 provides a
summary of container processes at a Container Terminal.
Most of the container terminals make use of manned equipments. However, a few
terminals are semi-automated using unmanned equipment for transport of
containers such as driver less SCs are used in Patrick Terminal in Brisbane, driver
less Rail Mounted Gantries (RTGs) are also tried at Patrick Terminal in Sydney,
some terminals in Rotterdam use Automated Guided Vehicles (AGVs) and
Automated Stacking Cranes (ASCs). Australian Container Terminals in Brisbane
and Sydney are currently re-developing their sites for use ASCs.
24
Figure 3.7 Container ship loading plan
25
Figure 3.8 Processes at Container Terminal
26
3.1.2 Electrical assets at container terminal
As a large electrical user and having a number of machines powered at high voltage
(HV) typically at 11kV level, container terminals are usually under HV tariff.
Following the above description, container terminal administration office is the first
area to look at for electrical assets. Typically, it consists of the following:
working areas and amenities (general office, first aid office, meeting room,
canteen, toilet, …) for its work forces,
control tower/room for computer system to observe and monitor all terminal
activities,
air conditioning, lighting and communication systems.
Electrical power at low voltage (three phase 415V in Australia) is required for these
services. Supply is normally via a step down transformer located near the office to
reduce the voltage drop.
Next type of electrical asset is the container handling equipment group: QCs, RTGs,
RMGs, ASCs, AGVs, SCs and Forklift. However, RTGs, AGVs, SCs and Forklift
are mobile machines which are either not electric powered or not connected to
electric grid. In other words, they are not electrical assets for the purposes of this
study. QCs, RMGs and ASCs are giant and very fast electric powered machines
which give the impression that they use a high amount of energy and require a very
high electrical demand. Due to their size and the capability of travel a relative long
distance (few hundreds meters), they are powered by HV, typically at 11kV.
27
Next electrical asset would be the refrigerated containers that require low voltage
electrical power to keep their cargo at the correct temperatures. Designated areas
with electrical infrastructure to allow these refrigerated containers to be connected
to the electrical grid are in the storage stack. These designated areas are normally
located close to the electrical substation to limit the voltage drop.
As container terminals are operated on 24 hours a day and 7 days a week basic,
lightings are required for night operation. Low voltage electrical supply to these
lightings is from the mention electrical substations.
A maintenance workshop is also a requirement at any container terminal; it is where
the repair and maintenance works to be carried out to keep all electrical assets in
good working order. Welding machines, lathes, power tools, measurement
instruments, spares,.. are in this workshop which required low voltage electric
power supply.
These electrical assets are divided into three groups for detailed study:
Container cranes group consists of QCs, RMGs and ASCs assets
Refrigerated containers group
Other load group consists of Office, Workshop and Lighting assets
Container group will be studied in Chapter 4, Chapter 5 investigates the refrigerated
container group. Demand of the other load group is well regulated and could be
calculated using the AS/NZS 3000 [62] or Construction handbook [21, 34] , it is the
responsibility of the building designer to provide the estimated demand; the
installed demand of this load group was taken as the maximum demand for this
study.
28
3.2 Definition of Electrical Demand
Electrical demand could mean different thing among the Utilities. As the purpose of
this study is to calculate the electrical maximum demand at the container terminal, it
is important that a clear definition of the term “demand” is needed ([99] provides
basic information). This was achieved by checking information provided by the
Utilities, analysing the actual electricity bill and examining measuring devices.
3.2.1 Definition from the Utilities
The following definitions are obtained from several different Utilities in Australia:
United Energy
Maximum Demand = Energy consumption over ½ hr period/ Time (1/2 hr). The Rolling Peak Demand Charge is based on the highest power (kVA at the highest kW) delivered during Peak periods (defined as 7am to 7pm Local Time weekdays excluding public holidays) over 12 months to the end of the billing period.
Powercor
Actual demand, which is measured as the energy consumption recorded over the demand integration period divided by the demand integration period in hours (the demand integration period is 15 minutes.
Energex
The customer’s connection point has a meter installed that is capable of measuring energy consumption (kW.h) and demand (kW). This meter records total energy consumption (kW.h) and demand over 30 minute periods. A customer’s demand is the average demand (kW) over the 30 minute period.
Western Power
The metered demand (MD) is a rolling 12-month maximum haft-hourly demand.
29
The electrical demand is actually calculated as defined above was confirmed in the
next section by examining the electrical bills of the container terminal and the raw
measured electrical usage.
3.2.2 Definition from Electricity Bills and measured energy
Electricity bill of an industrial HV customer is different from a residential LV
customer. By law, all the different charges have to be disclosed. Figure 3.9 shows
the electricity bill of the container terminal in Port Botany Sydney for November
2010. For the purposes of this study, the following information is of interested:
Total energy usage: 826,565 kWh and Maximum demand: 2317.41 kVA
Figure 3.9 Port Botany terminal – November 2010 Electricity bill
30
It was noted that there is no information on the feed back energy from the container
terminal (when container handling machine in lowering mode), by experience it is
small amount and ignored by the Utilities. The Utilities provided the electrical data
as requested by the container terminal operator to ensure the charges were correct.
As shown on the electricity bill, there are two meters so that two set of metered data
were provided. Data are time stamped for every 30 minutes during November 2010.
Table 3.1 and Table 3.2 list only part of the electrical data as full listing is not
necessary.
Table 3.1 Port Botany terminal – Meter 1 data for November 2010
31
Table 3.2 Port Botany terminal – Meter 2 data for November 2010
Calculation was performed to find the total energy usage and maximum demand
during November 2010. Calculations are:
kWhyUsageTotalEnerg (Eq. 3.1)
kWhh
kWhkW *2 as h = 30 minutes = 0.5 hours (Eq. 3.2)
kVArhh
kVArhkVAr *2 as h = 30 minutes = 0.5 hours (Eq. 3.3)
32
22 kVArkWkVA (Eq. 3.4)
)(kVAMAXDemandMax (Eq. 3.5)
Calculations are also shown in Table 3.1 and Table 3.2 and the results are
summarised in Table 3.3
Table 3.3 Port Botany terminal – Summary of Electricity November 2010
Electricity Bill Metered Data Unit Energy Usage Meter 1 559,913 559,840 kWh Meter 2 266,652 266,636 kWh Total 826,565 826,476 kWh Max Demand 2317.41 2301.40 kVA
The same results were found for all the electricity bills and metered data in 2010. It
was concluded that as defined by the Utilities, electrical demand is indeed
calculated from the metered energies over a time period of 30 minutes.
Digital meters are now used by the Utilities to measure the electrical usage; they are
capable of measuring the electrical demand. Information of how the digital meters
measure the electrical demand was examining in the next section for the definition.
3.2.3 Definition from the Digital Power meters
All digital power meters installed at large users including container terminal are
capable of recording energy both ways: deliver (from the electrical grid to the
customer (positive)) and receive (from the customer to the electrical grid
33
(negative)). However, unless the received energy is from small source (solar) or
agreed generator set the Utilities would not recognize this feed back energy.
The maximum electrical demand is calculated from the measured energies over a
period of 30 minutes as shown in the previous section. It is known that digital
meters are capable measure and calculate a lot more electrical parameters especially
the electrical demand value. It is possible that some Utilities may use this value
instead of calculate as previous section. To ensure this possibility would not affect
the out come, definition of electrical demand measured by the digital meters was
examined in this section.
Following descriptions are extracted from the manuals of some of the digital
meters that used at container terminals around the world:
ION 7300 series Power & Energy Meter from Schneider Electric [69]
Demand is a measure of average power consumption over a fixed time
interval. Peak (or maximum) demand is the highest demand level recorded
over the billing period.
Quantum Q1000 Multifunction Meter from SchlumbergerSema [156]
Demand is the average value of a measured quantity over a specified time.
9300 Series Power Meter from Siemens [11]
The demand modules (both Thermal Demand modules and Sliding Window
Demand module) are configured to calculate the average current demand
and kW, kVAR and kVA demand.
34
Mk Genius and Mk6E Energy Meters from EDMI [114]
The demand for the period is simply the accumulated energy divided by the
fraction of an hour that the demand period is.
DIN Integra 500 Series from Crompton [3]
Most electricity utilities base their charges on power consumption,
historically using a thermal maximum demand indicator (MDI) to measure
peak power consumption averaged over a number of minutes, thus avoiding
artificially high readings caused by surges.
It was confirmed that the digital meters calculate the electrical demand in the same
manner as the Utilities do from their metered energy values. That means value of
electrical demand is the same either it was read from the digital meter or calculated
by the Utilities.
The important result from the study so far is that electrical demand is the average
demand over a measure period. The measure period is 30 minutes for Melbourne
Container Terminal and Sydney Port Botany Container Terminal.
3.3 Focusing study on average electrical demand
With the understanding of maximum electrical demand as discussed in previous
section, the maximum demand calculation in later section would be the calculation
of the maximum average demand instead of the peak demand. Reasons for this
decision were given below.
35
3.3.1 Reasons for focusing study on average demand instead of peak demand
Recall from previous section, the electrical demand is the average demand over a
measure period that is normally 15 minutes or 30 minutes. Since the main purpose
of this study was to calculate the maximum electrical demand at container terminal
for negotiation the power supply contract either new supply or an upgrade one,
similar term (the average demand) should be used.
Some digital power meters do have the ability to calculate and record the
instantaneous maximum demand (secondly). However, as there is no way of
distinguishing between the actual electrical demand from the user and the network
disturbances; this ability of the meter was usually ignored.
An analogue maximum demand ammeter, such as BIQ96 from Ziegler, could also
be used to measure the maximum current demand then maximum power demand
could be calculated if required. Information from http://www.ziegler-
instruments.com/pdf/Bieq-c-ch.pdf (accessed on 22 May 2012) states that: “The
thermal bimetallic movement indicates the mean rms value over 15 minutes
(optional 8 min.) And deflects a reset-table red slave pointer which shows the
maximum value reached.” It was noted that peak demand was not measured.
Although peak electrical demand is important for any electrical network, it may
cause voltage flickering and trigger the supply interruption on a weak network, peak
demand is really the protection issue, which is out of this study scope. It is possible
to reduce this peak demand value to a manageable figure by using a synchronized
36
movements (central control) [42] scheme or using a “peak lopping” device that
would be discussed in Chapter 6.
3.3.2 Decision of focusing the study on average demand
A container terminal with a large number of container handling cranes would face a
very large value of maximum electrical demand if peak values were used.
Information of how electrical energy and demand were measure, calculated and
charged at container terminal together with the above reasons, using average
electrical demand was the correct way to calculate/estimate the maximum electrical
demand of a container terminal.
3.4 Set up at Melbourne Container Terminal for collecting data
With the approval and permission of Patrick management team, the new 11kV HV
reticulation with an Energy Management System (EMS) was designed and installed
at Swanson Dock in the Port of Melbourne. The change over from the old electrical
supply network to the new ones without interruption to the daily operation of the
terminal was completed in early 2006.
The selected EMS was the Power Logic System Management Software from
Schneider Electric (previously owned by Square D) because it was the only system
that provides a complete solution at that time. The EMS was designed for the ease
of communicating and collecting measured electrical data from a large range of
power meters, protection relays as well as tripping units of low voltage circuit
breakers especially for devices from Schneider Electric.
37
Figure 3.10 shows the HV single line diagram and location of measuring devices
while Figure 3.11 shows the layout of the EMS system.
For a fast changing electrical load, such that the QC, RMG and ASC, Schneider’s
circuit monitor CM3250 was used. This device is a powerful power meter with in
built memory large enough to record electrical data every second for at least 5
hours.
For a slow changing or steady load, such as the refrigerated containers, metering
features of the digital protection relay (SEPAM series 40) and digital tripping
circuit of circuit breaker (MicroLogic 5) were utilised. These devices do not have
built-in memory, the required electrical values were measured and calculated then
pass on to the EMS server when there was a “data collect” signal was issued from
the EMS server. Electrical data collection period can be varied between 1 minute
and 1 hour.
Power Logic System Management Software version 4 was installed on a computer
server which runs Windows Server 2003 operating software. Electrical data,
voltages, currents, powers, energies, power factor and harmonics from each of the
devices (shown in Figure 3.11) were collected and save in a database every 15
minutes. Historical data could be archived when required.
The system was set up as a stand alone system that was not connected to the
container terminal computer network for security reasons. Remotely access was via
the World Wide Web by using the service of Iburst wireless network.
Unfortunately, the Iburst network was closed several years ago and the only way to
access this system was via local direct log in to the server.
38
Figu
re3.
10
S
ingl
e L
ine
Dia
gram
with
mea
suri
ng d
evic
es lo
catio
ns
39
Figu
re 3
.11
Ene
rgy
Man
agem
ent S
yste
m la
yout
40
With permission from management team of Patrick, the Melbourne Container
Terminal Operator, and utilizing the fast 1 second data recording feature of the
CM3250, load profiles of number of QCs were obtained. The QCs electrical
specifications, characteristic, measure conditions and results would be discussed in
Chapter 4.
The management team also permitted the daily weekday reports of number of
refrigerated containers and dry cargo containers that were in storage stack of the
terminal be generated and provided via email. These reports would be discussed in
Chapter 4 and Chapter 5.
Although all circuit monitor CM3250s and Power Logic System Management
Software V4 are still in working order providing invaluable data for the study and
any future work, they are discontinued and no longer available, Schneider Electric
claims latest software version, Power Logic ION Enterprise, and newer
measurement devices would be more user friendly and provide better results.
3.5 Conclusions
Understanding the processes at container terminals helps to identify the electrical
assets and focus the study on interested areas.
The definition of the electrical demand term that is referred to by the Utilities is
clarified by reviewing the Utilities’ electricity bills, examining the raw data of
measured electrical parameters and investigating how those electrical parameters
are defined in modern digital power meters (smart meters), it is confirmed that
41
electrical demand is the average demand over a measure period (usually 15
minutes or 30 minutes period) and is not the instantaneous demand. The tasks
of finding the maximum demand of electrical assets became easier with this
confirmation.
With the measurement scheme set up for data collection as described, the studyied
results could be verified. The electrical energy usage and maximum demand of
container cranes group would be investigated in the next chapter – Chapter 4
Container Handling Crane.
42
CHAPTER FOUR
Container Handling Crane
With the electrical assets of container terminal have been identified in previous
chapter, the electrical demand and energy usage for the big machine group –
container handling cranes – could now be studied. These machines are used to:
move containers from container ship to ground or via versa (QCs),
move containers into stacking area, shuffle them within the stacking area or
move containers out of stacking area for delivery either with driver (RMGs)
or driverless (ASC).
This chapter began with a brief discussion on these machines and their operation
then investigates the following:
Quay crane profile – record load profiles of similar quay cranes with AC
drive and DC drive and compare the results for contribution to the AC verus
DC drive debate,
Container weight – payload of container, capacity and deadweight of
container ship and analysing actual weights of containers (daily data
collected more than one year) in stack of Melbourne Container Terminal for
contribution to the debate of what size (lifting capacity) of quay crane is
needed.
43
The reasons why this study was only focusing on the maximum average demand
instead of the peak instantaneous demand would be discussed before demand
calculations were performed and conclusions were drawn.
4.1 Brief Discussion of container handling cranes
A modern container terminal would be dominated by the giant quay cranes that can
reach out over the ships to load or unload containers. They are mounted on rails and
will be able to long travel up and down the quay to exactly align themselves with
the “bays” in or over the ship’s hold, where the container is to be handled. The
outreach of the horizontal boom permits a trolley to cross travel from over the quay
to over the ship, a spreader with four locks suspended from the trolley. These locks
nest into the four corners of the containers, make fast and enable the container to be
hoisted out of (or lowered into) the ship hold. A crane driver in his cab alongside
the trolley has an excellent view of the process he is controlling.
Apart from the sizes of the quay cranes that are capable of serving different type of
container ships (Panamax, Post Panamax,…), quay cranes are different in look: ‘A
frame’ quay cranes are the most common cranes as they are the lightest and
cheapest quay cranes that can be built. Articulated boom or goose neck quay cranes
are used when there is a restriction in crane’s height. Under severe crane height
restriction due to the container terminal is on the adjacent airport’s flight path,
shuttle boom or low profile quay cranes have to be used. Figure 4.1 shows these
types of quay cranes.
44
Another way of classifying quay cranes is their lifting capability or safe working
load (SWL) and how they lift containers. As 30 tones is the SWL of each 20’ or
40’ container, latest design tandem lift quay crane capable of lifting 6 x 20’
containers should have the rated load of 180 tones. Figure 4.2 shows different types
of lifts.
Figure 4.1 Different forms of quay cranes
45
Source : www.lifttech.net
Figure 4.2 Quay cranes - Types of Lifts
On the quay ground handling equipment (straddle carriers, fork lifts or automatic
guided vehicles) moves the containers from the quay cranes to the stack and via
versa. The container is then moved into stacking area by the rail mounted gantries
(RMGs) or driverless automatic stacking cranes (ASCs). The same machine will
deliver containers to the truck or rail when required. In general, these machines are
very similar to the quay cranes without the boom motion, hoist/lower and cross
46
travel motions are for a short distance only. RMGs are shown in Figure 4.3 and
ASCs are shown in Figure 4.4.
Figure 4.3 Rail Mounted Gantries
Figure 4.4 Automatic Stacking Cranes (no driver)
Due to the need to travel a long distance (few hundreds meters) and handle the
heavy containers that is drawing large current over a long cable, these machines are
electrical powered at high voltage level and the drive system can either be an AC
drive system or a DC drive system. Unless specified, all machines are now come
with AC drive system simply because they can operate at or close to unity power
factor.
47
It was not able to record load profiles for RMG or ASC as the Melbourne Container
Terminal does not have any of these machines. However, it was expected the load
profiles of RMG and ASC are very similar to that of the quay cranes as:
Hoist/Lower motion would be similar as the container loads are the same for
these machines,
The long travel motion would be the predominant one as RMGs and ASCs
need only hoist/lower a short distance but long travel a very long distance.
Demand and energy usage are also less than that of the quay crane due to the
nature of the long travel motion – overcome friction rather than lifting a
weight.
The Melbourne Container Terminal has both AC drive and DC drive quay cranes.
With the permission from the management team, load profile of these quay cranes
were obtained as described in next section.
4.2 Load Profiles of Quay Crane – Comparison between AC and DC drive systems
Since the introduction of IGBT based AC drive products in the late 1980s, there has
been much debate on which technology – AC or DC drive – should be used by the
crane industry for new container cranes. The AC technology appears to win the
debate as today almost all container cranes are AC. However, the electrical power
demand and energy usage of container cranes have not been mentioned in any
debate. With the new “bigger and faster” container cranes being built, the high
electrical cost of running these container cranes must now be closely analysed.
48
With the permission of management team of Melbourne Container Terminal, load
profiles of two very similar quay cranes – one with AC drive system and the other
with DC drive system - had been obtained on 29 January 2008 and 13 February
2008. As described in chapter 3, the Schneider circuit monitor CM3250 was used at
the high voltage supply end of each quay crane to capture the electrical data every
second then uploaded to the Energy Management System data base. The operation
data (time, container number, weight, quay crane motion, load or unload) were also
recorded for analysis. Details were discussed below.
4.2.1 AC and DC quay cranes under study
As there were no exactly match pair of quay cranes at the container terminal, two
very similar quay cranes (physical size, mechanical arrangement, year of
manufactured) had to be selected to produce comparable results.
Almost only Hoist and Cross Travel motions are used in loading/unloading
containers to/from container ship. These motions produce the peak demand and
around 99% of the energy usage. Therefore, this study concentrated mainly on these
two motions. The main electrical data of these quay cranes was listed in Table 4.1.
Table 4.1 Main data of Quay cranes under observation
49
The AC quay crane uses AC drive system with Active Front End technology that is
full compensation can be made for power factor and harmonics. The DC quay crane
uses DC drive technology with harmonic filter to compensate the generated
harmonics.
4.2.2 Study results
It was expected the peak demand would be larger for AC drive technology due to
the fact that:
the AC motor size that have to be larger in size to produce the same torque
and overload capability resulting in. larger rotational inertial, cooling
systems and power consumption,
the AC drives technology requires two steps, conversion and inversion while
DC drive technology needs only conversion. This means extra power
requirement, larger cooling devices as more heat would be generated for the
AC drives.
Electrical data were captured during actual working conditions: loading containers
to container ship. At the same time, loading sequence and container weight are also
recorded. Figure 4.5 and Figure 4.6 show graph of Real Power (kW), Reactive
Power (kVAr) and Apparent Power (kVA) of the quay cranes working on the same
ship hold, ie. minimum usage of gantry motion.
50
Figure. 4.5 AC quay crane – Graph of powers vs. time (second).
Figure 4.6 DC quay crane – Graph of powers vs. time (second).
The first impression is that DC quay crane handled more containers, there are
regenerative Real Power (-ve kW), DC quay crane requires larger kVA demand.
This data is used to calculate the energy usage of the quay crane for handling each
container.
To make comparison, a loading cycle with the similar container weight and similar
travel distances are used. Figure 4.7 and Figure 4.8 show the Power graphs of the
AC and DC quay cranes when handling container weight 26.1T and 26T
respectively.
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Figure 4.7 AC quay crane – Graph of powers vs. time (second) for one loading
cycle.
Figure 4.8 DC quay crane –Graph of powers vs. time (second) for one loading cycle.
A loading cycle comprises of::
- Lock the container to the spreader for a safe move,
- Hoist the container up, start cross travel (while hoisting) to sea side when clear of all obstacles,
- Lower the container to its final position and unlock,
- Hoist the empty spreader up, start cross travel (while hoisting) to land side when clear of all obstacles,
- Lower the empty spreader on top of the next container.
Therefore a graph of Power versus Time of a complete load cycle was expected to
have four peaks values. The Real Power should have two negative peaks
52
(regenerative when lowering). Figure 4.7 and Figure 4.8 confirmed these
expectations. The slightly differences in shape and duration were due to the
techniques of the quay crane drivers.
Peak Power Demand and Energy Usage
The results were summarised in Table 4.2. Theoretical average power demands
were calculated and also shown in the table for reference only. The formula were
from chapter two and actual mechanical data used in calculation of the average
demand would be shown in later section. To make a true comparison between AC
and DC quay cranes, the electrical conditions had to be the same. It was assumed
that the issue of poor power factor of DC drive quay crane was not a concern;
comparison was now based on the peak kW demand rather than the peak kVA
demand.
As shown in Table 4.2, peak demand from AC quay crane was 21.9% higher than
DC quay crane. When taking the Safe Working Load of the quay cranes into
account, the difference was still expected to be higher than 15%.
As discussed in Chapter 3, the Electrical Distribution Company (the Utility) does
not look at this instantaneous peak/maximum demand. The peak/maximum demand
was normally calculated from the “remotely read” energy kWHr and kVArHr every
15 or 30 minutes. That means the peak kW demand shown in the electrical bill was
actual the average kW demand.
53
Table 4.2 Results of measurement
Quay Crane with AC Drive DC Drive Differences Load condition Number of loads 29 47 Load Weights From 7T to 48.4T From 7T to 48.4T Results Net used energy (kWHr) 113.50 115.20 Average used energy per 3.91 2.45 For 26T load Peak demand (kW) 1476 1211 21.88% Average demand (kW) 147.75 105.26 40.37% Cal. Ave. demand (kW) 152.01 126.83 19.85% Power factor - Real time 0.087 – 1 0.006 - 0.838 Power factor - calculated 0.952 0.475 Total Harmonic Distortion (THD) Line Current Ia (%) 1.9 - 51.9 5.6 - 49.7 Ib (%) 1.6 - 830.3 5.3 - 56.9 Ic (%) 1.6 - 93.1 63. -50.9 Line Voltage Vab (%) 0.9 - 1.2 0.7 - 1.9 Vbc (%) 0.9 - 1.2 0.8 - 2.0 Va (%) 0.9 - 1.2 0.6 - 1.8
For 26T container load, the AC quay crane kW demand was 40% higher. Taking
into account the driver’s techniques, the final position of the container and other
containers on the ship, the difference was still expected to be in the low 20%.
With higher peak and average demand, the energy usage had to be higher for AC
drive quay crane. An average of 60% more energy was required to handle a
container during this observation. AC drive quay crane used 100% more energy
than DC drive quay crane had been observed at other time.
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Power Factor
Figure 4.9 and Figure 4.10 show graph of Power Factor vs. Time of AC and DC
drive quay cranes when handling 26T container and the numerical results were
shown in Table 4.2. An average Power Factor was also shown in the graphs. This
average Power Factor (as seen by the Utility) was the ratio of kWHr and kVAHr.
Figure 4.9 AC quay crane – Graph of power factor vs. time (second) for one
loading cycle.
Figure 4.10 DC quay crane – Graph of power factor vs. time (second) for one
loading cycle.
55
As expected, DC drive quay crane had a very poor power factor. However, it is
possible to solve this problem by using a dynamic power factor correction unit. A
dynamic power factor correction unit consists of capacitor banks and power
electronic switches. A microprocessor is used to control the switching to connect an
appropriate amount of corrective capacitance on the “per-cycle” basic (50 cycles
per second for 50Hz system) [55, 56]. The desired power factor can easily be
achieved.
“Crane Factor” from TM GE Automation system or “Pure wave AVC” from S and
C Electric Company are two examples of such unit.
The Melbourne Container Terminal used a Pure Wave AVC unit with a very good
result. For better utilization, the power factor correction unit was connected at the
main 11kV bus bar, which supplied three (3) DC drive quay cranes, two (2) AC
drive quay cranes and 500 outlets for refrigerated containers. Overall power factor
is always greater than 0.9.
So that with the right selection of equipment, poor power factor of DC drive quay
crane was no longer an issue.
Total Harmonic Distortion (TDH)
Measured TDHs of live voltage and current for AC and DC drive quay cranes
during the 26T loading cycle were plotted against time (second) as shown in Figure
4.11 and Figure 4.12. Different scales were used for voltages and currents.
56
Figure 4.11 AC quay crane – Graph of THD (%) vs. time (second) for one
loading cycle.
Figure 4.12 DC quay crane – Graph of THD (%) vs. time (second) for one
loading cycle.
The measurement shown an abnormal THD value of 830.3% of current on phase b.
AC drive quay crane achieves smaller variation of THDs of voltages and currents.
However, THDs of both AC and DC drive quay cranes were comparable.
57
4.2.3 Study conclusions
With observation and actual measured electrical data of quay cranes with AC drive
and DC drive systems, load profiles of these quay cranes were studied and
understood. It could be concluded that if proper power factor correction and
harmonic compensation were provided, a quay crane with DC drive technology was
a better choice as it produced lower Peak Demand and Energy Usage. However, this
conclusion was simply based on the electrical point of view. Other factors - such as
quay crane weight, wheel load, maintenance, spares, and cost – had to be
considered before making decision of selecting quay crane with AC or DC drive
system.
In the next section, the actual container weight at Melbourne Container Terminal
will be studied to have a better understanding of what sort of loads the container
handling machines have to work with and contribute information for the decision of
selecting the machines’ size.
4.3 Container Weight Analysis
Understanding of the weight of containers that were actually handled by the
container terminal would help in electrical demand calculation in later section.
4.3.1 Weight of container – container ship and ISO standard
Data of ship capacity and deadweight of a number of container ships are sourced
from [14] Wikipedia http://en.wikipedia.org/wiki/Container_ship#Size_categories.
Average container’s weight is calculated using deadweight figures as the weights of
containers.
58
Table 4.3 Container ship capacity and deadweight
Dimensions and weights of containers shown in Table 4.4 were sourced from [13]
www.atmgloballogistics.com/docsjpg/Documents/containerinfo.pdf, interested data
were:
Standard container: 20 foot Gross weight of 24 Tonnes 40 foot Gross weight of 30.48 Tonnes Refrigerated container: 20 foot Gross weight of 30.48 Tonnes 40 foot Gross weight of 34 Tonnes
Compare these maximum container weight with the average weight of container
from container ship data, it could be said that the container ship would:
- expect to carry a number of empty containers or
- expect loaded containers are not at their maximum weights or
- expect to travel with full capacity or combination of these.
59
60
4.3.2 Weight of container at Melbourne Container Terminal
Collection of container weight data
Details of containers in stacking area as shown in Figure 4.13 at Melbourne
Container Terminal under this study were daily reported around 7:00AM via email.
Due to the limitation of current terminal operator system, these reports could only
be done manually during the week.
Figure 4.13 Container stacking area at Melbourne Container Terminal
Container details were collected every week day for more than one year from
February 2007 till August 2008. There were some period without reports due to the
change of the Terminal’s personnel and failure of recording equipment.
Container report contained information of all container (including reefers) in the
Terminal on a specific date. Each container was detailed as:
location in the Terminal/Yard
identification
Category (import/export/transhipment)
61
size (20 or 40 foot)
weight (tonnes)
commodity – eg. general cargo, paper, frozen juice (FZJC), frozen meet (FZMT),…
in bound carrier (container ship name/truck)
out bound carrier (truck/container ship name)
Sample of container report is shown in Table 4.5.
Table 4.5 Sample of Container daily Report Current Position Container No. Cat Len Wt
Tns Comd I/B Carr O/B Car
BBK TOLU8971025 IMPORT 40' 29 OOG MMO9051 TRUCK C 0211 1 KKTU7263932 IMPORT 20' 4.8 GENL CIM9071 TRUCK C 0212 1 KKTU7521770 IMPORT 20' 6.1 GENL CIM9071 TRUCK C 0214 1 MSCU6400084 IMPORT 20' 21.5 GENL MMO9051 TRUCK C 0215 1 FCIU2217818 IMPORT 20' 24.1 GENL SDR9074 TRUCK C 0215 2 INBU3282777 IMPORT 20' 20.2 GENL MMO9051 TRUCK C 0217 1 NYKU2886113 IMPORT 20' 18.7 GENL CIM9071 TRUCK C 0217 2 MSCU6972201 IMPORT 20' 19.2 GENL MMO9051 TRUCK C 0611 1 CPSU6002343 IMPORT 40' 10.2 GENL SFB9055 TRUCK C 0611 2 MSCU9148865 IMPORT 40' 22.6 GENL MMO9051 TRUCK C 0613 1 GATU8487445 IMPORT 40' 14 GENL SFB9055 TRUCK C 0613 2 SUDU5608733 IMPORT 40' 28.1 GENL SFB9055 TRUCK C 0615 1 MSCU4319820 IMPORT 40' 17.5 GENL MMO9051 TRUCK C 0615 2 GLDU0463460 IMPORT 40' 25.7 GENL MMO9051 TRUCK C 0617 1 SUDU5848400 IMPORT 40' 19.8 GENL SFB9055 TRUCK E 0204 2 MWCU5664991 EXPORT 20' 20.4 FZJC R31037G TAT9067 E 0205 1 PONU2850924 EXPORT 20' 20.5 FZJC R31037G TAT9067 E 0209 1 PONU2948783 IMPORT 20' 20.4 REEF SFB9055 TRUCK E 0209 2 SUDU1089373 IMPORT 20' 17 REEF SFB9055 TRUCK E 0210 1 SUDU1024691 IMPORT 20' 20.5 REEF SFB9055 RAIL E 0301 1 CRLU1242231 EXPORT 40' 29.6 FZMT TRUCK TAT9067 E 0301 2 MWMU6431485 EXPORT 40' 22.7 CHMT R31037G TAT9067 E 0303 1 PONU2932426 EXPORT 20' 14.3 CHMT R30037G TAT9067 E 0303 2 MWCU5639751 EXPORT 20' 22 FZMT TRUCK TAT9067 E 0305 1 SUDU1013439 IMPORT 20' 18 REEF HSG9054 TRUCK U 3401 1 KHS400488 EXPORT 40' 32.5 STEL TRUCK OOF9079 U 3401 2 KHS400461 EXPORT 40' 32.5 STEL TRUCK OOF9079 U 3501 1 MSCU7346218 TRANSSHIP 20' 23.9 TIMB HRT9069 TAT9067 U 3601 1 TOLU8986180 IMPORT 40' 16 OOG UKI9056 TRUCK V 0101 1 MWCU5742760 IMPORT 20' 13 CHC MDH9077 TRUCK V 0401 1 PONU2870221 EXPORT 20' 22.5 FZMT TRUCK TAT9067 V 0501 1 TRLU1044459 EXPORT 20' 16 FZMT TRUCK UVA9072 V 0601 1 CBHU2933183 EXPORT 40' 10.9 CHIL TRUCK OOF9079 4/02/2007 7:08:20 AM
62
Although there were some missing data as the results of changing personnel at the
Terminal and measurement equipment malfunction, the number of report were still
too great to be listed or printed. They are available in electronic form upon request.
The main purpose of this data collection scheme was to study the weight of
containers that are handled by the Melbourne Container Terminal to see if there was
a need for quay cranes with higher lifting capacity and to help in demand
calculation in later section.
From the container reports, the following steps were carried out for analysing the
data:
counting the number of containers with weights less than 5T (empty),
between 5T and 10T, between 10T and 15T, between 15T and 20T, between
20T and 25T, between 25T and 30T and greater than 30T.
counting the number of containers for import, export or other
(transhipment).
tabulating these results and calculate the percentages of 40 foot containers,
percentage of containers weighted more than 30T and percentage of empty
containers.
Analysing the reports in their original forms would take too much time for
producing the above results. Microsoft Excel spreadsheet program was used and a
macro CONTAINERS was created to automate the described analysing task.
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Information of the CONTAINERS macro can be found in Appendix A. Table 4.6
shows the analysed results after running the macro CONTAINERS.
Table 4.6 Results of running “CONTAINERS” macro
4.3.3 Results of analysing data collection
The analysing data of daily container reports were then combined into another table
as shown in Table 4.7 for a long term view of the weight of containers handle at
Melbourne Container Terminal. The analysis results were visualised in Figured
4.14, Figure 4.15 and Figure 4.16 and in numerical were:
Average weight of 16.7 Tonnes per container
Average weight of 11.7 Tonnes per TEU
Less than 5% of containers were weighted more than 30 Tones,
Around 15% containers were empty,
Around 45% containers were 40 foot containers.
4.3.4 Conclusions of weight analysis
With collected daily reports of containers at Melbourne Container Terminal and the
analysed results as shown above, it could be concluded that:
64
Recall from section 4.3.2.1, the maximum gross weight of container was 34
tonnes, from the collected data the container’s maximum weight was 32.5
tonnes with only less than 5% of handled containers were weighted more
than 30 tones. Quay cranes with lifting capacity (safe working load) of 40
tonnes or 50 tonnes are large enough to handle all of the loads in single lift
mode and most of the loads in twin lift mode. As information of the
incoming load (ship stowage plan, container weight,…) would be known in
advance, the container terminal operator could decide if twin lift or tandem
lift was needed for speeding up the unloading of the ship.
Containers are handled by RMGs or ASCs at the stack yard, these machines
are most likely single lift type due to the need for fast process and the
complicity of computerised automation system. Therefore, a machine with
safe working load of 40 tones was appropriate size for RMG or ASC.
65
66
Melbourne Container TerminalNumber of Containers in stacking yard - 2007/2008
Date
Num
ber
Figure 4.14 Number of container at Melbourne Container Terminal in 2007 - 2008
Melbourne Container TerminalContainers in stacking yard - 2007/2008
Date
Perc
enta
ge
Figure 4.15 Percentage of 40’ container, empty container and heavy container at Melbourne Container Terminal in 2007 - 2008
67
Melbourne Container TerminalContainers in stacking yard - Average Weight
Date
Wei
ght (
Tone
s)
Figure 4.16 Average weight of container and TEU at Melbourne Container Terminal in 2007 - 2008
4.4 Calculate Demand and Energy usage of container handling cranes
All these container handling cranes are very similar. Only quay cranes have a boom
structure to allow loading/unloading containers to/from the container ship, this
boom has to be in stow position to avoid collision with the ship control bridge when
the container ship berthing or leaving the port. All machines use other motions:
hoist/lower, cross travel and long travel. The differences are the paths of travel.
Using the calculation formulas listed in Chapter 2 together with the actual
mechanical and electrical data of container handling machine to calculate the
demand and energy usage before conclusions were drawn.
4.4.1 Quay Cranes and maximum Electrical Demand
For QCs, the boom and long travel motions were ignored when calculating the
maximum electrical demand because:
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the boom motion is only used to put it in the working position to start
loading/unloading containers and to stow the boom at the end of its work,
other motions are not available when boom hoist is in use.
the boom’s electrical motor is not as large as the hoist’s electrical motor, the
demand is not the maximum demand,
similarly the long travel’s electrical motor is not the largest and the demand
is not the maximum demand.
After the container ship was securely berthed along the wharf, the assigned several
quay cranes were long travelled to a ship bay, the boom then be lowered to working
position ready for loading/unloading containers to/from the container ship.
Hoist/lower and cross travel motions were needed for loading and unloading the
container and they were normally operated concurrently. Quay crane was long
travelled to the next bay when finish the current bay.
The unloading (or loading) cycle time was depended on the location of the
container on the ship to be unloaded (or the location for the container to be loaded
onto the ship) that was the required travel distances. Average cycle time was less
than two (2) minutes as most of the container terminals publish their crane rate
around 45 containers per hour. The cycle time was well within the electricity
measure period of 15 minutes or 30 minutes of the Utilities so that average demand
calculation method can be used. Figure 4.17 showed how the quay crane electrical
demand was calculated.
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Figure 4.17 Calculation of average electrical demand of a quay crane
With actual specifications of at least four (4) different quay cranes from different
manufactures at Melbourne Container Terminal, a Microsoft Excel spreadsheet had
been created with all above formulas as the base model. Several simulations were
carried out:
with a specific loading/unloading cycle, average demands was calculated for
different loads for each crane,
with a specific crane, average demands were calculated for an actual
working shift,
It was found that:
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For a single lift operation (that is container load < 35 tones) with the same
cycle, the calculated average demands were slight different for different
quay cranes mainly because of the auxiliary loads.
For a single lift operation, the calculated average demands were found to be
less than 250kW. Therefore the maximum demand for a single lift quay
crane is 250kW.
For twin lift operation, the calculated average demands were found to be
less than 350kW. Therefore the maximum demand for a twin lift quay crane
is 350kW.
The average demand of RMG and ASC would be investigated in the next section
4.4.2 RMG and ASC and maximum Electrical Demand
RMGs and ASCs are very similar, the ASCs are actual the automation version of
the RMGs. Due to the working environment (within container stacking area with lot
of obstacle) and short lifting height and short cross travel distance, they are
normally single lift type and on “single motion” operation mode. For example, cross
travel motion only starts when hoist is at the top limit and long travel only starts
when cross travel motion is completed.
For stacking operation: after the container was delivered at the end of the stack by
staddle carrier, a RMG/ASC picked up the container, hoist to the top limit, cross
travelled to the safe position ready for long travel motion. The RMG/ASC then long
travelled to the pre-set row location in the stack, cross travelled to the correct lane
then lowered the container to its storage location, hoisted without load, cross
travelled to the travel position, long travelled back to the end of the stack and
71
complete the cycle by lowered to the next container. Deliver cycle was in reverse
order.
Again, the stacking (or deliver) cycle time was depended on the location of the
container in the stack to be stored (or the location of the container in the stack to be
pickup) that was the required travel distances. Average cycle time was also well
within the electricity measure period of 15 minutes or 30 minutes of the Utilities,
especially the cycle time of ASCs as they were controlled by computer and
optimised for speed. Figure 4.22 shows how the RMG/ASC electrical demand is
calculated.
Figure 4.18 Calculation of average electrical demand of a RMG/ASC
As there are not any RMG and ASC at Melbourne container terminal, these
machines specifications were obtained from other terminals: RMG specifications
were from Sydney container terminal at Port Botany and ASC specifications were
72
from Brisbane container terminal at Fisherman Island. Again a Microsoft Excel
spreadsheet had been created with all above formulas as the base model and similar
simulations were carried out:
with a specific cycle, average demands were calculated for different loads
for each machine,
with a specific machine, average demands were calculated for different
cycles.
Examples of these calculation simulations were listed in Appendix E.
It was found that:
For the same cycle, the calculated average demands were slight different for
different RMG and ASC mainly because of the auxiliary loads.
For container load of up to 35T, the calculated average demands were found
to be less than 200kW. Therefore the maximum demand for RMG and ASC
was 200kW.
4.5 Conclusions
Container handling cranes and their operation were briefly described to help
readers, who are not familiar with these machines, to have a basic understanding of
how they are operated.
The electrical characteristics of quay cranes with different drive systems: AC drive
and DC drive were recorded and comparisons were made as contribution to the
debate of AC verus DC control system.
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Formulas for calculation of the average demand of two (2) main motions of the
container handling cranes listed in Chapter 2 were used for calculation. These
formulas were used with the actual specifications of quay cranes in Melbourne,
RMG in Sydney and ASC in Brisbane to perform the spreadsheet simulation for
finding the maximum demand of each type of machine.
The results were found to be:
Maximum demand for a single lift quay crane was 250kW
Maximum demand for a twinlift quay crane was 350kW
Maximum demand for a RMG or ASC was 200kW
The next big electrical energy usage and large electrical demand – the refrigerated
containers – were investigated in the next chapter.
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CHAPTER FIVE
Refrigerated Container
Following the study of big electrical machines at Melbourne Container Terminal in
previous chapter, the refrigerated containers - the next major electrical usage –
would be now studied in this chapter.
In contrary to the impression that the big and fast cranes would have a huge
electrical usage and demand, refrigerated containers were actually the biggest
electrical usage and demand at the Container Terminal due to their number and the
need of electrical power for refrigeration all the time. Just over one thousand (1000)
spaces for 20 foot refrigerated containers were available at Melbourne Container
Terminal.
It was important to estimate the electrical demand of refrigerated container as
accurate as possible because it would have a huge impact on designing and
installing the electrical infrastructure to provide power for those refrigerated
containers.
5.1 Brief Discussion of Refrigerated Container
Refrigerated Containers or Reefers are used for goods which need to be transported
at a constant temperature above or below freezing point. These goods are divided
into chilled goods and frozen goods, depending on the specified transport
75
temperature. They principally include fruit, vegetables, meat and dairy products,
such as butter and cheese.
As described in [4], there are two types of refrigerated containers:
a. Porthole refrigerated containers, also called insulated containers, do not
have their own refrigeration unit. They are thus reliant on an external supply
of cold air. Refrigeration units of various types, permanently installed on the
ship (Figure 5.1), permanently installed in the terminal or clip-on units
(Figure 5.2) for individual containers, are used for this.
Figure 5.1 Refrigeration supply system Figure 5.2 Clip-on unit for for porthole container. transport by road.
Source: the container handbook [4]
Figure 5.3 Portholes at the end of a porthole container Source: the container handbook [4]
b. Integral refrigerated containers (Figure 5.4), on the other hand, have an
integral refrigeration unit for controlling the temperature inside the
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container. The refrigeration unit is generally powered by a 3 phase electric
power supply and is arranged in such a way that the external dimensions of
the container meet ISO standards.
Figure 5.4 Integral refrigerated containers
Technical details such as dimensions and construction as well as the reefer’s
operation and control were not discussed here as power usage and electrical demand
were the main purposes of this study.
Depending on the system used at the container terminal, the reefer maximum
electrical demand was the value of the permanent installed system or the individual
reefer or combination of both types.
5.2 Estimate Electrical Demand of Refrigerated Container
A permanent installed system at Container Terminal to supply cool air for portholes
reefers would only be run when there is a need for such refrigeration. The demand
of this system is only of interested if it is an electric powered system, the electrical
demand would therefore be the rated value of the system and be readily available
from the supplier.
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For an integral refrigerated container, it would be harder to estimate the electrical
demand due to the fact that many different products (kept at different temperatures)
could be carried by the same reefer. A reefer does no longer require full power all
the time after cooled down to the set temperature, thus a diversity factor shall be
applied to a large number of reefers connected to the same power supply source. In
other words, an average electrical demand was to be estimated.
To reach that estimation with degree of accuracy, the following steps were carried
out:
investigating current practice,
developing a way for calculate electrical demands of reefers,
analysing data collected over two (2) years period,
comparing the three results then concluding with the required electrical
demand.
5.2.1 Maximum Demand of a Reefer Stack
It is a fortunate that the author had access to a number of electrical assessment
reports that were done for various container terminals by different consultant group.
Calculation of maximum demand was part of the reports and mostly done with
practical assumptions. Those calculation approaches were described in the
following section.
5.2.1.1 Demand calculation using Australian Standard AS3000
Australian Standard AS3000 [62] does have information about the maximum
demand of an electrical installation. Four methods are listed in the Standard:
calculation, assessment, measurement and limitation as per following extraction:
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Extract from AS 3000 - 2007
2.2.2 Maximum demand
The maximum demand in consumers mains, sub-mains and final sub-circuits, taking account of the physical distribution and intended usage of electrical equipment in the electrical installation and the manner in which the present requirements might vary, shall be determined using one of the methods set out in Items (a) to (d).
If the actual measured maximum demand is found to exceed that obtained by calculation or assessment, the measured value shall be deemed to be the maximum demand.
NOTE: Guidance on the determination of maximum demand is provided for basic electrical installations in Appendix C.
(a) Calculation The maximum demand may be calculated in accordance with the guidance given in Appendix C for the appropriate type of electrical installation and electrical equipment supplied. It is recognized that there may be considerable differences in loading from one electrical installation to another. Alternative methods of calculating the maximum demand may be used taking account of all the relevant information available for any particular electrical installation.
(b) Assessment The maximum demand may be assessed where: (i) the electrical equipment operates under conditions of fluctuating or
intermittent loading, or a definite duty cycle; or (ii) the electrical installation is large and complex; or (iii) special types of occupancy exist.
(c) Measurement The maximum demand may be determined by the highest rate of consumption of electricity recorded or sustained over any 15 min period or periods when demand is at its highest by a maximum demand indicator or recorder.
(d) Limitation The maximum demand may be determined by the current rating of a fixed setting circuit breaker, or by the load setting of an adjustable circuit breaker.
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The maximum demand of consumers mains and sub-mains may be determined by the sum of the current settings of the circuit breakers protecting the associated final sub-circuit/s and any further sub-main/s.
Table 5.1 Maximum Demand non-Domestic Electrical Installation*
1 2 3
Load group
Residential Institutions, hotels, boarding
houses, hospitals, accommodation houses, motel
Factories, shops, stores, offices, business
premises, schools and churches
A. Lighting other than in load group F
75% of connected load Full connected load
B. (i) Socket outlets not
exceeding 10A other than those in B (ii)
1000W for first outlet plus 400W for each
additional outlet
1000W for first outlet plus 750W for each
additional outlet
(ii) Socket outlets not exceeding 10A in buildings or portions of building provide with permanent installed heating or cooling equipment or both.
1000W for first outlet plus 100W for each additional outlet
(iii) Socket outlets exceeding 10A
Full current rating of highest rated socket
outlet plus 50% of full current ration of
remainder
Full current rating of highest rated socket
outlet plus 75% of full current ration of
remainder C. Appliances for cooking,
heating and cooling, incl. instant water heaters but not appliances included in group D and J below.
Full connected load of highest rated appliance plus 50% of full load of
remainder
Full connected load of highest rated appliance plus 75% of full load of
remainder
* adapted from Table C2 of AS/NZS 3000
At container terminal, reefers are powered via a three phase 30 Amperes socket.
Using calculation method and guidance Table 5.1 as per above extraction, the
maximum demand of a reefer stack is given by:
75.0*30*)1__(30_ reefersofNumberDemandCurrent Max (A)
80
1000415*_*3 Max
MaxDemandCurrent
Demand (kVA)
In 2010, a consultant used this method of calculation for the redevelopment project
at Webb dock in the port of Melbourne. Result from this calculation was very
conservative, it was found to be more than double the results from other calculation
methods.
5.2.1.2 Demand calculation using diversity factor
For an expansion of P&O Sydney Container Terminal project in 2006, a Maximum
Demand Analysis submitted by a consultant using the following approach to
estimate the maximum demand of a reefer stack:
i. examining reefer nameplate data and deciding rated load values for 20 foot
reefer and 40 foot reefer,
ii. calculating a diversity factor (DF) by:
- choosing a day for observation, measuring an actual demand, recording
number of connected reefers and ambient temperature on that day then
with selected value in (i), the diversity factor was calculated as:
LoadRatedeferNumbereferActualDemandActualDF
__Re*_Re__
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- using historical maximum demand of an existing reefer stack and
assuming it occurred when the stack was full with a ration of 20 foot
reefers and 40 foot reefers is 2:1, the diversity factor was calculated as:
LoadRatedefereferofNumber
DemandMaximumHistoricalDFfullStack __Re*Re__
__
_
- select the higher value of calculated diversity factor
iii. the maximum demand for such reefer stack was then given by
)**(* _'40_'40_'20_'20 reeferreeferreeferreeferMax LoadNumberLoadNumberDFDemand
For Patrick Sydney Container Terminal redevelopment project in 2004, maximum
demand of a proposed reefer stack was calculated in the same way but with an
assumed diversity factor of 0.8 without any explanation.
5.2.1.3 Other demand calculation method
Another approach was used by consultant working on development of London
Gateway container terminal in England. Utilisation factor and simultaneous factor
are used in this approach. The definitions are:
Utilisation factor is the ratio of the Root Mean Square (RMS) power and the Peak
power.
Simultaneous factor is actually another name for diversity factor.
82
This approach was based on the information, especially the reefer’s average power
consumption value, in the Container Handbook [4] published by German Insurance
Association (Gesamtverband der Deutschen Versicherungswirtschaft e.V. (GDV).
i. using typical reefer’s nameplate rated load,
ii. the utilisation factor (UF) was calculated as:
24.015
6.3__
powerPeakpowerRMSUF
Where Peak power = 15 kW typical reefer peak load RMS power = 3.6 kW/TEU obtained from the Container Handbook 1 x 40 foot container = 2 TEU 1 x 20 foot container = 1 TEU
iii. simultaneous factor was already covered in above calculation as average
power was actually used instead of RMS value,
iv. discussing about this specific installation and providing reasons for choosing
different UF value (usually a higher value) then use this UF to calculate the
maximum demand :
UFLoadPeak
NumberNumberDemand reeferreeferreeferMax
_*)*2( _'40_'20
5.2.1.4 Reefer demand information from Container Handbook
The technical aspects of reefers, especially the actual electrical consumption, were
discussed in details and described in chapter 8 of The Container Handbook [4] by
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Dr. Yves Wild. On line version is available and full access to the book is provided
at http://www.containerhandbuch.de/chb_e/stra/index.html.
It appears that the published information was results of taking actual measurements
For the purposes of this study, the following conclusions of this discussion are of
interest:
For a very broad average value for all container types, ambient conditions and cargo types, the value 3.6 kW/TEU can be used. A 20' container tends to be closer to 4 kW and a 40' container tends towards 7 kW. As a result of new developments and the associated improvements in the efficiency of the containers, this value is dropping.
5.2.1.5 Demand calculation based on heat transfer and required cooling
Introduction
There is at least another way to calculate the reefer demand that was developed
during the course of this study. Considering the operation of the refrigeration unit as
the actual power consumption would depend on its operating status which has only
one purpose: keep the reefer at the required temperature.
Assuming the reefer was at the required temperature when connected to the
electrical grid at the container terminal. On the energy point of view, following
events would happen:
a) Since the required temperature was already reached, the refrigeration was
not required and because of the temperature difference, heat would be
transferred from ambient (high) to the reefer (low) through the reefer
surfaces trying to reach the equilibrium point,
84
b) When reefer temperature raises more than a preset value, the reefer
refrigeration unit would start drawing power from the electrical grid to cool
the cargo (ie. remove the above heat energy out of the reefer) and would not
be required when temperature drop to a preset value.
The cycle would be repeated with a time controlled defrost cycle (where the heating
element would be turned on) run independently.
The heat energy from event (a) had to be equal to the cooling energy from event (b)
because of the principle of energy conservation. Therefore the power consumption
of the reefer in this case is calculated as:
CoolHeat
AuxiliaryCoolAverage TimeTime
EnergyEnergyDemand
Detailed calculation would be discussed later in this section; it is now to investigate
the reefer specifications from different manufacturers for data needed for this
demand calculation method.
Reefer Manufacturers’ Data
There are numbers of reefer manufacturers in the world, the best known are
Thermal King, Carrier, Maersk, Daikin and Mitsubishi. The specifications of the
power units for reefer of those manufacturers could be obtained on line via their
web sites [1, 5, 8, 9, 12].
The interested Cooling Capacity values from these specifications were tabulated in
Table 5.2. All reefer power units from different manufacturers were very
compatible in providing similar cooling capacity at the same temperature. For
example, they all had around 6kW at -18oC available for further cool the cargo
85
and/or compensate for the heat transfer. More power was available at higher
temperature and less power at lower temperature.
Table 5.2 Cooling capacity of Reefer Power Unit
Reefer Power Unit
MakerThermal King Carrier Maersk Mitsubishi
Model CRR40 ThinLine Marine Reefer Reefer CPE Cooling
capacity at Watts Btu/hr Watts Btu/hr Watts Btu/hr(1) Watts Btu/hr(1)
1.7oC / 2oC 11,753 40,100 10,260 35,000 11,735 40,040 11,000 37,530 -18oC 6,009 20,500 6,008 20,500 6,540 22,310 6,280 21,430
-29oC 3,107 10,600 3,224 11,000 4,030 13,750 4,420 15,080 (1) Converted from Watts figures
However, this cooling capacity of the reefer was not the electrical demand or power
required to keep the reefer at the set temperature, this electrical demand was in fact
the sum of:
cooling power required to keep the cargo at set temperature within a tolerant
temperature band,
all other reefer electrical loads – compressor, fan, lights, control
processor,…
Demand Calculation formulas and calculation examples
It was assumed that the reefer was at the required temperature, ie. the cargo had
been cooled, when it was at the Container Terminal waiting for loading onto
container ship. In low-temperature mode (below -10°C) the refrigeration unit was
run in on/off mode, whereas in chilled mode (above -10°C) the output of the
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refrigeration circuit was regulated constantly. The operation is based on a
temperature range settings in the control section of the refrigeration unit (example ±
5oC) would:
turn on the refrigeration unit when the cargo temperature rise to 5oC above
the required temperature.
turn off the refrigeration unit when the cargo temperature drop 5oC below
the required temperature.
For the temperature increase over time t, the following equation applies for non-
respiring goods (goods with no respiration heat):
e tmc
kA
ambientambient pTTtT.
*)( (Eq. 5.1)
where: t time without refrigeration (seconds) T average temperature increase of the load after time t (oC) Tambient temperature different b/w internal and external of the reefer (oC) A surface area of the reefer (m²) k heat transition coefficient of the reefer (W/m²K) m mass of the load (kg)
cp specific heat capacity of the load (J/kgK) – see Table 5.3 For the cooling process the following equation can be used:
tTcm p ..
Q average (Eq. 5.2)
where: Qaverage average refrigerating capacity for cooling (W)
t cooling time (seconds) T cargo temperature after time t (oK) m mass of the load (kg)
cp specific heat capacity of the load (J/kgK) – see table 5.3 The average electrical demand would be calculated as:
87
21
212 )(**tt
ttPtPandAverageDem Auxiliary (Eq. 5.3)
Where: P cooling capacity at cargo temperature (W) PAuxiliary Auxiliary loads – controller, fan, lights… (W) t1 time reefer temperature rise (Hour) t2 time to cool reefer cargo (Hour)
Steps to calculate the average electrical demand of a reefer with specific cargo
were:
known T, Tambient, m, k, temperature tolerant range, cooling capacity and A
(from reefer dimensions), using Specific Heat Capacity Table in Appendix
C and references [39, 66, 107, 146] to find value of cp,
calculate time t1 using formula (Eq. 5.1), time that refrigeration is off and
reefer’s temperature is rising but still within the tolerant band,
calculate time t2 using formula (Eq. 5.2), time that requires refrigeration to
cool reefer cargo to within tolerant band,
calculate average demand using (Eq. 5.3).
Results of calculated electrical demand of reefers with different cargo and
assumption were tabulated in Table 5.3.
A number of calculations (shown in Appendix D) were done for a range of reefers
with different products that are at different temperature settings and carrying
different type of cargo as reported at Melbourne Container Terminal, it was
estimated that the electrical demand was between 2.5kW and 6kW. Thus the
electrical maximum demand for each reefer was 4.2 kW when applying a diversity
factor of 0.7.
88
Table 5.3 Calculated Average Electrical Demand of different reefer cargo
J/kgK)
W/m²K)T (oC)
Tambient (oC) T (oC)
Tambient (oC) P (W) PAxiliary (W) t1 (Hr) t2 (Hr) Ave. Demand (kW)
5.3 Measure the actual reefer electrical demand
5.3.1 Description
Reefers at the Melbourne Container Terminal under this study were located at two
areas namely E and L blocks, and were electrical powered via substations C and D
respectively. As mentioned in Chapter 3, the electrical parameters were recorded
every 15 minutes by the EMS server. Historical records could be set to report
automatically or manually.
Details of reefers within the Terminal were also reported daily around 7:00AM via
email. Due to the limitation of current terminal operator system, these reports could
only be done manually during the week.
By matching the number of reefers at each location with their power demands from
above reports, the average reefer actual power demand could be calculated.
89
Figure 5.5 Photo showing Reefer location at Melbourne Container Terminal
Figure 5.6 Drawing showing Reefer location at Melbourne Container Terminal
5.3.2 Data collection and analysing
Reefer details and Power Demand were collected every week day for more than two
(2) years from February 2007 till August 2009. There were some periods without
90
reports due to the change of the Terminal’s personnel and failure of recording
equipment.
Reefer report contains information of all reefers in the Terminal on a specific date.
Each reefer was detailed as:
location in the Terminal/Yard
when (date & time) it is in the Terminal/Yard
identification
set temperature
size (20 or 40 foot)
weight (ton)
commodity – eg. frozen juice (FZJC), frozen meet (FZMT),…
in/out bound (import/export)
others
Power Demand report contains data of electrical powers at four (4) reefer locations
shown in Figure 5.5 and Figure 5.6. Instantaneous apparent power, reactive power
and real power at specific time of specific date were reported.
Samples of the reefer report and power demand report were shown in Table 5.4 and
Table 5.5 respectively.
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Table 5.4 Example of Reefer daily Report
Although there were some missing data as the results of changing personnel at the
Terminal and measurement equipment malfunction, the number of report were still
too great to be listed or printed. They are available in electronic form upon request.
It should be noted that the measured actual load were more than just the reefers,
they are actual the whole low voltage loads within the substation that consists of air
conditioning system, computer system, general lights, battery chargers, exhaust fans
etc. So that they were already counted for in estimate the Container Terminal
electrical demand. However, the Container Terminal lights were not included as the
reports were done from 7:00AM when those lightings were switched off.
Current Position Yard In Container No. Temp C Len Wt Tns Comd Cat
E 0109 1 WE2250 SUDU1010337 -18.0¡C 20' 21.4 REEF IMPORT E 0111 1 SA2157 TOLU7804620 +5.0¡C 40' 21.5 REEF IMPORT E 0203 1 SA0719 POCU2817218 -25.0¡C 20' 20.8 FZJC EXPORT E 0203 2 SA0723 PONU2855314 -25.0¡C 20' 20.5 FZJC EXPORT E 0204 1 SA0726 POCU2827392 -25.0¡C 20' 20.6 FZJC EXPORT E 0204 2 SA0713 MWCU5664991 -25.0¡C 20' 20.4 FZJC EXPORT E 0205 1 SA0721 PONU2850924 -25.0¡C 20' 20.5 FZJC EXPORT E 0209 1 FR0919 PONU2948783 -18.0¡C 20' 20.4 REEF IMPORT E 0209 2 FR0934 SUDU1089373 +10.0¡C 20' 17 REEF IMPORT E 0210 1 FR1655 SUDU1024691 -18.0¡C 20' 20.5 REEF IMPORT E 0301 1 FR0845 CRLU1242231 -18.0¡C 40' 29.6 FZMT EXPORT E 0301 2 SA0719 MWMU6431485 -1.0¡C 40' 22.7 CHMT EXPORT E 0303 1 FR0646 PONU2932426 -1.0¡C 20' 14.3 CHMT EXPORT E 0303 2 FR1448 MWCU5639751 -18.0¡C 20' 22 FZMT EXPORT E 0305 1 WE1832 SUDU1013439 +7.0¡C 20' 18 REEF IMPORT E 0306 1 TH2045 CRXU5750618 -18.0¡C 20' 20.5 FZMT EXPORT E 0308 1 SA1448 MWCU5650998 -1.0¡C 20' 16.2 CHMT EXPORT E 0308 2 SA1451 PONU2974864 -1.0¡C 20' 15.8 CHMT EXPORT E 0309 1 SA1444 MWCU5678892 -1.0¡C 20' 17 CHMT EXPORT E 0309 2 SA1442 MWCU5801137 -1.0¡C 20' 17.1 CHMT EXPORT V 0501 1 TH1353 TRLU1044459 -18.0¡C 20' 16 FZMT EXPORT V 0601 1 WE1108 CBHU2933183 +13.0¡C 40' 10.9 CHIL EXPORT 4/02/2007 7:08:09 AM
92
93
The main purpose of this measurement scheme was to find the actual maximum
electrical demand of a reefer. The electrical demand could be express in term of
apparent demand (kVA) or real demand (kW) where:
FactorPowerDemandalkVADemandApparent _*_Re)(_
Power factor (PF) of a reefer was not a constant value, it was found to be between
0.5 and 0.7. The PF value vary between the reefer’s make, model and age. It may
even depend on the reefer status, for example PF may be different when
refrigeration is on (compressor + fan) or off (fan only). Therefore, the analysis
would concentrate on the maximum electrical real demand (in kW).
For each matching pair (same day) of reports shown in Table 5.4 and Table 5.5,
following steps were carried out for analysing the data:
from the reefer report, counted the number of reefer at each reefer location
– E block east, E block West, L block East and L block West.
at each reefer location counted the number of
o 20 foot reefers and 40 foot reefers
o for each reefer size, counted number of reefers in frozen mode (<
0oC), chill mode (> 0oC) or fan only mode,
o for each reefer size, counted number of reefers with weight < 10T,
between 10T and 20T, and > 20T,
from the power report, noted the powers at the same time or at closest time
to the reefer report time,
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tabulating these results and calculating the average demand for each reefer
at each reefer location.
Analysing the reports in their original forms would take too much time for
producing the usable results. Microsoft Excel spreadsheet program was used and a
macro REEFERS was created to automate the analysing task in following manner:
using an Excel file with pre-format tables for entering the analysis results,
combining the reports, rearranging the data, performing the calculation, and
entering the results into the pre-format tables.
saving the information in a new Excel file for further analysis.
Information of the REEFERS macro could be found in Appendix B. Table 5.6 and
Table 5.7 showed the analysed results after running the macro REEFERS.
Table 5.6 Results (temperature analysis) of running “REEFERS” macro
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Table 5.7 Results (weight analysis) of running “REEFERS” macro
5.3.3 Results of analysing data collection
Following information from the daily analysed result Excel file was extracted and
tabulated in another Microsoft Excel file:
number of 20 foot reefers
number of 40 foot reefers
average apparent power, real power and power factor of each reefer
A sample of such data was shown in Table 5.8. Average electrical demand per TEU
(not shown) was also calculated and saved in different columns of the same
worksheet using Excel function.
96
The daily average Electrical Demand per reefer and per TEU were then plotted
against time for year 2007, 2008 and 2009 as shown in Figures 5.7 to Figure 5.12.
So that the maximum value - purpose of this measurement - could be found quickly.
The aim of analysing of collected data is to find the maximum actual average
electrical demand of reefer, make comparison to the calculated values and come up
with the final value of the electrical demand as the result of this study.
Melbourne Container TerminalkW per Reefer 2007
Date
Dem
and
(kW
)
Figure 5.7 Electrical Demand per Reefer in 2007
97
98
Mel
bour
ne C
onta
iner
Ter
min
alkW
per
TEU
200
7
Dat
e
Demand (kW)
Fi
gure
5.8
E
lect
rica
l Dem
and
per
TE
U in
200
7
99
Mel
bour
ne C
onta
iner
Ter
min
al k
W p
er R
eefe
r 200
8
Dat
e
Demand (kW)
Fi
gure
5.9
E
lect
rica
l Dem
and
per
Ree
fer
in 2
008
100
Mel
bour
ne C
onta
iner
Ter
min
al k
W p
er T
EU -
200
8
Dat
e
Demand (kW)
Figu
re 5
.10
E
lect
rica
l Dem
and
per
TE
U in
200
8
101
Mel
bour
ne C
onta
iner
Ter
min
alkW
per
Ree
fer 2
009
Dat
e
Demand kW
Figu
re 5
.11
E
lect
rica
l Dem
and
per
Ree
fer
in 2
009
102
Mel
bour
ne C
onta
iner
Ter
min
alkW
per
TEU
- 2
009
Dat
e
Demand kW
Fi
gure
5.1
2
Ele
ctri
cal D
eman
d pe
r T
EU
in 2
009
103
The following were observed from these plots:
a. In early 2007, reefer electrical demand at L block West and E block East was
high with average maximum of 5kW and a few exceptions with the highest
value of 7.64kW on 7 June 2007. However, from August 2007 the reefer
electrical demand was consistently less than 4kW at all location.
b. In 2008, the reefer electrical demand was also less than 4kW
c. In 2009, the reefer electrical demand was similar to data in 2008 that was less
than 4kW but with larger variation and few exceptions that electrical demand
exceeded 4kW with the highest of 5.41 kW on 22 Jan 2009.
d. With above observation, it was safe to conclude that the maximum average
electrical demand per reefer was 4kW.
e. Electrical demand per TEU plot had similar shape to the corresponding
electrical demand per reefer plot as it was averaged over a higher number. The
maximum average electrical demand per TEU was found to be 3kW.
104
5.4 Comparison of maximum demand calculated by different
methods
A reefer stack with 510 outlets (power plugs) was used for calculating the maximum
demand using different methods that described above. The results were tabulated in
Table 5.9 for comparison. All assumptions were also listed in this table.
Table 5.9 Maximum Demand calculated using different methods
As shown in the table, the maximum demand calculation using heat transfer and
required cooling method gave the best result when compared to the actual
measurement.
The information from The Container Handbook was the second closest one. It stated
that “…improvements in the efficiency of the containers, this value is dropping…”,
however, the new value had not yet been published.
105
AS3000 calculation method was too conservative with a result almost triple the actual
measurement. The maximum current that can safety flow in the circuit was used for
calculation instead of the maximum current required by the reefer power unit.
Therefore, it could be stated that the calculation was for the maximum capacity of the
electrical infrastructure
Results from the other two methods were on the high side as too many assumptions
were made so that accuracy could not be always guaranteed.
Following reasons were used to help deciding which value of the reefer electrical
demand to be used for estimating the electrical power demand of a Container Terminal:
As mentioned before, the measure electrical demands were for all low voltage
loads that included air conditioning system, general lights, battery chargers,
exhaust fans, general power supply etc. There were always activities (substation
maintenance work, charging electrical tools,…) within the Container Terminal
that required electrical power resulting in a higher measured electrical demand.
The collected data were large enough to enable the inclusion of those extra
loads and ignoring a few abnormal values.
There was a potential problem with reefer load which is known to the industry
as “hot boxes”. The “hot boxes” are reefers that are not cold down to the
required set temperature when delivered to the Container Terminal for loading
onto a ship in a few day times. During these stays, electrical power is used to
reduce these reefer temperatures to the required values. This cost shifting
106
exercise will increase the reefer electrical demand at the Container Terminal
and might cause the “spikes” in the above plots.
The electrical demand per reefer was calculated for each reefer, it was not
distinguishing between the 20 and 40 foot reefers. In the author opinion, it was
the best way to use in estimating the average reefer electrical demand. The
reason for this is that both type of reefers were using the same refrigerated unit
especially the ones from Thermo King and Carrier the two leading reefer
manufacturers. By using number of reefer instead of the TEU number, the
calculated electrical demand would actually on the high side thus produce a
safety factor in electrical demand estimation. It was also noted that when the
ration between 20 foot reefers and 40 foot reefer was 2/1 then the maximum
demand will be the same using either demand per reefer value or demand per
TEU value.
It was normal practice to store 20 and 40 foot reefers together as shown in
Figure 5.13 . As a result, there was always empty or unused space in the reefer
area. For example, with a “two high” stacking area, a 40 foot reefer could not be
put on top of two 20 foot reefers or via versa. A “stacking factor”, which is
defined as the ratio between the actual number of reefers and the available
reefer spaces, could be used to describe this situation. By experiences, a
stacking factor of 0.7 could be used for this Melbourne Container Terminal. Not
applying this stacking factor, the estimate Reefer Electrical Demand would
have a safety margin for error.
107
Two years of daily observation was indeed a long time observation, collected
data would produce a reliable and accurate result that cover all variations due
to:
o number of reefer traffic in different seasons during the years,
o number of reefer with different cargo types (foods, fruits, dairies
products,…),
o extra loads from normal operation within the container terminal
Figure 5.13 Mix Reefer sizes in storage at Melbourne Container Terminal
Studying the comparison of the electrical demands estimated by using different
methods together with all above reasons, the result from actual measured reefer
electrical data would come first as the most reliable value to be used.
108
Therefore, an electrical demand of 4kW per reefer could be used for calculating the
Container Terminal electrical demand and decision of applying stacking factor or not
would be left for the engineer who would calculate the total electrical demand of the
terminal.
5.5 Conclusions
Refrigerated Containers were briefly described. Several different methods of
calculating the average electrical demand of a number of refrigerated containers were
presented and explained. A new approach to calculate electrical demand based on
energy conservation principle was also described with calculation example. The actual
electrical demand was also measured for more than two (2) years and the data were
carefully studied to yield a result of an electrical demand of 4kW for a 20 or 40 foot
reefer.
The study of major electric powered assets at the Container Terminal was now
completed producing results for calculating the maximum electrical demand of the
container terminal. The next chapter would exploring ways of reducing the total
electrical demand of the Terminal before verifying the results against the actual data.
109
CHAPTER SIX
Reducing electrical maximum demand & energy usage
The maximum demand of container terminal was now can be calculated with the
findings from previous chapters. However, before going to verify and check the
accuracy of such calculation and conclude the study, it was worth to discuss the
possibilities of reducing electric demand and energy usage at container terminal, a
required task for today terminal operator [17, 44].
This chapter investigates the methods of reducing the electrical demand and energy
usage at container terminal.
6.1 Reducing electrical maximum demand
Due to the operation conditions at container terminal, the author could see only two
ways to reduce the maximum demand: improving power factor and using cranes with
DC instead of AC drive system. Details were in the following sections.
6.1.1 Improving power factor to reduce maximum demand Recalling the power triangle: Apparent Power (VA) Reactive Power (VAr) Phase angle
110
Real Power (W) And the power factor is defined by:
PowerApparent
PoweralPF_
_Re
Most of the container terminals are on kVA tariff which the charged electrical demand
is the apparent power (kVA) and since the real power cannot be changed, the only way
to reduce the kVA demand is to increase the power factor or reducing the reactive
power component.
As accessed in Chapter 3, there were three groups of electrical load at a container
terminal: container handling cranes, refrigerated containers (reefers) and other loads.
The typical power factors of these groups were:
Container handling cranes :
with AC drive system - power factor is between 0.9 and 0.95
with DC drive system - power factor varies from 0.3 to 0.6
Reefers: power factor around 0.6 Other electrical loads: power factor around 0.8
The overall power factor at container terminal varies between 0.5 and 0.7 depending on
how many cranes with DC drive system at the container terminal, how often the cranes
are used and the number of refrigerated containers are in the yard. Improving power
factor would significantly reduce the electrical demand at the terminal. For example,
111
the kVA demand would be reduced by 22% if the power factor increases from 0.7 to
0.9. Power capacitors are used to improve the power factor and they are referred as
“power factor correction” or “VAR compensation” unit. There are two types of
compensation devices:
Static – using mechanical contactor for switching capacitors in/out to provide
the required reactive power. Used for slow change or constant load such as
reefers.
Dynamic [55, 126] – using solid state contacts for switching capacitors in/out to
provide the required reactive power. Used for fast change load such as cranes.
Typical cost for a static unit was about $90 per kVAr and about $200 per kVAr for a
dynamic power factor correction unit.
Melbourne container terminal had a very poor power factor around 0.5 due to all quay
cranes were with DC drive system. Although this terminal was on kW (real power
tariff) and there would be no financial benefit for improving the power factor, a 2
MVAr dynamic power factor correction unit was installed in 2006 to reduce the
terminal kVA demand and provide the spare capacity for connecting two new quay
cranes to the existing electrical infrastructure network without any upgrade work. The
effect of such installation was shown in Figure 6.1.
From Figure 6.1, maximum demand was 3100 kVA at power factor of 0.5 before the
commissioning of the power factor correction unit. This demand was reduced to 1800
kVA at unity power factor, a reduction of 42% and provided a spare capacity of 1300
112
kVA for new load or a substantial reduction of running cost (electricity bill) for the
terminal.
Reducing demand might also help to eliminate the voltage sag and flickering problems
for container terminal with weak power supply.
Figure 6.1 Reducing electrical demand by improving power factor
6.1.2 Using cranes with DC drive system to reduce maximum demand
Since the introduction of IGBT based AC drive products in the late 1980s, there had
been much debate on which technology – AC or DC drive – should be used by the
crane industry for new container cranes. References [15, 93, 148] give good guides for
selection between AC and DC drive.
113
It was fortunate that profiles of both quay cranes with DC and AC drive systems were
obtained in this study as described in Chapter 4 to enable the comparison of the peak
demand of different drive systems. Full details of comparisons were presented at the 6th
International Conference on Power Quality and Supply Reliability [58].
As detailed studied in Chapter 3 and Chapter 4, the maximum demand was the average
demand over the 15 or 30 minute interval and from Table 4.2, the AC quay crane kW
demand was 40% higher than that of the DC quay crane. Taking into account the
driver’s techniques, the final position of the container and other containers on the ship,
the difference was still expected to be in the low 20%.
Therefore, if all DC drive system was used on all container handling cranes and with
power factor correction unit(s) deployment, at least a 20% reduction in maximum
demand could be achieved. Dynamic power factor correction had to be used to provide
the correction as container handling cranes were the fast changing loads.
6.2 Reducing electrical energy usage
There were number of ways to reduce the energy usage at container terminal
6.2.1 Using cranes with DC drive system to reduce energy usage
Again as detailed studied in Chapter 4, with higher peak and average demand, the
energy usage had to be higher for AC drive quay crane. An average of 60% more
114
energy was required to handle a container during this observation. AC drive quay crane
used 100% more energy than DC drive quay crane had been observed at other time.
An 2005 internal study of energy consumption of quay cranes at Yantian international
container terminal in China [95] found similar results as shown in Table 6.1.
6.2.2 Utilisation of the regenerative energy to reduce energy usage
Depending on the capacity of the terminal there were a number of major items of plant
and equipment consuming electrical energy. These included:
Container handling cranes (when hoisting, trolleying and raising the boom plus
auxiliary loads such as air conditioners, fans, lighting, …),
Refrigerated containers (reefers),
Offices and workshops,
Lighting.
However when a container handling crane was lowering a load or the boom being
lowered, the relevant drive system motors became generators producing electrical
energy (regenerative energy) and sending it back into the electrical distribution
network.
115
Tab
le 6
.1
Ext
ract
from
Yan
tian
2005
inte
rnal
rep
ort o
n Q
C E
NE
RG
Y C
ON
SUM
PTIO
N S
TU
DY
DC
Qua
y cr
ane
AC
Qua
y cr
ane
116
This regenerative energy could be utilized within the container terminal by other
loads (other cranes, reefers, office,…) reducing the electrical energy demand from
the network. However, if the container terminal did not have sufficient overall
demand capacity to absorb this regenerative energy or had an internal electrical
distribution not configured to utilise this energy, the regenerative energy was then
returned to the network that was lost to the container terminal.
Magnitude of regenerative energy
As described in Chapter 3, a data collection scheme was set up at the Melbourne
container terminal with digital meter installed at every switch and circuit breaker.
Figure 6.2 shows the single line diagram of substation D at Melbourne container
terminal where supply to each of the quay cranes was fitted with digital meter
where the recorded consumed energies are presented in this section.
Accumulated real energies (kWHrs) were recorded for each quay crane; refer
supply and overall supply (feeder). Energy usage and regenerative energy were
calculated for different scenario and tabulated in Table 6.2 where:
In : energy flows from the electrical network to the load
Out: energy flows from the load to the electrical network (regenerative)
Sum : calculated sum for quay cranes and Terminal
117
Figure 6.2 Single line diagram of substation D
With data collected for 18 months tabulated in Table 6.2, followings are observed:
Regenerative energy from quay crane varied between 17.63% and 24.73%
of consumed energy of such crane.
If all quay cranes and reefer were individual metered – past practice at the
Port of Brisbane in Australia - then the regenerative energy was 9.21% of
the terminal’s total energy consumption.
These quay cranes were supplied from the same feeder and operate
independently of each other. The regenerative energy was partly utilised
(some quay cranes were lifting while the other were lowering) so that the
overall regenerative energy was 11.45% of the quay cranes’ energy
118
consumption or 4.40% of terminal’s total energy consumption. This amount
of energy would be lost if there was no other load.
With the reefer supply on the same feeder, the regenerative energy was
further utilised and the net regenerative energy lost to the network was only
0.47%.
Interesting articles on this topic could be found at [26, 28, 31]. The above observations were shown in Figure 6.3 and Figure 6.4.
119
Tab
le 6
.2
Rec
orde
d co
nsum
ed r
eal e
nerg
ies a
t sub
stat
ion
D
Dat
e3/
12/2
008
18/0
6/20
10
Con
sum
ptio
n T
ime
11:3
0 10
:30
3/12
/08
to 1
8/6/
10
kWhr
s kW
hrs
kWhr
sR
egen
erat
ive
Ene
rgy
(%)
No.
4 In
1,
054,
642
1,80
2,95
7 74
8,31
5
O
ut
194,
554
326,
499
131,
945
17.6
3%
No.
5 In
44
3,20
7 1,
382,
928
939,
721
Out
10
1,16
6 30
2,54
7 20
1,38
1 21
.43%
N
o.6
In
785,
137
1,25
0,77
2 46
5,63
5
O
ut
172,
753
262,
183
89,4
30
19.2
1%
No.
7 In
1,
495,
173
2,88
2,38
2 1,
387,
209
Out
33
9,42
4 68
2,45
3 34
3,02
9 24
.73%
N
o.8
In
1,53
0,62
6 2,
762,
080
1,23
1,45
4
O
ut
323,
057
614,
833
291,
776
23.6
9%
L B
lock
– E
AST
To
tal
4,81
6,57
4 7,
179,
158
2,36
2,58
4
L B
lock
– W
EST
Tota
l 7,
488,
193
11,8
38,1
47
4,34
9,95
4
Term
inal
– S
um
In
17,6
13,5
52
29,0
98,4
24
11,4
84,8
72
In
divi
dual
met
ered
O
ut
1,13
0,95
4 2,
188,
515
1,05
7,56
1 9.
21%
Q
uay
cran
e - S
um
In
4,19
5,23
0 11
.45%
of c
rane
s
Out
48
0,45
7 4.
40%
of t
erm
inal
In
18
,125
,541
29
,033
,309
10
,907
,768
Feed
er N
o. 2
O
ut
21,3
81
72,9
20
51,5
39
0.47
%
120
Figu
re 6
.3
Ene
rgy
cons
umpt
ion
with
out u
tiliz
atio
n of
reg
ener
ativ
e en
ergy
121
Figu
re 6
.4
Ene
rgy
cons
umpt
ion
with
util
izat
ion
of r
egen
erat
ive
ener
gy
122
Reducing Energy usage with the design of electrical infrastructure
As shown in Figure 6.3, if the container handling cranes and reefer supply were
individually metered then regenerative energy could not be utilised and would be lost.
This practice was used in the past at the Port of Brisbane in Australia as part of its
electricity redistribution from the Utilities.
Today, most of the container terminals have multiple feeders with a single meter
number so that the amount of utilised regenerative energy is depended on how the
electrical infrastructure was designed and what kind of base load (reefers, office,
workshop,…) it has.
Awareness of the existing of regenerative energy and its source when designing the
new electrical infrastructure or re-configuring the existing one of a container terminal
would help to reduce the electrical energy usage (up to 9.21% in Melbourne container
terminal case).
Reducing energy usage with ‘net metering’
Reference [150] provides good information. On this topic.This topic was presented at
the 2008 International Universities Power Engineering Conference in Italy [59].
123
The digital energy meters now used are capable of measuring and recording the
quantity of energy flow both from the electrical network into the container terminal and
the regenerative energy fed back into the network.
Under most electrical tariff structures the Utilities are not required to compensate the
consumer for regenerative energy or at least only charge the ‘net’ electricity. However,
the container terminal as a large electricity user in a competitive electricity market
would be in a strong position in negotiating for a supply contract which recognised the
presence of regenerative energy and only charge the ‘net’ electricity. To get a desired
result, the personnel who do the negotiation for the power supply contract should be
aware of the regenerative energy issue.
6.2.3 Reduce energy usage by lighting
Container terminal is a 24 hours per day operation area, lighting was fundamental to
the safe and secure of the container terminal for both vehicle and foot traffic. The
correct light levels were required to avoid simple trip and slip hazards to personnel and
the safe handling of goods and equipment that exists in a busy environment. Guide for
such lighting level could be found in [2] and [19].
124
Figure 6.5 High mast lighting at container terminal
To provide lighting for a large area with as uniform as possible, the high intensity
discharge (HID) lamps had to be mounted on high masts normally at 30 metres as
shown in Figure 6.5. Container terminal at night was shown in Figure 6.6.
Figure 6.6 Container terminal at night
125
For any redevelopment plan at the container terminal to cope with the forecast
throughput, there was no doubt that electrical infrastructure, especially the terminal
lighting system, would have to be part of the plan. Any electrical work had to meet all
new requirements from latest Standard, the existing lighting levels had to be checked
and the terminal operators would look to use the latest technologies to improve its
lighting system and reduce the energy usage.
There were several options to consider when upgrade the lighting system and saving
energy usage:
Prismalence system: most luminaries produce a globe like output, only a part of the
light volume reach the target area, the rest becomes stray light or glare. Prismalence
uses the prismatic lenses system to create pyramids of light where the target area is the
same while the light volume and glare is much less. A claim that its 150W unit is
comparable to a 400W high pressure sodium lamp. However, Prismalence could not
make an offer to a recent lighting upgrade project in Sydney due to the requirements
that existing high masts have to be used.
LED lighting system: similar to the prismalence system, the LEDs emit light in a
single forward direction. Properly designed LED modules can produce high lumen
output at lower wattage. They are also ‘instant-on’ and illuminate immediately upon
powering and even “dim” to any required level of luminance. However, maximum
mounting height for LED system is between 10 to 15 metres that is not suitable for high
mast application. A claim that a 2100W Phoenix LED system is comparable to a
126
5600W traditional lighting system for a RTG has been achieved. Saving maintenance
cost is also a main feature of the LED lighting system.
Active reactor system: the Active Reactor is a device for the efficient control and
operation of high intensity discharge (HID) lamps. The device uses a microchip and
electronics to control the starting and running of 150 watt to 2000 watt high pressure
sodium and metal halide lamps commonly used in street lighting, floodlighting and
industrial lighting. The Active Reactor delivers substantial energy savings, green house
gas reductions and lamp life extension when used with HID lamps. A claim of:
saving 18% of energy usage and increase lamp life of 50% for metal halide
lamps
saving 25% of energy usage and increase lamp life of 100% for high pressure
sodium lamps
had been achieved. Active reactor can also “dim” the lamps to lower lighting output
level to save energy and the lamps can be switch on to full power within seconds rather
than minutes as traditional lamp control system.
Depend on which system the container terminal operators choose to use, a saving of at
least 20% of energy usage could be achieved.
6.2.4 Energy Storage and Peak Lopping
As discussed in previous sections, regenerative energy existed at any container terminal
with modern container handling cranes. If the container terminal had no base load for
127
utilising this energy and could not get a “net metering” arrangement then energy
storage device could be used. Information on energy storage development and
application can be found at [36, 38, 40, 47, 67, 96, 122, 123, 133] for distributed
network or at [75, 76, 78, 79, 91, 92, 94, 97, 100, 103, 104, 109, 113, 117] for smaller
projects. The energy storage device could store and discharge the energy when the need
arise. Applications of these devices have been proven in transportation: rail [85], bus
and cranes. Current technology used for electrical storage devices is based on flywheel
[29, 30, 89] or super capacitor [16, 60, 61, 63]
It is possible to program the device to only discharge the stored energy when the
demand is higher than a pre-set value, that is peak lopping or peak shaving operation.
As amount of regenerative energy is not large enough for this kind of operation, the
device would get its major energy from the electrical network and top up with
regenerative energy and discharge the stored energy when required.
By lopping the peak, that is reducing the high demand some issues of the terminal’s
electrical supply (such as protection settings, voltage drop, light flickering due to the
very high peak demand in short time) could be eliminated.
Peak lopping device had been proposed to use for enable the connection of a new quay
crane to a weak electrical supply in Western Australia in 2007. Two different proposals
were submitted:
flywheel solution by Powercorp (Australia) and
super capacitor solution by S and C (America).
128
It was not in the scope of this study to explain the principle and how the system work
so that only the expected results were shown to prove technologies are available to use.
Figure 6.7 showed the load profile of the quay crane. Figure 6.8 showed the Powercorp
proposal which would kick in when the demand was greater than 500kW. Figure 6.9
showed the S and C proposal which would activate when the crane demand was greater
than 400kW.
Electronic Shock Absorber (ESA) Power
Time - seconds
kW
Figure 6.7 Quay crane load profile
129
Figure 6.8 Proposal from Powercorp using flywheel technology to limit peak
demand at 500kW and allow 100kW regenerative energy to be utilized by other
load
Net Source Power
Time - seconds
kW
Figure 6.9 Proposal from S and C using super capacitor technology to limit
peak demand at 400kW and capture all regenerative energy.
130
6.3 Conclusions
It was possible to reduce the electrical maximum demand and the energy usage at
container terminal as presented in this chapter.
Using container handling cranes with DC drive system would reduce both the demand
and energy usage, However, it might not be practical as the trend in production are for
AC drive system so that cost and delivery time of cranes with DC drive system would
not be justifiable.
The most effective way of reducing maximum demand is to improve the power factor;
a reduction of 50% had been achieved.
Utilising the regenerative energy and upgrading the lighting system would reduce the
energy usage at the container terminal. A saving at least 5% as in Melbourne container
terminal case.
In the next chapter, the findings from this study would be used to calculate the
maximum electrical demand of several different container terminals around the world,
the results would be then compared with the actual demand of those container terminal
for verification purposes.
131
CHAPTER SEVEN
Verification of this study results
The study of electrical usage and demand at the container terminal was based on the
operation data collected at Melbourne container terminal in Australia. Fortunately, the
findings of this study could be checked/verified as electrical energy and demand data of
a number of container terminals around the world were generously provided by Robert
Reid and Associates, the consultant who did the review electrical energy consumption
and management for those container terminals.
The majority of the utilities are now using kVA tariff for maximum demand charge to
reflect the true impact of the maximum demand on the electrical network. Therefore,
the kVA demand was used for results verification in this chapter.
This chapter begins with the description of how maximum electrical demand was
calculated by different methods:
using “calculation method” of AS/NZS 3000:2007 [62],
using “assessment method” of AS/NZS 3000:2007 [62] or the diversity factors
method as the norm in this field,
using the findings of this study – the new improve assessment method.
132
For each of the container terminals, that electrical data were available, expected
maximum electrical demand was calculated and the results were plotted against the
actual demand of that container terminal. By inspecting these plots, conclusions could
be drawn about the accuracy of using findings from this study for calculation of
maximum demand at container terminal.
7.1 Calculation of the maximum demand at container terminal
Three (3) different methods were used to calculate the maximum demand at container
terminal and the results were tabulated for comparison. All demands were in kVA and
following demand symbols were used:
PR Maximum reefer demand in kVA
PQC Maximum crane demand in kVA
PL Maximum terminal lighting demand in kVA
PW Maximum workshop demand in kVA
PO Maximum office demand in kVA
The differences from these calculations were how to calculate the reefer demand and
the container handling crane demand. All other loads were as their rated values (or as
installed) and would be the same for all three calculation methods; they were normally
expressed in kW and were converted to kVA using power factor of 0.8.
Lighting load PL in kVA
Workshop load PW in kVA
Office load PO in kVA
133
Maximum demand at container terminal is then:
PMAX = PR + PQC + PL + PW + PO (Eq. 7.1)
7.1.1 Calculation to AS/NZS 3000:2007
This method of calculation was based on the “Calculation method” mentioned
in AS/NZS 3000:2007 [62].
Reefer load (load group B (iii) from Table C2)
75.0*30*)1__(30_ reefersofNumberDemandCurrent Max
1000415*_*3 Max
RDemandCurrent
P (Eq. 7.2)
Crane load (load group E from Table C2)
Assumptions:
Hoist motor power for typical 60T cranes is PC = 1100kW
CnCCCQC PPPPP *5.0.....*5.0*75.0*25.1 321 (Eq. 7.3)
Where PC1 …PCi Hoist motor power of crane 1, … crane i and
PC1 > PC2 >….> PCi
Using only the hoist motor data as it is the largest motor on any container
handling crane. The motor power is normally given in kW, using power factor
134
of 0.95 to convert to kVA as most container handling cranes are now with AC
drive system.
Maximum demand of the container terminal was calculated using Eq. 7.1
7.1.2 Calculation using diversity factors
This method is normally used in this engineering field, it can also be described
as estimation of the maximum demand using “Assessment method” mentioned
in AS/NZS 3000:2007 [62].
Reefer load
Assumptions: Ratio of 40’ and 20’ reefers is 1:2
Rated load of 20’ reefer is 10.4 kVA
Rated load of 40’ reefer is 13.5 kVA
Reefer diversity factor is 0.5
Demand of 40’ reefers 3
5.13*40
NP (Eq. 7.4)
Demand of 20’ reefers 3
4.10**220
NP (Eq. 7.5)
Where N is the number of reefer outlets (sockets, plugs)
135
Reefer demand PR = 0.5 *(P40 + P20) (Eq. 7.6)
All demands are in kVA.
Crane load
Assumptions: Crane rated current @ 11kV 62.6A
Crane diversity factor is 0.5
Crane demand NPQC *6.62*11*3*5.0 (Eq. 7.7)
Where N is the number of cranes
Maximum demand of the container terminal was then calculated using Eq. 7.1
7.1.3 Calculation using findings of this study
Findings from this study:
Reefer demand 4 kW or 6.67 kVA @ pf of 0.6
Reefer stacking factor SF = 0.7
Crane demand 250 kW or 263 kVA @ pf of 0.95 (AC drive)
At most of container terminals, 20’ and 40’ reefers are stacked together causing
some unusable reefer outlets. A stacking factor is defined as the ratio between
used and available reefer outlets. A stacking factor of 0.7 (as typical value
found at Melbourne container terminal) would be used for any reefer stack that
136
has more than 100 reefer outlets and a stacking factor of 1 was used for reefer
stack with less than 100 outlets.
This calculation method is actually the new improved assessment method as
mentioned in [] as the actual operating data of container terminal were
examined/assessed resulting in a new way of calculating the maximum demand
at container terminal.
Reefer demand PR = SF * N * 4 / 0.6 (Eq. 7.8)
Crane demand PQC = 250 * C / 0.95 (Eq. 7.9)
Where N is the number of reefer outlets
SF is the reefer stacking factor
C is the number of cranes
Maximum demand of the container terminal was then calculated using Eq. 7.1
7.2 Maximum demand at Container Terminals
Calculations were done for comparison to the actual demand of the following
container terminals whose electrical energy consumption data were available:
Melbourne container terminal No. 1 at East Swanson Dock
Melbourne container terminal No. 2 at West Swanson Dock
Combined Melbourne two terminals with data on the same time frame
Sydney container terminal at Port Botany
Brisbane container terminal at Fisherman Islands
137
Yantian international container terminal – China
Fairview container terminal – Canada
Maher container terminal - USA
Calculation results were tabulated in Table 7.1 for Australian container terminals and in
Table 7.2 for overseas container terminals. Following were observations from these
tables:
Maximum demand calculated from the findings of this study was the lowest
value for all container terminals,
Except for the USA container terminal, calculation of maximum demand using
the findings from this study produced a reduction of between 34% and 47% of
maximum demand calculated using the Assessment method [62] that was the
norm in this field,
USA container terminal had a very large base load (office, lighting,..) as it had
to accommodate the custom building and lighting for a large area. However, a
reasonable reduction of 28% in the estimated maximum demand was obtained
using the findings of this study.
Chinese container terminal had even a larger base load than the USA container
terminal but a better reduction in estimated maximum demand (45%) due to the
fact that it had a very large number of quay cranes. It was noted that this
terminal had a lot of yard gantry cranes (around 200 machines) but they were
not electric powered so that they were not counted in this study and comparison.
138
139
140
It was good to find out a new way of calculating the maximum demand of
container terminal with an impressive reduction compared to the old way.
However, all the efforts were wasted if the new result was not stood up when
comparing to the actual demand at container terminal: the calculated maximum
demand should indeed be the MAXIMUM DEMAND. Therefore, in the next
section, comparison between calculated maximum demand and actual demand of
container terminal were performed to verify the usefulness of this new way of
calculation.
7.3 Comparison of the results
Figure 7.1 East Swanson Dock terminal – actual and calculated maximum
electrical demands
141
The results from Table 7.1 were visualized in the Figure 7.1 and the maximum
demand that was calculated using the new method was indeed the MAXIMUM
demand. It was about 47% less than the old assessment method of calculation. It
was also still 30% higher than the actual maximum demand so that there is safety
margin and scope for future terminal expansion.
Figure 7.2 West Swanson Dock terminal – actual and calculated maximum
electrical demands
The results from Table 7.1 were again visualized in the Figure 7.2 for the
Melbourne container terminal No. 2 – the West Swanson Dock terminal. The
calculated maximum demand using the new method was also the MAXIMUM
demand. It was about 46% less than the old assessment method of calculation and
was 30% higher than the actual maximum demand.
142
Figure 7.3 Swanson Dock terminals – actual and calculated maximum
electrical demands
As the available electrical energy consumption data were time stamped and the
same for both Melbourne container terminals, data from these two container
terminals were combined to create a larger terminal for study. Again, the results
from Table 7.1 were visualized in Figure 7.3 with similar observation: the
calculated maximum demand using the new method was also the MAXIMUM
demand. It was about 47% less than the old assessment method of calculation and
was 40% higher than the actual maximum demand.
143
Figure 7.4 Port Botany terminal – actual and calculated maximum electrical
demands
For Sydney container terminal, the results from Table 7.1 were visualized in the
Figure 7.4 with similar reduction in the calculated value compared to the old
calculation method. However, the new maximum demand was about 69% higher
than the actual maximum demand. This could only be understood with the terminal
operation conditions during the observation period:
there were a couple of old quay cranes that have lower safe working load –
lower demand
reefer traffic was low during this time period.
A maximum demand of 3940kVA was shown on the current electricity bill of this
terminal. It was around 30% spare capacity instead of 69%.
144
Figure 7.5 Fisherman Islands terminal – actual and calculated maximum
electrical demands
The results from Table 7.1 for Brisbane container terminal were visualized in the
Figure 7.5 and the calculated maximum demand using the new method was indeed
the MAXIMUM demand. It was about 34% less than the old assessment method of
calculation and 30% higher than the actual maximum demand of the terminal. The
reason for the smaller reduction in maximum demand was that this terminal had a
poor power factor (shown in the electrical energy consumption as around 0.6).
145
Figure 7.6 China – Yantian terminal – actual and calculated maximum
electrical demands
Yantian container terminal was a large and very busy terminal, there were a huge
number of machines in the terminal – more than 70 quay cranes and more than 200
yard gantry cranes. The yard gantry cranes were not electric powered at the time of
study so that they were not included. It was in the process of converting these
machines into electric powered. As only monthly electrical energy consumptions
were available, the analysis was not as good as other terminals.
Results for the Chinese container terminal from Table 7.2 were visualized in the
Figure 7.6 The calculated maximum demand was about 45% less than the old
assessment method of calculation and 26% higher than the actual maximum
demand.
146
Figure 7.7 Canada – Fairview terminal – actual and calculated maximum
electrical demands
In contrary, the Fairview container terminal in Canada is a relative small container
terminal with only three (3) quay cranes and 72 reefer outlets. However, it is also a
very busy terminal with regular shipments. Figure 7.7 shows almost a repeatable
pattern of peaks that when the quay cranes were working.
Results from Table 7.2 were visualised in Figure 7.7, the calculated maximum
demand was about 34% less than the old assessment method of calculation (due to
reefer poor power factor) and 58% higher than the actual maximum demand.
147
Figure 7.8 USA – Maher terminal – actual and calculated maximum
electrical demands
The Maher container terminal in USA is also an unusual terminal, it has a very large
base loads – lightings to cover a large stacking area and large office building to
accommodate the Custom office. Its base load of more than 2000 kW compared to
Melbourne terminal of 300 kW and both container terminals handled the same
number of containers. Real power (kW) tariff was used for this terminal so that
calculations and graphics were shown in kW.
The calculated maximum demand was only 28% less than the old assessment
method of calculation and 21% higher than the actual maximum demand. These
results were due to the rather larger base loads. However, there was still ample
spare capacity for safety margin and future expansion of terminal using this new
calculation method.
148
Summary of the comparisons were tabulated in Table 7.3 with following results
when using the new method of maximum demand calculation:
compared to the old assessment method, a reduction of around 45% could be
achieved for terminals with good power factor and around 34% for terminal
with poor power factor. As most modern container handling cranes have AC
drive system, the power quality (power factor) was all due to the reefers (0.6
or lower) and could be improved using the power factor correction or
automatic VAR compensation unit.
As the study findings were the results of analysing the actual container
terminal operation data, calculation of maximum demand using these
finding would always be higher than the actual demand. Such safety margin
was found to be more than 20%.
Table 7.3 Comparison of calculated and actual maximum demand
149
7.4 Conclusions
The maximum electrical demand of container terminal was calculated using method
described in [62] and the new improved method. The results from the new improved
method produced a more realistic value when comparing to other methods – a
reduction of at least 35% for the required maximum demand. And the new
maximum demand calculation at a container terminal result was also proved to be a
MAXIMUM DEMAND when compared to the actual demand of that terminal.
Using the new results for designing or upgrading a container terminal would require
smaller rated electrical infrastructure – switchgears, cables, size of substations. In
other words, a substantial saving in layout capital cost especially if the Utilities
insists of building a new electrical substation (that the terminal operator has to
contribute a major portion of the cost) to deliver the requested maximum demand.
The container terminal operator would also get a saving in running cost as the
requested maximum demand is charged as part of the monthly electricity bill and
this portion is around a fifth of the bill. A reduction of at least 35% maximum
demand would produce a big saving.
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CHAPTER EIGHT
Conclusions and directions for future research
The main objective of this research was to find a way of calculating the maximum
electrical demand at container terminal. There is a trend to build larger container
ship and bigger container handling cranes to care for the forecast increase in
container traffic for both dry cargos and refrigerated products. The main electrical
consumptions at container terminal are from those container handling cranes and
refrigerated containers. However, to the best of the author’s knowledge from the
literature review and long time working in the port, there was no published
academic research into the electrical energy usage and demand at container
terminal. The lack of published research and understanding about the electrical
demand and consumption at container terminal leading to over size the terminal
electrical infrastructure and wasting the needed capital for other work. To fill this
gap, actual operation data (container throughput and electrical energy consumption)
of Melbourne container terminal had been collected for over two years for
analysing, comparing to theoretical calculation and conclusions were drawn in this
research for a better way of determining the maximum demand at container
terminal.
This chapter summarises the findings of the research and recommends directions for
future research.
151
8.1 Conclusions
The main electrical demand and consumption at container terminal are from the
container handling cranes and refrigerated containers. Container handling cranes
were detailed studied in Chapter 4 via the following study steps:
Examining the load profile of quay cranes with AC and DC drive system to
understand the characteristics of these machine,
Analysing container weight at container terminal from the collected data,
Calculate the electrical demand for various loads (container weights) at
different location under loading (from land to container ship) and unloading
(from container ship to land) conditions using actual cranes data and
container weights.
The concluded results for this part of the study are:
for quay cranes: use 250kW for single lift cranes and 350 kW for twin lift
cranes as the maximum electrical demand value,
for rail mounted gantry or automatic stacking crane: use 200 kW for each
machine as the maximum electrical demand,
Similarly, the refrigerated containers (reefers) were detailed examined in Chapter 5
via the following study steps:
152
Several methods of how to calculate the maximum demand of reefers were
presented. These methods are more often than not used by the industry for
calculation leading to a very conservative result.
Power consumption by a reefer while in the container terminal is the power
required to keep that reefer at the pre-set temperature. Thus a new way of
calculating the demand of a reefer based on the heat transfer and required
cooling was developed and presented.
Analysing the reefers data that were collected for over two (2) years and
computed the reefer’s actual demand.
Results from different methods were compared and conclusion was drawn.
The concluded results for this part of the study is that 4 kW to be used for each
reefer as the maximum electrical demand.
Finally, the findings of this research were checked with the actual electrical demand
at a number of container terminals around the world in Chapter 7. The results were
very satisfactory with the calculated maximum demand was indeed the MAXIMUM
DEMAND with a large enough margin for safety and future expansion of the
container terminal.
In summary, the findings from this study provided a simpler way of calculating the
maximum demand at container terminal with a more accurate result leading to a
substantial saving to the container terminal operator both in capital layout for
electrical infrastructure investment and running cost of electricity.
153
8.2 Directions for future research
Despite of extensive time and effort spent on this study, it can be further improved
in many aspects. Followings are recommended for future research:
This thesis uses the number of container handling cranes and their average power
(kW) when handle the containers to compute the maximum demand for the
container handling cranes. There is another way of calculate their demand by
looking at the energy (kWHr) required to move a container and the number of
container throughput during a period (Hr) then compute the demand (kW). Be
reminded that the container handling cranes consume energy during idle time. This
method might produce a better result.
Originally, the simulation technique was intended to be used for this study of
electrical energy usage and demand at container terminals. However, suitable
simulation software (with electrical calculation module) was not available at the
time so that spreadsheet calculation was used. It appears that such simulation
software is now available as described in the news [7]. The actual container
terminal operation data collected for this thesis is still valid and can be used for
such model.
In this thesis, the weights of containers in stacking yard were analysed. This is not
the weight that would be lifted by the quay cranes as most of them capable of
operate in twin lift mode if the weight is within the quay cranes’ Safe Working
Load (SWL). Future research should look into the process of determining the
loading and unloading plans including the operation mode (single lift or twin lift) of
the quay cranes for better design input data for simulation model.
154
This study assumes all refrigerated containers (reefers) are at the set temperature
when arrive at the container terminal so that their electrical demand are computed
according to that assumption. However, there is an increased trend in the number of
“hot boxes” in Australia terminals. “Hot boxes” refers to the refrigerated containers
that are not cooled down to the required temperature when delivered to the
container terminal. During their stay, these “hot boxes” will draw electrical power
from the terminal electrical infrastructure for continuing the cooling process. In the
extreme case, they even use the power of container ship for cooling down to the
required temperature when arrive at the final destination. Future research should
pay attention on this fact as a large number of “hot boxes” would affect the out
come.
155
APPENDIX A
Daily container report, Code of Exel macro and Results
A1. Daily container report
For more than a year, the number of containers in the yard of Melbourne container
terminal was reported in the format shown below. As there are more than 6000
containers in the yard, only the report format and about 100 containers were listed.
Current Position Container No. Cat Len Wt Tns Comd I/B Carr O/B Car IMO
Plan Ql
BBK TOLU8971025 IMPORT 40' 29 OOG MMO9051 TRUCK C 0211 1 KKTU7263932 IMPORT 20' 4.8 GENL CIM9071 TRUCK C 0212 1 KKTU7521770 IMPORT 20' 6.1 GENL CIM9071 TRUCK C 0212 2 TOLU4589550 IMPORT 20' 23.7 GENL MMO9051 TRUCK C 0213 1 NYKU2778443 IMPORT 20' 2.7 GENL CIM9071 TRUCK J 2007 1 MSCU1544371 TRANSSHIP 20' 23.9 NICK HRT9069 KRI9068 J 2007 2 MSCU2703626 TRANSSHIP 20' 28.2 GENL HRT9069 KRI9068 J 2007 3 GLDU0332520 TRANSSHIP 20' 28.3 GENL HRT9069 KRI9068 J 2008 1 MSCU3183895 TRANSSHIP 20' 23.9 NICK HRT9069 KRI9068 J 2008 2 MSCU1266795 TRANSSHIP 20' 28.3 GENL HRT9069 KRI9068 L 0701 2 OOLU6092070 EXPORT 40' 24.3 FZMT TRUCK OOF9079 L 0703 1 CBHU2647235 TRANSSHIP 20' 11 REEF KKO9790 OOF9079 L 0705 1 APRU5074912 IMPORT 40' 27 REEF KKO9790 TRUCK L 0707 1 APRU5083107 IMPORT 40' 27.5 REEF KKO9790 TRUCK L 0709 1 CRLU1217218 EXPORT 40' 24.5 FZFS TRUCK KRI9068 L 0801 1 PCIU5999285 EXPORT 40' 29 FZMT TRUCK OOF9079 L 0801 2 OOLU5961950 EXPORT 40' 25 FZMT TRUCK OOF9079 R 1009 2 MSCU9196760 IMPORT 40' 24.9 PPR AYU9081 TRUCK R 1011 1 TRIU5674370 IMPORT 40' 26.8 PPR AYU9081 TRUCK R 1011 2 MSCU8661092 IMPORT 40' 27.8 PPR AYU9081 TRUCK R 1013 1 MSCU4167923 IMPORT 40' 28.1 PPR AYU9081 TRUCK R 1013 2 TRIU5449569 IMPORT 40' 27.2 PPR AYU9081 TRUCK R 1015 1 AMFU5001852 IMPORT 40' 14.1 MCH AYU9081 TRUCK R 1015 2 GATU8547842 IMPORT 40' 25.4 TIM UKI9056 TRUCK R 1101 1 TGHU0215794 IMPORT 20' 6.2 GENL AYU9081 TRUCK R 1101 2 CRXU0755448 IMPORT 20' 6.6 EFE AYU9081 TRUCK R 1102 1 MISU2363934 IMPORT 20' 23 GRAI MWA9062 TRUCK R 1102 2 TTNU2945935 IMPORT 20' 23 GRAI MWA9062 TRUCK R 1104 1 MISU2376818 IMPORT 20' 23 GRAI MWA9062 TRUCK R 1106 1 MEDU1200496 IMPORT 20' 15.3 MIP AYU9081 TRUCK R 1107 1 ECMU1377022 IMPORT 20' 6.5 FOD AYU9081 TRUCK
156
R 1108 1 TRLU8912861 IMPORT 20' 15.3 MIP AYU9081 TRUCK R 1111 1 MEDU1564409 IMPORT 20' 21.7 PPR AYU9081 TRUCK R 1113 1 LCRU2002026 IMPORT 20' 20.6 PPR AYU9081 TRUCK R 1201 1 ECMU9753189 IMPORT 40' 18 GEN UKI9056 TRUCK R 1201 2 TCKU9511018 IMPORT 40' 11.1 GEN UKI9056 TRUCK R 1203 1 SUDU5785934 IMPORT 40' 20.3 GENL HSG9054 TRUCK R 1203 2 HLXU4195509 IMPORT 40' 14.1 GENL HSG9054 TRUCK R 1211 1 TCNU9302922 IMPORT 40' 25.7 TIM UKI9056 TRUCK R 1213 1 GESU4282530 IMPORT 40' 10 GEN UKI9056 TRUCK R 1213 2 TRLU7085310 IMPORT 40' 14.9 GEN UKI9056 TRUCK U 2601 2 KHS400473 EXPORT 40' 32.5 STEL TRUCK OOF9079 U 2701 1 KHS400609 EXPORT 40' 24.5 STEL TRUCK OOF9079 U 2801 1 KHS400497 EXPORT 40' 31.5 STEL TRUCK OOF9079 U 2801 2 KHS400457 EXPORT 40' 32.5 STEL TRUCK OOF9079 U 2901 1 KHS400630 EXPORT 40' 24.5 STEL TRUCK OOF9079 U 2901 2 KHS400602 EXPORT 40' 32.5 STEL TRUCK OOF9079 U 3001 1 HLXU4603998 IMPORT 40' 25 OOG SFB9055 TRUCK U 3101 1 MSCU7347404 TRANSSHIP 20' 16.8 TIMB HRT9069 TAT9067 V U 3201 1 SUDU4881580 IMPORT 40' 11.1 OOG SFB9055 TRUCK T U 3301 1 TOLU4694257 IMPORT 20' 23.1 OOG CIM9071 TRUCK U 3401 1 KHS400488 EXPORT 40' 32.5 STEL TRUCK OOF9079 U 3401 2 KHS400461 EXPORT 40' 32.5 STEL TRUCK OOF9079 U 3501 1 MSCU7346218 TRANSSHIP 20' 23.9 TIMB HRT9069 TAT9067 V U 3601 1 TOLU8986180 IMPORT 40' 16 OOG UKI9056 TRUCK V 0101 1 MWCU5742760 IMPORT 20' 13 CHC MDH9077 TRUCK V 0201 1 PCIU5797598 EXPORT 20' 21.4 FZMT TRUCK OOF9079 V 0301 1 MAEU5661079 EXPORT 20' 22.5 FZMT TRUCK TAT9067 V V 0401 1 PONU2870221 EXPORT 20' 22.5 FZMT TRUCK TAT9067 V V 0501 1 TRLU1044459 EXPORT 20' 16 FZMT TRUCK UVA9072 V 0601 1 CBHU2933183 EXPORT 40' 10.9 CHIL TRUCK OOF9079 4/02/2007 7:08:20 AM
A2. Code listing of Excel macro CONTAINER
The daily report was analysed to gather interested information, in this case the
weight of the container. As ever daily report had more than 6000 containers data, an
Excel macro CONTAINER had been written to automate the task. The Visual Basic
code is listed below:
Sub Container() ' ' Container Macro
157
‘ count number of containers ‘ count number of 20’ containers and calculate the TEU ‘ count number of containers that weighted ‘ < 5T, b/w 5T and 10T, b/w 10T and 15T, b/w 15T and 20T, ‘ b/w 20T and 25T, b/w 25T and 30T and > 30T ‘ calculate average weight of each container and TEU ‘ ' Macro recorded 26/02/2007 by Thanh ' ' Keyboard Shortcut: Ctrl+c ' Dim LRow As Integer Dim counter As Integer Dim content As Single Dim RDate As String Dim work1 As String Dim Dtype As String Dim Weight As Single Weight = 0 Range("L2").FormulaR1C1 = "=LEFT(RC[-11],1)&LEFT(RC[-8],2)" Range("L2:L2").Select Selection.Copy ActiveCell.Offset(1, 0).Range("A1").Select Range(Selection, ActiveCell.SpecialCells(xlLastCell)).Select ActiveSheet.Paste ActiveCell.SpecialCells(xlLastCell).Select ActiveCell.Offset(0, -2).FormulaR1C1 = "=ROW() - 2" LRow = ActiveCell.Offset(0, -2).Value RDate = ActiveCell.Offset(0, -11).Value ActiveCell.Offset(0, -2).Value = "" ActiveCell.Offset(0, 0).Value = "" Range("E2").Select For counter = 1 To LRow - 1 Dtype = ActiveCell.Offset(counter - 1, 7).Value If Left(Dtype, 1) = "?" Then ActiveCell.Offset(counter - 1, 7).Value = "Other" & Right(Dtype, Len(Dtype) - 1) End If content = ActiveCell.Offset(counter - 1, 0).Value Weight = Weight + content If content < 5 Then ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "1" ElseIf content >= 5 And content < 10 Then ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "2" ElseIf content >= 10 And content < 15 Then ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "3" ElseIf content >= 15 And content < 20 Then ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "4"
158
ElseIf content >= 20 And content < 25 Then ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "5" ElseIf content >= 25 And content < 30 Then ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "6" Else ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "7" End If Dtype = ActiveCell.Offset(counter - 1, -2).Value If Left(Dtype, 1) = "E" Then ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "E" ElseIf Left(Dtype, 1) = "I" Then ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "I" Else ActiveCell.Offset(counter - 1, 7).Value = ActiveCell.Offset(counter - 1, 7).Value & "O" End If Next counter work = ActiveWorkbook.Name If Right(work, 3) = "XLS" Then work = Left(work, Len(work) - 4) End If Workbooks.Open Filename:= _ "C:\Documents and Settings\All Users\Application Data\Microsoft\Container_Form.xls" Sheets("Summary Weight").Copy After:=Workbooks(work).Sheets(1) Workbooks("Container_Form.xls").Close Range("I101").Select ActiveCell.Value = Weight Range("B7").Select For counter = 0 To 22 ActiveCell.Offset(counter, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-1]&""201?"")" ActiveCell.Offset(counter, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-2]&""202?"")" ActiveCell.Offset(counter, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-3]&""203?"")" ActiveCell.Offset(counter, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-4]&""204?"")" ActiveCell.Offset(counter, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-5]&""205?"")" ActiveCell.Offset(counter, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-6]&""206?"")" ActiveCell.Offset(counter, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-7]&""207?"")" ActiveCell.Offset(counter, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-8]&""20?I"")" ActiveCell.Offset(counter, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-9]&""20?E"")" ActiveCell.Offset(counter, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-10]&""20?O"")"
159
ActiveCell.Offset(counter + 29, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-1]&""401?"")" ActiveCell.Offset(counter + 29, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-2]&""402?"")" ActiveCell.Offset(counter + 29, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-3]&""403?"")" ActiveCell.Offset(counter + 29, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-4]&""404?"")" ActiveCell.Offset(counter + 29, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-5]&""405?"")" ActiveCell.Offset(counter + 29, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-6]&""406?"")" ActiveCell.Offset(counter + 29, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-7]&""407?"")" ActiveCell.Offset(counter + 29, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-8]&""40?I"")" ActiveCell.Offset(counter + 29, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-9]&""40?E"")" ActiveCell.Offset(counter + 29, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-10]&""40?O"")" ActiveCell.Offset(counter + 58, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-1]&""451?"")" ActiveCell.Offset(counter + 58, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-2]&""452?"")" ActiveCell.Offset(counter + 58, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-3]&""453?"")" ActiveCell.Offset(counter + 58, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-4]&""454?"")" ActiveCell.Offset(counter + 58, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-5]&""455?"")" ActiveCell.Offset(counter + 58, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-6]&""456?"")" ActiveCell.Offset(counter + 58, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-7]&""457?"")" ActiveCell.Offset(counter + 58, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-8]&""45?I"")" ActiveCell.Offset(counter + 58, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-9]&""45?E"")" ActiveCell.Offset(counter + 58, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C12:R" & Trim(Str(LRow)) & "C12,RC[-10]&""45?O"")" Next counter ' Range("AB32").Select ' ActiveCell.Offset(0, 0).FormulaR1C1 = "='Power Report'!R17C2" ' ActiveCell.Offset(0, 1).FormulaR1C1 = "='Power Report'!R17C3" ' ActiveCell.Offset(0, 2).FormulaR1C1 = "='Power Report'!R17C4" ' ActiveCell.Offset(1, 0).FormulaR1C1 = "='Power Report'!R17C5" ' ActiveCell.Offset(1, 1).FormulaR1C1 = "='Power Report'!R17C6" ' ActiveCell.Offset(1, 2).FormulaR1C1 = "='Power Report'!R17C7" ' Range("AB36").Select ' ActiveCell.Offset(0, 0).FormulaR1C1 = "='Power Report'!R17C14" ' ActiveCell.Offset(0, 1).FormulaR1C1 = "=-'Power Report'!R17C15" ' ActiveCell.Offset(0, 2).FormulaR1C1 = "='Power Report'!R17C16"
160
' ActiveCell.Offset(1, 0).FormulaR1C1 = "='Power Report'!R17C17" ' ActiveCell.Offset(1, 1).FormulaR1C1 = "=-'Power Report'!R17C18" ' ActiveCell.Offset(1, 2).FormulaR1C1 = "='Power Report'!R17C19" ' ActiveWindow.ScrollColumn = 24 Range("A93").Select ActiveCell.Offset(0, 9).Value = "='" & Trim(work) & "'!A" & Trim(Str(LRow + 2)) ActiveCell.Offset(0, 10).Value = "='" & Trim(work) & "'!B" & Trim(Str(LRow + 2)) End Sub A3. Results from running the macro CONTAINER The analysed results were:
161
APPENDIX B
Daily reefer report, Code of Excel macro and Results
B1. Daily reefer and power reports
For more than a year, the number of refrigerated containers (reefers) in the yard of
Melbourne container terminal and electrical demand at a specific time were
reported in the format shown below.
Daily reefer report
As there are more than 400 reefers, only the report format and about 50 reefers were
listed.
Current Position Yard In Container No. Temp C Len
Wt Tns Comd Special
Plan Ql Cat
E 0109 1 WE2250 SUDU1010337 -18.0¡C 20' 21.4 REEF #NAME? IMPORT E 0111 1 SA2157 TOLU7804620 +5.0¡C 40' 21.5 REEF #NAME? IMPORT E 0203 1 SA0719 POCU2817218 -25.0¡C 20' 20.8 FZJC #NAME? V EXPORT E 0203 2 SA0723 PONU2855314 -25.0¡C 20' 20.5 FZJC #NAME? V EXPORT E 0204 1 SA0726 POCU2827392 -25.0¡C 20' 20.6 FZJC #NAME? V EXPORT E 0204 2 SA0713 MWCU5664991 -25.0¡C 20' 20.4 FZJC #NAME? V EXPORT E 0205 1 SA0721 PONU2850924 -25.0¡C 20' 20.5 FZJC #NAME? V EXPORT E 0209 1 FR0919 PONU2948783 -18.0¡C 20' 20.4 REEF #NAME? IMPORT E 0209 2 FR0934 SUDU1089373 +10.0¡C 20' 17 REEF #NAME? IMPORT E 0408 1 FR2116 MAEU5667816 -20.0¡C 20' 18.4 FZMT #NAME? V EXPORT E 0408 2 SA0005 PONU2927292 -1.0¡C 20' 18.8 CHMT #NAME? V EXPORT E 0409 1 TH1943 SUDU1047871 -20.0¡C 20' 22.7 FZMT #NAME? EXPORT E 0409 2 FR0750 FSCU5214236 -20.0¡C 20' 22.1 FZMT #NAME? EXPORT E 0411 1 FR2112 CRLU1213721 -18.0¡C 40' 29.6 FZMT #NAME? EXPORT E 0411 2 SA1453 MWCU6818556 -1.0¡C 40' 21 CHMT #NAME? V EXPORT E 0501 1 FR2134 CRLU1319800 -21.0¡C 40' 29.6 FZMT #NAME? EXPORT E 0501 2 FR2145 MSCU7406641 -18.0¡C 40' 29.6 FZMT #NAME? EXPORT E 0503 1 FR0851 SCZU8263108 -18.0¡C 20' 16.9 FZMT #NAME? EXPORT E 0503 2 FR1212 MAEU5661274 +4.0¡C 20' 15.6 BUTR #NAME? EXPORT E 0504 1 FR0006 PONU2933931 -1.5¡C 20' 15.3 CHMT #NAME? V EXPORT E 0504 2 FR2125 MWCU5661950 -1.5¡C 20' 13.5 CHMT #NAME? V EXPORT E 0505 1 WE1650 KNLU2782677 -21.0¡C 20' 23.7 FZMT #NAME? V EXPORT E 0505 2 TH1433 MWCU5691235 -1.0¡C 20' 16.8 CHMT #NAME? V EXPORT E 0506 1 TH1731 MSCU3618149 -18.0¡C 20' 22 FZFS #NAME? V EXPORT
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E 0506 2 TH1734 MSCU3612475 -18.0¡C 20' 22.1 FZFS #NAME? V EXPORT E 0507 1 FR1759 PONU2917761 +4.0¡C 20' 15.6 BUTR #NAME? EXPORT E 0507 2 FR1800 PONU2882310 +4.0¡C 20' 15.6 BUTR #NAME? EXPORT E 0511 1 FR0110 HLXU7750415 +18.0¡C 40' 17.7 REEF #NAME? IMPORT E 0511 2 FR0445 MWCU6963322 -18.0¡C 40' 28.6 REEF #NAME? IMPORT E 0603 1 WE2054 MWCU5618595 -20.0¡C 20' 22.5 FZMT #NAME? V EXPORT E 0603 2 TH1443 MWCU5726178 +4.0¡C 20' 20.3 CHIL #NAME? V EXPORT E 0604 1 WE1652 PONU2865539 -21.0¡C 20' 23.6 FZMT #NAME? V EXPORT E 0604 2 TH1156 MWCU5702659 0.0¡C 20' 16 CHMT #NAME? V EXPORT E 0605 1 WE1651 POCU2831690 -21.0¡C 20' 16.7 FZMT #NAME? V EXPORT E 0605 2 FR0639 MWCU5711687 -1.0¡C 20' 13.6 CHMT #NAME? V EXPORT E 0606 1 WE1633 MWCU5669822 -18.0¡C 20' 19.5 FZCH #NAME? V EXPORT E 0606 2 WE1635 MWCU5622506 -18.0¡C 20' 19.5 FZCH #NAME? V EXPORT E 0607 1 WE1637 MWCU5800980 -18.0¡C 20' 19.5 FZCH #NAME? V EXPORT E 0607 2 WE1645 MWCU5628551 -18.0¡C 20' 19.5 FZCH #NAME? V EXPORT L 0801 2 FR2245 OOLU5961950 -20.0¡C 40' 25 FZMT #NAME? EXPORT L 0807 1 WE1547 GESU9341050 -20.0¡C 40' 25.5 FZPY #NAME? EXPORT L 0807 2 WE1957 CBHU2983760 -18.0¡C 40' 29.6 FZMT #NAME? EXPORT L 1001 1 WE1653 CRXU6805715 -18.0¡C 40' 21.9 REEF #NAME? IMPORT L 1003 1 FR1417 MWCU5672596 +4.0¡C 20' 15.6 BUTR #NAME? EXPORT L 1003 2 FR1654 CRLU3807430 +4.0¡C 20' 12.5 CHIL #NAME? EXPORT L 1004 1 TH0532 SUDU1031052 -18.0¡C 20' 21.6 REEF #NAME? IMPORT L 1005 1 FR1358 SCZU8677047 -18.0¡C 20' 14.9 FZFS #NAME? EXPORT L 1005 2 FR1351 SCZU8262354 -18.0¡C 20' 15.2 FZFS #NAME? EXPORT L 2105 1 TU2039 MWMU6363457 -1.0¡C 40' 25.4 CHMT #NAME? V EXPORT L 2105 2 TU2145 MWMU6342290 -1.5¡C 40' 24.7 CHMT #NAME? V EXPORT L 2107 1 WE0113 MWMU6306631 -1.5¡C 40' 26 CHMT #NAME? V EXPORT L 2107 2 TH0128 MWCU6718120 -1.5¡C 40' 24.9 CHMT #NAME? V EXPORT L 2109 1 WE1755 SUDU1102691 -18.0¡C 20' 19.7 REEF #NAME? IMPORT L 2109 2 WE1827 SUDU1043304 -18.0¡C 20' 21.2 REEF #NAME? IMPORT L 2110 1 FR2321 GESU9336310 -20.0¡C 20' 17.8 FZMT #NAME? EXPORT L 2111 1 TH1730 CRLU7229514 -1.0¡C 40' 29 CHMT #NAME? V EXPORT L 2111 2 FR0915 MWMU6430555 -1.0¡C 40' 29.1 CHMT #NAME? V EXPORT L 2201 1 FR1412 FSCU5645249 -18.0¡C 40' 29 FZMT #NAME? EXPORT L 2203 1 FR0003 CBHU2652695 +15.0¡C 20' 8.3 REEF #NAME? IMPORT L 2203 2 FR1815 CBHU2675509 -15.0¡C 20' 20.9 REEF #NAME? IMPORT L 2205 1 FR0650 MWCU5601329 -25.0¡C 20' 20.4 FZJC #NAME? V EXPORT L 2207 1 FR1656 FBLU6207507 -20.0¡C 20' 18 FZMT #NAME? EXPORT L 2209 1 FR1135 MSCU5611272 FAN 40' 29.1 FANT V EXPORT L 2211 1 FR1149 TRIU8282372 +15.0¡C 40' 17.2 CONF #NAME? EXPORT L 2301 1 FR0807 GCEU7720400 +15.5¡C 40' 16 REE #NAME? IMPORT L 2303 1 FR2121 PONU2976471 -20.0¡C 20' 17 FVG #NAME? IMPORT L 2304 1 FR0214 GCEU3128603 +3.0¡C 20' 23 CEE #NAME? IMPORT V 0201 1 FR1940 PCIU5797598 -18.0¡C 20' 21.4 FZMT #NAME? EXPORT V 0301 1 FR0106 MAEU5661079 -20.0¡C 20' 22.5 FZMT #NAME? V EXPORT V 0401 1 TH2240 PONU2870221 -20.0¡C 20' 22.5 FZMT #NAME? V EXPORT V 0501 1 TH1353 TRLU1044459 -18.0¡C 20' 16 FZMT #NAME? EXPORT V 0601 1 WE1108 CBHU2933183 +13.0¡C 40' 10.9 CHIL #NAME? EXPORT 4/02/2007 7:08:09 AM
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B2. Code listing of Excel macro REEFER
The daily reefer and power reports were analysed to gather interested information, in
this case the power demand of the whole reefer pads (there are 4 reefer pads at
Melbourne container terminal – L block East, L block West, E block East and E block
West). As ever daily report had more than 400 reefers data, an Excel macro REEFER
had been written to automate the task. The Visual Basic code is listed below:
Sub Reefers() ' ' Reefers Macro ' Macro recorded 7/02/2007 by Thanh ' ' Keyboard Shortcut: Ctrl+r ' Dim LRow As Integer Dim counter As Integer Dim content As Integer Dim RDate As String Dim work1 As String work1 = ActiveWorkbook.Name For Each w In Workbooks If Right(w.Name, 3) = "txt" Or Right(w.Name, 3) = "TXT" Then work = w.Name End If Next w Sheets("Sheet1").Copy After:=Workbooks(work).Sheets(1) Sheets("Sheet1").Name = "Power Report" Workbooks(work1).Close savechanges:=False Sheets(1).Select Range("L2").FormulaR1C1 = "=LEFT(RC[-11],1)&MID(RC[-11],3,2)&LEFT(RC[-7],2)" Range("N2").FormulaR1C1 = "=IF(LEFT(TRIM(RC[-10]),1)=""c"",RIGHT(TRIM(RC[-10]),LEN(TRIM(RC[-10]))-2),TRIM(RC[-10]))" Range("M2").FormulaR1C1 = "=IF(LEN(RC[1])>3,VALUE(LEFT(RC[1],LEN(RC[1])-2)),-100)" Range("L2:N2").Select Selection.Copy ActiveCell.Offset(1, 0).Range("A1").Select
165
Range(Selection, ActiveCell.SpecialCells(xlLastCell)).Select ActiveSheet.Paste ActiveCell.SpecialCells(xlLastCell).Select ActiveCell.Offset(0, -2).FormulaR1C1 = "=ROW() - 2" LRow = ActiveCell.Offset(0, -2).Value RDate = ActiveCell.Offset(0, -13).Value ActiveCell.Offset(0, -2).Value = "" ActiveCell.Offset(0, -1).Value = "" ActiveCell.Offset(-1, -1).Value = "" ActiveCell.Offset(-2, -1).Range("A1").Select Range(Selection, "M2").Select Selection.Copy Selection.PasteSpecial Paste:=xlPasteValues, Operation:=xlNone, SkipBlanks _ :=False, Transpose:=False Range("N2").Select For counter = 1 To LRow - 1 content = ActiveCell.Offset(counter - 1, -1).Value If content = -100 Then ActiveCell.Offset(counter - 1, 0).Value = ActiveCell.Offset(counter - 1, -2).Value & "F" ElseIf content < 0 Then ActiveCell.Offset(counter - 1, 0).Value = ActiveCell.Offset(counter - 1, -2).Value & "-" Else ActiveCell.Offset(counter - 1, 0).Value = ActiveCell.Offset(counter - 1, -2).Value & "+" End If content = ActiveCell.Offset(counter - 1, -8).Value If content < 10 Then ActiveCell.Offset(counter - 1, 1).Value = ActiveCell.Offset(counter - 1, -2).Value & "1" ElseIf content > 20 Then ActiveCell.Offset(counter - 1, 1).Value = ActiveCell.Offset(counter - 1, -2).Value & "3" Else ActiveCell.Offset(counter - 1, 1).Value = ActiveCell.Offset(counter - 1, -2).Value & "2" End If work = ActiveCell.Offset(counter - 1, -3).Value If Trim(work) = "EXPORT" Then ActiveCell.Offset(counter - 1, 0).Value = ActiveCell.Offset(counter - 1, 0).Value & "E" ActiveCell.Offset(counter - 1, 1).Value = ActiveCell.Offset(counter - 1, 1).Value & "E" Else
166
ActiveCell.Offset(counter - 1, 0).Value = ActiveCell.Offset(counter - 1, 0).Value & "I" ActiveCell.Offset(counter - 1, 1).Value = ActiveCell.Offset(counter - 1, 1).Value & "I" End If Next counter Columns("L").ClearContents Columns("M").ClearContents work = ActiveWorkbook.Name If Right(work, 3) = "XLS" Then work = Left(work, Len(work) - 4) End If Workbooks.Open Filename:= _ "C:\Documents and Settings\All Users\Application Data\Microsoft\Reefer_Form.xls" Sheets("Summary Temp").Copy After:=Workbooks(work).Sheets(2) Windows("Reefer_Form.xls").Activate Sheets("Summary Weight").Copy After:=Workbooks(work).Sheets(3) Sheets("Summary Temp").Select Workbooks("Reefer_Form.xls").Close Range("B2").Select For counter = 1 To 13 ActiveCell.Offset(counter + 5, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-1]&""20+?"")" ActiveCell.Offset(counter + 5, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-2]&""20-?"")" ActiveCell.Offset(counter + 5, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-3]&""20F?"")" ActiveCell.Offset(counter + 5, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 5, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 5, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-6]&""40+?"")" ActiveCell.Offset(counter + 5, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-7]&""40-?"")" ActiveCell.Offset(counter + 5, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-8]&""40F?"")" ActiveCell.Offset(counter + 5, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 5, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-10]&""40?E"")" ActiveCell.Offset(counter + 5, 11).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-1]&""20+?"")" ActiveCell.Offset(counter + 5, 12).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-2]&""20-?"")"
167
ActiveCell.Offset(counter + 5, 13).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-3]&""20F?"")" ActiveCell.Offset(counter + 5, 14).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 5, 15).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 5, 16).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-6]&""40+?"")" ActiveCell.Offset(counter + 5, 17).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-7]&""40-?"")" ActiveCell.Offset(counter + 5, 18).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-8]&""40F?"")" ActiveCell.Offset(counter + 5, 19).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 5, 20).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-10]&""40?E"")" Next counter For counter = 1 To 12 ActiveCell.Offset(counter + 25, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-1]&""20+?"")" ActiveCell.Offset(counter + 25, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-2]&""20-?"")" ActiveCell.Offset(counter + 25, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-3]&""20F?"")" ActiveCell.Offset(counter + 25, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 25, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 25, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-6]&""40+?"")" ActiveCell.Offset(counter + 25, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-7]&""40-?"")" ActiveCell.Offset(counter + 25, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-8]&""40F?"")" ActiveCell.Offset(counter + 25, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 25, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-10]&""40?E"")" ActiveCell.Offset(counter + 25, 11).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-1]&""20+?"")" ActiveCell.Offset(counter + 25, 12).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-2]&""20-?"")" ActiveCell.Offset(counter + 25, 13).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-3]&""20F?"")" ActiveCell.Offset(counter + 25, 14).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-4]&""20?I"")"
168
ActiveCell.Offset(counter + 25, 15).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 25, 16).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-6]&""40+?"")" ActiveCell.Offset(counter + 25, 17).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-7]&""40-?"")" ActiveCell.Offset(counter + 25, 18).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-8]&""40F?"")" ActiveCell.Offset(counter + 25, 19).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 25, 20).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-10]&""40?E"")" Next counter For counter = 1 To 2 ActiveCell.Offset(counter + 45, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-1]&""20+?"")" ActiveCell.Offset(counter + 45, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-2]&""20-?"")" ActiveCell.Offset(counter + 45, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-3]&""20F?"")" ActiveCell.Offset(counter + 45, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 45, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 45, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-6]&""40+?"")" ActiveCell.Offset(counter + 45, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-7]&""40-?"")" ActiveCell.Offset(counter + 45, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-8]&""40F?"")" ActiveCell.Offset(counter + 45, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 45, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C14:R" & Trim(Str(LRow)) & "C14,RC[-10]&""40?E"")" Next counter Range("AB32").Select ActiveCell.Offset(0, 0).FormulaR1C1 = "='Power Report'!R15C14" ActiveCell.Offset(0, 1).FormulaR1C1 = "=-'Power Report'!R15C15" ActiveCell.Offset(0, 2).FormulaR1C1 = "='Power Report'!R15C16" ActiveCell.Offset(1, 0).FormulaR1C1 = "='Power Report'!R15C17" ActiveCell.Offset(1, 1).FormulaR1C1 = "=-'Power Report'!R15C18" ActiveCell.Offset(1, 2).FormulaR1C1 = "='Power Report'!R15C19" Range("AB36").Select ActiveCell.Offset(0, 0).FormulaR1C1 = "='Power Report'!R15C2" ActiveCell.Offset(0, 1).FormulaR1C1 = "='Power Report'!R15C3" ActiveCell.Offset(0, 2).FormulaR1C1 = "='Power Report'!R15C4"
169
ActiveCell.Offset(1, 0).FormulaR1C1 = "='Power Report'!R15C5" ActiveCell.Offset(1, 1).FormulaR1C1 = "='Power Report'!R15C6" ActiveCell.Offset(1, 2).FormulaR1C1 = "='Power Report'!R15C7" ActiveWindow.ScrollColumn = 24 Range("Y5").Select ActiveCell.Offset(0, 11).Value = "='" & Trim(work) & "'!A" & Trim(Str(LRow + 2)) ActiveCell.Offset(0, 12).Value = "='" & Trim(work) & "'!B" & Trim(Str(LRow + 2)) Sheets("Summary Weight").Select Range("B2").Select For counter = 1 To 13 ActiveCell.Offset(counter + 5, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-1]&""201?"")" ActiveCell.Offset(counter + 5, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-2]&""202?"")" ActiveCell.Offset(counter + 5, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-3]&""203?"")" ActiveCell.Offset(counter + 5, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 5, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 5, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-6]&""401?"")" ActiveCell.Offset(counter + 5, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-7]&""402?"")" ActiveCell.Offset(counter + 5, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-8]&""403?"")" ActiveCell.Offset(counter + 5, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 5, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-10]&""40?E"")" ActiveCell.Offset(counter + 5, 11).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-1]&""201?"")" ActiveCell.Offset(counter + 5, 12).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-2]&""202?"")" ActiveCell.Offset(counter + 5, 13).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-3]&""203?"")" ActiveCell.Offset(counter + 5, 14).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 5, 15).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 5, 16).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-6]&""401?"")" ActiveCell.Offset(counter + 5, 17).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-7]&""402?"")"
170
ActiveCell.Offset(counter + 5, 18).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-8]&""403?"")" ActiveCell.Offset(counter + 5, 19).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 5, 20).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-10]&""40?E"")" Next counter For counter = 1 To 12 ActiveCell.Offset(counter + 25, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-1]&""201?"")" ActiveCell.Offset(counter + 25, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-2]&""202?"")" ActiveCell.Offset(counter + 25, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-3]&""203?"")" ActiveCell.Offset(counter + 25, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 25, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 25, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-6]&""401?"")" ActiveCell.Offset(counter + 25, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-7]&""402?"")" ActiveCell.Offset(counter + 25, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-8]&""403?"")" ActiveCell.Offset(counter + 25, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 25, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-10]&""40?E"")" ActiveCell.Offset(counter + 25, 11).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-1]&""201?"")" ActiveCell.Offset(counter + 25, 12).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-2]&""202?"")" ActiveCell.Offset(counter + 25, 13).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-3]&""203?"")" ActiveCell.Offset(counter + 25, 14).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 25, 15).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 25, 16).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-6]&""401?"")" ActiveCell.Offset(counter + 25, 17).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-7]&""402?"")" ActiveCell.Offset(counter + 25, 18).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-8]&""403?"")" ActiveCell.Offset(counter + 25, 19).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-9]&""40?I"")"
171
ActiveCell.Offset(counter + 25, 20).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-10]&""40?E"")" Next counter For counter = 1 To 2 ActiveCell.Offset(counter + 45, 0).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-1]&""201?"")" ActiveCell.Offset(counter + 45, 1).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-2]&""202?"")" ActiveCell.Offset(counter + 45, 2).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-3]&""203?"")" ActiveCell.Offset(counter + 45, 3).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-4]&""20?I"")" ActiveCell.Offset(counter + 45, 4).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-5]&""20?E"")" ActiveCell.Offset(counter + 45, 5).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-6]&""401?"")" ActiveCell.Offset(counter + 45, 6).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-7]&""402?"")" ActiveCell.Offset(counter + 45, 7).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-8]&""403?"")" ActiveCell.Offset(counter + 45, 8).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-9]&""40?I"")" ActiveCell.Offset(counter + 45, 9).FormulaR1C1 = "=COUNTIF('" & Trim(work) & "'!R2C15:R" & Trim(Str(LRow)) & "C15,RC[-10]&""40?E"")" Next counter Range("AB32").Select ActiveCell.Offset(0, 0).FormulaR1C1 = "='Power Report'!R15C14" ActiveCell.Offset(0, 1).FormulaR1C1 = "=-'Power Report'!R15C15" ActiveCell.Offset(0, 2).FormulaR1C1 = "='Power Report'!R15C16" ActiveCell.Offset(1, 0).FormulaR1C1 = "='Power Report'!R15C17" ActiveCell.Offset(1, 1).FormulaR1C1 = "=-'Power Report'!R15C18" ActiveCell.Offset(1, 2).FormulaR1C1 = "='Power Report'!R15C19" Range("AB36").Select ActiveCell.Offset(0, 0).FormulaR1C1 = "='Power Report'!R15C2" ActiveCell.Offset(0, 1).FormulaR1C1 = "='Power Report'!R15C3" ActiveCell.Offset(0, 2).FormulaR1C1 = "='Power Report'!R15C4" ActiveCell.Offset(1, 0).FormulaR1C1 = "='Power Report'!R15C5" ActiveCell.Offset(1, 1).FormulaR1C1 = "='Power Report'!R15C6" ActiveCell.Offset(1, 2).FormulaR1C1 = "='Power Report'!R15C7" ActiveWindow.ScrollColumn = 24 Range("Y5").Select ActiveCell.Offset(0, 11).Value = "='" & Trim(work) & "'!A" & Trim(Str(LRow + 2)) ActiveCell.Offset(0, 12).Value = "='" & Trim(work) & "'!B" & Trim(Str(LRow + 2)) End Sub
172
B3. Results from running the macro REEFER The analysed results were:
173
APPENDIX C
Specific Heat Capacity of various products
Source: Container Handbook [4]
APP
END
IX D
Cur
rent
Po
sitio
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Tem
p C
Len
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Spec
ial
IMO
Plan
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p (J
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0109
1W
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1010
337
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0¡C
20'
21.4
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F#N
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0.5
253
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35.0
6000
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10.1
93.
313.
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2157
TOLU
7804
620
+5.
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40'
21.5
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POR
T19
3013
5.26
0.5
230
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.012
000
2500
11.7
61.
924.
19E
0203
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0719
POC
U28
1721
8-2
5.0¡
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1074
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0.5
250
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5.0
35.0
6000
2500
15.7
94.
833.
91E
0203
2SA
0723
PON
U28
5531
4-2
5.0¡
C20
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15.5
64.
763.
91E
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2739
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15.6
44.
793.
91E
0204
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0713
MW
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5664
991
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20'
20.4
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2510
74.9
70.
52
50.0
-15.
035
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0025
0015
.49
4.74
3.91
E 02
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2850
924
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20'
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-15.
035
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.56
4.76
3.91
E 02
09 1
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2948
783
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20'
20.4
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F#N
AM
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T13
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0.5
253
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8.0
35.0
6000
2500
7.79
2.53
3.97
E 02
09 2
FR09
34SU
DU
1089
373
+10.
0¡C
20'
17R
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#NA
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IMPO
RT
3060
74.9
70.
52
25.0
10.0
35.0
1500
025
0032
.14
1.93
3.35
E 02
10 1
FR16
55SU
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1024
691
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0¡C
20'
20.5
REE
F#N
AM
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0.5
253
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8.0
35.0
6000
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7.83
2.54
3.97
E 03
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35.0
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8.23
4.82
4.72
E 03
01 2
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52
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3242
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14.3
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063.
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3.59
3.97
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043.
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0306
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2045
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618
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20.5
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343.
97E
0308
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9.19
1.21
3.66
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9.53
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APPENDIX E
Data Volume
184
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