1. 1

Abstract— This paper investigates the present energy
consumption pattern, energy demand by 2020 and viable demand
side management options for Jaya Container Terminal (JCT) at
Port of Colombo. Energy consumption was modeled using LEAP
energy planning software package and energy demand of the
container terminal by 2020 was forecasted. Per TEU energy
consumption for different container types (Refrigerated
containers, empty containers, loaded containers) transferred
through JCT is evaluated and the associated energy costs are
compared. In these comparisons, effects of future local fuel price
variations and electricity price variations are considered.
Index Terms— Container Terminal, Energy forecasting,
Demand side management
I. INTRODUCTION
ri Lanka Ports Authority (SLPA) is a bulk energy
consumer; mostly consumes Electricity and Diesel. Its
main energy suppliers are Ceylon Electricity Board (CEB) and
Ceylon Petroleum Corporation (CPC).
There are three main substations that are used to connect
CEB electricity supply with the electricity grid of SLPA. JCT
substation is used to electrify Jaya Container Terminal area
and consumes the highest fraction of total electrical energy
supplied to SLPA which is about 1.9GWhr per month.
Almost all the container handling operations in SLPA are
carried out in JCT at present; annual throughput of Unity
Container Terminal (UCT) is not significant compared to JCT.
JCT is comprised of various energy consumers such as Quay
cranes, Reefer containers, Buildings, Yard lights and Street
lights, Rubber Tired Gantry (RTG) cranes, Prime movers, Top
Lifters, Office vehicles, etc. Still the monitoring and
management of energy in SLPA is not in a very strong
position. Therefore it is worthwhile to investigate its energy
scenario.
Energy demand of Port of Colombo is increasing with the
development projects carried out. CEB is planning to draw
electricity connections from Kerawalapitiya Heavy Furnace
Oil (HFO) power plant and Kelanitissa Diesel Power plant to
establish a 500MVA substation to meet the increasing
electricity demand of Colombo City with these mega
developments.
With the escalation of energy requirements in Port of
Colombo it is essential to evaluate the demand side and supply
side options that could possibly be used in catering the future
energy requirement. Viable demand side management options
and their adoptability with JCT is evaluated with energy cost
and implementation aspects in this study.
Forecasting the energy requirement of Jaya Container
Terminal by 2020 is done based on the container forecast done
by Scott Wilson Co. Ltd. for Port of Colombo. The energy
cost increments at JCT with the increasing energy demand and
energy resource prices are forecasted. Finally the demand side
management options are analyzed to check the viability of
being implemented at JCT.
It is necessary to carry out this for whole Port of Colombo.
But with the limitations in availability of data and time
restrictions, the analysis is carried out for Jaya Container
Terminal as a pilot project. The procedure followed could be
extended for the whole Port of Colombo in future researches
or projects.
II. LITERATURE REVIEW
A. Jaya Container Terminal (JCT)
Jaya Container Terminal is one of the main container
terminals in Port of Colombo, and the biggest terminal owned
by SLPA at present. It has a container capacity of 3 million
TEUs while Colombo International Container Terminal
(CICT) and South Asia Gateway Terminal (SAGT) have
container capacities of 2.4 million TEUs and 2 million TEUs
respectively.
B. JCT Electrical system
There are five major electrical energy consumers in JCT
area contributing to (approximate) 1.9GWh monthly
electricity consumption; Quay Cranes (QC), Rail Mounted
Gantries (RMG), Refrigerated containers (Reefers), Yard
Lights and Buildings. Fig. 1 below shows the electricity usage
of JCT by different categories.
Analysis of energy demand at Jaya Container
Terminal and forecasting energy requirement
by 2020; proposing demand side management
options
H.G.W.T.Prabatha, U.K.D.Perera, K.H.I.U.Rathnayake, K.K.C.K.Perera, P.A.B.A.R.Perera and
M.M.I.D.Manthilake
S

2. 2
Inductive loads generated by QCs, RMGs and Reefers
reduce the Power Factor (PF) of the system and current system
PF vary between 0.5-0.95. The PF correction method used at
present is not effective (current method: manually switching
the capacitor banks), therefore PF correction methods and
Regenerative energy utilization procedures are discussed
under the Demand Side Management Options.
C. JCT Fuel system
Jaya Container Terminal consumes about 847,510 liters of
petroleum fuels annually and that amount is getting increased
with increasing container throughput. Rubber Tyred Gantry
Cranes (RTGs), Prime movers, Top lifters and Office vehicles
are the main fuel consumers that operate within JCT currently.
Fig. 2 below shows the fuel consumption of JCT by different
categories.
Diesel is the most frequently used petroleum fuel type in
JCT, and the usage of other petroleum fuels is negligible
compared to it. A little amount of Petrol 90, Petrol 95 and
Super Diesel is being used in the yard, especially for office
vehicles.
There are four main fuel sheds at SLPA and fuel issuing in
these sheds is done under to two main systems. I.e. Fuel
Management System (FMS) and Oracle (previously used
system named as PS CAPS).
D. Container terminals and green ports
Container terminals and ports are provided enormous
strength to local economic development by different means
[1]. They are consisted with different sub systems and utilize
different container handling equipment such as Quayside
cranes, RTGs, Prime movers, etc. [2]. Terminal operating
system and container handling equipment directly influence
the terminal performance and efficiency [3].
However, increased human activities with ports are caused
for serious environmental problems including air pollution,
water pollution and sound pollution [1]. With the pressure
developing from environmental regulations, lots of
international ports have chosen the “green port” concept as the
basement for port development [4]. Green container terminals
required to have minimum impact on the macro environment
and local environment. I.e. reduce air and noise pollution,
lower energy consumption, increase operating efficiency, etc.
[5]. Lower utilization of lights, electric yard cranes, hybrid/eco
yard cranes, low emission transport vehicles, electricity for
vessels at berth are basic feature that are available at green
ports [5].
Port of Los Angeles and Port of Long Beach is the largest
container handling ports in United States and the most
successful green ports in the world. They have taken several
green concept initializations and act as a green port model for
other worldwide ports [1].
E. Energy forecasting methods and software packages
As it is needed to forecast the energy requirement of JCT
for coming years, suitable forecasting methodologies and
software tools have to be identified by analyzing critical
factors such as accuracy, interpretation, number of variables
handling, application, etc.
Forecasting methods can be categorized under statistical,
structural and econometric approaches. In statistical methods,
different types of mathematical expressions are formulated
according to historical variations. Linear trend, polynomial
trend, logarithmic trend and exponential analysis are the
commonly used statistical forecasting methods [6]. But in the
structural methods such as “Multivariate regression analysis”,
“Box and Jenkins” models and “Ad Hoc” models [7],
forecasting functions are more analytical because of the ability
of capturing the effect of many factors [6].
Apart from these methods, more accurate results can be
achieved using analytical and econometric methods. ARIMA
and ANN are the popular models under this category because
of their ability to capture the effects of variable factors such as
population, employment, income, weather etc. Further
ARIMA is used as one of base model in LEAP and Forecast
Pro software tools. But the applicability of these models is
limited because of the complexity and the model is specific to
one application. Further, the requirement of data, time and
finance for formulating these models are much higher
compared to statistical models [8].
As it is required additional mathematical modeling and
analyzing skills for forecasting purposes, numerous
forecasting applications have been designed using these
complex models. Advantage of these software tools is their
ability to present the forecasted results graphically with
graphs, tables and animations. DAP (Demand Analysis
Planning) [9], LEAP (Long Range Alternatives Planning)
[10], Markel [11], Matrix ND [12], WASP [13] and TESLA
[14] are the mostly used energy forecasting software tools.
LEAP is usually adopted for medium to long term energy
forecasting of different energy systems and it is utilized in a
Fig. 1. JCT Electrical energy balance at year 2013
Fig. 2. JCT Fuel consumption at first half of year 2013

3. 3
range of modelling techniques for demand and supply
analysis. Depending on situation, bottom up end use
accounting to top down macro-economic techniques can be
used for modelling the demand. It is capable of evaluating
different scenarios for tracking energy demand, energy
production, greenhouse gas emissions and optimization
modelling [15]. It is a well renowned software package for
energy policy analysis and climate change mitigation
evaluations. LEAP has utilized over 5000 users at different
organizations, companies and institutions in more than 190
countries. Also it has been utilized for more than 70 peer
reviewed journal papers and many reports [16].
III. CONTAINER THROUGHPUT FORECAST FOR JCT
Scott Wilson ltd, UK has done a forecast on increasing
container throughput from 2010-2050. But due to the delays in
development progress during years after 2010, forecasted
throughput is not achieved. As shown in Fig. 3, actual
container throughput goes below the forecast.
At present the delays are rectified and the development is
continuing. Therefore now it can be assumed that forecast has
shifted by years and it is going to continue from the actual
container throughput value of 2014.
JCT container throughput forecast was done as a supporting
aid in finding the energy requirements in coming years. There
were two options in forecasting the container throughput of
JCT. One option (Case I) was to assume that the historical
throughput trend is going to continue in future. But it does not
capture the influences that are supposed act upon container
throughput due to world and local trends.
Next option (Case II) was to forecast container throughput
of JCT until 2020 based on the container forecast done by
Scott Wilson Company for Port of Colombo. As Scott Wilsons
has captured the external and internal influential factors for
container throughput in their forecast, this option is better in
obtaining an acceptable forecast for JCT throughput. It is
assumed that the total container throughput of Port of
Colombo is going to be distributed among the Container
Terminals proportionally to their container capacities. Table 1
shows the forecasted container throughput of JCT up to 2020
by Case II.
IV. DERIVATION OF ENERGY INTENSITY FACTORS
Energy consumption patterns are studied in relation to
the container throughput variation over the years in this study.
Therefore it is necessary to identify the variation of energy
consumption with container throughput in equipment used.
There the yard lights, building loads and fuel consumption of
office vehicles are assumed to be constant over the years until
2020, as those does not depend on throughput and no major
modifications are planned during the period. Use of Top lifters
in the terminal is highly unpredictable and there are no proper
duty allocations. Also the amount of fuel consumption is low
compared to other fuel consumers in the JCT. Therefore, its
fuel consumption is also assumed to be constant over the year.
Energy intensity factors are derived for QCs, RMGs,
Reefers, RTGs and Prime Movers with the aid of MiniTAB
statistical analysis software with regression modeling. The
regression models and intensity factors with the coefficients of
determination (R2
) are listed in Table 2 below.
A. Energy intensity of QC
5.74TEU+66=nconsumptioenergyQCDaily
For derivation of the above relationship, operational data of
a ZPMC AC QC was used. There are many factors that can
possibly be influencing the energy demand of a QC; daily
average weight of containers, number of containers handled,
idling of QC, operator skill, machine performance under
different ambient conditions. To minimize the influence from
machine performance and ambient conditions the time period
of observations were increased (here it is 2months data: this
could be further increased for better accuracy).
Fig. 3. Container forecast by Scott Wilson ltd. and actual throughput
TABLE 1
JCT CONTAINER FORECAST
Year Percentage container
capacity of JCT with
respect to PoC
JCT
CONTAINER
THROUGHPUT
2010 60% 1987058
2011 60% 2071887
2012 60% 2146577
2013 53.6% 2345539
2014 40.5% 1853108
2015 37.5% 1878375
2016 28.8% 1582212
2017 28.8% 1731058
2018 28.8% 1892596
2019 27.3% 1993363
2020 27.3% 2150620
TABLE 2
ENERGY INTENSITY VALUES AND R2
Equipment Energy intensity R2
QC 5.74kWh/TEU 78.6%
RMG 3kWh/TEU -
Reefer 4.87kWh/TEU -
RTG 2.29L/TEU 53.6%
Prime Movers 1.44L/TEU 40.5%

4. 4
Influence of TEU amount handled, daily average weight
and average cycle time for QC energy consumption was tested
with MINITAB software package. According to the results
TEU amount had an acceptable influence on QC energy
consumption with a coefficient of determination of 78.6%
while other factors showed very less significance. Therefore
the above relationship was accepted.
The constant “66” in equation above happens as the QC
starts; it can occur due to starting power, gantry motions,
boom hoist and lowering motions (the factors that are not
proportional to number of TEUs). When amount of TEUs
handled rises, significance of the constant (66) reduces. From
this relationship it could be taken that energy requirement per
TEU handled by QC to be equal to 5.74kWh.
Above value is compared with the value obtained for
average per cycle energy consumption, from the load profile
of QC (Figure 1). with the observations from the load profile it
is obtained that per TEU energy consumption by QC to be in
range of 2.4-4.2kWh with 90% confidence. This value is less
compared to the value obtained from Minitab. This may have
happened due the short time period that was captured in load
profile; load profile was only generated for half an hour due to
practical limitations. Therefore 5.74kWh/TEU is accepted for
future calculations in this research as energy intensity factor of
QC.
B. Energy intensity of RMG
RMGs mainly perform stacking and loading operations at
JCT. These are mostly used to handle empty container boxes
and only 4 cranes are operated in JCT. RMGs are powered by
grid electricity and connected through 300kVA transformer.
Max allowable load is 10 Tons and they cannot perform hoist
and trolley motion at the same time. When calculating energy
intensity for RMGs, various factors such as skills of the crane
operators, container stack configurations, and weight
distribution of the containers have to be considered. Lack of
power measurement instruments, previously done studies on
JCT RMGs and less frequent operations have made derivation
of intensity factor for RMGs at JCT a difficult task.
Average cycle time for one crane operation is calculated
using the observations recorded from all 4 cranes. But as per
the observations, it is found that the operating cycles are
identical in RMGs and RTGs. Both of them have similar
movements of hoisting, lowering, trolleying and gantry.
Movement profile is shown in figure below.
It is observed 23 moves in loading cycle which having 8
hoist moves, 4 trolley move and 1 gantry move. Motion and
time study for one crane operation is shown below in the
Table 3.
Power requirement for the each motion is analysed
according to the indicated power of the motors, and summary
is shown in Table 4 below. For each motion, idle time power
consumption of 13kW also added according to the practically
measured values acquired from the research done by
California Air Resource Board (CARB) for diesel emission
control of the RTGs [17].
Then the Load factor can be calculated using the following
equation.
kWh2.98=cyclepertRequiremenEnergy
kW44.763=(RTG)LF
25.0%)]xidle(LF+8.3%)gantry x(LF+
17.5%)trolley x(LF+49.6%)hoist x[(LF=(RTG)LF
Therefore energy intensity of rail mounted gantry cranes is
approximately 3 kWh per average loading cycle.
C. Energy intensity of Reefer containers
For practical determination of average energy consumption
of a reefer container per hour, energy data (kWh) were
recorded at a reefer yard section at JCT. kWh meter reading
and the number of reefers plugged in each hour was recorded
manually for 5 days. Due to practical difficulties in recording
this data was not logged in the time periods of 0000h to
Fig. 4. Load profile of ZPMC QC for one loading cycle
TABLE 3
RMG DUTY CYCLE
RMG Motion Time (Sec) Duty Cycle
Hoist 40+19 24.6%
Trolley 42 17.5%
Gantry 20 8.3%
Idle 119 49.6%
Total 240 100.0%
TABLE 4
RMG POWER REQUIREMENT
RMG Motion Power Required (kW) Duty Cycle
Hoist 90kW 24.6%
Trolley 45kW 17.5%
Gantry 100kW 8.3%
Idle 13kW 49.6%
Total 248kW 100.0%
Fig. 5. RMG Load cycle

5. 5
0800h. Usage of automatic data loggers in future studies are
recommended to avoid these difficulties. Based on the
collected data average energy consumption per container at
each hour of the day is obtained.
With the observations it can be said with 95% confidence
that average reefer energy consumption per hour falls in the
range from 4.82 kWh/TEU to 5.12 kWh/TEU Therefore 4.87
kWh/TEU is taken for the future calculations.
Despite of some random recordings, average energy
consumption per container at a particular time of the day has
not varied significantly for the days observed. It seems that
time of the day is a prominent factor that decides the energy
consumption of the reefer container compared to other factors.
Reefer energy consumption is depends on lots of factors.
Therefore the limited observations in this study could induce
large errors to the results obtained as the time is not sufficient
to observe the trends (In most studies carried out on reefer
container yards utilizes the data recorded for a time period
about 2 years). This happened due to lack of energy
monitoring system implementation at JCT, it is highly
recommended the maintenance of proper energy data records
for better energy performance.
According to the Container Handbook by German Marine
Insurers Power A 20' container tends to be closer to 4 kW and
a 40' container tends towards 7 kW [18]. Therefore it’s
acceptable to have an average power consumption of
4.87kWper reefer container (TEU) at JCT.
D. Energy intensity of RTGs
JCT is equipped with 59 RTGs which is capable of handle
either 35.5T or 40T weight, depending upon the model.
Existing RTGs use 450kW-550kW diesel generators as it
power source and regenerative power is dissipated through a
resistor bank. RTGs at JCT remain idle for long time periods.
For the analysis of RTG energy intensity, it is assumed that all
RTGs have similar performances and duty cycles.
Amount of fuel consumed per 8 months was plotted against
the amount of containers handled by RTGs. The best relation
between monthly fuel consumption and container handling
was given by 2nd
order regression polynomial as shown in Fig.
6.
According to the model developed, RTGs at JCT consume
2.29 Diesel liters per TEU.
E. Energy intensity of Prime movers
JCT consist 142 diesel prime movers. And that accounts for
61% of total Cargo Handling Equipment (CHE) used in JCT.
Prime movers are subjected to high amount of idling during its
duty cycle. For the analysis of Prime mover energy intensity,
it is assumed that all prime movers are consisted with similar
performances and duty cycles.
Amount of fuel consumed by prime movers was plotted
against the amount of TEUs handled, for 8 months. The best
relation between monthly fuel consumption and container
handling was given by 2nd
order regression polynomial as
shown in Fig. 7.
According to the model developed a prime mover requires
1.44 Diesel liters per TEU operation.
V. ENERGY FORECAST
Forecasting of energy demand is done by analysing
above data with LEAP. Variable energy consumers and
Fig. 6. RTG Fuel consumption variation
Fig. 7. Prime mover Fuel consumption variation
Fig. 8. LEAP Demand analysis model

6. 6
constant energy consumers are modelled appropriately with
yearly data. Fig. 8 shows the demand model utilized in LEAP.
- For variable energy consumers,
levelActivityYearlyxintensityEnergy=energyTotal
- For constant energy consumers,
nconsumptioenergyYearly=energyTotal
Forecasting is carried out depending on two basic scenarios.
 Business as Usual scenario
 Energy Efficiency scenario.
A. Business as Usual Scenario
Here it is assumed that, all the energy relevant activities that
carried out in JCT is going to continue till year 2020, without
introducing major changes to the system. Results obtained for
the forecast is shown in Fig. 9.
Energy demands from year 2010 to 2013 are based on the
historical records and values from 2014 to 2020 are generated
by LEAP energy forecast based on container throughput
variation. Energy demand of the JCT is getting reduce until
year 2016, because of the low container throughput, which is
affected by the Colombo South harbour expansion. Then it is
supposed to grow continuously, with the increment of
container handling at Port of Colombo. According to results,
Diesel provides dominant amount of energy to the system.
Electricity contributes the remaining energy needs while
energy supplied by Gasoline is negligible.
B. Energy Efficiency Scenario
In this scenario, it is considered two viable energy saving
options. I.e. convert all the existing yard lights into LED by
2020 as it is already started and electrification of existing RTG
fleet as proposed. The results that obtained according to the
scenario is shown in Fig. 9 below.
Here it is clearly seen that, energy saving measures that are
analyzed in this scenario is capable of reducing the total
energy consumption of JCT by a considerable amount.
According to the forecast, total energy consumption of JCT in
year 2020 will be 242.2 million MJ. This is 191.7 million MJ
of energy saving compared to the value obtained by Business
as Usual Scenario.
Efficiency improvement measures that are used in this
scenario is discussed under the topic Demand Side
management.
VI. ENERGY CONSUMPTION AND ENERGY COST PER TEU
There are three basic types of containers arriving at JCT;
reefer containers, empty containers and loaded containers.
Energy requirement for each container varies depending on
the operations it undergoes at the terminal. Therefore energy
consumption per TEU can be studied under six cases.
Case: 1 Empty containers going to country from ship
Case: 2 Empty containers transhipped
Case: 3 Loaded containers going to country from ship
Case: 4 Loaded containers transhipped
Case: 5 Reefer containers going to country from ship
Case: 6 Reefer containers transhipped
Energy intensity factors are mentioned as below for the
calculation shown in Table 4.
A. – kWh per TEU handling in QC (5.74kWh/TEU)
B. – Fuel litres per TEU handling in Prime Mover
(1.44L/TEU)
C. – Fuel litres per TEU handling in RTG (2.29L/TEU)
D. – kWh per TEU handling in RMG (3kWh/TEU)
E. – kWh per reefer TEU per hour plugged in
(4.87kWh/TEU/hour)
T. – Number of hours that a reefer plugged at JCT
Fig. 9. Energy demand of JCT – Business as Usual Scenario
Fig. 10. Energy demand of JCT – Energy Efficiency Scenario
TABLE 4
ENERGY CONSUMPTION PER TEU OPERATION AT JCT FOR SIX OPERATIONS
Case Expression for per TEU
energy consumption
Per TEU energy
consumption
1 (A+2D) + B 11.74kWh + 1.44l
2 2(A+D) + 2B 17.48kWh + 2.88l
3 A + (B+2C) 5.74kWh + 6.02l
4 2A+ 2(B+C) 11.48kWh + 7.46l
5 (A+ET) + (B+2C) (5.74+4.87T)kWh + 6.02l
6 (2A+ET)+ 2(B+C) (11.48+4.8T)kWh + 7.46l

7. 7
Per TEU energy cost variation over the years considering the
Diesel and Electricity price variations for the six cases are
indicated in Fig. 11 Below.
VII. DEMAND SIDE MANAGEMENT
With the rapid fossil fuel depletion and climate
change effects, world is in search for greener energy systems
with lowered emission levels. Demand side management and
efficiency improvements are one of major aspect that can
achieve greener targets.
A. RTG Energy Saving
Due to the high fuel demand and high emissions of
traditional RTGs, different technologies and systems have
been developed and tested in real time, in order to mitigate the
existing issues. Identified possible solutions are shown in the
Fig. 12.
RTG electrification using bus bar system more appropriated
for existing terminals, in order to minimize the disturbances
for yard operations. It’s capable of providing more than 80%
of energy saving and emission reduction. The investment for
the conversion of 59 RTGs is around 1220 million rupees and
payback period is about 2 years.
Hybrid RTGs also one of famous energy saving option,
which is practice in many container terminals worldwide. The
system consisted with low capacity genset (220kW-250kW),
battery pack, etc. It has capable of 60%-80% energy saving
and 60%-90% emission reduction. Converting all 59 RTGs
into hybrid system is cost 2330 million rupees and payback
period is 5 years.
Flywheel energy storage system is utilized low capacity
genset, flywheel system, etc. It is capable energy saving up to
38%. This systems can be installed to any RTG as a retrofit or
during the manufacturing of the equipment. Associated costs
and payback periods need to be calculated.
B. Prime mover Energy Saving
142 units of JCT prime movers consumed around 36% of
diesel fuel out of the total. Few technologies have penetrated
into the market in the sense of fuel saving and emission
reduction. There are few energy saving options are available
for prime movers as shown in Fig. 13.
LNG prime movers are capable of CO2 emission reduction of
18%. But it increased NOx level by 21% [19]. Also the typical
fuel consumption of a LNG prime mover is about 3.8 gal/hr,
while diesel prime mover consumes 1.7 gal/hr [19]. According
to the 2011 statistics, LNG prime mover cost around extra 5
million rupees. Therefore, monetary saving by LNG prime
movers are heavily depend on the price of both fuels. Since
there are no LNG based infrastructure at SLPA, it will cost
minimum of $700K [19]. Therefore, it causes for financially
unviable at this stage. But LNG exploration at Mannar basin
will provide a good opportunity with respect to low LNG fuel
price and infrastructure development in the near future.
Hybrid prime movers are emerging technology in container
terminals and capable of energy saving of 15% compared to
diesel prime movers [20]. According to the 2012 statistics,
hybrid prime mover costs around extra 6 million rupees. With
compared to its fuel saving, the payback period is around 15
year. Therefore, it is not financially viable at this stage and
may it able to consider in the near future with development of
hybrid prime mover technology.
VIII. CONCLUSION
In this study it has derived energy intensities of Quayside
Cranes, Reefer containers, RMGs, RTGs, and Prime
movers. According to the energy model develop for JCT
using the LEAP software, Energy demand was forecasted. It
shown a drawback of the energy demand up to year 2016
and then a continuous increment in the demand, according
Fig. 11. Per TEU Energy Cost variation over the years for the six cases
concerned (here the reefer plugged in time is taken as 5hours for Case5 and
Case6, to plot the graphs)
Fig. 12. Possible solutions for RTGs
Fig. 13. Possible solutions for Prime movers

8. 8
to the Business as Usual Scenario. The energy demand in
the terminal is heavily inherited by the container throughput
variation. As shown in the Energy Efficiency Scenario and
Demand Side Management options, it is capable of high
amount of energy saving. Out of various options, RTG
electrification is shown as the best opportunity at this stage.
IX. ACKNOWLEDGEMENT
Authors would like to acknowledge the staff of the
Department of Mechanical Engineering, University of
Moratuwa, Sri Lanka Ports Authority, Ceylon Electricity
Board, Ceylon Petroleum Corporation and all the
colleagues, for all the helps, assistance and guidance
provided throughout the project.
X. REFERENCES
[1] T.-L. H. a. S.-R. L. Jiuh-Bing Sheu, "THE KEY
FACTORS OF GREEN PORT IN SUSTAINABLE
DEVELOPMENT," 2013.
[2] Y. S. C. Won Young Yun, "A simulation model for
container-terminal operation analysis using an object-
oriented approach," March 1999.
[3] H.-O. G. a. K.-H. Kim, "Container terminals and
terminal operations," 2006.
[4] C.-J. T. a. S.-C. Tsai, "EFFECT OF CONSUMER
ENVIRONMENTAL ATTITUDEEFFECT OF
CONSUMER ENVIRONMENTAL ATTITUDE ON
GREEN CONSUMPTION DECISION-MAKING,"
2011.
[5] W.-M. C. Yi-Chih YANG, "Performance Analysis of
Electric-Rubber Tired Gantries from a Green Container
Terminal Perspective," 2013.
[6] R. S. T. Jin-Lung Lin, "Comparisons of Forecasting
Methods with Many Predictors," Taiwan.
[7] A. C. Harvey, "Forecasting, Structural Time Series
Models and the Kalman Filter," International Journal of
Forecasting, vol. 8, no. 4, pp. 24-50, 1992.
[8] S. -. A. Volkan S-. Ediger, "ARIMA forecasting of
primary energy demand by fuel in Turkey," Energy
Policy, vol. 35, 2007.
[9] [Online]. Available:
http://www.systemseurope.be/products/dap.en.php.
[10] [Online]. Available: http://www.energycommunity.org/.
[11] [Online]. Available:
http://www.matrica.co.uk/wb/en/index.html.
[12] [Online]. Available:
https://www.itron.com/na/productsAndServices/electricit
y/Pages/analysis-software_energy-forecasting--load-
research.aspx.
[13] "Incorporating Social And Environmental Concerns In
Long Term Electricity Generation Expansion Planning
In Sri Lanka," 2006.
[14] [Online]. Available: http://www.teslaforecast.com/.
[15] Octomber 2013. [Online]. Available:
www.energycommunity.org/default.asp?action=47.
[16] H. L. B. M. M. L. D. Connolly, "A review of computer
tools for analysing the integration of renewable energy
into various energy systems".
[17] Starcrest Consulting Group, LLC, "Rubber Tired Gantry
(RTG) Crane Load Factor Study," Port of Long Beach
and Port of Los Angeles, October 2009.
[18] [Online].
[19] CALSTART, "Liquefied Natural Gas (LNG) Yard
Hostler Demonstration and Commercialization Project,"
Port of Long Beach, 2008.
[20] CALSTART, "Revised Hybrid Yard Hustler
Demonstration and Commercialization Project Final
Report," The Port of Long Beach and The Port of Los
Angeles, 2012, August.