Assessment of energy efficiency improvement and technology uptake in the
shipping industry
Tristan Smith, UCL Energy Institute [email protected]
Subject Matter Experts Professor James Corbett (Delaware) Professor Henry Marcus (MIT)
David MacBrayne BPA WWF Exact Earth B9
Teekay BCS FFF KPMG ECF
Zodiac RINA CWR Fraunhofer Hawkins Wright
EA Gibson SEAaT CCC Clarksons Seas at Risk
BAe Systems ETI IMO Chalmers Svitzer
SSA ISL IEA, ITF KfW USP
~25 SCC Research Staff 9 SCC PhD Studentships
General approach
Assessing uncertain futures:
2010 2050
Annual CO2 emissions
2030
According to IMO 2nd GHG
Return of business as usual (corrected from IMO)
What might be happening now
What ‘needs’ to happen
What might be likely?
EEDI/SEEMP
Transport demand (tenm) x
Transport emissions (gCO2/tenm)
Input data and assumptions
Econometric analysis of historic prices (e.g. TCe) underpins forecast and market assumptions
0
5
10
15
20
25
0
1,00
0
2,00
0
3,00
0
4,00
0
5,00
0
Th
ou
san
d $
/ d
ay
teu
Container
Users can choose from a variety of existing trade scenarios, or input their own assumptions at country-country level for 100 individual commodities. Datasets and forecasts of container and empty container global country-country O-D flows derived for 2010-2050
!
Modelling method
From the ship owner’s perspective
€
π pa = Rpa −Cs _ pa −Cv _ pa
f(speed) f(technology) f(technology)
f(speed)
Speed
Speed / kts
Profit
High fuel prices, low freight rates
Low fuel prices, high freight rates
High fuel prices, high freight rates
Stock
A number of different technical approaches to energy efficiency increase can be considered and are assessed for their profitability both independently and in combination
Detailed analysis of the impacts of a technology on a ship’s technical, operational and economic characteristicss
Each option is given the following ‘impacts’:
• Compatability (with machinery/other tech) • Date available/mature • purchase cost • annual maintenance cost • dwt capacity • propulsion engine power (e.g. mewis duct) • propulsion sfoc (e.g. engine tuning) • propulsion MCR (e.g. wind assistance) • efficiency deterioration (e.g. paint/ hull and
prop maintenance) • auxiliary engine power (e.g. solar power) • auxiliary sfoc (e.g. shore power)
Sunk cost
Main engine fuel consumption
Revenue potential
Aux engine fuel consumption
Implement?
Example outputs
Three scenarios
0.00E+00%
5.00E+07%
1.00E+08%
1.50E+08%
2.00E+08%
2.50E+08%
3.00E+08%
2010% 2015% 2020% 2025% 2030% 2035% 2040% 2045% 2050%
HFO%
MDO%
LSHFO%
LNG%
Hydrogen%
Methanol%
0.00E+00%
5.00E+07%
1.00E+08%
1.50E+08%
2.00E+08%
2.50E+08%
3.00E+08%
2010% 2015% 2020% 2025% 2030% 2035% 2040% 2045% 2050%
HFO(fos+bio)%
MDO(fos+bio)%
LSHFO(fos+bio)%
LNG(fos+bio)%
HFO(bio)%
MDO(bio)%
LSHFO(bio)%
LNG(bio)%
0.00E+00%
2.00E+07%
4.00E+07%
6.00E+07%
8.00E+07%
1.00E+08%
1.20E+08%
1.40E+08%
1.60E+08%
2010% 2015% 2020% 2025% 2030% 2035% 2040% 2045% 2050%
HFO%
MDO%
LSHFO%
LNG%
Hydrogen%
Methanol%
“BAU”
High bio availability
High carbon price
Worked example for 2 foreseeable fuel price scenarios:
A - IEO Ref
B – IEO High
0
500
1000
1500
2005 2010 2015 2020 2025 2030 2035
Price&$/te HFO
MDO/MGO
LNG
CO2
0
500
1000
1500
2000
2005 2010 2015 2020 2025 2030 2035
Price&$/te HFO
MDO/MGO
LNG
CO2
Newbuilds e.g. panamax dry bulk
2
2.5
3
3.5
4
4.5
2005 2010 2015 2020 2025 2030 2035
EE
DI g
CO
2/te
nm
A
B
required
• Pre-swirl duct • Propeller/rudder
bulb • Covering hull
openings
• SS streamlining • Stern hydrodynamics • LNG
Newbuilds
11
11.5
12
12.5
13
13.5
14
2005 2010 2015 2020 2025 2030 2035
Op
erat
ing
sp
eed
kts
A
B
2
2.5
3
3.5
4
4.5
2005 2010 2015 2020 2025 2030 2035
EE
DI g
CO
2/te
nm
A
B
New and existing
2
2.5
3
3.5
4
4.5
2005 2010 2015 2020 2025 2030 2035
Ave
rag
e E
ED
I gC
O2/
ten
m
A
B
11
11.5
12
12.5
13
13.5
14
2005 2010 2015 2020 2025 2030 2035
Ave
rag
e sp
eed
kts
A
B
All ships
4
4.5
5
5.5
6
6.5
7
2005 2010 2015 2020 2025 2030 2035
Ave
rag
e E
EO
I gC
O2/
ten
m
A
B
Analysis performed both for technical and operational indicators - new and existing ships:
Concluding remarks
- Links to TIAM, Tyndall etc - Links to UCL law, NGOs etc - Bunkering modelling - Bioenergy availability
- Climate change impacts
- Elasticities of demand - Aviation/AIM - Modal split
- Improved trade data - S-AIS and Drewry/Clarksons data - Container hub/spoke model - Matching modelling
- Fuel consumption/performance data analysis
- Ship operation survey - Market barriers (split-
incentives) - Price/energy efficiency
premiums
- LCA/upstream - Post-processing of statistics - Carbon revenue - Iterative GloTraM (carbon price
and bioenergy availability)
What does this work mean for technology R&D • What are the objectives?
– Sell more technology
– Reduce transport costs
– Reduce operational emissions
– Reduce life-cycle impacts
• What is important?
– What is the commercial viability?
– There are important feedbacks between design and operation
– Isolated ‘technologies’ vs whole ship approach
• Model vs reality:
– Measurability challenge
– Reality of operation vs design
– Short-termism
– Owner/charterer market failure
Measurability challenge / operation vs. design
Low Carbon Shipping Conference, London 2013
8
Figure 5
The regression analysis was applied to this CM and noon report data. The results are as in Table 3.
Table 3: Regression analysis of noon reported and continuous monitoring data for the same LNG carrierNumber ofobservationsafter outlierremoval
AdjustedR2
RelativeConfidenceInterval, %(95% level)
Absolutestandarderror, tpd
RelativeStandardError, %
Noon reportdata
205 0.88 61.39 13.38 15.80
Continuousmonitoring
10633 0.95 18.19 5.97 4.64
The confidence interval and standard error derived from the noon report data of the LNG carrier arelarge relative to those of the oil tankers. This may be because of increased sources of uncertainty onLNG carriers owing to the presence of the steam power plant; it is more difficult to get an accuratemeasure of the gas and corresponding fuel oil equivalent that is burnt in a steam powered LNG ship.This depends however on the specific procedure involved in the measurement and on which componentof the power plant the fuel sensor is physically located. The temperature and composition of the boiloff gas used to derive the fuel oil equivalent mass may be assumed rather than measured and this mayvary according to the grade/quality of the LNG cargo loaded, it is also possible that the composition ofthe boil off may vary throughout the voyage, particularly for the natural boil off.
There is a substantial reduction in the uncertainty when the CM data is used, this relates to, amongother factors, the increased measurement variables that enable the gas composition, temperature,heating values and density to be recorded and therefore improve the accuracy of the gas consumptionmeasurement and calculation of foe. This is specific to the LNG carriers and the magnitude of thereduction in uncertainty may not be extrapolated to other ship types. There are some characteristics ofthe CM dataset however that would enable improvement in the certainty generally; the many elementsof human error described in the introduction will be eliminated, as well as the improved repeatability ofmeasurements, limited only by the precision of the sensors. Extra data fields that the automation andtherefore reduction in manpower allows for means that more refined data filtering techniques can beimplemented. The presence of both wind speed and direction allows true wind speed to be calculated,and the numeric rather than binary draft input further reduces rounding errors and improves theuncertainty.
0 5 10 15 200
20
40
60
80
100
120
140
160
180Ship Speed and Fuel Oil Equivalent Consumption
Ship Speed, Knots
FueloilEquavlent,tpd
Contnuous Monitoring DataNoon Reported Data
Low Carbon Shipping Conference, London 2013
11
Figure 8
Although still underestimating the fuel consumption at higher ship speeds, the noon report model nowcorresponds more closely with the theoretical and CM models across the speed range. This suggeststhat the standard error (13.38tpd) is now a better representation of the measurement/aleatoryuncertainty as the epistemic uncertainty has been reduced. This highlights the importance ofcorroborating with theory to cross check that the model does have some overlap with what is expectedin reality, standard error as a measure of uncertainty is only meaningful if the epistemic uncertainty islow. If it is not a larger dataset over a longer time period can be of assistance. This may be a secondmethod of ensuring that the sample size is adequate to justify the use of the standard error inrepresenting the measurement uncertainty in the data (the first being to randomly select samples ofincreasing size and ensuring the standard error fluctuates normally about the mean as in section 5.3).
The continuous model shows a good match to the trend of the theoretical and the differences might beseen as a representation of long term degradation of the ship relative to its age plus shorter term effectsdue to fouling since the last hull scrub. The standard error bars however are important; if the optimumtime to hull scrub or optimum speed is determined from a techno-economic model then the per centerror becomes significant and needs to be quantified and considered.
6. ConclusionThe numerous sources of uncertainty in noon reports have been described as well as the non-linearinteractions both between on board ship systems and between the ship and its environment. Thiscomplexity has been investigated through the use of a multiple linear regression model to capture theunderlying trends and to present the standard error associated with the fuel consumption. Thisproduced a number of key findings:
6.1. The viability of statistical modelsStatistical techniques and backwards regression were successfully applied to sets of noon reports for anumber of ships. A multi linear regression model found by a backwards elimination procedure isapplied to the noon reports from 89 tankers which has indicated that the relative standard error isgenerally in the range of 1-8% for various types of oil tanker. The equivalent values are higher for anLNG carrier (15.80%) which is possibly due to the nature of sensors and measurements specific to thedifferent ship type and power plant.
The high adjusted R2 values, lack of correlation between standard error and other features (in particularsample size) and the normality and homoscedasticity of the residuals implies generally that thestatistical model captures the underlying data trends and is credible for use in performance analysis.Furthermore, the standard error is thought to be generally representative of aleatory and measurementuncertainty and it enables users to assess the level of certainty from which they can use the data andmodel to assess the ship’s performance.
13 14 15 16 17 18 1970
80
90
100
110
120
130
140
150Ship Speed and Fuel Consumption for 3 Models
Ship speed, Knots
FuelConsumption,tpd
TheoreticalCM stats modelNoon report model
Ability to evidence savings in real conditions needs to be thought about at R&D stage, think more about uncertainty
Short-termism / owner-charterer failure
Solutions: short-term pay back, novel finance, technology portability?
Low Carbon Shipping Conference, London 2013
explaining the energy efficiency gap (Sorrel, 2004). The important distinction between general market barriers and market failures is to do with the legitimacy of policy intervention to rectify market failures (Sorrell et al., 2004; Thollander and Palm, 2013). It should be noted however, that the above classification of barriers is not entirely accurate (Thollander and Ottosson, 2008). According to Weber (1997) barriers are unobservable and it is “empirically impossible” to find the true reason for lack of action. Moreover, Blumstein (1980) suggests that the causes of barriers are often interlinked and follow a causal chain. Nonetheless, Sorrell et al. (2000 and 2004) provide a useful framework for investigating barriers to energy efficiency by categorizing them as shown in Figure 2. Figure 1: Classification of barriers
2.1. Principal agent problems Within the context of barriers to energy efficiency, agency theory has been utilized to explain some of the market failures (Levinson and Neimann, 2003, Murtishaw and Sathaye, 2006; Prindle et al. 2006; IEA, 2007; Grauss and Worrell, 2008; Gillingham et al. 2011; Vernon and Meier, 2012). The tenets of agency theory lie under the orthodox economics perspective (Sorrell et al., 2004). The theory aims to create the most efficient contracts for the ubiquitous agency relationship, in which one party (the principal) delegates work to another (the agent), who performs that work (Ross, 1973; cited by Eisenhardt, 1989) or when one individual depends on the action of another (Pratt and Zeckhauser, 1985) and delegates some decision making authority (Jensen and Meckling, 1976). From the perspective of the key stakeholders involved in shipping, the shipowner and the charterer can be seen as being involved in an agency relationship, where the principal i.e. the charterer hires the shipowner as an agent to provide service of carrying goods from A to B (Classification follows Murtishaw and Sathaye (2006), IEA (2007), Vernon and Meier (2008), Veenstra and Dalen (2011). The theory aims to resolve two agency problems that occur as a result of this relationship: • Problem 1: The desires or the goals of the principal and agent conflict (split incentives problem) • Problems 2: It is difficult or expensive to verify agent’s actions (informational problem) So, agency theory refers to the economic theory that aims to create most efficient contracts given the assumptions and problems of the agency relationship. Principal agent problems refers to agency theory being applied to the barriers debate, which results in several cases suggesting optimal or sub optimal outcomes. Principal agent problem has been investigated generally through applying a set methodology (IEA, 2007) to quantify the effect of principal agent problem on energy efficiency and energy end use.
2.2. Analysis of shipping’s abatement potential A common method to calculating the techno-economic potential of CO2 reducing measures and the order in which they may be adopted is through marginal abatement cost curves (MACC). A MACC
Market heterogeneity Hidden costs Access to capital Risk
Barriers to energy efficiency
Market failures
Behavioural Economic
Non market failures
Organisational
Power Culture
Bounded rationality Form of information Credibility and trust Inertia Values Priority accorded
Principal – agent problem Split incentives Adverse selection Moral hazard Imperfect/asymmetric information
Additional material
Assessment of wind-assist
Structure of the model
Pre - processor
Trade:
200 countries,
100 commodity categories,
50 years
Fleet:
80,000 ships
9 ship types
Trade:
Regional flows per commodity aggregation
Fleet:
4 ship types, age and size aggregation
baseline
trajectory
PP GUI
PP input spreadsheets
GloTraM
GloTraM GUI
GloTraM input spreadsheets
Pre - processor
GloTraM
Company X’s pax/freight activity
Company X’s fleet
PP GUI GloTraM GUI
GloTraM input spreadsheets
PP input spreadsheets
baseline
trajectory
Company X’s data
Specify time range and time-step
Select size categorisation
Select ship type
Specify operational assumptions
Specify trade scenario
Trade database
Regulation scenario
- which measures to include
- stringencies
Economic scenario
- Fuel/carbon price
- Shipping price (TCe)
- Barriers
- Investment parameters
Fleet evolution options
- Fuel options
- Abatement options
- Speed and MCR ranges
- Ship size growth relationship
Bunkers data
Port data
Time charter data
Ship impact database
Output Excel and .mat data
Glo
TraM
GU
I
Pre-processed input file
ship_stk_new_spd
Regulation data
ship_extra_data_in
ship _cost_data_in
ship_me_spec
ship _price_data_in
ship_port_op
ship_freight_rate
route_match
apport
output_data
ship_scrap
ship_stk_retro
ship_dem
ship_stk_new
mai
n
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