Assessment of energy efficiency improvement and technology uptake in the

shipping industry

Tristan Smith, UCL Energy Institute tristan.smith@ucl.ac.uk

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