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Incorporating Uncertainty into World Energy Modelling: the PROMETHEUS Model

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Abstract

In general, policy and most economic decisions like investments are formulated in a non-deterministic context. Their analysis can be considerably enhanced if probability information on future outcomes is available, especially in terms of unbiased estimates of the extent of unpredictability and stochastic dependence. For the sake of transparency, it is also important to be able to trace the justification of variability and its structure. This paper introduces PROMETHEUS, a stochastic model of the world energy system that is designed to produce joint empirical distributions of future outcomes concerning many variables that are important in terms of the evolution of the world energy system. The model methodology is based on Monte Carlo techniques, and the joint distributions of the model inputs are derived to a large extent not only from statistical econometric analysis but also from specialised studies. The emphasis is placed on the exhaustive coverage of variability including omitted variables. By incorporating detailed coverage of uncertainty into a comprehensive large-scale global energy system model, PROMETHEUS can be used to quantify probabilistic assessments of future model outcomes, which constitute critical parameters in formulating robust energy and climate policies. The description of the main model characteristics is complemented with an analytical example that illustrates the usefulness of stochastic PROMETHEUS results in the context of power generation investments under uncertainty.

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Notes

  1. A standard run of PROMETHEUS involves 2048 Monte Carlo experiments, although of course this number can be varied.

  2. Rail transport is included in the “other transport” sector of PROMETHEUS.

  3. The detailed manual and selected applications of GEM-E3 can be found in: http://www.e3mlab.eu/e3mlab/index.php?option=com_content&view=category&id=36%3Agem-e3&Itemid=71&layout=default&lang=en

  4. In the FSU and MENA regions, the effect of international fuel prices on their export revenues is taken into consideration.

  5. In Eqs. (4), (5) and (6), the subscript of the sector i is omitted for purposes of legibility.

  6. If Eq. (2) produces a negative value, the gap is assumed to be zero and no competition between technologies takes place.

  7. As obtained from a wide literature review.

  8. That includes the variable O&M cost and the fuel cost of each technology.

  9. In which all equation parameters are set at their mean values.

  10. In PROMETHEUS, international coal price represents the average OECD steam coal import price, which is the same used by IEA in its World Energy Outlook [48].

  11. This has been confirmed by econometric estimations performed over a relatively large set of historical data.

  12. PROMETHEUS uses the notion of floor costs determined by technology perspective analysis.

  13. For a detailed discussion on classification of sources of uncertainty, see Spiegehalter et al. [54].

  14. Bold type indicates vectors and bold type capital letters indicate matrices.

  15. The number of the rejected Monte Carlo runs depends on the variance of the estimated equation, but in general the ratio does not exceed 5 %.

  16. They do not include potential additions to hydrocarbon resources from reserve growth.

  17. Excluding the sector Land Use, Land Use Change and Forestry (LULUCF).

  18. These studies used a variety of established integrated assessment global energy-economy models, such as REMIND, POLES, WITCH, IMAGE, MESSAGE, GEM-E3, IMACLIM, and GCAM.

  19. This is magnified by the relatively high correlations between the profitabilities of alternative power generation options.

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Acknowledgements

The research leading to the study has been partly funded by the European Commission DG ENER within the framework contract for long-range energy modeling for the period up to 2050.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Politechniou Street, Zografou Campus, 15773, Athens, Greece

    Panagiotis Fragkos, Nikos Kouvaritakis & Pantelis Capros

Authors
  1. Panagiotis Fragkos
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  2. Nikos Kouvaritakis
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  3. Pantelis Capros
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Correspondence to Panagiotis Fragkos.

Appendices

Appendix 1

The 26 power generation technologies explicitly identified in the PROMETHEUS model are presented in the table below:

 

Power generation technologies

1

Conventional coal thermal

14

Nuclear PWR

2

Conventional lignite thermal

15

Nuclear 4th generation

3

Supercritical pulverised coal

16

Large hydro

4

Integrated coal gasification

17

Small hydro

5

Conventional gas thermal

18

Wind on-shore

6

Open cycle gas turbine

19

Wind off-shore

7

Gas turbine combined cycle

20

Photovoltaics

8

Combined heat and power

21

Concentrated solar power

9

Conventional oil thermal

22

Conventional biomass thermal

10

Open cycle oil turbine

23

Biomass gasification

11

Supercritical pulverised coal with CCS

24

Biomass gasification with CCS

12

Integrated coal gasification with CCS

25

Fuel cells using hydrogen

13

Gas turbine combined cycle with CCS

26

Fuel cells using natural gas

The table below summarises the technologies used in PROMETHEUS for hydrogen production:

 

Hydrogen production technologies

1

Gas steam reforming

10

Biomass pyrolysis

2

Gas steam reforming with CCS

11

Small scale biomass gasification

3

Solar methane reforming

12

Large scale biomass gasification

4

Coal partial oxidation

13

Large scale biomass gasification with CCS

5

Coal partial oxidation with CCS

14

Solar high temperature thermochemical cycles

6

Coal gasification

15

Nuclear high temperature thermochemical cycles

7

Coal gasification with CCS

16

Water electrolysis from dedicated nuclear plant

8

Oil partial oxidation

17

Water electrolysis from dedicated wind plant

9

Oil partial oxidation with CCS

18

Water electrolysis from electricity grid

Appendix 2

Properties of equation of international oil price

Econometric estimation of oil price (sample 1980–2010)

 

t statistic

Probability

C

16.880

0.000

LOG[PRODCAPEAST(−1)[

4.871

0.000

PDL01 [RPRATIO (−1)]

−10.877

0.000

R 2

0.954

 

Adjusted R 2

0.933

 

SE of regression

0.132

 

Lag distribution of LOG[RPRATIO(−1)]

t statistic

 

Sum of lags

−10.877

Properties of equation of international price for natural gas

Econometric estimation of natural gas price (sample 1980—2010)

 

t statistic

Probability

C

4.432

0.001

PDL01 [oilprice(−1)]

10.944

0.000

PDL02 [RPRATIO (−3)]

−3.150

0.007

R 2

0.974

 

Adjusted R 2

0.969

 

SE of regression

0.075

 

Lag distribution of LOG[oilprice(−1)]

t statistic

 

Sum of lags

10.944

Lag distribution of LOG[RPRATIO(−3)]

t statistic

 

Sum of lags

−3.150

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Fragkos, P., Kouvaritakis, N. & Capros, P. Incorporating Uncertainty into World Energy Modelling: the PROMETHEUS Model. Environ Model Assess 20, 549–569 (2015). https://doi.org/10.1007/s10666-015-9442-x

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