# Energy Security: A Robust Optimization Approach to Design a Robust European Energy Supply via TIAM-WORLD

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## Abstract

Energy supply routes to a given region are subject to random events, resulting in partial or total closure of a route (corridor). For instance, a pipeline may be subject to technical problems that reduce its capacity. Or, oil supply by tanker may be reduced for political reasons or because of equipment mishaps at the point of origin or again, by a conscious decision by the supplier in order to obtain economic benefits. The purpose of this article is to formulate a simplified version of the above issue that mainly addresses long-term uncertainties. The formulation is done via a version of the TIAM-WORLD Integrated Model, modified to implement the approach of robust optimization. In our case, the approach can be interpreted as a revival of chance-constrained programming under the name of distributionally robust, or ambiguous, chance-constrained programming. We apply the approach to improve the security of supply to the European Energy system. The resulting formulation provides several interesting features regarding the security of EU energy supply and has also the advantage to be numerically tractable.

## Keywords

Energy supply Robust optimization Ambiguous chance constraint programming TIAM-WORLD## Notes

### Acknowledgements

This work was supported by the FP7 European Research Project PLANETS and by GICC Research Grant form the French Ministry of Ecology and Sustainable Development (MEDDTL).

## References

- 1.Babonneau, F., Vial, J.-P., & Apparigliato, R. (2010). Robust optimization for environmental and energy planning. In J. A. Filar & A. Haurie (Eds.),
*Handbook on “Uncertainty and environmental decision making”. International series in operations research and management science*(pp. 79–126). Berlin: Springer.Google Scholar - 2.Behrens, A., Egenhofer, C., & Checchi, A. (2009). Long-term energy security risks for europe: a sector-specific approach. CEPS Working Documents 309, Centre for European Policy Studies, Brussels, Belgium.Google Scholar
- 3.Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009).
*Robust optimization*. Princeton: Princeton University Press.Google Scholar - 4.Ben-Tal, A., & Nemirovski, A. (1998). Robust convex optimization.
*Mathematics of Operations Research, 23*, 769–805.CrossRefGoogle Scholar - 5.Brown, S., & Huntington, H. G. (2008). Energy security and climate change protection: Complementarity or tradeoff.
*Energy Policy, 36*(9), 3510–3513.CrossRefGoogle Scholar - 6.Calafiore, G. C., & El-Gahoui, L. (2006). On distributionally robust chance-constrained linear programs.
*Journal of Optimization Theory and Applications, 130*, 1–22.CrossRefGoogle Scholar - 7.Charnes, A., Cooper, W. W., & Symonds, G. H. (1958). Cost horizons and certainty equivalents: an approach to stochastic programming of heating oil.
*Management Science, 4*, 235–263.CrossRefGoogle Scholar - 8.Dantzig, G. B. (1956). Linear programming under uncertainty.
*Management Science, 1*(3–4), 197–206.Google Scholar - 9.EC (2000). Towards a European strategy for the security of energy supply. Green Paper COM (2000) 769 final (27 p.), European Commission, Brussels, Belgium.Google Scholar
- 10.EC (2006). A European strategy for sustainable, competitive and secure energy. Green Paper COM (2006) 105 (20 p.), European Commission, Brussels, Belgium.Google Scholar
- 11.EC (2007). An energy policy for Europe. Green Paper COM (2007) 1 final (27 p.), European Commission, Brussels, Belgium.Google Scholar
- 12.El-Ghaoui, L., & Lebret, H. (1997). Robust solutions to least-square problems to uncertain data matrices.
*SIAM Journal of Matrix Analysis and Applications, 18*, 1035–1064.CrossRefGoogle Scholar - 13.Frogatt, A., & Levi, M. A. (2009). Climate and energy security policies and measures: synergies and conflicts.
*International Affairs, 85*(6), 1129–1141.CrossRefGoogle Scholar - 14.Grubb, M., Butler, L., & Twomey, P. (2006). Diversity and security in uk electricity generation: The influence of low-carbon objectives.
*Energy Policy, 34*(18), 4050–4062.CrossRefGoogle Scholar - 15.IEA (2007).
*Energy security and climate policy–Assessing interactions*(150 p.). Paris, France: International Energy Agency.Google Scholar - 16.IEA (2007).
*World Energy Outlook 2007, China and India insights*(674 p.). Paris, France: International Energy Agency.Google Scholar - 17.Iyengar, G., & Erdogan, E. (2006). Ambiguous chance constrained problems and robust optimization.
*Mathematical Programming Series B, 107*(1–2), 37–61.Google Scholar - 18.Jansen, J. C., van Arkel, W. G., & Boots, M. G. (2004). Designing indicators of long-term energy supply security. Technical Report ECN-C–04-007.Google Scholar
- 19.Kouvelis, P., & Yu, G. (1997).
*Robust discrete ooptimization and its applications*. London: Kuwer.Google Scholar - 20.Labriet, M., Loulou, R., & Kanudia, A. (2009). Modeling uncertainty in a large scale integrated energy-climate model. In J. A. Filar, & A. B. Haurie (Eds.),
*Environmental decision making under uncertainty*. Berlin: Springer.Google Scholar - 21.Loulou, R. (2008). ETSAP-TIAM: The TIMES integrated assessment model Part II: Mathematical formulation.
*Computational Management Science, 5*(1), 7–40.CrossRefGoogle Scholar - 22.Loulou, R., & Labriet, M. (2008). ETSAP-TIAM: The TIMES integrated assessment model Part I: Model structure.
*Computational Management Science, 5*(1), 7–40.CrossRefGoogle Scholar - 23.Loulou, R., Labriet, M., & Kanudia, A. (2009). Deterministic and stochastic analysis of alternative climate targets under differentiated cooperation regimes.
*Energy Economics, 31*(Supplement 2), 131–143.CrossRefGoogle Scholar - 24.Miller, L. B., & Wagner, H. (1965). Chance-constrained programming with joint constraints.
*Operations Research, 13*, 930–945.CrossRefGoogle Scholar - 25.Prékopa, A. (1970). On probabilistic constrained programming. In
*Proceedings of the Princeton symposium on mathematical programming*(pp. 113–138). Princeton: Princeton University Press.Google Scholar - 26.Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming.
*Operations Research, 21*, 1154–1157.CrossRefGoogle Scholar