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Annals of Operations Research

, Volume 274, Issue 1–2, pp 101–117 | Cite as

Energy technology environment model with smart grid and robust nodal electricity prices

  • Frédéric BabonneauEmail author
  • Alain Haurie
OR Modeling/Case Study
  • 28 Downloads

Abstract

This paper deals with the modeling of power flow in a transmission grid within the multi-sectoral multi-energy long-term regional energy model ETEM-SG. This extension of the model allows a better representation of demand response for flexible loads triggered by nodal marginal cost pricing. To keep the global model in the realm of linear programming one uses a linearized DC power flow model that represents the transmission grid with the main constraints on the power flowing through the different arcs of the electricity transmission network. Robust optimization is used to take into account the uncertainty on the capacity limits resulting from inter-regional transit. A numerical illustration is carried out for a data set corresponding roughly to the Leman Arc region.

Keywords

OR in energy Long-term energy model Power flow Robust nodal electricity prices Robust optimization 

Notes

Acknowledgements

This research is supported by the Qatar National Research Fund under Grant Agreement n\(^o\) NPRP10-0212-170447 and by Canadian IVADO programme (VORTEX Project).

References

  1. Andrey, C., Babonneau, F., & Haurie, A. (2015). Modélisation stochastique et robuste de l’atténuation et de l’adaptation dans un système énergétique régional. application à la région midi-pyrénées. Nature Science Société, 23(2), 133–149.CrossRefGoogle Scholar
  2. Babonneau, F., Caramanis, M., & Haurie, A. (2016). A linear programming model for power distribution with demand response and variable renewable energy. Applied Energy, 181, 83–95.CrossRefGoogle Scholar
  3. Babonneau, F., Caramanis, M., & Haurie, A. (2017). ETEM-SG: Optimizing regional smart energy system with power distribution constraints and options. Environmental Modelling and Assessment, 22(5), 411–430.CrossRefGoogle Scholar
  4. Babonneau, F., Haurie, A., Tarel, G. J., & Thénié, J. (2012). Assessing the future of renewable and smart grid technologies in regional energy systems. Swiss Journal of Economics and Statistics, 148(2), 229–273.CrossRefGoogle Scholar
  5. Babonneau, F., Kanudia, A., Labriet, M., Loulou, R., & Vial, J.-P. (2012). Energy security: A robust programming approach and application to european energy supply via tiam. Environmental Modeling and Assessment, 17(1), 19–37.CrossRefGoogle Scholar
  6. Babonneau, F., Klopfenstein, O., Ouorou, A., & Vial, J.-P. (2013). Robust capacity expansion solutions for telecommunication networks with uncertain demands. Network, 62(4), 255–272.CrossRefGoogle Scholar
  7. Babonneau, F., Vial, J.-P., & Apparigliato, R. (2010). Robust optimization for environmental and energy planning. In J. A. Filar & A. Haurie (Eds.), Uncertainty and environmental decision making. Berlin: Springer.Google Scholar
  8. Bacher, R. (1992). Power system models, objectives and constraints in optimal power flow calculations. In R. Bacher, K. Frauendorfer, & H. Glavitsch (Eds.), Optimization in planning and operation of electric power systems (pp. 217–264)., Lecture notes of the SVOR/ASRO tutorial Thun Berlin: Springer.Google Scholar
  9. Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust optimization. Princeton: Princeton University Press.CrossRefGoogle Scholar
  10. Ben-Tal, A., & Nemirovski, A. (1998). Robust convex optimization. Mathematics of Operations Research, 23, 769–805.CrossRefGoogle Scholar
  11. Berger, C., Dubois, R., Haurie, A., Lessard, E., Loulou, R., & Waaub, J.-P. (1992). Canadian MARKAL: An advanced linear programming system for energy and environmental modelling. INFOR, 30(3), 222–239.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. Fragnière, E., & Haurie, A. (1996). A stochastic programming model for energy/environment choices under uncertainty. International Journal Environment and Pollution, 6(4–6), 587–603.Google Scholar
  14. 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
  15. Office fédéral de l’énergie (OFEN). (2012). Die Energieperspektiven für die Schweiz bis 2050.Google Scholar
  16. ORDECSYS. (2013). Réseaux intelligents de transport/transmission de l’électricité en suisse. Technical report, ORDECSYS Technical report.Google Scholar
  17. ORDECSYS. (2014). Time of use (TOU) pricing: Adaptive and TOU pricing schemes for smart technology integration. Technical report, ORDECSYS Technical report.Google Scholar
  18. Ruiz, P.A., Foster, J.M., Rudkevich, A., Caramanis, M. (2011) On fast transmission topology control heuristics. In Proceedings of 2011 IEEE power and energy society general meeting, Detroit, MI. IEEE, July 2011Google Scholar
  19. Ruiz, P.A., Rudkevich, A., Caramanis, M.C., Goldis, E., Ntakou, E., Philbrick, R. (2012a). Reduced MIP formulation for transmission topology control. In 50th annual Allerton conference on communication, control, and computing, Monticello. USA University of Illinois at Urbana-ChampaignGoogle Scholar
  20. Ruiz, P. A., Foster, J. M., Rudkevich, A., & Caramanis, M. C. (2012). Tractable transmission topology control using sensitivity analysis. IEEE Transactions on Power Systems, 27, 1550–1559.CrossRefGoogle Scholar
  21. Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21, 1154–1157.CrossRefGoogle Scholar
  22. Stiel, A. D. J. (2011). Modelling liberalised power markets. Master’s thesis. ETH Zürich, Centre for Energy Policy and Economics. September 2011Google Scholar
  23. Weigt, H., & Schlecht, I. (2014). Swissmod a model of the Swiss electricity market. Technical report. WWZ-Discussion Paper.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Business SchoolUniversity Adolfo IbañezSantiagoChile
  2. 2.ORDECSYSChêne-BougeriesSwitzerland
  3. 3.University of GenevaGenevaSwitzerland
  4. 4.GERAD-HECMontrealCanada

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