Energy technology environment model with smart grid and robust nodal electricity prices
- 28 Downloads
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.
KeywordsOR in energy Long-term energy model Power flow Robust nodal electricity prices Robust optimization
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).
- 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
- 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
- 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
- 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
- Office fédéral de l’énergie (OFEN). (2012). Die Energieperspektiven für die Schweiz bis 2050.Google Scholar
- ORDECSYS. (2013). Réseaux intelligents de transport/transmission de l’électricité en suisse. Technical report, ORDECSYS Technical report.Google Scholar
- ORDECSYS. (2014). Time of use (TOU) pricing: Adaptive and TOU pricing schemes for smart technology integration. Technical report, ORDECSYS Technical report.Google Scholar
- 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
- 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
- Stiel, A. D. J. (2011). Modelling liberalised power markets. Master’s thesis. ETH Zürich, Centre for Energy Policy and Economics. September 2011Google Scholar
- Weigt, H., & Schlecht, I. (2014). Swissmod a model of the Swiss electricity market. Technical report. WWZ-Discussion Paper.Google Scholar