Journal of Applied Mathematics

Generation Expansion Models including Technical Constraints and Demand Uncertainty

Abstract

This article presents a Generation Expansion Model of the power system taking into account the operational constraints and the uncertainty of long-term electricity demand projections. The model is based on a discretization of the load duration curve and explicitly considers that power plant ramping capabilities must meet demand variations. A model predictive control method is used to improve the long-term planning decisions while considering the uncertainty of demand projections. The model presented in this paper allows integrating technical constraints and uncertainty in the simulations, improving the accuracy of the results, while maintaining feasible computational time. Results are tested over three scenarios based on load data of an energy retailer in Colombia.

Article information

Source
J. Appl. Math., Volume 2017 (2017), Article ID 3424129, 11 pages.

Dates
Accepted: 15 March 2017
First available in Project Euclid: 11 May 2017

https://projecteuclid.org/euclid.jam/1494468019

Digital Object Identifier
doi:10.1155/2017/3424129

Citation

Deossa, P.; De Vos, K.; Deconinck, G.; Espinosa, J. Generation Expansion Models including Technical Constraints and Demand Uncertainty. J. Appl. Math. 2017 (2017), Article ID 3424129, 11 pages. doi:10.1155/2017/3424129. https://projecteuclid.org/euclid.jam/1494468019

References

• F. Ueckerdt, R. Brecha, G. Luderer et al., “Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves,” Energy, vol. 90, pp. 1799–1814, 2015.
• T. Bo$\text{\ss}$mann and I. Staffell, “The shape of future electricity demand: exploring load curves in 2050s Germany and Britain,” Energy, vol. 90, pp. 1317–1333, 2015.
• K. Akbari, M. M. Nasiri, F. Jolai, and S. F. Ghaderi, “Optimal investment and unit sizing of distributed energy systems under uncertainty: a robust optimization approach,” Energy and Buildings, vol. 85, pp. 275–286, 2014.
• P. N. Georgiou, “A bottom-up optimization model for the long-term energy planning of the Greek power supply sector integrating mainland and insular electric systems,” Computers and Operations Research, vol. 66, pp. 292–312, 2016.
• I. Staffell and R. Green, “Is there still merit in the merit order stack? the impact of dynamic constraints on optimal plant mix,” IEEE Transactions on Power Systems, vol. 31, no. 1, pp. 43–53, 2016.
• C. De Jonghe, E. Delarue, R. Belmans, and W. D'haeseleer, “Determining optimal electricity technology mix with high level of wind power penetration,” Applied Energy, vol. 88, no. 6, pp. 2231–2238, 2011.
• J. Valinejad and T. Barforoushi, “Generation expansion planning in electricity markets: a novel framework based on dynamic stochastic MPEC,” International Journal of Electrical Power and Energy Systems, vol. 70, pp. 108–117, 2015.
• A. van Stiphout, K. Poncelet, G. Deconinck, and K. De Vos, “The impact of operating reserves in generation expansion planning with high shares of renewable energy sources,” in Proceedings of the IAEE European Energy Conference, pp. 1–6, 2014.
• A. J. Conejo, L. B. Morales, S. J. Kazempour, and A. S. Siddiqui, Investment in Electricity Generation and Transmission, Springer, New York, NY, USA, 2016.
• H. D. Sherali, A. L. Soyster, F. H. Murphy, and S. Sen, “Linear programming based analysis of marginal cost pricing in electric utility capacity expansion,” European Journal of Operational Research, vol. 11, no. 4, pp. 349–360, 1982.
• P. Tatjewski, “Advanced control and on-line process optimization in multilayer structures,” Annual Reviews in Control, vol. 32, no. 1, pp. 71–85, 2008.
• J. B. Rawlings, “Tutorial overview of model predictive control,” IEEE Control Systems Magazine, vol. 20, no. 3, pp. 38–52, 2000.
• K. De Vos, Sizing and allocation of operating reserves following wind power integration [Ph.D. thesis], KU Leuven, Departement Elektrotechniek (ESAT), 2013. \endinput