Commenced in January 2007
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Network-Constrained AC Unit Commitment under Uncertainty Using a Bender’s Decomposition Approach

Authors: B. Janani, S. Thiruvenkadam

Abstract:

In this work, the system evaluates the impact of considering a stochastic approach on the day ahead basis Unit Commitment. Comparisons between stochastic and deterministic Unit Commitment solutions are provided. The Unit Commitment model consists in the minimization of the total operation costs considering unit’s technical constraints like ramping rates, minimum up and down time. Load shedding and wind power spilling is acceptable, but at inflated operational costs. The evaluation process consists in the calculation of the optimal unit commitment and in verifying the fulfillment of the considered constraints. For the calculation of the optimal unit commitment, an algorithm based on the Benders Decomposition, namely on the Dual Dynamic Programming, was developed. Two approaches were considered on the construction of stochastic solutions. Data related to wind power outputs from two different operational days are considered on the analysis. Stochastic and deterministic solutions are compared based on the actual measured wind power output at the operational day. Through a technique capability of finding representative wind power scenarios and its probabilities, the system can analyze a more detailed process about the expected final operational cost.

Keywords: Benders’ decomposition, network constrained AC unit commitment, stochastic programming, wind power uncertainty.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1124001

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References:


[1] R. Baldick (1995), “The generalized unit commitment problem,” IEEE Trans.Power Syst., vol. 10, no. 1, pp. 465–475.
[2] F. Zhuang and F. D. Galiana (1988), “Towards a more rigorous and practical unit commitment by Lagrangian relaxation,” IEEE Trans. Power Syst., vol. 3, no. 2, pp. 763–773.
[3] N. J. Redondo and A. J. Conejo (1999), “Short-term hydro-thermal coordination by Lagrangian relaxation: solution of the dual problem,” IEEE Trans. Power Syst., vol. 14, no. 1, pp. 89–95.
[4] A. J. Conejo, M. Carrión (2010), and J. M. Morales, Decision Making Under Uncertainty in Electricity Markets, ser. Operations Research & Management Science. New York, NY, USA: Springer.
[5] S. Takriti, J. R. Birge, and E. Long (1996), “A stochastic model for the unit commitment problem,” IEEE Trans. Power Syst., vol. 11, no. 3, pp. 1497–1508.
[6] P. Carpentier, G. Gohen, J.-C. Culioli, and A. Renaud (1996), “Stochastic optimization of unit commitment: a new decomposition framework,” IEEE Trans. Power Syst., vol. 11, no. 2, pp. 1067–1073.
[7] J. M. Morales, A. J. Conejo, and J. Perez-Ruiz (2009), “Economic valuation of reserves in power systems with high penetration of wind power,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 900–910.
[8] F. Bouffard, F. D. Galiana, and A. J. Conejo (2005), “Market-clearing with stochastic security-Part I: formulation,” IEEE Trans. Power Syst., vol. 20, no. 4, pp. 1818–1826.
[9] F. Bouffard, F. D. Galiana, and A. J. Conejo (2005), “Market-clearing with stochastic security-Part II: case studies,” IEEE Trans. Power Syst., vol. 20, no. 4, pp. 1827–1835.
[10] Q. Wang, J. Wang, and Y. Guan (2015), “Price-based unit commitment with wind power utilization constraints,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2718–2726.
[11] A. Kalantari, J. F. Restrepo, and F. D. Galiana (2013), “Security-constrained unit commitment with uncertain wind generation: the loadability set approach,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1787–1796.
[12] A. Tuohy, P. Meibom, E. Denny, and M. O’Malley (2009), “Unit commitment for systems with significant wind penetration,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 592–601.
[13] J. Wang, M. Shahidehpour, and Z. Li (2008), “Security-constrained unit commitment with volatile wind power generation,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1319–1327.
[14] W. S. Sifuentes and A. Vargas (2007), “Hydrothermal scheduling using Benders decomposition: accelerating techniques,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1351–1359.
[15] Y. Fu, M. Shahidehpour, and Z. Li (2005), “Security-constrained unit commitment with AC constraints,” IEEE Trans. Power Syst., vol. 20, no.2, pp. 1001–1013.
[16] C. E. Murillo-Sanchez, R. D. Zimmerman, C. L. Anderson, and R. J. Thomas (2013), “Secure planning and operations of systems with stochastic sources, energy storage, and active demand,” IEEE Trans. Smart Grid, vol. 4, pp. 2220–2229.
[17] Q. P. Zheng, J. Wang, and A. L. Liu, “Stochastic optimization for unit commitment—a review,” IEEE Trans. Power Syst., to be published.
[18] J. Ostrowski, M. F. Anjos, and A. Vannelli (2012), “Tight mixed integer linear programming formulations for the unit commitment problem,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 39–46.
[19] S. Muller, M. Deicke, and R. W. D. Doncker (2002), “Doubly fed induction generator systems for wind turbines,” IEEE Ind. Appl. Mag., vol. 8, pp. 26–33.
[20] A. J. Conejo, E. Castillo, R. Minguez, and R. Garcia-Bertrand (2006), Decomposition Techniques in Mathematical Programming: Engineering and Science Applications. Heidelberg, Germany: Springer.
[21] D. P. Bertsekas and N. R. Sandell (1982), “Estimates of the duality gap for large-scale separable nonconvex optimization problems,” in Proc. 21st IEEE Conf. Decision and Control, vol. 21, pp. 782–785.
[22] Energy Information Administration (Online). Available: http://www.eia.gov/countries/data.cfm.
[23] T. N. Santos and A. L. Diniz (2009), “Feasibility and optimality cuts for the multistage Benders decomposition approach: Application to the network constrained hydrothermal scheduling,” in Proc. IEEE Power Energy Soc. General Meeting, 2009 (PES '09), pp. 1–8.