Skip to main content
Log in

Contingency-constrained unit commitment with post-contingency corrective recourse

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

We consider the problem of minimizing costs in the generation unit commitment problem, a cornerstone in electric power system operations, while enforcing an \(N\)\(k\)\(\varvec{\varepsilon }\) reliability criterion. This reliability criterion is a generalization of the well-known \(N\)\(k\) criterion and dictates that at least \((1-\varepsilon _j)\) fraction of the total system demand (for \(j = 1,\ldots , k\)) must be met following the failure of \(k\) or fewer system components. We refer to this problem as the contingency-constrained unit commitment problem, or CCUC. We present a mixed-integer programming formulation of the CCUC that accounts for both transmission and generation element failures. We propose novel cutting plane algorithms that avoid the need to explicitly consider an exponential number of contingencies. Computational studies are performed on several IEEE test systems and a simplified model of the Western US interconnection network. These studies demonstrate the effectiveness of our proposed methods relative to current state-of-the-art.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Arroyo, J. M. (2010). Bilevel programming applied to power system vulnerability analysis under multiple contingencies. IET Generation, Transmission & Distribution, 4(2), 178–190.

    Article  Google Scholar 

  • Benders, J. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4, 238–252.

    Article  Google Scholar 

  • Bienstock, D., & Verma, A. (2010). The N–k problem in power grids: New models, formulations, and numerical experiments. SIAM Journal on Optimization, 20(5), 2352–2380.

    Article  Google Scholar 

  • Capitanescu, F., et al. (2011). State-of-the-art, challenges, and future trends in security constrained optimal power flow. Electric Power Systems Research, 81, 1731–1741.

    Article  Google Scholar 

  • Carrión, M., & Arroyo, J. M. (2006). A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 21(3), 1371–1378.

    Article  Google Scholar 

  • Chen, R. L., Cohn, A., Fan, N., & Pinar, A. (2012). \(N\)\(k\)\(\epsilon \) power system design. In Proceedings of the 12th probabilistic methods for power systems conference. Istanbul, Turkey

  • Chen, R. L., Cohn, A., Fan, N., & Pinar, A. (2014). Contingency-risk informed power system design. IEEE Transaction on Power Systems, 29(5), 2087–2096.

  • Fan, N., Xu, H., Pan, F., & Pardalos, P. M. (2011). Economic analysis of the N–k power grid contingency selection and evaluation by graph algorithms and interdiction methods. Energy Systems, 2(3–4), 313–324.

    Article  Google Scholar 

  • FERC Staff Report. (2011). Recent ISO Software Enhancements and Future Software and Modeling Plans. Federal Energy Regulatory Commission

  • Frank, S., Steponavice, I., & Rebennack, S. (2012a). Optimal power flow: A bibliographic survey I—formulations and deterministic methods. Energy Systems, 3(3), 221–258.

    Article  Google Scholar 

  • Frank, S., Steponavice, I., & Rebennack, S. (2012b). Optimal power flow: A bibliographic survey II—nondeterministic and hybrid methods. Energy Systems, 3(3), 259–289.

    Article  Google Scholar 

  • Fu, Y., Shahidehpour, M., & Li, Z. (2005). Security-constrained unit commitment with AC constraints. IEEE Transactions on Power Systems, 20(3), 1538–1550.

    Article  Google Scholar 

  • Fu, Y., Shahidehpour, M., & Li, Z. (2006). AC contingency dispatch based on security-constrained unit commitment. IEEE Transactions on Power Systems, 21(2), 897–908.

    Article  Google Scholar 

  • Hedman, K. W., Ferris, M. C., O’Neill, R. P., Fisher, E. B., & Oren, S. S. (2010). Co-optimization of generation unit commitment and transmission switching with \(N-1\) reliability. IEEE Transactions on Power Systems, 24(2), 1052–1063.

    Article  Google Scholar 

  • Hobbs, B. F., Rothkopf, M. H., O’Neill, R. P., & Chao, H.-P. (Eds.). (2001). The next generation of electric power unit commitment models. Boston: Kluwer.

    Google Scholar 

  • IEEE Test Systems. http://www.ee.washington.edu/research/pstca

  • Lesieutre, B., Roy, S., Donde, V., & Pinar, A. (2006). Power system extreme event analysis using graph partitioning. In Proceedings of the 39th North American power symposium, Carbondale, IL.

  • Lesieutre, B., Pinar, A., & Roy, S. (2008). Power system extreme event detection: The vulnerability frontier. In: Proceedings of the 41st Hawaii international conference on system sciences (184 pp). Waikoloa, Big Island, HI.

  • Lotfjou, A., Shahidehpour, M., Fu, Y., & Li, Z. (2010). Security-constrained unit commitment with AC/DC transmission systems. IEEE Transactions on Power Systems, 25(1), 531–542.

    Article  Google Scholar 

  • North American Electric Reliability Corporation. (2014). Transmission Planning Standards, Accessed on April 2014. Available at http://www.nerc.com/pa/Stand/Reliability%20Standards/Forms/AllItems.aspx

  • O’Neill, R. P., Hedman, K. W., Krall, E. R., Papavasiliou, A., & Oren, S. S. (2010). Economic analysis of the N \(-\) 1 reliable unit commitment and transmission swtiching problem using duality concepts. Energy Systems, 1(2), 165–195.

    Article  Google Scholar 

  • Personal Communication. (2012). Dr. Eugene Litvinov.

  • Padhy, N. P. (2004). Unit commitment—a bibliographical survey. IEEE Transactions on Power Systems, 19(3), 1196–1205.

    Article  Google Scholar 

  • Pinar, A., Meza, J., Donde, V., & Lesieutre, B. (2010). Optimization strategies for the vulnerability analysis of the electric power grid. SIAM Journal on Optimization, 20(4), 1786–1810.

    Article  Google Scholar 

  • Price, J. E. (2011). Reduced network modeling of WECC as a market design prototype. In Proceedings of the 2011 IEEE general meeting of the power and energy society. San Diego, California.

  • Salmeron, J., Wood, K., & Baldick, R. (2004). Analysis of electric grid security under terrorist threat. IEEE Transactions on Power Systems, 19(2), 905–912.

    Article  Google Scholar 

  • Salmeron, J., Wood, K., & Baldick, R. (2009). Worst-case interdiction analysis of large-scale electric power grids. IEEE Transactions on Power Systems, 24(1), 96–104.

    Article  Google Scholar 

  • Street, A., Oliveira, F., & Arroyo, J. M. (2011a). Contingency-constrained unit commitment with n–K security criterion: A robust optimization approach. IEEE Transactions on Power Systems, 26(3), 1581–1590.

    Article  Google Scholar 

  • Street, A., Oliveira, F., & Arroyo, J. M. (2011b). Energy and reserve scheduling under an N–K security criterion via robust optimization. In Proceedings of the 17th power systems computation conference. Stockholm, Sweden.

  • Van Dinter, J., Rebennack, S., Kallrath, J., Denholm, P., & Newman, A. (2013). The unit commitment model with concave emissions costs: A hybrid Benders’ decomposition with nonconvex master problems. Annals of Operations Research, 210(1), 361–386.

    Article  Google Scholar 

  • Wang, J., Shahidehpour, M., & Li, Z. (2008). Security-constrained unit commitment with volatile wind power generation. IEEE Transactions on Power Systems, 23(3), 1319–1327.

    Article  Google Scholar 

  • Wang, Q., Watson, J.-P., & Guan, Y. (2013). Two-stage robust optimization for \(N\)\(k\) contingency-constrained unit commitment. IEEE Transactions on Power Systems, 28(3), 2366–2375.

    Article  Google Scholar 

  • Wood, A. J., & Wollenberg, B. J. (1996). Power generation, operation and control (2nd ed.). New York: Wiley.

    Google Scholar 

  • Wu, L., & Shahidehpour, M. (2010). Accelerating the Benders decomposition for network-constrained unit commitment problems. Energy Systems, 1(3), 339–376.

    Article  Google Scholar 

  • Yuan, W., Zhao, L., & Zeng, B. (2014). Optimal power grid protection through a defender-attacker-defender model. Reliability Engineering and System Safety, 121, 83–89.

    Article  Google Scholar 

  • Zhao, L., & Zeng, B. (2013). Vulnerability analysis of power grids with line switching. IEEE Transactions on Power Systems, 28(3), 2727–2736.

    Article  Google Scholar 

  • Zheng, Q. P., Wang, J., Pardalos, P. M., & Guan, Y. (2013). A decomposition approach to the two-stage stochastic unit commitment problem. Annals of Operations Research, 210(4), 387–410.

    Article  Google Scholar 

Download references

Acknowledgments

Sandia National Laboratories’ Laboratory-Directed Research and Development Program and the U.S. Department of Energy’s Office of Science (Advanced Scientific Computing Research program) funded portions of this work. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Neng Fan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, R.LY., Fan, N., Pinar, A. et al. Contingency-constrained unit commitment with post-contingency corrective recourse. Ann Oper Res 249, 381–407 (2017). https://doi.org/10.1007/s10479-014-1760-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-014-1760-x

Keywords

Navigation