Annals of Operations Research

, Volume 238, Issue 1–2, pp 449–473 | Cite as

Short-term balancing of supply and demand in an electricity system: forecasting and scheduling

  • Jeanne Aslak Petersen
  • Ditte Mølgård Heide-Jørgensen
  • Nina Kildegaard Detlefsen
  • Trine Krogh Boomsma
Article
  • 262 Downloads

Abstract

Until recently, the modelling of electricity system operations has mainly focused on hour-by-hour management. However, with the introduction of renewable energy sources such as wind power, fluctuations within the hour result in imbalances between supply and demand that are undetectable with an hourly time resolution. Ramping restrictions on production units and transmission lines contribute further to these imbalances. In this paper, we therefore propose a model for optimising electricity system operations within the hour. Taking a social welfare perspective, the model aims at reducing intra-hour costs by optimally activating so-called manual reserves based on forecasted imbalances. Since manual reserves are significantly less expensive than automatic reserves, we expect a considerable reduction in total costs of balancing. We illustrate our model in a Danish case study and investigate the effect of an expected increase in installed wind capacity. We find that the balancing costs do not outweigh the benefits of the inexpensive wind power, and that the savings from activating manual reserves are even larger for the high wind capacity case.

Keywords

OR in energy Scheduling Forecasting Power system balancing 

Notes

Acknowledgments

The authors gratefully appreciate many valuable comments and suggestions from two anonymous referees, Pierre Pinson from the Technical University of Denmark and Peter Meibom from the Danish Energy Association, as well as discussions of the problem with Energinet.dk. Jeanne Aslak Petersen acknowledges support through the CFEM project and Trine Krogh Boomsma through the ENSYMORA project, both funded by the Danish Council of Strategic Research (09-067008/DSF and 10-093904/DSF, respectively). Ditte Mølgård Heide-Jørgensen acknowledges the support through the iPower project also funded by the Danish Council of Strategic Research via the DSR-SPIR program (10-095378).

Supplementary material

10479_2015_2092_MOESM1_ESM.pdf (60 kb)
Supplementary material 1 (pdf 59 KB)
10479_2015_2092_MOESM2_ESM.pdf (55 kb)
Supplementary material 2 (pdf 54 KB)

References

  1. Bouffard, F., & Galiana, F. (2008). Stochastic security for operations planning with significant wind power generation. IEEE Transactions on Power Systems, 23(2), 306–316. doi: 10.1109/TPWRS.2008.919318.CrossRefGoogle Scholar
  2. Bunn, D. W., & Paschentis, S. N. (1986). Development of a stochastic model for the economic dispatch of electric power. European Journal of Operational Research, 27(2), 179–191. doi: 10.1016/0377-2217(86)90059-7.CrossRefGoogle Scholar
  3. Carøe, C. C., & Schultz, R. (1998) A two-stage stochastic program for unit commitment under uncertainty in a hydro-thermal power system. Tech. Rep. SC-98-11, ZIB, Takustr.7, 14195 Berlin.Google Scholar
  4. Carrión, M., & Arroyo, J. (2006). A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 21(3), 1371–1378. doi: 10.1109/TPWRS.2006.876672.CrossRefGoogle Scholar
  5. Dillon, T., Edwin, K.W., Kochs, H.D., & Taud, R.J. (1978). Integer programming approach to the problem of optimal unit commitment with probabilistic reserve determination. IEEE Transactions on Power Apparatus and Systems, PAS-97(6):2154–2166. doi: 10.1109/TPAS.1978.354719.
  6. Doorman, G., & Nygreen, B. (2003). Market price calculations in restructured electricity markets. Annals of Operations Research, 124(1–4), 49–67.CrossRefGoogle Scholar
  7. Ela, E., & O’Malley, M. (2012). Studying the variability and uncertainty impacts of variable generation at multiple timescales. IEEE Transactions on Power Systems, 27(3), 1324–1333. doi: 10.1109/TPWRS.2012.2185816.CrossRefGoogle Scholar
  8. Farahmand, H., & Doorman, G. (2012). Balancing market integration in the Northern European continent. Applied Energy, 96, 316–326. doi: 10.1016/j.apenergy.2011.11.041.CrossRefGoogle Scholar
  9. Garver, L. (1962). Power generation scheduling by integer programming-development of theory. Transactions of the American Institute of Electrical Engineers Part III Power Apparatus and Systems, 81(3), 730–734. doi: 10.1109/AIEEPAS.1962.4501405.CrossRefGoogle Scholar
  10. Gollmer, R., Nowak, M. P., Römisch, W., & Schultz, R. (2000). Unit commitment in power generation-a basic model and some extensions. Annals of Operations Research, 96(1–4), 167–189.CrossRefGoogle Scholar
  11. Heredia, F.-J., Rider, M. J., & Corchero, C. (2012). A stochastic programming model for the optimal electricity market bid problem with bilateral contracts for thermal and combined cycle units. Annals of Operations Research, 193(1), 107–127. doi: 10.1007/s10479-011-0847-x.CrossRefGoogle Scholar
  12. Hobbs, B. F., Rothkopf, M. H., O’Neill, R. P., & Chao, H.-P. (Eds.) (2001) The next generation of electric power unit commitment models. Springer Science & Business Media 36Google Scholar
  13. Jaehnert, S., & Doorman, G. L. (2012). Assessing the benefits of regulating power market integration in Northern Europe. International Journal of Electrical Power & Energy Systems, 43(1), 70–79. doi: 10.1016/j.ijepes.2012.05.010.CrossRefGoogle Scholar
  14. Jordà, Ò., & Marcellino, M. (2010). Path forecast evaluation. Journal of Applied Econometrics, 25(4), 635–662. doi: 10.1002/jae.1166.CrossRefGoogle Scholar
  15. Lindgren, E., & Söder, L. (2008). Minimizing regulation costs in multi-area systems with uncertain wind power forecasts. Wind Energy, 11(1), 97–108.CrossRefGoogle Scholar
  16. Louie, H. (2010) Evaluation of probabilistic models of wind plant power output characteristics. In Probabilistic methods applied to power systems (PMAPS), 2010 IEEE 11th international conference, Singapore: 442–447.Google Scholar
  17. Lund, H. (2007). Renewable energy strategies for sustainable development. Energy, 32(6), 912–919. doi: 10.1016/j.energy.2006.10.017. third Dubrovnik Conference on Sustainable Development of Energy, Water and Environment Systems.
  18. Morales, J., Conejo, A., & Pérez-Ruiz, J. (2009). Economic valuation of reserves in power systems with high penetration of wind power. IEEE Transactions on Power Systems, 24(2), 900–910. doi: 10.1109/TPWRS.2009.2016598.CrossRefGoogle Scholar
  19. Morales-España, G., Latorre, J., & Ramos, A. (2013). Tight and compact MILP formulation of start-up and shut-down ramping in unit commitment. IEEE Transactions on Power Systems, 28(2), 1288–1296. doi: 10.1109/TPWRS.2012.2222938.CrossRefGoogle Scholar
  20. Nowak, M. P., & Römisch, W. (2000). Stochastic lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty. Annals of Operations Research, 100(1–4), 251–272. doi: 10.1023/A:1019248506301.CrossRefGoogle Scholar
  21. Ostrowski, J., Anjos, M., & Vannelli, A. (2012). Tight mixed integer linear programming formulations for the unit commitment problem. IEEE Transactions on Power Systems, 27(1), 39–46. doi: 10.1109/TPWRS.2011.2162008.CrossRefGoogle Scholar
  22. Papavasiliou, A., & Oren, S. S. (2013). Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network. Operations Research, 61(3), 578–592. doi: 10.1287/opre.2013.1174.CrossRefGoogle Scholar
  23. Pritchard, G., Zakeri, G., & Philpott, A. (2010). A single-settlement, energy-only electric power market for unpredictable and intermittent participants. Operations Research, 58(4–Part–2), 1210–1219. doi: 10.1287/opre.1090.0800.CrossRefGoogle Scholar
  24. Sheble, G. B., & Fahd, G. N. (1994). Unit commitment literature synopsis. IEEE Transactions on Power Systems, 9(1), 128–135. doi: 10.1109/59.317549.CrossRefGoogle Scholar
  25. Takriti, S., Birge, J. R., & Long, E. (1996). A stochastic model for the unit commitment problem. IEEE Transactions on Power Systems, 11(3), 1497–1508. doi: 10.1109/59.535691.CrossRefGoogle Scholar
  26. Weber, C., Meibom, P., Barth, R., & Brand, H. (2009). WILMAR: A stochastic programming tool to analyze the large-scale integration of wind energy. In Optimization in the energy industry, chap 19, pp 437–458, Energy Systems, Springer, Berlin.Google Scholar
  27. Zheng, Q., Wang, J., Pardalos, P., & Guan, Y. (2012). A decomposition approach to the two-stage stochastic unit commitment problem. Annals of Operations Research, 210(1), 387–410. doi: 10.1007/s10479-012-1092-7.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.CORAL, Department of Economics and BusinessAarhus UniversityAarhus VDenmark
  2. 2.Department of Mathematical SciencesUniversity of CopenhagenKøbenhavn ØDenmark
  3. 3.Dansk FjernvarmeKoldingDenmark

Personalised recommendations