Short-term balancing of supply and demand in an electricity system: forecasting and scheduling
- 262 Downloads
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.
KeywordsOR in energy Scheduling Forecasting Power system balancing
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).
- 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
- 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.
- 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
- 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
- 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.
- 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