Balancing Energy Flexibilities Through Aggregation
One of the main goals of recent developments in the Smart Grid area is to increase the use of renewable energy sources. These sources are characterized by energy fluctuations that might lead to energy imbalances and congestions in the electricity grid. Exploiting inherent flexibilities, which exist in both energy production and consumption, is the key to solving these problems. Flexibilities can be expressed as flex-offers, which due to their high number need to be aggregated to reduce the complexity of energy scheduling. In this paper, we discuss balance aggregation techniques that already during aggregation aim at balancing flexibilities in production and consumption to reduce the probability of congestions and reduce the complexity of scheduling. We present results of our extensive experiments.
KeywordsEnergy data management Energy flexibility Flex-offers Balance aggregation
This work was supported in part by the TotalFlex project sponsored by the ForskEL program of Energinet.dk.
- 1.Totalflex project. http://www.totalflex.dk/
- 2.Bach, B., Wilhelmer, D., Palensky, P.: Smart buildings, smart cities and governing innovation in the new millennium. In: 8th IEEE International Conference on Industrial Informatics (INDIN), pp. 8–14 (2010)Google Scholar
- 3.Boehm, M., Dannecker, L., Doms, A., Dovgan, E., Filipic, B., Fischer, U., Lehner, W., Pedersen, T.B., Pitarch, Y., Šikšnys, L., Tušar, T.: Data management in the mirabel smart grid system. In: Proceedings of EnDM (2012)Google Scholar
- 4.European Wind Energy Association: Creating the internal energy market in Europe. Technical report (2012). http://www.ewea.org/uploads/tx_err/Internal_energy_market.pdf
- 5.Hermanns, H., Wiechmann, H.: Future design challenges for electric energy supply. In: IEEE Conference on Emerging Technologies Factory Automation, pp. 1–8 (2009)Google Scholar
- 7.Kaulakienė, D., Šikšnys, L., Pitarch, Y.: Towards the automated extraction of flexibilities from electricity time series. In: Proceedings of the Joint EDBT/ICDT 2013 Workshops, pp. 267–272. ACM (2013)Google Scholar
- 9.Kupzog, F., Roesener, C.: A closer look on load management. In: 5th IEEE International Conference on Industrial Informatics, vol. 2, pp. 1151–1156 (2007)Google Scholar
- 16.Srinivasan, D., Chazelas, J.: A priority list-based evolutionary algorithm to solve large scale unit commitment problem. In: International Conference on Power System Technology, vol. 2, pp. 1746–1751 (2004)Google Scholar
- 17.Tušar, T., Dovgan, E., Filipic, B.: Evolutionary scheduling of flexible offers for balancing electricity supply and demand. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2012)Google Scholar
- 18.Tušar, T., Šikšnys, L., Pedersen, T.B., Dovgan, E., Filipič, B.: Using aggregation to improve the scheduling of flexible energy offers. In: International Conference on Bioinspired Optimization Methods and their Applications, pp. 347–358 (2012)Google Scholar