Using Flexibility Information for Planning the Energy Demand of Households
Energy flexibility information describes the energy minimum and the maximum consumption profiles of a flexible load, and has been recently recognized as a significant enabler for smart energy management in the grid. In this paper we describe an energy management architecture in a residential grid that allows a flexibility operator to use the flexibility of household consumption. Among the benefits of this setting are, for instance, that the demand management can cope with higher household loads due to house heating, electric vehicle charging, and schedule the loads while respecting the total consumption limit. Another benefit of the architecture is the robustness of the system in case the communication between flexibility operator and households is disrupted. In addition to system architectural aspects, the paper describes the optimization problems in both controllers and presents numerical results from the use of a benchmark grid.
KeywordsFlexibility models Load predictive models Optimization models Energy scheduling EV charging HVAC PV generation Demand response OADR 2.0 Day-ahead pricing Setpoint following
The research leading to these results has received funding from the European Community Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 318023 for the SmartC2Net project.
- 1.Biegel, B., Andersen, P., Stoustrup, J., Hansen, L.H., Tackie, D.V.: Information modeling for direct control of distributed energy resources. In: 2013 American Control Conference (ACC), pp. 3498–3504. IEEE, June 2013Google Scholar
- 4.CEN-CENELEC-ETSI Smart Grid Coordination Group: Sustainable Processes (2012)Google Scholar
- 5.Harbo, S., Biegel, B.: Contracting flexibility services. In: 2013 4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), pp. 1–5. IEEE, October 2013Google Scholar
- 6.Pedersen, R., Sloth, C., Andresen, G.B., Wisniewski, R.: DiSC - a simulation framework for distribution system voltage control (2014)Google Scholar
- 7.Andersson, G.: Dynamics and control of electric power systems. Lecture notes, 227–0528 (2012)Google Scholar
- 8.Binding, C., Dykeman, D., Ender, N., Gantenbein, D., Mueller, F., Rumsch, W.C., Tschopp, H.: FlexLast: an IT-centric solution for balancing the electric power grid. In: IECON 2013–39th Annual Conference of the IEEE Industrial Electronics Society, pp. 4751–4755. IEEE, November 2013Google Scholar
- 10.Orda, L.D., Bach, J., Pedersen, A.B., Poulsen, B., Hansen, L.H.: Utilizing a flexibility interface for distributed energy resources through a cloud-based service. In: 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 312–317. IEEE, October 2013Google Scholar
- 11.Tus̆ar, T., Dovgan, E., Filipic, B.: Scheduling of flexible electricity production and consumption in a future energy data management system: problem formulation. In: Proceedings of the 14th International Multiconference Information Society IS 2011, pp. 96–99 (2011)Google Scholar
- 13.DeRidder, F., Hommelberg, M., Peeters, E.: Four potential business cases for demand side integration. In: Proceedings of the 6th European International Conference on Energy Market, EEM 2009, pp. 1–6, May 2009Google Scholar
- 14.SmartC2Net official webpage. http://www.SmartC2Net.eu
- 15.Gurobi Solver webpage. www.gurobi.com
- 16.Bessler, S., Drenjanac, D., Hasenleithner, E., Ahmed-Khan, S., Silva, N.: Using flexibility information for energy demand optimization in the low voltage grid. In: SmartGreens Conference, Lisbon, Portugal (2015)Google Scholar