Flexpower – testing a market design

Denmark | June 2010 - December 2013

In Denmark, central power plants have traditionally been the primary providers of regulating power, i.e. increases or decreases in electricity production with short notice. Expanding the share of electricity generation from intermittent sources (i.e. wind power) is anticipated to result in an increased demand for regulating power. In addition, as a greater portion of electricity production comes from intermittent sources, less production will come from central plants, thus further increasing the need for regulating power from new sources.

The Flexpower project has shown that regulating power consumption via a price signal can meet a portion of this growing demand for regulating power.

Utilising small-scale resources

The central idea behind FlexPower is to use 5-minute electricity prices to shift electricity usage from times with high prices, to times with lower prices, and thereby provide regulating power via an aggregated response from numerous units on a volunteer basis.

The project objective is to develop and test a real-time market for regulating power that will attract a large number of small-scale resources (demand and distributed energy resources) to the regulating power market.

It is fundamental that the market should co-exist with the current market structure, be technologically neutral, and be simple and straightforward for the end-user.

Predictable and reliable demand response

A field-test with electric heating and bottle coolers demonstrated that a price signal based communication system can produce a predictable and reliable demand response.

In the test, price signals were sent to SmartBoxes coupled to electricity-consuming devices, such as a refrigerators or heating units, and the boxes adjusted the temperature in accordance with pre-established consumer comfort settings and whether the electricity price was high or low.

Via the implementation of improvements related to control strategies, and the inclusion of various price, heat demand, and weather forecasts, it is concluded that a price signal based demand response system could provide a new source of reliable regulating power.

Summary report

For more on the market design, the field test, and tools for improved performance, please see the FlexPower summary report:

The project team included Ea Energy Analysis (coordinator), the Technical University of Denmark (DTU), Enfor, Actua, Eurisco, EC Power, SEAS‐NVE and NEAS Energy.

The project ran from June 2010 to December 2013.


  • Bacher, P., Madsen, H., & Nielsen, H. A. (2013). Modelling and forecasting heat load for single family houses. Technical University of Denmark, Department of Applied Mathematics and Computer Science. Lyngby: DTU Compute.
  • Bang, N. C., Fock, F., & Togeby, M. (2011). Design of a real time market for regulating power. Copenhagen: Ea Energy Analyses.
  • Bang, N. C., Fock, F., & Togeby, M. (2012). The existing Nordic regulating power market. Copenhagen: Ea Energy Analyses.
  • Bang, N. C., Fock, F., & Togeby, M. (2011). Development of market design with focus on demand side participation. Paper presented by Christian Bang at Risø International Energy Conference, 10-12 May 2011.
  • Bang, N. C., Hay, C., Togeby, M., Søndergren, C., & Hansen, L. H. (2010). Introducing electric vehicles into the current electricity markets. Copenhagen: EDISON.
  • Bang, N. C., Togeby, M., & Brus, R. (2013). Field test – Electric heating and bottle coolers. Copenhagen: Ea Energy Analyses.
  • Corradi, O., Ochsenfeld, H., Madsen, H., & Pinson, P. (2013, February). Controlling Electricity Consumption by Forecasting its Response to Varying Prices. IEEE Transactions on Power Systems, 28(1), 421-429.
  • Delikaraoglou, S., & Ding, Y. (2012). Development of simultaneous energy and reserve dispatch model and corresponding pricing mechanism. Lyngby: Centre for Electric Technology – Technical University of Denmark.
  • Dorini, G. F., Corradi, O., Ochsenfeld, H., Nielsen, H. A., & Madsen, H. (2013). FlexPower – Work Package 2. Informatics and Mathematical Modelling, Technical University of Denmark and ENFOR A/S.
  • Dorini, G., Pinson, P., & Madsen, H. (2012, October). Chance-constrained optimization of demand response to price signals reports. IEEE Transactions on Smart Grid, 1-9.
  • Ea Energy Analyses (2013): Activating electricity demands as regulation power. FlexPower – testing a market design proposal.
  • Ebert, B. (2013 a). Model and Algorithms for Electric Vehicle Charging. Århus: Actua.
  • Ebert, B. (2013 b). Simulation results. Århus: Actua.
  • ENFOR. (2011). Forecast requirements for house temperature control with flexible energy prices. Hørsholm: ENFOR A/S.
  • ENFOR. (2013 a). Simulation of 5 minute prices based on actual 1 hour data. Hørsholm: ENFOR A/S. Doc. ID: 08EKS0004A003-A.c
  • ENFOR. (2013 b). Modelling the Danish Real-Time Electricity Market. Hørsholm: ENFOR A/S. Doc. ID: 08EKS0004A002-A.d
  • ENFOR. (2013 c). Forecasts of actual imbalance unit costs and simulated 5 minute prices for the two Danish Nordpool Spot price areas. Hørsholm: ENFOR A/S. Doc. ID 08EKS0004A004-A
  • ENFOR. (2013 d). PRESS web-service API. Hørsholm: ENFOR A/S. Doc. ID 08EKS0004A005-A
  • ENFOR. (2013 e). FlexPower WP5 summary. Hørsholm: ENFOR A/S. Doc. ID 08EKS0004A006-A
  • EURISCO. (2012). FlexPrice – The definition: Definition of FlexPrice, the container for Control-by-price price signals. Odense: EURISCO.
  • EURISCO. (2013 a). Interface specification (D7.1) and Information Exchange specifications (D7.2). Odense: EURISCO.
  • EURISCO. (2013 b). D7.3 – Concept design report. Odense: EURISCO.
    Li, Y., Zhang, C., Ding, Y., & Østergaard, J. (2013). New organisations in electricity market. Lyngby: Danish Technical University.
  • Nielsen, D. L., Zimmermann, J. K., Rasmussen, C. B., & Pedersen, T. H. (2013). FlexPower WP9 Documentation. Electrical Engineering. Lygnby: DTU.
  • Nyeng, P., & Østergaard, J. (2011). Information and communications systems for control-by-price of distributed energy resources and flexible demand. Lynby: IEEE Transactions on Smart Grid.
  • Nørgaard, P., Sossan, F., & Nielsen, H. (2011, June 6-9). Indirect regulation of many DER units through broadcasted dynamic price signal. CIRED, pp. 1-4.
  • O’Connell, N.; P. Pinson; H. Madsen; M. O’Malley (2013): Benefits and Challenges of Demand Response: A Critical Review. Lyngby: Department for Mathematics and Computer Science, Technical University of Denmark.
  • Rasmussen, C. B., & Petersen, P. F. (2013). WP9 – Field test – Industrial and various on/off devices. Lygnby: DTU.
  • Sossan, F. (2013). Identification of the flexibility and control strategies for indirect con-trolled flexible demand. Lyngby: Center for Electric Power and Energy – DTU Elektro.
  • Sossan, F., & Bindner, H. (2012 a, December 10-13). Evaluation of the performance of indirect control of many DSRs using hardware-in-the-loop simulations. IEEE, pp. 5596-5591.
  • Sossan, F., & Bindner, H. (2012 b). A comparison of algorithms for controlling DSRs in a control be price context using hardware-in-the-loop simulations. IEEE.
  • Sossan, F., Kosek, A. M., Martinenas, S., Marinelli, M., & Bindner, H. (2013). Scheduling of Domestic Water Heat Power Demand for Maximizing PV Self-Consumption Using Model Predictive Control. Roskilde: Center for Electric Power and Energy – Technical University of Denmark.
  • Sossan, F., Marinelli, M., Costanzo, G. T., & Bindner, H. (n.d.). Indirect control of DSRs for regulating power provision and solving local congestions.