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Stochastic nonlinear modelling and application of price-based energy flexibility

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  • Junker, Rune Grønborg
  • Kallesøe, Carsten Skovmose
  • Real, Jaume Palmer
  • Howard, Bianca
  • Lopes, Rui Amaral
  • Madsen, Henrik

Abstract

If CO2-emissions are to be reduced, the shares of renewable energy sources will have to be significantly increased. However, energy flexibility is required to cope with the increased share of renewable energy. Utilising it necessitates mathematical models of the operational response of energy flexible consumers. In this paper we present an accurate and general dynamic model of energy flexibility based on stochastic differential equations. The intuitive interpretation of the parameters is explained, to show the generality of the proposed model. To validate the approach, the parameters are estimated for three water towers and three buildings controlled by economic model predictive controllers. The model is then used to offer the energy flexibility on the current electricity market of Scandinavia, Nord Pool, using the so called “flexi orders”. Finally, the energy flexibility is used by controlling the demand of the water towers indirectly, through price signals designed based on the proposed model. Compared to having perfect foresight of electricity prices and future demand, between 63% and 98% of the potential savings were obtained in for these case studies. This shows that even without direct control of energy flexible systems, most of the potential can be reached under the current market conditions.

Suggested Citation

  • Junker, Rune Grønborg & Kallesøe, Carsten Skovmose & Real, Jaume Palmer & Howard, Bianca & Lopes, Rui Amaral & Madsen, Henrik, 2020. "Stochastic nonlinear modelling and application of price-based energy flexibility," Applied Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:appene:v:275:y:2020:i:c:s0306261920306085
    DOI: 10.1016/j.apenergy.2020.115096
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    5. Schledorn, Amos & Junker, Rune Grønborg & Guericke, Daniela & Madsen, Henrik & Dominković, Dominik Franjo, 2022. "Frigg: Soft-linking energy system and demand response models," Applied Energy, Elsevier, vol. 317(C).
    6. Schledorn, Amos & Charousset-Brignol, Sandrine & Junker, Rune Grønborg & Guericke, Daniela & Madsen, Henrik & Dominković, Dominik Franjo, 2024. "Frigg 2.0: Integrating price-based demand response into large-scale energy system analysis," Applied Energy, Elsevier, vol. 364(C).

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