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A Network Flow Model for Price-Responsive Control of Deferrable Load Profiles

Author

Listed:
  • Juliano Camargo

    (Energy Department, Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, B-2400 Mol, Belgium
    Energy Department, EnergyVille, Thor Park, Poort Genk 8130, 3600 Genk, Belgium)

  • Fred Spiessens

    (Energy Department, Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, B-2400 Mol, Belgium
    Energy Department, EnergyVille, Thor Park, Poort Genk 8130, 3600 Genk, Belgium)

  • Chris Hermans

    (Energy Department, Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, B-2400 Mol, Belgium
    Energy Department, EnergyVille, Thor Park, Poort Genk 8130, 3600 Genk, Belgium)

Abstract

This paper describes a minimum cost network flow model for the aggregated control of deferrable load profiles. The load aggregator responds to indicative energy price information and uses this model to formulate and submit a flexibility bid to a high-resolution real-time balancing market, as proposed by the SmartNet project. This bid represents the possibility of the cluster of deferrable loads to deviate from the scheduled consumption, in case the bid is accepted. When formulating this bid, the model is able to take into account the discretized power profiles of the individual loads. The solution of this type of aggregation problems is necessary for the participation of small loads in demand response programs, but scalability can be an issue. The minimum cost network flow problem belongs to a restricted class of discrete optimization problems for which efficient and scalable algorithms exist. Thanks to its scalability, this technique can be useful in the control of a large number of smart appliances in future real-time balancing markets. The technique is efficient enough to be employed by an aggregation module with limited computational resources. Alternatively, when indicative price information is not made available by the system operator, the technique can be combined with an external forecast in order to minimize possible imbalance costs.

Suggested Citation

  • Juliano Camargo & Fred Spiessens & Chris Hermans, 2018. "A Network Flow Model for Price-Responsive Control of Deferrable Load Profiles," Energies, MDPI, vol. 11(3), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:613-:d:135560
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    References listed on IDEAS

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    1. Ottesen, Stig Odegaard & Tomasgard, Asgeir, 2015. "A stochastic model for scheduling energy flexibility in buildings," Energy, Elsevier, vol. 88(C), pages 364-376.
    2. Bushnell, James & Hobbs, Benjamin F. & Wolak, Frank A., 2009. "When It Comes to Demand Response, Is FERC Its Own Worst Enemy?," The Electricity Journal, Elsevier, vol. 22(8), pages 9-18, October.
    3. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
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    Cited by:

    1. Thomas I. Strasser & Sebastian Rohjans & Graeme M. Burt, 2019. "Methods and Concepts for Designing and Validating Smart Grid Systems," Energies, MDPI, vol. 12(10), pages 1-5, May.

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