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Uncertainty modelling of an industry facility as a multi-energy demand response provider

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  • Kostelac, Matija
  • Pavić, Ivan
  • Zhang, Ning
  • Capuder, Tomislav

Abstract

While the latest European energy regulations emphasise the active power system participation of the household level end-users, the large industrial facilities are still not fully exploiting all the market opportunities to decrease their costs and become more competitive. Significant cost reduction can be achieved by offering flexibility services in the electricity market. This is especially valid in the case when the industrial consumers are multi-energy hubs where shifting and optimising usage of input energy vectors creates additional opportunities. Research gaps were identified and a price responsive demand response model for a multi-energy industry facility under uncertainty was developed. The uncertainty aspects are modelled both by the robust optimisation and by the two-stage stochastic optimisation. Additionally, we develop a linear energy flow-based model of an industrial steam system which better encompasses losses and makes the model more realistic. The model is validated on a real-world case of a multi-energy industry facility and the results indicate that cost savings of up to 18 % can be achieved compared to the passive and deterministic, mass flow-based business-as-usual behaviour.

Suggested Citation

  • Kostelac, Matija & Pavić, Ivan & Zhang, Ning & Capuder, Tomislav, 2022. "Uncertainty modelling of an industry facility as a multi-energy demand response provider," Applied Energy, Elsevier, vol. 307(C).
  • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921014744
    DOI: 10.1016/j.apenergy.2021.118215
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    References listed on IDEAS

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    3. Oliver Gregor Gorbach & Jessica Thomsen, 2022. "Comparing the Energy System of a Facility with Uncertainty about Future Internal Carbon Prices and Energy Carrier Costs Using Deterministic Optimisation and Two-Stage Stochastic Programming," Energies, MDPI, vol. 15(10), pages 1-39, May.
    4. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).

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