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Model for optimal malfunction management in extended district heating networks

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  • Guelpa, Elisa
  • Verda, Vittorio

Abstract

The increasing number of district heating systems makes the management of operations both in normal conditions and in case of undesired events (failure of a device, breakups and leakages) a more and more important issue. In this paper, a methodology for the management of malfunctions in district heating systems are proposed. The approach is based on the optimization of the water transportation in the district heating network, with the goal of providing the end users with a heat flux as close as possible to their demand despite the failure. The optimizer includes a fluid dynamic model of the network able to take into account the pressure losses along the pipelines as well as the contributions due to pumping. Two kinds of malfunctions were considered: leakages of pipelines and failure of a pumping station. Both types of malfunctions are managed using two alternative strategies, which differ on the type of independent variables that can be modified: a partial operation strategy, where only the pumping settings are modified, and a complete operation strategy, where also the production share of the different plants can be modified. An application to the Turin district heating network is presented. Results show the potentials of the proposed methodology to overcome or limit the effects of malfunctions. In particular, it is shown that the methodology, especially when complete operation strategy is used, allows an effective management of malfunctions that otherwise could create issues in the heating supply.

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  • Guelpa, Elisa & Verda, Vittorio, 2018. "Model for optimal malfunction management in extended district heating networks," Applied Energy, Elsevier, vol. 230(C), pages 519-530.
  • Handle: RePEc:eee:appene:v:230:y:2018:i:c:p:519-530
    DOI: 10.1016/j.apenergy.2018.08.024
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    8. Guelpa, Elisa & Marincioni, Ludovica & Verda, Vittorio, 2019. "Towards 4th generation district heating: Prediction of building thermal load for optimal management," Energy, Elsevier, vol. 171(C), pages 510-522.
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