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Holistic modelling and optimisation of thermal load forecasting, heat generation and plant dispatch for a district heating network

  • Efficient operation of district heating networks requires a precise forecasting of the thermal loads and an optimised dispatch strategy for the available generation and storage portfolio. This paper presents a holistic modelling and optimisation approach: first, detailed process modelling and optimisation of power plants and thermal storages; second, a numerical model for dispatch optimisation; and third, machine-learning-based load forecasting. The work is based on operating data from the district heating network of the city of Ulm in Germany. The paper presents the modelling, validation and simulation results of stationary and instationary process simulation for a biomass-fired combined heat and power plant. The analysis identifies a potential to integrate additional renewable power by “power-to-heat” technologies into different parts of the process. The economic benefit is quantified by mixed-integer linear programming optimisation applied to the district heating network. In order to allow for real-time dispatch optimisation, aEfficient operation of district heating networks requires a precise forecasting of the thermal loads and an optimised dispatch strategy for the available generation and storage portfolio. This paper presents a holistic modelling and optimisation approach: first, detailed process modelling and optimisation of power plants and thermal storages; second, a numerical model for dispatch optimisation; and third, machine-learning-based load forecasting. The work is based on operating data from the district heating network of the city of Ulm in Germany. The paper presents the modelling, validation and simulation results of stationary and instationary process simulation for a biomass-fired combined heat and power plant. The analysis identifies a potential to integrate additional renewable power by “power-to-heat” technologies into different parts of the process. The economic benefit is quantified by mixed-integer linear programming optimisation applied to the district heating network. In order to allow for real-time dispatch optimisation, a machine-learning-based thermal load forecasting method was developed and evaluated, based on a 72-h forecast horizon. In addition, the economic impact of prediction uncertainties is analysed with the numerical dispatch optimisation tool.show moreshow less

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Metadaten
Author:Matthias FinkenrathORCiDGND, Till Faber, Fabian Behrens, Stefan LeiprechtORCiD
DOI:https://doi.org/10.1016/j.energy.2022.123666
Identifier:0360-5442 OPAC HS OPAC extern
Identifier:1164-0235 OPAC HS OPAC extern
Parent Title (English):Energy
Publisher:Elsevier Ltd.
Place of publication:Amsterdam
Document Type:Article
Language:English
Year of first Publication:2022
Tag:Dispatch optimisation; District Heating; Load forecasting; Machine Learning; Process simulation; deepDHC
Volume:250 (2022)
Article Number:123666
Number of pages:12 Seiten
First Page:1
Last Page:12
Institutes:Fakultät Maschinenbau
IEAT - Institut für Energie- und Antriebstechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Open Access:open_access
Research focus:FSP1: Energie
Publication Lists:Finkenrath, Matthias
Behrens, Fabian
Leiprecht, Stefan
Research Project:deepDHC (Deep Learning for Distinct Heating and Cooling: Entwicklung modernster maschineller Lernverfahren für die hochgenaue Fernwärmelastprognose)
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2022/04/12
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