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

  • Decarbonisation of district heating networks requires a heat generation and storage portfolio that allows for maximised integration of renewable energies. However, it is also essential to have a precise forecast of the thermal loads to be expected in the network and – based on this forecast – an optimised dispatch strategy in order to best match the available generation and storage portfolio with the actual heat demands in the grid. This paper presents a holistic approach that combines modelling and optimisation activities related to these three aspects: 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. This work, which was performed as part of the publicly funded research projects “KWKflex” and “deepDHC”, was 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 a stationary andDecarbonisation of district heating networks requires a heat generation and storage portfolio that allows for maximised integration of renewable energies. However, it is also essential to have a precise forecast of the thermal loads to be expected in the network and – based on this forecast – an optimised dispatch strategy in order to best match the available generation and storage portfolio with the actual heat demands in the grid. This paper presents a holistic approach that combines modelling and optimisation activities related to these three aspects: 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. This work, which was performed as part of the publicly funded research projects “KWKflex” and “deepDHC”, was 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 a stationary and instationary process simulation for a 58 MW thermal biomass-fired combined heat and power plant. The analysis identifies a potential to integrate additional renewable power of up to 17 MW thermal power by “power-to-heat” technologies into different parts of the process. The economic benefit is quantified with a mixed-integer linear programming dispatch optimisation model of the district heating network. In order to allow for real-time optimisation of the power plant and thermal energy storage dispatch, a machine-learning-based thermal load forecasting method was developed. The performance of different machine learning algorithms, including decision trees and deep learning techniques, is compared based on a 72-hour forecast horizon. In addition, the economic impact of uncertainties in thermal load prediction 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
Identifier:978-1-7138-4398-6 OPAC HS OPAC extern
Parent Title (English):34th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2021): Taormina, Italy, 27 June-2 July 2021
Publisher:Curran Associates, Inc.
Place of publication:Red Hook
Document Type:conference proceeding (article)
Conference:34th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, June 27 - July 2, 2021, Taomina, Italy
Language:English
Year of first Publication:2021
Tag:Fernwärme; KWK-Flex; Lastprognose; Maschinelles Lernen; Prozessimulation; deepDHC
Article Number:114
Number of pages:11 Seiten
Institutes:Fakultät Maschinenbau
IEAT - Institut für Energie- und Antriebstechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
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)
KWK-Flex – Hochflexible stromgeführte Kraft-Wärme-Kopplung durch thermische Speicher und "Power-to-Heat"-Technologien
Publication reviewed:begutachtet
Release Date:2021/09/30
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