TY - JOUR A1 - Finkenrath, Matthias A1 - Faber, Till A1 - Behrens, Fabian A1 - Leiprecht, Stefan T1 - Holistic modelling and optimisation of thermal load forecasting, heat generation and plant dispatch for a district heating network T2 - Energy N2 - 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, 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. KW - Machine Learning KW - deepDHC KW - Process simulation KW - District Heating KW - Load forecasting KW - Dispatch optimisation Y1 - 2022 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/1229 SN - 0360-5442 SN - 1164-0235 VL - 250 (2022) SP - 1 EP - 12 PB - Elsevier Ltd. CY - Amsterdam ER -