Frode Lie-Jensen
Faculty of technology, Arts & Design, Department of Mechanical, Electronic & Chemical engineering, OsloMet, Norway
Andreas Aannø
Faculty of technology, Arts & Design, Department of Mechanical, Electronic & Chemical engineering, OsloMet, Norway
Elena Aleksandrova
Faculty of technology, Arts & Design, Department of Mechanical, Electronic & Chemical engineering, OsloMet, Norway
Anders Westli
Faculty of technology, Arts & Design, Department of Mechanical, Electronic & Chemical engineering, OsloMet, Norway
Morten Nielsen
Fortum Oslo Varme AS, Norway
Tiina Komulainen
Faculty of technology, Arts & Design, Department of Mechanical, Electronic & Chemical engineering, OsloMet, Norway
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http://dx.doi.org/10.3384/ecp1815343Published in: Proceedings of The 59th Conference on Simulation and Modelling (SIMS 59), 26-28 September 2018, Oslo Metropolitan University, Norway
Linköping Electronic Conference Proceedings 153:7, p. 43-50
Published: 2018-11-19
ISBN: 978-91-7685-494-5
ISSN: 1650-3686 (print), 1650-3740 (online)
District heating system (DHS) is a widely used and increasingly popular energy source in cities. The uncertainty in the heat load (HL) due to customer demand fluctuations makes unit commitment (UC) and heat production unit (HPU) control a complex task. This case study of the DHS at Fortum Oslo Varme AS (FOV) aims to find a strategy to optimize and fully automate UC and HPU. Our results suggests this can be accomplished by using model predictive control (MPC) to control HPU power and flow rate, mixed integer linear programming (MILP) optimization to solve UC problem, and multiple linear regression (MLR) model to predict the HL. We also show that the fuel cost can be reduced significantly.
district heating, model predictive control, system identification, unit commitment problem, heat load prediction
