Print Email Facebook Twitter Optimal Energy Scheduling of Flexible Industrial Prosumers via Reinforcement Learning Title Optimal Energy Scheduling of Flexible Industrial Prosumers via Reinforcement Learning Author van den Bovenkamp, Nick (Student TU Delft; Sunrock Investments B.V) Giraldo, Juan S. (TNO) Salazar Duque, Edgar Mauricio (Eindhoven University of Technology) Vergara Barrios, P.P. (TU Delft Intelligent Electrical Power Grids) Konstantinou, Charalambos (King Abdullah University of Science and Technology (KAUST)) Palensky, P. (TU Delft Electrical Sustainable Energy) Department Electrical Sustainable Energy Date 2023 Abstract This paper introduces an energy management system (EMS) aiming to minimize electricity operating costs using reinforcement learning (RL) with a linear function approximation. The proposed EMS uses a Q-learning with tile coding (QLTC) algorithm and is compared to a deterministic mixed-integer linear programming (MILP) with perfect forecast information. The comparison is performed using a case study on an industrial manufacturing company in the Netherlands, considering measured electricity consumption, PV generation, and wholesale electricity prices during one week of operation. The results show that the proposed EMS can adjust the prosumer's power consumption considering favorable prices. The electricity costs obtained using the QLTC algorithm are 99% close to those obtained with the MILP model. Furthermore, the results demonstrate that the QLTC model can generalize on previously learned control policies even in the case of missing data and can deploy actions 80% near to the MILP's optimal solution. Subject Q-learningtile codingenergy management systemmixed-integer linear programming To reference this document use: http://resolver.tudelft.nl/uuid:42fcf01b-29f9-462d-a834-3f0158640409 DOI https://doi.org/10.1109/PowerTech55446.2023.10202699 Publisher IEEE, Piscataway Embargo date 2024-02-09 ISBN 978-1-6654-8779-5 Source Proceedings of the 2023 IEEE Belgrade PowerTech Event 2023 IEEE Belgrade PowerTech, 2023-06-25 → 2023-06-29, Belgrade, Serbia Series 2023 IEEE Belgrade PowerTech, PowerTech 2023 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2023 Nick van den Bovenkamp, Juan S. Giraldo, Edgar Mauricio Salazar Duque, P.P. Vergara Barrios, Charalambos Konstantinou, P. Palensky Files PDF Optimal_Energy_Scheduling ... arning.pdf 2.9 MB Close viewer /islandora/object/uuid:42fcf01b-29f9-462d-a834-3f0158640409/datastream/OBJ/view