Casagrande, Vittorio;
Boem, Francesca;
(2023)
A novel learning-based MPC with embedded profiles prediction for microgrid energy management.
In:
IFAC-PapersOnLine.
(pp. pp. 7954-7959).
Elsevier
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Abstract
This paper presents a novel algorithm for microgrid energy management based on a Differentiable learning-based Model Predictive Control (MPC) for jointly optimising profiles prediction and control performance. Specifically, we propose an algorithm for the online training of a Neural Network (NN) that predicts the unknown parameters of the MPC optimisation problem during control operation. Since the training is performed online at each time step the controller adapts to possible changes in the system parameters, while avoiding the offline training phase. Differently to standard methods in the literature, the proposed NN is trained by minimising a performance-based loss, i.e. the total cost of the energy trading with the utility grid. Simulation results show that the proposed approach outperforms the traditional approach minimising an estimation-only MSE loss, both when the model parameters are perfectly known and when they are uncertain.
| Type: | Proceedings paper |
|---|---|
| Title: | A novel learning-based MPC with embedded profiles prediction for microgrid energy management |
| Event: | IFAC World Congress 2023: 22nd World Congress of the International Federation of Automatic Control |
| Location: | Yokohama, Japan |
| Dates: | 9 Jul 2023 - 14 Jul 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ifacol.2023.10.915 |
| Publisher version: | https://doi.org/10.1016/j.ifacol.2023.10.915 |
| Language: | English |
| Additional information: | © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
| Keywords: | Model predictive control, Microgrid, Energy management, Convex optimisation, Energy systems, Learn-based control, Neural-network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10176325 |
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