Casagrande, V;
Boem, F;
(2022)
A distributed scenario-based stochastic MPC for fault-tolerant microgrid energy management.
In:
IFAC-PapersOnLine.
(pp. pp. 704-709).
Elsevier
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Abstract
This paper proposes a fault-tolerant energy management algorithm for microgrid systems composed of several agents. The method stems from the necessity to design an algorithm that takes explicitly into account the possibility of faults and their consequences to avoid solutions which are excessively conservative. A tree of possible fault scenarios is built in a completely distributed way by all the agents of the network; then the resulting optimization problem is solved through a distributed algorithm which not only does not require a high computational power for each agent, but keeps also private all local data and decision variables. The effectiveness of the proposed method is proved through simulation results.
| Type: | Proceedings paper |
|---|---|
| Title: | A distributed scenario-based stochastic MPC for fault-tolerant microgrid energy management |
| Event: | 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ifacol.2022.07.210 |
| Publisher version: | https://doi.org/10.1016/j.ifacol.2022.07.210 |
| Language: | English |
| Additional information: | © The Author 2022. Original content in this paper is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Energy management system, Fault-tolerant control, Microgrid, Stochastic MPC, Distributed optimization |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10155675 |
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