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Learning-based MPC with uncertainty estimation for resilient microgrid energy management

Casagrande, V; Ferianc, M; Rodrigues, M; Boem, F; (2024) Learning-based MPC with uncertainty estimation for resilient microgrid energy management. In: IFAC-PapersOnLine. (pp. pp. 556-561). Elsevier BV Green open access

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Abstract

To enhance fault resilience in microgrid systems at the energy management level, this paper introduces a novel proactive scheduling algorithm, based on uncertainty modelling thanks to a specifically designed neural network. The algorithm is trained and deployed online and it estimates uncertainties in predicting future load demands and other relevant profiles. We integrate the novel learning algorithm with a stochastic model predictive control, enabling the microgrid to store sufficient energy to adaptively deal with possible faults. Experimental results show that a reliable estimation of the unknown profiles' mean and variance is obtained, improving the robustness of proactive scheduling strategies against uncertainties.

Type: Proceedings paper
Title: Learning-based MPC with uncertainty estimation for resilient microgrid energy management
Event: 19th IFAC World Congress
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ifacol.2024.07.277
Publisher version: https://doi.org/10.1016/j.ifacol.2024.07.277
Language: English
Additional information: © 2024 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: Energy Management Systems, Microgrid, Model Predictive Control, Online Learning, Uncertainty Estimation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10197014
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