TY  - GEN
A1  - Casagrande, V
A1  - Ferianc, M
A1  - Rodrigues, M
A1  - Boem, F
PB  - Elsevier BV
Y1  - 2024///
N2  - 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.
AV  - public
EP  - 561
SN  - 2405-8963
N1  - © 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/)
ID  - discovery10197014
TI  - Learning-based MPC with uncertainty estimation for resilient microgrid energy management
KW  - Energy Management Systems
KW  -  Microgrid
KW  -  Model Predictive Control
KW  -  Online
Learning
KW  -  Uncertainty Estimation
SP  - 556
UR  - https://doi.org/10.1016/j.ifacol.2024.07.277
ER  -