| Issue |
ESAIM: ProcS
Volume 65, 2019
CEMRACS 2017 - Numerical methods for stochastic models: control, uncertainty quantification, mean-field
|
|
|---|---|---|
| Page(s) | 46 - 67 | |
| DOI | https://doi.org/10.1051/proc/201965046 | |
| Published online | 02 April 2019 | |
Regression Monte Carlo for microgrid management
1
EDF R&D - FIME, Palaiseau, France
e-mail: clemence.alasseur@edf.fr
2
University of Leeds, Woodhouse Lane, Leeds LS2 9JT, United Kingdom
e-mail: A.Balata@leeds.ac.uk
3
University of Tunis El Manar,ENIT-LAMSIN, BP.37, Le Belvédère 1002 Tunis, Tunisia
e-mail: sahar.benaziza@enit.utm.tn
4
University of California, Santa Barbara, USA
e-mail: aditya_maheshwari@umail.ucsb.edu
5
CREST-ENSAE, Palaiseau, France
e-mail: peter.tankov@ensae.fr
6
EDF R&D - FIME, Palaiseau, France
e-mail: xavier.warin@edf.fr
We study an islanded microgrid system designed to supply a small village with the power produced by photovoltaic panels, wind turbines and a diesel generator. A battery storage system device is used to shift power from times of high renewable production to times of high demand. We build on the mathematical model introduced in [14] and optimize the diesel consumption under a “no-blackout” constraint. We introduce a methodology to solve microgrid management problem using different variants of Regression Monte Carlo algorithms and use numerical simulations to infer results about the optimal design of the grid.
© EDP Sciences, SMAI 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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