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Regression Monte Carlo for microgrid management

انحدار مونت كارلو لإدارة الشبكات الصغيرة

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Preprint , Journal 01 Jan 2019Embargo end date: 01 Jan 2018Publisher:EDP SciencesJournal:ESAIM: Proceedings and Surveys, volume 65, pages 46-67 (eissn: 2267-3059,

Authors: Clémence Alasseur; Alessandro Balata; Sahar Ben Aziza; Aditya Maheshwari; Peter Tankov; Xavier Warin;

doi: 10.1051/proc/201965046 , 10.60692/4njtf-jwg30 , 10.48550/arxiv.1802.10352 , 10.60692/sjj6z-ptg42

arXiv: http://arxiv.org/abs/1802.10352 , 1802.10352

Regression Monte Carlo for microgrid management

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Abstract

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.

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University of California System
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École Nationale de la Statistique et de l'Administration Économique
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Tunis El Manar University
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National Engineering School of Tunis
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University of Leeds
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Keywords

Renewable energy, Microgrid, FOS: Mechanical engineering, Reliability engineering, Electric power system, Engineering, Microgrid Control, Energy management system, Mathematics - Optimization and Control, Battery (electricity), Photovoltaic system, T57-57.97, Applied mathematics. Quantitative methods, Physics, Mathematical optimization, Statistics, Energy management, Power (physics), 93E24, 90B05, 93E20, Mechanical engineering, Monte Carlo method, Physical Sciences, Control and Synchronization in Microgrid Systems, Diesel generator, Electricity Price and Load Forecasting Methods, Distributed Power Generation, Geometry, Automotive engineering, Quantum mechanics, Blackout, QA1-939, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Demand Response in Smart Grids, Electrical and Electronic Engineering, Constraint (computer-aided design), Grid, Power management, Load Forecasting, Computer science, Load Control, Optimization and Control (math.OC), Control and Systems Engineering, Electrical engineering, Wind power, Energy (signal processing), Short-Term Forecasting, Diesel fuel, Mathematics

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