Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)

To meet the target of carbon neutrality by the year 2050 and decrease the dependence on fossil fuels, renewable energy sources (RESs), specifically wind power, and Electric Vehicles (EVs) have to be massively deployed. Nevertheless, the integration of a large amount of wind power, with an intermitte...

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Bibliographic Details
Published inEnergies (Basel) Vol. 16; no. 5; p. 2179
Main Authors Khabbouchi, Imed, Said, Dhaou, Oukaira, Aziz, Mellal, Idir, Khoukhi, Lyes
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2023
Subjects
Air quality management
Algorithms
Alternative energy sources
Electric vehicles
Energy management
Energy management systems
Energy sources
Energy storage
Energy use
Environmental aspects
Forecasting
Game theory
game-theoretic model
Green technology
Industrial plant emissions
Investigations
Learning algorithms
Machine learning
Methods
Optimization models
Optimization techniques
Renewable energy sources
wind energy management system
Wind measurement
Wind power
Tunisia
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Summary:To meet the target of carbon neutrality by the year 2050 and decrease the dependence on fossil fuels, renewable energy sources (RESs), specifically wind power, and Electric Vehicles (EVs) have to be massively deployed. Nevertheless, the integration of a large amount of wind power, with an intermittent nature, into the grid and the variability of the load on the demand side require an efficient and reliable energy management system (EMS) for operation, scheduling, maintenance and energy trading in the modern power system. This article proposes a new Energy Management Protocol (EMP) based on the combination of Machine Learning (ML) and Game-Theoretic (GT) algorithms to manage the operation of the charging/discharging of EVs from an energy storage system (ESS) via EV supply equipment (EVSE) when the main source of energy is wind power. The ESS can be linked to the grid to overcome downtimes of wind power production. Case study results of wind power forecasting using an ML algorithm and 10 min wind measurements, combined with a GT optimization model, showed good performance in the forecasting and management of power dispatching between EVs to ensure the efficient and accurate operation of the power system.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16052179