Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)
نویسندگان
چکیده
To meet the target of carbon neutrality by year 2050 and decrease dependence on fossil fuels, renewable energy sources (RESs), specifically wind power, Electric Vehicles (EVs) have to be massively deployed. Nevertheless, integration a large amount with an intermittent nature, into grid variability load demand side require efficient reliable management system (EMS) for operation, scheduling, maintenance trading in modern power system. This article proposes new Energy Management Protocol (EMP) based combination Machine Learning (ML) Game-Theoretic (GT) algorithms manage operation charging/discharging EVs from storage (ESS) via EV supply equipment (EVSE) when main source is power. The ESS can linked overcome downtimes production. Case study results forecasting using ML algorithm 10 min measurements, combined GT optimization model, showed good performance dispatching between ensure accurate
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ژورنال
عنوان ژورنال: Energies
سال: 2023
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en16052179