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

Abstract

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.

Keywords

• machine learning
• game-theoretic model
• wind energy management system
• electric vehicles