Renewable　microgrids　are　new　solutions　for　enhanced　security,　improved　reliability　and　boosted　power　quality　and　operation　in　power　systems.　By　deploying　different　sources　of　renewables　such　as　solar　panels　and　wind　units,　renewable　microgrids　can　enhance　reducing　the　greenhouse　gasses　and　improve　the　efficiency.　This　paper　proposes　a　machine　learning　based　approach　for　energy　management　in　renewable　microgrids　considering　a　reconfigurable　structure　based　on　remote　switching　of　tie　and　sectionalizing.　The　suggested　method　considers　the　advanced　support　vector　machine　for　modeling　and　estimating　the　charging　demand　of　hybrid　electric　vehicles　(HEVs).　In　order　to　mitigate　the　charging　effects　of　HEVs　on　the　system,　two　different　scenarios　are　deployed;　one　coordinated　and　the　other　one　intelligent　charging.　Due　to　the　complex　structure　of　the　problem　formulation,　a　new　modified　optimization　method　based　on　dragonfly　is　suggested.　Moreover,　a　self-adaptive　modification　is　suggested,　which　helps　the　solutions　pick　the　modification　method　that　best　fits　their　situation.　Simulation　results　on　an　IEEE　microgrid　test　system　show　its　appropriate　and　efficient　quality　in　both　scenarios.　According　to　the　prediction　results　for　the　total　charging　demand　of　the　HEVs,　the　mean　absolute　percentage　error　is　0.978,　which　is　very　low.　Moreover,　the　results　show　a　2.5%　reduction　in　the　total　operation　cost　of　the　microgrid　in　the　intelligent　charging　compared　to　the　coordinated　scheme.