Developments　in　the　Internet　of　Things　and　cyber–physical　systems　have　enabled　intelligent　solutions　to　alleviate　peak　loads,　and　transmission　and　storage　concerns　in　MicroGrids　(MGs)　through　the　deployment　of　Electrical　Vehicles　(EVs),　as　well　as　provide　a　platform　for　EV　energy　exchange.　Nonetheless,　EV　participation　depends　upon　the　willingness　of　the　owners,　who　can　enter　and　leave　the　market　in　a　random　fashion　with　multiple　demands.　Clever　participants　may　be　tempted　to　misrepresent　their　information　to　achieve　a　greater　personal　reward.　Also,　utilizing　location　information　from　users　will　make　them　susceptible　to　privacy　leakage,　but　is　necessary　for　the　appropriate　allocation　of　services.　To　resolve　these　challenges,　we　propose　a　Location　Privacy-preserving　Online　(LoPrO)　scheme,　which　can　allocate　electricity　and　charging　stations　in　MicroGrids　to　EVs　when　the　energy　supply　is　limited.　In　our　scheme,　the　optimal　decision　of　winner　determination　and　the　electricity　and　charging　station　allocations　are　made　by　the　auctioneer,　without　prior　knowledge　of　EV　arrival　and　departure,　and　EV　location　information　privacy　is　differentially　guaranteed　by　leveraging　the　Laplace　mechanism.　Through　theoretical　analysis,　we　prove　that　LoPrO　scheme　achieves　the　properties　of　incentive　compatibility,　individual　rationality,　and　-differential　privacy.　The　results　of　our　experimental　evaluation　also　demonstrate　that　LoPrO　achieves　better　performance　with　respect　to　EV　utility,　buyer　satisfaction　ratio,　electricity　allocation　efficiency　and　EV　State-of-Charge　(SoC),　in　comparison　with　existing　schemes.　In　terms　of　privacy　preservation,　LoPrO　is　capable　of　protecting　EV　location　information　with　low　probability　of　leakage,　and　computation　overhead　is　reasonable.　©　2019　Elsevier　B.V.