The　increasing　proliferation　of　electric　vehicles　(EVs)　and　renewable　energy　sources　(RESs)　poses　challenges　to　the　operation　of　coupled　power　and　transportation　networks　due　to　their　uncertainties,　where　EV　users＇　routing　and　charging　behaviors　are　subjected　to　their　complex　decision-making　rationality.　To　economically　manage　numerous　fast　charging　stations　with　onsite　RESs,　this　article　focuses　on　the　optimal　day-ahead　bidding　and　intraday　scheduling　strategies　of　a　fast　charging　station　aggregator　(FCSA)　to　maximize　its　profit　in　the　electricity　market.　Traffic　simulation　based　on　boundedly　rational　dynamic　user　equilibrium　is　presented　to　model　charging　demand　under　bounded　rationality　of　EV　users.　To　efficiently　manipulate　large-scale　EVs,　a　group　charging　scheduling　framework　is　proposed　to　reduce　decision　variables.　Uncertainties　in　electricity　prices,　RES　generation,　traffic　demand,　and　user　rationality　are　addressed　by　stochastic　programming.　Case　studies　have　validated　the　effectiveness　of　the　proposed　method　in　reducing　the　FCSA＇s　operational　costs　and　RES　curtailment.