Planning　of　an　islanded　hybrid　system　(IHS)　with　different　sources　and　storages　to　supply　clean,　flexible,　and　highly　reliable　energy　at　consumption　sites　is　of　high　importance.　To　this　end,　this　paper　presents　the　design　of　an　IHS　with　a　wind　turbine,　photovoltaic,　diesel　generator,　and　stationary　(battery)　and　mobile　(electrical　vehicles)　energy　storage　systems　(ESS).　The　proposed　method　includes　a　multi-objective　optimization　to　minimize　the　total　cost　of　construction,　maintenance,　and　operation　of　sources　and　ESSs　within　the　IHS　and　the　emission　level　of　the　system　using　two　separate　objective　functions.　The　problem　is　subject　to　operational　and　planning　constraints　of　sources　and　ESSs　and　power.　Employing　the　Pareto　optimization　technique　based　on　the　epsilon-constraint　method　forms　a　single-objective　optimization　problem　for　the　proposed　design.　The　problem　involves　uncertainties　of　load,　renewable　energy,　and　energy　demand　of　mobile　ESSs　and　has　a　nonlinear　form.　Adaptive　robust　optimization　based　on　a　hybrid　meta-heuristic　algorithm　that　utilizes　a　combination　of　the　sine-cosine　algorithm　(SCA)　and　crow　search　algorithm　(CSA)　is　proposed　to　achieve　an　optimal　robust　structure　for　the　suggested　scheme.　In　this　scheme,　operation　model　of　the　mobile　storage　systems　in　the　IHS　considering　the　uncertainties　prediction　errors　and　its　model　using　HMA-based　ARO　besides　adopting　the　HMA　to　achieve　a　unique　optimal　solution　are　among　the　novelties　of　this　research.　Eventually,　considering　the　climate　data　and　energy　consumption　of　a　region　in　Rafsanjan,　Iran,　capabilities　of　the　method　in　extracting　a　robust　IHS　for　sources　and　ESSs　are　validated　depending　on　optimal　economic　and　environmental　conditions.　The　HMA　succeeds　to　reach　an　optimal　solution　with　an　SD　of　0.92%　in　the　final　response　and　this　underlines　its　capability　in　achieving　approximate　conditions　of　unique　responsiveness.　The　proposed　scheme　with　proper　planning　and　operation　of　sources　and　storages　in　the　form　of　a　HIS　finds　optimal　values　for　economic　and　environmental　conditions　so　that　the　difference　between　pollution　and　cost　values　from　its　minimum　values　at　the　compromise　point　is　roughly　22%.　For　17%　uncertainty　parameters　prediction　errors,　the　scheme　obtains　a　robust　structure　for　the　IHS.