Wind　energy　plays　a　significant　role　in　sustainable　power　generation　in　power　systems　such　as　energy　hubs,　microgrids,　smart　grids　and　smart　cities.　On　the　other　hand,　some　challenges　such　as　wake　effects　in　wind　farms　can　lead　to　reduced　efficiency　and　increased　maintenance　costs　for　the　wind　farms.　This　paper　presents　a　cutting-edge　approach　to　tackle　these　challenges　through　the　development　of　a　novel　deep　learning-based　digital　twin　model.　The　proposed　model　integrates　advanced　deep　learning　algorithms　with　digital　twin　technology　to　accurately　simulate　and　predict　wake　effects　within　wind　farms.　By　leveraging　data　from　various　sensors　and　weather　forecasts,　the　model　can　dynamically　adjust　turbine　settings　and　optimize　energy　production　in　real-time.　Key　features　of　the　digital　twin　include　a　convolutional　neural　network　(CNN)　for　spatial　analysis　of　wake　patterns,　a　recurrent　neural　network　(RNN)　for　temporal　modelling　of　wind　behaviour,　and　a　reinforcement　learning　(RL)　framework　for　autonomous　decision-making.　Through　extensive　simulations　and　validation　against　field　data,　the　model　demonstrates　superior　performance　in　mitigating　wake　effects　and　improving　overall　wind　farm　efficiency.　This　research　contributes　to　the　advancement　of　renewable　energy　technologies　by　providing　a　robust　and　scalable　solution　for　optimizing　wind　farm　operations　and　maximizing　energy　output.　©　2025　Elsevier　Ltd