To address the economic, security, and computational efficiency challenges in transmission-distribution coordinated dispatch with high renewable energy penetration, a multi-period optimization model and an adaptive improved grey wolf optimization (AIGWO) are proposed. First, a three-dimensional objective function framework is established by comprehensively considering economic cost, environmental penalty, and security risk, with complete modeling of unit ramping constraints, energy storage charging-discharging time-coupled constraints, and network security boundary conditions. On this basis, three innovative mechanisms are designed: (1) a nonlinear adaptive convergence factor to dynamically balance global exploration capability, (2) knowledge-guided population initialization to improve solution quality, and (3) hybrid binary-real encoding to cooperatively optimize continuous and discrete variables, thereby overcoming the low convergence efficiency and poor discrete decision space processing of conventional algorithms. Experiments are performed on a modified IEEE 33-bus system for verification. Results show that compared with the standard grey wolf optimization, particle swarm optimization, mixed integer linear programming, and deep Q-network, AIGWO reduces the total cost by 1.72%~5.03%, decreases line overload violations by 89.2%~93.5%, lowers voltage violations by 82.4%~90.3%, and shortens the computation time by 9.32%~94.22%. The proposed algorithm provides an efficient solution for coordinated transmission-distribution resource optimization in high-fluctuation source-load scenarios.