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.