基于自适应灰狼优化算法的新型电力系统输配资源调度OA

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.