针对高比例可再生能源接入下电力系统输配协同调度存在的经济性、安全性和计算效率难题，提出一种多时段优化模型及自适应改进灰狼优化(adaptive improvement grey wolf optimization, AIGWO)算法。首先，建立了综合考虑经济成本、环境代价与安全风险的三维目标函数体系，并完整计及机组爬坡、储能充放电时序约束及网络安全边界条件；在此基础上，设计了非线性自适应收敛因子动态平衡全局搜索能力、知识引导种群初始化提升解的质量、混合二进制-实数编码协同优化连续/离散变量这3项创新机制，以解决传统算法收敛效率低及离散决策空间处理缺陷。基于改进IEEE 33节点系统进行分析验证，结果表明：与标准灰狼优化算法、粒子群优化算法、混合整数规划及深度Q网络相比，AIGWO将总成本降低1.72%~5.03%，线路越限量减少89.2%~93.5%，电压越限量降低82.4%~90.3%，计算时间缩短9.32%~94.22%。该算法为高波动性源荷场景下的输配资源协同优化提供了高效解决方案。

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.