The increasing integration of renewable energy resources and active distribution networks has significantly increased the complexity of optimal power flow (OPF) problems in integrated transmission-distribution (T&D) power systems. To address these challenges, this paper proposes a novel tri-swarm adaptive hybrid optimizer (TAHO) that integrates particle swarm optimization (PSO), grey wolf optimizer (GWO), and jellyfish search (JS) within a unified adaptive optimization framework. The proposed method effectively balances exploration and exploitation to improve convergence stability and optimization accuracy. A multi-objective OPF model is developed to minimize generation cost, power loss, and voltage deviation under operational constraints. Experimental results on integrated IEEE 30-bus and IEEE 33-bus systems demonstrate that the proposed TAHO achieves superior performance with the minimum fitness value of 0.0008, faster convergence within 75 iterations, and the lowest standard deviation of 0.0005 compared with PSO, GWO, and JS. Benchmark evaluations further confirm the robustness and strong global search capability of the proposed framework for renewable-integrated smart grid optimization and real-time OPF applications.

The integration of renewable energy sources into modern power systems introduces stability and resilience challenges due to their intermittent and stochastic behavior. To address these issues, this study proposes an artificial intelligence (AI)-driven statistical complex network (AI-SCN) framework for stability assessments in renewable-integrated power grids. The framework models the grid as a weighted complex network, where the nodes represent generation, storage, and load units, and the edges capture electrical and statistical dependencies. By integrating network topology metrics with data-driven AI models, AI-SCN enables accurate stability margin estimation and resilience quantification under varying renewable penetration levels. Simulations on the Institute of Electrical and Electronics Engineers (IEEE) 39-bus and IEEE 118-bus systems show that AI-SCN outperforms conventional and long short-term memory (LSTM)-based approaches, achieving root mean square error (RMSE) values of 0.0185 and 0.0219, respectively, representing improvements of 40.7% and 58.9%. Furthermore, recovery time is reduced from 12.8 s to 8.4 s, demonstrating the system's enhanced recovery efficiency. These results confirm that AI-SCN offers a scalable and adaptive framework for improving stability and resilience in renewable-dominant power systems.