Optimization techniques for renewable-integrated active distribution networks: A comprehensive review

As the penetration of renewable energy sources (RESs) and distributed generations (DGs) continues to increase, the operational complexity of active distribution networks (ADNs) has grown significantly, making multi-objective dispatch optimization essential for achieving economic, reliable, and low-carbon operation. However, existing reviews mainly focus on the classification of optimization algorithms and models, while lacking a systematic synthesis of optimization modeling, solution approaches, and their interrelationships, as well as critical analysis of their engineering applicability. This review establishes a unified analytical framework covering optimization modeling, physical constraints, uncertainty modeling, and optimization approaches, and systematically analyzes representative optimization methods in terms of model characteristics, solver–model compatibility, and engineering applicability. Based on this analysis, the key challenges and future research directions are further discussed. The results indicate that ADN dispatch optimization is evolving from deterministic optimization toward hybrid frameworks integrating multi-objective optimization, uncertainty modeling, and intelligent optimization techniques. Nevertheless, model scalability, uncertainty handling, and engineering deployment remain the major challenges for future research. This review provides a systematic basis for understanding the relationships among modeling assumptions, solution methodologies, and practical requirements for the development of intelligent and scalable dispatch strategies in ADNs.