A comprehensive review of modern transmission expansion planning

Transmission Expansion Planning (TEP) has undergone a profound transformation over the past four decades, evolving from a deterministic, cost-driven task into a multi-dimensional optimization challenge that addresses uncertainty, renewable integration, resilience, and policy coordination. Early studies focused on meeting growing demand at minimum cost using fixed forecasts and static system assumptions. The rise of renewable energy, market deregulation, and grid digitalization has introduced new complexity, requiring advanced modeling and data-intensive methods. The objective of this paper is to summarize, classify, and critically evaluate recent advances in TEP modeling, highlighting how stochastic, robust, hybrid, and data-driven methods manage uncertainties in load growth, renewable generation, and system contingencies. It also examines the incorporation of resilience, reliability, environmental constraints, and policy considerations into planning frameworks. This review discusses the evolution of TEP methodologies, from traditional cost-minimization models to stochastic, robust, and hybrid frameworks that account for variability in load, generation, and contingencies. It highlights the growing role of data-driven and intelligent approaches, including machine learning and digital twin technologies, in enabling predictive planning, scenario reduction, and real-time adaptability. The integration of reliability and resilience metrics alongside environmental, policy, and market constraints is also discussed, with emphasis on multi-objective optimization for balanced, sustainable planning decisions. Finally, the review identifies current challenges, including high-dimensional uncertainty, correlated renewable outputs, and computational scalability. It concludes that future TEP frameworks must be hybrid, stochastic–robust, and resilience-oriented, combining advanced computational techniques with cross-disciplinary insights to design transmission networks that are economically efficient, environmentally sustainable, and operationally robust in high-renewable power systems.