A Review of Power Grid Frameworks for Planning Under Uncertainty

Power-system planning is being reshaped by rapid decarbonisation, electrification, and digitalisation, which collectively amplify uncertainty in demand, generation, technology adoption, and policy pathways. This review critically synthesises three principal optimisation paradigms used to plan under uncertainty in power systems: scenario-based stochastic optimisation, set-based robust optimisation (including adaptive and distributionally robust variants), and minimax-regret decision models. The review is positioned to address a recurrent limitation of many uncertainty-planning surveys, namely the separation between “method reviews” and “technology reviews”, and the consequent lack of decision-operational guidance for planners and system operators. The central contribution is a decision-centric framework that operationalises method selection through two explicit dimensions. The first is an information posture, which formalises what uncertainty information is credible and usable in practice (probabilistic, set-based, or probability-free scenario representations). The second is a flexibility posture, which formalises the availability, controllability, and timing of operational recourse enabled by smart-grid technologies. These postures are connected to modelling templates, data requirements, tractability implications, and validation/stress-testing needs. Smart-grid technologies are integrated not as an appended catalogue but as explicit sources of recourse that change the economics of uncertainty and, in turn, shift the relative attractiveness of stochastic, robust, and regret-based planning. Soft Open Points, Coordinated Voltage Control, and Vehicle-to-Grid/Vehicle-to-Building are treated uniformly under this recourse lens, highlighting how device capabilities, control timescales, and implementation constraints map into each paradigm. The paper also increases methodological transparency by describing literature-search, screening, and inclusion principles consistent with a structured narrative review. Practical guidance is provided on modelling choices, uncertainty governance, computational scalability, and institutional adoption constraints, alongside revised comparative tables that embed data credibility, regulatory interpretability, and implementation maturity.