A Comprehensive Framework for the Deployment of AI-Based and Data-Driven Supporting Tools in Generation Expansion Planning

Generation Expansion Planning (GEP) now must contend with high penetrations of variable renewable energy, deep electrification, and ambitious decarbonization targets, rendering traditional deterministic and static modelling approaches increasingly inadequate. AI-based and data-driven supporting tools offer a promising pathway to enhance GEP by improving input data quality, capturing system variability, and streamlining the optimization process. However, their diversity in maturity, data needs, and integration complexity creates a significant adoption challenge for planning utilities.

This paper proposes a novel, structured framework to assess the deployment readiness of AI and data-driven tools and prioritize their implementation based on a specific power system's context. The assessment framework involves: (1) functional capabilities inventory, (2) requirements profiling, (3) a multi-criteria assessment matrix adapting Technology Readiness

Level (TRL) and Application-specific Adoption Readiness Level (ARL*) frameworks to the planning domain, (4) a scoring system, and (5) a deployment prioritization guide to translate assessment scores into actionable adoption roadmaps. This decision-support framework evaluates AI capabilities through the lens of Information System readiness by addressing data governance and workflow-integration barriers. The Jordan case study demonstrates the framework’s value by identifying the high-readiness capabilities, such as dimensionality reduction and explainable diagnostics, as 'Quick Wins' that allow utilities to extract value from planning data within a six-month deployment horizon.