Data-driven distribution network expansion planning for hosting new green investments

As distribution networks transition to active systems, Distribution System Operators (DSOs) require advanced planning tools to delay or avoid costly infrastructure investments by procuring operational flexibility from controllable loads or generators. This paper proposes a modular framework for multi-year distribution network planning, developed and enhanced within the context of the SYNERGIES, OPENTUNITY, and ODEON Horizon Europe projects. The methodology integrates an efficient Mixed-Integer Linear Programming (MILP) optimization engine with high-performance computing modules, including a Fixed-Point Iteration (FPI) power flow engine, pre-trained AI analytics for forecasting, and Dynamic Time Warping