GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis
GENCO (GEometric Neural Corrective Optimizer) is a unified neural solver for steady-state transmission grid analysis that handles power flow (PF), optimal power flow (OPF), and state estimation (SE) within a single architecture and shared network representation. The open-source GridFM Development Framework standardizes synthetic data generation and training in a low-code environment. Large-scale datasets with millions of PF and OPF scenarios across diverse grid topologies are released. GENCO is evaluated on PFDelta and OPFData benchmarks against state-of-the-art neural solvers and classical solvers (Newton-Raphson, IPOPT), and on real-world Hydro-Québec SCADA data. For large-scale PF, GENCO recovers the full AC operating state, including voltage magnitudes and reactive power that DC-PF cannot provide, while matching DC-PF-level speed.
Researchers introduce GENCO, a unified neural solver for steady-state transmission grid analysis, capable of handling power flow, optimal power flow, and state estimation within a single architecture. The accompanying open-source GridFM Development Framework provides a low-code environment for synthetic data generation and training. Large-scale benchmark datasets with millions of scenarios are released. GENCO demonstrates competitive performance against classical and neural solvers on standard benchmarks and real-world Hydro-Québec SCADA data, recovering full AC states with speed comparable to DC-PF.
GENCO employs a geometric neural corrective optimizer architecture that enforces physical consistency across multiple grid analysis tasks. By sharing a network representation, it unifies PF, OPF, and SE, potentially reducing the need for task-specific models. The framework's low-code synthetic data generation may accelerate experimentation and reproducibility in neural power system solvers.
The introduction of a unified neural solver and a standardized development framework could lower barriers for adopting AI in power system operations. Utilities and grid operators may benefit from faster, more comprehensive steady-state analysis, especially where full AC solutions are needed. The release of large-scale datasets may spur further research and commercial tool development.
GENCO and the GridFM Framework could reduce computational costs and time for grid analysis, enabling more frequent and detailed operational planning. This may lead to improved grid reliability, better integration of renewables, and new software products for the energy sector.
Next signals include independent benchmarking on additional real-world grids, integration with existing energy management systems, and extensions to dynamic or transient stability analysis. Adoption by grid operators and contributions to the open-source framework will indicate practical viability.