Accelerating Quasi-Static Time Series Simulations with Foundation Models
This is an incremental approach aimed at improving the economic viability of neural solvers for power grid simulations, particularly for operators with limited resources.
The paper addresses the computational cost and convergence issues of iterative power flow solvers in quasi-static time series simulations by proposing grid foundation models to amortize training costs for neural power flow solvers, enabling broader access to AI benefits for grid operators.
Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. However, as grids expand and operate closer to their limits, iterative power flow solvers, central to QSTS simulations, become computationally prohibitive and face increasing convergence issues. Neural power flow solvers provide a promising alternative, speeding up power flow computations by 3 to 4 orders of magnitude, though they are costly to train. In this paper, we envision how recently introduced grid foundation models could improve the economic viability of neural power flow solvers. Conceptually, these models amortize training costs by serving as a foundation for a range of grid operation and planning tasks beyond power flow solving, with only minimal fine-tuning required. We call for collaboration between the AI and power grid communities to develop and open-source these models, enabling all operators, even those with limited resources, to benefit from AI without building solutions from scratch.