Long-term Power Grid Planning via Answer Set Programming

Problem
This paper addresses the gap in automated long-term power grid planning, a critical area for ensuring the sustainability and reliability of power infrastructure. The authors highlight that existing planning languages struggle to express the necessary topological and combinatorial invariants required for effective planning. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose an innovative approach utilizing Answer Set Programming (ASP) to automate and optimize the long-term planning of power grids. The core technical contribution lies in the formulation of planning problems as ASP programs, which allows for the succinct representation of complex constraints and invariants. The authors detail the encoding of various grid properties, including sustainability targets and demand patterns, into ASP. The experimental setup includes evaluations on both synthetic datasets and real-world grid data, although specific details regarding the training compute and hyperparameters are not disclosed.

Results
The experimental evaluations demonstrate the effectiveness of the ASP-based approach compared to traditional planning methods. The authors report significant improvements in planning efficiency and expressiveness, although specific quantitative metrics (e.g., execution time, solution quality) against named baselines are not provided in the abstract. The results indicate that the ASP framework can handle the complexities of long-term planning more effectively than conventional methods, suggesting a substantial advancement in the field.

Limitations
The authors acknowledge that their approach may face scalability issues when applied to very large and complex grid networks, as the computational complexity of ASP can increase with the size of the problem. Additionally, they do not address potential limitations related to the interpretability of ASP solutions or the integration of their method with existing grid management systems. The lack of detailed benchmarking against established planning frameworks is also a notable omission.

Why it matters
This work has significant implications for the future of power grid management, particularly in the context of increasing demands for sustainability and resilience in energy systems. By leveraging ASP, the authors provide a novel framework that could facilitate more adaptive and efficient planning processes, ultimately contributing to the development of smarter and more sustainable power infrastructures. The findings may inspire further research into the application of logic programming techniques in other areas of infrastructure planning and optimization.

Authors: Antonio Ielo, Francesco Doria, Sandra Castellanos-Paez, Marco Maratea, Francesco Percassi, Mauro Vallati
Source: arXiv:2605.20172v1
URL: https://arxiv.org/abs/2605.20172v1