Long-term Power Grid Planning via Answer Set Programming

2026-05-19

AI Summary

This study addresses the challenges of long-term power network planning, which must simultaneously account for topological and combinatorial invariances, sustainable development goals, growing electricity demand, and dynamic adjustments driven by urbanization. To this end, it introduces Answer Set Programming (ASP) into the domain for the first time, proposing an automated modeling and optimization framework. The approach enables concise yet precise representation of complex constraints and long-term invariants, overcoming the limited expressiveness of conventional planning languages. Experimental evaluation on both synthetic and real-world power grid data demonstrates the method’s superior modeling expressivity and computational efficiency, effectively supporting high-quality decision-making for grid evolution over horizons exceeding ten years.

Abstract

The Power grid is a critical infrastructure underpinning all aspects of modern society and its services. Maintaining its effectiveness requires continuous adaptations. In particular, addressing sustainability targets, demand patterns, and urbanisation trends requires implementing changes to the network. Actual developments can potentially span over a decade, with supply continuity and service quality that must be preserved throughout by ensuring conformance to several topological and combinatorial invariants. Long-term power grid planning deals with the above process, and although planning languages could be a natural choice, the kind of properties and invariants needed are cumbersome to express in such languages; on the contrary, they can be elegantly and succinctly encoded in Answer Set Programming (ASP). In this paper, we propose the first approach to automate and optimise the long-term power grid planning process using ASP. Experimental evaluations conducted on synthetic and real-world grid data confirm the expressive power of the proposed ASP-based approach and demonstrate its effectiveness.

Problem

• Long-term Power Grid Planning
• Sustainability Targets
• Urbanisation Trends
• Topological Invariants
• Combinatorial Constraints

Innovation

• Answer Set Programming
• long-term power grid planning
• combinatorial invariants
• topological constraints
• automated planning