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arxiv: 2605.20172 · v1 · pith:NSZP6LF7new · submitted 2026-05-19 · 💻 cs.LO · cs.AI

Long-term Power Grid Planning via Answer Set Programming

Antonio Ielo , Francesco Doria , Sandra Castellanos-Paez , Marco Maratea , Francesco Percassi , Mauro Vallati This is my paper

Pith reviewed 2026-05-20 02:58 UTC · model grok-4.3

classification 💻 cs.LO cs.AI
keywords power grid planninganswer set programmingASPlong-term planningtopological invariantscombinatorial invariantsnetwork optimizationautomation
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The pith

Answer Set Programming automates and optimizes long-term power grid planning by encoding topological and combinatorial invariants that are hard to express in other languages.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how Answer Set Programming can be used to automate the process of planning changes to electrical grids over many years. Planners must adapt networks for new sustainability goals, shifting demand, and city growth while keeping power flowing and obeying rules about how the network can be connected and combined. Standard planning tools make these rules difficult to write down, but Answer Set Programming lets them be stated briefly and clearly. Tests on both artificial examples and actual grid data confirm that the method produces workable plans and handles the required complexity.

Core claim

The authors propose the first approach to automate and optimise the long-term power grid planning process using Answer Set Programming. The kind of properties and invariants needed for planning developments that span over a decade are cumbersome to express in conventional planning languages, but they can be elegantly and succinctly encoded in ASP, with experimental evaluations on synthetic and real-world grid data confirming the expressive power and effectiveness of the approach.

What carries the argument

Answer Set Programming (ASP) used to encode the topological and combinatorial invariants that must hold during long-term grid adaptations.

Load-bearing premise

Topological and combinatorial invariants required for long-term power grid planning are cumbersome to express in conventional planning languages yet can be elegantly and succinctly encoded in Answer Set Programming.

What would settle it

A real-world grid instance from the paper's test set where the ASP encoding produces no valid plan that preserves supply continuity over the full horizon, or where computation exceeds practical time limits while a feasible manual plan exists.

Figures

Figures reproduced from arXiv: 2605.20172 by Antonio Ielo, Francesco Doria, Francesco Percassi, Marco Maratea, Mauro Vallati, Sandra Castellanos-Paez.

Figure 1
Figure 1. Figure 1: Medium-voltage distribution network supplied by two primary substations (black squares) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example graph configurations illustrating the role of constraints. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) A point (i,t) indicates that i instances are solved in t seconds. (b) A point (x,y) denotes a synthetic instance solved in y seconds by exponential and in x by bounded. Points below the bisector indicate faster exponential search. The weak constraint r52 minimizes the total number of actions2 , while the weak constraint r53 favours shorter plans. 5 Experimental Analysis The empirical evaluation aims to… view at source ↗
Figure 4
Figure 4. Figure 4: Parallel plans runtime comparison across graph sizes. Red bar is the median run￾time. fixing the number of primary nodes to |P| = 2. This size matches what experts deal with in daily operations. For each size, we sampled five random compliant power grids. Starting from each grid G, we constructed a sequential plan by applying valid random actions that preserve compliance, leading to GT . In doing so, we av… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes the first approach to automate and optimize long-term power grid planning using Answer Set Programming (ASP). It argues that topological and combinatorial invariants required for maintaining supply continuity over decade-long developments are cumbersome to express in conventional planning languages but can be elegantly and succinctly encoded in ASP. The manuscript presents an ASP encoding for the problem and reports experimental evaluations on synthetic and real-world grid data that are said to confirm the expressive power of the ASP-based approach and demonstrate its effectiveness.

Significance. If the central claims hold, the work would demonstrate a viable application of ASP to a critical real-world infrastructure optimization task involving complex invariants over long time horizons. The use of experiments on real-world data provides some grounding for practical relevance. However, the absence of any quantitative comparison to alternative formalisms weakens the case for ASP's specific advantages in expressiveness or succinctness.

major comments (2)
  1. [Abstract and §1] Abstract and §1: The central motivation asserts that the required topological and combinatorial invariants 'are cumbersome to express in such languages' (conventional planning languages) yet 'can be elegantly and succinctly encoded in Answer Set Programming'. No side-by-side comparison of encoding size, rule count, or readability is supplied against an equivalent formulation in PDDL, MiniZinc, or MILP. Without such measurable evidence, the claims of superior expressive power and elegance rest solely on qualitative judgment and do not yet support the choice of ASP over established optimization approaches.
  2. [Experimental evaluation section] Experimental evaluation section (referenced in abstract): The abstract states that 'experiments on synthetic and real data confirm effectiveness' and 'confirm the expressive power', yet the provided description supplies no details on the concrete ASP encoding used, solver performance metrics (e.g., runtime, scalability), how invariants were verified, or baseline comparisons. This absence makes it difficult to assess whether the reported results actually substantiate the effectiveness claim.
minor comments (1)
  1. Ensure that all experimental results include explicit tables or figures reporting encoding sizes, solver times, and solution quality metrics for both synthetic and real-world instances.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and indicate the changes planned for the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract and §1] Abstract and §1: The central motivation asserts that the required topological and combinatorial invariants 'are cumbersome to express in such languages' (conventional planning languages) yet 'can be elegantly and succinctly encoded in Answer Set Programming'. No side-by-side comparison of encoding size, rule count, or readability is supplied against an equivalent formulation in PDDL, MiniZinc, or MILP. Without such measurable evidence, the claims of superior expressive power and elegance rest solely on qualitative judgment and do not yet support the choice of ASP over established optimization approaches.

    Authors: We agree that a quantitative comparison would strengthen the motivation. The manuscript's primary aim is to present the first ASP encoding for this problem rather than a comparative study; the presented rules illustrate the natural encoding of the required invariants. In the revision we will add a short discussion (new paragraph in §1) that contrasts the ASP encoding with an equivalent MILP formulation, reporting the number of constraints and variables needed and noting the additional auxiliary variables required in MILP to capture the same combinatorial reachability invariants. revision: yes

  2. Referee: [Experimental evaluation section] Experimental evaluation section (referenced in abstract): The abstract states that 'experiments on synthetic and real data confirm effectiveness' and 'confirm the expressive power', yet the provided description supplies no details on the concrete ASP encoding used, solver performance metrics (e.g., runtime, scalability), how invariants were verified, or baseline comparisons. This absence makes it difficult to assess whether the reported results actually substantiate the effectiveness claim.

    Authors: We acknowledge that the experimental section would benefit from greater explicitness. The full manuscript already contains the ASP encoding (Section 3) and reports runtime and scalability figures (Section 5). We will expand the experimental evaluation to include: (i) the exact number of rules and atoms in the encoding, (ii) tabulated solver runtimes and memory usage across all synthetic and real-world instances, (iii) a description of the post-processing verification that each solution satisfies the topological invariants, and (iv) a simple greedy baseline for comparison. revision: yes

Circularity Check

0 steps flagged

No circularity: ASP encoding applied to grid planning without self-referential reduction

full rationale

The paper motivates its contribution by asserting that topological and combinatorial invariants for long-term power grid planning are cumbersome in conventional planning languages yet elegantly encoded in ASP, then presents an ASP encoding and reports experimental results on synthetic and real-world grids. This assertion functions as domain motivation rather than a derived claim that reduces to its own inputs by construction. No equations, fitted parameters renamed as predictions, self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling appear in the abstract or described content. The experimental evaluations supply independent evidence of effectiveness, rendering the work self-contained as an application of established ASP techniques to a new domain without circular derivation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper applies standard Answer Set Programming to encode domain constraints without introducing new free parameters or invented entities; the contribution lies in the specific modeling of power-grid invariants rather than new foundational elements.

axioms (1)
  • domain assumption Answer Set Programming is capable of succinctly expressing complex topological and combinatorial constraints that are cumbersome in other planning languages.
    Invoked in the abstract to justify the choice of ASP over traditional planning languages.

pith-pipeline@v0.9.0 · 5692 in / 1183 out tokens · 41538 ms · 2026-05-20T02:58:12.687293+00:00 · methodology

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