Energies 2026, 19(14), 3230; https://doi.org/10.3390/en19143230 (registering DOI) - 8 Jul 2026
Abstract
Institutional governance has traditionally been analyzed under the assumption that the space of potential violations is finite, enumerable, and progressively constrainable through rule refinement and calibrated enforcement. The rapid integration of large language models into strategic and documentary decision-making challenges this premise by
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Institutional governance has traditionally been analyzed under the assumption that the space of potential violations is finite, enumerable, and progressively constrainable through rule refinement and calibrated enforcement. The rapid integration of large language models into strategic and documentary decision-making challenges this premise by transforming feasible deviation spaces from bounded sets into generative manifolds. This paper develops a formal simulation framework for examining institutional stability under algorithmically amplified strategic exploration. Regulatory rules are modeled as a constraint manifold characterized by effective dimensionality, while generative systems expand the behavioral strategy space through semantic recombination under detection and sanction constraints. Stability is defined through a minimum deterrence margin evaluated across the generatively reachable domain rather than only through historical violation catalogs. The study uses a 2014–2023 regulatory and violation corpus to initialize and calibrate the simulation and to conduct a limited historical hold-out check; the 250,000 LLM-generated scenarios are treated as synthetic stress-test proposals rather than observed violations. The computational specification reports the generator checkpoint, embedding model, decoding parameters, prompt templates, random seeds, filtering rules, and label partitions used in the simulation. The model introduces a dimensional dominance principle: systemic vulnerability may emerge in the simulation when the effective dimensionality of generative strategic search expands faster than the independent constraint dimensionality of the rule system. Under the reported baseline setting, the synthetic simulations show a pipeline-specific dimensional crossover, convergence limits in rule-consistency classification, and a nonlinear detection–sanction response surface. These outputs are interpreted as diagnostics of the stated computational pipeline, not as universal empirical laws about real institutions. The power-grid component is delimited accordingly: the paper does not simulate physical grid operation, power flow, dispatch, or relay-protection dynamics; it interprets the model at the documentary governance layer of power-grid enterprises, including procurement, construction supervision, maintenance records, dispatch-related documentation, customer-service reporting, and internal audit. The framework therefore provides a reproducible and cautiously delimited basis for analyzing text-mediated institutional resilience in the age of generative intelligence.
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(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)
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