Modern energy systems operate as large-scale, distributed, cyber-physical networks with continuous-dynamics behavior, multi-agent interactions, and strict safety and stability requirements. Grid operators must coordinate generation, transmission, distribution, storage, and consumption across heterogeneous assets with varying reliability, latency, and environmental conditions. However, existing energy automation frameworks exhibit nondeterministic behavior due to asynchronous sensing, probabilistic forecasting, timing instability, and multi-agent disagreement. This nondeterminism undermines grid stability, reproducibility, and operational trust. I introduce Lume-Energy, a deterministic governance substrate for energy systems. Built on the DAIGS foundation, Lume-Energy integrates the invariant-preserving safety layer of Lume-V with the multi-agent arbitration and collective cognition capabilities of Lume-X. Lume-Energy defines power-flow invariants, grid-stability envelopes, real-time arbitration rules, deterministic override mechanisms, and certificate-based operational truth records tailored to energy environments. I formalize the Lume-Energy architecture, define its continuous-dynamics operational semantics, and present constructive proofs demonstrating invariant preservation, envelope enforcement, deterministic override correctness, multi-agent convergence, and replay-identical execution. I evaluate Lume-Energy in a simulated grid environment with load spikes, renewable intermittency, sensor drift, cyber-physical noise, and multi-agent conflict. Results show that Lume-Energy enforces deterministic grid stability, maintains certificate-chain integrity, and ensures reproducible outcomes even under degraded or adversarial conditions.
Ronald Jason Andrews (Thu,) studied this question.