inZORi PF-Δ Phase 1 — Streaming Power Flow Robustness

Abstract

We demonstrate inZORi's adaptive capacity in a challenging streaming power flow scenario under extreme load/generation shocks, limited iteration budget (nr_max=8), periodic state subsampling (pf_interval=5), and sudden topology changes (N-1 line outages). Traditional fixed-strategy baselines suffer significant convergence degradation under the severe_plus regime (S3 convergence ~84–86%). The inZORi Frozen-Elite-16 runtime extension — offline-evolved genomes deployed at O(1) cost — achieves S3 ≈ 100%, a +15.2 percentage point improvement over Baseline B and +3.1 pp over Baseline A. Recovery times after N-1 events are ~7× faster than the periodic-reset baseline. All results are validated across 30 seeds with CI95 confidence intervals.

99.1%

inZORi S3 convergence
CI95 [98.9%, 99.3%]

83.9%

Baseline B S3 convergence
(periodic reset)

+15.2pp

Advantage vs Baseline B
CI95 non-overlap confirmed

7×

Faster N-1 recovery
vs periodic-reset baseline

~100%

Frozen-Elite-16 runtime
S3 convergence

Seeds validated
50,000 steps each

1. Problem and Motivation

The PFΔ benchmark evaluates AI systems in realistic power grid operation scenarios where conditions drift continuously and solvers must adapt in real time. Unlike static benchmarks, PFΔ introduces:

Temporal dynamics: seasonal drift in base loads/generation over tens of thousands of steps
Regional shocks: sudden multi-zone load spikes simulating cascading events
Compute constraints: strict iteration budgets (nr_max=8) and periodic PF subsampling to mimic real-time SCADA refresh rates
Topology changes: N-1 line outages that invalidate prior warm-start solutions

Real-world grid operators cannot afford unlimited Newton-Raphson iterations per PF solve. When network conditions degrade (weak diagonal dominance, voltage collapse regimes), the solver either diverges, exhausts the budget, or requires manual intervention. This phase targets autonomous stability with zero online learning.

inZORi Core Principles

Principle	Description
No RL/NN/Backpropagation	No gradient-based learning, no neural networks, no reinforcement learning
Energy & Memory	Organisms gain/lose energy based on PF solve success; thresholds trigger reproduction/death
Genome-Driven Parameters	4-float genome (`risk`, `memory_lr`, `jump_chance`, `step_scale`) maps to PF parameters deterministically
Natural Selection	Only top-energy organisms reproduce; mutations are small Gaussian perturbations
World-Level PF Evaluation	Top-K candidates evaluated per step (not per organism) — 40–50× speedup vs naive implementation

2. Experimental Setup

Simulation Seasons

Season	Steps (FULL)	Conditions	N-1 events
S0 (Spring)	0–12,499	Mild load variation	None
S1 (Summer)	12,500–24,999	Elevated loads, regional shocks	None
S2 (Autumn)	25,000–37,499	Peak stress + occasional shocks	None
S3 (Winter)	37,500–49,999	Severe stress + N-1 topology shocks	Every 900 steps

Methods Compared

Method	Strategy	S3 Convergence
Baseline A	Warm-start tracking (always reuses previous solution)	95.9%
Baseline B	Periodic reset (flat start every 500 steps)	83.9%
inZORi FULL	Online evolutionary genome selection (K=2 top-K per step)	99.1%
Frozen-Elite-16	Pre-evolved pool, O(1) contextual selector, no online evolution	~100%

3. Results

Fig. 1 — S3 convergence rate comparison with CI95 error bars. inZORi Frozen Elite achieves ~100%, inZORi FULL 99.1%, vs 95.9% (Baseline A) and 83.9% (Baseline B). Frozen Elite outperforms all alternatives with zero online computation.

Fig. 2 — Convergence rate over simulation time (S0→S3). inZORi maintains stability through all seasons; Baseline B degrades sharply in S3 when N-1 shocks begin.

Fig. 3 — Distribution of N-1 recovery times. inZORi recovers in 1–2 steps; Baseline B requires 7–15 steps. The ~7× speedup comes from genome-encoded jump_chance triggering DC-init fallback.

Fig. 4 — Genome fitness progression across evolutionary evaluations. The best genome pool stabilizes by evaluation 4–5 and is then frozen for runtime deployment.

Fig. 5 — Convergence rate per season across evaluations. S3 (winter/stress) shows the largest improvement as genomes specialize for high-shock conditions.

Fig. 6 — N-1 shock event timeline showing load/topology perturbations in S3. Each spike corresponds to a line outage that invalidates the prior warm-start solution.

4. The Frozen Elite Strategy

The key innovation of this phase is the Frozen-Elite-16 runtime approach: genomes are evolved offline once, then deployed as a fixed lookup pool at runtime. The contextual selector chooses among 16 pre-computed genomes based on:

Season-awareness: Aggressive genomes preferred in S0/S1; conservative in S3
Convergence feedback: If recent window shows drops, switch to more stable genome
N-1 detection: On topology shock, switch to genome with high jump_chance (DC-init retry) — automatic flat-restart when warm-start becomes invalid

This approach has direct operational implications: no GPU, no neural network inference, no online gradient computation — just a lookup table evaluated in microseconds per step.

5. Claims & Limitations

CLAIMS: inZORi Frozen Elite achieves statistically significant (+15.2pp CI95 non-overlap) convergence improvement over static baselines in the severe_plus regime (multi-zone shocks + N-1)
CLAIMS: N-1 recovery is ~7× faster due to DC-init fallback encoded in genome jump_chance parameter — no explicit topology rule required
CLAIMS: Results validated across 30 independent seeds with CI95 non-overlapping intervals
DOES NOT CLAIM: This phase uses a nonlinear surrogate model, not a full AC power flow engine — see Phase 2 for real pandapower validation
DOES NOT CLAIM: Generalization to larger networks or real TSO topologies without further experiments
DOES NOT CLAIM: Equivalence to commercial EMS/OPF solvers

6. Reproducibility

Run Phase 1:

# Prerequisites
pip install pandapower numpy scipy matplotlib

# FULL mode (30 seeds × 50k steps, ~4h)
cd inzori/
python3 -m problems.zor_pf_real_118.run_full

# DEV mode (rapid validation, 5k steps)
python3 -m problems.zor_pf_real_118.run_dev

# Frozen Elite runtime demo
python3 -m problems.zor_pf_real_118.frozen_elite_demo
      

Key parameters: nr_max=8 · pf_interval=5 (DEV) / =1 (FULL) · N-1 every 900 steps (S3) · K=2 top-K world-level evaluation

inZORi PF-Δ Phase 1: Streaming Power Flow Robustness