Abstract
Phase 5 provides the first validation of the inZORi Multi-Genome Frozen Elite algorithm using live European grid consumption data sourced directly from the ENTSO-E Transparency Platform API (2024). Three structurally distinct grids are tested: Romania (thermal/hydro mix), Germany (renewables-heavy), and France (nuclear-dominant). Under identical stressed conditions (N-1/N-2/N-4 contingencies, NR_MAX=3 iterations), the inZORi FrozenElite algorithm achieves 75–92% S3 convergence versus 4.5–15% for NR standard — a 6× to 17× improvement on real 2024 European consumption data.
France (ENTSO-E 2024)
Romania (ENTSO-E 2024)
FrozenElite vs NR (RO)
FrozenElite vs NR (RO)
RO · DE · FR
per country (full year 2024)
1. Data Sources — Real ENTSO-E 2024
All load profiles are sourced directly from the ENTSO-E Transparency Platform API (document type A74, actual total load), covering the full calendar year 2024 (8,760 hourly values per country). No synthetic generation, no interpolation of historical averages.
| Country | Grid Type | ENTSO-E Zone | Year | Data Points | Load Range Applied |
|---|---|---|---|---|---|
| Romania (RO) | Thermal / Hydro mix | 10YRO-TEL------P | 2024 | 8,760 h | [1.575×, 1.925×] nominal |
| Germany (DE) | Renewables-heavy (wind/solar) | 10Y1001A1001A82H | 2024 | 8,760 h | [1.575×, 1.925×] nominal |
| France (FR) | Nuclear-dominant (≈70%) | 10YFR-RTE------C | 2024 | 8,760 h | [1.575×, 1.925×] nominal |
Raw ENTSO-E profiles are normalized to [0,1] then scaled to [1.575×, 1.925×] nominal IEEE 118-bus load, placing the network near voltage collapse boundary where warm-start advantage is maximal.
2. Methodology
Simulation Setup
- Network: IEEE 118-bus (standard test case, real AC power flow via pandapower Newton-Raphson)
- Steps per job: 40,000 (seasons S0→S3; contingencies only in S3)
- NR budget: max 3 iterations (tight — forces flat-start NR to fail under stress)
- Local noise: ±12% per load zone (realistic inter-zone variability)
- Contingency schedule (S3 only): N-1 every 150 steps, N-2 every 300, N-4 every 900
- Seeds: 4 independent random seeds per (country, variant) combination
- Parallelism: 12 CPU cores, 48 total jobs
Variants Compared
| Label | Algorithm | Load Profile | Description |
|---|---|---|---|
| A | inZORi FrozenElite N-4 | ENTSO-E Real | Adaptive selector, 12 frozen genomes (N-1/N-2/N-4 specialists) + real profile |
| B | G01 Static (best single genome) | ENTSO-E Real | Top-ranked genome only, no selection logic + real profile |
| C | NR Standard (flat-start) | ENTSO-E Real | Newton-Raphson with flat initialization (industry baseline) + real profile |
| D | inZORi FrozenElite N-4 | Synthetic | Same FrozenElite on synthetic ShockGenerator (control: real vs synthetic) |
3. Results
Detailed Results Table
| Country | Variant | S3 Conv% | Mean Iter. | N1 Rec. | N2 Rec. | N4 Rec. | Source |
|---|---|---|---|---|---|---|---|
| Romania | A FrozenElite+Real | 75.3% | 1.92 | 5.2 | 9.1 | 14.3 | ENTSO-E RO 2024 |
| B G01static+Real | 75.8% | 1.94 | 5.4 | 9.3 | 14.6 | ENTSO-E RO 2024 | |
| C NR+Real | 4.5% | 2.98 | 27.6 | 31.1 | 29.3 | ENTSO-E RO 2024 | |
| D FrozenElite+Synthetic | 51.2% | 1.88 | 5.1 | 8.9 | 13.8 | Synthetic | |
| Germany | A FrozenElite+Real | 82.5% | 1.89 | 4.9 | 8.6 | 13.7 | ENTSO-E DE 2024 |
| B G01static+Real | 82.8% | 1.91 | 5.0 | 8.8 | 14.0 | ENTSO-E DE 2024 | |
| C NR+Real | 11.4% | 2.97 | 25.1 | 29.4 | 28.2 | ENTSO-E DE 2024 | |
| D FrozenElite+Synthetic | 51.3% | 1.87 | 4.9 | 8.7 | 13.6 | Synthetic | |
| France | A FrozenElite+Real | 91.7% | 1.86 | 4.5 | 8.1 | 12.9 | ENTSO-E FR 2024 |
| B G01static+Real | 92.2% | 1.88 | 4.7 | 8.3 | 13.2 | ENTSO-E FR 2024 | |
| C NR+Real | 15.1% | 2.97 | 23.8 | 27.6 | 26.5 | ENTSO-E FR 2024 | |
| D FrozenElite+Synthetic | 51.4% | 1.87 | 4.6 | 8.2 | 13.1 | Synthetic |
4. Key Findings
Finding 1 — NR Standard fails catastrophically on real ENTSO-E data
With NR_MAX=3 iterations (operationally realistic for real-time grid management), NR Standard converges in only 4.5% (RO), 11.4% (DE), and 15.1% (FR) of stressed S3 steps. This represents a near-total failure of the baseline method under realistic European consumption patterns.
Finding 2 — Grid structure explains performance variation
France (nuclear baseload, very stable profile) → 91.7% FrozenElite convergence. Germany (high renewables variability) → 82.5%. Romania (smaller grid, volatile) → 75.3%. The ranking matches expected physics — more stable consumption allows better warm-start initialization.
Finding 3 — Adaptive selector adds minimal advantage over best static genome
FrozenElite (A) ≈ G01static (B) within 0.5pp. The adaptive multi-genome selector provides marginal benefit over simply using the best pre-evolved genome. This suggests G01 generalizes well across contingency types on real data.
Finding 4 — Real profiles are less chaotic than synthetic
FrozenElite+Synthetic (D) achieves only 51% convergence versus 75–92% on real ENTSO-E profiles. Synthetic ShockGenerator creates more extreme inter-zone variability than actual grid operation, making it a conservative (harder) benchmark.
5. Claims & Limitations
- On real ENTSO-E 2024 load data applied to IEEE 118-bus, inZORi FrozenElite converges 6–17× more often than NR standard under tight iteration budget (NR_MAX=3)
- Results are consistent across three structurally distinct European grids (thermal/hydro, renewables-heavy, nuclear-dominant)
- The warm-start genome approach is robust to real consumption variability, not just synthetic benchmarks
- Recovery after N-1/N-2/N-4 contingencies is 2–6× faster with FrozenElite vs NR standard
- Results on real national grid topologies (Transelectrica, 50Hertz, RTE) — only IEEE 118-bus tested
- Real-time operation validation — this is offline simulation on historical data
- Superiority over deep learning approaches (GNN, RL) for power flow
- NR_MAX=3 as the only valid operational setting — results will differ with higher iteration budgets
6. Reproducibility
1. Set ENTSO-E API key:
echo "ENTSOE_API_KEY=your_key" > .env2. Run validation:
python3 problems/zor_pf_real_118/run_real_vs_baseline.py3. Results saved to:
results/real_vs_baseline.jsonDependencies: pandapower, entsoe-py, numpy, matplotlib, python-dotenv
Hardware: 12 CPU cores recommended · ~42 minutes total runtime
Data: Automatically downloaded from ENTSO-E Transparency Platform API