Berlin Grid Real-Time Simulation Framework A Scalable Architecture for Probabilistic Edge Control

Author: Clifford Ondieki
Purpose: Demonstrate scalable real-time edge control (Redispatch 3.0) and grid hosting-capacity analytics using real German grid data.
Compliant targets: VDE-AR-N 4110, §14a EnWG.

🚀 Key Features

This repository implements a streaming digital twin with real-time validation:

Core Capabilities

• Real-Time Streaming Architecture – Streaming microkernel with <10 µs P99 Jitter, enabling deterministic control for hard real-time requirements.
• Hardware Benchmarking – High-throughput vectorization sustained at ~30 Million ops/sec on standard x86 hardware.
• Convergent Control Logic – Ablation study proving the Fuzzy Logic controller converges to 97% of optimal efficiency under heavy congestion while eliminating binary oscillation.
• Physics-Integrated Stochasticity – Monte Carlo simulations (n=50) propagating AR(1) correlated uncertainty through the AC Power Flow solver to verify voltage stability.
• Data-Agnostic Design – Decoupled architecture robust to topological scope mismatches.

Technical Stack

• Typed Python modules with Pydantic data models
• Pandapower for AC network simulation (Warm-Start Newton-Raphson)
• Fuzzy (sigmoid) smoothing control algorithm
• CI/CD pipeline (pytest, mypy, Docker)
• Comprehensive documentation (METHODOLOGY.md)

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⏱️ Real-Time Performance

The StreamingDigitalTwin class validates edge-readiness via tick-by-tick simulation:

# Measured on x86_64 Linux (Single Core)
Avg Latency: 4.35 µs
P99 Jitter:  9.94 µs (Deterministic)
Throughput:  ~230,000 Ops/Sec (Streaming Mode)

✅ Conclusion: The architecture exceeds 50Hz grid cycle requirements (<20ms) by three orders of magnitude, making it deployment-ready for ARM gateways (e.g., Raspberry Pi).

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📈 Controller Convergence (Ablation Study)

A sensitivity sweep compares the Fuzzy Logic controller against a standard Hard Cutoff baseline across congestion severities.

Scenario	Limit	Behavior	Efficiency Gap
Light Congestion	28.0 MW	Proactive: Dampens voltage ripple	+550 MWh (Stability Premium)
Heavy Congestion	22.0 MW	Convergent: Enforces physical limits	+34.5 MWh (3% Gap)

Key Insight: In deep congestion, the Fuzzy Controller automatically tightens to achieve 97% of the theoretical optimal efficiency of a binary relay, while retaining the benefits of smooth, differentiable control action.

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⚡ Multi-Constraint Validation

Pandapower AC power flow runs continuously in the loop to verify constraints:

Solver: Newton-Raphson (Warm-Start Optimized)
✓ Voltage Stability: Monitored continuously (0.90–1.10 p.u.)
✓ Thermal Loading: Transformers and lines checked dynamically at every tick.
✓ Stability: Smooth control response verified under dynamic load conditions.

Note: Initial validation against open-source upstream telemetry revealed a topological scope mismatch ($\rho &lt; 0.37$). Consequently, this framework focuses on architectural robustness and internal consistency rather than calibrating to mismatched open datasets.

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🎲 Stochastic Robustness

Monte Carlo simulation (n=50) with documented uncertainty models:

• PV Generation Error: Auto-Regressive AR(1) with high persistence ($\phi=0.95$).
• EV Charging Variability: Random arrival ($\phi=0.10$) for stochastic plug-in times.
• Result: Voltage confidence intervals (95% CI) remain bounded within safety margins despite correlated perturbations.

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Quick Start

1. Clone repository:

git clone [https://github.com/omari91/berlin-grid-project.git](https://github.com/omari91/berlin-grid-project.git)
cd berlin-grid-project

2. Create virtual environment & install:

python3 -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt

3. Add data: Create a data/ folder and add the required Energienetze Berlin CSVs (file names specified in main.py).

4. Run simulation:

python main.py

5. Run tests:

pytest -q

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Project Structure

berlin-grid-project/
├── data/                   # (excluded from repo) raw CSVs
├── docs/                   # mkdocs documentation
├── output/                 # generated graphs & artifacts
├── src/                    # typed source modules
├── tests/                  # pytest tests
├── .github/workflows/      # CI/CD pipeline
├── Dockerfile
├── METHODOLOGY.md          # Academic methodology (6 sections)
├── PORTFOLIO.md            # Portfolio highlights
├── requirements.txt
├── requirements-dev.txt
├── main.py                 # Golden master (working analysis)
├── mkdocs.yml
└── README.md

Note: The root main.py contains the validated analysis script with all features described above. The src/ directory provides a refactored, enterprise-grade modular version with CI/CD.

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📚 Documentation

• METHODOLOGY.md – Complete engineering methodology aligned with ISO/IEC Digital Twin standards
• PORTFOLIO.md – Recruiter-friendly project highlights
• docs/ – MkDocs technical documentation

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🛠️ Skills Demonstrated

• Programming: Python (pandas, numpy, pandapower, matplotlib, seaborn)
• Power Systems Engineering: Grid resilience, voltage stability, Redispatch 3.0, §14a EnWG
• Real-Time Systems: Streaming data processing, latency optimization, edge computing
• Software Engineering: Testing (pytest), CI/CD (GitHub Actions), Containerization (Docker)
• Stochastic Modeling: Monte Carlo simulation, uncertainty quantification
• German Energy Regulations: EnWG §14a, VDE-AR-N 4110

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Licensing

MIT License recommended.

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Contact

Clifford Ondieki
📧 [email protected]
🎓 M.Sc. Electrical Engineering (graduating 2026)
🔗 LinkedIn | GitHub www.cliffordomari.com