"A transformative Proof of Concept (PoC) modeling an energy-independent metro ecosystem. Integrating dynamic renewable energy sources (Solar/Wind) with a Smart Energy Management System (EMS) to achieve net-positive grid contribution."
The future of urban transportation lies in Energy Autonomy. This project simulates a dynamic environment where metro infrastructure transitions from an energy consumer to a green energy producer. By leveraging a Linear Programming (LP) optimization model, the system intelligently balances renewable generation, battery storage (BESS), and national grid interactions to minimize carbon footprint while maximizing financial returns.
This simulation is architected as a Digital Twin Framework:
- Dynamic Load Balancing: Utilizing hourly dynamic data profiles (train density, solar radiation, wind velocity) to model realistic energy consumption/generation cycles.
- LP-Based Optimization (PuLP): The heart of the system is an EMS Engine that solves complex cost-efficiency equations to determine the optimal flow between batteries, renewables, and the grid in real-time.
- BESS Lifecycle Modeling: Implementing Battery Energy Storage System (BESS) logic with strict SoC (State of Charge) thresholds, charging/discharging efficiency coefficients, and immediate demand fulfillment protocols.
- Financial & Carbon Analytics: A dual-objective analysis layer that calculates CAPEX/OPEX metrics alongside avoided carbon emissions, proving the project's Environmental & Economic Feasibility.
- Modular Architecture: Clean separation of concerns between the Simulation Engine, Visualization Layer, and Parameter Definition files.
- ⚡ Energy Positivity: Proven capability to meet total internal demand and export surplus energy back to the national grid.
- 📉 Grid Independence: Achieved near-zero dependency on external grid power during peak renewable production hours through intelligent battery orchestration.
- 🌱 Decarbonization: Significant reduction in carbon footprint by substituting grid-sourced electricity with localized green energy.
- 💰 Revenue Generation: Optimized selling cycles on the virtual spot market, turning a traditional cost center into a profit-generating asset.
| Energy Balance & Flow | Battery (BESS) Management |
|---|---|
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| Hourly tracking of generation vs. demand. | Dynamic management of Battery State of Charge (SoC). |
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| System-wide energy distribution dynamics. | Cost/Revenue analysis of grid interactions. |
To replicate this simulation environment on your local machine, follow these structured steps:
Ensure you have Python 3.8+ installed on your system.
git clone [https://github.com/emineugurlu/MetroEnerjiSimulasyonu.git](https://github.com/emineugurlu/MetroEnerjiSimulasyonu.git)
cd MetroEnerjiSimulasyonuInstall the necessary data processing, visualization, and optimization libraries:
pip install pandas matplotlib pulp openpyxlExecute the main orchestration script to start the engine and generate visual reports:
python main.py📂 Project Structure main.py: The entry point that orchestrates the simulation and triggers visualizations.
simulasyon_motoru.py: The core logic containing the PuLP optimization model and energy balance calculations.
gorsellestirme.py: Dedicated functions for generating high-fidelity Matplotlib graphs.
parametreler.py: Configuration file for battery capacities, efficiency coefficients, and pricing.
dinamik_veriler.xlsx: The dataset containing hourly consumption and generation profiles.
Developed by Emine Uğurlu - Computer Engineer. Focused on the intersection of AI, Energy, and Sustainable Infrastructure.