I built a Battery Degradation-Aware PV-BESS Energy Management and Sizing Simulation for Munich Households using Python.
This project focuses on how residential solar + battery systems perform over a 15-year lifecycle, considering not only energy savings but also real battery aging and replacement planning.
🔋 What the project includes:
✅ PV-BESS energy management simulation
✅ State of Charge tracking
✅ Depth of Discharge analysis
✅ 15-year battery degradation modeling
✅ Battery replacement year estimation
✅ Payback and LCOE analysis
✅ Battery sizing optimization
✅ New Li-ion vs second-life EV battery comparison
✅ Simple frequency support simulation
📍 Case Study: Munich, Germany
⚡ Best simulated option: 4 kWh second-life EV battery
📊 LCOE: ~0.400 EUR/kWh
💰 Payback: Year 11
One key takeaway:
Second-life EV batteries can make residential PV-BESS systems more economical, but degradation-aware planning is essential for realistic long-term performance.
This project helped me combine renewable energy, battery storage, data analysis, and Python simulation into one complete techno-economic study.
I’m excited to keep learning and building more energy-focused data science projects.
