PhD Researcher in Energy Economics | FaaS & BESS Optimization | Grid Congestion & DEA Efficiency
📍 University of Algarve, Portugal | 📍 Utrecht University (2027)
My doctoral research develops a tripartite cumulative framework bridging strategic management, non-parametric efficiency benchmarking (SBM-DEA), and explainable machine learning (Random Forest + SHAP) to optimize Battery Energy Storage System (BESS) flexibility arbitrage under volatile European electricity markets. Key contributions include:
- R² = 0.9655 day-ahead price forecasting in TenneT (Netherlands)
- ‑500 EUR/MWh negative price floor capture
- Cross-market arbitrage using NL‑ES decoupling (r = 0.702)
I am actively seeking a research traineeship (up to 12 months) with energy institutions in Ireland and across Europe (SEAI, EirGrid, ESB Networks, UCD Energy Institute, MaREI, and other TSO/DSO/energy market organisations) to apply my data‑driven frameworks to wind‑dominated and high‑RES electricity systems.
| Irish Challenge | My Research Transferable Solution |
|---|---|
| Short‑term grid congestion under high wind penetration | Grid congestion ML models (TenneT/MIBEL validated) |
| VPP integration & flexibility asset dispatch | FaaS platform optimization with SHAP‑based decision SOPs |
| Negative price event forecasting | RF + SHAP pipeline with structural break detection (Crisis_Dummy) |
| Cross‑border interconnection efficiency | SBM‑DEA panel benchmarking across 28 EU member states |
My methodologies are transferable to any market with:
- High renewable penetration and price volatility
- Active flexibility / ancillary service markets
- Cross‑border interconnection dynamics
Target regions: Germany, Netherlands (TenneT), Nordics, Iberia (MIBEL), and other EU bidding zones.
- ✅ Research Portfolio: Core methodological repositories are now populated and publicly accessible.
- 🔄 Mobility Seeking: Actively exploring Erasmus+ research traineeship opportunities with Irish and European energy institutions.
- 📧 Open to Collaboration: a92176@ualg.pt
| Domain | Core Areas |
|---|---|
| Energy Economics | Electricity market design (EMD), FaaS platforms, BESS arbitrage & degradation |
| Operations Research | SBM‑DEA, eco‑efficiency, cross‑border productivity, panel econometrics |
| Data Science | ML price forecasting, XAI/SHAP, grid congestion prediction, time‑series ETL |
| Category | Tools & Methods |
|---|---|
| Efficiency Analysis | SBM‑DEA, Dynamic DEA, Panel Econometrics (Tobit / FE) |
| Machine Learning | Python (Scikit‑learn), Random Forest, GPR, SHAP (XAI) |
| Deep Learning (planned) | LSTM/TCN for probabilistic forecasting |
| Grid Simulation | Pandapower, OpenSTEF |
| Data Pipeline | ENTSO‑E API, Automated ETL, UTC synchronization |
| Reproducibility | Git, Jupyter, VS Code + Cline (DeepSeek) |
| Repository | Tech Stack | Description | Type |
|---|---|---|---|
| BESS_Flexibility_ML | Python, SHAP, Scikit‑learn | Explainable ML pipeline for price volatility & BESS dispatch (R²=0.9655) | Core Research |
| Grid_Congestion_ML | Python, SHAP, Scikit‑learn | Grid congestion & negative price forecasting at system level | Core Research |
| European_FaaS_DEA | Python, Econometrics | SBM‑DEA efficiency benchmarking of 28 EU electricity systems | Core Research |
| DEA‑Efficiency‑Toolkit | Python, Econometrics | Reusable SBM‑DEA & Dynamic DEA modules | Toolkit |
| ENTSOE‑Data‑Pipeline | Python, ENTSO‑E API | Scalable ETL pipeline for hourly European power market data | Infrastructure |
Note: Proprietary grid datasets and commercial optimization parameters are maintained in private repositories.
- LinkedIn: linkedin.com/in/leonardo-xi
- Email: a92176@ualg.pt
- ORCID: 0009-0007-4728-0256
- GitHub: github.com/leo-energy