⚡ Energy Storage Optimization & Market Analytics 📌 Overview This project demonstrates how energy data analytics, SQL, and AI optimization can be applied to optimize dispatch from different sources i.e on grid supply & Battery Energy Storage Systems (BESS) in wholesale electricity markets. It brings together three pillars: SQL Queries for Energy Analytics → extracting insights from dispatch, market, and weather data. Forecasting Models → machine learning for price and load forecasting (LightGBM, Prophet, Transformers). Optimization Models → dispatch strategies for maximizing revenue under risk and operational constraints (MILP, CVaR, MPC). This project is inspired by challenges faced in virtual power plant (VPP) optimization, where assets like batteries and renewables are aggregated and traded in markets.
🎯 Objectives Analyze and visualize battery operations vs market conditions. Build probabilistic price forecasts to support bidding strategies. Optimize battery schedules for arbitrage, balancing, and risk-adjusted profit. Apply SQL, Python, and ML to real-world energy scenarios.
📂 Project Structure energy-vpp-analytics/ │ ├── data/ # Example datasets (battery dispatch, prices, weather) ├── notebooks/ # Jupyter notebooks for EDA, forecasting, optimization ├── sql/ # SQL queries for data extraction and analysis ├── models/ # Forecasting + optimization models ├── results/ # Generated reports, charts, KPIs └── README.md # Project documentation
🛠️ Tech Stack Languages: Python, SQL Libraries: pandas, numpy, scikit-learn, lightgbm, pyomo, matplotlib, pytorch Databases: PostgreSQL / SQLite Cloud Tools: AWS (S3, RDS), Google Cloud (BigQuery, Vertex AI)
Visualization: Plotly, Power BI 🔍 Example SQL Queries
- Battery Revenue per Hour SELECT b.battery_id, DATE_TRUNC('hour', b.timestamp) AS hour, SUM(b.power * m.price_eur) AS revenue FROM battery_dispatch b JOIN market_prices m ON b.timestamp = m.timestamp GROUP BY b.battery_id, hour;
- SOC Violations SELECT * FROM battery_dispatch WHERE soc < 0 OR soc > 100 ORDER BY battery_id, timestamp;
- Negative Price Opportunities SELECT timestamp, price_eur, CASE WHEN price_eur < 0 THEN 'Charge Opportunity' ELSE 'No Action' END AS action FROM market_prices;
🤖 Forecasting Models ARIMA / Prophet → baseline time series forecasting. LightGBM & XGBoost → gradient boosting with weather + load features. Transformer Models → advanced deep learning for intraday price dynamics. Probabilistic Forecasting → quantile regression, CRPS, pinball loss.
🔧 Optimization Models Mixed-Integer Linear Programming (MILP) for day-ahead scheduling. Stochastic Optimization using price scenarios. Model Predictive Control (MPC) for intraday re-optimization. Risk-Aware Dispatch → CVaR-based revenue optimization.
📊 KPIs Tracked Revenue uplift vs baseline. Forecast accuracy: RMSE, CRPS, pinball loss. Risk-adjusted return: CVaR at 95%. Battery health: cycles/year vs design. Imbalance penalties avoided.
🧠 My Background Engineering: Hands-on experience in electrical projects & operations (asset reliability, grid systems). Network Ops: Real-time infrastructure monitoring & decision-making under uncertainty. Data Science MSc: Forecasting, ML, AI for digital business. Cloud Expertise: AWS Certified, Google Cloud ML APIs. This project integrates my engineering grounding with my data science & AI skills to deliver real-world energy analytics.