Combining Power System Modeling with AI Forecasting for Renewable Grid Integration

A recent electrical engineering graduate built an end-to-end Python pipeline that combines power system modeling, machine learning load forecasting, and battery dispatch optimization for renewable grid integration. The project links a pandapower 132kV/11kV substation model, a PyPSA solar-plus-battery microgrid optimizer, and a scikit-learn next-day load forecasting model into a single workflow that turns raw network data into an actionable dispatch plan.

Renewable energy sources like solar are great for sustainability, but terrible for predictability. Grid operators need to know, ahead of time, roughly how much power will be available and how to dispatch storage efficiently around it. I built a small end-to-end pipeline to explore this problem using Python. Unlike a traditional power plant, solar generation depends on weather, time of day, and season — it's not something you can simply "turn up" to meet demand. To integrate renewables into a grid reliably, you need three things working together: a model of the electrical network, a way to optimize how storage is dispatched, and a way to forecast tomorrow's load and generation. Most tutorials handle these separately. I wanted to see how they connect. 1. Substation Load Flow & Short Circuit Model pandapower A 132kV/11kV substation model to study transformer loading, bus voltages, and fault currents — the base network that everything else operates on. 2. Solar + Battery Microgrid Optimization PyPSA Using PyPSA's optimization engine, I modeled a microgrid with solar generation and battery storage, solving for the dispatch schedule that minimizes cost while meeting demand. 3. AI Load Forecasting Tool scikit-learn A machine learning model trained to predict next-day electrical load from historical patterns — giving the optimization step something realistic to plan against. 4. The Capstone — AI-Driven Renewable Grid Integration Finally, I combined all three: the substation model provides the network context, the forecasting model predicts tomorrow's load, and the optimization engine decides how to dispatch solar and battery storage against that forecast — an end-to-end pipeline from raw network data to an actionable dispatch plan. As grids add more renewables, this kind of pipeline — network model + forecast + optimizer — isn't just an academic exercise anymore, it's close to what real control room and planning tools need to do. Building it from scratch gave me a much deeper appreciation for where the real engineering challenges are. Code: GitHub — AI-Driven Renewable Grid Integration https://github.com/muhammadshahzaibshahzaib92-dev/ai-driven-renewable-grid-integration Full portfolio: muhammadshahzaibshahzaib92-dev.github.io https://muhammadshahzaibshahzaib92-dev.github.io/ I'm a recent Electrical Engineering Power graduate combining ETAP-based power systems work with Python and AI-driven grid tools. Always happy to talk power systems, grid optimization, or renewable integration — feel free to connect on LinkedIn https://www.linkedin.com/in/muhammad-shahzaib-b41a3b26b .