Combining Power System Modeling with AI Forecasting for Renewable Grid Integration

Muhammad Shahzaib — Wed, 30 Sep 2026 03:35:33 +0000

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.

The Problem

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.

What I Built

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.

What I Learned

Power system engineering and machine learning use very different mental models — one is grounded in physical laws (Kirchhoff's laws, impedance, per-unit systems), the other in statistical patterns. Getting them to talk to each other cleanly took more thought than I expected.
Forecast accuracy directly affects how "safe" an optimizer can afford to be — a noisy forecast means the battery dispatch plan needs more of a buffer, which has a real cost.
This kind of integration is exactly where the power industry seems to be heading — utilities are increasingly hiring for exactly this intersection of skills.

Why This Matters

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
Full portfolio: 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.

Building a GOOSE-Based Substation Protection Pipeline in Python( IEC61850)

Muhammad Shahzaib — Wed, 30 Sep 2026 03:28:16 +0000

# Building a GOOSE-Based Substation Protection Pipeline in Python

Substation protection has traditionally relied on hardwired relay-breaker connections. Modern substations, however, are moving toward IEC 61850 — a standard that lets protection devices (IEDs) communicate over a network using the GOOSE (Generic Object Oriented Substation Event) protocol instead of physical wiring.

I wanted to understand this shift hands-on, so I built a small end-to-end pipeline that simulates it in Python.

The Problem

In a traditional substation, if a fault occurs, a relay detects it and sends a hardwired trip signal to a circuit breaker. This works, but it's rigid — every new connection needs new wiring, and there's no easy way to monitor the system's real-time state remotely.

IEC 61850's GOOSE protocol solves this by letting IEDs (Intelligent Electronic Devices) publish and subscribe to trip signals over a standard network, the same way services communicate in modern software systems.

What I Built

The project has three layered components:

1. GOOSE Relay-Breaker Communication
Using libiec61850, I set up a publisher-subscriber pair simulating two IEDs — one relay, one breaker — exchanging GOOSE trip messages over the network.

2. Fault-to-GOOSE Protection Logic
I connected this to pandapower for fault current simulation. When a fault current crosses a threshold in the simulated network, the logic automatically triggers a GOOSE trip message — mimicking how a real protection scheme would respond to a fault.

3. Substation Live Monitoring Dashboard
Finally, I built a real-time dashboard using streamlit that shows relay/breaker status, an event log, and a live fault graph — giving visibility into what the automated protection system is doing, the way a control room operator would see it.

What I Learned

GOOSE messaging is fast (sub-4ms in real IEC 61850 networks) because it's built for time-critical protection events, not general data transfer.
Simulating fault currents with pandapower before triggering protection logic gave me a much better intuition for how relay coordination and protection settings actually matter in the field — something that's hard to appreciate from textbooks alone.
Building the dashboard forced me to think about protection systems from an operator's perspective, not just an engineer's.

Why This Matters

As grids get more automated (and increasingly need to respond to renewable variability), protection systems built on standards like IEC 61850 are becoming the norm rather than the exception. Having hands-on experience with both the protocol and the underlying power system logic feels like a genuinely useful skill set going forward.

Code: GitHub — IEC 61850 Substation Automation
Full portfolio: muhammadshahzaibshahzaib92-dev.github.io

I'm a Electrical Engineering (Power) graduate combining ETAP-based power systems work with Python and grid automation. Always happy to discuss protection systems, grid modeling, or anything in between — feel free to connect on LinkedIn.

DEV Community: Muhammad Shahzaib

Combining Power System Modeling with AI Forecasting for Renewable Grid Integration

The Problem

What I Built

What I Learned

Why This Matters

Building a GOOSE-Based Substation Protection Pipeline in Python( IEC61850)

The Problem

What I Built

What I Learned

Why This Matters