PyPSA-Eur: A Sector-Coupled Open Optimisation Model of the European Energy System

A library of power system component models written in the Modelica language that can be used for power system dynamic analysis, such as phasor time-domain simulations.

🎭 Quebec's 735kv power lines can survive the apocalypse, but can they run TCP?!

A Graph Reinforcement Learning model that combines RL and Graph Neural Networks to solve Dynamic Economic Power Dispatch

This project contains an extensible GAN Framework which can be used to generate power grid related data for simulations.

Code and data for "Recovery Coupling in Multilayer Networks" ( https://doi.org/10.1038/s41467-022-28379-5)

Julia package for educational and experimental power-system analysis: rectangular Newton-Raphson power flow, PV/PQ switching, MATPOWER import, and WLS state estimation.

A Python implementation of signal-power integrity co-analysis framework for inter-chiplet links.

Suite of scripts developed to emulate operational technologies and industrial control systems.

multilayer network for power grid with multiple voltage levels

Interactive CGMES XML translation to OpenIPSL using XSLT running in EditiX.

大数据分析：电力需求

Spire is an intrusion-tolerant SCADA system for the power grid.

Power system .dyr data parser

Factorio 2.0 mod that automatically optimizes power pole wiring using Kruskal's MST algorithm

Geometric framework for extracting structure, transitions, and stability from dynamical systems.

AI-powered electricity demand forecasting for Delhi Power Grid. Predicts demand at 5-min, hourly, and daily resolutions using 7 ML models (0.18% MAPE). Built with FastAPI, Next.js, LightGBM, XGBoost, PyTorch. Deployed on Vercel + Render + Supabase.

GridFlow - Web-based Power System Load Flow Simulator with Newton-Raphson, Gauss-Seidel and Fast Decoupled algorithms. Supports IEEE 14, 30, and 118 bus test systems.

Electrification Futures Insight (EF-Insight)

FedRetinaNet is a federated learning-based object detection framework built on RetinaNet for insulator defect detection. It enables multiple clients to collaboratively train a high-performance detection model without sharing raw data, addressing data privacy challenges in power grid inspection systems. Optimized for detecting small-scale defects.