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active-set-prediction-dcopf

Prediction models for learning active sets in the DC-optimal power flow optimization.

combined-heat-and-power-system-economic-dispatch

Deep reinforcement learning approaches for CHP system economic dispatch

deep-reinforcement-learning-in-real-time-optimization

dnn-opf

fault_location

graphneuralsolver

grid2op

Grid2Op a testbed platform to model sequential decision making in power systems.

gym-anm

Design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.

icpe-639

This is an introductory course discussing machine learning fundamentals and methods commonly seen in data science with potential applications in energy and power systems. Machine learning methods to be discussed include basic supervised and unsupervised learning methods, performance evaluation, neural networks and other advanced predictive models, as well as an introduction to reinforcement learning.

learningx

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

machine-learning-engineering-for-production-public

Public repo for DeepLearning.AI MLEP Specialization

mapdn

This repository is for an open-source environment for multi-agent active voltage control on power distribution networks (MAPDN).

metaoptopf.jl

Code for paper: Learning an Optimally Reduced Formulation of OPF through Meta-Optimization

mlforopt

mlopf.jl

opengridgym

opfsampler.jl

Takes a power grid case and generates OPF samples by changing the input parameters.

pc_reasearch_poster

Showcase the research result of mine during Summer 2021 with a poster. I was hired by McGill EDA Lab to work on this result, so it is licensed under it. Therefore, I am not allowed to share my codes but only the demo.

pgsim

PGSIM Simulator

power-flow-balancing-with-decentralized-graph-neural-networks

Repository code for Power Flow Balancing with Decentralized Graph Neural Networks

power-system-reinforcement-learning-agent

power-systems-optimization

Power systems optimization course materials

powergrid

simulate centralized and distributed power grid, in order to implement state estimation and bad data detection. Also, simplest FDI Attack.

powergridrlagent

Implementations of DQN, DQN with PER, DDQN with PER, and DDDQN with PER agents to maximise reward in a 14 node power grid station

powergridworld

PowerGridworld provides users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). https://arxiv.org/abs/2111.05969

reinforcement-learning-forpowergrid-operation_and_maineinance

smart-grid-end-to-end

The Smart Grid will be the power grid of the future. It will offer a two way communication flow, between consumers and providers, to ensure energy is distributed in the most efficient way. With the advance of the internet and technology in general, we have the ability to improve the traditional power grid and turn it into an intelligent, automated and distributed energy delivery network that will be able to assess the state of the grid in real time and adopt an appropriate mode of operation. A key component of an efficient Smart Grid operation will be the accurate prediction of future supply and demand trends.

source

topology-aware-learning-assisted-branch-and-ramp-constraints-screening-for-dynamic-economic-dispatch

keywords—Dynamic economic dispatch, branch and ramp constraints, topology change, machine learning, constraint classification.