There are 1 repository under power-grids topic.
🏆 A weekly updated ranked list of popular open-source libraries and tools for Power System Analysis.
An open-source platform for applying Reinforcement Learning for Grid Control (RLGC)
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.
Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code runs on the pypownet environment (https://github.com/MarvinLer/pypownet). It is released under a license of LGPLv3
Nordic44-Nordpool: An Open Data Processing Software Toolset for an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data
The relevant codes of our work "Revealing Structural and Functional Vulnerability of Power Grids to Cascading Failures".
PowerSAS.m - A power grid analysis toolbox based on semi-analytical solutions (SAS) for Matlab/GNU Octave
Web application for the simulation of day-ahead energy markets
Network data repository
Audur - A Platform for Synchrophasor/PMU-Based Power System Wide-Area Control System Implementation
Minimalistic IEEE C37.118 synchrophasor real-time data mediators for application development in LabVIEW
This project will present an applied and game-like approach to simulating the load growth, investment decisions by two types of generation technologies, demand-price responsiveness, and reliability, of a test-case power system. The simulation begins as a 9-bus system with existing generation (3 generators) and transmission lines (8 lines). System topology can be viewed in a figure throughout the game with the yearly generation and load at each bus. In addition, dynamic color-coding is used to highlight transmission lines that exceed MVA ratings and highlight bus voltages that violate any limits. The winning objective of the player company (you) is to maximize his profit. Reliability can be tracked by viewing the N-1 generator and line contingencies every year, but this does not influence profits. There are two generation technologies used: coal and gas turbine. Each technology will have a similar competitor in the simulation. The competitor can bring down the market price and reduce the player’s profits significantly. The clock starts at T=0 in the investment game with a historical record of past prices and projected prices based on lack of investment. As time moves forward in yearly increments, the load, prices, investment costs, and other variables are adjusted to that of the player’s performance. The player has the opportunity to study various profitable and unprofitable investment alternatives each year of the simulation. If he invests at the right location, and in the right planning year, his company can make windfall profits. Competitors randomly participate in adding extra generation in random areas of the system based on the competition level settings. The challenge for the user is to study the effects of his investment decisions on market prices, reliability, and his profitability.
This project contains an extensible GAN Framework which can be used to generate power grid related data for simulations.
Demonstration of various grid energy management benchmarks using GEKKO
Research on Control of Cyber-Physical Systems
Tools for single node generation adequacy analysis
Models used in the paper "Power System Modeling for Identification and Control Applications using Modelica and OpenIPSL" by L. Vanfretti and C.R. Laughman.
Dynamical model for power grid frequency fluctuations