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-line-outage-distribution-factors-lodf-based-on-a-user-given-branch-outage-number---power-flows-

The project required the computation of the Line Outage Distribution Factors (LODF) based on a user given branch outage number using the fast-decoupled XB version. The LODF values were used to approximate the post-contingency branch flows based on a pre-contingency branch flow and the base branch flow before the contingency. The AC power flows were compared with the DC method for the MW and MVA flows. Source code is provided in the same WinZip file for the functions calculating the LODF values and the approximated post-contingency branch flows.

deep_reinforcement_learning_course

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

dynasim

DynaSim toolbox for modeling and simulating dynamical systems

egret-1

Tools for building power systems optimization problems

genco-investment-strategies-by-simulation-for-demand-side-role-for-investments-and-capacity-adequacy

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.

hellow-world

pandapower

Convenient Power System Modelling and Analysis based on PYPOWER and pandas

pypsa

PyPSA: Python for Power System Analysis

pypsa-eur

PyPSA-Eur: An Open Optimisation Model of the European Transmission System

ypea

Yarpiz Evolutionary Algorithms Toolbox for MATLAB