Optimization and Electricity Market

Part 1: LP-Basics

Objective

This section provides a comprehensive introduction to Linear Programming, which forms the computational backbone for large-scale decision-making problems. The primary goal is to equip learners with the ability to model real-world optimization problems using Python.

Linear Programming basics

• Introduction to LP Optimization Models

• Using Gurobi for Linear Programming

• Solving Optimization Models

• Extracting optimization results

Part 2: An example of a power system optimization problem

Objective

This module leverages cyber infrastructure to conduct a day-ahead economic dispatch on a modified PJM 5-bus system, demonstrating how cyber infrastructure can be utilized to model and solve a power system optimization problem. The cyber infrastructure leveraged in this module includes Python, Jupyter Notebook, NumPy, GurobiPy, and Matplotlib.

Key Components

1. Inputting system parameters and calculating the Shift Factors matrix.

2. Formulating the optimization model and solving the optimization problem.

3. Extracting the optimization results and displaying the outcomes.

This module provides an effective PowerCyber training, facilitating the utilization of cyber infrastructure to conduct studies and research in the power domain.