Optimization and Electricity Markets

Module Overview

Efficient power-system operation is, at its core, an exercise in optimization. Every five minutes operators solve a large-scale mathematical program that decides which generators run, how power flows through the network, and what price each bus pays or receives. The same machinery supports day-ahead markets, long-term planning studies, and real-time security assessments.

Restructuring did not lessen the need for optimization—it multiplied it. Market operators now use variants of these models to maximize social welfare, mitigate market power, and settle transactions, all while honoring the physical and reliability constraints inherited from the vertically integrated era.

This module demystifies those models. We start with the building blocks of optimization theory, progress to linear programming in Python, and culminate in market clearing and security-constrained dispatch case studies drawn from real grids.

By the end you will be able to

• translate power-system questions into mathematical optimization problems

• build and solve linear programs in Python

• explain how markets use optimization to set dispatch and locational marginal prices

• tackle economic dispatch, unit commitment, and optimal power-flow problems

• address uncertainty, multiple objectives, and computational scale

Prerequisites

You should already be comfortable with

• Python fundamentals (functions, data structures)

• NumPy and Pandas

• basic linear algebra

Completing Module 02 satisfies these requirements.

How We Will Learn

Short concept explanations are paired with hands-on notebooks. You will write code, run solvers, and interpret results using open-source tools. Hints and full solutions are available when you need them.