Power System Economic Dispatch: Storage & Carbon Optimization

Managing modern electrical grids requires balancing cost, reliability, and environmental impact. As renewable energy and storage assets expand, power system optimization becomes essential for efficient grid operations. This text-based course guides you through the foundational principles of economic dispatch, advancing from basic concepts to complex multi-bus systems. You will learn how to formulate mathematical optimization models and implement them programmatically to solve real-world grid challenges. What you'll learn: - Understand the fundamental mathematical principles of economic dispatch and power system optimization. - Build economic dispatch models using Pyomo in Python and GAMS to solve grid allocation problems. - Integrate energy storage systems, modeling charge/discharge cycles and state-of-charge constraints. - Apply carbon constraints and carbon pricing mechanisms to optimization formulations. - Scale optimization models from simple single-bus systems to multi-bus grid configurations. - Analyze solver outputs to interpret marginal costs, locational marginal pricing, and system constraints. The course starts with essential terminology and mathematical foundations before introducing hands-on coding. You will progress from simple single-bus formulations to multi-bus systems incorporating wind generation, battery dynamics, and emissions limits through written explanations and code exercises. This course is designed for power systems engineers, energy analysts, and students with basic programming knowledge who want to learn grid optimization from scratch. No advanced background in optimization theory is required. Start mastering the mathematical and programming tools driving the modern energy transition today.