Power System Economic Dispatch: Python Model & Optimization

Use Python, Pyomo and GAMS for optimization.

Description

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3. Course Overview: You will learn to build and solve economic dispatch models (optimization) using Python (Pyomo) and GAMS, applying them to realistic scenarios with and without energy storage and CO₂ constraints. Through case studies, you will analyze the economic impact of energy storage in power systems, while developing skills to debug and refine optimization models.   
In addition to modeling and optimization, you will develop valuable debugging skills to identify and resolve common issues, ensuring that your solutions are both accurate and robust. You will also learn how to interpret model results effectively, enabling you to make informed assessments of operational strategies and investment decisions. Emphasis is placed on real-world data and scenarios, helping bridge the gap between theoretical understanding and practical application. By the end of the course, you will be equipped with a strong foundation in economic dispatch and the tools necessary to analyze and improve power system performance.
There are no prerequisites – the course is very detailed and you will have no difficulties.
This course is ideal for students in electrical engineering, energy systems, or operations research; aspiring energy economists seeking quantitative modeling skills; and professionals in utilities, consulting, or policy-making roles who need to understand the technical and economic trade-offs in power system operations. Careers that benefit from this knowledge include energy analysts, power system engineers, energy modelers, grid operators, and sustainability consultants. Mastery of economic dispatch modeling is also valuable for roles in government agencies, system operators (like ISOs and TSOs), and clean tech startups focused on energy storage, smart grids, or renewable integration.

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