Industrial Energy Optimization Modeling with Python and GAMS

Industrial facilities face massive energy costs and complex operational challenges that require smart, automated scheduling. Mastering mathematical optimization allows you to design highly efficient systems that minimize expenses while meeting strict demand constraints. This written course guides you through the foundational concepts of mathematical programming to model real-world industrial energy systems. You will transition from understanding core optimization theory to writing clean, executable Pyomo and GAMS code for integrated components like furnaces, heat pumps, and battery storage. What you'll learn: - Understand the fundamental concepts of linear and mixed-integer programming in industrial contexts. - Build mathematical models for key assets including natural gas furnaces, chillers, batteries, and combined heat and power (CHP) units. - Formulate multi-stage optimization problems that balance operational limits, energy demand, and contract constraints. - Implement models seamlessly in both Pyomo (Python) and GAMS using modern, clean coding practices. - Apply modern environmental constraints such as carbon emission tracking and dynamic pricing structures to your models. - Analyze solver outputs to make data-driven decisions for facility energy management. You will start with core optimization terminology and basic mathematical formulations before moving on to hands-on modeling scenarios. Through step-by-step written explanations and practical code snippets, you will learn how to structure, solve, and refine complex multi-energy system models. This course is designed for engineers, analysts, and developers who are new to mathematical optimization. No prior experience with Pyomo or GAMS is required, though a basic familiarity with Python is helpful. Start building your first industrial optimization model today.