A Beginner’s Guide to Sustainable Energy Systems Modelling and Optimization

A Beginner’s Guide to Sustainable Energy Systems Modelling and Optimization is a hands-on, tutorial-style reference designed to equip engineers, planners, researchers, and energy professionals with practical tools and methods to tackle energy planning and decarbonization challenges. The book provides a step-by-step guide to building, analyzing, and optimizing sustainable energy systems—starting from fundamental concepts and progressing through applications using widely accessible platforms like Microsoft Excel, LINGO, P-graph, and Python. By illustrating real-world energy systems, supply chain, and regional/national energy planning problems through a consistent structure across chapters, this book demystifies the modelling and optimization process, helping readers gain confidence in applying what they learn to their specific domains.

• Bridges the gap between engineering, policy, and sustainability.
• Offers clear explanations, structured examples, and direct pathways to practical outcomes.
• Walks the reader through solving the same three types of energy problems using different tools throughout the book, reinforcing learning and enabling direct comparison.
• Requires no programming experience needed to get started: starts with user-friendly and accessible platforms and gradually introduces more advanced tools.
• Each section ends with a practical evaluation of the strengths, limitations, and ideal use-cases for the featured tool, providing foundational exposure to readers about when and why to use each platform.
• Features case studies at three levels of energy problems, ranging from energy system design problems, supply chain synthesis problems to regional/national energy planning problems.

Whether transitioning from spreadsheet modelling or looking to expand their toolkit to more sophisticated or open-source platforms, professional engineers in energy systems design, policy, and industrial decarbonization will find this guide particularly valuable for generating data-driven insights, testing different planning scenarios, and optimizing for cost, emissions, and energy efficiency.