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.