The PyPSA Handbook: Integrated Power System Analysis and Renewable Energy Modeling is a comprehensive guide to the landmark open-source platform for modeling modern energy systems. Starting from core concepts in energy system analysis, the book builds a clear theoretical foundation before progressing to advanced topics such as renewable integration, storage, grid expansion, system stability, and future developments. Designed for both newcomers and experienced users, it features layered examples, hands-on exercises, and real-world case studies. This book is an indispensable resource for students, researchers, and energy professionals.
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Table of Contents
1. Introduction to Power Systems and PyPSA2. Getting Started with PyPSA
3. Modeling Components in PyPSA
4. Advanced Modeling Techniques
5. Decarbonization and Renewable Integration
6. Microgrids and Distributed Energy Resources (DERs)
7. Transmission Planning and Expansion
8. Reliability and Resilience
9. Integrating PyPSA with Other Tools
10. Future Trends and Challenges
Authors
Neeraj Dhanraj Bokde Lead Researcher, Renewable and Sustainable Energy Research Center, Technology Innovation Institute, Abu Dhabi, UAE. Dr. Neeraj Dhanraj Bokde is a Lead Researcher at the Technology Innovation Institute (TII), Abu Dhabi, with over 12 years of interdisciplinary experience in renewable energy, data science, and AI. He has held academic positions at Aarhus University in Denmark and contributed to applied research at DEWA and VNIT Nagpur. Dr. Bokde develops open-source tools in R and Python for data analysis, forecasting, and optimization. His work supports the energy transition through transparent modeling, advanced analytics, and integrated planning strategies. He has published extensively in the fields of energy modeling, renewable integration, and data-driven decision support. Carlo Fanara Executive Director of the Energy Modelling team at the Technology Innovation Institute (TII), Abu Dhabi..Dr. Carlo Fanara is Executive Director of the Energy Modelling team at the Technology Innovation Institute (TII), Abu Dhabi. He has extensive experience spanning nuclear and plasma physics, materials science, and machine learning, and has held academic and industrial roles across Europe. He holds a Ph.D. in plasma physics and has led research in energy beams, diagnostics systems, and data-driven modeling. His current work focuses on advancing energy systems analysis through high-performance computing, algorithm design, and applied data science. He is member of the IEEE Nuclear and Plasma Physics division and ordinary member of the ACM.
