Energy Systems Analysis using OSeMOSYS

The program begins with a presentation of the course structure (university module, short course, or tailored training). Students are introduced to the importance of energy planning and the role of energy system modelling in supporting policy decisions, investment strategies, and sustainable development.

Students are introduced to the mathematical foundations of optimization models, focusing on linear programming (LP). Key concepts include objective functions, constraints, decision variables, and feasible regions. Examples are provided to show how LP underpins energy system models like OSeMOSYS.

The class reviews Modules 1 to 4 of the Open University’s OSeMOSYS online course. This covers introductory exercises, installation and setup, and the first modelling steps. Sessions include hands-on troubleshooting to ensure that all students can run and test simple models successfully.

Focus shifts to Modules 5 to 7, which deal with building more detailed energy systems focused on the power sector. Students learn about input data structures, resource representation, and basic demand projection.

The class works through Modules 8 to 10, introducing topics such as renewable technologies, energy storage, and emissions accountability. At the end of the week, students complete Midterm I, which tests their understanding of the fundamentals and their ability to run and analyse a simple OSeMOSYS model.

This week focuses on multisectoral modelling aspects presented in Modules 11 and 12, such as representation of the residential and industrial sectors. Students continue to refine troubleshooting skills and work on interpreting increasingly complex model results.

Students explore Module 13, which introduces transport sector modelling. The week emphasizes how OSeMOSYS can be used to assess cross-sectoral policies.

This week deepens the focus on model calibration, with Module 14 covering residual capacities and historical generation profiles in OSeMOSYS. Students learn how models can be adjusted to simulate historical data.

The final Open University module, Module 15, introduces scenario analysis. Students practice designing and running alternative scenarios. At the end of the week, they complete Midterm II, which evaluates their progress and ability to perform multisectoral modelling.

Students are introduced to the class project, which will run until the end of the course. The week is dedicated to defining project objectives, assigning groups (if applicable), and beginning data collection for national or regional case studies.

The project work begins with a focus on the power sector. Students learn to collect electricity demand, generation, and technology data, and implement these into their OSeMOSYS model.

Students are guided through model calibration, ensuring that the model replicates known historical data before future assessments are made. They learn how to fine-tune assumptions and check data consistency.

Attention shifts to the transport sector, adding demand and technology representation into the model. By the end of the week, students deliver their first project milestone, including a working model structure and initial calibration.

Building on earlier work, students refine their calibration with transport data. The week emphasizes model validation, reproducibility, and error reduction.

Students learn how to design and implement scenarios such as renewable energy integration, policy interventions, or different demand pathways. They practice running comparative analyses and interpreting scenario outcomes.