Generating robust evidence for decision-making and promoting high-level technical cooperation
Good decision-making requires understanding of the likely consequences of policy choices. One of the pillars of the RESET Network’s work is the development and strengthening of open-source energy planning models, adapted to national realities. The aim is to generate robust evidence for decision making and to promote high-level technical cooperation across the Global South.
Practical application
In 2025, MCET (the modelling arm of the RESET Network) launched a competitive small-grants program to support the practical application of open-source energy models in real-world decision-making processes in developing countries. Five projects were selected, each involving more than one modeling team and outreach to policy makers. The results of these projects are currently being written up as academic articles and policy briefs.
Multi-country Energy Transition (MCET) modeling team examples
As examples of the kind of work undertaken by MCET teams who are now folded into the RESET Network, check out the the following examples.
China
A team from Beihan University, with support from Climate Imperative, modeled the energy transition for Lvliang, Shanxi, a city heavily dependent on coal. Their results and subsequent discussion with local officials highlighted the labor market and economic challenges facing Lvliang and have led to a new project focused on finding innovative solutions. In addition, the team from the China Energy Modeling Forum used the Switch-China version to model different electricity system transition paths, combining applied research with seminars, training, and dialogue spaces with public actors, researchers, and energy policy makers.
Colombia
The team at Universidad de los Andes calibrated the Switch 2.0 model to reflect the Colombian electricity system. This model allows for the simulation of capacity expansion scenarios and has been compared with official scenarios from Colombia’s Energy Mining Planning Unit through 2037, providing a solid tool for analyzing optimal generation portfolios and evaluating the impact of different energy policies.
Bangladesh
Nine energy scenarios to the year 2050 were developed and evaluated using official data from the Bangladesh Power Development Board and the Power Grid Company of Bangladesh. Optimal paths for generation and transmission expansion were analyzed using the Switch model, with emphasis on costs, emissions, and technical feasibility. The results were systematized and made available to key sector stakeholders to facilitate policy design aligned with decarbonization goals.
India
Open source electricity capacity expansion modeling teams in India have been involved in MCET since the very beginning and a flourishing community has developed. The Indian Institute of Science, Bengaluru and Javadpur University, Kolkata began the collaboration, which resulted in the IDEEA model. The community has since expanded to include the Indian Institute of Technology-Roorkee and beyond working with models including SWITCH and TIMES. This work has built significant new capacity through students and workshops. Indian MCET members have also engaged in a variety of research areas including demand-responsive pricing and, in collaboration with teams in Vietnam and Chile, comparing the roles of diverse strategies for transition across the three countries. The team is increasingly providing support to key decision-makers.
Vietnam
The team from Fulbright University adapted the Switch model to project possible pathways toward carbon neutrality by 2050. The aim of the work is to incorporate more realistic and policy relevant pricing structures into Vietnam’s national electricity planning analysis. The project has strengthened partnerships with governmental and academic actors, and has promoted technical knowledge exchange initiatives, aiming to strengthen local capacities in energy planning.
Explaining energy modeling
Energy modeling uses computer software to simulate the growth and function of energy systems in the real world.
Models have been used for many years to plan, research and forecast how to react to different future scenarios.
Recent advances in data science capabilities mean models can capture more complex and dynamic scenarios with increasingly sophisticated, flexible, and affordable results.
