Novel Approaches for Sustainable Energy System Assessment

Dear Colleagues, 

The urgent need for rapid decarbonisation has made the design and assessment of sustainable energy systems a critical global priority. This challenge is particularly acute in developing countries, where access, equity, and affordability continue to shape the energy transition. In such contexts, novel approaches are needed to integrate technological, environmental, economic, and social dimensions into the modelling of energy systems, especially in developing countries, where structural inequalities and institutional heterogeneity influence energy outcomes. 

The recent literature shows a clear trend toward using diverse and hybrid modelling approaches to assess the deployment of renewable energy technologies and the transition toward cleaner systems. These methodologies—ranging from system dynamics, agent-based modelling, and optimisation tools to lifecycle assessment and spatial analysis—have enabled a better understanding of the interdependencies among actors, technologies, infrastructures, and policy environments. The complexity of these interactions calls for interdisciplinary perspectives that go beyond technical analysis and incorporate behavioural, cultural, and socioeconomic factors. 

Furthermore, machine learning and data-driven techniques have gained traction as complementary tools in energy system assessments. These methods offer significant advantages in handling large and complex datasets, detecting nonlinear patterns, and improving predictive accuracy. When combined with traditional modelling approaches, machine learning can enhance scenario analysis, demand forecasting, and allow for the assessment of policy impacts across different population groups. 

In the context of developing countries, where data limitations and resource constraints are common, combining quantitative models with participatory and qualitative methods has also shown promise. These integrated approaches can bridge knowledge gaps, empower local communities, and support bottom-up planning processes that reflect local needs and aspirations. 

This Special Issue seeks to highlight innovative research and applied studies that advance the understanding and assessment of sustainable energy systems through novel and interdisciplinary modelling approaches. We particularly welcome contributions focused on developing regions, social inclusion, and methodological integration. Case studies, comparative analyses, and methodological contributions that bridge the gap between modelling and implementation are particularly encouraged to be submitted to this Special Issue. By bringing together diverse perspectives and experiences, this Special Issue aims to support more just, inclusive, and context-sensitive energy transitions worldwide. 

In this Special Issue, original research articles and reviews are welcome to be submitted. Research areas may include, but are not limited to, the following: 

• System dynamics and agent-based and hybrid modelling in energy systems;
• Machine learning and AI applications in renewable energy forecasting and planning;
• Modelling the social dimensions of energy access and technology adoption;
• Integrated assessment of energy policies in developing countries;
• Participatory and bottom-up approaches for energy planning;
• Spatial and demographic analysis of energy inequality;
• Cross-sectoral models linking energy, water, food, and climate systems;
• Tools and metrics for evaluating energy justice and distributional impacts;
• Modelling behavioural responses to clean energy interventions.

Dr. Milton M. Herrera
Dr. Mauricio Uriona-Maldonado
Guest Editors

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• sustainable energy systems
• modelling approaches
• developing countries
• machine learning
• system dynamics