This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

The escalating global energy demand, driven by heavy reliance on fossil fuels, worsens environmental degradation and triggers socio-economic shifts in extraction and refinery hubs. In Colombia, the energy matrix is predominantly fossil-based (76%), with hydroelectric power accounting for 70% of electricity generation. However, renewable energy sources only contribute 2% to the national energy mix. To reduce emissions by 20% by 2030, Colombia has presented an energy transition roadmap. The need for bioenergy production in Colombia arises from the residual biomass availability, the potential to provide sustainable energy access, and the potential to mitigate climate change impacts, while addressing energy poverty and enhancing energy security. This study presents an overview of biogas production in Colombia, emphasizing the need for financial resources to overcome barriers. Policy incentives, awareness campaigns, and research and development play a vital role in fostering social acceptance, technology adoption, and optimizing biogas production processes. Collaborative efforts among the government, private sector, and local communities are recommended to ensure wide-scale adoption of biogas, promoting economic, social, and environmental sustainability. By enabling informed decision-making, this research supports the transition to renewable energy sources and the achievement of sustainable development goals (SDGs), with a particular focus on bioenergy. The aim of this study is to explore the challenges and opportunities associated with biogas production in Colombia, including technical, economic, social, and environmental aspects, and provide recommendations for promoting its sustainable implementation and widespread adoption in the country.

The worldwide population growth and the technological advancements reported in the past few years have led to an increase in the production and consumption of energy. This has increased greenhouse gas (GHG) emissions, the primary driver of climate change. As a result, great attention has been paid to sustainable and green energy sources that can replace or reduce reliance on non-sustainable energy sources. Among the different types of renewable energy sources currently available, bioenergy has been reported as an attractive resource mainly due to its low cost and great availability. Bioenergy can be produced from different biomass sources and converted into biofuels or value-added products through thermochemical, biochemical, and chemical processes. Gasification is a thermochemical process commonly used for bioenergy production, and it is particularly attractive mainly due to its high efficiency. However, its performance is influenced by parameters such as type of feedstock, size of biomass particle, feed rate, type of reactor, temperature, pressure, equivalence ratio, steam to biomass ratio, gasification agent, catalyst, and residence time. In this paper, the influence of different performance parameters in the gasification process is analyzed, and optimization and modelling techniques are proposed as a strategy for product yield enhancement.