Greenenergy Awards

1. Introduction Decentralized renewable energy systems are transforming the global energy landscape, yet surplus generation from solar mini-grids often remains underutilized. Integrating green finance mechanisms and vertically coordinated economic structures can unlock this excess capacity to produce low-carbon hydrogen and support electric mobility ecosystems. This research introduces a systemic framework that connects renewable surplus utilization, hydrogen value chain development, and sustainable transport electrification to enhance carbon mitigation and economic resilience. 2. Solar Mini-Grid Surplus Optimization and Energy Storage This topic investigates technical strategies for capturing and optimizing surplus electricity generated by solar mini-grids. It evaluates storage technologies, demand-side management, and power-to-hydrogen conversion systems to ensure efficient utilization of excess renewable energy while minimizing curtailment losses. 3. Green Finance Mechanisms for Hyd...

  1. Introduction Fluoride contamination in groundwater poses significant risks to human health, including dental and skeletal fluorosis. Developing cost-effective and environmentally sustainable removal techniques is therefore essential. This research introduces the application of Chara vulgaris biomass as a biosorbent for fluoride ion adsorption. By integrating kinetic and thermodynamic evaluations, the study provides a scientific foundation for understanding adsorption mechanisms, efficiency, and feasibility under different environmental conditions. 2. Characterization of Chara vulgaris Biomass as a Biosorbent This topic focuses on the physicochemical properties of Chara vulgaris biomass that influence fluoride adsorption performance. Surface morphology, functional groups, porosity, and active binding sites are examined using analytical techniques. The findings explain how natural biomass structure enhances adsorption capacity and supports its application in low-cost water...

  1. Introduction The transition toward renewable energy systems requires sustainable and resilient biomass supply chain networks capable of operating under uncertainty and disruption risks. This research introduces an integrated multi-period, multi-objective mathematical modeling framework that simultaneously optimizes economic, environmental, and social objectives. By incorporating scenario-based uncertainty analysis, the model enhances decision-making robustness while addressing long-term sustainability challenges in biomass energy infrastructure planning. 2. Multi-Objective Mathematical Modeling Framework This topic explores the development of a comprehensive mathematical model that optimizes cost efficiency, carbon emissions, and social welfare simultaneously. Using multi-objective programming techniques, the study generates Pareto-optimal solutions that help decision-makers balance trade-offs between profitability, sustainability, and community development goals. 3. Scena...

  1. Introduction Forest biomass estimation plays a crucial role in understanding carbon sequestration potential, ecosystem productivity, and climate regulation. Nepal’s diverse topography and ecological gradients require spatially explicit modeling approaches to capture regional biomass variability. This research introduces a data-driven bioregionalisation framework combined with machine learning algorithms to enhance prediction accuracy across forest types. By integrating ecological zoning with predictive modeling, the study supports national climate commitments and sustainable forest governance strategies. 2. Data-Driven Bioregionalisation Framework This topic explores the methodological foundation of dividing Nepal into ecologically homogeneous bioregions using environmental variables such as elevation, precipitation, soil type, and vegetation indices. Bioregionalisation improves predictive consistency by reducing spatial heterogeneity and strengthening model generalization ...

  1. Introduction Geothermal energy exploration requires accurate subsurface characterization to reduce drilling risks and improve resource assessment. This research introduces an integrated methodology combining radon gas measurement and conventional electrical prospecting to enhance geothermal detection efficiency. By analyzing radon anomalies associated with subsurface fault zones and correlating them with resistivity data, the study demonstrates a multidisciplinary approach to identifying geothermal reservoirs. The Lantian Section case study provides a practical framework for sustainable geothermal exploration and contributes to advancing renewable energy resource mapping techniques. 2. Radon Measurement in Geothermal Prospecting Radon gas anomalies are closely linked to tectonic fractures and geothermal pathways. This topic examines radon concentration mapping techniques and their role in identifying permeable zones associated with geothermal reservoirs. The study highlight...

  1. Introduction Geothermal energy exploration plays a crucial role in advancing sustainable and renewable energy systems. Accurate identification of geothermal reservoirs requires integrated geophysical and geochemical methods to reduce uncertainty and enhance exploration success rates. This study introduces the combined use of soil radon measurement and conventional electrical prospecting techniques to improve geothermal anomaly detection. By analyzing subsurface gas migration patterns alongside resistivity variations, researchers can better interpret fault structures and heat pathways, contributing to efficient geothermal resource assessment and sustainable energy planning. 2. Radon Measurement as a Geochemical Indicator This topic explores radon gas as a tracer for identifying geothermal activity. Elevated radon concentrations often correlate with fault zones and fracture systems that facilitate heat flow. The research evaluates radon anomaly mapping techniques and discusse...