The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Opti...The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Optimization generally involves finding the configuration of the renewable generation and hydrogen system components that maximizes return on investment.Previous studies have included many aspects into their optimizations,including technical parameters and different costs/socio-economic objective functions,however there is no clear best-practice framework for model development.To address these gaps,this critical review examines the latest development in renewable hydrogen microgrid models and summarizes the best modeling practice.The findings show that advances in machine learning integration are improving solar electricity generation forecasting,hydrogen system simulations,and load profile development,particularly in data-scarce regions.Additionally,it is important to account for electrolyzer and fuel cell dynamics,rather than utilizing fixed performance values.This review also demonstrates that typical meteorological year datasets are better for modeling solar irradiation than first-principle calculations.The practicability of socio-economic objective functions is also assessed,proposing that the more comprehensive Levelized Value Addition(LVA)is best suited for inclusion into models.Best practices for creating load profiles in regions like the Global South are discussed,along with an evaluation of AI-based and traditional optimization methods and software tools.Finally,a new evidence-based multi-criteria decision-making framework integrated with machine learning insights,is proposed to guide decision-makers in selecting optimal solutions based on multiple attributes,offering a more comprehensive and adaptive approach to renewable hydrogen system optimization.展开更多
Efforts towards achieving high access to cooking with clean energy have not been transformative due to a limited understanding of the clean-energy drivers and a lack of evidence-based clean-energy policy recommendatio...Efforts towards achieving high access to cooking with clean energy have not been transformative due to a limited understanding of the clean-energy drivers and a lack of evidence-based clean-energy policy recommendations.This study addresses this gap by building a high-performing machine learning model to predict and understand the mechanisms driving energy poverty-specifically access to cooking with clean energy.In a first-of-a-kind,the estimated cost of US$14.5 trillion to enable universal access to cooking with clean energy encompasses all the intermediate inputs required to build self-sufficient ecosystems by creating value-addition sectors.Unlike pre-vious studies,the data-driven clean-cooking transition pathways provide foundations for shaping policy and building energy models that can transform the complex energy and cooking landscape.Developing these path-ways is necessary to increase people’s financial resilience to tackle energy poverty.The findings also show the absence of a linear relationship between electricity access and clean cooking-evidencing the need for a rapid paradigm shift to address energy poverty.A new fundamental approach that focuses on improving and sustaining the financial capacity of households through a systems approach is required so that they can afford electricity or fuels for cooking.展开更多
基金financial support offered to M.D.M.for his doctoral research by the UK Engineering and Physical Sciences Research Council and Loughborough University through the EPSRC Sustainable Hydrogen Centre for Doctoral Training funded by the UK Research and Innovation(UKRI)[grant number EP/S023909/1].
文摘The renewable hydrogen economy is recognized as an integral solution for decarbonizing energy sectors.However,high costs have hindered widespread deployment.One promising way of reducing the costs is optimization.Optimization generally involves finding the configuration of the renewable generation and hydrogen system components that maximizes return on investment.Previous studies have included many aspects into their optimizations,including technical parameters and different costs/socio-economic objective functions,however there is no clear best-practice framework for model development.To address these gaps,this critical review examines the latest development in renewable hydrogen microgrid models and summarizes the best modeling practice.The findings show that advances in machine learning integration are improving solar electricity generation forecasting,hydrogen system simulations,and load profile development,particularly in data-scarce regions.Additionally,it is important to account for electrolyzer and fuel cell dynamics,rather than utilizing fixed performance values.This review also demonstrates that typical meteorological year datasets are better for modeling solar irradiation than first-principle calculations.The practicability of socio-economic objective functions is also assessed,proposing that the more comprehensive Levelized Value Addition(LVA)is best suited for inclusion into models.Best practices for creating load profiles in regions like the Global South are discussed,along with an evaluation of AI-based and traditional optimization methods and software tools.Finally,a new evidence-based multi-criteria decision-making framework integrated with machine learning insights,is proposed to guide decision-makers in selecting optimal solutions based on multiple attributes,offering a more comprehensive and adaptive approach to renewable hydrogen system optimization.
文摘Efforts towards achieving high access to cooking with clean energy have not been transformative due to a limited understanding of the clean-energy drivers and a lack of evidence-based clean-energy policy recommendations.This study addresses this gap by building a high-performing machine learning model to predict and understand the mechanisms driving energy poverty-specifically access to cooking with clean energy.In a first-of-a-kind,the estimated cost of US$14.5 trillion to enable universal access to cooking with clean energy encompasses all the intermediate inputs required to build self-sufficient ecosystems by creating value-addition sectors.Unlike pre-vious studies,the data-driven clean-cooking transition pathways provide foundations for shaping policy and building energy models that can transform the complex energy and cooking landscape.Developing these path-ways is necessary to increase people’s financial resilience to tackle energy poverty.The findings also show the absence of a linear relationship between electricity access and clean cooking-evidencing the need for a rapid paradigm shift to address energy poverty.A new fundamental approach that focuses on improving and sustaining the financial capacity of households through a systems approach is required so that they can afford electricity or fuels for cooking.
