Difference between revisions of "Muhamad Azkhariandra Aryaputra"

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Hydrogen storage optimization entails enhancing hydrogen storage technologies' efficiency, capacity, safety, and cost-effectiveness. Because hydrogen has a low energy density, efficient storage is essential for its broad use as an energy carrier.
 
Hydrogen storage optimization entails enhancing hydrogen storage technologies' efficiency, capacity, safety, and cost-effectiveness. Because hydrogen has a low energy density, efficient storage is essential for its broad use as an energy carrier.
  
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One of the case study of hydrogen storage optimization is Long-Term Hydrogen Storage—A Case Study Exploring Pathways and Investments. Future low-carbon systems with very high shares of variable renewable generation require complex models to optimise investments and operations, which must capture high degrees of sector coupling, contain high levels of operational and temporal detail, and when considering seasonal storage, be able to optimise both investments and operations over long durations. Standard energy system models often do not adequately address all these issues, which are of great importance when considering investments in emerging energy carriers such as Hydrogen. An advanced energy system model of the Irish power system is built in SpineOpt, which considers a number of future scenarios and explores different pathways to the wide-scale adoption of Hydrogen as a low-carbon energy carrier. The model contains a high degree of both temporal and operational detail, sector coupling, via Hydrogen, is captured and the optimisation of both investments in and operation of large-scale underground Hydrogen storage is demonstrated. The results highlight the importance of model detail and demonstrate how over-investment in renewables occur when the flexibility needs of the system are not adequately captured. The case study shows that in 2030, investments in Hydrogen technologies are limited to scenarios with high fuel and carbon costs, high levels of Hydrogen demand (in this case driven by heating demand facilitated by large Hydrogen networks) or when a breakthrough in electrolyser capital costs and efficiencies occurs. However high levels of investments in Hydrogen technologies occur by 2040 across all considered scenarios. As with the 2030 results, the highest level of investments occur when demand for Hydrogen is high, albeit at a significantly higher level than 2030 with increases in investments of large-scale electrolysers of 538%. Hydrogen fueled compressed air energy storage emerges as a strong investment candidate across all scenarios, facilitating cost effective power-to-Hydrogen-to-power conversions.
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One of the case study of hydrogen storage optimization is Long-Term Hydrogen Storage—A Case Study Exploring Pathways and Investments. Future low-carbon energy systems require radical changes to the structure and operation of the system. Planning for such a future requires complex modelling solutions which must be capable of adequately capturing a high degree of sector coupling at high levels of operational and temporal detail. Insufficient levels of model detail leads to misleading solutions, for example the under estimation of renewables integration costs and curtailment levels, and subsequently over investments in renewable generation. Hydrogen technologies can provide solutions on both the supply and demand side. However it is essential that these investments are not considered in isolation. Hydrogen electrolysers and generators such Hydrogen fuelled CAES can have a synergistic impact on renewable generation investments where investments in one would not be economically viable without the other. Seasonal storage, such as underground Hydrogen storage, can play a vital role in the long-term balancing of the energy system. However, it is difficult to optimise as part of a standard investment model. New methodologies are emerging which allow both investments in and operation of seasonal storage to be optimised, such as using ordered representative periods, mapped to equivalent periods across the year.

Revision as of 10:48, 30 May 2023

Introduction

Azkha.jpg

Name: Muhamad Azkhariandra Aryaputra

NPM: 2106657765

Major: Mechanical Engineering KKI

DoB: 1 January 2003

E-mail: azkha.aryaputra@gmail.com


Hello everyone ! My name is Muhamad Azkhariandra Aryaputra, you can call me Azkha. Right now I'm a Mechanical Engineering student at University of Indonesia and taking Numerical Method class with Pak DAI as the lecturer.

Hydrogen Storage Optimization

Hydrogen storage optimization entails enhancing hydrogen storage technologies' efficiency, capacity, safety, and cost-effectiveness. Because hydrogen has a low energy density, efficient storage is essential for its broad use as an energy carrier.

One of the case study of hydrogen storage optimization is Long-Term Hydrogen Storage—A Case Study Exploring Pathways and Investments. Future low-carbon energy systems require radical changes to the structure and operation of the system. Planning for such a future requires complex modelling solutions which must be capable of adequately capturing a high degree of sector coupling at high levels of operational and temporal detail. Insufficient levels of model detail leads to misleading solutions, for example the under estimation of renewables integration costs and curtailment levels, and subsequently over investments in renewable generation. Hydrogen technologies can provide solutions on both the supply and demand side. However it is essential that these investments are not considered in isolation. Hydrogen electrolysers and generators such Hydrogen fuelled CAES can have a synergistic impact on renewable generation investments where investments in one would not be economically viable without the other. Seasonal storage, such as underground Hydrogen storage, can play a vital role in the long-term balancing of the energy system. However, it is difficult to optimise as part of a standard investment model. New methodologies are emerging which allow both investments in and operation of seasonal storage to be optimised, such as using ordered representative periods, mapped to equivalent periods across the year.

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