Novel graphical framework for optimal deployment of renewable power-to-X technology across multiple regions

Karen Gah Hie Kong, Nuo Wang, Dominic Chwan Yee Foo, Xiaodong Hong, Jingdai Wang, Yongrong Yang and Zuwei Liao

Energy, 2025, vol. 322, issue C

Abstract: To effectively mitigate anthropogenic climate change, the global community must endeavour towards achieving 'net zero' emissions. Hence, the use of renewable energy and its generation has been increasing rapidly in the past decades. More recently, efforts have been observed to make use of renewables for the decarbonization of CO2-intensive industries via various power-to-X technologies, such as heat pumps, green hydrogen, etc. Given the variability in the effectiveness of CO2 emission reductions among power-to-X technologies and the potential constraints in renewable energy supply, it is crucial to allocate renewables optimally to maximize overall CO2 emission reductions. To address this, a novel graphical framework was proposed. The newly proposed framework involves two stages – stage I: assessing renewables surplus in various regions, and stage II – allocating the renewables among different power-to-X technologies in different regions optimally. The newly proposed framework is demonstrated with a case study based in China involving three distinct scenarios. The framework demonstrates that the trading of surplus renewable energy across regions can ensure better utilization of renewables in order to achieve the CO2 emissions reduction goal. The optimal scenario in the case study requires only 37 % of the total surplus renewables, emphasizing the importance of strategic renewables distribution for achieving the CO2 emissions reduction goal. Through the proposed framework, decision-makers and policy-makers can now visualize the “big picture” of renewable energy systems while providing valuable insights into priority order of power-to-X technologies for the effective use of renewables.

Keywords: Renewable energy planning; Graphical approach; Carbon emission pinch analysis (CEPA); Process integration; Targeting (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225010953

DOI: 10.1016/j.energy.2025.135453

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