Yunfei Mu , Haochen Guo , Zhijun Wu , Hongjie Jia , Xiaolong Jin , Yan Qi
{"title":"A two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub","authors":"Yunfei Mu , Haochen Guo , Zhijun Wu , Hongjie Jia , Xiaolong Jin , Yan Qi","doi":"10.1016/j.egyai.2024.100435","DOIUrl":null,"url":null,"abstract":"<div><div>Building a low-carbon park is crucial for achieving the carbon neutrality goals. However, most research on low-carbon economic planning methods for park-level integrated energy systems (PIES) has focused on multi-energy flow interactions, neglecting the “carbon perspective” and the impact of the dynamic coupling characteristics between multi-energy flows and carbon emission flow (CEF) on carbon reduction and planning schemes. Therefore, this paper proposes a two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub (CESH). Firstly, this paper establishes a CESH model for PIES to describe the synergistic relationship between CEF and multi-energy flows from input, conversion, storage, to output. Secondly, a PIES two-layer low-carbon economic planning model with CESH is proposed. The upper model determines the optimal device types and capacities during the planning cycle. The lower model employs the CESH model to promote carbon energy friendly interactions, optimize the daily operation scheme of PIES. The iterative process between the two layers, initiated by a genetic algorithm (GA), ensures the speed and accuracy. Finally, case studies show that, compared to planning methods without the CESH model, the proposed method is effective in reducing carbon emissions and total costs during the planning cycle. From a dual “carbon-energy” perspective, it enhances investment effectiveness and carbon reduction sensitivity by deeply exploring the energy conservation and carbon reduction potential of PIES.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100435"},"PeriodicalIF":9.6000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and AI","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666546824001010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Building a low-carbon park is crucial for achieving the carbon neutrality goals. However, most research on low-carbon economic planning methods for park-level integrated energy systems (PIES) has focused on multi-energy flow interactions, neglecting the “carbon perspective” and the impact of the dynamic coupling characteristics between multi-energy flows and carbon emission flow (CEF) on carbon reduction and planning schemes. Therefore, this paper proposes a two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub (CESH). Firstly, this paper establishes a CESH model for PIES to describe the synergistic relationship between CEF and multi-energy flows from input, conversion, storage, to output. Secondly, a PIES two-layer low-carbon economic planning model with CESH is proposed. The upper model determines the optimal device types and capacities during the planning cycle. The lower model employs the CESH model to promote carbon energy friendly interactions, optimize the daily operation scheme of PIES. The iterative process between the two layers, initiated by a genetic algorithm (GA), ensures the speed and accuracy. Finally, case studies show that, compared to planning methods without the CESH model, the proposed method is effective in reducing carbon emissions and total costs during the planning cycle. From a dual “carbon-energy” perspective, it enhances investment effectiveness and carbon reduction sensitivity by deeply exploring the energy conservation and carbon reduction potential of PIES.