{"title":"基于 \"资源-需求 \"平衡的低碳区域综合能源系统适宜性匹配评估","authors":"Zhenyu Zhao, Kun Yang","doi":"10.1016/j.seta.2024.104099","DOIUrl":null,"url":null,"abstract":"<div><div>The integrated energy system (IES) stands out due to its advantages of multi-energy complementarity, green and efficiency. This paper aims to provide a practical model for suitable region site selection of low-carbon regional IES based on the “resource-demand” balance. A “resource-demand” suitability matching analysis indicator system is established. By using prospect theory and variable precision rough set theory, the decision-making preferences and the cognitive fuzziness influence of expert scoring were corrected respectively, and reasonable subjective weights were obtained. Then, the objective weights are obtained using the entropy method, and the optimal combined weights are obtained through the game theory weighting method. Finally, this paper establishes a “resource-demand” suitability matching model consisting of three sub models: similarity matching degree, numerical matching degree, and suitability matching degree. An empirical study is conducted on 16 regions in Beijing, and the results show that the numerical matching degree of “resource-demand” gradually decrease from the central urban regions to the surrounding regions. The similarity matching degree in most regions reaches the highest range (0.776,1.00). There are 4 regions with a suitability matching rating of perfect, 7 regions with a rating of great, 1 region with a rating of good, and 4 regions with a rating of poor. The suitability matching model established in this paper can guide energy project decision-makers to select regions with greater potential for the development of IES. In addition, adjusting the similarity matching coefficient and numerical matching coefficient can meet the diversified needs of decision-makers for the similarity matching degree and numerical matching degree of the “resource-demand” balance, providing support for energy project planning decisions.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"72 ","pages":"Article 104099"},"PeriodicalIF":7.1000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Suitability matching evaluation of the low-carbon regional integrated energy system based on the “resource-demand” balance\",\"authors\":\"Zhenyu Zhao, Kun Yang\",\"doi\":\"10.1016/j.seta.2024.104099\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The integrated energy system (IES) stands out due to its advantages of multi-energy complementarity, green and efficiency. This paper aims to provide a practical model for suitable region site selection of low-carbon regional IES based on the “resource-demand” balance. A “resource-demand” suitability matching analysis indicator system is established. By using prospect theory and variable precision rough set theory, the decision-making preferences and the cognitive fuzziness influence of expert scoring were corrected respectively, and reasonable subjective weights were obtained. Then, the objective weights are obtained using the entropy method, and the optimal combined weights are obtained through the game theory weighting method. Finally, this paper establishes a “resource-demand” suitability matching model consisting of three sub models: similarity matching degree, numerical matching degree, and suitability matching degree. An empirical study is conducted on 16 regions in Beijing, and the results show that the numerical matching degree of “resource-demand” gradually decrease from the central urban regions to the surrounding regions. The similarity matching degree in most regions reaches the highest range (0.776,1.00). There are 4 regions with a suitability matching rating of perfect, 7 regions with a rating of great, 1 region with a rating of good, and 4 regions with a rating of poor. The suitability matching model established in this paper can guide energy project decision-makers to select regions with greater potential for the development of IES. In addition, adjusting the similarity matching coefficient and numerical matching coefficient can meet the diversified needs of decision-makers for the similarity matching degree and numerical matching degree of the “resource-demand” balance, providing support for energy project planning decisions.</div></div>\",\"PeriodicalId\":56019,\"journal\":{\"name\":\"Sustainable Energy Technologies and Assessments\",\"volume\":\"72 \",\"pages\":\"Article 104099\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Energy Technologies and Assessments\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213138824004958\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Energy Technologies and Assessments","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213138824004958","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Suitability matching evaluation of the low-carbon regional integrated energy system based on the “resource-demand” balance
The integrated energy system (IES) stands out due to its advantages of multi-energy complementarity, green and efficiency. This paper aims to provide a practical model for suitable region site selection of low-carbon regional IES based on the “resource-demand” balance. A “resource-demand” suitability matching analysis indicator system is established. By using prospect theory and variable precision rough set theory, the decision-making preferences and the cognitive fuzziness influence of expert scoring were corrected respectively, and reasonable subjective weights were obtained. Then, the objective weights are obtained using the entropy method, and the optimal combined weights are obtained through the game theory weighting method. Finally, this paper establishes a “resource-demand” suitability matching model consisting of three sub models: similarity matching degree, numerical matching degree, and suitability matching degree. An empirical study is conducted on 16 regions in Beijing, and the results show that the numerical matching degree of “resource-demand” gradually decrease from the central urban regions to the surrounding regions. The similarity matching degree in most regions reaches the highest range (0.776,1.00). There are 4 regions with a suitability matching rating of perfect, 7 regions with a rating of great, 1 region with a rating of good, and 4 regions with a rating of poor. The suitability matching model established in this paper can guide energy project decision-makers to select regions with greater potential for the development of IES. In addition, adjusting the similarity matching coefficient and numerical matching coefficient can meet the diversified needs of decision-makers for the similarity matching degree and numerical matching degree of the “resource-demand” balance, providing support for energy project planning decisions.
期刊介绍:
Encouraging a transition to a sustainable energy future is imperative for our world. Technologies that enable this shift in various sectors like transportation, heating, and power systems are of utmost importance. Sustainable Energy Technologies and Assessments welcomes papers focusing on a range of aspects and levels of technological advancements in energy generation and utilization. The aim is to reduce the negative environmental impact associated with energy production and consumption, spanning from laboratory experiments to real-world applications in the commercial sector.