Yingbo Zhang , Hong Tang , Hangxin Li , Shengwei Wang
{"title":"下一代以人工智能为重点的数据中心与智能电网和区域能源系统的集成和交互:最新技术、机遇和挑战","authors":"Yingbo Zhang , Hong Tang , Hangxin Li , Shengwei Wang","doi":"10.1016/j.rser.2025.116097","DOIUrl":null,"url":null,"abstract":"<div><div>The rapid evolution of artificial intelligence (AI) and high-performance computing (HPC) has significantly increased the demand for data center capacity, particularly for Graphics Processing Unit (GPU) data centers. These data centers offer enhanced computational capabilities, but they also consume significantly more electricity than traditional data centers. However, existing reviews primarily focus on the role of traditional data centers for general-purpose computing concerning energy aspects. This paper rethinks the role of next-generation AI-focused GPU data centers as prosumers-both producers and consumers of energy, when integrated with and interacting within smart grids and district energy systems. First, we systematically review the existing strategies and methods to enhance the energy flexibility of data centers within the smart grids and highlight unique computing workload characteristics and AI-driven flexibility of GPU data centers in comparison with traditional data centers. Second, we comprehensively summarize transformative cooling technologies, particularly liquid cooling, the higher-grade waste heat recovery potential of GPU data centers and their various applications. Third, we thoroughly discuss the opportunities and technologies for renewable energy integration and curtailment as key strategies for GPU data center decarbonizations. Furthermore, this study elaborates on potential challenges and future perspectives of GPU data centers within smart grids and district energy systems as prosumers.</div></div>","PeriodicalId":418,"journal":{"name":"Renewable and Sustainable Energy Reviews","volume":"224 ","pages":"Article 116097"},"PeriodicalIF":16.3000,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integration and interaction of next-generation AI-focused data centers with smart grids and district energy systems: The state-of-the-art, opportunities and challenges\",\"authors\":\"Yingbo Zhang , Hong Tang , Hangxin Li , Shengwei Wang\",\"doi\":\"10.1016/j.rser.2025.116097\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The rapid evolution of artificial intelligence (AI) and high-performance computing (HPC) has significantly increased the demand for data center capacity, particularly for Graphics Processing Unit (GPU) data centers. These data centers offer enhanced computational capabilities, but they also consume significantly more electricity than traditional data centers. However, existing reviews primarily focus on the role of traditional data centers for general-purpose computing concerning energy aspects. This paper rethinks the role of next-generation AI-focused GPU data centers as prosumers-both producers and consumers of energy, when integrated with and interacting within smart grids and district energy systems. First, we systematically review the existing strategies and methods to enhance the energy flexibility of data centers within the smart grids and highlight unique computing workload characteristics and AI-driven flexibility of GPU data centers in comparison with traditional data centers. Second, we comprehensively summarize transformative cooling technologies, particularly liquid cooling, the higher-grade waste heat recovery potential of GPU data centers and their various applications. Third, we thoroughly discuss the opportunities and technologies for renewable energy integration and curtailment as key strategies for GPU data center decarbonizations. Furthermore, this study elaborates on potential challenges and future perspectives of GPU data centers within smart grids and district energy systems as prosumers.</div></div>\",\"PeriodicalId\":418,\"journal\":{\"name\":\"Renewable and Sustainable Energy Reviews\",\"volume\":\"224 \",\"pages\":\"Article 116097\"},\"PeriodicalIF\":16.3000,\"publicationDate\":\"2025-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Renewable and Sustainable Energy Reviews\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1364032125007701\",\"RegionNum\":1,\"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":"Renewable and Sustainable Energy Reviews","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364032125007701","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Integration and interaction of next-generation AI-focused data centers with smart grids and district energy systems: The state-of-the-art, opportunities and challenges
The rapid evolution of artificial intelligence (AI) and high-performance computing (HPC) has significantly increased the demand for data center capacity, particularly for Graphics Processing Unit (GPU) data centers. These data centers offer enhanced computational capabilities, but they also consume significantly more electricity than traditional data centers. However, existing reviews primarily focus on the role of traditional data centers for general-purpose computing concerning energy aspects. This paper rethinks the role of next-generation AI-focused GPU data centers as prosumers-both producers and consumers of energy, when integrated with and interacting within smart grids and district energy systems. First, we systematically review the existing strategies and methods to enhance the energy flexibility of data centers within the smart grids and highlight unique computing workload characteristics and AI-driven flexibility of GPU data centers in comparison with traditional data centers. Second, we comprehensively summarize transformative cooling technologies, particularly liquid cooling, the higher-grade waste heat recovery potential of GPU data centers and their various applications. Third, we thoroughly discuss the opportunities and technologies for renewable energy integration and curtailment as key strategies for GPU data center decarbonizations. Furthermore, this study elaborates on potential challenges and future perspectives of GPU data centers within smart grids and district energy systems as prosumers.
期刊介绍:
The mission of Renewable and Sustainable Energy Reviews is to disseminate the most compelling and pertinent critical insights in renewable and sustainable energy, fostering collaboration among the research community, private sector, and policy and decision makers. The journal aims to exchange challenges, solutions, innovative concepts, and technologies, contributing to sustainable development, the transition to a low-carbon future, and the attainment of emissions targets outlined by the United Nations Framework Convention on Climate Change.
Renewable and Sustainable Energy Reviews publishes a diverse range of content, including review papers, original research, case studies, and analyses of new technologies, all featuring a substantial review component such as critique, comparison, or analysis. Introducing a distinctive paper type, Expert Insights, the journal presents commissioned mini-reviews authored by field leaders, addressing topics of significant interest. Case studies undergo consideration only if they showcase the work's applicability to other regions or contribute valuable insights to the broader field of renewable and sustainable energy. Notably, a bibliographic or literature review lacking critical analysis is deemed unsuitable for publication.