Jian Zhao , Keran Huang , Yuan Gao , Xiaoyan Bian , Kai Zhang , Dongdong Li , Haoyang Cui
{"title":"考虑计算资源动态池化的计算中心微电网协同调度优化","authors":"Jian Zhao , Keran Huang , Yuan Gao , Xiaoyan Bian , Kai Zhang , Dongdong Li , Haoyang Cui","doi":"10.1016/j.apenergy.2025.125971","DOIUrl":null,"url":null,"abstract":"<div><div>The computility center (CC) is a flexible load that regulates its power demands through workload dispatching. CC microgrid can employ the flexibility of CC load to coordinate with the volatility of photovoltaic (PV) generation. However, the computing resources of CC are heavily fragmented. Such situation limits workload distribution and then results in CC load incapable of coordinating with microgrid. To address the above issue, this paper proposes a coordinated scheduling method for CC microgrid using computing resources dynamic pooling (CRDP). Specifically, a workload-core mapping model is proposed to transform workload into power load by formulating the matrix of processor core states. Subsequently, the CRDP method is proposed to integrate and allocate remaining available cores according to the core real-time states. Then a self-regulating CC microgrid coordinated framework is proposed to regulate the CC load by adjusting the scale of computing resource pool to the volatility of PV generation. The effectiveness of the proposed method in improving PV consumption is validated across different microgrid simulation scenarios.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"393 ","pages":"Article 125971"},"PeriodicalIF":10.1000,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Coordinated scheduling optimization for Computility center microgrid considering computing resources dynamic pooling\",\"authors\":\"Jian Zhao , Keran Huang , Yuan Gao , Xiaoyan Bian , Kai Zhang , Dongdong Li , Haoyang Cui\",\"doi\":\"10.1016/j.apenergy.2025.125971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The computility center (CC) is a flexible load that regulates its power demands through workload dispatching. CC microgrid can employ the flexibility of CC load to coordinate with the volatility of photovoltaic (PV) generation. However, the computing resources of CC are heavily fragmented. Such situation limits workload distribution and then results in CC load incapable of coordinating with microgrid. To address the above issue, this paper proposes a coordinated scheduling method for CC microgrid using computing resources dynamic pooling (CRDP). Specifically, a workload-core mapping model is proposed to transform workload into power load by formulating the matrix of processor core states. Subsequently, the CRDP method is proposed to integrate and allocate remaining available cores according to the core real-time states. Then a self-regulating CC microgrid coordinated framework is proposed to regulate the CC load by adjusting the scale of computing resource pool to the volatility of PV generation. The effectiveness of the proposed method in improving PV consumption is validated across different microgrid simulation scenarios.</div></div>\",\"PeriodicalId\":246,\"journal\":{\"name\":\"Applied Energy\",\"volume\":\"393 \",\"pages\":\"Article 125971\"},\"PeriodicalIF\":10.1000,\"publicationDate\":\"2025-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306261925007019\",\"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":"Applied Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306261925007019","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}
Coordinated scheduling optimization for Computility center microgrid considering computing resources dynamic pooling
The computility center (CC) is a flexible load that regulates its power demands through workload dispatching. CC microgrid can employ the flexibility of CC load to coordinate with the volatility of photovoltaic (PV) generation. However, the computing resources of CC are heavily fragmented. Such situation limits workload distribution and then results in CC load incapable of coordinating with microgrid. To address the above issue, this paper proposes a coordinated scheduling method for CC microgrid using computing resources dynamic pooling (CRDP). Specifically, a workload-core mapping model is proposed to transform workload into power load by formulating the matrix of processor core states. Subsequently, the CRDP method is proposed to integrate and allocate remaining available cores according to the core real-time states. Then a self-regulating CC microgrid coordinated framework is proposed to regulate the CC load by adjusting the scale of computing resource pool to the volatility of PV generation. The effectiveness of the proposed method in improving PV consumption is validated across different microgrid simulation scenarios.
期刊介绍:
Applied Energy serves as a platform for sharing innovations, research, development, and demonstrations in energy conversion, conservation, and sustainable energy systems. The journal covers topics such as optimal energy resource use, environmental pollutant mitigation, and energy process analysis. It welcomes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gap between research, development, and implementation. The journal addresses a wide spectrum of topics, including fossil and renewable energy technologies, energy economics, and environmental impacts. Applied Energy also explores modeling and forecasting, conservation strategies, and the social and economic implications of energy policies, including climate change mitigation. It is complemented by the open-access journal Advances in Applied Energy.