Yongling Wu, Kang Li, Ning Li, Shaoyuan Li, Lin Wang
{"title":"Optimization of Controller Parameters for Energy Saving","authors":"Yongling Wu, Kang Li, Ning Li, Shaoyuan Li, Lin Wang","doi":"10.3182/20140824-6-ZA-1003.02361","DOIUrl":null,"url":null,"abstract":"Abstract Among various technologies to tackle the twin challenges of sustainable energy supply and climate change, energy saving through advanced control plays a crucial role in decarbonizing the whole energy system. Modern control technologies, such as optimal control and model predictive control do provide a framework to simultaneously regulate the system performance and limit control energy. However, few have been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper investigates the correlations between control energy consumption and system performance using two popular control approaches widely used in the industry, namely the PI control and subspace model predictive control. Our investigation shows that the controller design is a delicate synthesis procedure in achieving better trade-off between system performance and energy saving, and proper choice of values for the control parameters may potentially save a significant amount of energy.","PeriodicalId":13260,"journal":{"name":"IFAC Proceedings Volumes","volume":"67 1","pages":"10281-10286"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IFAC Proceedings Volumes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3182/20140824-6-ZA-1003.02361","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Abstract Among various technologies to tackle the twin challenges of sustainable energy supply and climate change, energy saving through advanced control plays a crucial role in decarbonizing the whole energy system. Modern control technologies, such as optimal control and model predictive control do provide a framework to simultaneously regulate the system performance and limit control energy. However, few have been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper investigates the correlations between control energy consumption and system performance using two popular control approaches widely used in the industry, namely the PI control and subspace model predictive control. Our investigation shows that the controller design is a delicate synthesis procedure in achieving better trade-off between system performance and energy saving, and proper choice of values for the control parameters may potentially save a significant amount of energy.