{"title":"微电网性能改进和成本优化的灰狼优化算法","authors":"Charivil Sojy Rajan, M. Ebenezer","doi":"10.1109/icgea54406.2022.9791902","DOIUrl":null,"url":null,"abstract":"The traditional grid is undergoing a rapid transition from its conventional unidirectional form to an interactive, smart, bidirectional form. Microgrids are an integral part of smart grids playing a prominent role in supplying power to regions lacking electrical infrastructure. Since there may be diverse Distributed Generation (DG) sources in a microgrid, it is challenging to maintain the bus voltage at the desired value, which may adversely affect the performance of microgrids. This demands the implementation of controllers. The classical PID controller would be an apt choice in such a scenario. To obtain the desired output, it is necessary to perform the tuning of control parameters-proportional, integral and derivative gains, Kp, Ki and Kd, respectively. This paper presents the implementation of Grey Wolf Optimizer (GWO) Algorithm tuned PID Controller to maintain the DC link voltage of a microgrid under study. The latter part of the paper presents a multi-microgrid interconnection scheme. The GWO Algorithm has been implemented for the cost optimization of this multi-microgrid interconnection scheme, consisting of thermal units, solar PV array and wind generation. It has been proved that there is considerable savings in the total cost due to the integration of solar PV array and wind generation. The microgrid modeling and simulations in both cases are performed in the MATLAB/Simulink environment.","PeriodicalId":151236,"journal":{"name":"2022 6th International Conference on Green Energy and Applications (ICGEA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Grey Wolf Optimizer Algorithm for Performance Improvement and Cost Optimization in Microgrids\",\"authors\":\"Charivil Sojy Rajan, M. Ebenezer\",\"doi\":\"10.1109/icgea54406.2022.9791902\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The traditional grid is undergoing a rapid transition from its conventional unidirectional form to an interactive, smart, bidirectional form. Microgrids are an integral part of smart grids playing a prominent role in supplying power to regions lacking electrical infrastructure. Since there may be diverse Distributed Generation (DG) sources in a microgrid, it is challenging to maintain the bus voltage at the desired value, which may adversely affect the performance of microgrids. This demands the implementation of controllers. The classical PID controller would be an apt choice in such a scenario. To obtain the desired output, it is necessary to perform the tuning of control parameters-proportional, integral and derivative gains, Kp, Ki and Kd, respectively. This paper presents the implementation of Grey Wolf Optimizer (GWO) Algorithm tuned PID Controller to maintain the DC link voltage of a microgrid under study. The latter part of the paper presents a multi-microgrid interconnection scheme. The GWO Algorithm has been implemented for the cost optimization of this multi-microgrid interconnection scheme, consisting of thermal units, solar PV array and wind generation. It has been proved that there is considerable savings in the total cost due to the integration of solar PV array and wind generation. The microgrid modeling and simulations in both cases are performed in the MATLAB/Simulink environment.\",\"PeriodicalId\":151236,\"journal\":{\"name\":\"2022 6th International Conference on Green Energy and Applications (ICGEA)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 6th International Conference on Green Energy and Applications (ICGEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icgea54406.2022.9791902\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 6th International Conference on Green Energy and Applications (ICGEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icgea54406.2022.9791902","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grey Wolf Optimizer Algorithm for Performance Improvement and Cost Optimization in Microgrids
The traditional grid is undergoing a rapid transition from its conventional unidirectional form to an interactive, smart, bidirectional form. Microgrids are an integral part of smart grids playing a prominent role in supplying power to regions lacking electrical infrastructure. Since there may be diverse Distributed Generation (DG) sources in a microgrid, it is challenging to maintain the bus voltage at the desired value, which may adversely affect the performance of microgrids. This demands the implementation of controllers. The classical PID controller would be an apt choice in such a scenario. To obtain the desired output, it is necessary to perform the tuning of control parameters-proportional, integral and derivative gains, Kp, Ki and Kd, respectively. This paper presents the implementation of Grey Wolf Optimizer (GWO) Algorithm tuned PID Controller to maintain the DC link voltage of a microgrid under study. The latter part of the paper presents a multi-microgrid interconnection scheme. The GWO Algorithm has been implemented for the cost optimization of this multi-microgrid interconnection scheme, consisting of thermal units, solar PV array and wind generation. It has been proved that there is considerable savings in the total cost due to the integration of solar PV array and wind generation. The microgrid modeling and simulations in both cases are performed in the MATLAB/Simulink environment.