Qiao Lin;Li Ding;Zhengmin Kong;Zhen-Wei Yu;Xin Li;Haijin Wang
{"title":"基于多时间尺度模型预测控制的微电网需求方管理","authors":"Qiao Lin;Li Ding;Zhengmin Kong;Zhen-Wei Yu;Xin Li;Haijin Wang","doi":"10.1109/TSG.2024.3493958","DOIUrl":null,"url":null,"abstract":"The microgrid (MG) integrating clean renewable energy has increasingly emerged as a critical solution for addressing energy challenges and promoting sustainable energy development. However, the inherent uncertainties in renewable energy output and load consumption present significant challenges to the economic and stable operation of the MG. This paper investigates a multi-time model predictive control (MSMPC) strategy for the optimal scheduling of grid-connected MG. The proposed method can dynamically update the optimal scheduling of the MG on two-time scales based on real-time measurement data. The dispatchable thermostatically controlled loads (TCLs) are incorporated into the demand side management (DSM) system to enhance flexibility and satisfy the future trend of a larger proportion of controllable TCLs. Furthermore, the TCL model considers the aging problem associated with excessive compressor cycling and the satisfaction of end users. Simulation results demonstrate that the proposed method significantly improves the economic performance and robustness of the MG system. Moreover, a real-time experiment conducted using RT-LAB further verifies the feasibility of the proposed approach.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1181-1193"},"PeriodicalIF":8.6000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-Time Scale Model Predictive Control-Based Demand Side Management for a Microgrid\",\"authors\":\"Qiao Lin;Li Ding;Zhengmin Kong;Zhen-Wei Yu;Xin Li;Haijin Wang\",\"doi\":\"10.1109/TSG.2024.3493958\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The microgrid (MG) integrating clean renewable energy has increasingly emerged as a critical solution for addressing energy challenges and promoting sustainable energy development. However, the inherent uncertainties in renewable energy output and load consumption present significant challenges to the economic and stable operation of the MG. This paper investigates a multi-time model predictive control (MSMPC) strategy for the optimal scheduling of grid-connected MG. The proposed method can dynamically update the optimal scheduling of the MG on two-time scales based on real-time measurement data. The dispatchable thermostatically controlled loads (TCLs) are incorporated into the demand side management (DSM) system to enhance flexibility and satisfy the future trend of a larger proportion of controllable TCLs. Furthermore, the TCL model considers the aging problem associated with excessive compressor cycling and the satisfaction of end users. Simulation results demonstrate that the proposed method significantly improves the economic performance and robustness of the MG system. Moreover, a real-time experiment conducted using RT-LAB further verifies the feasibility of the proposed approach.\",\"PeriodicalId\":13331,\"journal\":{\"name\":\"IEEE Transactions on Smart Grid\",\"volume\":\"16 2\",\"pages\":\"1181-1193\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2024-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Smart Grid\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10750304/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Smart Grid","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10750304/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Multi-Time Scale Model Predictive Control-Based Demand Side Management for a Microgrid
The microgrid (MG) integrating clean renewable energy has increasingly emerged as a critical solution for addressing energy challenges and promoting sustainable energy development. However, the inherent uncertainties in renewable energy output and load consumption present significant challenges to the economic and stable operation of the MG. This paper investigates a multi-time model predictive control (MSMPC) strategy for the optimal scheduling of grid-connected MG. The proposed method can dynamically update the optimal scheduling of the MG on two-time scales based on real-time measurement data. The dispatchable thermostatically controlled loads (TCLs) are incorporated into the demand side management (DSM) system to enhance flexibility and satisfy the future trend of a larger proportion of controllable TCLs. Furthermore, the TCL model considers the aging problem associated with excessive compressor cycling and the satisfaction of end users. Simulation results demonstrate that the proposed method significantly improves the economic performance and robustness of the MG system. Moreover, a real-time experiment conducted using RT-LAB further verifies the feasibility of the proposed approach.
期刊介绍:
The IEEE Transactions on Smart Grid is a multidisciplinary journal that focuses on research and development in the field of smart grid technology. It covers various aspects of the smart grid, including energy networks, prosumers (consumers who also produce energy), electric transportation, distributed energy resources, and communications. The journal also addresses the integration of microgrids and active distribution networks with transmission systems. It publishes original research on smart grid theories and principles, including technologies and systems for demand response, Advance Metering Infrastructure, cyber-physical systems, multi-energy systems, transactive energy, data analytics, and electric vehicle integration. Additionally, the journal considers surveys of existing work on the smart grid that propose new perspectives on the history and future of intelligent and active grids.