{"title":"Understanding the Role of Microgrid Topology for Decentralized Model-Based Control","authors":"Matthew K. Chu Cheong, Dongmei Chen, P. Du","doi":"10.1115/dscc2019-9103","DOIUrl":null,"url":null,"abstract":"\n This paper identifies how the topology of a microgrid, particularly with respect to localized power injections, can affect the overall stability of the system. Microgrids are smaller-scale power networks that can disconnect from, and operate independently to, the main grid if necessary; accordingly, distributed and local generation is much more common in these systems. Of these local power sources, a significant proportion interface with the microgrid via inverters, and therefore lack physical inertia. This absence of physical inertia exacerbates the control challenge in a microgrid. These issues motivate the question of how to best control distributed generators to realize grid-wide improvements to power quality. We outline how the placement of controlled distributed generators can result in varying degrees of improved transient behavior, following disturbances to a microgrid. In this resulting simulations and analysis, we find that when the power sources in a microgrid are of varying capacity or rating, then the network topology can have a significant effect on transient performance deterioration. Notably, we find that if even a single a lower rated power source is ‘near’ or adjacent to a grid disturbance, then the microgrid may experience severe harmonic disturbances. In addition, we show that if such sources are controlled with a decentralized optimal controller, rather than a typical droop mechanism, then the overall microgrid performance is significantly improved.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":"48 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechatronic Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/dscc2019-9103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper identifies how the topology of a microgrid, particularly with respect to localized power injections, can affect the overall stability of the system. Microgrids are smaller-scale power networks that can disconnect from, and operate independently to, the main grid if necessary; accordingly, distributed and local generation is much more common in these systems. Of these local power sources, a significant proportion interface with the microgrid via inverters, and therefore lack physical inertia. This absence of physical inertia exacerbates the control challenge in a microgrid. These issues motivate the question of how to best control distributed generators to realize grid-wide improvements to power quality. We outline how the placement of controlled distributed generators can result in varying degrees of improved transient behavior, following disturbances to a microgrid. In this resulting simulations and analysis, we find that when the power sources in a microgrid are of varying capacity or rating, then the network topology can have a significant effect on transient performance deterioration. Notably, we find that if even a single a lower rated power source is ‘near’ or adjacent to a grid disturbance, then the microgrid may experience severe harmonic disturbances. In addition, we show that if such sources are controlled with a decentralized optimal controller, rather than a typical droop mechanism, then the overall microgrid performance is significantly improved.
期刊介绍:
This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.