{"title":"PMU Placement for Maximal Power System Observability Using Topology Transformation","authors":"Haneen Bawayan, Mohamed Younis","doi":"10.1109/STICT.2019.8789378","DOIUrl":"https://doi.org/10.1109/STICT.2019.8789378","url":null,"abstract":"A power system is considered to be fully observed when all bus voltages and currents are known. Phasor Measurement Unit (PMUs) opt to achieve system's observability when placed at selected buses throughout the system. Optimal PMU placement is concerned with determining the least count and position of PMUs needed to fully observe the system. This paper considers such optimization problem in the presence of zero injection buses (ZIBs). A novel solution is proposed where the system observability is modeled as a graph. A special transformation is presented to reduce the complexity of the optimization while factoring in the contribution of a ZIB to the system's observability. The optimization problem is then formulated as an integer linear program. Validation is conducted using different IEEE bus systems and using randomly generated power grid topologies. The results confirm the performance advantage of the proposed approach over competing scheme in the literature.","PeriodicalId":209175,"journal":{"name":"2019 IEEE Sustainability through ICT Summit (StICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125437734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SeyedMorteza MirhoseiniNejad, Fernando Martinez-Garcia, Ghada H. Badawy, D. Down
{"title":"ALTM: Adaptive learning-based thermal model for temperature predictions in data centers","authors":"SeyedMorteza MirhoseiniNejad, Fernando Martinez-Garcia, Ghada H. Badawy, D. Down","doi":"10.1109/STICT.2019.8789370","DOIUrl":"https://doi.org/10.1109/STICT.2019.8789370","url":null,"abstract":"To design effective control schemes for energy efficiency in data centers, it is crucial to have a thermal model of the system. Constructing thermal models of data centers for temperature prediction is extremely challenging, due to inherent complexity. Computational fluid dynamics (CFD) simulations or physical heat transfer equations are conventionally used to construct such thermal models. More recent approaches combine physical heat transfer rules and data-driven methods in an effort to obtain more accurate models. Our proposed adaptive learning-based thermal model (ALTM) is fast, adapts to thermal changes in the data center environment, and does not require prior knowledge of heat transfer rules between data center entities. Unlike other methods, ALTM is a holistic thermal model that predicts temperature of critical zones using data center operational variables as inputs. The operational variables are the controllable parameters and easily obtained measurements from IT and cooling units. A key use case for ALTM is that it can be effectively used for thermal-aware workload schedulers or cooling system controllers. Our results confirm the accuracy and adaptability of the model.","PeriodicalId":209175,"journal":{"name":"2019 IEEE Sustainability through ICT Summit (StICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133665587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"PReS: Power Peak Reduction by Real-time Scheduling for Urban Railway Transit","authors":"Zekun Yang, Yu Chen, N. Zhou, Shiqiong Tong","doi":"10.1109/STICT.2019.8789376","DOIUrl":"https://doi.org/10.1109/STICT.2019.8789376","url":null,"abstract":"Railway transportation is one of the most popular options for Urban Massive Transportation Systems (UMTS) because of many attractive features. A robust electric power supply is essential to enable normal operation. However, the power peaks appearing at the start time of the vehicles put heavy pressure on the power grid. Reduction of the power peak is a key issue in improving urban railway transit's power efficiency. Researchers have tried to address this problem by making a delicate timetable, but this method often failed to serve the purpose because of the punctuality problem. In this work, taking advantage of real-time estimation of the single train's power consumption, an online Power peak Reduction by real-time Scheduling (PReS) solution for trains' departure is proposed. Particularly, a Binary Integer Programming (BIP) model is introduced that is able to avoid power consumption peak caused by multiple trains departure simultaneously. The simulation result verified that the proposed real-time scheduling approach can effectively reduce the occurrences of power peak without bringing in additional train travel delay.","PeriodicalId":209175,"journal":{"name":"2019 IEEE Sustainability through ICT Summit (StICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124607939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}