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Titlebook: Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of ; Jelena Pono?ko Book 2020 Springer Na

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發(fā)表于 2025-3-21 17:09:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of
編輯Jelena Pono?ko
視頻videohttp://file.papertrans.cn/263/262718/262718.mp4
概述Provides an exhaustive summary of the latest tendencies in demand side management.Presents examples of data analytics and text mining applied to power system studies.Offer in-depth analysis of smart m
叢書名稱Springer Theses
圖書封面Titlebook: Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of ; Jelena Pono?ko Book 2020 Springer Na
描述.This thesis deals with two important and very timely aspects of the future power system operation - assessment of demand flexibility and advanced demand side management (DSM) facilitating flexible and secure operation of the power network. It provides a clear and comprehensive literature review in these two areas and states precisely the original contributions of the research..The book first demonstrates the benefits of data mining for a reliable assessment of demand flexibility and its composition even with very limited observability of the end-users. It then illustrates the importance of accurate load modelling for efficient application of DSM and considers different criteria in designing DSM programme to achieve several objectives of the network performance simultaneously. Finally, it demonstrates the importance of considering realistic assumptions when planning and estimating the success of DSM programs..The findings presented here have both scientific and practical significance; they gained her BSc and MSc degrees in electrical engineering from the University of Belgrade in 2011 and 2012 respectively. She graduated with her PhD from the University of Manchester. She has prese
出版日期Book 2020
關(guān)鍵詞Demand Side Management; Distribution Network; Smart Metering; Demand Response; Load Modelling; Load Disag
版次1
doihttps://doi.org/10.1007/978-3-030-39943-6
isbn_softcover978-3-030-39945-0
isbn_ebook978-3-030-39943-6Series ISSN 2190-5053 Series E-ISSN 2190-5061
issn_series 2190-5053
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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The Need for and Application of Data Analytics in Distribution System Studies,ibility and advanced demand side management (DSM) facilitating flexible and secure operation of the power network. The thesis provides a very clear and comprehensive literature review in these two areas and states precisely the original contributions of the research. It first demonstrates the benefi
5#
發(fā)表于 2025-3-22 12:35:25 | 只看該作者
Advanced Demand Profiling,eful to the distribution network operator (DNO) and/or other demand response (DR) responsible parties, information about time varying demand composition and its flexibility (both in close to real time and forecast) should also be provided.
6#
發(fā)表于 2025-3-22 13:39:00 | 只看該作者
Multi-objective Demand Side Management at Distribution Network Level,ilds on the results of the methodology on Advanced Demand Profiling, detailed in the previous chapter. Information about demand composition is used to model demand at each load bus of the network, facilitating that way further studies of the effect DSM may have on network performance indicators.
7#
發(fā)表于 2025-3-22 20:17:41 | 只看該作者
Conclusions and Further Work,ssment, and optimised DSM. The main aim of the research was to develop a methodology for multi-objective DSM in distribution network in support of transmission network operation, relying on the existence of a certain number of SMs with sub-metering technologies and application of data analytics meth
8#
發(fā)表于 2025-3-23 00:48:25 | 只看該作者
Jelena Pono?koProvides an exhaustive summary of the latest tendencies in demand side management.Presents examples of data analytics and text mining applied to power system studies.Offer in-depth analysis of smart m
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發(fā)表于 2025-3-23 02:08:56 | 只看該作者
Springer Theseshttp://image.papertrans.cn/d/image/262718.jpg
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