{"title":"Deep learning-based post-disaster energy management and faster network reconfiguration method for improvement of restoration time","authors":"","doi":"10.1016/j.epsr.2024.111081","DOIUrl":null,"url":null,"abstract":"<div><div>Natural disasters in the world often cause power outages. To improve post-disaster response and restoration time, a method for energy management and network reconfiguration in emergencies has been proposed, where energy storages systems (ESSs) in distribution networks are used to support the system balance immediately and then the network reconfiguration is accelerated based on deep learning techniques. First, when power failures occur, the outputs of ESSs are calculated optimally in terms of the proposed optimization model under the constraints of minimizing line losses. After the injections of ESSs in emergencies, the network reconfiguration is still needed due to the limitation of ESS capacities. To avoid calculations of power flow repeatedly, a deep-learning based reconfiguration method for distribution networks has been proposed, where the deep-learning model is trained offline and can output the parameters of power flow immediately when a state combination of distribution generators (DGs), loads and power lines, which is an effective way to accelerate the network reconfiguration. If many reconfiguration schemes are found, a decision-making method with preferences are used to select one according to different situations. Finally, cases are designed, and simulation results show that the calculation time of a reconfiguration schemes are reduced significantly by almost one hundred multiples.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378779624009660/pdfft?md5=387754fbfe06c33cee7f788d539f2ad6&pid=1-s2.0-S0378779624009660-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electric Power Systems Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378779624009660","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Natural disasters in the world often cause power outages. To improve post-disaster response and restoration time, a method for energy management and network reconfiguration in emergencies has been proposed, where energy storages systems (ESSs) in distribution networks are used to support the system balance immediately and then the network reconfiguration is accelerated based on deep learning techniques. First, when power failures occur, the outputs of ESSs are calculated optimally in terms of the proposed optimization model under the constraints of minimizing line losses. After the injections of ESSs in emergencies, the network reconfiguration is still needed due to the limitation of ESS capacities. To avoid calculations of power flow repeatedly, a deep-learning based reconfiguration method for distribution networks has been proposed, where the deep-learning model is trained offline and can output the parameters of power flow immediately when a state combination of distribution generators (DGs), loads and power lines, which is an effective way to accelerate the network reconfiguration. If many reconfiguration schemes are found, a decision-making method with preferences are used to select one according to different situations. Finally, cases are designed, and simulation results show that the calculation time of a reconfiguration schemes are reduced significantly by almost one hundred multiples.
期刊介绍:
Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview.
• Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation.
• Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design.
• Substation work: equipment design, protection and control systems.
• Distribution techniques, equipment development, and smart grids.
• The utilization area from energy efficiency to distributed load levelling techniques.
• Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.