LEADER 04653nam 2200985z- 450 001 9910580204303321 005 20231214133242.0 035 $a(CKB)5690000000012047 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/87492 035 $a(EXLCZ)995690000000012047 100 $a20202207d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSituation Awareness for Smart Distribution Systems 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (214 p.) 311 $a3-0365-4525-5 311 $a3-0365-4526-3 330 $aIn recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $acommunity integrated energy system 610 $aenergy management 610 $auser dominated demand side response 610 $aconditional value-at-risk 610 $aelectric heating 610 $aload forecasting 610 $athermal comfort 610 $aattention mechanism 610 $aLSTM neural network 610 $asmart distribution network 610 $asituation awareness 610 $ahigh-quality operation and maintenance 610 $acritical technology 610 $acomprehensive framework 610 $adistributionally robust optimization (DRO) 610 $aintegrated energy system (IES) 610 $ajoint chance constraints 610 $alinear decision rules (LDRs) 610 $aWasserstein distance 610 $aload disaggregation 610 $adenoising auto-encoder 610 $aREDD dataset 610 $aTraceBase dataset 610 $amachine learning 610 $asecondary equipment 610 $aCNN 610 $ashort text classification 610 $aelectric vehicle 610 $ashort-term load forecasting 610 $aconvolutional neural network 610 $atemporal convolutional network 610 $aclimate factors 610 $acorrelation analysis 610 $asustainable wind-PV-hydrogen-storage microgrid 610 $apower-to-hydrogen 610 $areceding horizon optimization 610 $astorage 610 $aphotovoltaic (PV) system 610 $aDC series arc fault 610 $apower spectrum estimation 610 $aattentional mechanism 610 $alightweight convolutional neural network 610 $acapacity configuration 610 $awind-photovoltaic-thermal power system 610 $acarbon emission 610 $amulti-objective optimization 610 $ainertia security region 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aGe$b Leijiao$4edt$01297583 702 $aYan$b Jun$4edt 702 $aSun$b Yonghui$4edt 702 $aWang$b Zhongguan$4edt 702 $aGe$b Leijiao$4oth 702 $aYan$b Jun$4oth 702 $aSun$b Yonghui$4oth 702 $aWang$b Zhongguan$4oth 906 $aBOOK 912 $a9910580204303321 996 $aSituation Awareness for Smart Distribution Systems$93024575 997 $aUNINA