Название: Smart Grid and Enabling Technologies
Автор: Frede Blaabjerg
Издательство: John Wiley & Sons Limited
Жанр: Физика
isbn: 9781119422457
isbn:
In Chapter 15, the latest taxonomy of Artificial Intelligence (AI) applications in SGs is discussed, including load and renewable energy forecasting, power optimization, electricity price forecasting, fault diagnosis, and cyber and physical layers security. Chapter 16 discusses the current state of simulation‐based approaches including multi‐domain simulation, co‐simulation, and real‐time simulation and hardware‐in‐the‐loop for SGs. Furthermore, some SG planning and analysis software are summarized with their advantages and disadvantages. Chapter 17 presents an overview of SG standards; new standardization studies, SG policies of some countries, and some important standards for the smart grid. Chapter 18 depicts the concepts of distributed generation, micro‐grid, SG, and distributed operation, which all pose more complexity and challenges to the modern power systems. This chapter presents the challenges and barriers that modern SGs face from different perspectives.
This book has the typical attributes of a contemporary book and discusses several aspects that will appeal to students, researchers, professionals, and engineers from various disciplines looking to increase their knowledge, insights, and ideas for the future development of SG as the next energy paradigm. This work perfectly fills the current gap and contributes to the realization and a better understanding of SG and its enabling technologies.
List of Abbreviations
3 GPPThird Generation Partnership ProjectABCAnt bee colonyAdaboost–MLPAdaptive boosting-Multilayer perceptronAERall-electric-rangeAMIAdvanced metering infrastructureAMRAutomated Meter ReadingAMSAutomatic metering servicesAManalytical methodsANSIAmerican National Standards InstituteACAlternative CurrentAVRAutomatic Voltage RegulatorAWSAmazon Web ServicesAPIApplication programming interfaceANNartificial neural net-workARIMA-XGBoostAutoregressive integrated moving Average-Extreme gradient boostingARMA-TDNNAutoregressive and moving average-Time delay neural networkARMAAutoregressive and Moving AverageARIMAAutoregressive integrated moving averageAdaboost–MLPAdaptive boosting-Multilayer perceptronARIMA-ANFISAutoregressive integrated moving average-adaptive neuro-fuzzy inference systemANFISAdaptive neuro-Fuzzy inference systemAODEAggregating One-Dependence Estimators classifierARMAAutoregressive moving averageACOAnt colony optimizationAnt colonyAnt colonyABCAnt bee colonyARIMA Neurofuzzy-Artificial neural networks-fuzzy logic-Autoregressive integrated moving averageARIMA MixedMixed autoregressive integrate moving averageImproved ARIMAXImproved Autoregressive integrated moving average process with exogenous inputsBACBuilding Automation and ControlBMbusiness modelBPEBuilding Produced EnergyBANsBuilding/Business Area NetworkBPLBroadband over Power LineBayesianWavelet-Extreme learning machineBoosting additive quantile regressionBoosting additive quantile regressionBNNBayesian neural networkBACBayesian actor-Critic algorithmsBBNBayesian belief networkBioBiological swarm chasing algorithmbioenergyBiomass energybiofuelsliquid fuelsBEVsbattery electric vehiclesCCCloud ComputingCIMCommon Information ModelCPPcritical peak pricingCHILcontroller HILCHPCombined heat and power systemsCISCustomer Information SystemCEPRIChina Electric Power Research InstituteCOAGCouncil of Australian GovernmentsCAEScompressed air energy storageCHBcascaded H-bridge converterCMCcentral management controllerFaster R-CNNFaster Region-based Convolutional neural networkCNN-WTConvolutional neural network-Wavelet transformCVAELMComplementary ensemble empirical mode decomposition with adaptive noise- Variational mode decomposition – Adaptive boosting-Extreme learning machineCRfsConditional random fieldsCRO-SLCoral reefs optimization algorithmCSPconcentrating solar powerc-SisiliconCO2carbon dioxideCAESCompressed Air Energy StorageCICEVsconventional internal-combustion-engine vehiclesDGDistributed GenerationDRDemand ResponseDMSDistribution Management SystemDSIDemand-Side IntegrationDSMDemand Side ManagementDSLDigital Subscriber LineDSOdistribution network operatorDERDistributed Energy ResourcesDRMSDemand Response Management SystemDFIGdoubly fed induction generatorDCDirect CurrentDPRdigital protective relayDFRdigital fault recorderDGsdistributed generatorsDESDistributed Energy StorageDBNDeep belief networksDCNNDeep convolutional neural networkDRN-DWWCDeep residual networks - Dynamically weighted wavelet coefficientsDQLDeep Q-learningDNIdirect componentDoDDepth of DischargeE2EEnd-to-EndEMSEnergy management systemEUEuropean UnionEPBDEnergy Performance of Building DirectiveETLextract, transform and loadETAPElectrical Transient Analyzer ProgramEPRIElectric Power Research InstituteEEGIEuropean Electricity Grid InitiativeESSsenergy storage systemsEMIelectromagnetic interferenceERPEnterprise Resource PlanningEVElectric VehiclesESNEcho state networksEKF-based NNExtended Kalman filter method Neural NetworkExtra treeExtra treeETCevacuated tube solar collectorsESOIenergy stored on energy investedEVSEElectric Vehicle Supply EquipmentEPSElectric power systemFACTSFlexible AC transmission systemsFERCFederal Energy Regulatory CommissionFMIFunctional Mockup InterfaceFMUFunctional Mock-up UnitsFANField Area NetworkFCflying capacitor converterFLFuzzy logicFCRBMFactored conditional restricted Boltzmann MachineFaster R-CNNFaster Region-based Convolutional neural networkFIS-LSEFuzzy inference system-least-squares estimationFLC-FAFuzzy logic controller-Firefly algorithmFRFresnel ReflectorFPCflat-plate solar collectorsFESFlywheel Energy StorageFCEVsfuel-cell electric vehiclesGOOSEgeneric object-oriented system eventGSMGlobal System for Mobile CommunicationsGISGeographic Information SystemGPSGlobal Positioning SystemGWACGrid Wise Architecture CouncilGASVMGenetic functionality support vector machineGRU NNGated recurrent unit neural networkGPGaussian processGANsGenerative adversarial networksGBMGradient boosting machineGARCHGeneralized Autoregressive Conditional HeteroskedasticGA-NNGenetic algorithm-Neural networkGBRTGradient boosted regression treeGlow-wormGlow-worm optimizationGWOgrey wolf optimizationGBTDGradient boosting theft detectorGHPGeothermal Heat PumpGWECThe Global Wind Energy CouncilGHGgreenhouse gasesHANsHome area networksHEMshome energy managementHILHardware-in-the-LoopHPCCHomePlug Command and ControlHVDCHigh-Voltage Direct CurrentHCShill climbing searchingHESSshybrid energy storage systemsHPFhigh pass filterHDFSHadoop Distributed File SystemHNNHybrid neural networkHMMHidden Markov modelsHPPsHydropower СКАЧАТЬ