vehiclesICTInformation and Communication TechnologiesIEDsIntelligent Electronic DevicesIHDIn-Home DisplayIANsIndustrial Area Networks andIECInternational Electro Technical CommissionIEEEInstitute of Electrical and Electronics EngineersITLInformation Technology LaboratoryISOInternational Organization for StandardizationISAInternational Society of AutomationICinternal combustionIaaSInfrastructure as a ServiceIGCCIntegrated Gasification Combined CycleITUInternational Telecommunication UnionISACAInformation Systems Audit and Control AssociationIpso-ANNImproved article Swarm Optimization Algorithm-Artificial neural networkImproved ARIMAXImproved Autoregressive integrated moving average process with exogenous inputsIRENAInternational Renewable Energy AgencyIEAInternational Energy AgencyKPIsKey Performance IndicatorsKATSKorean Agency for Technology and Standardskbpskilobits per secondKNNK-nearest neighborsKNN-ANNK-nearest neighbor-Artificial neural networkKAISTKorea Advanced Institute of Science and TechnologyLPFlow pass filterLANlocal area networkLVDClow-voltage direct currentLVAClow-voltage alternating currentLSTM NNLong short-term memory Neural networkLESLinear exponential smoothingLightGBMLight gradient boosting methodLCOElevelized cost of electricityLCOSlevelized cost of storageLi-ionLithium-IonLi-PoLithium-PolymerMGMicrogridMDMSMeter Data Management SystemMPPTmaximum power point trackingMMCmodular multilevel converterMVDCMedium Voltage DCMPCModel Predictive ControlMOPSOMulti-Objective Particle Swarm OptimizationMOGAMulti-Objective GeneticMDMSMeter Data Management SystemMVACmedium voltage ACMASmulti agent-based control systemMVmedium voltageMSS-AdaMSS-Adaptive boostingAdaboost–MLPAdaptive boosting-Multilayer perceptronMixed ARIMAMixed autoregressive integrate moving averageMNBMultinomial naïve bayesMSMolten SaltMPmathematical programmingNANsneighbored area networksNISTNational Institute of Standards and TechnologyNPCneutral-point clamped converternZEBnet Zero Energy BuildingsnZECnet Zero Energy CommunityNANNeighborhood Area NetworkNNNeural networkNeuro-FuzzyArtificial neural networks-Fuzzy logicNeurofuzzy-ARIMAArtificial neural networks-fuzzy logic-Autoregressive integrated moving averageNARXNonlinear autoregressive exogenousNBNaïve bayesNi-CdNickel-CadmiumNi-MHNickel-Metal HydrideOMSOutage Management SystemOPFROptimal Power Flow ReservesOpenADROpen Automated Demand ResponseOPFOptimal Power Flow ToolOTECOcean thermal energy conversionOTEGsocean thermo-electric generatorsOLEVon-line electric vehicleORNLOak Ridge National LaboratoryPHILpower HILPHEVPlug-in Hybrid Electric VehiclePSCADPower Systems Computer Aided DesignPLCPower Line CommunicationPVphotovoltaicP2Ppeer-to-peerPLLPhase Locked LoopPVQVVoltage Adequacy and Stability ToolPESPower and Energy SocietyPSFpower signal feedbackPIproportional integralPIDproportional–integral–derivativePMUphasor measurement unitsPaaSPlatform as a ServicePSOParticle swarm optimizationPCCpoint of common couplingPCA-LSSVMPrincipal component analysis- Least squares support vector machinesPDRNNPooling-based deep recurrent neural networkPTParabolic TroughPHSPumped Hydroelectric StoragePEVsplug-in electric vehiclesPHEVsplug-in hybrid electric vehiclesPb-acidLead-AcidPATHPartners for Advance Transit and HighwaysQoSQuality of ServiceRTSpower grid real-timeRERsrenewable energy resourcesROCOFrate of change of frequencyRTPReal-Time PricingRVM-XGBoost EnsembleEnsemble of relevance vector machines and boosted treesRF-XGBoostRandom forest-extreme gradient boostingRBFNNRadial basis neural networksRFRandom forestRFLReinforcement learningRErenewable energyRCResistance to ChangeRFRadio FrequencySMssmart metersSCADASubstation supervisory control and data acquisitionSTATCOMStatic Synchronous CompensatorSGSmart GridSCADASupervisory Control and Data AcquisitionSCOPFSecurity Constrained Optimal Power Flow ToolSGCCState Grid Corporation of ChinaSCsupercapacitorsSMESsuperconducting magnetic energy storageSRF-PLLsynchronous reference frame PLLSGssynchronous generatorsSMssubmodulesSSRSub-synchronous resonanceSERsequence of event recorderSQLStructured Query LanguageSaaSSoftware as a ServiceSVMSupport vector machinesSD-EMD-LSTMSimilar days-selection, empirical mode decomposition-long short-term memory neural networksSDAsStacked denoising AutoencodersSARSAState–action–reward–state–actionSTARMASpatiotemporal auto-regressive moving average modelSELMStacked extreme learning machineSMLEStacking heterogeneous ensemble learning modelSMOspider monkey optimizationSASimulated annealingSARIMAXSeasonal autoregressive integrated moving average process with exogenous inputsSTPARSmooth transition periodic autoregressiveSTARMASpatiotemporal modelsSDSolar DishSTSolar TowerSTSSolar thermal systemsSFSolar FuelsSNGSynthetic Natural GasSLIstarting, lighting, and ignitionSoCState of ChargeSoHState of HealthSGCCSmart Grid Consumer CollaborativeTSOtransmission system operatorToUTime of use pricingTEPCOTokyo Electric Power Co.TSRtip speed ratioTD learningTemporal difference learningTFthin-filmsTESThermal Energy StorageTRATheory of Reasoned ActionTPBTheory of Planned BehaviorTAMTechnology Acceptance ModelTTMTranstheoretical ModelUNFCCCUnited Nations Framework Convention on Climate ChangeVSCVoltage Source ConvertersVSGVirtual synchronous generatorVICvirtual impedance controlVPNvirtual private networkVSIvoltage source inverterVMD-CNNVariational mode decomposition -Convolutional neural networkVARMAXVector autoregressive moving Average with exogenous inputsV2Gvehicle-to-gridG2Vgrid-to-vehicleV2Hvehicle to homeV2Vvehicle to vehicleVAMValue-based Adoption ModelWASAwide-area situational awarenessWANWide Area NetworkWAMSWide Area Monitoring SystemsWLANwireless local area networkWPT-RFWavelet packet transform-Random forestWESNWavelet echo state networksWT-TES-WNNWavelet transform combined with Holt-Winters- Weighted nearest-neighbor modelWNNWeighted nearest neighborsWCDWorld Commission on DamsWECswave energy convertersWPTwireless power transferXMLExtensible Markup LanguageXGboost-DWTExtreme gradient boosting- Discrete wavelet transformXGBoostExtreme gradient boostingZEBszero energy buildings
About the Companion Website
Smart Grid and Enabling Technologies is accompanied by a companion website:
www.wiley.com/go/ellabban/smartgrid
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PowerPoint Slides for Lecturers
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1 Smart Grid Architecture Overview
The electric power system is the largest and best engineering invention and achievement in human history. However, this grid paradigm faces serious challenges with regard to the increasing demand for electricity, the expanding penetration of intermittent renewable energies, and the need to respond to emerging needs such as wide usage of electric vehicles. The newly faced and expected challenges and expectations from the grid are forcing drivers to transform the current power system into a smarter grid. Smart grid (SG) is a new paradigm shift that combines the electricity, information, and communication infrastructures to create a more reliable, stable, accessible, flexible, clean, and efficient electric energy system. The SG comprises two main parts, SG infrastructure, and smart applications and operation. SG infrastructure entails a smart power system, information technology (IT), and communication system, while SG applications and operation are categorized into fundamental and emerging areas. The fundamental ones refer to energy management strategies, reliability models, security, privacy, and demand‐side management (DSM). Emerging applications include the wide deployment of electric vehicles and mobile charging and storage stations. All this indicates that SGs are characterized by automated energy generation, delivery, monitoring, and consumption with stakeholders from smart utilities, markets, and customers.
Initially in this chapter, the principles of current electrical power systems will be briefly discussed. After that, the implications of the transformation trend toward SG architecture will be investigated. Following this, SGs are addressed in greater depth, covering fundamentally diverse concepts and classifications. Lastly, some SG architectures will be highlighted and the future challenges and directions will be addressed.
1.1 Introduction
Today, power grids are being challenged to meet the ever‐growing energy demands of the twenty‐first century. Energy usage is projected to rise by 50% by 2050, according to
