Demand Response Application in Smart Grids: Operation Issues - Volume 2

290 Pages·2020·11.439 MB·English

by  Sayyad Nojavan, Kazem Zare

Sayyad Nojavan
Kazem Zare  Editors
Demand
Response
Application in
Smart Grids
Operation Issues - Volume 2
Demand Response Application in Smart Grids
(cid:129)
Sayyad Nojavan Kazem Zare
Editors
Demand Response
Application in Smart Grids
Operation Issues - Volume 2
Editors
SayyadNojavan KazemZare
DepartmentofElectricalEngineering FacultyofElectricalandComputerEngineering
UniversityofBonab UniversityofTabriz
Bonab,Iran Tabriz,Iran
ISBN978-3-030-32103-1 ISBN978-3-030-32104-8 (eBook)
https://doi.org/10.1007/978-3-030-32104-8
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Preface
After restructuring and deregulation of the electricity industry, it is stated that the
power system will be more efficient if the differences between peak and low load
periods are kept as small as possible. It has been demonstrated that the perfect
balance between thesupply and demand inthe real time is necessary for a reliable
operationofelectricitysystem.Demandresponseprogramisdefinedaschangesin
electricconsumptionpatternsofend-userclientsinresponsetochangesofelectricity
priceovertimeortoincentivepaymentsdesignedtodecreasehighelectricityusage
athighwholesalemarketpricestimesorwhenthesystemreliabilityproblemsoccur.
Inotherwords,theprocedurethroughwhichconsumersrespondtothepricesignals
inserted in tariffs by changing their consumption patterns is called the demand
response programs (DRPs). Moreover, DRPs can help the independent system
operator (ISO) to reduce the price volatility during peak demand hours. Different
DRPscanbeclassifiedintotwomaincategories:incentive-basedprograms(IBPs),
which are further divided into classical programs and market-based programs, and
price-based programs, (PBPs), which are based on the dynamic pricing rates in
which the electricity tariffs are not flat. The rates fluctuate following the real-time
costofelectricity.Theultimateobjectiveoftheseprogramsistoflattenthedemand
curvebyofferingahighpriceduringpeakperiodsandlowerpricesduringoff-peak
periods.Theseratesincludethetimeofuse(TOU)rate,criticalpeakpricing(CPP),
extreme day pricing (EDP), extreme day CPP (ED-CPP), and real-time pricing
(RTP). The basic type of PBP is the TOU rates, which are the rates of electricity
price per unit consumption that differ in different blocks of time. The rate during
peakperiodsishigherthantherateduringoff-peakperiods.ThesimplestTOUrate
hastwotimeblocks:thepeakandtheoff-peak.Theratedesignattemptstoreflectthe
average cost of electricity during different periods. The CPP rates include a
pre-specified higher electricity usage price superimposed on TOU rates or normal
flat rates. CPP prices are used during contingencies or high wholesale electricity
prices for a limited number of days or hours per year. On the other hand, EDP is
similartoCPPinhavingahigherpriceforelectricityanddiffersfromCPPinthefact
that the price is in effect for the whole 24 h of the extreme day. Furthermore, in
ED-CPPrates,CPPratesforthepeakandoff-peakperiodsarecalledduringextreme
v
vi Preface
days.RTPsaretheprogramsinwhichthecustomersarechargedhourlyfluctuating
pricesreflectingtherealcostofelectricityinthewholesalemarket.RTPcustomers
areinformedaboutthepricesonaday-aheadorhour-aheadbasis.Manyeconomists
areconvincedthatRTPprogramsarethemostdirectandefficientDRPssuitablefor
competitive electricity markets and should be the focus of policymakers. In other
words, PBPs have a wide range of planning horizon from a few minutes to many
years. DRP can be categorized into long-term, midterm, and short-term programs
based on their planning intervals. Programs with more than a day period are long-
term or midterm programs. Finally, this book seeks to analyze economic and
technicaleffectsofdemandresponseprogramsinsmartgridsinoperationissues.
Bonab,Iran SayyadNojavan
Tabriz,Iran KazemZare
Contents
1 SmartGridsandGreenWirelessCommunications. . . . . . . . . . . . . 1
FarzadH.PanahiandFereidounH.Panahi
2 ImplementationofDemandResponsePrograms
onUnitCommitmentProblem. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
FarkhondehJabari,MousaMohammadpourfard,
andBehnamMohammadi-Ivatloo
3 HourlyPrice-BasedDemandResponseforOptimal
SchedulingofIntegratedGasandPowerNetworks
ConsideringCompressedAirEnergyStorage. . . . . . . . . . . . . . . . . 55
MohammadAminMirzaei,MortezaNazari-Heris,
BehnamMohammadi-Ivatloo,KazemZare,MousaMarzband,
andAmjadAnvari-Moghaddam
4 EnergyManagementofHybridAC-DCMicrogrid
UnderDemandResponsePrograms:Real-TimePricing
VersusTime-of-UsePricing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
RaminNourollahi,KazemZare,andSayyadNojavan
5 DistributionFeederReconfigurationConsidering
Price-BasedDemandResponseProgram. . . . . . . . . . . . . . . . . . . . . 95
EhsanHooshmandandAbbasRabiee
6 Risk-ConstrainedIntelligentReconfiguration
ofMulti-Microgrid-BasedDistributionSystems
underDemandResponseExchange. . . . . . . . . . . . . . . . . . . . . . . . . 119
AtaAjoulabadi,FarhadSamadiGazijahani,
andSajadNajafiRavadanegh
vii
viii Contents
7 ACOptimalPowerFlowIncorporatingDemand-Side
ManagementStrategy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
FarkhondehJabari,MousaMohammadpourfard,
andBehnamMohammadi-Ivatloo
8 DemandSideIntegrationintheOperation
ofLVSmartGrids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
SusannaMocciandSimonaRuggeri
9 Multi-ObjectiveOptimizationModelforOptimal
PerformanceofanOff-GridMicrogridwithDistributed
GenerationUnitsinthePresenceofDemand
ResponseProgram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
AfshinNajafi-Ghalelou,KazemZare,SayyadNojavan,
andMehdiAbapour
10 OptimalOperationoftheMicrogridConsidering
NetworkLossesandDemandResponsePrograms
UnderConditionofUncertainty. . . . . . . . . . . . . . . . . . . . . . . . . . . 217
KazemZareandSaberMakhandi
11 Techno-EconomicFrameworkforCongestionManagement
ofRenewableIntegratedDistributionNetworks
ThroughEnergyStorageandIncentive-Based
DemandResponseProgram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
AryaAbdolahi,FarhadSamadiGazijahani,
NavidTaghizadeganKalantari,andJavadSalehi
12 StochasticOptimalPreventiveVoltageStabilityControl
inPowerSystemsunderDemandResponseProgram. . . . . . . . . . . 265
MortezaNojavan,HereshSeyedi,
andBehnamMohammadi-Ivatloo
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
Chapter 1
Smart Grids and Green Wireless
Communications
FarzadH.PanahiandFereidounH.Panahi
Nomenclature
AMI Advancedmeteringinfrastructure
AP Accesspoint
BS Basestation
CR Cognitiveradio
D2D Devicetodevice
DR Demandresponse
DSM Demand-sidemanagement
EE Energyefficiency
EH Energyharvesting
FQL FuzzyQ-learning
FSL FuzzySARSAlearning
GA Geneticalgorithm
GHG Greenhousegas
HetNet Heterogeneousnetwork
HPPP HomogeneousPoissonpointprocess
ICT Informationandcommunicationstechnology
IoT Internetofthings
LTE-A Long-termevolutionadvanced
M2M Machinetomachine
MNO Mobilenetworkoperator
NE Nashequilibrium
OPEX Operationalexpenditure
PLC Powerlinecommunications
PSM Powersavingmode
F.H.Panahi(*)·F.H.Panahi
DepartmentofElectricalEngineering,UniversityofKurdistan,Sanandaj,Kurdistan,Iran
e-mail:[e-mail]
©SpringerNatureSwitzerlandAG2020 1
S.Nojavan,K.Zare(eds.),DemandResponseApplicationinSmartGrids,
https://doi.org/10.1007/978-3-030-32104-8_1
2 F.H.PanahiandF.H.Panahi
QoS Qualityofservice
RES Renewableenergysource
RPS Renewablepowersupplier
SEP Smartenergyprofile
SG Smartgrid
SGFAN Smartgridfieldareanetwork
SGHAN Smartgridhomeareanetwork
SGNAN Smartgridneighborhoodareanetwork
SGWAN Smartgridwideareanetwork
SINR Signaltointerferenceandnoiseratio
UDN Ultradensenetwork
UE Userequipment
UMTS Universalmobiletelecommunicationssystem
WSN Wirelesssensornetwork
1.1 Introduction
Inatraditionalelectricgrid,themaincausesofpowerinefficiencyarehigh-voltage,
long-distancetransmission,andlarge-scalecentralizedelectricitygeneration[1].To
improve thepower efficiency and reliability of the grid, the concept of smartgrids
(SGs) has been proposed by using information and communications technology
(ICT). Demandresponse(DR),decentralized powergeneration, demand-sideman-
agement (DSM), and price signaling are the key characteristics of a SG associated
withgreenwirelesscommunications.WithDRandDSM,bothpowergeneratorsand
consumers can interact to optimize the process of power supply and consumption.
The power generation may be performed by small distributed power plants (e.g.,
small wind turbines and solar panels) and consumers using decentralized design.
Therefore,thiscouldhelpconsumerstobelessdependentonthemainelectricalgrid.
With price signaling, the consumers will know about the present power price.
Moreover, the generators can encourage consumers to consume electrical energy
when the demand is low, i.e., during the off-peak period, by giving them a lower
priceforelectricityduringthosetimes.Thiswillresultinalowerinvestmentforthe
infrastructureasthepeakloadwillbereduced.
Inrecentyears,theintegrationofwirelesscommunicationsandSGshasattracted
a significant research attention [2]. On one hand, wireless communication technol-
ogieswillplayanessentialroleintherevolutionofSGsbycommunicatingavariety
ofdataandmeasurementoverallnodesoftheelectricalgrid.Ontheotherhand,fora
better power usage when providing a wireless service to mobile units, SGs can be
usedtosupportgreenwirelesscommunications.Inwirelessnetworks,eachwireless
basestation(BS)poweredbyaSGmightbeselfishinoptimizingitsownoperation
in terms of capacity or quality of service (QoS). In this chapter, how to design
energy-efficient communication infrastructures without negative effects on the