Energy Management System in Smart Microgrid Using Multi Objective Grey Wolf Optimization Algorithm

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Energy Management System in Smart Microgrid Using Multi Objective Grey Wolf Optimization Algorithm

2

1M. E. Power Systems Engineering, K. S. Rangasamy College of Technology, Tiruchengode, Tamil Nadu, India.

2Professor/EEE, K. S. Rangasamy College of Technology, Tiruchengode, Tamil Nadu, India. ***

Abstract Nowadays the population is increasing day by day, so that the demand for energybecomes high which inturn increases the demand for coal. This rapid increase in demand of electricity becomes uneconomical, detrimental and high in power losses. Also the conventional grid is unable to adjust to the growing energy demands and locating gridfailures. Hence there is the need for other energy resources like renewable sources and this integration may cause unbalanced power flow to the grid which needs an energy management system. This proposed work aims at maximizing the use of local generation, minimizing the consumption price and reducing the emission of greenhouse gases. This efficient energy management system is achieved with the help of two controllers: Energy Market Management Controller (EMMC) and Home Energy Management Controller (HEMC). HEMC shares the information about load andenergystorage systems to EMMC which will contain all details about the energy providers, local generation and its price details. The problems in smart grid can be solved using the strategies that were followed in demand response. Among various optimization methods, Multi Objective Grey Wolf Optimization (MOGWO) is preferred due to its fast converging capability compared to other optimizationtechniques. The simulationresult shows the reduction in pollution and consumption price in this work.

Key Words: Microgrid, smart grid, energy market management controller, home energy management controller, multi objective grey wolf optimization, energy providers, renewable energy resources.

1. INTRODUCTION

Energyplaysacrucialpartinacountry'sgrowthofits socialandeconomicposition.Becauseithasadirectimpact ontheeconomyandislinkedtoraisingthecountry'sliving standards.Astheworld'spopulationgrows,moreenergyis requiredtomeetthegrowingdemandforenergy.Asaresult of these energy constraints in emerging countries, smart energy management (SEM) can help to alleviate both technicalandeconomicissues.

SEMisconcernedwithintegratinglocalgeneration,such asphotovoltaic(PV),wind,andfuelcells,aswellaseffective

energytradingbetweenenergyprovidersandcustomers.By combining both generation and consumption, researchers are attempting to design improved structures for optimal energyandmarketmanagement.

Consumer basedenergymanagementtoincreaseprofit forconsumersbyemployingastochasticgamestrategythat combines prosumer decision and the stochastic nature of renewable energy is proposed in [1]. [2] provides task classification based home energy management, which identifiesthebestactivationtaskwithindevicerestrictions.

The ideal activation time for each type of work is determinedusingaquadraticutilityfunction.[3]proposesa decision makingcontrollerthatoptimizesgeneration,load, andstorage.Tomakedecisionsmoreintelligent,intelligent fuzzy logic is offered. In [4,] the integration of a storage system is proposed in order to achieve high energy independenceinanSMGthatisbasedonhomeloadcontrol. [5] investigates data driven home energy management (HEM),whichisoptimizedusingaBayesianalgorithmand includesrenewableenergyresources(RER)andanenergy storage system. Within micro grid (MG) and multi MG environments,theenergymarketmanagementsystemin[6] executes day ahead optimization of distribution network addressing(MMG).

Thegoalsaretoreducecostsbyusingtwooperatorsina dynamic games function. Researchers in [7] developed a power loss based energy transaction inside the MG and MMGparadigmstominimizepowerloss.TheMultiEnergy RouterSystemisusedtoachievethisstrategy(MERS).[8] proposes a market mechanism for average pricing that is utilizedindistributionnetworks.Thegoalistodecentralize theformulationoftheaveragepricemarketmechanismin ordertospreadthecostproductionofenergyresourceswith azeromargin.

Using Mixed Integral Linear Programming, [10] proposesamulti objectiveoptimizationtohandletheenergy management based social and ecologicalproblem for microgrid(MILP). Approach for maximum utilization of renewable distribution is proposed in [11], and the same conceptisaddressedin[12]toreduceenergylossinorderto recognize the economic benefits. [13] presents a quick overviewofvariouscontrolstrategies.

In addition, the authors recommended intelligent and IoT basedcontrolsolutionsforfutureclusteredmicrogrids. Accordingtoasurveyofrelatedliterature,researchershave solved technological challenges for SMG, such as user

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comfort, consumption, generation, storage, and trading. However, in order for it to be impactful and useful, more research is required. The majority of the study in this literaturefocusedonenergyandmarketmanagementissues. However, environmental implications such as greenhouse gases (GHGs) and other related problems are not well addressed.

Themaingoalofthisstudyistoofferendcustomersa practical answer to their energy management problems specifically,intermsofloadcontrol,loweringconsumption costs, and promoting users to use domestic generation within their limitations. Objectives of this paper are summarizedasfollows:

a) This work proposes greenhouse minimization by encouraging the users to use renewable energy resources.

b) Italsohelpstheprosumerstoreducetheirenergy consumptionprice.

c) Theseobjectivesareachievedwiththehelpofmulti objective grey wolf optimization technique which gives faster convergence compared to other optimizationtechniques.

Thispaperissectionedasfollows:Section2dealswith theworksrelatedtothispaper.Modelingofthesystemand load for the microgrid is discussed in section 3. The proposedworkoftheEMSisillustratedinsection4.Section 5 shows and discusses the result of this work. Section 6 presents the conclusion of this research and section 7 providesanideaforthefutureresearchwork.

NOMENCLATURE

2. RELATED WORK

Demandresponse(DR)systemthatisbasedonoptimal planningissuggestedin[14].RERandintelligentcontrolof domestic heating and cooling systems for smart grid that reducescostsbyregulatingsmartdeviceswereadded.[15] proposed a revolutionary market management structure (transaction rules) for industrial consumption, based on block chain and peer to peer electricity markets. Load managementonthedemandsideisalsoinvestigatedinthis study.[16]comparedtrendsandassociateddifficultiesinthe microgrid.ThewritersofthispublicationcoveredtypicalMG concerns. In a regulated environment, there are also obstacles for managing and protecting. For controlling energy for smart distribution systems, encompassing implementation, current development, and ongoing research, [17] addresses classification, limitations, and problems.

In[18],theauthorsdevelopedaToUgaspricing based trading model for MG at two levels, in which the goal is realized using Game Theory. The suggested approach is tested on a case study with two scenarios: a single gas pricing scenario and a gas pricing scenario with two scenarios. For multi home MG, an energy management systemisintroduced,whichreducesmarketclearingprice by15%andloadconsumptionfactorby30%foradefined timeinterval[19].ForenergytransferfromhometoMGor viceversa,aswellasloadcontrol,netmeteringandsmart devicesareexplored.

3. SYSTEM MODEL AND LOAD MODEL

Withinthesmartgridconcept,theMicro grid(MG) haswell definedelectricalandcommunicationboundaries forsharingpowerandcommunicationsignals.

Fig -1: Microgridenergymanagementsystem

The proposed work dealswiththemicrogridthat supplies energy to three areas that has its own local generation. EMMC will get the information about power fromDGs.ThisdatawillbesharedwithHEMCtoschedule the load. The microgrid needs an effective energy management system for which the concepts like battery energy storage system, RER’s, greenhouse emission, load schedulingandconsumptionpricingshouldbewellplanned andthendesigned.Fig 1representsthemicrogridenergy

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managementsystem.Thedetailedsystemmodelingwillbe discussedasfollows:

3.1. Solar Panel Modeling

Renewableenergyresourceslikesolarcellandwind turbineareconsideredasalocalgenerationforeacharea. ThisusageofRERwillhelptheconsumertominimizetheir consumptionprice.Theoutputpowerfromeachresourceis expressedbelow.Thesolarpowerisexpressedinequation (1)[20].

Ppv=(Rp/1000)×Ppv,rated×ηMPPT ……….(1)

3.2. Wind Turbine Modeling

The mechanical and electrical powers of wind turbine can be expressed in equation (2) and (3) respectively[21]. The detailsofRER’sandthermal power plantarelistedinTable 1.

individualdemandof1MW.Thenetloadofacommunitywill be3MW.

4. PROPOSED DESIGN OF EMS

EMScanfortifytheefficiencyofagridandcansupply thedemandwithoutanyinterruptionorlossesandmakesthe powersupplyreliable.Forthis,therearemanyfactorslike consumption price, greenhouse gases, renewable energy resources,etc.shouldbetakenintoaccountandalsotobe controlled [23]. Fig 2 presents the block diagram of proposeddesignofEMS.

……….. (2)

Pe = ɳ ×Pw ..………(3)

Table 1: Ratingsofenergyproviders

Energyproviders Ratings

Thermalpower 1MW Solarpower 500kW Windpower 500kW

3.3. ESS Modeling

Theoutput powerof renewableenergy resources always depends upon the weather conditions such as sunlightandwind.Duetothechangesinclimaticconditions, thepowerproduceswillalwaysbefluctuating.Toencounter thisinstability,theusageofbatteryenergystoragesystem becomesessential[22].Ratingofbatteryisveryimportantin case of energy storage system. The equation [4] and [5] represents the charging and discharging state of battery [21].

PBch(t) =Pch(t) if PRER(t)>PD(t) ……….(4)

PBdis(t)=Pdis(t) if PRER(t)<PD(t) ……….(5)

3.4. Load Modeling

Theproposedsystemhasbeenimplementedona communityhavingthreeareasthathavedifferenttypesof loads with different ratings. Each area is setup to have an

4.1.

Fig 2:ProposeddesignofEMS

Home Energy Management Controller (HEMC)

HomeEnergyManagementController(HEMC)is veryessentialineveryresidentialareawhichenhancesthe energy efficiency [24]. Reducing PAR, energy bills and maximizinghe user comfortfor multi residential homes is proposed in [25]. An optimum home energy management controller is implemented in [26] which minimize the electricitybillupto21.5%.HEMCwillcollectthedetailsabout loadanditsneed,localgenerationcapacitiesandbatterySOC state.ThemainobjectiveofHEMCistoscheduletheloadat minimum consumption price. Thus the cost objective functioncanbegivenin(6).

Cost=Minimize( ……….(6)

4.2.

Energy Market Management Controller(EMMC)

Inatraditionalgridthereisnopossibilityoftwo way communication and feedback. This will affect the efficiency of the grid. But in today’s era there are lots of methodsavailablethatwillmakethegridsmarter.EMSwill makethegridandconsumertointeractwhichpavestheway forhealthycommunication.

In our proposed system, the Energy Market Management Controller (EMMC) will collect all the informationfromenergyproviderslikecapacity,costprice andemittinggasdetails[20].Afterreceivingalldetails,the informationwillbesharedtoHEMC.ThenHEMCwillmanage and schedule the load. This will be done before t=1. Now optimizationwilltakeplacewiththehelpofmultiobjective

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grey wolf optimization. After that, EMMC will begin to forecastthedatafromindividualHEMC.ThenHEMCwillsend thesignaltoEMMCaboutthedemandateacharea.Allthese detailswillbesharedtocontrolagentwhichhastheauthority todecidewhichenergyprovidershouldsupplythedemand. Thisprocesswillberepeatedforevery24hours.Afunction ofEMMCisshowninFig 3[20].

Fig 3:FunctionsofEMMC[20]

4.3. Multi Objective Grey Wolf Optimization Technique (MOGWO)

MultiGreyWolfOptimization(MOGWO)techniqueis one of the effective meta heuristic algorithm proposed in [20]. Because of its excellent precision in solution, low processingcost, andavoidance of prematureconvergence, this optimization outperforms other algorithm such as ParticleSwarmOptimization(PSO),AntBeeColony(ABC), GeneticAlgorithm(GA),HarmonicSearchAlgorithm(HSA), etc.

Fig -4:Hierarchyofgreywolves[27]

Grey wolves are the inspiration for this optimization technique. Grey wolves live in packs, with each pack consistingof5to12wolves.Thesepacksorgroupshavebeen divided into many categories based on their hunting behavior. The leader of a grey wolf pack is known as the 'alpha,'anditisresponsibleforoverseeingallofthepack's operations. The 'Beta' level of wolves is responsible for reinforcingalpha'sinstructionsandprovidingfeedbacktothe leaders.The'Omega'levelisthethirdandlastlevel,andits roleinthepackissimilartothatofascapegoat.

If the wolf in the pack does not fall into the above mentionedcategories,itwillbe'Delta,'beingthesecondbest optionanddeltabeingthethirdbestposition.Thehierarchy ofgreywolvesisshowninFig 4[27].Theflowchartofthe proposedworkisshowninFig 5.TheparametersofMOGWO ofproposedEMSarelistedinTable 2

Fig -5:Flowchartofproposedwork Table

5. RESULT AND DISCUSSION

Inthissection,theoutputofMATLABsimulationis discussed.Theproposedworkhasbeenframedtooperatein multi residential areas, in a community of many areas, in industry,etc.Inthiswork,solarandwindenergyareusedas renewableenergyresources.Theproposedworkhasmetmy objectives.

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as alpha and then the optimization will continue for next cycle.

Fig 6: SelectiondatainMOGWOupto20th cycle

WhentheinputdatawerefedinMOGWO,itwillnot operateforafullcycle.Insteaditwilltakethedatasetbyset andthenthebestpositionwillbeselected.Fig 6showsthe featureselectiondatainMOGWOupto20th cyclewhichhas thebestvalueatthatinstance.

Fig 7: Clustersampling

a) b)

Fig 9: Percentageofpollutionreductioninarea1 a)solarandthermal b)windandthermal

Thisproposedworkhelpsinfindingthesolutionto reducetheemissionofgreenhousegases,increasedusageof renewablesourcesandreducestheconsumptionpricefor prosumers.Withthehelpofequation(1),(3),(4)and(5)the information about solar power, wind power and battery capacity were forecasted to EMS. As the next step, load schedulingtakesplace.

Fig 8: Clusteredoverheaddata

Thebestpositionforeachcycleisselectedandthen grey wolf optimization algorithm will create a cluster samplingwhichisshowninFig 7.Outofallthebestposition ineverycycle,theaveragepositionofalliterationswillbe sortedouttocreateaclusteroverheaddatawhichisshown inFig 8.Afterthis,thealgorithmwillchosethebestposition

c)

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Here in the graph, initially the demand will be forecasted and then CA will make the RER to deliver its energy.Iftheenergyisinsufficientfortheload,thenCAwill send signals to receive the energy from grid, so that the usage of RER will be high. Thus with the help of MOGWO withtheforecastedinformationforatimeperiodof24hrsor foraday,thepollutioncanbereducedto39.52%to45.97%.

d)

Fig 10:Percentageofpollutionreductioninarea2 c)solarandthermald)windandthermal

Fig 9(a),Fig 10(c)andFig 11(e)representsthe percentage of pollution reduction in area 1, 2 and 3 respectivelyinwhichthedemandis suppliedbysolarand thermalenergy.SimilarlyFig 9(b),Fig 10(d)andFig 11 (f)representsthepercentageofpollutionreductioninarea 1,2and3respectivelyinwhichthedemandissuppliedby windandthermalenergy.

e)

f)

Fig 11:Percentageofpollutionreductioninarea3 e)solarandthermalf)windandthermal

g) h)

Fig 12:Percentageofreductioninconsumptionprice g)solarandthermalh)windandthermal

Fig 12:(g)and(h)representsthepercentageofprice reduction in all the three areas for receiving energy from solarandthermalandwindandthermalrespectively.The consumptionpriceforprosumerstobuyenergyisreduced to a range of 48.51% to 54.69%. Thus the objective of proposedworkisachievedwiththehelpofproperEMS.

6. CONCLUSION

Thispaperproposesanenergymanagementsystem foraneffectiveoperationofthemicrogridinsmartway.It enablesaninteractionbetweentheprosumersandenergy providers. In this three areas in a community of different ratings were taken into consideration for load which is supportedbysolarandwindenergyastheirlocalgeneration, andalsosupportedbymicrogrid.ForEMS,thereisaneedof twocontrollersviz.EnergyMarketManagementController (EMMC)andHomeEnergyManagementController(HEMC) with the help of Multi Objective Grey Wolf Optimization (MOGWO)technique. Asaninitialstagethedetailsofenergy

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providers,itscapacities,limits,gasemissionratesandcost priceswereforecastedtoEMMC.Thentheinformationabout eacharea,itsdemand,localgenerationdetails,itscostprice and battery SOC were forecasted to HEMC. As a first step, EMMCwillshareitsforecasteddetailstoHEMC,sothatitwill scheduleandmanagetheloadaccordingtothesources.Then theinformationwillbesenttoControlAgent(CA)whereit decides whether the demand will be supplied by the local generationorformthegrid.Thentheoptimizationprocess takes place with the help of MOGWO which gives faster convergence. As a result of this work, the energy consumptionpriceofprosumerscanbereducedupto48.51% to54.69%.Thistechniqueprovidesanintellectualsolution foreconomicalandtechnicalissues.

7. FUTURE SCOPE

Thisworkhasbeenimplementedwithtwocontrollers namelyEnergyMarketManagementController(EMMC)and HomeEnergyManagementController(HEMC)withthehelp of Multi Objective Grey Wolf Optimization (MOGWO) technique.Infuturethesameworkcanbecarriedoutwith different algorithm that converges even faster and also be testedwithdeeplearningalgorithmwhichisbecomingthe futureofautomationinbigdataanalysis.

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