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Real-time energy resources schedulingconsidering intensive wind penetrationMarco SilvaPolytechnic of Portomasi@isep.ipp.ptHugo MoraisPolytechnic of Portohgvm@isep.ipp.ptZita ValePolytechnic of Portozav@isep.ipp.ptAbstractThe use of distributed energy resources, based on naturalintermittent power sources, like wind generation, inpower systems imposes the development of new adequateoperation management and control methodologies.A short-term Energy Resource Management (ERM)methodology performed in two phases is proposed in thispaper. The first one addresses the day-ahead ERMscheduling and the second one deals with the five-minuteahead ERM scheduling.The ERM scheduling is a complex optimization problemdue to the high quantity of variables and constraints.In this paper the main goal is to minimize the operationcosts from the point of view of a virtual power playerthat manages the network and the existing resources. Theoptimization problem is solved by a deterministic mixedintegernon-linear programming approach.A case study considering a distribution network with33 bus, 66 distributed generation, 32 loads with demandresponse contracts and 7 storage units and 1000 electricvehicles has been implemented in a simulator developedin the field of the presented work, in order to validate theproposed short-term ERM methodology considering thedynamic power system behavior.Keywords: Mixed-integer non-linear programming;Short-term energy resources management; Smart grid;Virtual power player;1. Nomenclaturec Excess generated energy costEGEcCut ( b)Consumption curtailment cost for bus bcRed ( b)Consumption reduction cost for bus bcSupplierSupplier energy costcStorageChargeStorage charge costcStorageDischargeStorage discharge costcV 2GChargeV2G charge costcV 2GDischargeV2G discharge costc Non-supplied power costNSPcDG( g)Generation cost of generation unit gngTotal number of generatorsnbTotal number of busesP Load powerLoadPCut ( b)Consumption curtailment for bus bP Maximum consumption curtailment inCutMax ( b)bus bPDG ( g)PDGMax ( g)PDGMin ( g)PEGEPRed ( b)PRedMax ( b)Generation power of generation unit gMaximum generation power of generationunit gMinimum generation power of generationunit gExcess generated energy costPSupplierSupplier powerPStorageStorage powerConsumption reduction for bus bMaximum consumption reduction inbus bPStorageInitialInitial stored powerPStorageChargeStorage charge powerPStorageChargeMaxMaximum storage charge powerPStorageDischargeStorage discharge powerPStorageDischargeMaxMaximum storage discharge powerPStorageMaxMaximum storage powerP V2G powerV2GP Initial stored power in V2G batteriesV 2GInitialPV 2GChargeStorage charge power in V2G batteries2 V GChargeMaxP Maximum storage charge power inV2G batteries

P Storage discharge power in V2G batteriesV 2GDischargeP Maximum storage discharge power inV 2GDischargeMaxV2G batteriesP Maximum storage power in V2G batteriesV 2GMaxP Non-supplied powerNSPX Binary variable for consumption curtailment,for busCut ( b)bXDG( g)XStorageBinary variable for generation unit gBinary variable for storage chargeYStorage Binary variable for storage dischargeXV2GBinary variable for V2G chargeYV2GBinary variable for V2G discharge1. IntroductionThe increasing use of Distributed Generation (DG),mainly based on renewable energy resources, and otherDistributed Energy Resources (DER), including DG,Demand Response (DR) programs, storage systems andelectric and plug-in hybrid vehicles poses new challengesto Power Systems planning and operation. Also theintroduction of liberalized markets in the electricity sectorhas caused significant changes in power systemsagents relationships. DER use in distribution network hasbeen increased significantly [1, 2], bringing new challengesto power systems agents and leading to the smartgrid concept [3-8].In some cases, the investment made in RES is not usedin its full extent. In some periods, with high wind generationand low demand consumption, a wind curtailment isalso necessary. Presently, operation planning methodsare not adequate to the characteristics of most of DERand even with a lot of ongoing research work some problemsremain unsolved. This is the case of real-time DERmanagement which should take into account all the relevanttechnical and economic issues [9, 10].The high number of wind energy is worrisome, sincewind power is stochastic, especially in the very shortterm (e.g., over any given hour, 30 minutes, 15 minutesor 5 minutes period) [11]. This has created a completelynew challenge to the system operators so maintain continuouslybalance electricity supply and demand.The main difficulties with renewable energy resourcesare the dispatchability and reliability problems associatedwith their operation. The output of some renewable generation,such as wind generators and photovoltaic systems,is determined by the climate and weather conditionsand operating patterns will therefore follow thesenatural conditions. The intermittent nature of thesesources leads to an output which often does not suit theload demand profile. Smart grids introduce new managementconcepts with new operation methods for adequatelyscheduling renewable based generation and allDER.Storage systems and electric vehicles could be veryuseful in the Energy Resources Management (ERM)process. These units increase the consumption in generationsurplus cases (charge batteries) and increase thegeneration in shortage generation cases (discharge batteries).Demand response programs can be used in a moreflexible way guaranteeing that the most costly generationresources are managed so that operation costs are keptwithin acceptable limits [12, 13]. The new context includesa large number of players (electricity consumers,DG owners, aggregating entities such as Virtual PowerPlayers (VPP) [14-16], and system operators) acting incompetitive Electricity Markets.Short-Term energy resource management is a veryrelevant task in modern energy systems [9, 10]. It consistsin correctly scheduling the available DER in orderto reduce the operation costs. The number of variablesconsidered in this approaches, and the need for obtaininga rapid response, requires the usage of advanced optimizationtechniques, such as artificial intelligence techniques,namely metaheuristics such as Particle SwarmOptimization, Genetic Algorithms or Simulated Annealing[7, 9, 17-20].The coordination of all these resources is a quite challengingissue requiring distributed intelligence accordingto the concept of the smart grid [3, 6, 8]. This can beachieved through an integration of the behavior and actionsof all users connected to it, and so, adequatelyscheduling renewable based generation and all DER,including the available load curtailment opportunities [1,10].This work contributes to overcome this situation conceiving,developing and implementing methodologiesadequate for energy resource management in a distributionnetwork, considering intensive penetration of DG,storage, electric vehicles and load curtailment opportunitiesenabled by demand response programs in the contextof future power systems. The proposed methodologiesare based on the characteristics of the problem and of theinvolved resources.2. Energy Resource ManagementMethodologyProper use of optimization techniques in the DERreal-time scheduling is very relevant for smart grids.This is mainly due to the lack of accuracy in wind forecastingwhen the forecasting anticipation is increased. In[21] the authors demonstrate that wind forecasting can bevery accurate for very short-term forecasting, using thelast 5 hours of wind speed data to predict the next 5minutes. This methodology can be used in this case toupdate 5 minutes ahead optimization input data. In [22]very short-term wind forecasting is also discussed for areal world application using data provided by HydroTasmania. A 2.5 minutes horizon is proposed in the usedneuro-fuzzy methodology with less than 4% error. How-

ever, the forecast accuracy significantly drops when thetime horizon is extended, with much higher errors whenthe prediction is made several hours ahead, namely formedium-term forecasting, with over 6 hours of anticipation.Due to the difficulties of having accurate forecastingfor natural resources, mainly due to forecast wind, thescheduling of energy resources should be undertakenwith little anticipation. This leads to the proposal of atwo-phase short-term energy resources scheduling, withdifferent time anticipations (1 hour and 5 min) (Figure 1),each one considering the most updated forecasts, thealready established contracts and market transactions andthe market opportunities. The authors propose a newmethodology to the real-time energy resource managementthat considers all the referred resources and aims atminimizing the operation costs. The developed methodologyconsiders two different algorithms. The first one isused in each hour and the objective function consists inminimizing the operation cost. The difference betweenthe energy resource scheduling computed in the previousday and the energy resource scheduling necessary to respondto the forecasted load demand. The second one isrun in real-time for each period of 5 minutes [23]. It considersthe adjustment according to the forecast of generationto the verified load demand. The difference betweenalgorithms concerns the resources managed by each one.All the resources (generators, storage units, electric vehicles,demand response programs, and the intra-day market)are considered by the first algorithm. The secondalgorithm (5 minutes) only manages the connected generators(spinning reserve) with available power capacity,storage units, electric vehicles, demand response withload reduction contracts, and considers market penalties.The main goal is minimize the operation cost and minimizethe impact in the day ahead and hour ahead energyresources scheduling. Considering these goals the aggregatorreduces the operation costs and at same time themarket penalties.To test the methodology, a distribution network hasbeen implemented in an Power Systems Simulation Tool(PSST) [24]. In each period of optimization, PSST exportsthe instant data (bus voltages, generation, load consumptions,line power flows, etc) to optimization toolsoftware (OTS). The inputs to the algorithms of optimizationare the actual data of generation and consumptionsent by PSST and existent data base with equipmentcharacteristics, DR contracts, and day-ahead electricitymarket information.Fig. 1. Proposed methodology architecture

2.1. Mathematical formulationThis sub-section presents the mathematical formulationof the problem proposed to be solved. This problemis classified as mixed-integer non-linear. The objectivefunction (1) of this mixed-integer non-linear model isformulated with the aim of finding the minimal cost ofsupplying the demand.MinimizengPSupplier cSupplier ( PDG ( g ) cDG( g )) g 1PStorageChargecStorageChargePStorageDischargecStorageDischargef PV 2GCharge cV 2GChargePV 2GDischarge cV 2GDischargePNSP cNSP PEGE cEGE nb PCut( b) cCut( b) b1 PRed ( b) c Red ( b)Two demand response capacities (consumption reductionand consumption curtailment) are considered as aresource. The existence of storage units and several generators,as well as the energy supplied by a supplier, arealso considered.Equations (2) to (13) refer to the constraints that areconsidered. Equation (2) refers to the first Kirchhoff Lawor power balance constraint.ngP P P PSupplier DG( g) StorageDischarge V 2GDischargeg 1nb P P PNSP Cut( b) Red ( b)b1 P P P PLoad StorageCharge V 2GCharge EGEEquations (3) to (7) represent the constraints concerningthe maximum capacity considering the available resources,for both generation (3, 4) and load response (5,6), and for storage units (7). In the consumption curtailmentprogram, the participation of each load only can beby its total curtailment power.P P(3)DG( g) DGMax ( g)DG( g) DGMin ( g) DG( g) DG( g)(2)P P X ; X 0,1 (4)P P X ; X 0,1(5)Cut ( b) Cut ( b) Cut ( b) CutA( b)P P(6)Red ( b) RedMax ( b)(1)considered in equation (8) and the charge capacity inequation (9). In each instant, the battery only can becharging or discharging, as imposed in equation (10).P P X ; X 0,1StorageDischarge StorageDischargeMax Storage StorageP P Y ; Y 0,1 (9)StorageCharge StorageChargeMax Storage StorageX Y 1; X and Y 0,1 (10)Storage Storage Storage StorageIt is also necessary to impose that it is not possible todischarge more than the stored energy (11). Similarly,the power to be charged plus the power stored cannot behigher than the total storage resource capacity (12). Finally,the storage state is obtained considering the initialstored energy, the charge, and the discharge in each timeperiod (13).PStorageDischargeP 0(11)StorageInitialPStorageCharge PStorageInitial PStorageMax(12)PStorage PStorageInitial PStorageDischarge PStorageCharge(13)Electric vehicles with gridable capability (V2G) resourcesrequire a special treatment due to specific operationconstraints. The discharge capacity is considered inequation (14) and the charge capacity in equation (15). Ineach instant, the battery only can be charging or discharging,as imposed in equation (16).P P X ; X 0,1 (14)V 2GDischarge V 2GDischargeMax V 2G V 2GP P Y ; Y 0,1 (15)V 2GCharge V 2GChargeMax V 2G V 2GX Y 1; X and Y 0,1 (16)V 2G V 2G V 2G V 2GIt is also necessary to impose that it is not possible todischarge more than the stored energy (17). Similarly,the power to be charged plus the power stored cannot behigher than the total storage resource capacity (18). Finally,the storage state is obtained considering the initialstored energy, the charge, and the discharge in each timeperiod (19).PV 2GDischargePV 2GInitial0 (17)P P P(18)V 2GCharge V 2G V 2GMaxP P P P(19)V 2G V 2GInitial V 2GDischarge V 2GCharge(8)PStorage P(7)StorageMaxStorage resources require a special treatment due tospecific operation constraints. The discharge capacity is

2.2. Short-term scheduling simulatorIn this work the DICOPT solver is used to the MINLPapproach for the short-term energy resources management,the OTS is used for interface between the resultsof scheduling and the simulation tool [28-30]. To simulatethe use of DER in power systems, it is necessary tocreate models in simulation tools to test scheduling solutionsprior to actual implementation. The tool for simulationof electricity network and energy resources used toapply the proposed methodology is PSST.The choice of these three software packages fulfilledthe requirements, providing us with powerful mathematicalresources of DICOPT solver, and the use of the OTSwith the advantage of an efficient connection with thePSST power system simulator. This tool allows buildcustom models using PSST Design Editor. PSST hasbeen widely used in the study of distributed energy resources[31-37].To simulate the distribution network for the hourlyoperation planning, the authors had to implement thenetwork in PSST and to create models of distributedgeneration units, loads, lines and substation. During thesimulation, PSST receives information concerning distributionnetwork data, network state, DG and DR shorttermscheduling resulting from the optimization process.The optimization process, needs the following data: generationdata, generation costs, DR contracts, day-aheadDER scheduling and the intra-day market price, with theobjective to minimize the cost of the DG, load curtailmentand the intra-day market.PSST has the capability of interfacing with OTScommands and toolboxes through a special interface.OTS programs or block-sets that are to be interfacedwith PSST must be designed and saved as an OTS programfile. Then, a user-defined block must be providedin PSST, with the necessary inputs and outputs, to interfacethe OTS file. In this paper, an interfacing block hasbeen created in PSST to link the OTS files defined withinthe block.Fig. 2 shows components and the connection betweenthe PSST and OTS.Fig. 2. Components and connections of a bus implementedin PSSTwhere:pgnqgnvivjplkqlkpijxqijxpjixqjixfckIfgnfgnActive power of DG unit n in BUS iReactive power of DG unit n in BUS iVoltage magnitude in BUS iVoltage magnitude in BUS jActive power demand of load k in BUS iReactive power demand of load k in BUS iActive power in line x from BUS i to BUS jReactive power in line x from BUS i to BUS jActive power in line x from BUS j to BUS iReactive power in line x from BUS j to BUS iLoad control variable of load kMax. instantaneous active power generator ofDG unit nGenerator control variable of DG unit nThe network values obtained for period t and withload forecast and generation forecast for period t+1, areimportant data for optimization process. The obtainedoptimized solution is sent to PSST, through the followingvariables: the load control variable in each load, themaximum instantaneous active power in each distributedgeneration unit, and the generator control variable ineach distributed generation unit. These variables will setthe new state of the generators and loads.4. Case studyThe case study shows the simulation of a distributionnetwork with high DER penetration using PSST simulationtool and DICOPT solver to optimize the energy resourcesusage, and OTS to interface between the optimizationand the simulation tool. The method considers the5 minutes operation planning, in each phase, all theavailable resources (DG, demand response, electric vehiclesand storage) respecting their technical limits, contracts,day-ahead and intra-day scheduling, and aims atminimizing the VPP operation costs.The simulator will iterate with the optimization of theDER short-term scheduling, in terms of the 5 minutesoperation planning during 2 hours scenario. The casestudy was implemented on the distribution network with33 buses, from [19, 25], with load and Distributed Generation(DG) evolution prediction for the year 2040 [26],with 32 load, 66 DG and 1000 electric vehicles, 7 storageand 10 external supplier [26]. The electric vehicles usescenarios were developed in EVeSSi Simulator tool. Thisapplication allows the creation of different scenariosconsidering the trip parameters, electric vehicles classesand types parameters, and electric vehicles specific modelparameters [27].Short-term scheduling is used to reschedule the previouslyobtained schedule taking advantage of the betteraccuracy of short-term forecasting in order to obtainmore efficient resource scheduling solutions.The used optimization is based on a MINLP approachthat has proved to achieve a satisfactory cost operatingpoint in a competitive time.The proposed methodology demonstrated to be able toprovide users with significant cost reductions, lowering

the power losses and resource use costs. Moreover, itincludes a dynamic analysis of the power system simulation,which is based on the use of PSST.Table 1 summarizes the considered energy resourcescosts for the case study and the number of DG units.Table 1. Case study energy resource data.Energy Resources Number of unitsPrice(m.u./MWh)case studyBiomass 4 0.0500 – 0.0720Cogeneration 15 0.0416 – 0.0600Fuel cell 7 0.5833 – 0.8400Hydro small 2 0.0458 – 0.0660Photovoltaic 31 0.0167 – 0.0240Waste to energy 1 0.0375 – 0.0540Wind 6 0.0250 – 0.0360Load DR 32 1.000 – 15.000Energy supply 10 0.5833 – 0.8400ElectricvehiclesDischarge 1000 0.2000 – 0.2500The results of loads consumption and the DG (PV andWind) prediction can be seen in Fig. 3.The MINLP based optimization approach described insection 2 has been used for determining the DistributedGeneration and Demand Response short-term schedulingfor this case study. It is important to note that all 288optimizations, each one undertaken for 5 minutes, aredependent from each other, because of the state of storageand state of electric vehicles. DER scheduling forperiod t is undertaken in period t-1, considering the operationstate resulting from the schedule already used forthe previous periods.The methodology used to simulate the power systemof this case study has been tested on a PC compatiblewith one Intel Xeon W5450 3.00 GHz processor, with 8Cores, 12GB of random-access-memory (RAM) andWindows Sever Enterprise.4.2. Resultsscheduling in the day-ahead and in the hour-ahead andthe transients effects between the scheduling periods. Inthis paper are presented the transient effects simulated inPSST.Figure 4 shows the results in PSST of an example ofthe several energy resources evolution along a set of periodsin bus 18. In Figure 5 is possible to see an exampleof the energy in a storage unit and an electric vehicle forthe same periods of time5. ConclusionsThis paper presented a short-term energy resourcemanagement methodology involving day-ahead and fiveminutesahead scheduling. Short-term scheduling is usedto reschedule the previously obtained schedule takingadvantage of the better accuracy of short-term wind andsolar generation and demand consumption forecasting inorder to obtain more efficient resource schedulingsolutions.The proposed method considers distributed generators,storage units, electric vehicles and two distinct demandresponse programs –consumption reduction and consumptioncurtailment.The optimization process use a deterministic approachmixed integer non-linear programming (MINLP). Theobtained feasibility solution is technically validatedusing realistic power system simulation, based on PSST.The presented case study is based on a 33 busdistribution network with distributed generation, storageunits, electric vehicles and controllable loads.AcknowledgementsThis work is supported by FEDER Funds throughCOMPETE program and by National Funds throughFCT under the projects FCOMP-01-0124-FEDER:PEST-OE/EEI/UI0760/2011,PTDC/EEA-EEL/099832/2008, and PTDC/SEN-ENR/099844/2008.Several results can be obtained from the simulations inthis case study. The most important ones are energyFig. 3. Load forecasting and the DG forecasting.

Fig. 4. Example of evolution of energy resources (EV – Electric Vehicle, S – Storage, PV – Photovoltaic, W – Wind,L – Load) and voltage (V)Electric vehicleout off gridFig. 5. Example evolution of energy in a storage unit and in a electric vehicle batteries

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Soares, "Intelligentenergy resource management considering vehicle-to-grid: Asimulated annealing approach," Accepted for Publication onIEEE Transaction on Smart Grid, Special Issue onTransportation Electrification and Vehicle-to-Grid Applications,2011.[21] Sérgio Ramos, João Soares, Zita Vale, and Hugo Morais, "AData Mining Based Methodology for Wind Forecasting,"presented at the 16th International Conference on IntelligentSystem Applications to Power Systems (ISAP 2011), Crete,Greece, 2011.[22] C. W. Potter and M. Negnevitsky, "Very short-term windforecasting for Tasmanian power generation," Ieee Transactionson Power Systems, vol. 21, pp. 965-972, May 2006.[23] California ISO, "Renewables Integration Market Vision &Roadmap Day-of Market," Initial Straw Proposal07/06/20112011.[24] PSCAD, "Applications of PSCAD / EMTDC," 2008.[25] M. E. Baran and F. F. Wu, "Network Reconfiguration inDistribution-Systems for Loss Reduction and Load Balancing,"Ieee Transactions on Power Delivery, vol. 4, pp. 1401-1407, Apr1989.[26] P. Faria, Z. A. Vale, and J. Ferreira, "Demsi — A demandresponse simulator in the context of intensive use of distributedgeneration," in Systems Man and Cybernetics (SMC), 2010 IEEEInternational Conference on, 2010, pp. 2025-2032.[27] J. Soares, "Modified PSO for day-ahead distributed energyresources scheduling including vehicle-to-grid," Master degreethesis, Polytechnic of Porto, Portugal, 2011.BiographiesMarco Silva received the BSc degree inElectrical Engineering from the PolytechnicInstitute of Porto (ISEP/IPP), Portugal in2007. Presently, he is an Assistant Researcherat GECAD – Knowledge Engineering andDecision-Support Research Center ofISEP/IPP. His current research activities arefocused on future electrical networks withintensive use of distributed generation.Hugo Morais (M’11 S’08) received theBSc and Master degrees in Electrical Engineeringfrom the Polytechnic Institute ofPorto (ISEP/IPP), Portugal in 2005 and 2010respectively. He is a Researcher at GECAD –Knowledge Engineering and Decision-Support Research Center and a PhD student.His research interests include smart grids,virtual power players, and electricity markets.Zita A. Vale (SM’10 M’93 S’86) is thedirector of the Knowledge Engineering andDecision Support Research Center (GECAD)and a professor at the Polytechnic Institute ofPorto.She received her diploma in Electrical Engineeringin 1986 and her PhD in 1993, bothfrom University of Porto. Her main researchinterests concern Artificial Intelligence (A.I.)applications to Power System operation andcontrol, Electricity Markets, DistributedGeneration, and Smart Grids.