Outline

PLANNING DISTRIBUTION SYSTEM RESOURCE ISLANDS CONSIDERING RELIABILITY, COST AND THE IMPACT OF PENETRATION OF PLUG-IN HYBRID ELECTRIC VEHICLES

Julieta Girldez

Graduate StudentDivision of EngineeringCSMMarch 2011

1 Outline Introduction Design of distributed resource islands Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on distributed resource islands Conclusions and future work

22 Introduction Optimization of islanded distribution systems from a design perspective Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island Conclusions and future work

Outline3Smart Grid Initiative [1]: what is the evolution of electric power distribution systems?

Distributed Energy Resources (DER) or Distributed Generation (DG) Incorporate ways of physical and virtual storage to balance consumption and production including PHEVs Increased used of technologies: advanced meters, advanced inverters, distribution automation, communication systems, etc.

Introduction4[1] 110thCongress of the United States, "Title XIII (Smart Grid)," in Energy Independence and Security Act of 2007. Washington, DC: Dec. 2007, pp. 292 303.Why? Contribute to the load relief of the transmission system by increasing the generation in the distribution system and new ways of energy management Higher reliability and power quality Integration of green technologies into the grid

Introduction5[2] N.Hatziargyriou, H.Asano, R.Iravani, and C.Marnay , Microgrids, IEEE Power & Energy Magazine, pp.78-94, July/Aug. 2007. How to implement the smart grid? Microgrid concept: a distributed resource island Self-contained autonomous subset of the area electric power system Has local Distributed Energy Resources (DER) Operates semi-autonomously of the grid, being able to island and reconnect as circumstances dictate Able to provide power quality and reliability different from general macro-grid standards

Introduction67

[3] Distributed Energy Resources Integration, Consortium for Electric Reliability Technology Solutions (CERTS), [Online]. Available:http://certs.lbl.gov/certs-der.html Introduction Design of distributed resource islands Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island Conclusions and future work

Outline8Distribution systems Traditional electric distribution systems:GridGridLineTransformerLoadInfinite bus Design of distributed resource islands9Distribution systemsEvolving distribution systems:

GridGridDGIncrease annual RELIABILITY at a feasible COST Design of distributed resource islands10Modeling of annual load Annual demand: two ways of modeling annual load annual average demand at every load: i.e. 1 load level representative of the annual demand 6 step-load duration curve representation (hourly demand reordered in increasing demand): i.e. 6 load levels representative of the annual demand

Design of distributed resource islands11[4] R. Billinton, S. Kumar, et al., "A Reliability Test System for Educational Purposes - Basic Data," IEEE Transactions on Power Systems, vol. 4, pp. 1238-1244, August 1989.T1 =100 hT2=1900h Design of distributed resource islandsModeling of DG DG: aggregate power output of Renewable Energy (RE) and Conventional Distributed Generation (CDG) [5]

Pout = CDG + RE + DS

Capacity Factor: ratio of the actual output of a power source and its output if it had operated at fullcapacity Total DG rating R=RRE + RCDG

Pout = 12[5] H. Brown, Implications on the Smart Grid Initiative on Distribution System Engineering: Improving Reliability on Islanded Distribution Systems with Distributed Generation Sources, M.S thesis, Dept. Elec. Eng., Colorado School of Mines, 2010. Basic Reliability Concepts: ASAI: The time as a fraction of a year for which the system is available Annual Outage Time, U : Time as a fraction of a year for which the system is NOT available ~ Power Not Supplied (PNS) [MW]: Unserved load or demand that the system cannot attend Reliability metric: Energy Not Supplied [MWh]

Design of distributed resource islands13Power systems simulation tool: Computer program to solve a power flow: Generation supplies the demand, to control the frequency of the system Bus voltage magnitudes remain close to the rated values Lines and transformers are not overloaded PowerWorld SimulatorTM is usedI. Enter the power system component dataII. Solve the Power Flow under balanced three phase conditions

2.1.3.Slack bus: slack bus is modeled as a generator that absorbs or supplies generation in order to balance the load and generation~ Power Not Supplied~ Design of distributed resource islands14 Outline Introduction Optimization of islanded distribution systems from a design perspective Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island Conclusions and future work

15 MOGAMulti-objective redesign problem

Investment cost versus reliability: Pareto-optimality ~ no single optimal solutions but a set of alternative solutions ~ Non-linear problem, discrete and non-convex feasible region Intractability of the problem as the size of the system grows [5] Evolutionary methods

16[5] H. Brown, Implications on the Smart Grid Initiative on Distribution System Engineering: Improving Reliability on Islanded Distribution Systems with Distributed Generation Sources, M.S thesis, Dept. Elec. Eng., Colorado School of Mines, 2010. there is no single optimal solution, but rather a set of alternative solutionsFor instances, a redesign solution in a distribution system might increase the reliability by a certain amount at a high cost versus a different redesign solution with lower reliability measure but associated with a lower cost of the project. None of these two solutions can be said to be superior if we do not include preference information of the objectives. Thus if no such information is available, it may be useful to have knowledge about those alternative architectures.16 MOGAMathematical formulation Variables

Objective function 1: COST

CDG: Cost of DG [$/MW]PDGj: Power output of DGlocated at bus j [MW]CC :Cost of conductor [$/km]li : Length of connection i [km]17 MOGAMathematical formulation Objective function 2: RELIABILITY ~ Energy Not Supplied

Annual average loads:

Six step load duration curve:

18 MOGAMathematical formulation Constraints Voltage within 5% of the nominal value at every bus j:

Loading of the Line from bus j to k:

19

GAs and the fitness function

A population is comprised of individuals or chromosomes ~ a potential solution to the optimization problem Evolutionary operators are used to create randomly individuals which may move to a higher level of fitness such as mutation, recombination, and crossover. MatlabTM Genetic Algorithm Optimization Toolbox (GAOT) inbuilt functions The fitness function determines how likely an individual is to survive to the next generation ~ output of fitness function ~

MOGA

****20 MOGAImportance of the initial population for convergence

Explore 3 ways of selecting the initial population

21[6] R. Billinton and S. Jonnavithula, "A Test System for Teaching Overall Power System Reliability assessment," IEEE Transactions on Power Systems, vol. 11, pp. 1670-1676, November 1996. MOGA: RBTS test systemApplication to a test system RBTS System [6]:Possible Connections 302. We input only the 164 connections which length is less than 3km.Possible DG Location 27 busesDG:

DESIGN PARAMETERSCF: wind, solar, conventional DG0.25, 0.3, 0.8Total DG penetration80% Total Annual Average LoadRE penetration20% Total DG penetration

22Customer TypeLoad points iAverage Load, [MW]Max. Peak Load, [MW]Residential1, 4-7, 20-24, 32-360.46840.8367Residential11, 12, 13, 18, 250.47580.8500Residential2, 15, 26, 300.43390.7750Small Industrial8, 9, 100.84721.0167Commercial3, 16, 17, 19, 28, 29, 31, 37, 380.28860.5222Office Buildings14,270.56800.9250Application to a test system

Results: look-up table for the decision maker A more expensive solution may be chosen if the Value of Lost Load (VOLL) [$] of the system is greater than the investment cost

Solution #Connection (s)DG (s) bus location #Cost [106 US $]ENS [MWh]1Line 11-171517.9731.402Line 1-7Line 11-174 & 1518.0621.963Line 1-7Line 11-17Line 23-294 & 15 & 2518.1221.884Line 11-17Line 23-2913 & 2918.3121.625Line 11-17Line 17-23Line 23-297 & 13 & 2318.4721.366Line 1-7Line 9-15Line 21-2711 & 13 & 2318.9121.33 MOGA: RBTS Test system23which estimates the amount that customers receivingelectricitywithfirm contractswould be willing to pay to avoid adisruptionin their electricity service []. If this value is greater than the difference in costs between the cheaper and expensive solutions, then the expensive solution may be chosen.23 MOGA: RBTS Test system24

If VOLL Cost Solution 6 might be chosen

which estimates the amount that customers receivingelectricitywithfirm contractswould be willing to pay to avoid adisruptionin their electricity service []. If this value is greater than the difference in costs between the cheaper and expensive solutions, then the expensive solution may be chosen.24 MOGA: RBTS Test systemApplication to a test system

Very similar redesign solutions for the RBTS with annual average loads and with step-load duration curve ENS overestimated with annual average demand Computational time : modeling of the annual load connection from Matlab to PowerWorld Simulator initial population

25which estimates the amount that customers receivingelectricitywithfirm contractswould be willing to pay to avoid adisruptionin their electricity service []. If this value is greater than the difference in costs between the cheaper and expensive solutions, then the expensive solution may be chosen.25 Outline Introduction Optimization of islanded distribution systems from a design perspective Multi-Objective Genetic Algorithm (MOGA) Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island Conclusions and future work

26[7] Vehicle to Grid (V2G) Electricity , Global Greenhouse Warming, [Online]. Available: http://www.global-greenhouse-warming.com/vehicle-to-grid.html Impact of PHEVs in distributed resource islandsIntroduction to PHEVs IEEE definition: vehicles that have a battery storage system rating of 4 kWh or more, a means of recharging the battery form an external source, and the ability to drive at least 10 miles in all electric mode Vehicle-to-grid (V2G): using the battery of a vehicle as a Distributed Energy Resource (DER) New way of electric energy management Existing power system infrastructure may not be adequate to deal with the increased demand and new patterns of consumption and power flows in the grid

27 Impact of PHEVs in distributed resource islandsModeling PHEVs in distribution systems How many PHEVs? What is the behavior of the driver? For how long does a PHEV behave as a load? For how long does a PHEV behave as DG?

~ KEY ASSUMPTIONS TO STUDY THE IMPACT ~28 Impact of PHEVs in distributed resource islandsModeling PHEVs in a distribution system

How many vehicles?

How many PHEVs in the system?

Electric customer consumes 2 kW and has 1.5 vehicles for residential; 38 workers per office building and 17 workers per commercial and 1.5 vehicles per worker

30% penetration of the total transportation fleet

What kind of PHEVs? What design and operational characteristics? What is the behavior of the driver?

Probabilistic simulation methodology Driving factors Peak-shaving Owners benefit Linear Programming (LP) algorithms to optimize charging patterns

For how long does the PHEV behaves as a load? and as a generator?

29Methodology Probabilistic simulation methodology [8] Contributions made by this thesis: LP algorithm ~ determine the loading Impact on design and reliability of distributed resource islands

Impact of PHEVs in distributed resource islandsProbabilistic simulation of PHEV fleet for 8760 hours [8]PHEV Class 2PHEV Class 1PHEV Class 4PHEV Class 3Daily vehicle data for optimizationEnergy requiredMiles drivenDeparture timeArrival timeLP Optimization of daily charging pattern of PHEVs for 1 yearObjective/s: maximize owners profit and/or utility peak shaving (Demand response) Incorporate optimized PHEV load (hourly) to load duration curve of distribution systemImpact of PHEV fleet on annual reliability of islanded legacy radial distribution systemsImpact of PHEV fleet on annual reliability of islanded networked distribution systemsTools & methodsResults30[8] S. Meliopoulos, J. Meiselrge and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. Impact of PHEVs in distributed resource islandsParameters for the Prob. Sim. Methodology [8] Four vehicle classes (types) Design characteristics (SOC):

Vehicle class cBc [kWh]MaxMin112821410321174231931[8] S. Meliopoulos, J. Meisel and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. Battery size Parameters for the Prob. Sim. Methodology [8] Amount of driving supplied from electric battery? From fuel? kphev=0 represents a charge sustaining (CS) mode in which on average all the drive energy comes from gasoline kphev=1 represents a charge depleting (CD) mode, all of the drive energy comes from electricity Simulations run in Powerdrive Simulation Analysis Tool (PSAT) Performance parameter Ec: required energy per mile [kWh/mi.]

Impact of PHEVs in distributed resource islands32[8] S. Meliopoulos, J. Meiselrge and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. weighted nonlinear least squares approximation method32 Impact of PHEVs in distributed resource islandsParameters for the Prob. Sim. Methodology [8] Vehicle control strategy: drive in CD from battery while in SOC ranges and switch to CS to maintain SOC relying on gas Charge depleting distance MD

Vehicle class ckphevcMaxmin10.59760.244720.61510.275030.54280.321740.48000.322433[8] S. Meliopoulos, J. Meiselrge and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. On trips longer than the depleting driving distance, after the battery is depleted to a specified lower level determined by the state of charge (SOC) in the battery, the control strategy is switched to the charge sustaining mode. The charge sustaining mode relies on gasoline to maintain a constant average SOC where on average all the energy used to drive the PHEV comes from gasoline. The limits of the kphev parameter represent the percentage of energy per mile on average which comes from a battery on board a PHEV, within the assumed vehicle control strategy.33 Impact of PHEVs in distributed resource islandsParameters for the Prob. Sim. Methodology [8] Four random paramaters kphevc and Bc# Vehicles per classDaily Miles driven per vehicleDrivers behavior ~ Time parameters

34[8] S. Meliopoulos, J. Meiselrge and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. On trips longer than the depleting driving distance, after the battery is depleted to a specified lower level determined by the state of charge (SOC) in the battery, the control strategy is switched to the charge sustaining mode. The charge sustaining mode relies on gasoline to maintain a constant average SOC where on average all the energy used to drive the PHEV comes from gasoline. The limits of the kphev parameter represent the percentage of energy per mile on average which comes from a battery on board a PHEV, within the assumed vehicle control strategy.34 Impact of PHEVs in distributed resource islandsProbabilistic simulation methodology [8]Vehicle design characteristics kphevc and usable battery capacity Bc are distributed according to a bivariate normal distribution with mean vector and covariance matrix with 0.8 parameter correlation Performance parameter Ec is determined knowing kphevc

Vehicle class cBC [kWh]kphevc114.30150.5976214.18270.6151319.15160.5428421.32110.4800

35[8] S. Meliopoulos, J. Meiselrge and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. weighted nonlinear least squares approximation method35[8] S. Meliopoulos, J. Meiselrge and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. Impact of PHEVs in distributed resource islandsProbabilistic Sim. Meth. applied to the RBTS test system[8]Vehicles per class: normal distribution with mean #PHEVs*Probability vehicle class and 1% standard deviation Total # vehicles (light transportation fleet): 15, 269 = 14,925 res + 230 com+ 114 off Uniform distribution of the #PHEVs throughout the load points of the RBTS per demand type~ daily parameters generated only for the #PHEVs in one load type~ Vehicle population size per class:

Approximate to the average #PHEV per class per load type

36Vehicle classVehicles per load point typecResidentialCommercialOffice building144157248189345146449198weighted nonlinear least squares approximation method36 Impact of PHEVs in distributed resource islandsProbabilistic simulation methodology [8]Miles driven per vehicle per day Md,c,v : log normal distribution with mean 3.37 and standard deviation of 0.5 Daily energy required per vehicle from the grid [kWh]:

, if MD Md,c,v, if Md,c,v MD 37[8] S. Meliopoulos, J. Meiselrge and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. weighted nonlinear least squares approximation method37[8] S. Meliopoulos, J. Meiselrge and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct. 2009. Impact of PHEVs in distributed resource islandsProbabilistic simulation methodology [8] Drivers behavior ~ time parameters: Gaussian distribution

Only residential charging in [8], what about office and commercial loads?

Departure (am)Arrival (pm)ParameterWeekdayWeekendWeekdayWeekendc79615c1.732.451.732.45

average urban driving speed 25 [mi./h]38weighted nonlinear least squares approximation method38 Impact of PHEVs in distributed resource islandsLP algorithms By now we know: Size and design characteristics of the PHEV fleetDaily energy required per vehicle from the gridDaily available time for charging per vehicle

DETERMINE DAILY CHARGING PATTERNS: Utility peak shaving or benefit of the owner39weighted nonlinear least squares approximation method39 Impact of PHEVs in distributed resource islandsMathematical formulation of the LPs Sets:

I = set of load types , from 1 NIC= set of PHEV classes, from 1NCV= set of PHEVs per class, from 1 NVD=set of days in a year, from 1NDT= set of hours in a day, from 1 NT40weighted nonlinear least squares approximation method40 Impact of PHEVs in distributed resource islandsMathematical formulation of the LPs Parameters:

Bc = Battery size per vehicle class c [kWh]DEd,c,v = Daily energy required per day d, vehicle class c and vehicle v [kWh] Ad,c,v = Daily arrival time per day d, vehicle class c and vehicle v [h] Dd,c,v = Daily departure time per day d, vehicle class c and vehicle v [h]

From the Probabilistic Simulation MethodologyCmaxc = Maximum hourly charge rate per vehicle class c [kW] Lbased,i,t = Base load (without PHEVs) on day d, load type i and hour t [kW] Lavd,i = Average base load (without PHEVs) on day d and load type i [kW] Pd,t = Price of energy on day d and hour t [$/kWh]41weighted nonlinear least squares approximation method41 Impact of PHEVs in distributed resource islandsApplication to the RBTS test system Base load of the system:

Day% Annual Peak LoadMonday93Tuesday100Wednesday98Thursday96Friday94Saturday77Sunday7542[9] Reliability Test System Task Force of the Application of Probability Methods Subcommittee, IEEE reliability test system, IEEE Transactions on Power Apparatus and Systems, vol. PAS-98, no. 6, pp. 2047-54, November 1979. Customer TypeLoad points iMax. Annuak Peak Load, [MW]Residential1, 4-7, 20-24, 32-360.8367Residential11, 12, 13, 18, 250.8500Residential2, 15, 26, 300.7750Small Industrial8, 9, 101.0167Commercial3, 16, 17, 19, 28, 29, 31, 37, 380.5222Office Buildings14,270.9250WeekPeak LoadWeekPeak LoadWeekPeak LoadWeekPeak Load182.214752775.54072.42901572.12881.64174.3387.816802980.14274.4483.41775.4308843805881883.73172.24188.1684.119873277.64588.5783.2208833804690.9880.62185.63472.947949742281.13572.648891073.723903670.54994.21171.52488.9377850971272.72589.63869.5511001370.42686.13972.45295.2weighted nonlinear least squares approximation method42 Impact of PHEVs in distributed resource islandsApplication to the RBTS test systemCharge rates assumptions:Residential: Classes 1&2 ~ Level 1 (120V;15A)Classes 3&4 ~ Level 2 (240V;30A) Non-residential: all classes at Level 2

Price of energy: Time Of Use (TOU) pricing 2 seasons 3 price levels: on-peak, medium peak and off-peak

43

0.0520.210.950.850.0520.190.19*the numbers inside the pie charts express the energy rate in $/kWhMidnightMidnightNoonNoonPast 66.00043Mathematical formulation of the LPs Variables:

C+d,c,v,t= Amount charged on day d, vehicle class c, vehicle v and time t [kW] C-d,c,v,t= Amount discharged on day d, vehicle class c, vehicle v and time t [kW] Cd,c,v,t = Energy stored on day d, vehicle class c, vehicle v and time t [kWh]

Impact of PHEVs in distributed resource islandsW+d,c,v,t = Absolute value of the difference between C+d,c,v,t and C+d,c,v,t+1 [kW]

Ld,i,t = New load on day d, load type i and hour t [kW] Zd,i,t = Absolute value of the difference between Ld,i,t and Lavd,i [kW]Hourly charge (+) or discharge (-)Energy inventory If positive, a change in the direction of power in the battery44weighted nonlinear least squares approximation method44 Impact of PHEVs in distributed resource islandsMathematical formulation of the LPs Battery constraints:

C+d,c,v, t Cmaxc for every d, c, v, tC-d,c,v, t Cmaxc for every d, c, v, t Limit the charge/discharge to the available connectionCd,c,v, t = Bc - DEd,c,v for t=Ad,c,v 1 and every d,c,v Energy in the battery when the PHEV arrives homeCd,c,v, t = Cd,c,v, t-1 + C+d,c,v, t - C-d,c,v, t for Ad,c,v t Dd,c,v and every d,c,vInventory balanceCd,c,v, t = Bc for t=Dd,c,v and every d,c,v Battery fully charged by dep. time45weighted nonlinear least squares approximation method45-W+d,c,v,t C+d,c,v, t - C+d,c,v, t+1 W+d,c,v,t for Ad,c,v t Dd,c,v -1 and every d, c, vW+d,c,v,t 3*Cmaxc for Ad,c,v t Dd,c,v -1 and every c, v Mathematical formulation of the LPs Battery constraints:

Impact of PHEVs in distributed resource islandsHourC+d,c,v,t [kW]

C-d,c,v,t [kW]t170t270t307t407t570t670t707t807C+d,c,v,t 77007700W+d,c,v,t?W+d,c,v,t070707046complete discharge, followed by a complete charge of a battery

46 Impact of PHEVs in distributed resource islandsMathematical formulation of the LPs Load constraints:

for every d, i, t

for every d, i, tNew load with PHEVsPeak-shaving measure47weighted nonlinear least squares approximation method47 Impact of PHEVs in distributed resource islandsMathematical formulation of the LPs Objective function: Utility peak-shaving

Customer profit

SOLVE ONE OBJECTIVE AT A TIME AND COMPARE IMPACT IN RELIABILITY48weighted nonlinear least squares approximation method48

Impact of PHEVs in distributed resource islandsResults Loading of the RBTS system with PHEVs

Peak demand [kW]Base Load[kW]49(1) RBTS Base Load(2) RBTS Base load + PHEV for peak shaving (3) RBTS Base load + PHEV for customer benefit(4) RBTS Base load + PHEV uncontrolled charging & no V2G

RBTS Power demand [kW]Time [h]weighted nonlinear least squares approximation method49

Impact of PHEVs in distributed resource islandsResults Loading of the RBTS system with PHEVs

Peak demand [kW]Base Load[kW](1) RBTS Base Load(2) RBTS Base load + PHEV uncontrolled charging & no V2G (3) RBTS Base load + PHEV delayed charging & no V2GTime [h]RBTS Power demand [kW]50weighted nonlinear least squares approximation method50 Impact of PHEVs for peak-shavingResults Individual charging patterns and daily load with PEAK-SHAVING:

Daily peak demand shiftedDaily base load shiftedSome charging before base load peak demandGeneral charging during the night51weighted nonlinear least squares approximation method51 Impact of PHEVs for PEAK-SHAVING versus UNCONTROLLED chargingResultsDaily load with PEAK-SHAVING versus UNCONTROLLED charging:

52

Daily average of the base load with no PHEVsweighted nonlinear least squares approximation method52 Impact of PHEVs for customer benefitResults Individual charging patterns and daily load with TOU PRICING:

Daily peak demand shiftedDaily base load shiftedCharging before base load peak demandNo valley-fillingDischarge in the morning53weighted nonlinear least squares approximation method53 Impact of PHEVs in distributed resource islandsReliability impact in the RBTS radial system Same annual average loads for RBTS test system with PHEVs optimized for peak-shaving & benefit of PHEV owner

Using step-load duration curve modeling:

RBTSBase loadBase load + PHEVsENS [MWh]44.5247.45Load levelsBase loadBase load + PHEVs Peak shavingBase load + PHEVs Customer benefitBT [hours]PNS [MW]T [hours]PNS [MW]T [hours]PNS [MW]13*10-423.643*10-426.27127.852222520.2024921.968722.863253116.76263217.64199217.884197513.32323513.33281712.90515839.8819659.2127837.9264226.456544.7110562.95ENS [MWh]39.8845.8739.2654

weighted nonlinear least squares approximation method54

Impact of PHEVs in distributed resource islandsResults Step-load duration curve:

Valley filling of Peak-shavingReduce consumption for customer benefit55(1) RBTS Base Load(2) RBTS Base load + PHEV for peak shaving (3) RBTS Base load + PHEV for customer benefit(4) RBTS Base load + PHEV uncontrolled charging & no V2G

RBTS Power demand [kW]Time [h]weighted nonlinear least squares approximation method55 Impact of PHEVs in distributed resource islandsReliability impact in the RBTS with DG + feeder interties ENS reduced in the redesigned RBTS with PHEVs However, the optimal solutions for the base load of the RBTS system without PHEVs and the cost and reliability are directly influenced by the demand per load point which has changed MOGA applied to the RBTS with PHEVs

OPTIMAL SOLUTIONS CHANGE?56weighted nonlinear least squares approximation method56 Impact of PHEVs in distributed resource islandsMOGA applied to the RBTS with PHEVs Annual average modeling ~ same for peak-shaving and TOU pricing

Solution #Connection (s)DG (s) bus location #Cost [106 US $]ENS [MWh]1Line 11-171517.6030.722Line 1-7Line 11-175 & 1417.7021.553Line 1-7Line 11-175 & 1117.8721.314Line 1-7Line 11-17Line 23-294 & 14& 2318.6421.625Line 1-7Line 11-17Line 17-245 & 11 & 1918.3321.00Annual average load, [MW]Customer TypeLoad points iBase loadBase load + PHEVs peak shavingBase load + PHEVs TOU pricing Residential1, 4-7, 20-24, 32-360.46840.49390.4940Residential11, 12, 13, 18, 250.47580.50110.5012Residential2, 15, 26, 300.43390.46060.4607Small Industrial8, 9, 101.01671.01671.0167Commercial3, 16, 17, 19, 28, 29, 31, 37, 380.18890.20240.2024Office Buildings14,270.33450.47780.4778No PHEVs: 2nd highestWith PHEVs: highest57weighted nonlinear least squares approximation method57 Impact of PHEVs in distributed resource islandsConclusions in the RBTS test system Several assumptions required Peak demand may be increased and shifted in time Charging patterns for customer benefit (TOU pricing) without demand charges increase the peak-demand by 25% but increase the reliability of the system (reduce energy consumption) Charging pattern for peak-shaving increase the peak demand by 8% and reduce the reliability (valley filling) The redesign solutions of distribution systems considering PHEVs may change

58weighted nonlinear least squares approximation method58 Future workMOGA methodology Time dependency on the power output of DG (Stochastic approach) JISEA project on Verifiable Decision-Making Algorithms for Reconfiguration of Electric Microgrids in collaboration with University of Colorado-Boulder: Acceleration technique for filtering potentially infeasible and/or suboptimal inputs, based on Machine Learning [10] Explore other evolutionary approaches to the redesign problem

5910] J. Girldez, A. Jaintilal , J. Walz, H. E. Brown, S. Suryanarayanan, S. Sankaranarayanan, E. Chang, An evolutionary algorithm and acceleration approach for topological design of distributed resource island, accepted in Proc. 2011 IEEE PES PowerTech, Trondheim, Norway, Jun 2011.weighted nonlinear least squares approximation method59 Future workStudy of PHEVs Develop a study or a survey on how a future vehicle fleet in distributions systems will look like Acquire PHEV simulation software to run performance, design and behavioral simulationsModeling of a vehicle battery in the LPs can be extended and more detail on the operation included Refine the LP algorithms: peak-shaving: define a new average load, explore dynamic approach customer benefit: explore other demand response pricing schemes Probabilistic based methodology to model the distribution of PHEVs throughout the load points of a medium voltage system

60weighted nonlinear least squares approximation method60 AccomplishmentsPresentations J. Girldez, A multi-objective genetic algorithmic approach for optimal allocation of distributed generation and feeder interties considering reliability and cost, student poster contest, IEEE PES Power Systems Conference and Exposition, Phoenix, AZ, Mar 2011. S. Suryanarayanan, J. Girldez , S. Rajopadhye, S. Natarajan, S. Sankaranarayanan, E. Chang, D. Grunwald, J. Walz, A. Jaintilal Verifiable Decision-Making Algorithms for Reconfiguration of Electric Microgrids, poster presentation at JISEA Annual Meeting, Mar. 2011.J. Girldez, An evolutionary algorithm for planning distributed resource islands, presentation, IEEE Powel Electronics Society (PELS) , Colorado School of Mines, Golden CO, Nov. 2010 J. Girldez, S. Suryanarayanan, S. Sankaranarayanan, Modeling and simulation aspects of topological design of distributed resource islands, presentation, Joint Institute for Strategic Energy Analysis (JISEA), Natl Renewable Energy Lab (NREL). [Online] Available http://www.jisea.org/pdfs/20101214_seminar.pdf (Dec 2010).Publications J. Girldez, A. Jaintilal , J. Walz, H. E. Brown, S. Suryanarayanan, S. Sankaranarayanan, E. Chang, An evolutionary algorithm and acceleration approach for topological design of distributed resource island, accepted in Proc. 2011 IEEE PES PowerTech, Trondheim, Norway, Jun 2011. Chapter 4 is leading to a paper that will be submitted to IEEE International Conference or Transactions61weighted nonlinear least squares approximation method61 AccomplishmentsUnique contributions Enhancement of an existing technique (MOGA) for planning distributed resource islands: Simultaneous location of DG and feeder interties in a given radial distribution systemExploration of 2 ways of modeling the annual load and its effect in the redesignRedesign of distribution systems considering PHEV penetration with V2G technology: methodology to model the behavior of a PHEV fleet as load and as generation in residential and non-residential demand types impact on the reliability of distributed resource islands of different charging strategies of a PHEV fleet

62weighted nonlinear least squares approximation method62

Julieta Girldez

Graduate StudentDivision of EngineeringCSM

Thank you! Questions?63