1. PLANNING DISTRIBUTION SYSTEM RESOURCE ISLANDS CONSIDERING RELIABILITY, COST AND THE IMPACT OF PENETRATION OF PLUG-IN HYBRID ELECTRIC VEHICLES PLANNING DISTRIBUTION SYSTEM RESOURCE ISLANDS CONSIDERING RELIABILITY, COST AND THE IMPACT OF PENETRATION OF PLUG-IN HYBRID ELECTRIC VEHICLES Julieta Giráldez Graduate Student Division of Engineering CSM March 2011 1

2. Outline Outline o Introduction o Design of distributed resource islands o Multi-Objective Genetic Algorithm (MOGA) o Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on distributed resource islands o Conclusions and future work 2

3. o Introduction o Optimization of islanded distribution systems from a design perspective o Multi-Objective Genetic Algorithm (MOGA) o Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island o Conclusions and future work Outline Outline 3

4. Smart 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. Introduction Introduction 4 [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.

5. 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 Introduction Introduction 5

6. [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 Introduction Introduction 6

7. 7 [3] “Distributed Energy Resources Integration”, Consortium for Electric Reliability Technology Solutions (CERTS), [Online]. Available:http://certs.lbl.gov/certs-der.html

8. o Introduction o Design of distributed resource islands o Multi-Objective Genetic Algorithm (MOGA) o Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island o Conclusions and future work Outline Outline 8

9. Distribution systems Traditional electric distribution systems: Grid Line Transformer Load Infinite bus Design of distributed resource islands Design of distributed resource islands 9

10. Distribution systems Evolving distribution systems: Grid DG Increase annual RELIABILITY at a feasible COST Design of distributed resource islands Design of distributed resource islands 10

11. Modeling 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 islands Design of distributed resource islands 11 [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 h ∆T 2 =1900h

12. Design of distributed resource islands Design of distributed resource islands Modeling of DG DG: aggregate power output of Renewable Energy (RE) and Conventional Distributed Generation (CDG) [5] P out = CDG + RE + DS  Capacity Factor: ratio of the actual output of a power source and its output if it had operated at full capacity  Total DG rating R=R RE + R CDG P out = 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.

13. 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 islands Design of distributed resource islands 13

14. Power systems simulation tool: Computer program to solve a power flow: 1. Generation supplies the demand, to control the frequency of the system 2. Bus voltage magnitudes remain close to the rated values 3. Lines and transformers are not overloaded PowerWorld Simulator TM is used I. Enter the power system component data II. 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 islands Design of distributed resource islands 14

15. Outline Outline o Introduction o Optimization of islanded distribution systems from a design perspective o Multi-Objective Genetic Algorithm (MOGA) o Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island o Conclusions and future work 15

16. MOGA MOGA Multi-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.

17. MOGA MOGA Mathematical formulation Variables Objective function 1: COST C DG : Cost of DG [$/MW] P DG j : Power output of DGlocated at bus j [MW] C C :Cost of conductor [$/km] l i : Length of connection i [km] 17

18. MOGA MOGA Mathematical formulation Objective function 2: RELIABILITY ~ Energy Not Supplied Annual average loads: Six step load duration curve: 18

19. MOGA MOGA Mathematical formulation Constraints  Voltage within 5% of the nominal value at every bus j:  Loading of the Line from bus j to k: 19

20. 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. Matlab TM 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 MOGA * * * * 20

21. MOGA MOGA Importance of the initial population for convergence Explore 3 ways of selecting the initial population 21

22. [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 system MOGA: RBTS test system Application 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 buses DG: DESIGNPARAMETERS CF: wind, solar, conventional DG 0.25, 0.3, 0.8 Total DG penetration80% Total Annual Average Load RE penetration20% Total DG penetration 22 Customer TypeLoad points i Average Load, [MW] Max. Peak Load, [MW] Residential1, 4-7, 20-24, 32-360.46840.8367 Residential11, 12, 13, 18, 250.47580.8500 Residential2, 15, 26, 300.43390.7750 Small Industrial 8, 9, 100.84721.0167 Commercial 3, 16, 17, 19, 28, 29, 31, 37, 38 0.28860.5222 Office Buildings 14,270.56800.9250

23. Application 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 [10 6 US $]ENS [MWh] 1Line 11-171517.9731.40 2 Line 1-7 Line 11-17 4 & 1518.0621.96 3 Line 1-7 Line 11-17 Line 23-29 4 & 15 & 2518.1221.88 4 Line 11-17 Line 23-29 13 & 2918.3121.62 5 Line 11-17 Line 17-23 Line 23-29 7 & 13 & 2318.4721.36 6 Line 1-7 Line 9-15 Line 21-27 11 & 13 & 2318.9121.33 MOGA: RBTS Test system MOGA: RBTS Test system 23

24. MOGA: RBTS Test system MOGA: RBTS Test system 24 If VOLL ≤ Cost Solution 6 might be chosen

25. MOGA: RBTS Test system MOGA: RBTS Test system Application 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 25

26. Outline Outline o Introduction o Optimization of islanded distribution systems from a design perspective o Multi-Objective Genetic Algorithm (MOGA) o Impact of Plug-in Hybrid Electric Vehicles (PHEVs) on an electric distributed island o Conclusions and future work 26

27. [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 islands Impact of PHEVs in distributed resource islands Introduction 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

28. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Modeling 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

29. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Modeling 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 1. Peak-shaving 2. Owner’s benefit Linear Programming (LP) algorithms to optimize charging patterns For how long does the PHEV behaves as a load? … and as a generator? 29

30. Methodology 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 islands Impact of PHEVs in distributed resource islands Probabilistic simulation of PHEV fleet for 8760 hours [8] PHEV Class 2 PHEV Class 1 PHEV Class 4 PHEV Class 3 Daily vehicle data for optimization Energy required Miles driven Departure time Arrival time LP Optimization of daily charging pattern of PHEVs for 1 year Objective/s: maximize owners profit and/or utility peak shaving (Demand response) Incorporate optimized PHEV load (hourly) to load duration curve of distribution system Impact of PHEV fleet on annual reliability of islanded legacy radial distribution systems Impact of PHEV fleet on annual reliability of islanded networked distribution systems Tools & methods Results 30 [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.

31. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Parameters for the Prob. Sim. Methodology [8] Four vehicle classes (types) Design characteristics (SOC): Vehicle class cB c [kWh] MaxMin 1128 21410 32117 42319 31 [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

32. 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 E c : required energy per mile [kWh/mi.] Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands 32 [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.

33. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Parameters 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 M D Vehicle class c kphev c Maxmin 10.59760.2447 20.61510.2750 30.54280.3217 40.48000.3224 33 [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.

34. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Parameters for the Prob. Sim. Methodology [8] Four random paramaters 1. kphev c and B c 2. # Vehicles per class 3. Daily Miles driven per vehicle 4. Driver’s 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.

35. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Probabilistic simulation methodology [8] 1. Vehicle design characteristics kphev c and usable battery capacity B c are distributed according to a bivariate normal distribution with mean vector μ and covariance matrix ∑ with 0.8 parameter correlation Performance parameter E c is determined knowing kphev c Vehicle class c BC [kWh]kphev c 114.30150.5976 214.18270.6151 319.15160.5428 421.32110.4800 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.

36. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Probabilistic Sim. Meth. applied to the RBTS test system [8] 2. 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 36 Vehicle class Vehicles per load point type cResidentialCommercialOffice building 144157 248189 345146 449198

37. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Probabilistic simulation methodology [8] 3. Miles driven per vehicle per day M d,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 M D ≤ M d,c,v, if M d,c,v ≤ M D 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.

38. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Probabilistic simulation methodology [8] 4. Driver’s behavior ~ time parameters: Gaussian distribution Only residential charging in [8], what about office and commercial loads? Departure (am)Arrival (pm) ParameterWeekdayWeekendWeekdayWeekend μcμc 79615 σcσc 1.732.451.732.45 average urban driving speed 25 [mi./h] 38

39. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands LP algorithms By now we know:  Size and design characteristics of the PHEV fleet  Daily energy required per vehicle from the grid  Daily available time for charging per vehicle DETERMINE DAILY CHARGING PATTERNS: Utility peak shaving or benefit of the owner 39

40. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Sets:  I = set of load types, from 1 … N I  C= set of PHEV classes, from 1…N C  V= set of PHEVs per class, from 1 … N V  D=set of days in a year, from 1…N D  T= set of hours in a day, from 1 … N T  I = set of load types, from 1 … N I  C= set of PHEV classes, from 1…N C  V= set of PHEVs per class, from 1 … N V  D=set of days in a year, from 1…N D  T= set of hours in a day, from 1 … N T 40

41. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Parameters:  B c = Battery size per vehicle class c [kWh]  DE d,c,v = Daily energy required per day d, vehicle class c and vehicle v [kWh]  A d,c,v = Daily arrival time per day d, vehicle class c and vehicle v [h]  D d,c,v = Daily departure time per day d, vehicle class c and vehicle v [h]  B c = Battery size per vehicle class c [kWh]  DE d,c,v = Daily energy required per day d, vehicle class c and vehicle v [kWh]  A d,c,v = Daily arrival time per day d, vehicle class c and vehicle v [h]  D d,c,v = Daily departure time per day d, vehicle class c and vehicle v [h] From the Probabilistic Simulation Methodology  C max c = Maximum hourly charge rate per vehicle class c [kW]  L base d,i,t = Base load (without PHEVs) on day d, load type i and hour t [kW]  L av d,i = Average base load (without PHEVs) on day d and load type i [kW]  P d,t = Price of energy on day d and hour t [$/kWh]  C max c = Maximum hourly charge rate per vehicle class c [kW]  L base d,i,t = Base load (without PHEVs) on day d, load type i and hour t [kW]  L av d,i = Average base load (without PHEVs) on day d and load type i [kW]  P d,t = Price of energy on day d and hour t [$/kWh] 41

42. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Application to the RBTS test system Base load of the system: Day% Annual Peak Load Monday93 Tuesday100 Wednesday98 Thursday96 Friday94 Saturday77 Sunday75 42 [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 Type Load points i Max. Annuak Peak Load, [MW] Residential 1, 4-7, 20-24, 32- 36 0.8367 Residential11, 12, 13, 18, 250.8500 Residential2, 15, 26, 300.7750 Small Industrial 8, 9, 101.0167 Commercial 3, 16, 17, 19, 28, 29, 31, 37, 38 0.5222 Office Buildings 14,270.9250 Week Peak Load Week Peak Load Week Peak Load Week Peak Load 182.214752775.54072.4 2901572.12881.64174.3 387.816802980.14274.4 483.41775.430884380 5881883.73172.24188.1 684.119873277.64588.5 783.2208833804690.9 880.62185.63472.94794 9742281.13572.64889 1073.723903670.54994.2 1171.52488.937785097 1272.72589.63869.551100 1370.42686.13972.45295.2

43. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Application to the RBTS test system Charge 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 0.052 0. 21 0. 95 0. 85 0.052 0. 19 *the numbers inside the pie charts express the energy rate in $/kWh Midnight Noon

44. Mathematical 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]  C d,c,v,t = Energy stored on day d, vehicle class c, vehicle v and time t [kWh]  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]  C d,c,v,t = Energy stored on day d, vehicle class c, vehicle v and time t [kWh] Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands  W + d,c,v,t = Absolute value of the difference between C + d,c,v,t and C + d,c,v,t+1 [kW]  L d,i,t = New load on day d, load type i and hour t [kW]  Z d,i,t = Absolute value of the difference between L d,i,t and L av d,i [kW]  L d,i,t = New load on day d, load type i and hour t [kW]  Z d,i,t = Absolute value of the difference between L d,i,t and L av d,i [kW] Hourly charge (+) or discharge (-) Energy inventory If positive, a change in the direction of power in the battery 44

45. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Battery constraints: C + d,c,v, t ≤ C max c for every d, c, v, t C - d,c,v, t ≤ C max c for every d, c, v, t C + d,c,v, t ≤ C max c for every d, c, v, t C - d,c,v, t ≤ C max c for every d, c, v, t Limit the charge/discharge to the available connection C d,c,v, t = B c - DE d,c,v for t=A d,c,v – 1 and every d,c,v Energy in the battery when the PHEV arrives home C d,c,v, t = C d,c,v, t-1 + C + d,c,v, t - C - d,c,v, t for A d,c,v ≤ t ≤ D d,c,v and every d,c,v Inventory balance C d,c,v, t = B c for t=D d,c,v and every d,c,v Battery fully charged by dep. time 45

46. -W + d,c,v,t ≤ C + d,c,v, t - C + d,c,v, t+1 ≤ W + d,c,v,t for A d,c,v ≤ t ≤ D d,c,v -1 and every d, c, v ∑W + d,c,v,t ≤ 3*C max c for A d,c,v ≤ t ≤ D d,c,v -1 and every c, v -W + d,c,v,t ≤ C + d,c,v, t - C + d,c,v, t+1 ≤ W + d,c,v,t for A d,c,v ≤ t ≤ D d,c,v -1 and every d, c, v ∑W + d,c,v,t ≤ 3*C max c for A d,c,v ≤ t ≤ D d,c,v -1 and every c, v Mathematical formulation of the LPs Battery constraints: Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands W + d,c,v,t ? W + d,c,v,t 0 7 0 7 0 7 0 46

47. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Load constraints: for every d, i, t New load with PHEVs Peak- shaving measure 47

48. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Mathematical formulation of the LPs Objective function: 1. Utility peak-shaving 2.Customer profit SOLVE ONE OBJECTIVE AT A TIME AND COMPARE IMPACT IN RELIABILITY 48

49. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Results 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]

50. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Results 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 V2G Time [h] RBTS Power demand [kW] 50

51. Impact of PHEVs for peak-shaving Impact of PHEVs for peak-shaving Results Individual charging patterns and daily load with PEAK-SHAVING: Daily peak demand shifted Daily base load shifted Some charging before base load peak demand General charging during the night 51

52. Impact of PHEVs for PEAK-SHAVING versus UNCONTROLLED charging Impact of PHEVs for PEAK-SHAVING versus UNCONTROLLED charging Results Daily load with PEAK-SHAVING versus UNCONTROLLED charging: 52 Daily average of the base load with no PHEVs

53. Impact of PHEVs for customer benefit Impact of PHEVs for customer benefit Results Individual charging patterns and daily load with TOU PRICING: Daily peak demand shiftedDaily base load shifted Charging before base load peak demand No valley-fillingDischarge in the morning 53

54. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Reliability 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 + PHEVs ENS [MWh]44.5247.45 Load levelsBase load Base load + PHEVs Peak shaving Base load + PHEVs Customer benefit B∆T β [hours]PNS [MW]∆T β [hours]PNS [MW]∆T β [hours]PNS [MW] 13*10 -4 23.643*10 -4 26.27127.85 2222520.2024921.968722.86 3253116.76263217.64199217.88 4197513.32323513.33281712.90 515839.8819659.2127837.92 64226.456544.7110562.95 ENS [MWh]39.8845.87 39.26 54

55. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Results Step-load duration curve: Valley filling of Peak-shaving Reduce consumption for customer benefit 55 (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]

56. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Reliability 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? 56

57. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands MOGA applied to the RBTS with PHEVs Annual average modeling ~ same for peak-shaving and TOU pricing Solution #Connection (s)DG (s) bus location #Cost [10 6 US $]ENS [MWh] 1Line 11-171517.6030.72 2 Line 1-7 Line 11-17 5 & 1417.7021.55 3 Line 1-7 Line 11-17 5 & 1117.8721.31 4 Line 1-7 Line 11-17 Line 23-29 4 & 14& 2318.6421.62 5 Line 1-7 Line 11-17 Line 17-24 5 & 11 & 1918.3321.00 Annual average load, [MW] Customer TypeLoad points iBase load Base load + PHEVs peak shaving Base load + PHEVs TOU pricing Residential1, 4-7, 20-24, 32-360.46840.49390.4940 Residential 11, 12, 13, 18, 25 0.4758 0.50110.5012 Residential2, 15, 26, 300.43390.46060.4607 Small Industrial8, 9, 101.0167 Commercial3, 16, 17, 19, 28, 29, 31, 37, 380.18890.2024 Office Buildings14,270.33450.4778 No PHEVs: 2 nd highest With PHEVs: highest 57

58. Impact of PHEVs in distributed resource islands Impact of PHEVs in distributed resource islands Conclusions 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 58

59. Future work Future work MOGA 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 59 10] J. Giráldez, 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.

60. Future work Future work Study 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 simulations Modeling 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 60

61. Accomplishments Accomplishments Presentations  J. Giráldez, “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. Giráldez, 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. Giráldez, “An evolutionary algorithm for planning distributed resource islands,” presentation, IEEE Powel Electronics Society (PELS), Colorado School of Mines, Golden CO, Nov. 2010  J. Giráldez, S. Suryanarayanan, S. Sankaranarayanan, “Modeling and simulation aspects of topological design of distributed resource islands,” presentation, Joint Institute for Strategic Energy Analysis (JISEA), Nat’l Renewable Energy Lab (NREL). [Online] Available http://www.jisea.org/pdfs/20101214_seminar.pdf (Dec 2010). Publications  J. Giráldez, 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 Transactions 61

62. Accomplishments Accomplishments Unique contributions Enhancement of an existing technique (MOGA) for planning distributed resource islands:  Simultaneous location of DG and feeder interties in a given radial distribution system  Exploration of 2 ways of modeling the annual load and its effect in the redesign  Redesign of distribution systems considering PHEV penetration with V2G technology: o methodology to model the behavior of a PHEV fleet as load and as generation in residential and non-residential demand types o impact on the reliability of distributed resource islands of different charging strategies of a PHEV fleet 62

63. Julieta Giráldez Graduate Student Division of Engineering CSM Thank you! Thank you! Questions? Questions? 63