PSERC Webinar - September 27,

Transcription

1 PSERC Webinar - September 27,

2 [1]. S. Meliopoulos, J. Meisel and T. Overbye, Power System Level Impacts of Plug-In Hybrid Vehicles (Final Project Report), PSERC Document 09-12, Oct PSERC Webinar - September 27,

3 PSERC Webinar - September 27,

4 4

5 PSERC Webinar - September 27,

6 Source: IEEE P "Draft Guide for Design, Operation and Integration of Distributed Resource Island Systems with Electric Power Systems,, Work Group web site PSERC Webinar - September 27,

7 7

8 KEY QUESTIONS TO STUDY THE IMPACT KEY ASSUMPTIONS TO STUDY THE IMPACT 8

9 9

10 Probabilistic simulation of PHEV fleet for 8760 hours PHEV Class 1 PHEV Class 2 PHEV Class 3 PHEV Class 4 Daily vehicle data for optimization Tools & methods Miles driven Energy required Arrival time Departure 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 Results 10

11 Battery size Vehicle class c B c [kwh] Max Min

12 E c E a ( kphev ) c c E b c 12

13 M M D D B E c c 40miles E a ( kphev ) c E c c E b c Vehicle class c Max kphev c min

14 14

15 Vehicle class c BC [kwh] kphev c

16 DE d, c, v Bc M d, c, v * E, if M D M d,c,v c, if M d,c,v M D 16

17 Departure (am) Arrival (pm) Parameter Weekday Weekend Weekday Weekend μ c σ c Dep Arr nonres d, c, v nonres d, c, v Arr Dep res d, c, v res d, c, v M S M d, c, v d, c, v S average urban driving speed 25 mph 17

18 DETERMINE DAILY CHARGING PATTERNS: Utility peak shaving using LP algorithms 18

19 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 19

20 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 20

21 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] 21

22 Hourly charge (+) or discharge (-) Energy inventory 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] 22

23 If positive, a change in the direction of power in the battery 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] 23

24 Limit the charge/discharge to the available connection Energy in the battery when the PHEV arrives home 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 = B c - DE d,c,v for t=a d,c,v 1 and every d,c,v Inventory balance 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 Battery fully charged by dep. time C d,c,v, t = B c for t=d d,c,v and every d,c,v

25 -W + d,c,v,t C + d,c,v, t - C + d,c,v, t+1 W + d,c,v,t for every d, c, v A d,c,v t D d,c,v -1 W + d,c,v,t 3*C max c for every d, c, v A d,c,v t D d,c,v -1 25

26 W + d,c,v,t? Hour C + d,c,v,t [kw] C - d,c,v,t [kw] C + d,c,v,t W + d,c,v,t t1 7 0 t2 7 0 t3 0 7 t4 0 7 t5 7 0 t6 7 0 t7 0 7 t

27 New load with PHEVs Ld,i,t L Base d, i, t N V v1 C d, c, v, t C d, c, v, t for every d, i, t Peakshaving measure - Z d,i,t av L d, i, t Ld, i Zd, i, t for every d, i, t 27

28 28 Mathematical formulation of the LPs Peak Objective function: Utility peak-shaving shaving Minimize d, i, t Z d, i, t Measure of deviation of daily peak load from average load 28

29 29

30 Customer Load points i Average Max. Peak Type Load, [MW] Load, [MW] Residential 1, 4-7, 20-24, Residential 11, 12, 13, 18, Residential 2, 15, 26, Small Industrial Commercial 3, 16, 17, 19, 28, 29, 31, 37, 38 8, 9, Office Buildings 14,

31 Day % Annual Peak Load Monday 93 Tuesday 100 Wednesday 98 Thursday 96 Friday 94 Saturday 77 Sunday 75 Week Peak Load Week Peak Load Week Peak Load Week Peak Load

32 Vehicle Vehicles per load point type class c Residential Commercial Office building

33 PSERC Webinar 9/27/

34 Typical weekday profile of residential load type in RBTS Typical weekend profile of residential load type in RBTS 34

35 Typical weekday profile of non-residential load type in RBTS 35

36 36

37 37