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An outage work planningunder the weather uncertainties
(不確定条件下での作業系統計画について)
N. Yorino, *Y. Zoka (Electric Power & Energy System Lab.)(餘利野 直⼈,*造賀 芳⽂,電⼒・エネルギー⼯学研究室)
Hiroshima University(広島⼤学)
ECC Workshop 2016
Table of Contents
¤ Overview of the current research (Hiroshima Univ.)¤ Our research works
¤ Outage works¤ What is the outage work?
¤ Problems to be appeared under uncertainties¤ Difficulties against weather conditions
¤ A solution for supporting system (IEEJ paper)¤ Formulation¤ Simulation studies
¤ Summary
Oct. 19, 2016 2
Overview of the current research(Hiroshima Univ. Team)
Oct. 19, 2016 3
Outline of Research Themes (HU)
Fields Groups Themes/Keywords
Analysis, Evaluation
Reliability analysis, evaluation (Y, S) Robust stability & security, Feasible region, Uncertainty, PV + Battery
Transient stability analysis (Y, S) Critical Trajectory, Computation efficiency, unbalanced fault
Reactive power evaluation (Y, S) Deregulation, “Q” pricing, OPF
EV/PHEV (Z) Frequency control, Uncertainty, Economic value evaluation
Frequency control analysis (Y) Performance indices for LFC
Operation, Control
Load forecasting (Z)Renewable-energy forecasting (Y, S)
Min/Max Load forecast, continuous-updating regressionPV/WT output prediction, Uncertainty
Dynamic ELD / Stochastic OPF (Y, S) Dynamic Economic Load Dispatch including PV/WT, Uncertainty
System stabilizing control (Z) PSS, Robust control (Uncertainty),Support Vector Machine
Microgrid operation, control (Y, Z, S) Demand-Supply Control, Plug-and-Play, Stabilization, Uncertainty
Autonomous distributed control (Y, Z) Voltage control for distribution systems, PV, Multi-agent system, Uncertainty
Power electronics application (Y, Z, S)Inverter Design (Y, S)FACTS Controller Design (Y)
Quasi-synchronizing power invertors, FACTS design
Planning, Optimization
OPF (O, Y)Optimal FACTS allocation (Y)
OPF for distribution system, Security-constrained OPF (SCOPF)Cost minimization, Load shedding, Voltage stability and Security
Outage-work planning (Z, O) PV, Support system, UncertaintyUnit Commitment with RE (Y, S)Operation Planning Including batteries, PV, forecast error, Uncertainty
(Y): Yorino, (Z): Zoka, (S): Sasaki, (O): OutsideOct. 19, 2016 4
Treatment of Uncertainty
future
Variance of EstimationError
Stochastic Power Flow
Robust Power System Security
Present t0
Power System Security Analysis, Planning, Operation and Control
s
Co-variance Matrix Confidence Intervals (CI)
Increasing variance w.r.t. time
Future t1
CI2 CI4
2s4s
t1 t2 t3 t4
Oct. 19, 2016 5
Subject (1)
1. Robust Power System Security (RPSS)
> Robust stability under N-1 contingencies inside CI
Security Analysis with Uncertainty
Voltage Stability
Over Loading
Voltage Limitation
Transient Stability
Freq. Dev.
System must be stable for all single
contingencies.
Conventional N-1 Security Criterion
Robust N-1 Security (More strict concept)where uncertain parameters are allowed to vary in
Confidence Intervals
Definition of RPSS
Oct. 19, 2016 6
Robust Static Security Region (RSS)
7
ü Power Flow Equations 𝐹(#) 𝑢, 𝑝 =0ü Constraints 𝐺(#) 𝑢, 𝑝 ≤ 0, 𝑛 = 0,1,⋯ , 𝑁
ü Control Variables 𝑢 ≤ 𝑢 ≤ 𝑢
Measure of RSS Region
ü Uncertainties in CI 𝑝 ≤ 𝑝 ≤ 𝑝
The Worst Case Max & Min inside RSS Region with Uncertainties
Upper bound 𝛼122 = min6,7
{max6
𝑐<𝑢}
Lower bound 𝛼122 = max6,7
{min6𝑐<𝑢}
Objective function
Constraints
Oct. 19, 2016
Definition of RRS
13
Robust Static Security region (RSS)
Robust Dynamically Reachable Security region (RRS)RRS : (RSS) + (Ramp Rate Constraints of u)
V
Time
t
t0
RSS
Reachable from u0
OperatingPoint
RSS
u0
Oct. 19, 2016
Robust Dynamic Analysis Example
9
Controllable parameterØ Generator outputs
Constraints for RRS : LinearØ Demand and supply balance Ø Generator output limits Ø Power flow equation (DC power
flow) Ø Security limits of line flowsØ Generator Ramp Rate ConstraintsØ Initial Operating Point at t=t0
ContingencyØ 1 of 2 Lines Trip at A
UncertaintiesØ CI for RE generations
Test System
40%
60%
G1 G2
G3Battery
① ②
③
④
⑤ ⑥
1F
2F
3F
4F 5F
6F7F
PVWT
PVWT
34z
36z56z
15z
12z 24z
25z
A
u=[G1, G2, G3, S]’
p=[RE1, RE2]’
Objective functionα = c@u : Total Generation
Oct. 19, 2016
Results of RRS Evaluations
Oct. 19, 2016
20002500300035004000450050005500
13:00 13:20 13:40 14:00
Total Thermal Generation
Operating Point
Time Time
20002500300035004000450050005500
13:00 13:20 13:40 14:00
Total Thermal Generation
Lower bound
Demand & Supply Mismatch
Upper bound of Gen
Case 2Case 1
RRS analysis at 13:00 for 1 hour ahead cT = [ 1 1 …1]cTu = Total Generation
Subject (2)
Transient Stability Analysis
¤ Critical Trajectory Method
Fast Stability Analysis
Oct. 19, 2016 11
Critical waveform by methods A & Bcompared with conventional simulation method
Rotor angle of generator 1 for fault at point G
-100
-50
0
50
100
150
200
250
300
350
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Time [s]
Roto
r A
ngle
[deg]
CCT by Method B = 0.2726 [s]
CT = 0.272 [s]
Conventional Simulation
CT = 0.273 [s]
CCT by Method A = 0.2732 [s]
Rot
or A
ngle
[deg
]
Time [s]
End Point of Method B Agrees with CUEP by Shadowing
End Point of Method A
Critical Trajectory by Method B
Critical Trajectory by Method A
Oct. 19, 2016 12
No. 17
Simulation Case for CT =0.272 [s] for fault at point G
-100
-50
0
50
100
150
200
250
300
350
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Time [s]
Roto
r A
ngl
e [
deg]
q0130 q1030
q1130
q0230
q2030
q2830
q2930
q1930
CT = 0.272 [s]
Time [s]
Rot
or A
ngle
[deg
]
CCT by Method B = 0.2726 [s]
Critical Trajectory by Method B
Conventional Simulation
CCT by Method A = 0.2732 [s]
Critical Trajectory by Method A
UEP by ShadowingEnd Point of Method A
End Point of Method B
Critical waveform by methods A & Bcompared with conventional simulation method
Oct. 19, 2016 13
No. 18
Simulation case for CT=0.273 [s] for fault at point G
-100
-50
0
50
100
150
200
250
300
350
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Time [s]
Rot
or A
ngle
[de
g]
Critical Trajectory by Method B
Conventional Simulation
Critical Trajectory by Method A
UEP by Shadowing
CT = 0.273 [s]
End Point of Method A End Point of Method B
Time [s]
Rot
or A
ngle
[deg
]
CCT by Method B = 0.2726 [s]CCT by Method A = 0.2732 [s]
q0130
q1030
q1130 q0230
q2030
q2830 q2930
q1930
Critical waveform by methods A & Bcompared with conventional simulation method
Oct. 19, 2016 14
Formulation of Critical Trajectory Method
Variables: CCT, Boundary conditions
>Initial point Condition for >End point Conditions for
Trapezoidal Conditions for numerical integrationNumber of points (m): specified. (Typically m=10)
0x
CP
xm
xk
ee
eEach point is connected by using Trapezoidal Method
x0 ~ xm+1: critical trajectoryx0
xm+1
x1
0 1, ,..., mx xe +
1mx +
Oct. 19, 2016 15
□ New End Condition, ■ Previous End Condition
0.45 0.28 0.12
4.242.39
3.92
0.930.00
1.09
5.68 4.29
21.26
-3.000
2.000
7.000
12.000
17.000
22.000
3machine 4machine 6machine 7machine 10machine 30machine
AverageerrorinCCT
computation[%
]
Powersystemmodel
%Errors in CCT
16Oct. 19, 2016
0.03 0.04 0.05 0.070.15
1.80
0.04 0.07 0.05 0.060.11
1.00
0
0.5
1
1.5
2
2.5
3machine 4machine 6machine 7machine 10machine 30machine
AverageCPU[s]
Powersystemmodel
CPU Time
17
□ New End Condition, ■ Previous End ConditionOct. 19, 2016
Subject (3)
Demand & Supply Management (Micro-EMS Controller)¤ PV Generation & Load Forecasts¤ Operation Planning (Unit Commitment) using BT¤ Computation of Dynamic Feasible Region ¤ Real Time Fast Economic Load Dispatch¤ Stochastic Power Flow¤ Frequency Control using BT, etc.
G
Thermal Gen.
GThermal Gen.
Residential
Industrial
WT
Battery (BT)
PV
Demand & Supply Manager
System Operation
under Uncertainty
Oct. 19, 2016 18
Micro-EMS Controller
�Power Demand, Weather Info.�Generator, Battery(BT), Electric
Vehicle(EV) Info., Network Info., etc.
�Real-time Demand, Weather Info.,�Gen./BT/EV Operating Info.,�Network Operating Info., etc.
Day-ahead Forecast�Power Demand�Photovoltaic Gen. (PV)�Wind-turbine Gen. (WT)�Confidence Interval(CI)
Gen. Scheduling�Unit Commitment (UC) �Battery(BT) Operation�Robust Security�Reserve Evaluation
Real-time Forecast�Power Demand�PV, WT�CI
Gen. dispatching�UC�Economic Load
Dispatching (ELD,TDF, PLF)
�Robust Security�BT re-scheduling
Normal Control�Load Frequency
Control (LFC)�AR EvaluationEmergency Control�Gen. Shedding�Load Control(Shedding,
BT, Electric Vehicle(EV))
Re-dispatching�Gen./BT re-dispatching�S&D Mismatch(SDM)
�GOV
-
PV, WT
Customer
Information Board
>
<=
SDM
Red: under VerificationBlue: under DevelopmentBlack: under Consideration
Day-Hour (UC) orderDay-ahead Planning
Manager
Hour-Min. (ELD) orderReal-time Operating
Manager
Min.–Sec.(LFC,Gov)orderReal-time Operating
Manager
Off-line Database On-line Database
�GOV
-
�GOV
-
Customer
Customer
BT, EV
Oct. 19, 2016 19
Stochastic Line Flow Model
Nde
Line Flow Limit
Line Flow [pu]
Pr(X>Xlimit)
Pr
Expected Line Flow
Node: Cov(P) à Line Flow: Cov(F) à Line Flow Limits Assumption: Normal distribution of RE Prediction Error
≦0.26%
[ ] [ ] [ ]TijCov Cov s= × Þ = × × =F S P F S P S
Linear Line Flow Model
Line Flow Control :Pr (Line Flow Violation) < a
Oct. 19, 2016
QP Problem to be solved every 5 minutes to update 1 hour Generation Schedule (GS).minimize:
subject to:
Formulation for Robust Dynamic ELD
21
( 1) ( )k Gk Gk kt tP Pd d-- £ - £
1 1( ) ( ) ,( )
n nN N
Gk Dkk k
t tP E P= =
=å å D-S Balance
( )kt Gk kttPa a£ £ FOR
Ramp rate
(1)
(2)
(3)
(4)
1( ) ( ) ( )
nN
l ll l lj Gj l ll lj
F D t S P t F D tbs bs=
- + + £ £ - +å (5)
1
1
2
1
( )
( )2
,
( ) ( )n
T t
t tN
kk k k k
k
f f t
af t P b P ct t
+
=
=
=
= + +æ öç ÷è ø
å
å
Network
Oct. 19, 2016
t = t1,!, t1 +T
24-hours scheduling resultsPG1 PG2 PG3 BTout PD Pt PPV PWT BTSOC
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
0:00 5:00 10:00 15:00 20:00Time [hour]
Out
put p
ower
[MW
]
0
0.4
0.8
1.2
1.6
2
2.4
SOC
[MW
h]
G1 G2 G3 Bd Pd Pt Ppv Pwt BsPG1 PG2 PG3 BTout PD Pt PPV PWT BTSOC
(a) Weekday (b) Weekend
12:15 12:25 12:35 12:45 12:55 13:05 13:150
1000
2000
3000
Time
PG
1[k
W]
TDF and Output Power
Upper limit of TDFLower limit of TDFOutput Power
12:15 12:25 12:35 12:45 12:55 13:05 13:150
1000
2000
3000
Time
PG
2[k
W]
12:15 12:25 12:35 12:45 12:55 13:05 13:150
1000
2000
3000
Time
PG
3[k
W]
10:00 10:10 10:20 10:30 10:40 10:50 11:000
0.5
1
1.5
2
2.5
3
Time
TD
F P
1[M
W]
TDF
0% Upper limit of TDF10% Upper limit of TDF0% Lower limit of TDF10% Lower limit of TDF
The feasible region and dispatch value on weekday TDF at 10:00 on weekday.Oct. 19, 2016 22
High PV penetration case
0
400
800
1200
1600
2000
2400
0
1000
2000
3000
4000
5000
6000
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
SO
C[k
Wh]
Pow
er D
eman
d[kW
]
Time[hour]
G1 G2 G3 Bd Pd Pt Ppv Pwt Bs
The feasible region and dispatch valueOct. 19, 2016 23
Daily Operation SimulationTotal Demand Total Demand - PV,WT OutputsGen. Output
Oct. 19, 2016 24
An outage work planningunder the weather uncertainties
IEEJ Transactions on Power and Energy, (to be published)
Oct. 19, 2016 25
Outage Work?
¤ Stable power supply¤ Important mission of power systems¤ Inspection, repair, reinforcement, …¤ à Outage works are necessary
¤ Outage work planning is to determine…¤ System configuration, work combination, work
schedule, etc.
¤ In this study,¤ Regarded as the problem of system configuration
Oct. 19, 2016 26
At planning stage, a maximum load is assumed for work days.
However, if the weather is different from the assumed condition…
Outage work planning must be done one-year aheadbased on annual plan.
PV output
Load forecastOutage work plan
Power flow changes depending on PV outputs
PV impacts?
27
PV output
Load forecastOutage work plan
Oct. 19, 2016
IEEJ Technical Report (Analysis Tech.)
¤ IEEJ Technical Report¤ Hiroshima Univ.¤ Chugoku
Electric Power Co.
¤ Universities¤ Manufacturers¤ Institutes¤ Gas companies¤ Generation Co.¤ 10 Utilities
28Oct. 19, 2016
Typical actual workflow of utilities
¤ Utilities
¤ Outage works
Oct. 19, 2016 29
Typical actual workflow of utilities
¤ Utilities
¤ Outage works
Oct. 19, 2016 30
Power BalancePower Grid related
Dec
ades
Seve
ral y
ears
Year
ly
Mon
tyly
Wee
kly
Day
lyRe
al ti
me
Generators Networks
Flowchart (general)
Oct. 19, 2016 31
Normal system
LoadG SS
SS
SS
SS
Power supply = Stable
: SubstationSS
Oct. 19, 2016 32
Normal system
LoadG SS
SS
SS
SS
Target line
No route!
Supply failure to Loads
Outage work = infeasible!
Oct. 19, 2016 33
LoadG SS
SS
SS
SS
Target line
Outage work system
Power supply = stable
Outage work = feasible
Oct. 19, 2016 34
Flow of the planning
35
Make ranking of the set
Select the outage work system
Calculate the index
Build a feasible candidate set
Check the constraints
Enumerate candidates
Define of the search space
*) Hamming Distance constraint: the number of switching from the normal system.**) N-2 supply failure power: the failed amount of power supply when simultaneous 2-lines outage occur.
1st=sys7, 2nd=sys2, 3rd=sys10
Outage work system = sys7
Index** = N-2 supply failure power
{sys2, sys7, sys10}
Power balance, voltage, line power flow, …
{sys1, sys2, …, sysN} | N: total candidates
Search space based on Hamming Distance*
Oct. 19, 2016
Background
Oct. 19, 2016 36
*) Outage work: temporal, partial stop for inspection, repair, etc. (not blackout)
Outage work planning* = determine work schedules, orders, combinations, system configurations
Outage work system candidates
All system configurations
Usual systems
Outage work system candidates
Highly reliable outage work systems
Determinethe work-date
Rearrange of work-date
Built up a chart日 1 2 3 4 5 6
作業設備 月 火 水 木 金 土作業1作業2作業3作業4作業5作業6作業7
LoadG SS
SS
SS
SS
Research Target
In case of huge, rapid change of PV outputs
Due to the over loads, feasible outage work systems are different depending on PV output.
New outage work planning taking into account PV output uncertainties is necessary.
An impact to outage work by PVs
37
N1
N3
N4N2
N6N5
L1L2L3
L4
L7
L5
L6
PV
PV
PV
Over load!
Outage work system
Target line
Oct. 19, 2016
Intersection of the candidates
Oct. 19, 2016 38
PV
PV
PV
PV
PV
PV
���
PV
PV
PV
{sys1, sys2, sys3}
{sys2, sys3, sys4}
{sys2, sys4}
PV installed n nodesà 2n patterns
If prepared as sys2, the plan will be feasible even if any patterns of PV output occur.
A typical example
¤ Data¤ Total load: 3,000MW¤ 7 generators
¤ 109 nodes, 138 branches¤ Upper system: loop-based¤ Lower system: radial-based
39
System model
Oct. 19, 2016
Assumptions (example)
¤ PV assumption¤ Divided into 3 areas (A, B, C)
¤ Same PV output states within the same area
¤ PV Install conditions¤ PV installation types
¤ Mega-solar : Roof-top PV= 1 : 1
¤ Extrapolate for larger cases
¤ Amount of installation¤ Mega-solar = actual data¤ Roof-top = based on
household statistics
¤ Outage works¤ Target = L57 (1 cct)
40Oct. 19, 2016
Lost of feasible plan due to PVs
Oct. 19, 2016 41
Finally, no outage work plan obtained because of no feasible solution.
The more PV penetration, the fewer feasible outage work system candidates due to overload line.
N1
N3
N4N2
N6N5
L1L2L3
L4
L7
L5
L6
PV
PV
PV
Over load!
Outage work system
Target line
0
20
40
60
80
100
120
140
0 2 4 6 8 10
実行可能な作業系統候補数[個]
PV導入量 [%]PV penetration rate (%)
Feas
ible
sys
tem
can
dida
tes
Lost of feasible plan due to PVs
42
Feasible system candidates depending on PV output patterns and penetration rates
0
20
40
60
80
100
120
140
0 2 4 6 8 10
� ��������[�]
PV��� [%]PV penetration rate (%)
Feas
ible
sys
tem
can
did
ates
*) PV output conditionsØ 1 = 100%Ø 0 = 0%
PV output conditions* PV penetration rate assumptions
Area A Area B Area C
Common feasible systems
Rapid reduction in the cases 2, 5, 6, and 8.
In area C, large number of roof-top PVs in residential area and many mega-solar systems in industry area.
Large power flow change occursdepending on PV output patterns.
Oct. 19, 2016
Problem to be solved
¤ Problem¤ No feasible outage work system obtained by PVs
¤ Mainly due to overload problem¤ à An additional function to avoid overload
conditions.
Oct. 19, 2016 43
¤ All weather feasible
Oct. 19, 2016 44
PV
PV
PV
PV
PV
PV
・・・PV
PV
PV
sys1 sys2 sys4
sys3 sys2 sys4
sys2 sys4
PV installed n nodesà 2n patterns*
*) PV output is assumed 0% or 100% because the most severe cases must be considered.
If prepared as sys2, the plan will be feasible even if any patterns of PV output occur.
Formulation (minimization of N-2)
Oct. 19, 2016 45
ååå¹= = =
•=MC
jii
MC
j
MB
bkbbijk pLyxpyxA
1 1 1)(),(),,( w
),,(min 1pyxA
Minimize the index: N-2 supply failure power(decision variables: x = facilities connection)Subject to: feasible for any PV output patterns
Objective function
Subject to
N-2 supply failure power
!n
kF kXxx
2
1
)(,=
Î"
*1
*2
kp : Load parameters for PV output pattern k
*1
*2 )(kXF : Feasible solution set for PV output pattern k
Find the system with minimum N-2 supply failure power when the weather is fine and feasible for any PV output patterns.
¤ Overload cases
Oct. 19, 2016 46
PV
PV
PV
PV
PV
PV
・・・PV
PV
PV
sys1 sys2 sys4
sys3 sys2 sys4
sys5 sys2
PV installed n nodesà 2n patterns*
*) PV output is assumed 0% or 100% because the most severe cases must be considered.
If not obtained feasible solution, generator output adjustment (control) takes place and adopt it as additional candidates.
Formulation (G adjustment)
Oct. 19, 2016 47
Minimize: amount of G adjustment(decision variables: G = output adjustment)Subject to: operation restrictions
Determine the amount of G adjustment and its locationfor N-1 overload banishing.
)(min1
-
=
+ +å m
MP
mm GG
maxmaxjjj FFF ££-
0)(1
=+ -
=
+å m
MP
mm GG
maxminmmm PPP ££
Objective function
Constraints
: Amount of G adjustment
: Limits of line flows
: Power balance
: Limits of G outputs
Flowchart of the algorithm
Oct. 19, 2016 48
Numerical simulations
¤ Data¤ Total load: 3,000MW¤ 109 nodes, 138 branches
¤ (Same as before)
¤ PV assumption¤ Divided into 3 areas (A, B, C)¤ PV Install conditions
¤ (Same as before)
¤ Outage works¤ Target = L57 (1 cct)¤ Target = L30 (1 cct) ß
overload case
Oct. 19, 2016 49
Case Area A Area B Area C1 1 1 12 1 1 03 1 0 14 0 1 15 1 0 06 0 1 07 0 0 18 0 0 0
Area A
Area C
Area B
L30
×
×L57
Simulation results
¤ All weather feasible¤ Obtained a solution feasible for all weather
conditions¤ Shaded part of the table below¤ Covering all cases
Oct. 19, 2016 50
Case off→on on→off Connection change off→on on→off Connection change off→on on→off Connection change off→on on→off Connection change
1 L107 L29 L129(N13→N14) L107 L29 L107 L29 L127(N13→N14) L107 L29 L128(N14→N13)
2 L107 L29 L107 L29 L127(N13→N14) L107 L29 L128(N14→N13) L107 L29 L131(N16→N17)
3 L107 L29 L107 L29 L127(N13→N14) L107 L29 L128(N14→N13) L107 L29 L131(N16→N17)
4 L107 L29 L107 L29 L127(N13→N14) L107 L29 L128(N14→N13) L107 L29 L131(N16→N17)
5 L107 L29 L107 L29 L127(N13→N14) L107 L29 L128(N14→N13) L107 L29 L131(N16→N17)
6 L107 L29 L107 L29 L127(N13→N14) L107 L29 L128(N14→N13) L107 L29 L131(N16→N17)
7 L107 L29 L127(N13→N14) L107 L29 L30(N14→N13) L110 L29 L107 L29 L30(N14→N13)
8 L107 L29 L127(N13→N14) L107 L29 L30(N14→N13) L110 L29 L110 L29 L131(N16→N17)
4th
Ranking
1st 2nd 3rd
Simulation results
¤ Overload case¤ In Case 7 & 8, no feasible system obtained.¤ à Additional G adjustment works well.
¤ Shaded part of the table below
Oct. 19, 2016 51
Case off→on on→off Connection change off→on on→off Connection change off→on on→off Connection change off→on on→off Connection change
1 L107 L30 L129(N13→N14) L107 L30 L107 L30 L128(N14→N13) L107 L29 L131(N16→N17)
2 L107 L30 L107 L30 L128(N14→N13) L107 L30 L131(N16→N17) L107 L30 L132(N17→N16)
3 L107 L30 L107 L30 L128(N14→N13) L107 L30 L131(N16→N17) L107 L30 L132(N17→N16)
4 L107 L30 L107 L30 L128(N14→N13) L107 L30 L131(N16→N17) L107 L30 L132(N17→N16)
5 L107 L30 L107 L30 L128(N14→N13) L107 L30 L131(N16→N17) L107 L30 L132(N17→N16)
6 L107 L30 L107 L30 L128(N14→N13) L107 L30 L131(N16→N17) L107 L30 L132(N17→N16)
7 L107 L30
8 L107 L30
Ranking
1st 2nd 3rd 4th
Conclusions
¤ Summary¤ PV penetration affects outage work planning
¤ It has been found out that the number of feasible outage work systems decreases depending on PV penetration.
¤ A new method has been proposed¤ To avoid overload cases.
¤ Future works¤ PV penetration assumption should be brushed up.¤ PV installation patterns should be analyzed more in detail.¤ PV output classification (area) should be studied based
on actual data.
Oct. 19, 2016 52