Transcript Slide 1

Wind Power Variability in the Grid:
Regulation & Load Following
J. McCalley
Outline
1.
2.
3.
4.
AGC
AGC and wind
Control performance standards (CPS)
Effect of AGC on CPS
2
Two Area System
BA 1
BA 2
P12
X
Stiffness coefficient: T 
 e0
X
And if the two areas are operating with only primary
control, then their “slow” dynamics are represented by
the following block diagram.
3
Two area system with primary control
dynamics only
1
R1
+
ΔPref,1(s)
1
1+sTG,1
Σ
1
ΔPV1(s)
ΔPm1(s) +
-
1+sTT,1
T1(s)
1
Σ
Δω1(s)
ΔPtie(s)
ΔPNL1(s)
M1s+D1
G1(s)
+
T
s
ΔPNL2(s)
ΔPtie(s)
+
+
ΔPref,2(s)
1
1+sTG,2
Σ
-
1
ΔPV2(s)
ΔPm2(s) +
Σ
Σ
1
1+sTT,2
M2s+D2
T2(s)
G2(s)
Δω2(s)
1
R2
See http://home.eng.iastate.edu/~jdm/ee553/AGC1.pdf
4
State equations for this system

PV 1 (t )  
1
1
1
PV 1 (t ) 
1 (t ) 
Pref 1 (t )
TG1
TG1 R1
TG1

1
1
PV 1 (t ) 
Pm1 (t )
TT 1
TT 1

1
D
1
1
Pm1 (t )  1 1 (t ) 
Ptie (t ) 
PNL1 (t )
M1
M1
M1
M1
Pm1 (t ) 
1 (t ) 

PV 2 (t )  
1
1
1
PV 2 (t ) 
2 (t ) 
Pref 2 (t )
TG 2
TG 2 R2
TG 2

1
1
PV 2 (t ) 
Pm 2 (t )
TT 2
TT 2

1
D
1
1
Pm 2 (t )  2 2 (t ) 
Ptie (t ) 
PNL 2 (t )
M2
M2
M2
M2
Pm 2 (t ) 
2 (t ) 
Form of equations is
the same, except for
sign of ΔPtie term in 3rd
equation of each set.

Ptie (t )  T1 (t )  T2 (t )
5
Will this work?
For a load change in area 1, we desire:
∆ Pm1∞=∆PNL1 Each BA compensates for
∆Pm2∞=0
its own load change.
∆ω∞=0
∆Ptie∞=0
Steady-state values
The system of the previous two slides consists of just
primary control, and as we have seen will distribute the
generation imbalance to all units, leaving a non-zero
steady-state frequency error. Thus, ΔPm2∞≠0; Δω∞≠0,
and ΔPtie∞≠0. We need an additional control loop.
6
Introduce Area Control Error
ACE1=-B1∆ω-∆Ptie,
ACE2=-B2∆ω+∆Ptie
PNL1
The additional loop is an
integral control loop, which
provides the ability to zero the
steady-state error of the
system output (frequency) in
response to a unit step
disturbance.
See http://home.eng.iastate.edu/~jdm/ee553/AGC1.pdf
PNL2
7
State equations for this system

PV 1 (t )  
1
1
1
PV 1 (t ) 
1 (t ) 
Pref 1 (t )
TG1
TG1 R1
TG1

1
1
PV 1 (t ) 
Pm1 (t )
TT 1
TT 1

1
D
1
1
Pm1 (t )  1 1 (t ) 
Ptie (t ) 
PL1 (t )
M1
M1
M1
M1
Pm1 (t ) 
1 (t ) 

Pref 1 (t )   KB11 (t )  KPtie

1
1
1
PV 2 (t )  
PV 2 (t ) 
2 (t ) 
Pref 2 (t )
TG 2
TG 2 R2
TG 2

1
1
PV 2 (t ) 
Pm 2 (t )
TT 2
TT 2

1
D
1
1
Pm 2 (t )  2 2 (t ) 
Ptie (t ) 
PL 2 (t )
M2
M2
M2
M2
Pm 2 (t ) 
2 (t ) 

Pref 2 (t )   KB2 2 (t )  KPtie

Ptie (t )  T1 (t )  T2 (t )
8
AGC and participation factors
ACE, being a measure of
how much the total system
generation needs to
change, is allocated to the
various units that comprise
the balancing area via
participation factors.
The participation factors
are obtained by linearizing
the economic (market)
dispatch about the last
base point solution (see
Wood & Wollenberg,
section 3.8).
Base point calculation is performed
by the real-time market every 5 mins.
See http://home.eng.iastate.edu/~jdm/ee553/AGC1.pdf
9
Summary of power balance control levels
No.
Control Name Time frame
1
Inertial response
0-5 secs
2
Primary control,
governor
1-20 secs
3
Secondary
control, AGC
4 secs to 3
mins
4
5
Real-time market Every 5 mins
Day-ahead
market
Every day
Control objectives
Power balance and
transient frequency dip
minimization
Power balance and
transient frequency
recovery
Function
Transient frequency
control
Transient frequency
control
Power balance and steadystate frequency
Regulation
Power balance and
economic-dispatch
Load following and
reserve provision
Power balance and
Unit commitment and
economic-unit commitment
reserve provision
We are
addressing the
system’s ability
to control
steady-state
frequency.
Why consider
the real-time
market?
The real-time market has a secondary influence on the system’s ability to
control steady-state frequency because it computes base points based on a net
load forecast. The accuracy of this forecast determines how much the units will
be moved by AGC and as a result, how much frequency variability is present.
10
So let’s take a look at how the real time market uses a net load forecast.
Base point calculation via real-time market
Focus on interval 2, { t+5, t+10}.
ADS: automatic dispatch system
DOT: dispatch operating target
For interval 2, a short-term net
load forecast is made 7.5 min
before interval 2 begins, at t-2.5,
and generation set points are
computed accordingly.
At t+2.5, which is 2.5 minutes
before interval 2 begins, the units
start to move.
The units are ramped at a rate
which provides that they reach the
desired base point at t+7.5 min,
which is 2.5 min after the interval
begins.
Source: Y. Makarov, C. Loutan, J. Ma, and P. de Mello, “Operational impacts
of wind generation on California power systems,” IEEE Trans on Power
Systems, Vol. 24, No. 2, May 2009.
Key point: The base point is
computed from a net load
forecast. There is error in this
forecast, which typically
increases as wind penetration
increases. This error contributes
to frequency deviation.
11
Wind farm participation in AGC
PNL1
Most windfarms do not
participate in AGC today.
However, windfarms do
affect the net load seen by
AGC, as indicated here.
PNL2
12
Control performance standards
Control Performance Standards CPS1 and CPS2 evolved from earlier metrics and
were enacted by NERC in 1997 to evaluate a balancing area’s frequency control
performance in normal interconnected power system operations.
The motivation underlying CPS is to ensure a targeted long term frequency control
performance of the entire interconnection.
CPS measures each balancing area’s frequency control performance in achieving
control objectives.
N.Jaleeli and ,L.VanSlyck, “Control performance standards and procedures for interconnected operation,” Electric Power
Research Institute, Dublin, Ohio, Tech.Rep. TR-107813, Apr.1997.
N.Jaleeli and L.S.Vanslyk, “NERC’s new control performance standards. IEEE Trans. Power Syst.,” vol.14, pp.1092-1099,
Aug.1999.
13
Control performance standards
CPS1
CPS2
NERC Standard BAL-001-0.1a — “Real power balancing control performance,” http://www.nerc.com/files/BAL-001-0_1a.pdf.
14
CPS1:a measure of a balancing area’s long
term (12 mo) frequency performance.
• Control objective - bound excursions of 1-minute average
frequency error over 12 months in the interconnection.
• Measures control performance by comparing how well a
balancing area’s ACE performs in conjunction with the
frequency error of the interconnection.
Ref: M. Terbrueggen, “Control Performance Standards” 2002
ACE  ( Ptie,a  Ptie, s ) | B | F

Ptie
Average ACE, ΔF over 1 min to compute:
CP1min 
ACE1min
 F1min
10 | B |
10B to give units of Hz.
ΔF is an interconnection measure. ΔPtie is a balancing area measure.
When ΔF<0, the interconnection needs generation, so desire BA to make
ΔPtie large  ACE>0 (helping). If ACE<0, it means BA is undergenerating  “hurting.”
So we want to see CP negative, large in mag.
15
CPS1:a measure of a balancing area’s long
term (12 mo) frequency performance.
• Control objective - bound excursions of 1-minute average
frequency error over 12 months in the interconnection.
• Measures control performance by comparing how well a
balancing area’s ACE performs in conjunction with the
frequency error of the interconnection.
Ref: M. Terbrueggen, “Control Performance Standards” 2002
ACE  ( Ptie,a  Ptie,s ) | B | F
Average ACE, ΔF over 1 min to compute:
ACE1min
 F1min
10 | B |
Average CP 1min
(CP1min )12Month
over 12 mo to
CF 
compute:
(1 ) 2
CP1min 
ε1 =target bound for 12 month of 1min avg freq error. e.g., 0.018Hz in EI, 0.228Hz
in WECC, 0.020 Hz for ERCOT. Must be squared to normalize Hz2 in numerator.
CPS1  (2  CF ) 100  100%
16
CPS1:a measure of a balancing area’s long
term (12 mo) frequency performance.
• Control objective - bound excursions of 1-minute average
frequency error over 12 months in the interconnection.
• Measures control performance by comparing how well a
balancing area’s ACE performs in conjunction with the
frequency error of the interconnection.
Ref: M. Terbrueggen, “Control Performance Standards” 2002
ACE  ( Ptie,a  Ptie,s ) | B | F
Average ACE, ΔF over 1 min to compute:
ACE1min
 F1min
10 | B |
Average CP 1min
(CP1min )12Month
over 12 mo to
CF 
compute:
(1 ) 2
CP1min 
CPS1  (2  CF ) 100  100%
Problem: balancing area can grossly
over- or under-generate (as long as
it is opposite frequency error) and
get very good CPS1, yet impact its
neighbors with excessive flows
(large ACEPtie,a>>Ptie,s).
17
CPS2: measure of a balancing area’s ACE
over all 10-minute periods in a month.
• Control objective – limit ACE variations & bound
unscheduled power flows between balancing areas.
• Developed to address “problem” of previous slide.
Requirement: |ACE10min |< L10
 1.6510
10Bi 10Bs 
CPS2=100%-(Percent of 10 min periods in violation)>90%
• L10 is max value within which ACE10min must be controlled
• BS=sum of B values for all control areas.
• ε10 =target bound for 12 mo RMS of10-min avg freq error:
e.g., 0.0057Hz in EI, 0.0073 for the WI and ERCOT.
• In 2003, the 10Bs were ~ -5692 mw/0.1hz for EI, -1825
mw/0.1hz for WEEC, -920 mw/0.1Hz for ERCOT.
18
Simulation System
•Two Area System (Area A and Area B)
Wind power is assumed in area A
•Each area consists of 10 conventional units, with inertia and with
speed governing
• Based points are computed from net load forecast made 7.5 min
ahead, with an assumed error of 1% for load and 4.5% for wind.
•Wind penetration levels- 6%, 10%, 13%, 17%, 21%, 25% (Pw/Pnw)
are considered (by capacity).
• Wind is assumed to displace conventional units
• Actual sec-by-sec p.u. value of load and of wind power data from
one wind farm is used.
Con
units
Wind
units
A
B
Con
units
C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
19
2 Area Simulation System
reg
PNL
1 (t )
ACE  ( Ptie,a  Ptie,s ) | B | F
reg
PNL
2 (t )
C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
Inputs for 2 Area Simulation System
Area 2 input
Area 1 input
reg
PNL
1 (t )  [ PNL1 (t )]  [ PG1, RTED (t )]

(t )   P

(t ) 
 PL1 (t )  Pw1 (t )  PL1, fcst (t )  Pw1, fcst (t )
 PL1 (t )  PL1, fcst
 P (t )  P (t )
reg
L1
reg
w1
w1
(t )  Pw1, fcst
reg
PNL
2 (t )  [ PNL 2 (t )]  [ PG 2 , RTED (t )]

 PL 2 (t )  PL 2, fcst (t )
 PL 2 (t )  PL 2, fcst (t ) 

 PLreg
2 (t )
The sec-by-sec generation levels in each case
(PG1,RTED and PG2,RTED) are determined by linearly
interpolating between their respective 5 minute
load and wind forecasts.
C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
Study results
1
Normalized CPS1 Score
0.9
0.8
Case A
Case B
0.7
0.6
Normalized CPS1
0.5
Case A: Area 1, Area 2
have same size.
Case B: Area 1
unchanged. Area 2 load
and gen scaled up by 10.
0.4
0.3
0.2
0%
6.67%
10.14%
13.71%
17.37%
Wind Energy Penetration Level in Area 1
21.12%
24.94%
Conclusions:
1. CPS1 and CPS2 deteriorates
with increasing wind
penetration.
2. The effect is larger for “smaller”
interconnections.
Normalized CPS2 Score in Area 1
1
0.9
Case A
Case B
0.8
0.7
0.6
Normalized CPS2
0.5
0.4
0.3
0.2
0%
6.67%
10.14%
13.71%
17.37%
Wind Energy Penetration Level in Area 1
21.12%
24.94%
C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
22
Study results
Measures to improve CPS1, CPS2:
• M1: Increase primary frequency control capability in Area 1
• M2: Increase the forecast accuracy of wind power
• M3: Control wind power output to be no more than a band around forecasted value
• M4: Combining control areas.
CPS1 AND CPS2 SCORE WITH DIFFERENT MEASURES AT 25% WIND POWER
ENERGY PENETRATION LEVEL IN AREA1, CASE A
Measures
CPS1
Improvement
CPS2
Improvement
over Original
over Original
CPS1
CPS2
M1
52.93%
31.74%
59.71%
3.75%
M2 *
65.58%
63.21%
71.22%
23.75%
M2 **
92.90%
131.21%
100%
75.00%
M3
60.76%
51.22%
66.19%
15.00%
M4
73.84%
83.78%
In M2*, NRMSE of wind power forecast is assumed to be 3%; In M2**, NRMSE of wind power forecast is assumed to be 0%.
CPS1 AND CPS2 SCORE WITH DIFFERENT MEASURES AT 25% WIND POWER
ENERGY PENETRATION LEVEL IN AREA 1, CASE B
Measures
CPS1
Improvement
CPS2
Improvement
over Original
over Original
CPS1
CPS2
M1
91.50%
4.61%
96.52%
2.21%
M2 *
93.78%
7.21%
98.61%
4.41%
M2 **
96.56%
10.40%
100%
5.88%
M3
91.16%
4.22%
97.92%
3.68%
M4
99.07%
13.25%
In M2*, NRMSE of wind power forecast is assumed to be 3%; In M2**, NRMSE of wind power forecast is assumed to be 0%.
C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
23
Solutions to variability & uncertainty
1. Do nothing: fossil-plants provide reg & LF (and die ).
2. Improve forecasts (M2)
3. Increase control of the wind generation
a. Control wind to band around forecasted value (M3)
b. Provide wind with primary control
• Reg down (4%/sec), but spills wind following the control
• Reg up, but spills wind continuously
c. Limit wind generation ramp rates
• Limit of increasing ramp is easy to do
• Limit of decreasing ramp is harder, but good forecasting can warn
of impending decrease and plant can begin decreasing in advance
4. Increase non-wind MW ramping capability during periods of
expected high variability using one or more of the below (M1):
a. Conventional generation
%/min $/mbtu $/kw LCOE,$/mwhr
b. Load control
Coal
1-5
2.27
2450 64
c. Storage
Nuclear 1-5
0.70
3820 73
d. Expand control areas
NGCC
5-10
5.05
984 80
5. Combine control areas (M4) CT
20
5.05
685 95
24
24
Diesel
40
13.81