impact of variability on control performance metrics
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James D. McCalley Harpole Professor of Electrical & Computer Engineering Iowa State University. Impact of Variability on Control Performance Metrics. Wind penetration is small now, so NERC says decreasing freq gov charac β not due to wind , - PowerPoint PPT PresentationTRANSCRIPT
Impact of Variability on Control Performance Metrics
James D. McCalleyHarpole Professor of Electrical & Computer Engineering
Iowa State University
1
Impact of variability on control performance1. Wind penetration is small now, so NERC says
decreasing freq gov charac β not due to wind,Source: “Comments Of The North American Electric Reliability Corporation Following September 23 Frequency Response Technical Conference,” Oct. 14, 2010
but…at higher wind penetrations, • β will decrease if non-wind units with primary control
are displaced by wind units without primary control; • Reduced inertia and increased net load variability will
cause greater frequency excursions.2. Will higher wind penetrations degrade freq perf in
terms of NERC’s Control performance metrics?Each Balancing Authority shall achieve, as a minimum,• Requirement 1: CPS1 compliance of 100%• Requirement 2: CPS2 compliance of 90%• Potential requirements: BAAL and others….
3
Control performance standardsControl 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.
4
Control performance standards
NERC Standard BAL-001-0.1a — “Real power balancing control performance,” http://www.nerc.com/files/BAL-001-0_1a.pdf.
CPS1 CPS2
5
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.
FBPPACEtieP
stieatie
||)( ,,
min1min1
min1 ||10F
BACECP
Ref: M. Terbrueggen, “Control Performance Standards” 2002
Average ACE, ΔF over 1 min to compute:
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.
6
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.
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.
FBPPACE stieatie ||)( ,,
%100100)2(1 CFCPS
21
12min1
)()(
MonthCPCF
min1min1
min1 ||10F
BACECP
Ref: M. Terbrueggen, “Control Performance Standards” 2002
Average ACE, ΔF over 1 min to compute:
Average CP 1min over 12 mo to compute:
7
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.
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 ACEPtie,a>>Ptie,s).
FBPPACE stieatie ||)( ,,
%100100)2(1 CFCPS
21
12min1
)()(
MonthCPCF
min1min1
min1 ||10F
BACECP
Ref: M. Terbrueggen, “Control Performance Standards” 2002
Average ACE, ΔF over 1 min to compute:
Average CP 1min over 12 mo to compute:
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.
10 101.65 10 10i sL B B
• 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.
Requirement: |ACE10min |<
CPS2=100%-(Percent of 10 min periods in violation)>90%
• L10 is max value within which ACE10min must be controlled
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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.
A BWind units
Con units Con
units
9C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
2 Area Simulation System
FBPPACE stieatie ||)( ,,
)(1 tP regNL
)(2 tP regNL
C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
)()(
)()()()(
)()()()(
)]([)]([)(
11
,11,11
,1,111
,111
tPtP
tPtPtPtP
tPtPtPtP
tPtPtP
regw
regL
fcstwwfcstLL
fcstwfcstLwL
RTEDGNLregNL
)(
)()(
)()(
)]([)]([)(
2
,22
,22
,222
tP
tPtP
tPtP
tPtPtP
regL
fcstLL
fcstLL
RTEDGNLregNL
Area 1 input Area 2 input
Inputs for 2 Area Simulation System
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
0% 6.67% 10.14% 13.71% 17.37% 21.12% 24.94%0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Nor
mal
ized
CP
S1
Sco
re
Wind Energy Penetration Level in Area 1
Case ACase B
0% 6.67% 10.14% 13.71% 17.37% 21.12% 24.94%0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1N
orm
aliz
ed C
PS
2 S
core
in A
rea
1
Wind Energy Penetration Level in Area 1
Case ACase B
Normalized CPS1
Normalized CPS2
Case A: Area 1, Area 2 have same size.Case B: Area 1 unchanged. Area 2 load and gen scaled up by 10.
12
Conclusions:1. CPS1 and CPS2 deteriorates
with increasing wind penetration.2. The effect is larger for “smaller”
interconnections.
C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
Study resultsMeasures 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 over Original
CPS1
CPS2 Improvement over Original
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 over Original
CPS1
CPS2 Improvement over Original
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%.
13C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.
Why Does CPS1 CPS2 Degradation Matter?
NERC penalties Indication of greater frequency variability:
Some loads may lose performance (induction motors) UFLS relay operation Volts per Hz relay operation Lifetime reduction of turbine blades Frequency dip may increase for given loss of generation
Indication of greater ACE variability: Increased inadvertent flows Increase control action of generators
Indication BA is “leaning on” othersQUESTION: Why do we divide the interconnection into BAs?
MOST IMPORTANT FACTORS
Solutions to variability & uncertainty1. 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 advance4. Increase non-wind MW ramping capability during periods of
expected high variability using one or more of the below (M1):a. Conventional generation b. Load controlc. Storaged. Expand control areas
5. Combine control areas (M4)15
%/min $/mbtu $/kw LCOE,$/mwhr
Coal 1-5 2.27 2450 64
Nuclear 1-5 0.70 3820 73
NGCC 5-10 5.05 984 80
CT 20 5.05 685 95
Diesel 40 13.8115