J. McCalley

Wind Power Variability in the Grid: Regulation & Load Following

Outline1. AGC2. AGC and wind3. Control performance standards (CPS)4. Effect of AGC on CPS

2

Two Area System

3

BA 1 BA 2P12

X

XT e0

And if the two areas are operating with only primary control, then their “slow” dynamics are represented by the following block diagram.

Stiffness coefficient:

Two area system with primary control dynamics only

4

Σ ΔPm2(s) + 1

M2s+D2

ΔPtie(s)

Δω2(s)

1

1+sTT,2

ΔPV2(s) 1 1+sTG,2

T2(s) G2(s)

+

1 R2

Σ ΔPref,2(s)

+

-

Σ ΔPm1(s) + 1

M1s+D1 ΔPtie(s)

Δω1(s)

1

1+sTT,1

ΔPV1(s) 1 1+sTG,1

T1(s) G1(s)

-

1 R1

Σ +

-

ΔPref,1(s)

T s Σ

ΔPNL1(s)

ΔPNL2(s)

-

-

+

-

See http://home.eng.iastate.edu/~jdm/ee553/AGC1.pdf

State equations for this system

5

)(1

)(1

)()(1

)(

)(1

)(1

)(

)(1

)(1

)(1

)(

111

11

11

11

11

11

1

11

111

11

1

tPM

tPM

tM

DtP

Mt

tPT

tPT

tP

tPT

tRT

tPT

tP

NLtiem

mT

VT

m

refGG

VG

V

)(1

)(1

)()(1

)(

)(1

)(1

)(

)(1

)(1

)(1

)(

222

22

22

22

22

22

2

22

222

22

2

tPM

tPM

tM

DtP

Mt

tPT

tPT

tP

tPT

tRT

tPT

tP

NLtiem

mT

VT

m

refGG

VG

V

)()()( 21 tTtTtPtie

Form of equations is the same, except for sign of ΔPtie term in 3rd equation of each set.

Will this work?

6

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.

Each BA compensates for its own load change.

For a load change in area 1, we desire:∆ Pm1∞=∆PNL1

∆Pm2∞=0∆ω∞=0∆Ptie∞=0

Introduce Area Control Error

7See http://home.eng.iastate.edu/~jdm/ee553/AGC1.pdf

ACE1=-B1∆ω-∆Ptie, ACE2=-B2∆ω+∆Ptie

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.

PNL1

PNL2

State equations for this system

8

)()()( 21 tTtTtPtie

tieref

Ltiem

mT

VT

m

refGG

VG

V

PKtKBtP

tPM

tPM

tM

DtP

Mt

tPT

tPT

tP

tPT

tRT

tPT

tP

)()(

)(1

)(1

)()(1

)(

)(1

)(1

)(

)(1

)(1

)(1

)(

222

222

22

22

22

22

22

2

22

222

22

2

tieref

Ltiem

mT

VT

m

refGG

VG

V

PKtKBtP

tPM

tPM

tM

DtP

Mt

tPT

tPT

tP

tPT

tRT

tPT

tP

)()(

)(1

)(1

)()(1

)(

)(1

)(1

)(

)(1

)(1

)(1

)(

111

111

11

11

11

11

11

1

11

111

11

1

AGC and participation factors

9See http://home.eng.iastate.edu/~jdm/ee553/AGC1.pdf

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.

Summary of power balance control levelsNo. Control Name Time frame Control objectives Function

1 Inertial response 0-5 secsPower balance and

transient frequency dip minimization

Transient frequency control

2 Primary control, governor 1-20 secs

Power balance and transient frequency

recovery

Transient frequency control

3 Secondary control, AGC

4 secs to 3 mins

Power balance and steady-state frequency Regulation

4 Real-time market Every 5 mins Power balance and economic-dispatch

Load following and reserve provision

5 Day-ahead market Every day Power balance and

economic-unit commitmentUnit commitment and

reserve provision

10

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.

So let’s take a look at how the real time market uses a net load forecast.

Base point calculation via real-time market

11

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.

Focus on interval 2, { t+5, t+10}.

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.

ADS: automatic dispatch systemDOT: dispatch operating target

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.

Wind farm participation in AGC

12

Most windfarms do not participate in AGC today.

However, windfarms do affect the net load seen by AGC, as indicated here.

PNL1

PNL2

13

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.

14

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

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.

FBPPACE

tieP

stieatie

||)( ,,

min1min1

min1 ||10F

B

ACECP

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.

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.

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

)(

)(

MonthCP

CF

min1min1

min1 ||10F

B

ACECP

Ref: M. Terbrueggen, “Control Performance Standards” 2002

Average ACE, ΔF over 1 min to compute:

Average CP 1min over 12 mo to compute:

17

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

)(

)(

MonthCP

CF

min1min1

min1 ||10F

B

ACECP

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

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.

A BWind units

Con units Con

units

19C. 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 A

Case 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 A

Case 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.

22

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%.

23C. Wang and J. McCalley, “Impact of Wind Power on Control Performance Standards,” under review, IEEE Trans on Pwr Sys.

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)

24

%/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 13.8124