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Reserve and Congestion Management Using Wind Power

Probabilistic Forecast: A Real Case-Study

Ricardo Bessa1 (rbessa@inescporto.pt)Leonardo Bremermann1, Manuel Matos1

Rui Pestana2, Nélio Machado2 Hans-Peter Waldl3, Christian Wichmann3

1 INESC Porto, Portugal2 REN, Portugal

3 Overspeed GmbH & Co. KG, Germany

2011 MAR 17

+

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Introduction

• In the ANEMOS.plus European project power system management tools were developed, and are now being demonstrated at several end-users

• Two of these management tools will be presented (on-going demonstration for REN)

• Robust Reserve Setting (RRS) tool

– Objectives: estimation of the operational reserve needs to account for units outages, wind power and load uncertainty

– Output: reserve levels for each hour of a predefined period (i.e. day-ahead, intraday) obtained with different decision-aid methods

• Fuzzy Power Flow (FPF) tool

– Objectives: identify possible voltage violations and branch congestions

– Output: list of nodes with possible voltage limits violations and branches with possible congestions

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Robust Reserve Setting Tool

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Robust Reserve Setting (RRS) Tool

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G: Uncertain Generation

L: Uncertain Load

Decision Methods

Preferred Operating

Reserve Level

Evaluation

Decision Maker(REN)

Probabilistic Model

Decision-aid Phase

Demonstration at the Portuguese SO

(REN)

(risk vs reserve cost)

Deterministic Multicriteria

Problem

System Gen. Margin Model

SM=G-L

Risk Indices

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Uncertainty Modeling

• Conventional generation: discrete probability distribution of the possible capacity states (capacity outage probability table, COPT)

• Load: Gaussian distribution with a given standard deviation and zero mean

• Wind generation: set of quantiles forecasted by the ANEMOS platform

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System Generation Margin Distribution (Probabilistic Model)

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risk of loss of load

LOLP=0.49EPNS=157.1 MW

PWRE=0.037EWRE=4.13 MW

upward reserve

+ 700 MW

LOLP=0.036EPNS=5.4 MW

risk of generation surplus

PWRE=0.51EWRE=129.1 MW

downward reserve

- 600 MW

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Risk/(Reserve or Cost) Curves and Decision-aid

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max accepted

LOLP

Recommended upward reserve

max accepted

PWRE

Recommended downward

reserve

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Demonstration Case Design

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RRS(ANEMOS.plus)

Upscaled Probabilistic WPF

Load and Special Regime Generation

(e.g. mini-hydro, CHP) Forecasts

4 GW4 times per day

(ANEMOS)

7 times per day

Market Dispatch andInterconnection Levels

7 times per dayDaily, 6 Intraday Markets

Hourly Upward and Downward Reserve Needs

7 times per dayDaily, 6 Intraday Markets

Running since 28 Sept 2010

Sequential Market

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0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

h

Intra1 Intra2 Intra3 Intra4 Intra5

Intra6 Intra7 Mob. Reserve Benchmark Rule

Output Results (Upward Reserve)

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LOLP=0.1%

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Upward Reserve Results (Oct-Feb, 4 Months)

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Market Session

LOLP=0.1%

LOLP=0.5%

LOLP=1%

Benchmark Rule

Daily 1.44 % 2.25 % 2.76 % 4.34 %

Intraday 1 0.83 % 1.39 % 1.79 % 3.13 %

Intraday 2 1.23 % 1.76 % 2.14 % 3.18 %

Intraday 3 1.15 % 1.77 % 2.33 % 2.47 %

Intraday 4 1.28 % 2.02 % 2.51 % 2.08 %

Intraday 5 1.18 % 1.72 % 2.37 % 2.35 %

Intraday 6 0.70 % 0.70 % 1.10 % 2.47 %

Reliability (or calibration) of probabilistic forecasts is the key requirement

Sharpness is important, but it is not the critical factor

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Fuzzy Power Flow Tool

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Fuzzy Power Flow (FPF)

• Fuzzy numbers for generation and load (active and reactive)

• The midpoint is computed by the deterministic AC power flow

• The FPF consists of a linearization step and a non-iterative algorithm to deal with uncertainties

• Output data

– e.g. fuzzy node voltages’ magnitudes and angles; fuzzy active and reactive power flows; fuzzy active and reactive losses and currents

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00.10.20.30.40.50.60.70.80.9

1

30 40 50 60 70

u(x)

MW

00.10.20.30.40.50.60.70.80.9

1

10 20 30 40 50u(

x)MW

00.10.20.30.40.50.60.70.80.9

1

10 15 20 25 30 35 40

u(x)

MW

Load about 50 MW Load more or less between 30 and 40 MW Load between 15 and 30 MW

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Demonstration Case Design

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Deterministic AC PowerFlow

AC Fuzzy PowerFlow (ANEMOS.plus)

Network physical data

Conventional generation and load for day D+1

Deterministic andprobabilistic WPF for D+1

(ANEMOS)

Transformation of WPF uncertainty

into fuzzy sets

Running since 25 Oct 2010

1 time per day and for 24 hours of the next day

Fuzzy setsVoltage module and phase

P and Q power flowsActive losses

Q5%

Point Forecast

Q95%

Transmission Network of Portugal 1 time per day and for 24 hours

1 time per day and for 24 hours

forecast launched at 6AM38 Wind farms

6 network nodes~2 GW

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Output Information

• List of possible bus voltage violations and branch congestion

• Voltage violation: >1.05 pu and <0.95 pu

• Congestion: greater than line limit power

• Severity index of the congestion and voltage violation (in %)

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0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

apparent Power Flow (MVA)

Severity of the congestion

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Output Results

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• Possibility of overvoltage situations in two nodes at 9PM 31 Oct

• Possibility of network congestions in two lines on 31 Oct at 9PM

1.03 1.035 1.04 1.045 1.05 1.0550

0.2

0.4

0.6

0.8

1

1.2Node 102911

Voltage (pu) Voltage limit (pu)

1.03 1.035 1.04 1.045 1.05 1.0550

0.2

0.4

0.6

0.8

1

1.2 Node 102662

Voltage (pu) Voltage limit (pu)

700.

00

800.

00

900.

00

1000

.00

1100

.00

1200

.00

1300

.00

1400

.00

1500

.00

0.00

0.20

0.40

0.60

0.80

1.00

1.20line 0399

Apparent Fuzzy Power Flow (MVA)Line Capacity (MVA)

100.00 120.00 140.00 160.00 180.00 200.000

0.2

0.4

0.6

0.8

1

1.2 line 0090

Apparent Fuzzy Power Flow (MVA)Line limit

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Output Results

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

3

1 1 1 1 1

3

1

3

2

1

3 3 3

2 2

Number of network congestions

31 Oct 2010

31 network congestion along this day

27 Oct 2010

0 network congestion along this day

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Conclusions

• The tools were developed according to the end-users prerequisites and necessities

• Robust reserve setting tool

– avoids making assumptions on the errors distributions

– defines the reserve dynamically

– models different attitudes and values of the decision-maker

• Fuzzy power flow tool

– allows the inclusion of probabilistic WPF in day-ahead security evaluation

– contribute to identify weak points of the transmission network during operational phases

• Next step: quantitative and qualitative evaluation results for the whole demonstration period (until June 2011)

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