reserve and congestion management using wind power probabilistic forecast a real case study
Download
Skip this Video
Download Presentation
Reserve and Congestion Management Using Wind Power Probabilistic Forecast: A Real Case-Study

Loading in 2 Seconds...

play fullscreen
1 / 17

Reserve and Congestion Management Using Wind Power Probabilistic Forecast: A Real Case-Study - PowerPoint PPT Presentation


  • 84 Views
  • Uploaded on

2011 MAR 17. Reserve and Congestion Management Using Wind Power Probabilistic Forecast: A Real Case-Study. Ricardo Bessa 1 ( [email protected] ) Leonardo Bremermann 1 , Manuel Matos 1 Rui Pestana 2 , Nélio Machado 2 Hans-Peter Waldl 3 , Christian Wichmann 3

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Reserve and Congestion Management Using Wind Power Probabilistic Forecast: A Real Case-Study' - tamera


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
reserve and congestion management using wind power probabilistic forecast a real case study
2011 MAR 17

Reserve and Congestion Management Using Wind Power Probabilistic Forecast: A Real Case-Study

Ricardo Bessa1 ([email protected])

Leonardo Bremermann1, Manuel Matos1

Rui Pestana2, Nélio Machado2

Hans-Peter Waldl3, Christian Wichmann3

1 INESC Porto, Portugal

2 REN, Portugal

3 Overspeed GmbH & Co. KG, Germany

+

introduction
Introduction

EWEA Annual Conference, 14-17 March 2011

    • In the ANEMOS.plusEuropean 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
slide3
EWEA Annual Conference, 14-17 March 2011

Robust Reserve Setting Tool

robust reserve setting rrs tool
Robust Reserve Setting (RRS) Tool

L: Uncertain Load

Decision-aid Phase

(risk vs reserve cost)

System Gen. Margin Model

SM=G-L

Deterministic Multicriteria Problem

Preferred Operating Reserve Level

Decision Methods

Risk Indices

G: Uncertain Generation

Probabilistic Model

Decision Maker

(REN)

Demonstration at the Portuguese SO (REN)

Evaluation

EWEA Annual Conference, 14-17 March 2011

uncertainty modeling
Uncertainty Modeling

EWEA Annual Conference, 14-17 March 2011

  • 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
system generation margin distribution probabilistic model
System Generation Margin Distribution (Probabilistic Model)

upward reserve

+ 700 MW

LOLP=0.036

EPNS=5.4 MW

risk of loss ofload

LOLP=0.49

EPNS=157.1 MW

PWRE=0.037

EWRE=4.13 MW

risk of generationsurplus

PWRE=0.51

EWRE=129.1 MW

downward reserve

- 600 MW

EWEA Annual Conference, 14-17 March 2011

risk reserve or cost curves and decision aid
Risk/(Reserve or Cost) Curves and Decision-aid
  • Recommended downward reserve
  • Recommended upward reserve
  • max
  • accepted
  • LOLP
  • max
  • accepted
  • PWRE

EWEA Annual Conference, 14-17 March 2011

demonstration case design
Demonstration Case Design

Running since 28 Sept 2010

Hourly Upward and Downward

Reserve Needs

Load and

Special Regime Generation (e.g. mini-hydro, CHP) Forecasts

7 times per day

Daily, 6 Intraday Markets

7 times per day

Sequential Market

RRS

(ANEMOS.plus)

Upscaled Probabilistic WPF

Market Dispatch and

Interconnection Levels

4 GW

4 times per day

(ANEMOS)

7 times per day

Daily, 6 Intraday Markets

EWEA Annual Conference, 14-17 March 2011

output results upward reserve
Output Results (Upward Reserve)

LOLP=0.1%

EWEA Annual Conference, 14-17 March 2011

upward reserve results oct feb 4 months
Upward Reserve Results (Oct-Feb, 4 Months)

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

Sharpness is important, but it is not the critical factor

EWEA Annual Conference, 14-17 March 2011

fuzzy power flow fpf
Fuzzy Power Flow (FPF)

Load about 50 MW

Load more or less between 30 and 40 MW

Load between 15 and 30 MW

EWEA Annual Conference, 14-17 March 2011

  • 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
demonstration case design1
Demonstration Case Design

Running since 25 Oct 2010

Deterministic AC Power

Flow

Network physical

data

Transmission Network of Portugal

1 time per day and for 24 hours

AC Fuzzy Power

Flow (ANEMOS.plus)

Conventional generation

and load for day D+1

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

Fuzzy sets

Voltage module and phase

P and Q power flows

Active losses

1 time per day and for 24 hours

Transformation of

WPF uncertainty

into fuzzy sets

Deterministic and

probabilistic WPF for D+1

(ANEMOS)

forecast launched at 6AM

38 Wind farms

6 network nodes

~2 GW

Q5%

Q95%

Point Forecast

EWEA Annual Conference, 14-17 March 2011

output information
Output Information

Severity of the congestion

EWEA Annual Conference, 14-17 March 2011

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 %)

output results
Output Results

EWEA Annual Conference, 14-17 March 2011

  • Possibility of overvoltage situations in two nodes at 9PM 31 Oct
    • Possibility of network congestions in two lines on 31 Oct at 9PM
output results1
Output Results

31 Oct 2010

31 network congestion along this day

EWEA Annual Conference, 14-17 March 2011

27 Oct 2010

0 network congestion along this day

conclusions
Conclusions

EWEA Annual Conference, 14-17 March 2011

    • 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)
ad