Risk Based Methods for Short Term Wind Power Commitment

Transcription

1 Risk Based Methods for Short Term Wind Power Commitment Rajesh Karki, Roy Billinton and Suman Thapa Power Systems Research Group University of Saskatchewan Saskatoon, Canada

2 Introduction Rapid growth of wind power, Expected to Continue Increasing variability/uncertainty in power generation System operating problems, Operating reliability Methods/Tools for operating decisions Deterministic/Probabilistic methods Operating risks Risk-based operating decisions 2

3 3 Introduction This presentation discusses probabilistic risk based methods for wind capacity estimates and for unit commitments, and presents examples to illustrate the impacts of different system parameters on system operating risks.

4 Diurnal Variation of Wind Wind Speed (km/h) Time of day (hour) mean, day-1 SD, day- 1 mean, day-161 SD, day-161 Day-1: Jan 1 Day-161: June 10 Average hourly wind speed variations at Toronto location

5 Wind Power Modeling for Short- term Reliability Evaluation Short-term wind model: knowledge of initial wind power condition Persistence model ARMA Model: historical wind speed data Conditional Probability Model: using ARMA Model for selected lead time, and knowledge of initial wind speed Wind power model: wind power curve Quantify operating risk 5

6 Probability 6 Conditional Wind Speed Models Lead time = 1 hour Probability Wind Speed (km/h) Wind Speed (km/h) Initial wind speed at Hour 8 20 km/h km/h km/h 30 km/h

7 Wind Speed Models Considering Different Lead Times Probability Wind Speed (km/h) Initial wind speed = 25 km/h Lead time 1 hour 2 hours 4 hours 7

8 Evaluation of Wind Power Commitment Risk Probability that the wind power in the short future time is less than the estimated and committed value - WPCR Probability Hour ahead wind speed distribution (initial speed=25km/h) WPCR=0.52 Wind Power Curve Wind Speed (km/h) Wind Power (% rated capacity) Regina wind data, initial wind speed 25 km/h 8

9 WPCR WPCR in Hour-ahead Wind Power Estimates km/h Regina Data % 80% 50% Wind Power Commitment (% of Initial Power) Initial Wind Power = 0.1 p.u. 9

10 WPCR Deterministic Wind Power Commitment and WPCR Variation Regina Toronto Initial Wind Speed (km/h) Next hour commitment = 100% of initial wind power 10

11 Wind Power Committment (% of initial power) 11 Risk Based Wind Power Commitment WPCR= Wind Power (p.u. of rated capacity) Lead time =1 hour

12 Committment in the next hour (% Rated Capacity) 12 Simplified Method for Wind Power Commitment WPCR=0.3 WPCR=0.2 WPCR=0.1 y = 0.63x y = 0.57x y = 0.47x Initial Power (% Rated Capacity) R. Karki, S. Thapa and R. Billinton, A Simplified Risk-Based Method for Short-Term Wind Power Commitment, IEEE Transactions on Sustainable Energy, 3(3),

13 13 Impact of Lead Time and Rising/Falling Wind Trend on WPCR Initial Wind Power = 0.2 p.u.

14 Day-ahead Wind Power Commitment 14

15 Evaluation of Unit Commitment Risk (UCR) Wind capacity estimate input to unit commitment Probability of the committed generating capacity being equal or less than the forecast load during a future specified duration (or lead time) - UCR. Operating reliability lead time, initial condition UCR Evaluation: Capacity model of committed conventional units - COPT Wind capacity model conditional probability distribution Overall capacity model convolution / area risk approach 15

16 with wind power (0-0.5 hr.) Probability Probability 16 Probability Probability Incorporating Wind Power in Area Risk Concept for UCR Evaluation with wind power ( hrs.) with wind power ( hrs.) with wind power ( hrs.) with wind power (3.5-4 hrs.) 4.9 f(r) Wind Power States Wind Power States Wind Power States Wind Power States Lead Time (Hours) S. Thapa, R. Karki, R. Billinton (2013). Utilization of the Area Risk Concept for Operational Reliability Evaluation of a Wind-Integrated Power System, IEEE Transactions on Power Systems, 28(4),

17 17 Studies Using the IEEE-RTS 2 Operating conditions were considered: High Load Scenario (HLS) 22 units (total capacity of 3177 MW) from the priority loading order of IEEE RTS are committed for a lead time of 4 hours. Largest committed unit 400 MW, Smallest unit 12 MW. Low Load Scenario (LLS): 11 units from the priority loading order (total 2096 MW). Largest committed unit 400 MW, Smallest unit 50 MW. 300 MW wind farm with Toronto wind data is used.

18 18 UCR Analysis HLS with initial 90 MW wind power (30% of rated wind capacity)

19 19 UCR at Different Operating Conditions HLS (2600MW) LLS (1500MW) UCR x % 30% 60% Initial Wind Power (% Rated Capacity) Initial Time is Hour 20 with Falling wind trend

20 20 Capacity Contribution of Wind Power The capacity contribution of wind at an operating condition can be assessed by the increase in the load carrying capability due to wind power for a specified UCR criterion. HLS, UCR criterion of

21 Operating Capacity Credit of Wind The increase in the load carrying capability due to wind power expressed as a percentage of the total installed wind capacity is called the operating capacity credit (OCC). OCC (%) 40 Rising Wind 40 LLS HLS Falling Wind Falling HLS, UCRC Wind, = Initial Power 30% Initial Wind UCR Power Criterion (% (%) of Rated)

22 OCC Considering Wind Speed Correlation r = 0.75 Two wind farms, 45 MW each initially available 22

23 Well-being Analysis Incorporates a deterministic criterion, such as the N-1 criterion, in a probabilistic framework A system can be in any one of these 3 states during its operation. Healthy State Probability, P(H) Marginal State Probability, P(H) 23 Unit Commitment Risk, UCR

24 Well-being Indices at Different Wind Conditions Initial Wind Rising Trend Falling Trend Ph Pm UCR Ph Pm UCR 30% % HLS (3177 MW committed from IEEE-RTS) with 300 MW wind farm (Toronto wind data) 2770 MW load, 4 hour lead time considered. 24

25 Load Carrying Capability of Wind Considering Risk and Health Criteria Initial Wind power UCRC= P(h)=0.99 LCC, MW OCC (%) LCC, MW OCC (%) No Wind MW Rising MW Falling LLS (2096 MW committed from IEEE-RTS) with 300 MW wind farm (Toronto wind data) 4 hour lead time considered. 25

26 26 Conclusion Distinct and different indices: WPCR, UCR, Wellbeing Capacity value (credit) of wind power in a short future time depends on: initial conditions, rising/falling trend, acceptable risk criteria. WPCR, UCR generally increases with lead time. Initial wind speed has little or no effect on day-ahead wind power commitment. Wind speed correlation between multiple wind farms has considerable impact on the system risk.