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ALBERTA WIND POWER VARIABILITY STUDY Represented by Tommi Pensas

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CONTENTS 1.Introduction 2.Preliminary analysis 3.Purpose of the study Objective 1 Objective 2 4.Conclusion 5.Summary

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INTRODUCTION Growth of wind energy industry in Alberta What is the impact of wind power variability to grid reliability? PURPOSE: Objective 1.Development and validation of a simulation model Objective 2.Analysis for one existing scenario and three future scenarios WPF = wind power facility (wind park)

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PRELIMINARY ANALYSIS INPUT DATA AESO provided wind power generation data for five existing WPFs (10s and 1min) WPF1: – , time interval 1 minute WPF2: – , time interval 10 seconds WPF3: – , time interval 1 minute WPF4: – , time interval 10 seconds WPF5: – , time interval 1 minute Fluctuation statistics were calculated in terms of the wind power output for WPFs

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DEVELOPMENT AND VALIDATION OF A SIMULATION MODEL OBJECTIVE 1

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A MODIFIED MULTI-TURBINE CURVE APPROACH WAS SELECTED 10-minute model Assumptions All turbines similar Wind speeds normally distributed around the average wind speed Wind speed measured from the middle of a WPF 1-minute model Generated by linearly interpolating from 10-minute model Assumptions Sda Dsa

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MODEL VALIDATION Comparison between measurements and simulations At first with one WPF Then combined with other four existing WPFs Sensitivity analysis WPF size, turbulence intensity, air density, wake/array losses, electrical losses

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ANALYSIS FOR ONE EXISTING SCENARIO AND FOUR FUTURE SCENARIOS OBJECTIVE 2

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SCENARIO A – 5 EXISTING WPFs Nameplate capacity 254 MW Pincher Creek and Fort Macloed/Magrath SCENARIO B – 5 EXISTING AND 9 PROPOSED WPFs Nameplate capacity 895 MW Pincher Creek, Fort Macloed/Magrath and Taber SCENARIO C – 5 EXISTING AND 17 PROPOSED WPFs Nameplate capacity 1445 MW Pincher Creek, Fort Macloed/Magrath, Taber and Medicine Hat SCENARIO D – 5 EXISTING AND 22 PROPOSED WPFs Nameplate capacity 1994 MW Pincher Creek, Fort Macloed/Magrath, Taber, Medicine Hat and Waterton

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RESULTS Scenarios95,0%97,5%99,5%Maximum Scenario A (254 MW)14,70 MW18,76 MW29,69 MW82,01 MW Scenario B (895 MW)31,68 MW40,08 MW62,99 MW202,07 MW Scenario C (1445 MW)44,84 MW56,33 MW84,85 MW208,48 MW Scenario D (1994 MW)55,34 MW69,60 MW105,58 MW259,12 MW Percentiles of simulated 10-minute fluctuations for scenarios A, B, C and D Percentiles of simulated 1-minute fluctuations for scenarios A, B, C and D Scenarios95,0%97,5%99,5%Maximum Scenario A (254 MW)3,48 MW4,69 MW8,24 MW65,63 MW Scenario B (895 MW)6,81 MW8,82 MW14,63 MW115,86 MW Scenario C (1445 MW)9,14 MW11,67 MW18,64 MW148,61 MW Scenario D (1994 MW)11,09 MW14,17 MW22,87 MW221,87 MW

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CONCLUSION The modified multi-turbine power curve approach was adopted 10-minute1-minute RegionMeasuredSimulatedMeasurdSimulatd WPF114,6 %12,5 %2,6 %3,1 % WPF218,3 %21,1 %3,8 %4,9 % WPF317,2 %17,1 %5,4 %4,2 % WPF416,1 %19,9 %5,1 %4,7 % Aggregate Scenario8,4 %8,5 %1,7 %2,1 % The approach succeeded to capture the magnitude and frequency of short-term fluctuations with reasonable accuracy Normalized 97,5% of the measured and simulated fluctuations

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CONCLUSION With different wind energy penetration levels Magnitudes of power fluctuations were seldom extreme With aggregate scenarios there were lower fluctuations than with any single WPFs Magnitude of fluctuatons increased when nameplate capacity increased

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SUMMARY + Clear construction + Many diagrams -Time series from different time periods -Input data does not take into account the controlled outages and startups of WPFs -> data distortion -Had to read several times to get a grip of the study QUESTIONS?

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