Analysis of pre and post construction wind farm energy yields with focus on uncertainties

Transcription

1 Analysis of pre and post construction wind farm energy yields with focus on uncertainties Abdelbari Redouane REMENA Master Program 2014 Contact: Lahmeyer International GmbH

2 Defense of Master Thesis Analysis of pre and post construction wind farm energy yields with focus on uncertainties Improvement of Lahmeyer s spreadsheet application software for estimating wind farm energy yield uncertainties Author: Supervisor: Supervisor: Co-supervisor: Supervisor on-site: Abdelbari Redouane Prof. Adel Khalil Prof. Siegfried Heier Dr. Kurt Rohrig Dipl. Ing. Nicolás Veneranda 2

3 Agenda Introduction Motivation Uncertainty concept Model Input data for the Model Results Conclusion 3

4 Introduction Data Source: GLOBAL WIND STATISTICS 2013, GLOBAL WIND ENERGY COUNCIL,

5 Motivation Predict Gross Yield Wind speed measurements Long Term Prediction Process Terrain and surface roughness model Wind flow model Turbine layout Turbine power curve Calculate Losses and Predict NET Yield Wake Effects Availability Turbine Performance Electrical Environmental Curtailment Estimate Uncertainties Financial Model Site developer Independent Consultants Turbine Manufacturers Financiers 5

6 Uncertainty concept Error sources that can contribute in the uncertainty of the final result. Incomplete definition of the measurand ; Imperfect realization of the definition of the measurand ; Non-representative sampling if a sampling is used; Inadequate knowledge of the effects of environmental conditions on the measurement or imperfect measurement of environmental conditions; Personal bias in handling the instruments; Finite instrument resolution or discrimination threshold; JCGM 100:2008: Evaluation of measurement data Guide to the expression of uncertainty in measurement (ISO/IEC Guide 98-3) 6

7 Uncertainty concept Type A evaluation: Method of evaluation of uncertainty by the statistical analysis of series of observations In most cases, the best available estimate of the expectation or expected value q of a quantity q that varies randomly [a random variable], and for which n independent observations q k have been obtained under the same conditions of measurement, is the arithmetic mean or average of the n observations Type B evaluation: Method of evaluation of uncertainty by means other than the statistical analysis of series of observations Previous measurement data; Experience with or general knowledge of the behavior and properties of relevant materials and instruments; Manufacturer s specifications; Data provided in calibration and other certificates; Uncertainties assigned to reference data taken from handbooks. JCGM 100:2008: Evaluation of measurement data Guide to the expression of uncertainty in measurement (ISO/IEC Guide 98-3) 7

8 Wind Study Uncertainty Model Calibration Data Integrity Data Analysis Long-term correlation Total Uncertainty on Gross Production (1- year)deviation Total Uncertainty on Gross Production (N-year) Measurement and Data Processing - wind speed related 1-year wind Total N-year wind deviation Measurement and Data Processing wind to Energy converted Prediction horizon [years] Flow modeling Type of Anemometer Measurement - wind speed related Wake modeling Total Uncertainty Mounting Power curve 8

9 Model Standard deviation of the monthly energy production of a set of turbines Calculate the different standard deviation and average them and scale the average to the AEP level Production data Variability Sum the root square of the individual standard deviations Use of the correlation and the covariance matrices Wind related uncertainties Combining uncertainties Sensitivity analysis Wind to Energy uncertainties Energy related uncertainties Combining uncertainties Wind Study uncertainties

10 Input data for the Model Farm Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 number WTG Nominal Power 2 2, ,3 2 1,5 0,85 Hubhight m , mast with measurement height 30 m 40 m 50 m 30 m.50 m 53 m 49, m, 66.0 m and 42.0 m 49,5 81, 40 m 31, 50 m N/A number of met masts distance from 1 On site mast to WTGs On site On site On site On site On site On site On site, 1 22km On site Extension climate 4 years 8 1 year (16 8.5, , 5 4 years 6 years 1 year 1 year data input months months) years years 2 years Farm Nominal Power terrain complexity Neighboring Farms 38 37, ,5 Flat, hilly 2 existing 2 authorized data for the LT adjustment 4 Flat (modestly hilly agricultura l) Complex Flat, hilly Flat, hilly 1 existing Weather Station and 1 NCEP/NCA 12 (limited to 10 - grid requireme nt) 49,5 34 Complex Flat Flat Complex 0(repoweri ng) 2 authorized NCEP / 2 Weather 1 Weather Weather NCEP/NCA NCEP/NCA NCEP/NCA NCEP/NCA NCAR wind Station Station Station R ts R R R ts data +NCEP +NCEP R ts flow modelling WAsP WAsP WAsP WAsP WAsP WAsP WAsP WAsP WAsP production data Monthly turbines Data Monthly turbines Data Monthly turbines Data Monthly Farm Data Monthly Farm Data Monthly Farm Data Monthly Farm Data Monthly turbines Data 2 Monthly turbines Data Image source: Farm Location Designation Site1 Italy Confidential Site 2 Italy Confidential Site 3 Italy Confidential Site 4 France Confidential Site 5 France Confidential Site 6 France Confidential Site 7 Portugal Confidential Site 8 Pakistan Confidential Site 9 Italy Confidential

11 Input data for the Model (Example, site 1) Site / Graphs illustrating the monthly production and the variability related to it Image source: Lahmeyer International Farm Site 1 Number of WTG 19 Nominal Power of WTG MW 2 Hub height m

12 Results (Example, site 1) Site 1 Site 1 Correlation Turbines Correlation Months AEP PD [kwh] Average Monthly PD [kwh] StdDev Monthly PD [kwh] Average StdDevPD [kwh] SQRT(Sum(StdDev²)) PD [kwh] SQRT(Sum(Sum COVi)) PD [kwh] Average R 75% 48% 12

13 Results (Example, site 1) Site 1 Site 1 PD WS AEP NET [kwh/a] StdDev Variability [%] 8% Uncertainty WS [%] 15% Site 1 Site 1 PD WS PoE AEP NET [kwh/a] AEP NET[kWh/a] 50% % %

14 Results (Example, site 1) 14

15 Results Farm Terrain FLH WS [h] FLH PD [h] Site 1 Flat Site 2 Flat Site 3 Complex Site 4 Flat Site 5 Flat Site 6 Complex Site 7 Flat Site 8 Flat Site 9 Complex Farm Terrain WS Uncertainty [%] PD Variability [%] Site 1 Flat 15% 8% Site 2 Flat 13% 4% Site 3 Complex 12% 8% Site 4 Flat 12% 4% Site 5 Flat 13% 5% Site 6 Complex 12% 4% Site 7 Flat 12% 7% Site 8 Flat 16% 8% Site 9 Complex 9% 7% 15

16 Results 16

17 Results 17

18 Results PoE(50%) WS AEP PoE(50%) PD AEP -> Average estimation elative deviation of +15% PoE(90%) WS AEP PoE(50%) PD AEP -> Avrage estimation relative deviation of -2% PoE(90%) WS AEP PoE(90%) PD AEP -> Average estimation relative deviation of +15% 18

19 Conclusion Review of the uncertainty analysis tool New approach for uncertainty learning curve was implemented providing a powerful tool for comparing wind assessment uncertainties and the production data variabilities. Two components of the variability were calculated as correlated uncertainties using the correlation and the covariance matrices Two main aspects of variability of production data are presented here, temporal and spatial variability. A method is presented for combining the variabilities as a combined uncertainty that arises in wind production as correlated and uncorrelated variabilities. The variability at the wind farm level and annual energy production is scaled accordingly to the production data available. A comparison between the results of many farms with different characteristics is presented. 19

20 Recommendations In the list below some extension areas are given: Correct the NET estimated PD AEP with the Long Term of Wind Resources correction Include more sites and deep analysis of the different aspects of every site, and even better if some aspects could be quantified and statistically processed. Resolution of the PD and the time stamp for shorter than monthly production data. Uncertainties details of the wake modeling, curtailment, losses estimation and availability of the turbines vs. spatial variability. Including nacelle wind data in the analysis for directional behavior investigation 20

21 Contact: