A Climate Modeling based approach for Wind Resource Assessments and Power Potential determination for Suriname in future perspective

Peter Donk1

1 National Power Utility Co. Suriname

ABSTRACT This research presents a case study of a climate modeling based approach, used to assess the impact of possible climate change on the available wind resources in Suriname, for the sustainable development of wind power in future perspective. Two locations were considered in the coastal area, at potential wind sites, near the east and west borders of Suriname.Wind velocity measurements were done for one year (2009 – 2010), on a 10 minute interval basis, at 20 and 30 meters height. These ground measurement data, considering daily averages, and remote-sensing data in addition, were used as basis to conduct this study. Global Climate Models (GCMs), ECHAM4 and Headley, were used for future climate simulations, considering two SRES (Standard Report on Emission Scenarios) emission scenarios, i.e. A2 and B2, developed by the Intergovernmental Panel on Climate Change (IPCC).Two scenario periods were considered for the climate model simulations, i.e. 2020 – 2050 and 2070 – 2100. A Regional Climate Model (RCM), PRECIS, was used to downscale the coarse scale GCM outputs to a resolution suitable for wind resource assessments on local scale. The consistency of the models in simulating the current (local) climate, i.e. in terms of the temporal (frequency) and spatial distribution of wind velocities, was evaluated based on a consideration of baseline series (1960 – 1989) of the considered models, relative to the observed data. The ground measurement data were used to evaluate the temporal consistency on a statistical basis, whereas the remote-sensing data was used to evaluate the spatial consistency. Given the acceptable performance of the models, the simulated future climate series were effectively used for wind resource assessments and power potential determination in future perspective. The weibull probability distribution method was used to assess the possible impact on power density and capacity factor, important for wind turbine selection. The results reveal significant changes towards 2050 and 2100.

METHODOLOGY

MODEL VALIDATION (SPATIAL CONSISTENCY):

MODEL VALIDATION (TEMPORAL CONSISTENCY):

WEIBULL PROBABILITY DISTRIBUTION ANALYSIS:

RESULTS (SITE NICKERIE)

Fig 7. Future wind probability distributions; Long term (30 years)

Weibull probability Distributions

Fig 8. Power Potential in future perspective; Weighted average power density

calculated based on long term (5 years) Weibull Probability Distributions

DISCUSSION GENERAL:

CONSIDERING THE SHORTCOMINGS AND STRENGTHS OF THE USED MODELS IN RESEMBLING THE CURRENT/ LOCAL

CLIMATE OF SURINAME (SEASONAL VARIABILITY IN WIND VELOCITIES AND ASSOCIATED EXTREMES), IT IS IN FACT

INCONCLUSIVE TO STATE WETHER ONE MODEL IS BETTER THAN THE OTHER, ESPECIALLY CONSIDERED FROM A POWER

GENERATION PERSPECTIVE (CAPACITY FACTOR DEPENDENT ON WIND REGIME/ FREQUENCY OF FAVOURABLE WIND

VELOCITIES). ECHAM4 SIMULATES LESS VARIABILITY AND SLIGHTLY UNDERESTIMATES THE SEASONAL EXTREMES

COMPARED TO HEADLEY, BUT AT THE OTHERHAND PERFORMES BETTER AT RESEMBLING THE OVERALL SEASONAL

VARIABILITY (OVERALL BETTER CORRELATION). THEREFORE, IT IS ALSO IMPORTANT TO CONSIDER GRAPHICAL

INFORMATION IN ADDITION FOR AN OBJECTIVE VALIDATION OF THE MODEL PERFORMANCES. THE PERFORMANCE OF BOTH

MODELS IS ACCEPTABLE AND THESE ARE THEREFORE USEFUL FOR FUTURE PREDICTIONS.

SPECIFIC:

THE MODELS PREDICT GREATER WIND VELOCITIES FOR THE NEAR FUTURE (2020-2050) BASED ON THE

CONSIDERED EMISSION SCENARIOS, FOLLOWED BY A DECREAS IN THE LATER DECADES, AS PREDICTED BY

ECHAM4 SPECIFICALLY (2070-2100), BUT NO FURTHER DECREAS BEYOND THE CURRENT CONDITIONS.

GIVEN THE RESULTS, WIND COULD POTENTIALLY BE A VIABLE OPTION CONSIDERING THE MORE

FAVOURABLE CONDITIONS PREDICTED ON THE LONG TERM, THUS, THERE SHOULD BE LESS CONCERN

ABOUT THE POTENTIAL RISKS ASSOCIATED WITH HIGH INNITIAL INVESTMENTS (SUSTAINABILITY). THIS IS

IMPORTANT FOR POLICY/ DECISSION MAKING IN THE FUTURE (STRATEGIC PLANNING AND DEVELOPMENT OF

AN OPTIMAL/ SUSTAINABLE ENERGY MATRIX CONSIDERING UNCERTAINTIES).

REMARKABLE:

ECHAM4 PREDICTS A SLIGHTLY CHANGING WIND REGIME IN ADDITION, I.E. GREATER VARIABILITY BESIDES GREATER

WIND VELOCITIES (2020-2050 SERIES RELATIVE TO BASELINE AND 2070-2100 SERIES), BUT POSSIBLE/ INHERENT SYSTEMATIC

BIAS SHOULD ALSO BE TAKEN INTO CONSIDERATION. THIS NEEDS FURTHER ANALYSIS TO CONCLUDE ON THE SUBJECT.

FUTURE SCOPE:

IT IS IMPORTANT TO CONSIDER MORE MODELS AND

DIFFERENT EMMISSION SCENARIOS, WHICH ENABLES A

CONSIDERATION OF A BROAD SPECTRUM OF FUTURE

CLIMATE POSSIBLITIES, TO GAIN GREATER INSIGHT ON THE

EXTENT OF UNCERTAINTIES (ASSOCIATED WITH BOTH; THE

POTENTIAL SYSTEMATIC BIAS INHERENT TO MODELS AND

FUTURE CLIMATE PREDICTIONS), AND A BETTER

UNDERSTANDING OF THE ASSOCIATED RANGE OF POSSIBLE

FUTURE IMPACTS ON WIND RESOURCE AVAILIBILITY AS A

RESULT OF POSSIBLE CLIMATE CHANGE.

REFERENCES Zekai Şen, Abdüsselam Altunkaynak, and Tarkan Erdik, “Wind Velocity

Vertical Extrapolation by Extended Power Law,” Advances in Meteorology, vol. 2012, Article ID 178623, 6 pages, 2012. doi:10.1155/2012/178623

ACKNOWLEDGEMENTS Special thanks to the AdeKUS University of Suriname for providing the PRECIS model, Dr. Riad Nurmohamed (Department Infrastructure, AdeKUS University of Suriname) for providing access to the climate modeling data and Anand Kalpoe M.Sc (Department Power Engineering, AdeKUS University of Suriname) for providing the wind measurements data, used for this research.

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Tim

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BIN (WIND VELOCITY (M/s))

FUTURE WIND PROBABILITY DISTRIBUTIONSOBSERVED (2009-2010)

FITTED WEIBULL DISTRIBUTION(2009-2010)

ECHAM4 (60-89) Baseline

HEADLEY (60-89) Baseline

ECHAM4 (2020-2050) A2

ECHAM4 (2070-2100) A2

ECHAM4 (2070-2100) B2

HEADLEY (2070-2100) A2

HEADLEY (2070-2100) B2

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5 10 15 20 25 30

WEI

GHTE

D A

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POW

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ENSI

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/ M

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YEARS

POWER POTENTIAL: WEIGHTED AVERAGE POWER DENSITY (WATT/ M2)

ECHAM4 (60-89) Baseline

HEADLEY (60-89) Baseline

ECHAM4 (2020-2050) A2

ECHAM4 (2070-2100) A2

ECHAM4 (2070-2100) B2

HEADLEY (2070-2100) A2

HEADLEY (2070-2100) B2

POWER POTENTIAL DETERMINATION:

STUDY AREA

INTRODUCTION

The development of Wind Power projects is commonly known for high initial investments. It is therefore very important to conduct careful wind resource assessments to determine the feasibility/ sustainability of a proposed project, both technically and (especially) economically dependent on resource availability, and more importantly, to quantify possible future changes in wind regimes (risks), which in turn enables adequate decision making. Thus, the purpose of this research is to use Climate Modeling as a tool to: I. assess the impact of possible

climate change on the availability of wind resources by 2050 and 2100 and;

II. the resulting impact on the Wind Power Potential in the coastal area of Suriname on the long term.

Data acquisition/ Observed data:

Ground measurements (at 20 and 30 M) Remote-sensing (Res: 2 kM)

Climate Modeling:

GCMs: Large scale predictions data RCM: Dynamic Downscaling/ local scale (at 10 M/ Res: 25 kM)

Model Validation (ability to resemble current/ local climate): Using Model baseline data (1960-1989) to assess:

I. Spatial consistency (based on Remote sensing data) II. Temporal consistency (based on Ground measurements data)

Data extrapolation to 60M (standard hub-height for most medium scale wind turbines) for 1:1 comparison based on Power Law method (Şen et al., 2012)

Analyzing possible climate change impact on wind resources, hence Wind Power Potential in future perspective

Fig. 2. Schematic presentation of applied methodology

Resource assessments using future climate predictions data: I. Weibull Probability Distribution analysis

II. Power Potential determination: Weighted average power density

Fig. 1.Study area; Potential wind sites Nickerie and Galibi at the coast of Suriname (Remote sensing data image)

Fig. 3.Model Validation; assessment of spatial consistency based on long term (30 years) annual average model baseline (1960-1989) data; Example of ECHAM4 versus remote sensing data

Fig. 4.Model Validation; assessment of temporal consistency based on model baseline (1960-1989) data; Lowest, Mean and Highest correlation

BASELINE YEAR ECHAM4 CORRELTION

HEADLEY CORRELATION

60 0.865097002 0.734351452

61 0.886699614 0.446214569

62 0.783465018 0.321934141

63 0.591710817 0.158421949

64 0.864181701 0.668867276

65 0.704680086 -0.140081703

66 0.82478662 0.467898336

67 0.472322838 0.066007204

68 0.801432909 0.458744173

69 0.750931895 0.35678674

70 0.812887601 0.355407791

71 0.72921984 0.613161097

72 0.851569209 -0.169824369

73 0.40554643 0.882375698

74 0.879342124 0.465625984

75 0.66154214 0.793996748

76 0.830723165 -0.262442066

77 0.395149277 0.574657939

78 0.782832215 0.129727188

79 0.648356908 0.230373912

80 0.583938146 0.018065707

81 0.946266202 0.460930894

82 0.738760794 -0.181857699

83 0.784780706 0.659306041

84 0.513556675 0.796074836

85 0.739124154 0.69972515

86 0.729459358 0.434086077

87 0.622557541 0.478664465

88 0.786296321 0.850543369

89 0.531233617 0.772242957

LOWEST 0.395149277 -0.262442066

MEAN 0.717281697 0.404666195

HIGHEST 0.946266202 0.882375698

MODEL VALIDATION (TEMPORAL CONSISTENCY):

Fig. 5.Weibull Probability Distribution analysis; Fitted Weibul l Distribution Functions (FWDs) for long term model baseline (1960-1989) data versus observed data Fig. 6.Power Potential determination; Weighted average power de nsity calculation

per class of Weibull Probability Distribution

= 12