Portfolio theory application in wind potential assessment

doi:10.1016/j.renene.2014.11.033

Renewable Energy

Volume 76, April 2015, Pages 494-502

https://doi.org/10.1016/j.renene.2014.11.033 Get rights and content

Highlights

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We use portfolio theory to combine multiple wind measurements on one site.
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Resulting portfolios in general have lower uncertainty than individual measurements.
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The method can help in making measurements more efficient.
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The method can be used to incorporate remote sensing data.
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The method is flexible in terms of wind models used (e.g. WAsP or CFD models).

Abstract

Development of a wind farm project includes a lot of interconnected steps and one of the most important ones is the proper energy yield assessment. Wind energy yield assessment is typically based on wind measurements on a measurement mast that are later used in one of the wind flow software models. In cases where there are multiple wind measurements on the potential wind farm site, a question arises on how to optimally use all the available data. This paper shows a method of using such data through the application of the portfolio theory, a well-established theory in economics and frequently used in other scientific disciplines. The method shown is very flexible in terms of input data and software models, and the results of its application show that it is possible to increase accuracy and reduce uncertainty of energy yield assessment. The key result of the method is the possibility to achieve better quality of input data for the energy yield assessment without spending additional resources. The method opens up a wide space for further research and improvements, all with the objective of achieving better results of energy yield assessment and finally, better prepared wind project.

Introduction

Wind power is becoming increasingly important electricity source in the world. According to The Global Wind Energy Council (GWEC) the global cumulative installed capacity in 2013 in the world was 318.137 MW (Fig. 1), an increase of nearly 200 GW in the past five years [1]. This installed capacity in 2013 produce more than 580 TWh and thus meeting 2% of global electricity demand. GWEC expects a global capacity of 536 GW by the end of year 2017.

Profitability of a wind farm project depends on wind potential of the site, total investment and purchasing price of electricity. Therefore, it is important to properly estimate wind potential and select the adequate wind turbines for the site. Wind turbines usually account for 75% of total investment in a wind farm [2].

Every improvement in estimating wind potential, either by increasing accuracy or reducing the uncertainty of estimation, improves the quality of the wind project.

Wind potential is normally calculated on the basis of wind measurements on a prospective wind farm site. The objective of this paper is to demonstrate methodology for improving wind potential estimation by using portfolio theory on locations with multiple wind measurements. The described method addresses both the accuracy of estimation and the corresponding uncertainty.

Section snippets

Literature review

Accurate estimation of wind speed is critical for the assessment of wind energy potential. Usually, wind speed is modeled by using Weibull distribution [3], but there are also new methods [4]. The approach in this paper takes the existing methods into account and proposes an additional step in wind speed analysis based on the portfolio theory and normal distribution.

Modern portfolio theory was developed by Harry Markovitz in 1952 for the purpose of investing in stocks and bonds. In his paper [3]

Basic concept

In order to evaluate wind potential of site, we need to set up wind measurements lasting at least one year [16] on at least two heights and on a location that best represents the overall characteristics of the site [10]. Raw data is then imported into the wind modeling software to calculate the wind potential.

Wind is strongly influenced by topography, orography, roughness and obstacles [11], [12]. For each of the categories a special map that describes the relevant characteristics is derived

Site description

The observed location is relatively flat but surrounded by hills and a river canyon. Land cover is relatively even and consists of low bushes.

Orography map is taken from official digital elevation model with 10 m vertical resolution and fine-tuned according to more detailed maps to 5 m vertical resolution. Roughness map is prepared according to topographic maps and site visits [21].

Measurement data

There were four measurement masts on the location and the modeling was done based on the actual measured data.

Conclusion

The objective of the research shown in this paper was to improve wind potential assessment by using portfolio theory in cases when there are more wind measurements on a prospective wind farm site, as well as to increase the efficiency of using the available wind measurement data. The basic hypothesis was that by using more than one measurement it is possible to increase the accuracy and reduce uncertainty of wind potential assessment, compared to using only one measurement for the entire

Symbols and abbreviations

CDF: cumulative distribution function
CFD: computerized fluid dynamics
Cov: Covariance
E(R_i): expected return
IEC: International Electrotechnical Commission
LIDAR: LIght Detection And Ranging
MCP: measure–correlate–predict
MEASNET: network of European measuring institutes
r_i,j: correlation coefficient
SODAR: SOnic Detection And Ranging
WAsP: Wins Atlas Analysis and Application Program
σ: uncertainty/risk

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View full text

Portfolio theory application in wind potential assessment

Highlights

Abstract

Introduction

Section snippets

Literature review

Basic concept

Site description

Measurement data

Conclusion

Symbols and abbreviations

Renew Energy

World wind energy report 2012

Basic cost of wind energy

Portfolio selection

J Finance

Modern portfolio theory and investment analysis

Applying portfolio theory to EU electricity planning and policy-making

The modern portfolio theory applied to wind farm financing

DEWI Mag

Round Robin numerical flow simulation in wind energy

The Bolund experiment, part I: flow over a steep, three-dimensional hill

Boundary-Layer Meteorol

Reduction of uncertainty in resource assessment through wind flow model “ensemble”

WAsP 10 course notes