Portfolio theory application in wind potential assessment
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(Ri)
- expected return
- IEC
- International Electrotechnical Commission
- LIDAR
- LIght Detection And Ranging
- MCP
- measure–correlate–predict
- MEASNET
- network of European measuring institutes
- ri,j
- correlation coefficient
- SODAR
- SOnic Detection And Ranging
- WAsP
- Wins Atlas Analysis and Application Program
- σ
- uncertainty/risk
References (23)
- et al.
Analysis about sampling, uncertainties and selection of a reliable probabilistic model of wind speed data used on resource assessment
Renew Energy
(February 2013) World wind energy report 2012
(2013)Basic cost of wind energy
(2011)Portfolio selection
J Finance
(1952)- et al.
Modern portfolio theory and investment analysis
(2002) - et al.
Applying portfolio theory to EU electricity planning and policy-making
(2003) The modern portfolio theory applied to wind farm financing
DEWI Mag
(2011)- et al.
Round Robin numerical flow simulation in wind energy
(2008) - et al.
The Bolund experiment, part I: flow over a steep, three-dimensional hill
Boundary-Layer Meteorol
(2011) - et al.
Reduction of uncertainty in resource assessment through wind flow model “ensemble”
WAsP 10 course notes
Cited by (10)
Future studies in Iran development plans for wind power, a system dynamics modeling approach
2020, Renewable EnergyCitation Excerpt :Many of these changes in recent years has led to a drop in wind power generation. Medimorec et al. [9] have used hypothesis testing method in order to assess the uncertainty of wind power generation capacity in wind turbines. To generalize the results, they have used different data in long-term intervals.
A probabilistic portfolio-based model for financial valuation of community solar
2017, Applied EnergyCitation Excerpt :MVP has been widely applied in various fields [10] and proven effective, especially in the energy domain. For example, researchers have applied MVP to identify optimized portfolios that offer the lowest cost of power generation [11–15], highest internal rate of return [16], highest wind capacity factor [17], highest wind speed [18], and highest electricity generation [7,19]. Despite the widespread application of MVP in other fields, there is no model suitable for implementing MVP in community solar.
Wind resource assessment of Northern Cyprus
2016, Renewable and Sustainable Energy ReviewsCitation Excerpt :However, the need for high quality wind data measurement at different locations and altitudes for better estimation of wind resource potential is also emphasized. In order to improve the wind potential estimation, Medimorec and Tomšić [2] used portfolio theory on locations with multiple wind measurements. Their study showed that the method reduced both the uncertainty and error, even when using least accurate and most uncertain measurements.
PACPIM: New decision-support model of optimized portfolio analysis for community-based photovoltaic investment
2015, Applied EnergyCitation Excerpt :Their results indicate that renewable energies (including wind, biomass, and solar) could account for more than 25% of the optimized portfolio for the year 2030, while fossil fuels may account for less than 10% of the optimized portfolio. So far, energy researchers have used MVP to find optimized portfolios that offer the lowest cost of energy production [47–51], increased internal rate of return (IRR) [52], increased wind capacity factor [44], increased wind speed [42], and increased electricity production [53]. Despite a number of existing MVP studies, no identified study has specifically used MVP to evaluate community-based PV investments in terms of risk and return.
Design, Analysis, and Fabrication of Water Turbine for Slow-Moving Water
2022, Journal of Energy Resources Technology, Transactions of the ASME