Turbine Efficiency and Grid Integration

Impact of wind speed uncertainty and variability on the planning and design of wind power projects in a smart grid environment

Submitted to Bahri Uzunoglu as part of the Turbine Efficiency and Grid Integration course

Uppsala University Dept. of Earth Sciences, Campus Gotland

by Orkhan Baghirli

January 16, 2015

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ABSTRACT Key words: variability in annual mean wind speed, wind speed prediction, energy production estimation Wind energy is known to be an integral part of sustainable power systems of the future. However, intermittent nature of wind imposes certain limitations on wind energy penetration into the power grids. Due to variability in wind speed, energy production estimation has become a tedious work for the wind project developers. In this paper, effect of variability in annual mean wind speed on the energy production estimations is discussed. For this purpose, two different scenarios are studied, where one considers the variability in annual mean wind speed while the other one doesn’t. Based on the results, it is shown that the energy production estimations assuming fixed annual mean wind speed are subject to 3.16% error on average due to these variations. This might not be a critical issue in today`s energy market, however as the share of wind power increases in the power grid, uncertainty in wind resource assessments and energy estimations is expected to be more carefully inspected to facilitate the implementation of smart grids.

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TABLE OF CONTENTS

ABSTRACT ........................................................................................................................................... I

TABLE OF CONTENTS ................................................................................................................. II

CHAPTER 1. INTRODUCTION .................................................................................................. 1

CHAPTER 2. LITERATURE REVIEW ...................................................................................... 2 UNCERTAINTY IN WIND .............................................................................................................................. 2 SMART GRID .................................................................................................................................................... 2

CHAPTER 3. METHODOLOGY .................................................................................................. 3 CASE STUDY ...................................................................................................................................................... 3

Scenario 1............................................................................................................................................................3 Scenario 2............................................................................................................................................................3

CHAPTER 4. RESULTS .................................................................................................................. 6

CHAPTER 5. DISCUSSION ......................................................................................................... 12

CHAPTER 6. CONCLUSION ...................................................................................................... 13

REFERENCES ................................................................................................................................. 14

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CHAPTER 1. INTRODUCTION Sustainable power systems of the future will be characterized by significant penetration of renewable energy sources. Increasing the share of power generation from renewable energy sources is the key to the achievement of sustainable growth and minimization of environmental impacts. However, renewable energies tend to be intermittent and less economically attractive than conventional generation technologies. Fluctuations in the mean annual wind speed lead to increments or decrements in the power production of wind power projects (WPPs) during some years, and thus reduce or increase profits for the projects. Significant research is necessary to integrate new technologies into the system to increase the wind power penetration [1]. Planning and operation of power systems to ensure reliability and security of supply will require application of smart grid techniques [2]. Planning and design of generation projects based on renewable energies, such as WPPs, should therefore be predicated on the assumption that they will operate in a smart grid. The main factor affecting the planning and design of WPPs is the wind resource, especially the annual mean wind speed. This paper describes the impacts of variability and uncertainty in the planning and design of WPPs in a smart grid environment. It is reasonably predicted that variations in annual mean wind speed will have certain adverse effects on the energy production estimations. The annual wind energy production can be estimated by utilizing different statistical approaches. It is also conventional to predict the energy content in wind based on the annual mean wind speed of long-term wind data from previous years. However, mean wind speeds are also subject to variations with different levels of uncertainty. In this paper, two different scenarios are studied where one includes the variations in mean speed while the other one doesn’t.

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CHAPTER 2. LITERATURE REVIEW

Uncertainty in Wind

The variability of the wind resource comprises the natural fluctuations of wind speed throughout time. Wind speed uncertainty indicates the confidence project planners have in the accuracy of the wind recourse assessment. The uncertainty associated to the wind recourse assessment is a function of the characteristics of the wind site and its wind resource, available reference data, and WRA technique used [3]. Literature addressing the planning and design of WPPs focuses on the continuous variability of the wind resource within a year, and typically assumes the long-term wind speed is known with certainty [4]. Accordingly, the tools used for the planning and design of WPPs can be potentially improved by addressing the average annual mean wind speed variations. The significance of this factor will be affected by the characteristics of future smart grids leading to significant wind power penetration. Smart grid increases the significance of the wind resource and allows consumers to respond to the availability of cheap wind power. The continuous variability of the wind resource is typically modeled using different probability density functions, such as Weibull and Rayleigh [5], whereas the annual mean wind speed variability of the wind resource can be reasonably modeled with Gaussian distributions [6].

Smart Grid

The electricity generation, transmission and distribution infrastructure in many developed countries is aging and was not designed to handle the energy challenges of present times, such as, environmental concerns and significant penetration of renewable energies amongst others. Over the time, the renewable energy gradually displaces the coal, oil and gas in our energy consumption patterns. If we are to integrate a large amount of renewable energy into the power system and an efficient socio-economic way, we must reconfigure our energy systems. The intelligent power grid or smart grid is the key to realize this transformation [2]. At its core, a smart grid utilizes digital communications and control systems to monitor and control power flows, with the goal of making the power grid more resilient, efficient and cost effective. In contrast to conventional power grid, the new emerging smart grid uses two-way flows of electricity and information to create an automated and distributed advanced energy delivery network [7].

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CHAPTER 3. METHODOLOGY In this section, the methodology of the study set up is discussed. The main purpose of the study is to illustrate the effect of variations in annual mean speed on the estimated annual energy content in any prospective wind farm site. Wind resource assessment is an integral part of the wind power plant development process, thereby accuracy of wind resource analysis should carefully taken into account. However, wind energy estimations are always subject to certain amount of uncertainty due to intermittent nature of wind. Therefore, developers should be careful about uncertainty of estimations. This also finds its reflection in a case study proposed in this paper.

Case Study

In this case study, two different techniques to estimate the annual energy content of wind in a specific site are proposed. The assumption is that energy content of wind is proportional to extracted energy from the wind turbines. The investigated scenarios are given as follows.

Scenario 1.

In this scenario, average annual energy content is estimated based on the 20 years of existing wind speed measurements. It is assumed that during the lifetime of wind power project (20 years), average annual wind speed will be exactly the same as average annual wind speed observed in the past 20 years. In other words, it is estimated that energy content in wind for the next 20 years will be the replica of energy content of 20 years in the past. This model excludes the effects of variability in mean annual wind speed.

Scenario 2.

Scenario 2 is more complex that the first one. It considers the variability in mean wind speed over the years. It is assumed that during the next 20 years annual mean speed will have some degree of uncertainty, thereby for different uncertainty levels, annual mean wind speeds will have different distributions and different average annual energy content which might be different than what is estimated via the scenario 1. For this purpose, several annual mean wind speed distributions over next 20 years are modeled based previous years. The methodology to investigate the results from both of the scenarios can best be explained in steps. It is worthy to note that “ average annual energy content ” and “ annual average energy content” are two distinct concepts used in this paper. Average annual energy content: it is the average of annual energy content over 20 years calculated as arithmetic mean of total energy content in wind. Annual average energy content: it is the weighted mean wind speed within a specific year based of generated frequency distributions.

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Step 1: Wind statistics is downloaded and exported using WindPro for a specific site in mainland Sweden. From the file, 20 years of wind data covering years from 1995 up to 2014 is extracted. Afterwards, this data is broken into its annual terms. Step 2: After obtaining the annual wind data over past 20 years, frequency distributions of wind speed for each year are calculated. Based on these frequency distributions, weighted annual mean wind speeds have been calculated for each year (Note: arithmetic mean wind speed can not be used here because of the Weibull like distribution of data). Step 3: Analysis of variability in annual mean wind speed over past 20 years is studied. The study includes the calculations on standard deviation and variance of actual and two years averaged variability in annual mean wind speed distribution and modeling of the variability distribution. Step 4: Based on the generated frequency distributions and using the energy content

formula 1

2𝜌𝑣3 , average annual energy content (MWh/m2/year) in wind is estimated and

defined to be constant over the next 20 years. Step 5: Using the data from previous years, a relationship between annual mean speed and actual energy content is modeled. This relationship is important since it will be used to estimate annual average energy content from annual mean wind speed modeled for the next 20 years. After having this relationship, annual mean wind speed will be enough to directly estimate the energy content rather than generating wind data for each year and then calculating the energy based on their frequency distributions (Note: uncertainty added by using this relationship has been mitigated by adding extra term to energy estimation calculations to obtain more realistic results). Step 6: Different levels of uncertainty ranging from 1% to 10% is applied to actual averaged annual mean wind speed to generate variability in predicted annual mean speeds over the next 20 years. To generate random variables, Gaussian distribution has been used and it is shown that it is a nice a fit to describe variability in annual mean wind speed. Step 7: The predicted mean wind speeds are studied and their standard deviations along with their correlations to actual mean wind speed distribution are calculated and plotted for different levels of uncertainties applied.

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Step 8: Based on the predicted annual mean speeds and generated relationship between mean speed and energy content, the annual average energy content for the next 20 years are estimated. Furthermore, standard deviation of estimated annual average energy contents and their correlation to actual data are calculated and plotted. Step 9: Based on estimated annual average energy content over next 20 years for a specific level of uncertainty, average estimated annual energy content is calculated and the same procedure is followed for other uncertainty levels. Afterwards, a relationship between different uncertainty levels in estimated annual mean wind speeds and estimated average annual energy content is depicted. Step 10: Comparison between results from scenario 1 and scenario 2 regarding the estimated average annual energy content is made and percentile error is calculated and plotted for different levels of uncertainty. The potential effects of this mean wind speed variability on smart grid environment is discussed.

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CHAPTER 4. RESULTS In this section, results of the conducted study are presented in accordance with methodology. All of the simulations and calculations are done by using MatLab. WindPro is used to obtain the site-specific wind statistics. Discussion of the results is left for next chapter. Step 1:

Figure 1: Chosen site and wind statistics

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Step 2:

Frequency distributions of actual wind data

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Mean speed

7.278 6.469 6.847 6.736 7.026 6.798 6.787 6.438 6.614 6.650

Year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Mean speed

6.806 6.423 6.867 6.760 6.264 6.356 7.014 6.803 6.677 6.674

Figure 2: Frequency distributions and annual mean speeds

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Step 3:

std=3.66% / var= 0.9% / RMSE=0.19 / mean=6.7147 m/s

Figure 3: Years / Actual Annual Mean Wind Speed

std = 2.76% / var=0.51% / RMSE=0.24 / mean =6.7147 m/s

Figure 4: 2 years averaged Actual Mean Wind Speed / Years

Figure 5: Actual annual mean wind speed distribution / Gaussian

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Step 4:

Scenario 1: estimated average annual energy content= 2.9630 MWh/m2/year

std=10.45% / var=3.23% / RMSE=0.2714

Figure 6: Years / Actual annual average energy content Step 5:

Regression line: f(x) = 1.211*x - 5.169 / RMSE=0.08814

Figure 7: Actual annual average energy content / actual annual mean wind speed

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Step 6 & Step 7:

Green color: actual distribution, Blue color: predicted distribution

Figure 8: Time series / actual annual mean wind speed vs. predicted annual mean wind speed

Step 8:

Figure 9: Time series / actual annual average energy content vs. predicted annual average energy content

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Step 9:

Worst case scenario: std=3.63%

Figure 10: std. [%] in estimated average annual energy content / std. [%] in estimated annual mean wind speed

Step 10:

std=2.4634 / mean=3.16 / RMSE=2.17 / worst case scenario: 6.6% error

Figure 11: std [%] in annual mean wind speed / percentile error of estimated average annual energy content from scenario 1

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CHAPTER 5. DISCUSSION Step 1: Wind statistics are downloaded and separated into its annual terms for years ranging from 1995 up to 2014. NCAR_BASIC_N60.0_E17.5 meteorological station is used to obtain the wind statistics. Step 2: Frequency distributions are generated and annual mean speeds are calculated. Frequency distributions can best be described as a Weibull distribution, therefore mean wind speed is calculated by multiplying the wind speed with corresponding probability of occurrence and then summed up. Step 3: It is calculated that actual annual mean wind speeds have mean of 6.7147 m/s and std of 3.66%. Two years averaged mean speed shows less variation with a std of 2.76%. Therefore, using long-term wind data as a reference will result in better estimations. It is also observed that there are cyclical variations in mean speed in time, which can be explained by seasonal variations in wind speed. Furthermore, it is shown that annual mean wind speeds can be modeled as Gaussian distribution. Thereby, Gaussian distribution is used to predict the annual mean speeds for the next 20 years. Step 4: The variation in actual annual average energy content also shows cyclic behavior in accordance with mean speed variation. The estimated annual energy content from scenario one, wherein it is expected to be constant for the next 20 years is calculated to be 2.9630 MWh/m2/year. Step 5: The relationship established between annual mean wind speed and energy content in wind is found to be f(x) = 1.211*x - 5.169 with an RMSE of 0.08814 which can be describes as a nice fit. Step 6 & 7 & 8: Annual mean wind speeds and annual average energy contents are predicted for the next 20 years for several levels of uncertainty. The comparison between predicted data and actual data are illustrated and found to be reasonable. Step 9: The relationship between uncertainty in annual mean speed and uncertainty in estimated average annual energy content is established. The worst-case scenario shows that due to uncertainty of 10% in annual mean wind speed, estimated average annual energy variations may result in 3.63% uncertainty. Step 10: The comparison between estimated average annual energy content from scenario 1 and 2 shows that the assumption of fixed annual mean wind speed based on the previous wind data is exposed to 3.16% error on average due to variations in annual mean wind speeds. The worst-case scenario is more pessimistic with 6.6% error.

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CHAPTER 6. CONCLUSION In this document, effect of the annual mean wind speed variations on energy production estimations has been discussed. Two different scenarios have been studied; wherein scenario 1 assumed fixed annual mean wind speed over the next 20 years, while scenario 2 included annual mean wind speed variations in energy estimations. The study is done for a specific site in Sweden with an annual mean wind speed of 6.7147 m/s between the years 1995 and 2014. In the end, it is shown that energy production estimation assuming fixed annual mean wind speed is subject to 3.16% margin of error due to mean wind speed variations. This, in turn, may have certain adverse consequences with regard to wind power project development in a smart grid environment where accuracy of energy estimation is a key factor. Considering the increment in wind energy penetration into the power grids in the future, the cumulative uncertainty in wind energy estimations will indeed raise a problem especially for the implementation of smart grids.

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REFERENCES [1] E.A. Martinez-Cesena. “Impact of Wind Speed Uncertainty and Variability on the Planning and Design of Wind Power Projects in a Smart Grid Environment” IEEE. [Online] Available from: http://ieeexplore.ieee.org.ezproxy.its.uu.se/stamp /stamp.jsp?tp=&arnumber=6162613 [Accessed: January 12, 2015] [2] Abdul Motin Howlader and Ahmed Yousuf Saber, “Control Strategies for Wind Farm Based Smart Grid System ”, IEEE. [Online] Available from: http://ieeexplore.ieee.org.ezproxy.its.uu.se/stamp/stamp.jsp?tp=&arnumber=6527063 [3] B. Bailey, Wind resource assessment handbook. Albany, NY: AWS Scientific, 1997. [4] A. Mahmood, M. Aamir, and M.I. Anis, “Design and implementation of AMR smart grid system, “in Proc. IEEE Canada Electric Power Conference, Vancouver, Canada, Oct. 2008, pp. 6 - 7. [5] J. Carta, P. Ramrez, and S. Velzquez, “A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands,” Renewable and Sustainable Energy Reviews, vol. 13, no. 5, pp. 933 – 955, 2009. [6] (2009) Wind energy: The facts. European Wind Energy Association. [Accessed: Apr. 21, 2010]. [Online]. Available: http://www.wind-energy-the-facts.org/the-annual-variability-of-wind-speed.html [7] F. XI, M. Satyajayant , X. Guoliang , Y. Dejun, “Smart Grid-The New and Improved Power Grid: A Survey,” IEEE Communications Survey & Tutorials, vol. 14, no. 4, pp. 944-980, 2012.