Wind Energy: Developments, Potential and Challenges 1634842294, 9781634842297

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ENERGY SCIENCE, ENGINEERING AND TECHNOLOGY

WIND ENERGY DEVELOPMENTS, POTENTIAL AND CHALLENGES

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ENERGY SCIENCE, ENGINEERING AND TECHNOLOGY

WIND ENERGY DEVELOPMENTS, POTENTIAL AND CHALLENGES

DESIREE FLEMING EDITOR

New York

Copyright © 2016 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description. This button is linked directly to the title’s permission page on copyright.com. Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470 E-mail: [email protected].

NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data Names: Fleming, Desiree, editor. Title: Wind energy : developments, potential and challenges / editor, Desiree Fleming. Other titles: Wind energy (Nova Science Publishers : 2016) Description: Hauppauge, New York : Nova Science Publishers, Inc., 2016. | Series: Energy science, engineering and technology | Includes bibliographical references and index. Identifiers: LCCN 2015043139 (print) | LCCN 2016000648 (ebook) | ISBN 9781634842297 (hardcover) | ISBN 9781634842303 (eBook) Subjects: LCSH: Wind power. | Wind turbines. Classification: LCC TJ820 .W536 2016 (print) | LCC TJ820 (ebook) | DDC 621.31/2136--dc23 LC record available at http://lccn.loc.gov/2015043139

Published by Nova Science Publishers, Inc. † New York

CONTENTS Preface Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Index

vii Basic Theory to Conceptualize a Wind Farm: Research of Potential Wind Sites, Impacts of Wind Installation, Thermo Economic Modeling Mohamed Habib Sellami Comparative Study between Classical pi and Sliding Mode Controller for a Grid Connected Wind Turbine Based on DFIG Walid Ouled Amor and Moez Ghariani

1

39

Comparison for Policy and Promotion Strategy of Wind Energy Developments between Taiwan and Japan Tzu-Yi Pai, Keisuke Hanaki, Yi-Ti Tung and Pei-Yu Wang

79

Wind Energy Integration Into the South African Grid: Prospects and Challenges Komla Agbenyo Folly

93

Policy Challenges for the Deployment of Wind Energy Projects in the European Union Pablo del Río and Cristina Peñasco

121 163

PREFACE Renewable energy, such as sunlight, wind, rain, tides, waves, and geothermal heat, is the type of energy that can naturally replenish on a human timescale. Renewable energy can replace conventional fossil fuels in several distinct areas and can eliminate the need for fossil fuels. This book presents current research on the developments, potential and challenges of wind energy. The first chapter provides research on potential wind sites, the impacts of wind installation, and thermo economic modeling. The following chapter makes a comparative study of the dynamic behavior of an aero-generator with a double fed induction generator (DFIG) connected to the grid through two controller techniques: SMC and PI Controller. Chapter three discusses the policy and promotion strategy of wind energy developments between Taiwan and Japan. Chapter four looks at the current status of wind power in South Africa, prospects for growth in integrating wind energy in the South African grid and discusses possible challenges that may arise due to high penetration of wind power and outline some possible solutions. The last chapter provides an inventory of challenges for the deployment of wind energy in a 2030 timeframe in the EU. Chapter 1 – The wind as renewable energy, either inshore or offshore, is a key factor that attracts either economists and policy makers or academicians, scientists, researchers, designers and developers. To optimize the exploitation of this resource, the authors have to search the sites with high wind potential, to ameliorate the efficiency of every component in a wind turbine, to conceptualize the most efficient wind farms and to minimize their impacts during their operating timespan and across all correspondent production phases: manufacturing, transport, foundation, installation and operating. The core question asked by the specialists in this field is which global model to

viii

Desiree Fleming

develop will permit the evaluation of regional wind potential, the quantification of the socioeconomic and environmental impacts of wind farms installations, the test of various design alternatives and the detection of inefficiencies? In this chapter, the authors will try to participate in responding to this question by presenting in an organized manner the theoretical basic equations permitting to develop those kinds of models. Primary application for the case of wind energy projects in Tunisia will be discussed. Chapter 2 – The aim of this work is to make a comparative study of the dynamic behavior of an aero-generator with a double fed induction generator (DFIG) connected to the grid through two controller techniques: SMC and PI Controller. The stator of the DFIG is directly connected to the grid while its rotor is connected to it via a cascade (Rectifier, Inverter and Filter). Thus, the current control and the continuous bus of the wind turbine is carried out by the adjustment of the DFIG rotor sizes based on the maximum power point tracking (MPPT) technique. The results of simulation obtained under the MATLAB/SIMULINK environment present the performance of the sliding mode controller due to its robustness face to the wind speed variation of thus its simplicity of implementation and the robustness even in the presence of internal and external disturbances. By conclusion, it enabled the authors to justify the reliability of the suggested model and the elaborated command. Chapter 3 – Wind energy is regarded as one of the potential renewable energy and has been actively promoted by many countries. In this study, the policy and promotion strategy of wind energy developments between Taiwan and Japan were surveyed and compared. The results showed that the wind power increased significantly in the past ten years. The cumulative capacity of wind energy (CCWE), wind power generation (WPG), and the ratio of WPG to total power generation for Taiwan in 2014 gave on 26.5, 16.5, and 14.4 times than those in 2005. The CCWE, WPG, and the ratio of WPG to TPG for Japan in 2014 gave on 2.7, 2.7, and 2.9 times than those in 2005. Besides, an analytic hierarchy process (AHP) structure was suggested to aid decision makers making decisions to prioritize and select policy and promotion strategy of wind energy developments. For the first two important criteria, both Taiwan and Japan have collected the basic data and assessed wind power potential in the early 1990s. In the early 2000s, Taiwan government formulated the Renewable Energy Development (RED) Act and rewarded the wind power generation system settings of folk investment to promote the renewable energy. Japan formulated the Renewables Portfolio Standards Law to obligate the electric utilities to use a certain amount of new energy and implemented Feed-in Tariffs policy to set prices for the renewable power.

Preface

ix

Chapter 4 – In the last few years, the South African economy has grown considerably which led to a dramatic increase in load demand without a corresponding increase in the available power generation. A lack of sufficient and reliable electricity generation has been plaguing the South African’s economy, while the heavy reliance on fossil fuel for power generation continues to contribute not only to increasing emissions of greenhouse gases but also the costs of electricity. Reserve margins continued to be eroded and, as a result the country is experiencing rolling blackouts. Globally, the depletion and increase in fossil fuel prices, climate change and environmental pollution, unprecedented growth in energy insecurity, etc., have all contributed to the interest in renewable energy sources. Furthermore, the long term cost of non-renewable generation are projected to increase in subsequent years due to fast economic growth in the emerging nations, increasing electricity demand, depletion of the stocked non-renewable resources, etc. As a result, it is anticipated that in the next 20 years, a high portion of the South Africa electricity generation would come from renewable energy. A transition from non-renewable energy to renewable energy will promote public awareness on energy saving, as well as building a low carbon society. Renewable energy sources such as solar, wind, etc., have enormous potentials in contributing to the South African’s electricity portfolio and security, enhancing her social and economic growth, reducing the total dependency on fossil fuel power generation, as well as mitigating the increasing emissions of greenhouse gases. However, the integration of renewable energy sources such as wind, solar, etc., into the grid will bring new challenges. For instance, in the case of wind power, what would be the acceptable level of the penetration without compromising the stability and the reliability of the system? In this chapter, the authors will look at the current status of wind power in South Africa, prospects for growth in integrating wind energy into the South African grid and discuss possible challenges that may arise due to high penetration of wind power and outline some possible solutions. Chapter 5 – Policy makers in the Member States (MS) of the European Union (EU) face a difficult task: how to support renewable electricity deployment successfully (i.e., effectively and efficiently) in the short and medium terms and, in particular, in a 2030 horizon. Within renewable electricity, wind energy is a crucial energy source, expected to substantially contribute to the 2030 EU target (27% of overall energy consumption should come from renewable energy sources). This task is directly and negatively affected by certain factors or challenges which have to be dealt with. The aim of this chapter is to provide an inventory of challenges for the deployment of

x

Desiree Fleming

wind energy in a 2030 timeframe in the EU. This chapter is circumscribed to policy challenges, understood as those challenges directly and indirectly related to factors affecting wind energy deployment in a 2030 timeframe in the EU and which can be influenced by policy. The list of challenges contained in this chapter is based on an analysis of relevant literature,. The challenges are diverse, and include technological, macroeconomic, administrative, social acceptance and policy design aspects. In particular, the following specific challenges have initially been considered: how to adapt support levels to the trends in the costs of wind energy technologies and the uncertain evolution of resource potentials, how to cope with lower budgets for wind energy support, problems in accessing finance (credit restrictions), institutional challenges related to the implementation of market-based instruments (MBIs) in general and auctions in particular, making auctions and others MBIs effective and efficient, challenges related to target setting (an EU target without MS targets and an EU target with MS targets), merit order effect reducing wholesale prices and revenue for wind energy in particular, trade-offs between a greater stability and flexibility to adapt to new circumstances, delays in administrative procedures, trade-offs between not-in-my-back-yard (NIMBY) phenomena related to the concentration of wind energy projects and allocative efficiency, social rejection of high or escalating support costs and costs falling disproportionately on a given group of the population. There is no intention to rank the relevance of these challenges for different stakeholders and, in particular, for policy makers.

In: Wind Energy Editor: Desiree Fleming

ISBN: 978-1-63484-229-7 © 2016 Nova Science Publishers, Inc.

Chapter 1

BASIC THEORY TO CONCEPTUALIZE A WIND FARM: RESEARCH OF POTENTIAL WIND SITES, IMPACTS OF WIND INSTALLATION, THERMO ECONOMIC MODELING Mohamed Habib Sellami, Dr. Institute of Research and Higher Agricultural Education Department of Hydraulic, High School of Rural Engineering, Medjez El Bab, University of Jendouba, Tunisia Unity of Research of Thermal Radiation, Department of Physics, Faculty of Science Tunis, University Tunis El Manar Tunisian Association of Science, Technology and Development, Tunisia

ABSTRACT The wind as renewable energy, either inshore or offshore, is a key factor that attracts either economists and policy makers or academicians, scientists, researchers, designers and developers. To optimize the exploitation of this resource, we have to search the sites with high wind potential, to ameliorate the efficiency of every component in a wind turbine, to conceptualize the most efficient wind farms and to minimize 

[email protected], [email protected], 0216 98 442 549.

2

Mohamed Habib Sellami their impacts during their operating timespan and across all correspondent production phases: manufacturing, transport, foundation, installation and operating. The core question asked by the specialists in this field is which global model to develop will permit the evaluation of regional wind potential, the quantification of the socioeconomic and environmental impacts of wind farms installations, the test of various design alternatives and the detection of inefficiencies? In this chapter, we will try to participate in responding to this question by presenting in an organized manner the theoretical basic equations permitting to develop those kinds of models. Primary application for the case of wind energy projects in Tunisia will be discussed.

1. INTRODUCTION The wind, defined as air mass motions at the surface of the planet enclosed inside the atmosphere, is initiated by both warming the terrestrial surface by solar energy and the Earth’s rotation (Kalnay 2003, Konstantinov and Sakaly 1974). Also, the winds behind the entire meteor-climatic phenomenon are classified according to their spatial extent, speeds, geographical localisation, generating forces, their effects and impacts (Lynch 2008, 2006, World Meteorological Organisation 1974). “How can we localise the zones with high wind potential?” “How can we test the wind turbine efficiency?” are questions asked frequently by either farms generating electricity from wind energy or particulars who want to produce it for individual uses (Roland 1981, Vladimír 2011, Wiser and Bolinger 2011). Wind power has low ongoing costs, but a moderate capital cost. The marginal cost of wind energy once a plant is constructed is usually less than 1 cent per kWh (Robert Castellano 2012). As wind turbine technology improves, costs are coming down (Eric et al. 2012, Angelika et al. 2010, Thomas et al. 2010, Christian et al. 2009). There are now longer and lighter wind turbine blades, improvements in turbine performance and increased power generation efficiency. Also, wind project capital and maintenance costs have continued to decline. That’s why the primary objective of research developers in the field of wind energy valorization is to develop universal manufacturer-independent wind turbines and wind power plant models that can be shared, used, and improved without any restrictions by project developers, manufacturers, and engineers (Angelika et al. 2010, Christian 2009, Chang 2003). Those generic

Basic Theory to Conceptualize a Wind Farm

3

models asked have always to be approximates, and can be relied on for good estimates rather than precision so we do not need large datasets for validation with scalability of models from single wind turbines to large wind power (Chang et al. 2003, G. W. E. C. 2010). In fact the models focus on establishing codes and standards, launching awareness campaigns for decision makers, the general public and end-users and assisting renewable energy technology companies guarantee schemes (U.S. Energy Information Administration 2010). So, the better the equations forming the models represent the behavior of the installation units, the better the cost information that will be obtained. In other words, the more physical and realistic information is contained in the characteristic equations, the more physical significance the calculated costs will. We will in this work present in an organized manner the theoretical basic equations permitting to develop those kinds of models with a case study from installed wind farms.

2. BASIC THEORY TO DEVELOP MODELS FOR SEARCHING REGION WITH HIGH WIND ENERGY POTENTIAL Proposing models for searching regions with a high potential of wind, means to propose tools and equations permitting the detection of wind directions and the estimation of wind velocities. So we can fix the orientation of wind turbines and we can calculate the global amount of wind power in the tested zone. After we can conceptualize the proper wind turbines that transform all the existing power or that ensure only the energy needs of consumers.

2.1. Equations for Mass Air Motion The wind, considered by many specialists as an indirect form of the solar radiation, is the result of the move of an air mass as water vapour under the effect of a pressure difference. That water vapour pressure differences is created in many points in the atmosphere under the warming effect of solar radiations in their trajectories from the sun to the earth. The mass of air has tendency to displace vertically from position of high pressure to another characterised by a weak pressure value. The effect of earth rotation (Coriolis

4

Mohamed Habib Sellami

force) will generate the displacement of that mass in different directions. The direction of motion of the air mass represents the wind direction and the speed of its displacement is exactly the wind velocity (Kalnay 2003, Konstantinov and Sakaly 1974, Lamb 1974, Landsberg 1974). Characterising the wind sites and estimating the wind potential in a region means to determine the directions of air motions and the speed of their displacements in the studied zone. Applying the Navier Stocke’s equations to the air mass in motion permits to give a global expression to wind velocity. They can be expressed as follow (Comolet 1963, Dixon and Eng 1998):

 g : Gravity acceleration Pres: Pressure K : Volumetric absorptivity at the frequency T: Temperature t: Time

V : Velocity vector σ : Diffusivity coefficient at the frequency ε, , : Exchange coefficients ρ: Volumic mass Hence by resolving the equations of mass transfer, thermal transfer, radiation transfer, motion transfer in the media studied we can evaluate the amount of wind power that exists in the region and to take decision about installing or not the wind farm.

2.2. Weibull and Exponentiated Weibull Distribution for Wind Analysis To choose the place of implantation of wind farms, and to make the optimal design of wind propellers we have to analyze accurately the measured or calculated wind data. In this part we will introduce the Weibull distribution

Basic Theory to Conceptualize a Wind Farm

5

and the exponential Weibull distribution which are the most used to characterize both wind direction and its intensity In fact many researchers judged the two cited functions as the most careful ones fitting wind speed data (Debanshee and Datta 2013, Lavagnini et al. 2006; Jaramillo and Borjazool 2004).

2.2.1. Weibull Distribution Function If we consider the wind speed as random variable having Weibull distribution, its probability density function (Fw-pdf) can be expressed as follow: −

And its cumulative distribution function (Fw-cdf) can be represented by: =

−

−

Cshape Weibull shape parameter Cscale Weibull scale parameter Vwind-speed: Wind speed : Probability density function for the wind speed : Cumulative density function for the wind speed

2.2.2 Exponential Weibull Distribution Function Exponentiated Weibull distribution (EW) has a scale parameter and two shape parameters. The probability density function of a random variable V described by the exponential Weibull distribution is:

=

−

−

Cext-shape: Parameter of extra shape

−

6

Mohamed Habib Sellami : Probability density function for wind speed

described by exponential Weibul distribution. The cumulative density function of a random variable V described by the exponential weibull distribution is =

−



−

2.2.3. Determination of Weibull Parameters The relationship between Weibull parameters and the mean wind speed is =

+

Fgamma: Gamma function : Mean wind speed The variance of the wind speed is given by

=

The scale, shape and extrashape parameters associated to exponential Weibull distribution are related to the wind speed by the following relationships: =

∑

P: Variable characterizing the extra shape

2.3. Formulating Wind Direction Many researchers, to formulate the effect of wind directions offshore or inshore on the intensity, consider the scale and shape parameters and use the

Basic Theory to Conceptualize a Wind Farm

7

Weibull probability density function, presented above, for the total wind distribution over all directions. Based on that equation many models have been established in order to predict and draw maps for the potential wind energy sites specially the offshore ones (Sellami 2010, 2012, Lynch 2006, 2008, Kalnay 2003, Lamb 1974, Richardson’s 1922). The meteorologists have agreed on the following global directions of wind: North (N), South (S), East (E), West (W), North West (NW), North East (NE), South West (SW), South East (SE). In the field those directions could be found by installing weathercocks on masts at different highs. Mathematically they are considered in the expressions of the wind speed coordinates and the correspondent intensities as we will show later. At globe level researchers distinguish three circulations zones between equator and poles. The first called Hadley zone, between the equator and 30 degree North-South, we found winds blowing in the direction North East in the north hemisphere and in the direction South-East in the south hemisphere. The second, in the middle latitude, characterised by transient low-pressure systems in the west direction. Finally, polar cellule in the North and south of the parallel number 60 with an East surface circulation. At large scale and for the north hemisphere, the wind turns in the hourly sense around anticyclone and antihourly around a depression. Inversely, for the south hemisphere, winds turn in the hourly sense around a depression and anti-hourly around anticyclone (Lavagnini et al. 2006, Kalnay 2003, Konstantinov and Sakaly 1974, Lamb 1974, Landsberg 1974, World Meteorological Organisation 1974).

2.4. Formulating Wind Velocity For atmospheric circulation, by considering, hypothesis related to air motion in the atmosphere, the Navier Stockes equations have been simplified to obtain the atmospheric primitive equations (Comolet 1963). They are applied after considering the following hypothesis: Fluid in motion over the surface of a sphere, the vertical component of the motion is negligible in front of its horizontal component, the deep of fluid layer is very feeble compared to the radius of the sphere. Those hypotheses correspond to the flows motion at large scale as the synoptic scale for the earth atmosphere (Lynch 2008, 2006). They are well used in meteorology and oceanography for numerical models of time forecasting and when simulating the future behaviour of the atmosphere

8

Mohamed Habib Sellami

(Lynch 2008, 2006, Lavagnini et al. 2006, World Meteorological Organisation, 1974, Richardson’s 1922). From this general equation, and for the gestrophic conditions, we can obtain the following equations of air motion either onshore or offshore (Sellami 2010, 2012; Lamoureux et Fortuné 2001; Comolet 1963; Richardson’s 1922):

du  ,  fv   dt x

 dv  fu   , y dt  RT ,  p p

u v w    0, x y p  T RT  H f T T T u v  w   ,  p pc p  c p t x y     x   u t  x

   y   v    y

   z   z    z

 . 

cp: Specific heat. Hf: Heat flux by unity of time and mass. T: Temperature. u, v, w: Coordinates of the wind speed, respectively zonal, meridional and vertical. Φ: geo-potential. Ψ: Water content of the air. R: Perfect gas constant.

Basic Theory to Conceptualize a Wind Farm

9

By considering the stereographic polar projection and the sigma coordinate we can obtain the following expressions: For the temperature:

T T T T u v w . t x y z For the wind speed coordinates u:

E u  u  v   c p p xn  z  t x x 

u 

x

2 x

For the wind speed coordinates v:

E v v u     c p p xn  z  t y  v y

2



v

2



y

2 y

.

 .

For the air water content:

  x   u t  x

   y   v    y

   z   z    z

 . 

Ψx,y,z: Air waters content in the direction x, y et z. θp: Potential temperature. Exn: Exner function. A general analytical solution of the primitive equations that consider the latitude and the altitude is:

u,v, uˆ,vˆ,ˆ ei(s t) ,

s et σ are the zonal wave number and the frequency.

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Mohamed Habib Sellami

With these equations we can then estimate the wind speed, the temperature, the pressure at any points inshore or offshore to evaluate later if we can install our turbines or not. We can also, on behalf of this resolution, elaborate new wind atlas and wind maps, to make their bringing up to date and to actualise those existing.

3. PHYSICS AND BASIC EQUATIONS FOR WIND TURBINE DESIGN AND WIND FARM CONCEPTION Wind farm installed and wind turbines in use today are continually being updated with modern technology for design, conception, positioning and installation either inshore or offshore (Sinisa et al. 2009, Lavagnini et al. 2006). But evaluating the efficiency of that modern technology to adopt before and after being used is a key factor that occupies the vision of those interested by research development in the field of wind energy. Dominating the theoretical background of how wind turbine features operate and the basic equations that formulate the functioning of all components of a wind farm, permits to achieve the goal of research developers.

3.1. Physics of the Airfoils All the components of a wind propeller in particular blades are considered as airfoils (Dixon and Eng 1998). When the air in motion past through their upper and lower surfaces, it exerts a force on them called aerodynamic force. Lift is the component of this force that is perpendicular to the direction of oncoming wind so it’s a tangential force over the blade area. The parallel component to the wind flow direction is called drag force and it is perpendicular to the blade surface. The following Figure 1 presents the components of the aerodynamic force over a blade with the angle of attack. The driving force of wind turbine is lift force. Lift force, perpendicular to apparent velocity increases with angle of attack and supports blade rotation. The drag force also increases with the angle of attack but it opposes blade rotation. The optimum angle of attack for which the propeller reaches its maximum performance is when lift to drag ratio is maximum. The critical angle of attack is the angle of attack which produces maximum lift coefficient. This is also called the “stall angle of attack.”

Basic Theory to Conceptualize a Wind Farm

11

Figure 1. Schematization of the angle of attack with the aerodynamic forces.

Calculating the lift coefficient adequately can help in the design of the blades: shape, orientation, material of fabrication. The following expression for lift coefficient CLift , drag coefficient Cdrag and aerodynamic components can be used (Brearly 1998, Dixon and Eng 1998, Nicolas and Trevor 2007).: = = =

sin

sin

= .

= .

: Lift component of the aerodynamic force Drag component of the aerodynamic force CLift: Lift coefficient CDeag: Drag coefficient ρ: Density of the fluid Sref :Reference area V: Velocity of the undisturbed flow αa-a: Angle of attack

12

Mohamed Habib Sellami

3.2. Bernoulli Principle for Propeller Design Formulating The deflection of the air in the vicinity of the airfoil creates curved streamlines which results in lower pressure on one side of blade and higher pressure on the other (Figure 2). This pressure difference is accompanied by a velocity difference, expressed via Bernoulli’s principle. It’s by applying this principle that we can make the optimal design of all components of a wind propeller (Fingersh et al. 2006, Dixon and Eng 1998, Comolet 1963). Bernoulli principle expresses that the total energy accompanying the airflow is constant and it’s the sum of the static energy and dynamic energy. In fact, knowing that the gravitational potential can be assumed constant, energy in a given volume of air moving through the swept area of blades is due to its pressure and its velocity. Along a given streamline the sum of these quantities must remain constant. We can write (Dixon and Eng 1998, Comolet 1963):

V2 ) Et-airflow = cte= P + ρ ( 2g Et-airflow: Total energy in the airflow P: pressure that represent the static energy ρ(

V2 ): Dynamic pressure or energy 2g

ρ: Density of the air V: Wind speed g: gravity acceleration The equation of Bernoulli permits to say that the energy at any point in the flow path is the same and that when the kinetic energy decreases the pressure energy increases. For the air flow passing through the blades, we can say that the total energy at a point in the upstream just before the blade is the same as the total energy at a point just after the blade, at the downstream (Figure 2). By applying Bernoulli between a point at the upstream and that at the downstream of the blade we can write then: ,

+

,

=

,

+

,

We obtain the following expression for pressure difference before and after the rotor:

Basic Theory to Conceptualize a Wind Farm ,

−

,

=

,

−

,

=

,

Vup,1: Wind speed at a point 1 in the upstream Vdow, 2: Wind speed at a point 2 in the downstream Pup,1: Pressure at a point 1 in the upstream Pup,2: Pressure at a point 2 in the downstream

−

13 ,

Figure 2. Schematization of the surface and volume occupied by air flow over a propeller with the streamlines formed.

3.3. Formulating the Aerodynamic Forces Acting on Airfoils On behalf of the aerodynamic force components we found drag-based wind turbines and lift based wind turbine. More energy can be extracted from wind using lift rather than drag, but this requires specially shaped airfoil surfaces (Chang et al. 2003, Brearley 1998). That’s why our interest here will be about the design of lift based wind turbine. Let’s remind that the blade shape must be conceptualized in the purpose to create a differential pressure between the upper and lower surfaces, leading to a net force in the direction perpendicular to the wind direction. Rotors of this type must be carefully oriented and the rotor pitch that represents that orientation have to be optimized in order to increase the propeller global efficiency and to protect it from the very high wind power (Magdi and Adam 2011, Manwell et al. 2002). The intensity of the aerodynamic force acting on the blade as airfoil can be deduced from the following:



=

,

−

,

,

−

,

=

∆

=

14

Mohamed Habib Sellami

Faer: The intensity of the aerodynamic force Sairfoil: Surface occupied by the airfoil in touch with the air flow : Pressure difference between a position in the upstream and a ∆ position in the downstream The rate of change of axial momentum which represents the force acting on the rotor to absorb the energy from airflow can be expressed as the difference between the initial and final axial velocities of the fluid, multiplied by the mass flow rate: =



=

,

−

,



,

+



,

,

−

,

+

,

The mass flow rate of air touching the blade considered as the sensor capturing the airflow can be expressed: =

=

,

+

,



=

,



Rm-f: Mass flow rate Vm-dow-up: Mean speed of the air along a streamline The mean speed of the air along a streamline is generally expressed under the following form: = − Vup: Wind speed at a position in the upstream distant from the airflow Cax-ind-fac: Coefficient characterizing the axial induction factor The aerodynamic force acting on the airfoil (blade alone or the entire rotor) can then be expressed by: =



=



−



,

−



,



,

−

,

Basic Theory to Conceptualize a Wind Farm

15

The wind velocity far downstream can be expressed by:

=

−



3.3. Formulating Wind Power Extraction The power taken from the wind is equal the force acting by the air mass on the blade multiplied by the velocity of the air at the point of power extraction. We write then: =

If we replace the wind velocity far downstream by its expression we obtain: =

−

The purpose is to conceptualize a propeller or a wind farm permitting to transform all the power accompanying the air mass in displacement. The total wind power in the zone of the wind farm is given by: =

Suppose we are interested in finding the maximum power that can be extracted from the wind, we have to propose a method permitting to analyze the wind turbine efficiency alone or that of all the wind farm every time we introduce design amelioration on each component of a wind turbine. That efficiency can globally be expressed by:

=



: Power efficiency of a propeller alone or in a wind farm

16

Mohamed Habib Sellami

The axial induction factor for which the power efficiency reaches its maximum is determined by resolving the following equation for each design ameliorations proposed: =

×

=

A close look at a wind turbine will reveal that it is having airfoils cross sections from root to tip. Knowing the fact that increasing wind turbines and wind farms efficiencies means make change on the design or on the conception of each component of a propeller: blades, rotors, transmission system, generator, braking system, yaw system, control and monitoring system, and the tower. These major components are usually inside or connected to the nacelle, the capsule at the tip of the tower where the rotor attaches, and every component presented is in contact direct with the wind so we consider it as an airfoil. On most airfoil shapes, as the angle of attack increases, the upper surface separation point of the flow moves from the trailing edge towards the leading edge. At the critical angle of attack, upper surface flow is more separated and the airfoil is producing its maximum coefficient of lift. This later is a dimensionless coefficient that relates the lift generated by a lifting body to the air density around the body, the mass air velocity and an associated reference area. The lift coefficient for the blade is generally expressed by: = Clift: Lift coefficient associated to the aerodynamic lift component : Lift component of the aerodynamic force Sblade: Surface occupied by the blade Vup: Wind velocity at the upstream : Air density Calculating the lift coefficient can help in the design of the blades: shape, orientation, material of fabrication (Magdi and Adam 2011, Nicholas and Trevor 2007, Fingersh et al. 2006, Manwell et al. 2002). In fact the lift coefficient of a blade varies with angle of attack. Increasing angle of attack is

Basic Theory to Conceptualize a Wind Farm

17

associated with increasing lift coefficient up to the maximum lift coefficient, after which lift coefficient decreases.

3.4. Introducing Rotational Considerations for Propeller Conception To make a full analysis of the efficiency amelioration after introducing a new design in each component of a wind turbine, we have to take account about rotational considerations (Manwell et al. 2002, Lewis 2001, Dixon and Eng 1998). In fact, the lift force acting tangentially at a distance from the hub creates torque thus rotating the turbine. The blade pushes down on the air and the air pushes up on the blade creating lift. The blade is reacting to the difference in air pressure. Rotational power is torque times angular velocity. This means if torque decreases and rotational speed of the blade increases then power can remain constant. We can express the extracted power as follow: =

−

: Angular induction factor ρ: Air density : Rotation angular velocity r: Intermediate radius of the propeller And the power coefficient interpreted as the power turbine efficiency is given by: =

8

−

: Angular induction factor : Axial induction factor : Tip speed ratio , : Local speed ratio for the intermediate radius r

,

,

The tip speed ratio is a concept relating to the power of wind turbines the blade airfoil profile used, the number of blades, and the type of wind turbine course.

18

Mohamed Habib Sellami

It is defined as the ratio of the blade tip speed to the free stream wind speed. We write: = : Rotation angular velocity Vup: Wind speed at the upstream R: Radius of the blade The local speed ratio is the ratio of the rotor speed at some intermediate radius to the wind speed:

,



=

=



r: Intermediate radius in the blade The angular induction factor is given by: =



: Angular velocity imparted to the flow stream. The axial induction factor can be interpreted as the fractional decrease in wind velocity between the free stream and the rotor plane. For maximum power it can be formulated as a function of the local tip speed ratio in each annular ring. We write (.Manwell et all 2002):

,

=

−

−

−

For maximum power, in each annular ring, we can write the following relationship between the angular induction factor and the axial induction factor:

Basic Theory to Conceptualize a Wind Farm =

−

19

−

Finally we can say that when the wind creates a torque on the turbine, the turbine must also place an equal and opposite torque on the wind. Thus causing the air to accelerate tangentially as it passes through the turbine. Which means that limits can be imposed on the wind turbines by a specific design of its rotors. Material selection is not the only aspect of rotor design, but also the physical principles that are important for efficient energy production.

4. THERMOECONOMIC MODELING TO CONCEPTUALIZE WIND TURBINES AND WIND FARMS Thermo economic analysis combines economic and thermodynamic analysis by applying the concept of cost, originally an economic property, to exergy of every production process or every element in a system. Which permits the analysis and the optimization of wind turbine or wind farm functioning (Ensinas et al. 2013, Erlachet al. 1999). Then we can calculate the efficiency of the individual component in the propeller or in the wind farm and locates the irreversibility. Also, the thermo economic analysis provides means on how to allocate costs among them, evaluate their significance in terms of the overall production process, and propose solution of amelioration with its economic value (Rivarolo et al. 2012). Analyzing the exergy destroyed by each component in a process, we can see where we should be focusing our efforts to improve system efficiency and to orient our research development axis (Roksana Mazurek 2011).

4.1. Formulation for the Costs of Investments in Wind Farm The cost of investments for a propeller alone or in a wind farm must be evaluated by considering equipments, accessories, field, constructions, installing work, operating and maintenance, environmental consideration. Which all are dependent of the power to convert and the energy to produce (Sellami 2015, Sellami and Marzouk 2013, Ensinas et al. 2013). The global equation for cost calculation is the following;

20

Mohamed Habib Sellami

=

Cai: Equipment Costs : Equipment number Ci: Cost of the installation F: Factor that characterizes the type of material When we don’t have much information about the new technology of amelioration introduced for a component in the turbine we can estimate the investment from a reference installation. Then the formula to apply is: =

,

C: Cost of the technology to use C : Cost of the reference installation P: Power to produce by the technology to introduce P : Power produced from the reference installation

There are many empirical formulas to express the equipment purchase price. They are functions of the equipment kind. We give the following for a heat exchanger: Log

=

,

+

,

+

,

Caii: Buying cost of the equipment i in the technology used Ai: Surface occupied by the equipment i K1,i , K2,i et K3,i: Correlation coefficient The actualized total cost can be expressed by: = +

+

+

Ctot-act: Actualized total cost I: Initial investment C: Operating cost of the technology process I: Interest rate

−

Basic Theory to Conceptualize a Wind Farm

21

4.2. Formulation of the Thermo Economic Function for Wind Farms Generally this type of function depends on three terms: the investment (I), operating cost (CO) and the maintenance cost. For an annual evaluation they can be calculated from the following equations (Sellami 2015, Ensinas et al. 2013, Lozano et al. 2009, Erlachet al. 1999):

+

= = =∑

+

+ −∑

+

∗

∗

∗

∗

−

+

−

t:Time (integration over a year)' i: Interest rate Ii: Investment of the installed equipment i ni: Lifetime for the equipment i ne: Number of the equipment in the installation CO(t): Operating cost of the installation mi(t): Amount of material expressed in debit unity Pi,j: Selling price of the products i and j E: Electric power Pe: Electricity price M(t): Annual price of maintenance

4.3. Economic Quantification of the Environmental Effect The objective from considering the environmental impact is to evaluate the indirect cost when using the wind turbines for all phases: fabrication, transport, installation and functioning.

22

Mohamed Habib Sellami

It gives to our model a more realistic considerations because it represents the price to pay in order to make back the environment in which exist the installation to its initial situation. So we can say that we have considered all the aspects in the thermo economic function (Fahad et al. 2015, Sellami 2015, Vladimír 2011). The environmental cost (CE) can be expressed by:

∑

=

,

∗ ∑

+

,

+

,

ṁj: Output flux j ṁr: Debit of the resource r xi,j: Component of the pollutant i in the flux j xi,r: Emitting rate of the pollutant i per unit of resource r Ti: Tax applied to the pollutant i njpolluant: Pollutant number in the output flux j nout: Number of emitting flux from the installation na: Life time of the installation nh: Functioning time of the installation in hour per year Se: Size occupied by the equipment e ne: Number of equipment in the installation xi,e. Rate of production of pollutant i of the equipment e per unit of size The signification of a pollutant can change from a component to another and it depends on the phase considered. It can be a gas and chemical product emitted for the phase of fabrication, fuel consumption and CO2 emission for the transport phase to the site, pollutants clearance when installing turbines, and effect on surface land, interference with radio wave, bird life and visibility for the entire wind farm when functioning. When considering all those parameters, the total thermo economic function can be expressed by:

=

∗

+

∗

+

Basic Theory to Conceptualize a Wind Farm

23

When producing electricity from wind energy we need the following accessories: blades, mast, turbine, power transformer, converter, regulator, battery for storage, cables for transport power. The thermo economic equation that considers all the components, equipment and accessories of a wind farm added to the environmental impact is:

∗

=

,,

∗

+

+

,

,

: Operating cost of the equipment i in the turbine j inside a wind farm : Environmental cost of the equipement i in the turbine j inside the wind farm Ii,j: Cost of the investment in the equipment i of the turbine j ni,j,: Adjustment coefficient depending on the type of accessory and the kind of turbine

5. CASE STUDY FROM WIND POWER CALCULUS AND WIND FARMS SIZING 5.1. Applied Formulas for Wind Farm Sizing In reality most optimal wind turbine design becomes a compromise between optimal aerodynamic design, and optimal structural design (Miguel et al. 2014, Manwell et al. 2002). The intensity gives the importance of the wind force blowing locally in a region. It’s on behalf of the wind intensity that we can estimate the amount of electricity we can generate in a distinguished zone and after to take the decision about installing or not our propellers and turbines, to determine their number and the concentrators capacities and to choose the corresponding blades (diameter, material nature) (Manwell et al. 2002, Fingersh et al. 2006). We can express the wind intensity in unity of power as follow (Magdi and Adam 2011, Sellami 2010, 2012, Lamoureux et Fortuné 2001):

I

wind



m

V



d

V dt



24

Mohamed Habib Sellami Iwind: Intensity of the wind

V : Wind velocity m: Air mass in the limited space volume studied t: Time Statistical analysis and mathematical models, census, general plan for durable development by region are tools to calculate the need of energy par person at many scales for time (second, hour, day …), space (house, village, town, country …), usage (lighting, air conditioning, cleaning, industrial, agricultural …). So we can write:

P

cons



need





P

i

i

Pcon-need: Total energy needed by consumers expressed in unity of power Pi: Energy needed by class identified i: Class of calculus adopted (person, usage, sector…) Wind turbine efficiency defines its capability to transform all the wind flow touching the blades of a propeller into electrical energy (Sellami 2012, Roland Roger 1981). We can write

Yw-tur: Yield of a wind turbine Pw-tur: Energy to be transformed by a wind turbine in unity of power Pw-blade: Energy of wind flux in the site touching the blades of a propeller The energy to be transformed by a wind turbine can be expressed by the following empirical formula: Pw-tur = 0.12 D2 V3 D: Diameter of a blade depends on wind speed pressure V: Wind speed

Basic Theory to Conceptualize a Wind Farm

25

The energy of wind flux in the site touching the blades of a propeller can be expressed as follows: Pw-blade = ρ g Qair-flow D ρ: Air density g: gravity acceleration Qair-flow Incident air flow touching the blade of a propeller in the site (depend on wind speed) Determining the capacity of a wind farm means calculating its load to change all the wind energy of the sites into electrical energy. We can then write:

L

w





farm

P P

trfor site

Lw-farm: Load of the wind farms Ptrfor: Energy to be transformed expressed in unity of power Psite: Energy existing in the potential sites expressed in unity of power. The energy to be transformed can be calculated by:

P trfor

  gQ

1 tot

Y

w  tur

4V

nD

space

 farm 2

ρ: Air density g: gravity acceleration Qtot: Airflow to be transformed in the site Vspace-farm: Volume of space occupied by the wind farm (all the wind turbines) in the site D: Diameter of a wind turbine n: Number of wind turbines used Yw-tur: Yield of a wind turbine Also we have to consider the eventual lost in the transport system (cables, concentrators, batteries…) in order to offer the exact need of consumer. Conceptualizing the blades of a wind turbine means to determine their number by a propeller, the shapes and correspondent sizes, material of fabrication …to do that it’s the following reasoning that we have to adopt:

26

Mohamed Habib Sellami

The blades characteristics are materials of their fabrication, their diameter and their number by propeller (Sellami 2012, 2015). All those parameters depend on the wind speed. In fact the blade must resist to the pressure exerted by the wind in order to not be deformed or extracted. In order that the blade resists to the wind force we must write:

  Fpres + Fbladeres = 0.  Fpres : Resultant force of the air pressure.  Fbladeres : Force of resistance of the blade. Yet we calculate the force of air pressure from the dynamic fundamental equation, the atmospheric primitive equations and the expression of wind speed presented above, after we deduce the force of resistance of the blade:

  Fblade  res  Fpres . Also, at the same time, we can express this force of resistance by considering the blade fabrication material density and its geometric form as follow:

  Fbladeres = b Sb eb 1V . t ρb: Density or volume mass of the blade material of fabrication. Sb: Surface of the blade. eb: Depth of the blade. t: Time of functioning depending on the wind blowing duration’s.

 V : vector of the wind speed.

For a circular form of the blade we can write the following simplified form.

Fbladeres = b

Db2 4

V2 .

Db: Blade diameter.

Basic Theory to Conceptualize a Wind Farm

27

From the equality between the two expressions of the blade resistance force we can calculate the density or volume mass of the adequate material to fabricate the blade, its specific diameter and its optimal depth. The number of blades by propeller could be fixed according to the desired wind power we want to produce with the idea that we must minimize the lost of wind between the blades. If we suppose a propeller with (N) blades and in the case when we went to transform all the wind blowing and touching the blades into electricity and not only the energy needed by the consumers to satisfy, we can write the following relationship: Iwind = Nb-p Sb eb ρair V2 1 .

t

Iwind: Wind intensity in unity of power. ρair: The air volume mass. Sb: Surface of a blade. eb: Depth of a blade. Nb-p : Number of blades on a propeller. V: Wind speed. t: Time of functioning of a propeller. For a circular form of the propeller we can write : Iwind = Nb-p ρair

Db2 4

V3.

Db: Diameter of the blade.5.2. Recapitulation of costs from projects of conceptualized wind farm: For the investment cost of a wind firm it depends on the kind of turbine installed, its number and its accessories (tower, nacelle, hub, and blades), type of the storage system, sort of cable for power transport. Wind turbines can be found to have many shapes and sizes. The cost depends upon the project size, purchase agreement, construction contracts, type of machines, and the projects locations. Also we have to consider wind resource assessment, site analysis expenses, the freight of the turbine and its price, construction expenses, interconnection studies, utility system upgrades, protection, transformers, as well as metering equipment, operations, warranty, maintenance, repair, insurance, legal and consultation fees, the size of the project and applicability of taxes (Eric et al. 2012, Angelika et al. 2010, Thomas et al. 2010, Christian et al. 2009).

28

Mohamed Habib Sellami

We can say that the cost of the commercial wind turbines varied from $1 to $2 million per MW of nameplate capacity installed. The same turbines 2 MW in size cost roughly $2.8 million installed capacity. These turbines undergo significant economies scale. Smaller farm or residential scale turbines cost less but they are more expensive when per kilowatt of energy producing capacity is applied on the rates. This would indicate a 10 kilowatt machine might cost roughly around $48,000. In this manner 100 kilowatts wind turbines would cost roughly $2,800 to $4,800 per kilowatt of capacity. Also for the costs of the different component of a wind turbine we can recapitulate the following values: rotor capital cost 0,187 $/kw, blade capital cost 0,115 $/kw, hub capital cost 0,033 $/kw, gear box 0,0913 $/kw, generator 0,056 $/kw, foundation 0,038 $/kw , turbine transportation 0,026 $/kw , soft cost 0,308 $/kw , construction financing cost 0,042 $/kw.

Figure 3. Rate of different categories of operating and maintenance cost for wind turbines.

Based on experiences from Germany, Spain, the UK and Denmark, operating and maintenance costs are, in general, estimated to be at a level of approximately 1.2 to 1.5 c€/kWh of produced wind power seen over the total lifetime. As long the wind turbine is fairly new, the share might constitute 10%-15% increasing to at least 20%-35% by the end of its life. Thus,

Basic Theory to Conceptualize a Wind Farm

29

operating and maintenance costs are increasingly attracting the attention of manufacturers seeking to develop new designs requiring fewer regular service visits and less out-time. The turbine’s share of the total cost is, on average, around 76 per cent, while grid connection accounts for around 9 per cent and foundations for around 7 per cent. Figure 1 presents the percentage of costs for the majority of operating and maintenance actions (Rivarolo et al. 2012).

5.3. Recapitulation of External Costs from Realized Projects: Environmental Effects of Wind Farms Here we will try to evaluate the economic value of all the effects of wind farm on the environment (CO2 elimination, bird fatalities, noise, visibility, land use….) this by analyzing results recorded from the realized projects at worldwide. From many projects realized we can say that 500 megawatt wind farm (a large wind farm) powers almost 200,000 homes – saving on average 1.3 million tonnes of greenhouse gas emissions annually. Also 180 GW of wind energy would be generating 425 TWh per annum, wind power will provide: annual saving of 215 million tonnes CO2, annual saving of 261,000 tonnes SO2, annual savings of 333,000 tonnes NOx. In 2000, the total avoided external costs through the use of wind power amounted to nearly €1.8 billion. By 2020, taking EWEA projections for wind energy to be generating 425 TWh/a, the level of avoided external costs would have risen to an annual €25 billion. Also at 20% wind energy penetration the cost per tonne CO2 avoided is $2,500/t CO2 avoided. The energy consumed to manufacture, transport the materials used to build a wind power plant is equal to the new energy produced by the plant within a few months. In fact the CO2 emissions coming from manufacturing, transporting the materials used to build a wind power plant and producing the concrete for wind-turbine foundations range from 14 to 33 tonnes (15 to 36 short tons) per GWh of wind energy produced (Wiser and Bolinger, 2011). There are reports of bird and bat mortality at wind turbines as there are around other artificial structures. The scale of the ecological impact may or may not be significant, depending on specific circumstances. In fact with current capacity at roughly 50,000 megawatts, that comes to 150,000 to 200,000 birds per year each wind turbine kills an average of 4.27 birds per year. Wind farms are responsible for 0.3 fatalities per gigawatt-hour (GWh) of electricity.

30

Mohamed Habib Sellami

There are anecdotal reports of negative effects from noise on people who live very close to wind turbines. Peer-reviewed research has generally not supported these statements. Also the risk of interference with the communication waves emitted by planes when flying is a problem signaled but not approved by scientific researchers. The noise measured at 300 meters from a wind farm is less than that from normal road traffic or in an office. In fact at a distance of 300 meters from a 1 MW wind turbine, the expected sound level would be 45 decibels. But some physicians and acoustic engineers have reported problems from wind turbine noise, including sleep deprivation, headaches, dizziness, anxiety, and vertigo. The effect on land use we suggest that between 1-3% of a wind farm area is utilized by turbines, so up to 99% of the land is available for other uses. One wind turbine is expected to take up an agricultural land from 0.10 to 0.13 ha, where the own built-up area for the machine is about 200 m2. For the social effect of wind energy many researchers suggest that wind firms installed in rural zones can have positive socio-economic effects. In fact wind projects are reported to boost local tax bases, helping to pay for schools, roads and hospitals. Wind projects also revitalize the economy of rural communities by providing steady income to farmers and other landowners. Also some wind farms have become tourist attractions. The World Wind Energy Report indicated that the wind sector had created 440,000 jobs worldwide in 2008, up from 235,000 in 2005, almost doubling in just three years. This is clearly an indication of the growth of the wind market as well as its potential to create employment. As recapitulation we can evolve that Exterior cost for a turbine of 1000 kw costs 0,6 c €/kwh, the social cost is about 4,26 c €/kwh. The Figure 4 presents an economic evaluation of the CO2 avoided when using the wind energy: An important environmental enhancement from the use of wind energy is the protection of humanity from the effect of greenhouse gases which can be evaluated economically. In fact, the total CO2 costs and fuel costs avoided during the technical lifetime of a wind turbine (about 20 years and for offshore wind turbines of 25 years) is evaluated at €25/t CO2 and €42M/TWh. Furthermore, it is assumed that wind energy avoids an average of 690 g CO2/kWh produced and the power produced, is equivalent to an oil price throughout the period of $90 per barrel.

Basic Theory to Conceptualize a Wind Farm

31

Figure 4. Cost of CO2 avoided as function of wind energy penetration in the grid at International level (Lozano et al. 2009).

5.4. Application of the Model to the Case of Wind Farms Installed in Tunisia Currently, the use of the wind energy in Tunisia and, consequently, the installation of wind farms have become unavoidable realities, due to the environmental problems posed by the traditional energy sources, of the aero generators technological progress and the energy deficit recorded from the year 2000. In the purpose to meet the country's energy needs in the best economic conditions, of quality and respect of the environment as well as the users' safety, Tunisia has fixed as objective to have an electrical production from wind energy of about 4.6 TWh at the horizon of 2030 which means saving a cumulated energy equivalent to 2.8 Mtep and realising a reduction in cumulated CO2 emission of about 37 Mte CO2 yearly. So many wind farms were established (Ben Amar and Elamouri 2011):

32

Mohamed Habib Sellami

Sidi Daoud wind farm currently comprises 70 wind turbines of an installed power generation capacity of 53.6 MW. The first section of a power capacity of 10.56 MW, created in 2000, incorporates 32 aero generators (Made AE-32) with a asynchronous motor, having the unit nominal power of 330 kW. The second section of power capacity of 8.72 MW, created in 2003, comprises 12 aero generators: one wind turbine Made AE-52 with a synchronous motor of 800 kW and 11 wind turbines with asynchronous motor of which one machine Made AE-61 of 1.3 MW capacity and 10 machines Made AE-46, each of them with a capacity of 660 kW. The third section of power capacity of 34.32 MW, created in 2009, comprises 26 powerful wind turbines (MADE AE-61). Thala wind farm: The wind energy project involves the implementation of a park that would generate between 60 and 120 Megawats, and is intended to supply energy-consuming industries established in the region, such as concrete plants for instance. Theoretically, the Thala wind farm should include at the starting of the project a minimum of around 30 wind turbines with a unit capacity of 2 Megawatts. Other studies are required to determine the choice of equipment to be installed. The project is expected to kick-start mid-2013, once the complementary studies are complete. Bizerte wind farm: With a Total power product of about 69 MW. The Estimated annual production is 171 GWh (for an equivalent of 2,500 hours of full load/year). It’s an onshore wind farm and it is under construction. The turbines are installed in two different zones. The Kachbata zone with 26 turbines (model Made AE-61 power 1320 kW, diameter 61 m). The total nominal power is 34 320 kw. The second zone Elmetline, formed from 26 turbines with the same characteristics. With the construction of these two wind firms, authorities hope to produce about 400,000 MWh of renewable electricity annually. Both the Spanish and Tunisian parties emphasize that this kind of energy production is totally free of CO2 emissions. The investment amounting is 199,068,681 $/year. We have applied the thermo-economic analysis formulated above to the Tunisian wind farms by considering technical characteristics of turbines, external costs and political orientation. Results are in Table 1 (Sellami 2015):

Basic Theory to Conceptualize a Wind Farm

33

Table 1. Results from the thermo-economic analyzing for the wind firms in Tunisia

Number of turbines Mean capacity of one turbine Total capacity of the station TCO2 avoided per year TSO2 avoided per year TNOx avoided per year Number of birds can be dead per year Distance from habitat Land occupied Installed capital cost O and M cost CO2 cost avoided yearly SO2 cost avoided yearly NOx cost avoided yearly Social cost to realize per year Total cost avoided yearly Total cost

Didi daoued farm 70 0,76 MW/year

Thala farm 30 2 MW/year

Bizerte farm 54 1,277 MW/year

53,58 MW/year

60 MW/year

69 MW/year

63980 TCO2 77,691 TSO2 99,123 TNOx 280

71660 TCO2 87 TSO2 111 TNOx 120

82416 TCO2 100,050 TSO2 127,65 TNOx 216

More than 400 m 197820 m² 115465 $ 28866,25 $/year 159950 $ 194,225 $ 247,8075 $ 2280,25 $

More than 400 m 84780 m² 129300 $ 32325 $/year 179150 $ 217,5 $ 277,5 $ 2550,6 $

More than 400 m 152604 m² 148695 $ 37173,75 $/year 206040 $ 250,125 $ 319,125 $ 2939,4 $

160392,0325 $ 144331,25 $

179090 $ 161625 $

206609,25 $ 185868,75 $

CONCLUSION Evaluating the efficiency of modern technology to adopt for wind farm conception is a key factor that occupies the vision of those interested by research development in the field of wind energy. Dominating the theoretical background of how wind turbine features operate and the basic equations that formulate the functioning of all components of a wind farm, permits to achieve the goal of research developers. In this work, we have presented the basic theory permitting to all the intervening in wind energy sector to model the efficiency of new technologies to insert in the purpose to diminish costs and attract investors. In fact, we have detailed the theoretical background to develop models for searching region with high wind energy potential. We have introduced the Weibull distribution and the exponential Weibull distribution

34

Mohamed Habib Sellami

which are judged as the most careful functions fitting wind speed data. We have explained and formulated the physics and basic equations for wind turbine design and wind farm conception. We have established the thermo economic function for wind farm installations and we have recapitulated results from many case studies.

REFERENCES Abdallah H. (2008). Dangers menacing golf petroleum’s. International Politics Journal, Al-Ahram; Issue 171; pp. 34-37. Angelika P., Sven T., and Alexandra D. (2010). Wind power to provide a fifth of world’s electricity by 2030. Press release October 12, 2010, Greenpeece International and G.W.E.C, http://www.greenpeace.org/ and http:// www.windfair.us/press/8220.html. Ben Amar F and Elamouri M (2011). Wind Energy Assessment of the Sidi Daoud Wind Farm-Tunisia, Wind Farm-Technical Regulations, Potential Estimation and Sitting Assessment, Dr. Gastón Orlando Suvire (Ed.), ISBN: 978-953-307-483-2, InTech. Brearley M.N 1998 “Modeling the rowing stroke in racing shells,” The Mathematical Gazette, Nov 1998. Chang T. J., Wu Y. T., Hsu H. Y., Chu C. R., Liao C. M. (2003). Assessment of wind characteristics and wind turbine characteristics in Taiwa. Renewable Energy, No. 28, pp. 851-871. Christian K., Bruce D., Raffaella B., Elke Z. (2009). Wind Energy. The Facts. Executive Summary. European Wind Energy Association, 32 pages. Comolet R. (1963). Mécanique expérimentale des fluides. Dynamique des fluides réels, turbomachines. Masson et CLE editeurs, France, 442 pages. Debanshee Datta1 and D. Datta 2013 “Comparison of Weibull Distribution and Exponentiated Weibull Distribution Based Estimation of Mean and Variance of Wind Data” International Journal of Energy Information and Communications, Vol. 4, Issue 4, August, 2013. Dixon S. L., Eng B. (1998). Fluid Mechanics, Thermodynamics of Turbo machinery. Fourth Edition in SI/Metric Units, Oxford OX2 8DP; ISRN 0 7506 7059 2; 353 pages. Ensinas A. V., Codina V., Marechal F., Albarelli J., Silva M. A., 2013 Thermo-economic optimization of integrated first and second generation sugarcane ethanol plant, Chemical Engineering Transactions, 35, 2013, pp. 523-528.

Basic Theory to Conceptualize a Wind Farm

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Eric L., Ryan W. and Maureen H. 2012 “Future cost of wind energy” IEA Wind Task 26, idea wind, National Renewable energy Laboratory, US Department of energy, http://www.osti.gov/bridge. Technical report NREL/TP-6A20-53510, pp. 126. Erlach B., Serra L., and Valero A. 1999 Structural theory as standard for thermoeconomics. Energy Conversion and Management 40, 1999, pp. 1627–1649. Fahad A. Al-Sulaiman, Bekir S. Yilbas 2015 “Thermoeconomic analysis of shrouded wind turbines” Energy Conversion and Management, Volume 96, Issue null, Pages 599-604. Fingersh L., M. Hand, and A. Laxson 2006 “Wind Turbine Design Cost and Scaling Model” US National Renewable energy Laboratory Technical Report NREL/TP-500-40566. pp 39 pages. G.W.E.C. (2010). Global wind Energy outlook 2010. Report 2010 for Global Wind Energy Council http://www.gwec.net/fileadmin/documents/ Publications/GWEO%202010%20final.pdf, conuslted 15/01/2011. Jaramillo O. A., Borjazool M. A. (2004). Bimodal versus Weibull wind speed distributions: an analysis of wind energy potential in La Venta, Mexico. Wind Engineering, Volume 28, No. 2, pp. 225-234. Kalnay Eugenia (2003). Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press. Konstantinov A. R., Sakaly L. I. (1974). The significance of the components of heat and water balances in the formation of Micor and local climate. In Physical and dynamic climatology. WMO, Leningrad, pp. 99-105. Lamb H. H. (1974). Climates and circulation regimes developed over the Northen hemisphere during and since the last ice age. In Physical and dynamic climatology. WMO, Leningrad, pp. 233-261. Lamoureux L. et Fortuné D. (2001). Mécanique générale. Aide mémoire. Dunod, Paris, ISBN 2 10 005389 2, 181 pages. Landsberg H. E. (1974). Man-made climatic changes. In Physical and dynamic climatology. WMO, Leningrad, pp. 262-303. Lavagnini A., Sempreviva A. M., Transerici C., Accadia C., Casaioli M., Mariani S., Sperenza A. (2006). Offshore wind climatology over Mediterranean Basin. Wind Energy, No. 9, pp. 251-266. Lewis R. I. (2001). Development of vortex dynamics for simulation of turbo machine cascades and blade rows. Journal of Computational and Applied Mechanics, Vol. 2, No. 1, pp. 73-85.

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Lozano M.A, M. Carvalho, J. C. Ramos, L. M. Serra 2009 «Thermo economic Analysis of Simple Trigeneration Systems» Int. J. of Thermodynamics Vol. 12 (No. 3), pp. 147-153. Lynch P. (2008). The origins of computer weather prediction and climate modeling. Journal of Computational Physics, No. 227, Vol. 7, pp. 34313444. Lynch P. (2006). Weather Prediction by Numerical Process. The Emergence of Numerical Weather Prediction. Cambridge University Press. ISBN 9780521857291. pp. 1-27. Magdi Ragheb and Adam M. Ragheb (2011). Wind Turbines Theory - The Betz Equation and Optimal Rotor Tip Speed Ratio, Fundamental and Advanced Topics in Wind Power, Dr. Rupp Carriveau (Ed.), ISBN: 978953-307-508-2, InTech. Manwell, J. F., McGowan, J. G. and Rogers, A. L. (2002) Wind Energy Explained: Theory, Design and Application, 704 pp., 2nd ed., Wiley, ISBN: 978-0-470-01500-1. Second Edition John Willey & Sons, Ltd, Chichester, UK doi: 1010029781114367. Miguel Toledo Velázquez, Marcelino Vega Del Carmen, Juan Abugaber Francis, Luis A. Moreno Pacheco, Guilibaldo Tolentino Eslava, (2014). Design and Experimentation of a 1 MW Horizontal Axis Wind Turbine. Journal of Power and Energy Engineering, 2014, 2, 9-16. Nicholas Caplan & Trevor N. Gardner, 2007” A fluid dynamic investigation of the Big Blade and Macon oar blade designs in rowing propulsion,” Journal of Sports Sciences, April 2007; 25(6): 643-650. Richardson’s Lewis Fry (1922). Weather prediction by numerical process. Cambridge University Press, 236 pages. Rivarolo M., A.Greco, F.Travi, A.F. Massardo 2012 “Influence of renewable generators on the thermo-economic multi-level optimization of a polygeneration smart grid” Proceedings of ECOS 2012. June 26-29, 2012 Perugia. Robert Castellano 2012 “Alternative Energy Technologies. Opportunities and Markets” Old city Publishing Inc and Editions des archives contemporaine; Philadelphia, USA. Roksana Mazurek 2011 “Modeling and thermo economic analysis of the municipal CHP station (PEC) in Stargard Szczecinski” Master´s thesis done at RES|The School for Renewable Energy Science in affiliation with University of Iceland & University of Akureyri. pp. 87. Roland Roger (1981). Produire son énergie avec le vent. Editions de la Lanterne, Paris, IBSN 286588-007-9, 114 pages.

Basic Theory to Conceptualize a Wind Farm

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Sellami M. H. (2010). A modelling approach for evaluating regional wind potential, conceptualising wind turbines and testing their efficiencies. International Renewable Energy Congress, IREC2010 (www.irec. cmerp.net), Sousse, Tunisia, 5-7 November. Sellami M.H. 2012 “Basic concepts for modeling in different and complementary ecological fields: plants canopies conservation, thermal efficiency in buildings and wind energy producing,” Chapter in the book Ecological Modelling, ISBN: 978-1-61324-567-5 Editor Wen-Jun Zhang. Nova Science Publisher 2012. Sellami M.H. and Marzouk H 2013 “Thermo-economic modeling to conceptualize a biogas digester destined to energetic valorization of waste water under products.” International Journal of Renewable Energy and Biofuels, vol 2013 (2013) ID 563795 DOI: 105171/2013 563795. Sellami MH 2015 “ Thermo economic modeling of renewable energy system. Case study for wind farm,” International Journal of Energy Technology and Policy, Vol. 11 n. 1 pp. 53-67. Sinisa Stankovic, Neil Campbell, Alan Harrie’s (2009). Urban wnd energy. Published by Earth Scan UK and USA, ISBN 978-1-84407-282-8, 187 pages. Thomas Klaus, Carla Vollmer, Kathrin Werner Harry Lehmann, Klaus Müschen, 2010 «Energy target 2050:100% renewable electricity supply» Umwelt Bundes Amt for the Environment, 38 pages. U.S. Energy Information Administration (2010). International Energy Outlook 2010. U.S. Department of Energy. Washington. DOE/EIA 0484(2010). www.eia.gov/oiaf/ieo/index/.html.,338 pages. Vladimír Lapčík 2011. Wind Farms and Their Impact on Environment, Wind Farm-Technical Regulations, Potential Estimation and Sitting Assessment, Dr. Gastón Orlando Suvire (Ed.), ISBN: 978-953-307-483-2, 2011. Wiser, R. and Bolinger, M. (2011). 2010 Wind Technologies Market Report. DOE/GO-102011-3322. Washington, DC: U.S. Department of Energy Office of Energy Efficiency and Renewable Energy. World Meteorological Organisation (1974). Physical and dynamic climatology, WNO, No. 347, Leningrad, 582 pages.

In: Wind Energy Editor: Desiree Fleming

ISBN: 978-1-63484-229-7 © 2016 Nova Science Publishers, Inc.

Chapter 2

COMPARATIVE STUDY BETWEEN CLASSICAL PI AND SLIDING MODE CONTROLLER FOR A GRID CONNECTED WIND TURBINE BASED ON DFIG Walid Ouled Amor* and Moez Ghariani Department of Electrical Engineering, National School of Engineering of Sfax, Laboratory of electronics and information technology, University of Sfax, Sfax, Tunisia

ABSTRACT The aim of this work is to make a comparative study of the dynamic behavior of an aero-generator with a double fed induction generator (DFIG) connected to the grid through two controller techniques: SMC and PI Controller. The stator of the DFIG is directly connected to the grid while its rotor is connected to it via a cascade (Rectifier, Inverter and Filter). Thus, the current control and the continuous bus of the wind turbine is carried out by the adjustment of the DFIG rotor sizes based on the maximum power point tracking (MPPT) technique. The results of simulation obtained under the MATLAB/SIMULINK environment present the performance of the sliding mode controller due to its robustness face to the wind speed variation of thus its simplicity of *

Corresponding author: [email protected].

40

Walid Ouled Amor and Moez Ghariani implementation and the robustness even in the presence of internal and external disturbances. By conclusion, it enabled us to justify the reliability of the suggested model and the elaborated command.

Keywords: wind turbine, Double Fed Induction Generator (DFIG), Sliding Mode Controller (SMC), Maximum Power Point Tracking (MPPT), PI controller

INTRODUCTION The wind power conversion systems have gained a key role in the feeding of electric power generation systems. The great power type of this system (1.5MW) is primarily based on a Double Fed Induction Generator (DFIG), which has been the object of many research works [1]. Our power system presented by Figure 1, operates at a variable speed, consists of a wind turbine, a DFIG, a continuous bus, two static power inverters and a three-phase current filter. Several command strategies have been developed to ensure the control of the energy transferred between the machine and the grid to which is connected. Indeed, the stator is directly connected to the grid while the rotor is connected to it via two bidirectional static inverters cascaded through a continuous bus [2].

Figure 1. Configuration of studied system.

Comparative Study between Classical PI …

41

The aim of this work is to compare two command techniques: the classical PI and the Sliding Mode Controller (SMC). Our study is based on the performance analysis in terms of tracking, stability face to the disturbances and robustness. Indeed, our controller is used for the vectorial command of the DFIG, the control of the continuous bus and the control of the grid converter. Figure 1 presents the conversion systems to be studied. This paper evolves through three parts. The first focuses on the mathematical modelling of the studied wind power conversion system. The second part highlights the system command with the two techniques along with the setting parameters. The simulations and interpretation results are presented in the last part.

WIND TURBINE MODEL Wind Model In order to evaluate the potential wind, determining the speed frequency distribution of the wind is a significant factor in our study. In this context, we adopt the Weibull distribution law where the probability density is presented as follows [3]:

With:  

=

.

exp −

(1)

K: is the form factor. It characterizes the form of the wind frequency distribution C: is the scale factor. It determines the wind quality

In our case, we apply to our turbine a variable wind speed between 7 and 14 m/s during 30s presented by Figure 2. This choice of the wind model helps us to determine the DFIG operating modes and the static inverters reversibility.

42

Walid Ouled Amor and Moez Ghariani

Figure 2. Wind speed profile.

TURBINE MODEL The aero-generator converts kinetic energy of the wind into mechanical energy. Indeed, the kinetic power collected by the blades of the wind turbine is given by the following expression [4]: P =

With:

. .

(2)

S = π × R ʎ=

.Ω

(3)

The coefficient Cp characterizes the aerodynamic performance of the turbine. It defines the power which can be extracted during the kinetic energy transformation into mechanical energy described by the following expression: C λ, β = C



−C β−C e

+C λ

(4)

Comparative Study between Classical PI …

43

With: =

.

=0.5176; =116;

=0.4;

−

.

(5)



=5; =21; =0.0068

The aerodynamic power on the level of the turbine tree is given by the following expression:

=

P

ʎ . . .

(6)

In order to extract the maximum capacity generated by the turbine, it is necessary to fix λ = λ _ which corresponds to C _ . Indeed, the reference

magnetic torque C

is presented by the following expression:

_

C

. .

=C .

_

_

.Ω

(7)

. .

For reasons of simplification, the reference magnetic torque can be defined as follows: C

With:

_

A=C .

= A. Ω

_

. .

(8)

(9)

. .

Based on the assumption that the wind speed varies very little in permament mode, we obtain the static equation from the turbine equation: J.

Ω

=C −C

− f. Ω = C

=

(10)

The effect of the viscous friction torque is neglected: C

≅

(11)

44

Walid Ouled Amor and Moez Ghariani We obtained: C =C

(12)

DFIG Model The modelling of the DFIG, presented by Figure 3, is described in the PARK referential. This work is based on the following simplifying assumptions:    

The air-gap is constant and the nick effect is negligible The flow distribution is sinusoidal The saturation of the magnetic circuit is negligible The influences of the heating and the skin effect on the characteristics of the DFIG are not taken into account.

Figure 3. Block diagram of DFIG model.

Comparative Study between Classical PI …

45

The following equation system makes it possible to establish the total modelling of the generator [5]:

Stator: V

V

= R .i

= R .i ɸ

ɸ

Rotor: V

V

= L .i

= R .i ɸ

+

= L .i

= R .i

ɸ

+

+

+

= L .i

= L .i

ɸ –ɸ .W

(13)

+ M. i

(15)

ɸ

+ ɸ .W

(14)

+ M. i

(16)

ɸ –ɸ .W

(17)

+ M. i

(19)

ɸ

+ ɸ .W

+ M. i

W = W − p. W

(18)

(20) (21)

Electromagnetic torque is presented by the following equation: C

= p. ɸ . i

− ɸ .i

(22)

Power Converters Model The studied wind power conversion system is based on the electronic power converters. Indeed, the same modelling is valid for the rectifier and the

46

Walid Ouled Amor and Moez Ghariani

inverter. They comprise three IGBT arms. They consist of two commutation cells assembled in series that do not function simultaneously. In the ideal case, each cell can be comparable with a switch command at the opening and closing. Indeed, we associate a connection function f for each switch " i ∈ , ; j ∈ , , ". If f = : The Switch is open If f = : The switch is closed At the output of the inverter, the two modulated voltages are determined according to the conversion functions ′m and ′m and the continuous bus voltage by the following equations [6]: =

U − −

=

In the same way the simple voltages, V the following matrix:

With:

V V V

=

− −

−

−

(23) f f f

,V − −

I = m .I + m .I

(24)

and

f f f

V

are presented by (25)

(26)

DFIG Vectorial Command Model The vectorial DFIG command law is based on the stator flow orientation according to the axis d. We obtain the following results [7, 8]: If we take ɸ = So ɸ = ɸ V

= R .i

+

ɸ

(27)

Comparative Study between Classical PI … V

= R .i

In the steady state we have:

i

ɸ With:

ɸ

i

=

ɸ

+ ɸ .W

(28)

=

(29) .

.

=

= L . σ. i ɸ

(30)

+M

= L . σ. i

σ=

47

−

(31) .i

(32) (33)

(34)

.

For simplicity we reached the following expressions:

V

With: V

V

V

= V

= V

= R .i

= R . i

+e

(35)

+ e + eɸ

(36)

+ L . σ.

(37)

+ L . σ.

e = −W . L . σ. I e = W . L . σ. I

I

I

(38) (39) (40)

48

Walid Ouled Amor and Moez Ghariani eɸ =



.W .ɸ

(41)

The electromagnetic torque is expressed by the following expression:

From the V

and V

C

= −p. ɸ .



.i

(42)

voltage, we have the following transfer function: =

=



(43)

. .

The statoric active and reactive power is expressed by the following expression: P = V .I

Q = V .I

=

=−

. M. I

.ɸ −

(44) . M. I

From these two equations, we determined the I I

I

=

ɸ

=−

−

.ɸ

. .

.Q

.C

_

(45) and I

_

currents:

(46) (47)

Grid Side Converter Command Model To guarantee the interconnection between the rotor and the grid, an RL filter is installed to eliminate the harmonics communication from the converter operation [8, 9]. V = V V = V

− W .L .I + V

+ W .L .I + V

(48) (49)

49

Comparative Study between Classical PI … With: V

From the V function:

and V

V

= R .I + L .

I

= R . I + L .

(50)

I

voltage we have obtained the following transfer =

=

(51)

.

Similarly, the active and reactive powers are expressed by the following expressions: P = V .I + V .I

Q = V .I − V .I

From these two equations, we determined the I I

=

I

=

.

.

.

.

(52) (53) _

and I

_

currents:

(54)

(55)

Like any generator connected to the grid, the power factor can be set to 1 as Q =0. Neglecting losses in the filter, it leads to the following expressions [10]. V So we get:

V

=V =V =V =

P =V ∗i

(56) (57)

(58)

50

Walid Ouled Amor and Moez Ghariani

We deduce:

Q = −V ∗ i

(59)

_

(60)

I

=

I

=

_

(61)

PI CONTROLLER Generally, most of industrial regulation systems use PI Controllers thanks to its simplicity of synthesis. It can be presented by the following form [11, 12]:

The transfer function H is:

C=K +

G s =

=

(62)

.

The regulation block is presented by Figure 4:

Figure 4. Diagram regulation block of closed loop.

(63)

Comparative Study between Classical PI …

51

In closed loop, the transfer function is expressed as follows: FTBF =

.

.

.

(64)

To determine the regulation of PI parameters, K and K , our study consists in identifying the FTBF denominator with the following polynomial characteristics [13, 14]:

With:

P=

+

s+

s

(65)

ζ: Damping coefficient (without unit) W0: Pulsation of the unamortized oscillations We Obtained: K = . ξ. b. W − K = b. W

In order to determine the response time of the second order system, two conditions must be considered: ζ=

√

t .ω =

The wind turbine has three regulation blocks. The first corresponds to current regulation Ird and Irq. The second for the regulation of the continuous bus Udc and the third corresponds to the grid current command IGd and IGq. Table II.1 illustrates the PI controller parameters.

52

Walid Ouled Amor and Moez Ghariani Table 1. PI regulation parameters

I I

Parameters /i /I

. ξ. L . σ. W − R . ξ. C . W . ξ. L . W − R

L . σ. W C .W L .W

SLIDING MODE CONTROLLER The sliding mode command technique consists in bringing the state trajectory of a system towards the sliding surface and to commutate it using suitable commutation logic around it until reaching a balance point. This technique has several advantages: high precision, stability, simplicity, a very weak response time and especially the robustness [15, 16]. In the phase plan presented by Figure 5, the trajectory comprises three distinct modes: convergence mode, sliding mode and permanent operation mode. The study of this technique can be divided into three principal steps:   

The choice of the sliding surface Setting of the convergence conditions Setting of the command law

Figure 5. Phase plan of Sliding mode controller.

Comparative Study between Classical PI …

53

Choice of the Sliding Surfaces We consider the following state model: x = A x + B U

€ ℜ present the state vector, U € ℜ present the command vector with n > m. Generally, the choice of the number of the sliding surfaces is equal to the dimension of the command vector U . To ensure the convergence of a state variable x towards its reference value x , several works suggested the following general form [17, 18]:

With: : Gain positif

=

+

=

− x

r: relative degree, it is the smallest positive entity representing the necessary number of derivations to obtain the command, with: ensuring controllability.

≠

Convergence Condition Setting To allow the dynamic system to converge towards the sliding surface, we consider the following condition which corresponds to the convergence mode: S x .S x < Based on the Lyapunov function presented by the positive scalar function V x > for the state variables of the system, with V x > . Generally, this function is used to guarantee the stability of the nonlinear systems. We define the Lyapunov function by [19, 20]: V x = S x

54

Walid Ouled Amor and Moez Ghariani

To ensure the convergence of the Lyapunov function, it is sufficient to guarantee that: S x .S x



σ. L . ı

K

= cte >

V _

So: S I

.

+

+

=K

+

. .

Control of q-axes rotor current V

= R .i

+

L .i

−

.

(87)




sign S I

L .i

. .

− i . g. W . L . σ

L .i

+ σ. L . g. W . i

(88)

+ σ. L . g. W . i

+ L .i

+ M.

ɸ

(89)

.

. W

(90)

58

Walid Ouled Amor and Moez Ghariani V

V

= R .i

+ L .ı

= R .i V

ı

S ı

S ı

=ı

−

+ L .ı

.

+ W . L . i

−

+ W . L . i

.

= L .ı .σ + R .i

ı

=

.

=−

=ı _

_

+

V −

+

.

+

+

.i

. .

ɸ

−W.

−

+ W . M.

+ W . L . i . σ + W . M. − g. W . i +

. .

+

+ W . M.

−

.

.

.

(91)

ɸ

(92)

ɸ

ɸ

−

.

V

.

.

(93) (94)

V − g. W . i

.

. .

−

.

(95)

V + g. W . i

(96)

.

(97)

+V

+ g. W . i

At the steady state and during the sliding mode, we considerate: =

S I So:V V

S I

=

. .

= σ. L . ı

_

=

+

+

S I

.S I

During the convergence mode:

According to the Lyapunov theorem I

L .i

. .


L . σ. I

= cte K

>

So: S I =K _

.i

+

59

>

>
L .I

K

_

I

sign S I

+ R .I + L .ω I

_

= cte

> R .I + L .ω I

(111)

Control of Q-Axes Filtered Current S I

=I

_

−I

The surface derivative is presented by the following expression:

(112)

62

Walid Ouled Amor and Moez Ghariani S I

=I

We consider the following system: V =R I +L

Likewise:

−I

_

(113)

− ω L I + V

(114)

I

=

V −V

−

I +ω I

(115)

I

=

V −V

−

I +ω I

(116)

This study is based on the vector control of d-axis filtered current, this implies that: V = |V | and V =

=I

S I

S I

S I

−

_

=I

_

=I

_

V −V

−I

−I

+

(117) (118)

I −ω I

(119)

At the steady state and during the sliding mode, we considerate: =

S I

V

So:

V

,

= L .I

.

S I

=V .

_

.

=

=

+V

.

+ R .I − L .ω I + V

(120)

(121)

Comparative Study between Classical PI …

63

During the convergence mode: .S I

S I


So S I

If: S I

Either:

K

V

.

> L .I

K

_

=K

sign S I


R .I − L .ω I + V

Figure 8. Power coefficient curve Cp.

(122)

(123)

64

Walid Ouled Amor and Moez Ghariani

SIMULATION RESULTS AND INTERPRETATION Figure 8 presents the optimal point which corresponds to the Beta angle = 0°. This value is called the Betz limit. It is the point at the maximum power coefficient. We note that the wind turbine operation at these points makes it possible to maximize power extraction [10].

Figure 9. mechanical speed.

Figure 10. Mechanical speed limitation by pitch control.

Comparative Study between Classical PI …

65

Figure 9 illustrates the mechanical speed which follows the wind curve. Figure 10 corresponds to the mechanical speed with a limitation. This limitation is due to the variation of the blade orientation angle. Due to this variation, the power coeficient decreases [10].

Figure 11. Mechanical speed.

Figure 12. DFIG rotoric current.

66

Walid Ouled Amor and Moez Ghariani

Figure 13. Electric power generated.

Figure 11 describes the correct operation of the DFIG in both operating modes and the reversibility of both power converters. Indeed, during operation in hyposynchronous mode (mechanical speed lower than the speed of synchronism), the rotor absorbs the power of the grid. In the contrary case (hypersynchronous mode) where mechanical speed is higher than the speed of synchronism, the rotor injects power to the grid [10].

Figure 14. voltage no filtered curve.

Comparative Study between Classical PI …

67

Figure 15. Harmonic spectrum before the filter.

Figure 13 presents the aerodynamic power generated by the wind turbine. According to the the curve of the stator power we notice that it is limited to the nominal value and the power excess is transferred by the rotor. The total power injected into the grid corresponds to the sum of the Ps and Pr [10]. Figure 14, shows that the presence of a disturbed signal formed by a fundamental signal of frequency (f = 50 Hz) and other signals. Indeed, Figure 15 presents the range of the harmonics which disturb the voltage on the output of the inverter, located on a harmonic spectrum on the output of the inverter to evaluate the quality of voltage wave [10]. The result obtained by the spectrum reveals the cause of the noises in the voltage curve. In fact, the odd harmonics (k = 2 until k = 19) disturbs the voltage curve. V t =E ∑

sin k +

wt with n = k +

(124)

This equation illustrates the voltage signals forming the curve presented previously. To minimize the effect of the harmonics, a filter must be placed on the installation whose role is to eliminate the undesirable frequencies and to confine the useful frequency bands in a complex system. In our case, we use a

68

Walid Ouled Amor and Moez Ghariani

passive filter of type RL. According to the spectrum of harmonics on the output of the filter, the totality of the frequencies beyond the fundamental frequency F = 50Hz are eliminated. The voltage curve on the output of the filter is presented by Figure 16 [10]:

Figure 16. Voltage curve filtered.

Figure 17. Harmonic spectrum after filter.

Comparative Study between Classical PI …

69

In order to evaluate the performance of the suggested command strategy for the conversion system of wind turbine based on a DFIG, a comparison of performance between the classical PI and the first order SMC.

Figure 18. continuous bus voltages by PI controller.

Figure 19. Continuous bus voltage by SMC.

70

Walid Ouled Amor and Moez Ghariani

Figure 20. Irdq current before filter by PI controller.

Figures 18 and 19 respectively present the continuous bus voltage by Pi Controller and SMC regulated to the standard reference voltage fixed at1000 V. Indeed, in spite of the fluctuation of wind, Vcd remains quasi stationary. The analysis of the dynamic aspect of the two systems shows that the system using PI presents an overtaking at the beginning of the cycle on the one

Comparative Study between Classical PI …

71

hand, and a remarkable disturbance rate as compared to the system using the SMC. Consequently, the SMC eliminates the overtaking and minimizes the effects of disturbance. It thus proves the robustness of the SMC compared to PI. We mention that regulator PI is more sensitive to the variation of the wind compared to the SMC.

Figure 21. Irdq current before filter by SMC.

72

Walid Ouled Amor and Moez Ghariani

Figures 20 and 21 show respectively the rotor currents curves Ir dq before the filter in the two systems. These curves show that the static error with the SMC is significantly lower than that of the PI controller. Indeed, in permanent mode, the static error with SMC is weaker as compared to the PI controller. We infer that the SMC corrector is more performing than PI.

Figure 22. Irdq current after filter by PI controller.

Comparative Study between Classical PI …

73

Figures 22 and 23, illustrate the performance of the nonlinear command which is characterized by its robustness towards the disturbances. Firstly, we notice that the two controllers show almost the same behaviour dynamics under nominal condition. However, the results of simulation show that the control device in sliding mode realizes a better dynamic behaviour of the system with a fast stabilization and boarding time and a better performance than the PI regulator in terms of the reduction of the static error in steady operation and minimizes the disturbance effects.

Figure 23. Irdq current after filter by SMC.

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Walid Ouled Amor and Moez Ghariani

CONCLUSION Our study is interested in modelling and commanding a wind turbine based on DFIG. Indeed, the extraction of maximum power by the MPPT strategy makes it possible to provide the totality of the active power produced to the grid with a unitary power-factor. Two regulation techniques of are used in our study, namely the classical PI and SM controller. They are used for DFIG command, the continuous bus voltage regulation and the grid converter command. The obtained simulation results show that the SMC outperforms the PI controller thanks to its robustness face to the wind speed variation. The strength of SMC is the simplicity of implementation and the robustness even in the presence of the internal and external disturbances.

NOMENCLATURE P

β

S R V C Ω G C Ω C f j V V i i

Kinetic power (KW) Tip speed ratio Pitch angle (°) Power coefficient Area blade (m2) Blade radius (m) Air density (ρ = 1.22 kg/ ). Wind speed (m/s) Aerodynamic torque Mechanical turbine speed (rad/s) Multiplication ratio Electromagnetic torque Viscous friction torque Mechanical torque Rotational speed (rad/s) Speed multiplication torque Viscous friction coefficient Inertia (kg.m2) d and q components stator voltages (V) d and q components of the rotor voltages (V) d and q components stator currents (A) d and q components rotor currents (A)

Comparative Study between Classical PI … ɸ ɸ R ,R W ,W L , L M p ,

,

75

d and q components stator flux (Wb) d and q components rotor flux (Wb) Stator and rotor winding resistance (Ω) Stator and rotor pulse (rad/s) Stator and rotor winding inductance (H) Mutual inductance (H) Number of poles d and q grid voltage (V) Stator and rotor active power (KW) Stator and rotor reactive power (KVAR)

REFERENCES [1]

[2]

[3]

[4]

[5]

[6]

[7]

Hachicha, F; Krichen, L. “Performance Analysis of a Wind Energy Conversion System based On a Doubly-Fed Induction Generator,” IEEE Trans, 8th International Multi-Conference on Systems, Signals & Devices, 2011, 978-984. Belabbes, A; hamane, 1B; bouhamida, M; draou, A. “Power Control of a Wind Energy Conversion System based on a Doubly, Fed Induction Generator using RST and Sliding Mode Controllers” ICREPQ‟12, Santiago de Compostela (Spain), 28th to 30th March, 2012. Tapia, A; Tapia, G; Ostolaza, JX; Saenz, JR. “Modeling Control of a wind turbine driven doubly fed induction generator,” IEEE, Trans. Energy Convers., 18 (2) (2003), 194–204. Muller, S; Deicke, M; Rik, W; De Doncker, “Doubly fed induction generator systems for wind turbines,” IEEE Ind. Appl Magazine, 8 (3) (May/June 2002), 26–33. Datta, R; Ranganathan, VT. “Variable-speed wind power generation using doubly fed wound rotor induction machine a comparison with alternative schemes,” IEEE Transactions on Energy Conversion, 17 (3) (2002), 414–421. Poitiers, F; Machmoum, M; Le Daeufiand, R. aim, ME. “Control of a doubly-fed induction generator for wind Energy conversion systems,”IEEE Trans. Renewable Energy, Vol. 3, N°. 3, December 2001, 373-378. Machmoum, M; Poitiers, F; Darengosse, C; Queric, A. “Dynamic Performances of a Doubly-fed Induction Machine for a Variable-speed

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[8]

[9]

[10]

[11]

[12]

[13] [14]

[15]

[16]

[17]

[18]

[19]

Walid Ouled Amor and Moez Ghariani Wind Energy Generation ,” IEEE Trans. Power System Technology, vol.4, Dec. 2002, 2431-2436. El Aimani, S. “Towards a Practical Identification of a DFIG based Wind Generator Model for Grid Assessment,” 2nd International Conference on Advances in Energy Engineering (ICAEE 2011). El Aimani, S. “Modélisation de différentes technologique d’éoliennes intégrées dans un réseau de moyen tension,” thèse préparé au sein de laboratoire L2EP de l’école centrale de lille en 6 decembre 2004. Ouled Amor, W; Ltifi, A; Ghariani, M. “Study of a wind energy conversion systems based on double fed induction generator,” IREMOS, Vol 7, N 4, August 2014. Aouani, N; Bacha, F; Dhifaoui, R. “Control Strategy of a Variable Speed Wind Energy Conversion System Based on a Doubly Fed Induction Generator.” Kojooyan Jafari, H; Kojooyan Jafari, H. “Comparison of Self Tuning P and PI Voltage Control of DFIG in Wind Power Generation Considering Two Mass Shaft Model.” Krishan Gopal Sharma, Annapurna Bhargava, Kiran Gajrani, Stability Analysis of DFIG based Wind Turbines Connected to Electric Grid. Maimaitireyimu Abulizi, Ling Peng, Bruno Francois, Yongdong Li, “Performance Analysis of a Controller for Doubly-Fed Induction Generators Based Wind Turbines Against Parameter Variations.” Riouch, T; El-Bachtiri, R; Salhi, M. ‘Robust Sliding Mode Control for Smoothing the Output Power of DFIG Unde Fault Grid’, International Review on Modelling and Simulations (IREMOS) Vol 6, No 4 (2013). Abdelhak Djoudi, Hachemi Chekireb, El Madjid Berkouk, Seddik Bacha, Low-cost sliding mode control of WECS based on DFIG with stability analysis, Turkish Journal of Electrical Engineering & Computer Sciences. Belgacem, Kh; Mezouar, A; Massoum, A. ‘Sliding Mode Control of a Doubly-fed Induction Generator for Wind Energy Conversion’, International Journal of Energy Engineering. Bekakra, Youcef; Ben Attous, Djilani. “Sliding Mode Controls of Active and Reactive Power of a DFIG with MPPT for Variable Speed Wind Energy Conversion,” Australian Journal of Basic and Applied Sciences, Ltifi, A; Ghariani, M; Neji, R. “Comparison of Two Techniques for Control Nonlinear Systems: The PI Regulator and Sliding Mode Control,” International Conference on Control, Engineering & Information Technology (CEIT’14).

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[20] Ouled Amor, W; Ghariani, M; Neji, R. Study of the Contribution to the robust control of a small variable speed wind turbine based on permanent magnet synchronous generator: Comparison between PI controller and sliding mode controller, International Review of Automatic Control (IREACO), Vol.7, n.6, November 2014. [21] Ltifi, Arafet; Ghariani, Moez; Neji, Rafik. International Conference on Control, Engineering & Information Technology (CEIT’14). [22] Ltifi, A; Ghariani, M; Neji, R. “Performance comparison of PI, SMC and PI-Sliding Mode Controller for EV.” [23] Ghariani, Moez; Hachicha, Mohamed Radhouan; Ltifi, Arafet; Bensalah, Ibrahim; Ayadi, Moez; Neji, Rfik. ‘Sliding mode control and neurofuzzy network observer for induction motor in EVs applications’, International Journal of Electric and Hybrid Vehicles.’ [24] Ltifi, Arafet; Ghariani, Moez; Neji, Rafik. 'Performance comparison on three parameter determination method of fractional PID controllers,’ 14th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA’13). [25] Ltifi, A; Ghariani, M; Neji, R. “PI sliding mode control for the nonlinear system,” 10th International Multi-Conference on Systems, Signals & Devices (SSD’13). [26] Ltifi, A; Ghariani, M; Neji, R. “Sliding mode control of the nonlinear systems,” 11th International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD’10).

In: Wind Energy Editor: Desiree Fleming

ISBN: 978-1-63484-229-7 © 2016 Nova Science Publishers, Inc.

Chapter 3

COMPARISON FOR POLICY AND PROMOTION STRATEGY OF WIND ENERGY DEVELOPMENTS BETWEEN TAIWAN AND JAPAN Tzu-Yi Pai1,2,, Keisuke Hanaki2, Yi-Ti Tung3,4 and Pei-Yu Wang1 1

Master Program of Environmental Education and Management, Department of Science Application and Dissemination, National Taichung University of Education, Taichung, Taiwan, ROC 2 Department of Urban Engineering, School of Engineering, University of Tokyo, Bunkyo-ku, Tokyo, Japan 3 School of Medical Sociology and Social Work, Chung Shan Medical University, Taichung, Taiwan, ROC 4 Research Consultant, Social Service Section, Chung Shan University Hospital, Taichung, Taiwan, ROC



Corresponding authors: Tzu-Yi Pai, Master Program of Environmental Education and Management, Department of Science Education and Application, National Taichung University of Education, Taichung City, 40306, Taiwan, ROC , Tel: +886-4-22183541 Fax: +886-4-22183540, Email: [email protected].

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ABSTRACT Wind energy is regarded as one of the potential renewable energy and has been actively promoted by many countries. In this study, the policy and promotion strategy of wind energy developments between Taiwan and Japan were surveyed and compared. The results showed that the wind power increased significantly in the past ten years. The cumulative capacity of wind energy (CCWE), wind power generation (WPG), and the ratio of WPG to total power generation for Taiwan in 2014 gave on 26.5, 16.5, and 14.4 times than those in 2005. The CCWE, WPG, and the ratio of WPG to TPG for Japan in 2014 gave on 2.7, 2.7, and 2.9 times than those in 2005. Besides, an analytic hierarchy process (AHP) structure was suggested to aid decision makers making decisions to prioritize and select policy and promotion strategy of wind energy developments. For the first two important criteria, both Taiwan and Japan have collected the basic data and assessed wind power potential in the early 1990s. In the early 2000s, Taiwan government formulated the Renewable Energy Development (RED) Act and rewarded the wind power generation system settings of folk investment to promote the renewable energy. Japan formulated the Renewables Portfolio Standards Law to obligate the electric utilities to use a certain amount of new energy and implemented Feed-in Tariffs policy to set prices for the renewable power.

INTRODUCTION Natural resources have seriously exhausted because of human beings’ massive consumption. At present, fossil fuels offer about 88 percent of all commercial energy in the world. The annual crude oil price increases because of the almost diminished reserves. The World Energy Council estimates that oil shale and other nontraditional deposits contain huge amounts of petroleum, so the shale oil is extracted by several countries recently. Although the extraction of shale oil inhibits the increase of oil price, high energy price seriously influences energy safety of Taiwan and Japan because both nations strongly rely on exported energy. Meanwhile, a large amount of carbon dioxide emission due to the combustion of fossil fuel will enhance the global warming [1, 2]. Renewable energy, such as sunlight, wind, rain, tides, waves, and geothermal heat, is the type of energy that can naturally replenish on a human timescale. Renewable energy can replace conventional fossil fuels in several

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distinct areas and can eliminate the need for fossil fuels [3, 4, 5, 6, 7, 8]. Hence active promotion of renewable energy has become an important option worldwide to simultaneously solve the problem of energy shortage and global warming. Wind energy is regarded as one of the potential renewable energy and has been aggressively promoted by many countries. Wind energy exhibits advantages in several aspects. First, wind is cheap and available almost everywhere compared to other alternative sources. Second, inhabitants do not migrate and land uses do not change because wind turbines only occupy small footprint. Third, to reduce dependence on fossil fuel imports, local wind power has also been the major focus. Community inhabitants may regard the wind farm as energy alternatives as well as the tools for earning money [1, 2]. Wind turbines have any negative impacts too. Wind farms are usually installed at the places where wind and weather are unattractive for inhabitation or other development activities. But they can obstruct the particular landscape in remote areas and perish natural beauty and a sense of isolation. Additionally, it has been frequently reported that birds, bats, and other flying animals are killed by the wind turbines in some places. With the proliferation of wind turbines, inhabitants at the neighborhoods often complain about noise of wind turbines and shadows of blades [1]. To actively promote wind energy, many studies have pointed out that the policy and promotion strategy of wind energy developments are the successful key. Therefore the policy and promotion strategy of wind energy developments between Taiwan and Japan were compared in this study. By studying the survey results, the viewpoints will be summarized to serve as a reference for the government to promote the wind energy in the future.

WIND POWER GENERATION IN TAIWAN AND JAPAN Cumulative Capacity of Wind Energy In the past decade, Taiwan’s total power generation (TPG) continuously increased from 227419.9 GWh in 2005 to 260026.7 GWh in 2014, i.e., increased by 14.3% as shown in Figure 1 [9, 10]. Contrarily, Japan’s TPG slightly increased from 882558.6 GWh in 2005, peaked in 2007 (919543.891 GWh), gradually decreased to 823004.9 GWh in 2014, i.e., totally decreased by 6.7%. Especially, after the Fukushima No.1 nuclear incident in 2011, Japan’s TPG significantly decreased from 906417.2 GWh in 2010 to 859808.7 GWh in 2011, i.e., decreased by 5.1% in the single year [11]. Since the impact

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of Fukushima No.1 nuclear incident was broad and long-lasting, not only the public’s behavior of energy usage changed dramatically, but also the public opinion switched from “accepting-nuclear” stance to “anti-nuclear and prorenewables.”

Total power generation (GWh)

1000000 900000 800000 700000

Taiwan

600000

Japan

500000 400000 300000 200000 100000 0 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Year

Figure 1. The total power generation for Taiwan and Japan from 2005 to 2014.

Cumulative Capacity of Wind Energy In the recent ten years, the wind energy was developed in Taiwan. According to Figure 2, the cumulative capacity of wind energy (CCWE) in Taiwan significantly increased from 24 MW in 2005 to 633 MW in 2014. The CCWE for Taiwan in 2014 gave on 26.5 times more capacity than that in 2005 [9, 10].

Cumulative Capacity of Wind Energy (MW)

3,000 2,500 Taiwan

2,000

Japan 1,500 1,000 500 0 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Year

Figure 2. The cumulative capacity of wind energy for Taiwan and Japan from 2005 to 2014.

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Wind power generation (GWh)

6000 5000 4000 Taiwan 3000

Japan

2000 1000 0 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Year

Figure 3. The wind power generation for Taiwan and Japan from 2005 to 2014.

Wind Power Generation Since the CCWE is actively installed in both Taiwan and Japan, the wind power generation (WPG) increased significantly from 2005 to 2014 as shown in Figure 3. In accordance with Figure 3, the WPG in Taiwan significantly increased from 91 GWh in 2005 to 1500 GWh in 2014. The WPG for Taiwan in 2014 gave on 16.5 times more generation than that in 2005 [9, 10]. Japan’s WPG also increased continuously from 1910 GWh in 2005 to 5100 GWh in 2014. The WPG for Japan in 2014 gave on 2.7 times more generation than that in 2005 [11].

Ratio of WPG to TPG With the WPG and TPG data, the ratio of WPG to TPG could be calculated further. In the past decade, the ratio of WPG to TPG was promoted in both Taiwan and Japan. According to Figure 4, the ratio in Taiwan significantly increased from 0.04 in 2005 to 0.58 in 2014. The ratio for Taiwan in 2014 gave on 14.4 times than that in 2005. Japan’s ratio also increased continuously from 0.22 in 2005 to 0.62 in 2014. The ratio for Japan in 2014 gave on 2.9 times than that in 2005.

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Ratio of WPG to TPG

0.60 0.50 0.40

Taiwan Japan

0.30 0.20 0.10 0.00 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Year

Figure 4. The ratio of WPG to TPG for Taiwan and Japan from 2005 to 2014.

Application of Analytical Hierarchy Process To promote wind energy, the analytic hierarchy process (AHP) theory was adopted to evaluate the priority of the policy and promotion strategy of wind energy developments [3, 4, 5, 6]. AHP is an analysis measure proposed by Saaty in the 1970s [12, 13]. An unsystematic question can be decomposed into hierarchy levels using AHP. In such a way, the unsystematic question become a hierarchy with hierarchical relationships between different levels [3, 4, 5, 6].

First Stage Test The criteria affecting the policy and promotion strategy of wind energy developments were surveyed. Based on literature review, related evaluation criteria were summarized and arranged to compile the questionnaire of first stage test. The questionnaire of first stage test was compiled using Likert 5point scale. Ten practitioners from environmental organizations of Taiwan were invited to answer the questionnaires. The items were selected by means and standard deviation. After calculation, 16 items with the mean top 16 weights were selected as the evaluation criteria to design the hierarchy structure of policy and promotion strategy of wind energy developments. The structure was decomposed in the second level containing four major criteria: (1) policies aspect, (2) educational promotion, (3) technical research and development, and (4) economic incentives. There are total 16 sub-criteria in

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the third level, as shown in Figure 5 [6]. Subsequently, the criteria were used to edit the AHP relative weight questionnaire. Low-carbon and low-pollution energy supply and consumption

Policies aspect

Reduction of the dependency on fossil energy and imported energy Increase of the wind power percentage in total power supply The government’s active development of green energy industry

Criteria for Promoting the Wind Energy Policy

To construct energy education plans according to local characteristics To provide complete wind power education information and resources Educational promotion

Implementation of international exchange and cooperation of wind power education Wind power educational promotion and activities

Patents for wind power products

Technical research and development

Prior implementation of items with mature techniques and economic benefits Improvement of wind power saving technique

R&D of off-shore wind power technique

The government’s favorable purchase of wind power Tax breaks or preferences for wind power industry Economic incentives Expansion of international market

To study the innovative service model of wind turbines and create business opportunities

Figure 5. The hierarchical structure for promoting the wind energy [6].

Prior implementation of items with mature techniques and economic benefits

0.149

The government’s active development of green energy industry

0.123

Low-carbon and low-pollution energy supply and consumption

0.122

R&D of off-shore wind power technique

0.115

To construct energy education plans according to local characteristics

0.102

Improvement of wind power saving technique

0.060

Increase of the wind power percentage in total power supply

0.054

To study the innovative service model of wind turbines and create business…

0.050

Reduction of the dependency on fossil energy and imported energy

0.050

Tax breaks or preferences for wind power industry

0.041

Patents for wind power products

0.041

To provide complete wind power education information and resources

0.028

Wind power educational promotion and activities

0.027

Expansion of international market Implementation of international exchange and cooperation of wind power… The government’s favorable purchase of wind power 0.000

0.017 0.013 0.006 0.020

0.040

0.060

0.080

Priority weight

Figure 6. The weights of all sub-criteria for policy and promotion strategy of wind energy developments.

0.100

0.120

0.140

0.160

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Second Stage Test According to the first stage questionnaire test, the criteria were used to form the AHP relative weight questionnaire of second stage test. The questionnaire of first stage test was compiled using pair-wise comparison. Ten practitioners from environmental organizations of Taiwan were invited to answer the questionnaires of second stage test. The pair-wise comparison data were allocated in the form of a matrix and calculated based the eigenvector procedure proposed by [12, 13]. To ensure the suitability of AHP, the consistency ratio for each of the matrices and overall inconsistency for the hierarchy were calculated. After calculation, the global weights of 16 sub-criteria were depicted in Figure 6.

POLICY AND PROMOTION STRATEGY OF WIND ENERGY DEVELOPMENTS The policy and promotion strategy of wind energy developments in both Taiwan and Japan were surveyed from 1990 to present [11, 14, 15, 16]. Both Taiwan and Japan have collected the basic data and assessed wind power potential in the early 1990s. In the middle 1990s, the New Energy and Industrial Technology Development Organization (NEDO) commenced the field testing and data gathering in Japan. Meantime, two projects including the regional new energy promotion business and new energy companies support measures were implemented. In the late 1990s, Taiwan formulated the Environmental Impact Assessment Law to protect environment and to reduce the inappropriate influence by the development activities. In some development activities, the usage of renewable energy was required. In the early 2000s, Taiwan formulated the Renewable Energy Development (RED) Act to promote the application of renewable energy, to increase the diversification of energy, to improve the quantity of environment, to impel the development of related industries, and to keep the sustainable development of countries. At the same time, the government began to reward the wind power generation system settings of folk investment. Japan formulated the Special Measures Law Concerning the Use of New Energy by Electric Utilities or Renewables Portfolio Standards Law (hereinafter referred to as RPS Law) in the early 2000s. The RPS Law

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obligated the electric utilities to use a certain amount of new energy towards the aim of promoting the new energy electricity. In the early 2100s, Taiwan implemented the offshore wind power system demonstration incentives project to encourage the installation of offshore wind power system. Subsequently, Taiwan launched the thousand wind turbines project in 2013 and formulated the Greenhouse Gas Reduction and Management Act (GGRM) in 2015. The GGRM Act is to manage and reduce the release of greenhouse gas, to implement the environmental justice, to take the responsibility to protect the global environment and to ensure that national sustainable development in response to global climate change. Japan implemented Feed-in Tariffs policy to set prices for the renewable power to compensate electric utilities for the higher cost of producing new energy electricity. The evolution and progress for policy and promotion strategy of wind energy developments in both Taiwan and Japan are summarized in Figure 7. According to the results of AHP, 5 sub-criteria were ranked most importantly. For the first two criteria of “prior implementation of items with mature techniques and economic benefits” and “the government’s active development of green energy industry,” both Taiwan and Japan have collected the basic data and assessed wind power potential in the early 1990s. In the early 2000s, Taiwan formulated the RED Act to promote the renewable energy. Taiwan government also began to reward the wind power generation system settings of folk investment. Japan formulated the RPS Law in the early 2000s to obligate the electric utilities to use a certain amount of new energy towards the aim of promoting the new energy electricity. In addition, Japan implemented Feed-in Tariffs policy to set prices for the renewable power. It revealed that the actions of governments and viewpoints of practitioners from environmental organizations were consistent. By studying the survey results, the consistent common viewpoints and actions were summarized to serve as a reference to promote the wind energy in the future.

CONCLUSION In this study, the policy and promotion strategy of wind energy developments between Taiwan and Japan were surveyed and compared from 1990 to present.

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2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990

Basic data

Wind conditions entire country

J

Partial predict wind model The wind power of assessment and research

T

Cooperation

J

NEDO field testing and data gathering

T

Construction allowance research Provided

J



Development of wind power assessment Regional new energy promotion business New energy companies support measures

T

Reward wind power generation system settings of folk Investment Offshore wind power system demonstration incentives Perform RPS act Feed-in Tariffs

J

Revised building basic act

Law, Policy

Natural Parks Law correction Environmental Impact Assessment Law Noise Control Act

T

Renewable Energy Development Act Thousand wind turbines project Greenhouse Gas Reduction and Management Act

J

Japan

T

Taiwan

Figure 7. The evolution and progress for policy and promotion strategy of wind energy developments in both Taiwan and Japan.

The results showed that the wind power increased significantly in the past ten years. The CCWE, WPG, and the ratio of WPG to TPG for Taiwan in 2014 gave on 26.5, 16.5, and 14.4 times than those in 2005. The CCWE, WPG, and the ratio of WPG to TPG for Japan in 2014 gave on 2.7, 2.7, and 2.9 times than those in 2005. Besides, an AHP structure was suggested to aid decision makers for prioritizing and selecting policy and promotion strategy of wind energy developments.

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Both Taiwan and Japan have collected the basic data and assessed wind power potential in the early 1990s. In the early 2000s, Taiwan formulated the RED Act and rewarded the wind power generation system settings of folk investment to promote the renewable energy. Japan implemented the RPS Law and Feed-in Tariffs policy towards the aim of promoting the new energy electricity. By studying the survey results, the consistent common viewpoints and actions could serve as a reference to promote the wind energy in the future.

ACKNOWLEDGMENTS The authors are grateful to the Ministry of Science and Technology of R.O.C. for financial support under the grant number MOST 104-2621-M-142001.

REFERENCES [1]

W.P. Cunningham and M.A. Cunningham, Principles of Environmental Science. Inquiry & Applications. McGraw-Hill, New York (2012). [2] Y.T. Tung and T.Y. Pai, CLEAN-Soil Air Water, 43, 627 (2015). [3] Y.T. Tung, T.Y. Pai, S.H. Lin, C.H. Chih, H.Y. Lee, H.W. Hsu, Z.D. Tong, H.F. Lu and L.H. Shih, Procedia Environ. Sci., 20, 526 (2014). [4] Y.T. Tung, T.Y. Pai, J.J. Yu, M.S. Lin, P.Y. Yang, L.H. Shih and J.W. Adv. Mat. Res., 1008-1009, 3 (2014). [5] Y.T. Tung, T.Y. Pai, Y.C. Kang, Y.P. Chen, T.C. Huang, W.J. Lai and H.Y. Lee, Adv. Mat. Res., 1008-1009, 89 (2014). [6] Y.T. Tung, T.Y. Pai, I.T. Li, T.Y. Ou, C.P. Lin, Y.Z. Jiang and J.W. Adv. Mat. Res., 1008-1009, 133 (2014). [7] Y.T. Tung, T.Y. Pai, H.Y. Lee, Y.Z. Jiang, and L.Y. Chan, Adv. Mat. Res., 1044-1045, 1737 (2014). [8] Y.T. Tung, T.Y. Pai, L.H. Shih, S.W. Fan, W.C. Lin and T.H. Lu, Adv. Mat. Res., 1044-1045, 1872 (2014). [9] Bureau of Energy, Taiwan Energy Statistics Yearbook. Bureau of Energy, Taipei (2014). [10] Directorate - General of Budget, Accounting and Statistics, National Accounts Yearbook, Taipei (2014).

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[11] Federation of Electric Power Companies of Japan, Electric Power Report. Federation of Electric Power Companies of Japan, Tokyo (2015). [12] T.L. Saaty, The Analytical Hierarchy Process. McGraw Hill, New York (1980). [13] T.L. Saaty, Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process. RWS Publications, PA (1994). [14] Council for Economic Planning and Development, Adaptation Strategy to Climate Change in Taiwan. Council for Economic Planning and Development, Taipei (2014). [15] Institute for Sustainable Energy Policies, Renewables Japan Status Report 2014 – Executive Summary. Institute for Sustainable Energy Policies, Tokyo (2014). [16] E. Mizuno Overview of wind energy policy and development in Japan. Renew. Sust. Energ. Rev., 40, 999 (2014).

In: Wind Energy Editor: Desiree Fleming

ISBN: 978-1-63484-229-7 © 2016 Nova Science Publishers, Inc.

Chapter 4

WIND ENERGY INTEGRATION INTO THE SOUTH AFRICAN GRID: PROSPECTS AND CHALLENGES Komla Agbenyo Folly Department of Electrical Engineering, University of Cape Town, Rondebosch, Cape Town, South Africa

ABSTRACT In the last few years, the South African economy has grown considerably which led to a dramatic increase in load demand without a corresponding increase in the available power generation. A lack of sufficient and reliable electricity generation has been plaguing the South African’s economy, while the heavy reliance on fossil fuel for power generation continues to contribute not only to increasing emissions of greenhouse gases but also the costs of electricity. Reserve margins continued to be eroded and, as a result the country is experiencing rolling blackouts. Globally, the depletion and increase in fossil fuel prices, climate change and environmental pollution, unprecedented growth in energy insecurity, etc., have all contributed to the interest in renewable energy sources. Furthermore, the long term cost of non-renewable generation are projected to increase in subsequent years due to fast economic growth in the emerging nations, increasing electricity demand, depletion of the stocked non-renewable resources, etc. As a result, it is anticipated that in the next 20 years, a high portion of the South Africa electricity generation would come from renewable energy. A transition

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Komla Agbenyo Folly from non-renewable energy to renewable energy will promote public awareness on energy saving, as well as building a low carbon society. Renewable energy sources such as solar, wind, etc., have enormous potentials in contributing to the South African’s electricity portfolio and security, enhancing her social and economic growth, reducing the total dependency on fossil fuel power generation, as well as mitigating the increasing emissions of greenhouse gases. However, the integration of renewable energy sources such as wind, solar, etc., into the grid will bring new challenges. For instance, in the case of wind power, what would be the acceptable level of the penetration without compromising the stability and the reliability of the system? In this chapter, we will look at the current status of wind power in South Africa, prospects for growth in integrating wind energy into the South African grid and discuss possible challenges that may arise due to high penetration of wind power and outline some possible solutions.

INTRODUCTION Energy derived from wind has been used since antiquity by mankind to pump water, grind grain, etc. For thousands of years, wind energy was used in windmills and watermills to produce mechanical power [1-2]. In recent years, wind energy has become one of the most seasoned, efficient and economically very competitive renewable energy [3]. Wind energy is the least cost option when adding new generation capacity to the grid. It is an excellent energy source since it is widely available and does not produce pollution during generation [3, 4]. Wind energy is playing an important and growing role in modern energy supply. The world’s wind capacity has doubled approximately every 3 years during the last decade of the 20th century. The global total cumulative installed capacity at the end of 2014 was 369.6 GW, representing a cumulative market growth of more than16%, which is lower than the average growth rate over the last 10 years (2005-2014) of almost 23% [5]. According to the global wind report [6], 2014 was a very good year for wind industry as annual installations reached more than 50 GW for the first time. This is a sharp rise compared to 2013 when the global installed capacity was just over 35.6 GW. The previous record was set in 2012 when over 45 GW of new capacity was installed globally [6]. According to the IEA New Policies scenario projections, annual wind energy markets will increase gradually until 2016, and then shrink to just under 40 GW per year by the end of the decade. Under this scenario, the cumulative installed capacity would reach 611 GW by 2020 and 964 GW by 2030 [5].

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Currently, the largest overall market for wind power is China. In 2014, China reached more than 100 GW of total installed capacity, adding another milestone to its already exceptional history of renewable energy development since 2005 [5, 6]. In Asia, India has the second highest installed wind capacity of 22.465GW after China, followed by Japan (2.789GW). Globally, in terms of wind market, Asia is ranked first, Europe is in second place followed by North America. In Europe, Germany still leading the wind market with a total installed capacity of 39.165 GW, followed by Spain (22.987 GW) and the UK (12.44GW). In North America, the USA is leading the wind market with a total installed capacity of 65.879 GW, followed by Canada (9.964GW) and Mexico (2.551 GW). In Africa and Middle East, Morocco is leading the wind market with a total installed capacity of 7.87 GW, followed by Egypt (6.10GW) and South Africa (5.70GW). While several developed countries have exploited or are exploiting their wind energy resources to their full potentials, many developing countries have enormous renewable energy resources which remain mostly untapped. This is the case in Sub-Saharan Africa where more than 600 million of people do not have access to electricity [7-8]. In the last few years, there has been an increase pressure on Sub-Saharan countries to electrify the deprived (rural) areas. Efforts to promote electrification in Sub-Saharan is gaining momentum although the high population growth is still a challenge. One of the alternative solutions to the electric power crisis in Sub- Saharan Africa would be the use of mini-grid based Distributed Generator (DG) [7]. DG is defined as an integrated or standalone use of small modular electricity generation resources by utility customers and/or third parties in applications that benefit the electric system, specific end-use customers or both. DG is predicted to play an increasing role in the electric power system of the future, in particular, DG based on renewable energy [7]. This is particularly true for the South African electricity supply which has been plagued in recent years with a lack of sufficient and reliable electricity generation. This has resulted in rolling blackouts in 2008 and more recently in 2014 and the first half of 2015 [17]. Historically, South Africa has been dependent on cheap coal for approximately 90% of its electricity generation. The heavy reliance on fossil fuel energy generation by the growing South African economy has contributed immensely to increasing emissions of greenhouse gases. South Africa is the 12th largest emitter of carbon dioxide (CO2) in the world. It is responsible for nearly half of the CO2 emission for the entire African continent and for about 1.6% of global emissions [9]. The energy sector, including electricity generation, petroleum refining and transportation is responsible for more than 85% of the

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country’s emissions, with the largest contribution coming from the power sector [9]. It is estimated that the South African power utility Eskom‘s carbon dioxide was approximately 228 million tons in 2013, making Eskom the largest emitter of greenhouse gases on the African continent. Eskom, which currently generates 95% of the country’s electricity, is facing increasing challenges in managing and improving the reliability and the security of the South African grid. Since the 2008 rolling blackouts, the government has pledged to reduce the country’s greenhouse gas emissions by 34% by 2020 and 42% by 2025 [20]. As a result, it is anticipated that in the next 20 years, a high portion of the South Africa electricity generation would come from renewable energy. A transition from non-renewable energy to renewable energy will promote public awareness on energy saving, as well as building a low carbon society. Renewable energy sources such as wind, solar, etc., have enormous potentials in contributing to the nation’s electricity portfolio and security, enhancing her social and economic growth, reducing the total dependency on fossil fuel power generation, as well as mitigating the increasing emissions of greenhouse gases. In particular, the cost of electricity generation using wind energy has fallen by more than 80% [4]. Wind energy has many advantages over other energy sources. These include very quick break-even time, no emissions produced during operation. However, the integration of wind energy into power systems will bring new challenges. Several issues related to the penetration of wind energy need to be investigated. For instance, what would be the acceptable level of the penetration of wind generator without compromising the stability and the security of the system? If wind energy is used to support the grid how this can be achieved in a safe and reliable manner? This chapter will look at the current status of wind energy in South Africa, prospects for growth, the integration of wind energy into the South African grid and possible challenges and suggest some solutions to these challenges.

WIND ENERGY Wind is a form of solar energy. The uneven heating of the atmosphere by the sun, the irregularities of the earth’s surface, and rotation of the earth contribute to the creation of winds [10-12, 24]. Wind energy production depends primarily on wind speed and how the winds are changing during the day, a season, a year or even over decades. Wind energy is one of the most affordable forms of electricity today [11]. Wind energy, like solar energy, is a

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free renewable energy source and will never run out. Currently, the technology available to harness the wind energy can be regarded as mature and reliable [13-15]. It is beyond doubt that the wind industry has made considerable progress, even if the question as to whether wind energy would still be competitive without promotional support is still an open one. The first commercial wind energy converters entered service back in the 1980s, although the wind energy boom as such did not begin until the mid 1990s, when the total installed wind generation capacity in the world was only 5,000 MW. Since then, the installed capacity has increased at double-digit rates of annual growth [5-6]. Average nameplate capacity, turbine hub height and rotor diameter have all increased substantially over the last decade, enabling wind project developers to economically build projects on lowerwind-speed sites [4-6]. Projects in high-wind-resource regions are seeing a boost in capacity factors because of improved turbine performance [18, 19], [22]. The ‘capacity factor’ of a wind turbine or a wind farm refers to the percentage of the nameplate capacity that a turbine will deliver in terms of electricity generation over the course of a year. This is primarily governed by the wind resources in the particular location, but is also affected by the efficiency of the turbine, its suitability for the particular location, the reliability of the turbine, how well the wind project is managed, and whether or not it is subjected to curtailment by the grid operator. Average capacity factors globally today are about 28%, but vary widely from region to region, and are generally increasing with rapid new developments in very windy locations [4]. In the early 2011, the average cost of installed per kW capacity of wind power was about $2,230. In 2013, the average cost of installed per kW has decreased to $1,630 [23]. According to DoE report, wind turbine prices have fallen 20% to 40% from their peak in 2008, and these declines are driving project costs down [18, 23]. It is expected that the cost of installed per kW capacity will continue to drop. Approximately 75% of the total cost of a wind farm is related to upfront costs such as the cost of the turbine, foundation, electrical equipment, grid-connection and so on. Fluctuating fuel costs have no significant impact on wind power generation costs. A wind turbine is capitalintensive compared to conventional fossil fuel fired technologies such as a natural gas power plant, where as much as 40-70% of costs are related to fuel and operations and maintenance. Generally speaking, there are economies of scale in the construction of wind farms, both in terms of the total size of the wind farms (the number of turbines sharing a common substation and sharing development and construction costs) – and in terms of the size of turbines. Larger turbines generally have comparatively lower installation costs per

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swept rotor areas. The advantages of mass production have been further boosted by considerable increases in the efficiency of turbines (greater hub height, larger rotor diameter, etc.), which have improved the economics of wind energy [4]. The amount of energy that can be extracted from the wind depends on its speed. The higher the wind speed, the more energy can be harnessed to generate electricity on a large scale. However, this requires large tracts of land to install enough wind turbines or generators. There are now turbines on the market with a rated output of up to 6 MW [4]. This trend further illustrates that the growth market in the wind industry is mainly seen in electricity generation and grid feed-in tariffs. According to [18], “in 2013, utility Xcel Energy (in the USA) proposed adding 550 megawatts of wind capacity to its system not due to environmental motivations or state renewable-energy mandates, but because new wind power was the cheapest supply option from a list that included gas combined-cycle …” The wind energy potential in many developing and emerging countries is substantial. In many of these locations, generating electricity from wind energy presents an economically viable alternative to the use of conventional fossil energy sources such as coal or diesel [7, 16]. In comparison to fossilfueled power stations, wind energy can now be cost-effective in many places, as well as being non-polluting and reducing dependence on imports of fossil fuels. Wind energy has many advantages over other energy sources. These include easy expansion of wind farms, very quick break-even time, no emissions produced during operation. Also, wind energy contributes to the power supply diversification, and the development of local resources in terms of labour, capital and materials, etc. [9]. In addition, the wind industry creates a large number of skilled, semi-skilled jobs [7, 9]. The main drawback of wind energy is the intermittence and mismatch with power demand. Other disadvantages include acoustic noise emission, negative visual impact on landscapes and electromagnetic interference with radio, TV and radar signals [7].

WIND TURBINE Wind turbines, like aircraft propeller blades, turn in the moving air and power an electric generator that supplies an electric current. Simply stated, a wind turbine is the opposite of a fan. Instead of using electricity to make wind, like a fan, wind turbines use wind to make electricity. The wind turns the blades, which spin a shaft, which connects to a generator and makes electricity

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[4]. Early turbines were fairly small in size (50-100 kW, 15-20m diameter), but the size of commercial wind turbines has steadily increased. “The rated output, rotor diameter and average height of wind turbines have steadily increased over the years. While the average size of turbines varies substantially by country and region, the average turbine installed in 2013 was 1.93 MW, against an average of 1.34 MW for all currently operating turbines worldwide, continuing the steady increase since the industry began. This trend is expected to continue as larger and larger machines are developed for the offshore industry, and larger and more efficient turbines are developed to extract the most energy from new sites as well as for repowering old sites, many of whose turbines are nearing their design lifetimes of 20 years” [5, 6]. Modern wind turbines fall into two basic groups: the horizontal-axis design, like the traditional farm windmills used for pumping water, and the vertical-axis design, like the eggbeater-style Darrieus model, named after its French inventor [24]. Most large modern wind turbines are horizontal-axis turbines as shown in Figure 1. The Horizontal turbine components include: (a) blade or rotor, which converts the energy in the wind to rotational shaft energy; (b) a drive train, usually including a gearbox and a generator; (c) a tower that supports the rotor and drive train; and other equipment, including controls, electrical cables, ground support equipment, and interconnection equipment [24].

Figure 1. Horizontal axis wind turbines [28].

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Wind turbines are available in a variety of sizes, and therefore power ratings. The largest machine has blades that span more than the length of a football field, stands 20 building stories high, and produces enough electricity to power 1,400 homes. A small home-sized wind machine has rotors between 2.4 m and 7.6 m in diameter and stands upwards of 9m and can supply the power needs of an all-electric home or small business [4]. In wind electric systems, the rotor is coupled via a gearing or speed control system to a generator, which produces electricity [24]. Several wind generator technologies such as fixed-speed (i.e., Squirrel Cage Induction Generator-SCIG), and variable speed (i.e., Doubly-Fed Induction GeneratorDFIG, and Converter Driven Synchronous Generator-CDSG) are in use today [22]. The Squirrel cage induction generator (SCIG) was used in every wind turbine at the beginning of the development of wind energy conversion systems. The generator is directly connected to the grid through a step up two winding transformer. The gearbox is connected between the wind turbine and the induction generator [23-26]. The generator speed is determined by the grid frequency [27]. The maximum power that can be generated is limited by the stall control of the turbine blade [22, 26, 27]. SCIG draws reactive power from the grid for magnetization and behaves like a conventional asynchronous (induction) motor during system contingency. Capacitor banks are usually connected in parallel with the generator to limit the amount of reactive power being drawn from the grid [23]. The SCIG is the oldest technology and in general is unable to meet the grid code requirements in terms of participating in voltage control [22, 28]. The SCIG schematic diagram is shown in Figure 2 [26].

Figure 2. Squirrel Cage Induction Generator (SCIG) [22].

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Doubly-Fed Induction Generator (DFIG) is arguably the most used generator for wind turbine generating units today [22]. Similarly, to SCIG it has a gearbox connected between the turbine rotor and the generator. The generator is connected to the grid through a three-winding transformer [26], [27]. The stator winding of the generator is connected directly to the power grid. The rotor winding is connected via frequency converter and the accompanying power transformer as shown in Figure 3. One of the advantages of the DFIG is that real power and reactive power can be controlled separately [22, 23]. With DFIG, sub-synchronous and super-synchronous operations are possible. At low wind speeds, the rotor speed is reduced, this cause subsynchronous operation, in this mode a motoring torque is produced and the generator absorbs power from the grid. On the other hand, in supersynchronous mode, the rotor speed is greater than the synchronous speed. The machine will supply power to the grid [2]. However, at reasonable wind speeds, the generator operates at synchronous speed. Therefore, in DFIG rotational speeds determine whether the generated power is delivered to grid or not. Converter Driven Synchronous Generator (CDSG) is generally used in large conventional power plants and can be electrically excited or made of permanent magnet synchronous generator. Figure 4 shows a schematic diagram of a CDSG. It has a gearbox connected between the turbine rotor and generator [26].

Figure 3. Double Fed Induction Generator (DFIG) [22].

The generator is connected to the grid similarly to the DFIG; it is connected through a back-to-back full converter. The converter can be either voltage source or diode rectifier to enable variable speed operation [22]. The electrical frequency of the generator may vary as the wind speed changes but

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the network frequency is not influenced by the wind speed, this is because the converter completely decouples the generator from the network. Larger, more sophisticated wind energy converters are used to feed power into the grid. For small turbines, the electricity generated can be used to charge batteries or used directly. Small turbines intended for battery charging have a turbine diameter of between 0.5 –5 m and a output power of 0.5 – 2 kW. Installed costs vary between US$ 4 – 10 per watt. Medium sized turbines are used in small independent grids in hybrid with a diesel or PV generator. These turbines have diameters of between 5-30 m and a power output of 10- 250 kW. Large wind turbines are normally grid connected. This category includes diameters of 3090 m and power outputs 0.5 – 3 MW [4].

Figure 4. Converter Driven Synchronous Generator [22].

Figure 5. Wind turbine Power curve [28].

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The most common approach utilized for the estimation of wind power outputs of a wind turbine is based on the use of turbine power curve as shown in Figure 5. The turbine power curve is a technical datasheet which illustrates the variation of the power outputs of the wind turbine as a function of the wind speed, ranging between the calm wind speed and the cut-out speeds of a given wind turbine. The use of turbine power curve for wind energy analysis is often been considered as a basic guide for small scale energy generation. However, this method does not take into consideration the varying site atmospheric conditions as this is not accurate for large (utility) scale energy application. The use of this approach often leads to overestimation of the wind energy potential. As a result, many wind studies have proposed alternative methods to estimate the wind energy at a given wind site. Some of the techniques adopted for estimating the energy generations of a wind turbine at a given site are based on the relationship that exists between the site atmospheric conditions, turbine parameters, surface roughness and the power outputs of a wind turbine. Wind generators are designed to work over a given range of wind speeds, usually 4– 12m/s. This means that the technology can only be used in areas with sufficient winds.

ELECTRICITY SUPPLY IN SOUTH AFRICA Historically, South Africa has been dependent on cheap coal for approximately 90% of its electricity generation. South Africa is the world’s fifth largest producer of coal [9]. Electricity supply in South Africa is dominated by the power utility Eskom, which currently generates 95% of the country’s electricity. Eskom network is part of the Southern African Power Pool (SAPP), which connects Eskom to the networks of other utilities in neighboring countries. Eskom generates over 40% of all electricity on the African continent making it one of the ten largest power utilities in the world. Eskom generates, transmits and distributes electricity to industrial, mining, commercial, agricultural and residential customers together with redistributors [29]. The power-generation pool comprises of the following sources, namely, coal, nuclear, hydro with pump storage, gas, wind and solar. South Africa has the world’s seventh largest coal reserves, so it is no surprise that about 69% of South Africa’s primary energy comes from coal, followed by crude oil and solid biomass and waste. South Africa’s energy balance also includes relatively small shares of natural gas, nuclear, and hydroelectricity as can be

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seen in Figure 6 [35]. As of June of 2015, Eskom’s total generating capacity was 45656MW. Eskom’s generation type is as follows: coal 82.7%, nuclear 4.25%, wind 0.23%, hydro 7.3%, gas 5.3% and solar 0.22% [29]. The power stations can be divided into base-load power generations and peaking power generations. Base-load power stations are characterized by power stations that run continuously; examples are Kendal, Koeberg, Matla and Majuba, to name a few. Peak power stations refer to generation stations that are used only during high consumption periods, examples are Ankerlig, Gourikwa, and Palmiet [29]. The base-load stations contributes up to 90% of the generation source, compared to peaking power stations that make up only 10% of the generation sources [2]. In South Africa, coal and nuclear dominate the baseload supply power stations, whereas wind, hydro (renewable energy), gas and solar constitute the peaking contingent of the generation sources. The heavy reliance on fossil fuel energy generation by the growing South African economy has contributed immensely to increasing emissions of greenhouse gases. South Africa is the 12th largest emitter of carbon dioxide (CO2) in the world. It is responsible for nearly half of the CO2 emission for the entire Africa continent and for about 1.6% of global emissions [9]. The energy sector, including electricity generation, petroleum refining and transportation is responsible for more than 85% of the country’s emissions, with the largest contribution coming from the power sector [9]. It was estimated that the total South African greenhouse gas emission in 2010 amounted to 579 million tonnes of carbon dioxide (CO2)-equivalent, excluding land use. This represents approximately, a 25% increase since 2000 and 50% above the 1994 levels [9]. Currently, South Africa is facing an energy crisis: the existing production capacity cannot meet the growing demand for electricity. However, the crisis has been in the making for the past three to four decades when the government of the day commenced the roll out of a massive power investment programme within Eskom. The historic role that the mining industry played in the establishment of electricity supply industry fundamentally influenced the management culture of Eskom up to the early 1980. Demand growth was overestimated and the construction of new coal fired stations resulted in an over capacity of power generation over the following 20 years [21]. As the cost of this investment was paid off, it culminated in South Africa enjoying the cheapest electricity prices in the world [33]. However, with virtually no new investment in new and replacement power generation over the next 20 years, the country was lulled into a false sense of security. Although South Africa’s 1998 energy white paper stated that up to 30% of the country’s generation could come from independent power producers (IPPs), the appropriate

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legislation was never enacted and no private generation was incorporated. In 2004, the power reserve margins dropped sharply as economic growth accelerated. The reserve margins continue to drop below 10%, and this led to rolling blackouts (i.e., load shedding) in 2007-2008 (see Figure 7). South African suddenly realized that the era of cheap and reliable electricity, which was of particular benefit to the country’s large energy intensive users, was over.

Figure 6. Total Primary Energy Supply in South Africa, 2012 [35].

After the 2008 rolling blackouts, the government called for expanded demand side management (DSM), where consumers were persuaded to use more efficient energy appliances and technology, or to switch to other form of energy supply (i.e., gas, solar water heating, etc.). Demand market participation scheme was introduced where industry is being paid for switching off machines at certain time of the day. Certain companies were compensated for self-generation. The government and Eskom realized the importance of generation mix, and diversification of energy sources was encouraged. It was decided to build new base load and peaking plants and return some old plants to service. In 2010, the World Bank granted the country a $3 billion loan for Medupi. However, Medupi took longer to build than expected. In July 2013, Eskom stated that construction of the coal-fired

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Medupi power station had fallen behind schedule, and the first of six 800 MW units at the site are not likely to begin operations until the second half of 2014 instead of December 2013. However, Medupi came on line only in the second half of 2015. In the meantime, between 2014 and the first half of 2015, there have been new waves of rolling blackouts. It is estimated that these blackouts cost the country’s economy between $1.7 billion (R20 billion) and $6.8 billion (R80.1 billion) a month [31]. Since the first rolling blackout in 2008, electricity price has been rising sharply. It is estimated that from 2007 to 2015, electricity tariffs increased by 300%, while inflation over this period was 45% [33]. In 2015, Eskom requested a further 10% increase in the price of electricity (on top of the already approved 12% increase) from the South Africa’s National Energy Regulator (NERSA) which oversees electricity matters in the country, including issues related to pricing and the licensing of electricity generation, transmission and distribution.

Figure 7. Electricity load demand increase and reserve margins [29].

The renewable energy white paper published in 2003 set a target of 10,000 GWh of electricity from renewable energy by 2013. This target was met late in 2012 [9]. In 2008, South Africa’s announced the renewable energy feed-in tariffs (REFITs) to attract independent renewable generation. In 2009, NERSA approved the REFIT policy. However, the REFIT was suddenly abandoned for a competitive bidding process for renewable energy, known as the Renewable Energy Independent Power Producer Procurement (REIPPP) program in 2011. This program has been very successful according to the government [30], [36].

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In 2010, the Department of Energy (DOE) promulgated an Integrated Resource Plan (IRP) which envisaged the doubling of power generation in South Africa by 2030 and significantly reducing the country’s greenhouse gas emissions by 34% by 2020 [20]. The IRP set to allocate 42% (17800 MW) of the new build to renewable power by 2025, the rest comprising coal, nuclear, gas and diesel fired power generation [9]. As a result, it is anticipated that in the next 20 years, a high portion of the South Africa electricity generation would come from renewable energy. A transition from non-renewable energy to renewable energy will promote public awareness on energy saving, as well as building a low carbon society. There is growing interest in the country's gas resources, with plans to develop an offshore gas field. As such, it is expect that gas could have a greater role to play in the long term. Other factors that will shape the development of South Africa's electricity market include speculation over a carbon tax, which could be introduced by 2015, and would affect the profitability of fossil-fuel electricity generation. Nuclear power is also included in the future generation mix in South Africa. It is expected that this will attract investment into the country by the international community [30].

WIND ENERGY POTENTIALS IN SOUTH AFRICA Renewable energy sources such as the solar, wind, and ocean wave have enormous potentials in contributing to the nation’s electricity portfolio and security, enhancing her social and economic growth, reducing the total dependency on non-renewable (fossil fuel) energy generation, as well as mitigating the increasing emissions of greenhouse gases. South Africa like the rest of the Sub-Saharan Africa has a huge potential for a number of different renewable energy technologies including: wind, solar, biomass, etc. The greatest potential for wind power exists in West Africa [11]. According to the World Bank, areas with promising wind potential include Ethiopia, Ghana, and Kenya. Several Sub-Saharan African countries are now taking advantage of this potential. For example, A 150 MW and a 52 MW wind farms were recently built in northern Senegal and Ethiopia, respectively. According to [11], “wind energy commitments in Kenya skyrocketed from zero in 2011 to $1.1 billion in 2012.” South Africa’s wind resource is exceptional. It is one of 15 countries with the best wind resources in Africa [10]. Research on wind power potential for South Africa estimated that wind power potential is generally good along the entire coast with localised areas, such as the coastal promontories, where

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potential is very good, i.e., mean annual speeds are above 6 m/s and power exceeds 200 W/m2. Moderate wind power potential areas include the Eastern Highveld Plateau, Bushmanland, the Drakensberg foothills in the Eastern Cape and KwaZulu-Natal. Areas with low wind power potential include the folded mountain belt (vast region of very complex and diverse terrain), the Western and Southern Highveld Plateau, the Bushveld basin, the Lowveld, the Northern Plateau, the Limpopo basin, Kalahari basin, the Cape Middleveld and the KwaZulu-Natal interior (see Figure 8) [10].

Figure 8. Wind power potential in South Africa [31].

Over the years, there have been several wind resource studies done in South Africa, these include the study by Roseanne Diab in 1995 which found a 4% potential, the 2001 study by Eskom/CSIR, the 2008 Mesoscale Wind Atlas which find a 35% potential (see Figure 9) and a recent initiative by the Department of Mineral and Energy (DME) to develop the first Wind Atlas for South Africa (WASA) that is still ongoing [10]. A mesoscale climate model with a 5km x 5km resolution had been created, using 30 years’ worth of meteorological global reanalysis data, in order to generate the wind atlas.

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Figure 9. 2008 Mesoscale wind atlas [34].

This wind atlas will help identify wind energy development sites in three provinces as the country pushes to increase its use of renewable energy sources [34]. Wind as an energy source is only practical in areas that have strong and steady winds. Table 1 lists the names of some of the wind farms in South Africa that are currently operating or are under constructions [36, 37]. According to [39], “Sere wind farm achieve its full commercial operation in June 2015. It is Eskom’s first large scale renewable project which will contribute to saving nearly 6 million tons of greenhouse gas emissions over it 20 years’ expected operating life with average annual energy production of about 298,000 MWh, enough to supply about 124,000 homes” [39]. The wind industry in South Africa is in a very rapid growth phase. The development of the wind industry has taken place within a relatively short period of about three years, placing South Africa amongst the leading new wind markets globally [10, 34]. The wind industry and its supply chain are becoming firmly established with several large wind farms now fully operational, and many more under construction. The cost of wind power is already competitive and it is expected that it will reach grid parity by 2016/2017 [34].

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Komla Agbenyo Folly Table 1. List of some of the wind farms in South Africa Name

Amakhala Chaba Cookhouse Darling Dassiesklip Dorper Gouda Grassridge Hopefield Jeffrey Bay Khobab Kouga Red Cape Klipheuwel prototype Loeriesfontein 2 Longyuan Mulilo De Aar Maanhaarberg Longyuan Mulilo Green Energy De Aar 2 North Metrowind Van Stadens Nobelsfontaine Nojoli Noupoort Oyster Bay Red Cap-Gibson Bay Sere Tsitsikamma community West coast 1

Capacity (MW) 133.70 21.00 138.60 5.20 27.00 100.00 138.00 61.50 66.60 138.00 137.74 80.00 3.16 138.23 96.48 138.96 27.00 75.00 86.60 79.05 80.00 110.00 106.00 94.80 90.82

SOUTH AFRICAN GOVERNMENT WIND VISION The Energy White Paper of 1998 adopted the policy that South Africa needs to diversify its generation supply options. The White Paper on Renewable Energy of 2003 set the target for the country to achieve 10,000GWh of renewable energy by 2013 or about 4% of current generation capacity [9]. The South African Integrated Resources Plan 2010 (IRP 2010) outlines the proposed power generation mix for South Africa for 2010 to 2030 [20]. The IRP 2010 promulgated in March 2011 seeks to increase the overall contribution of new renewable energy generation to about 17,800MW by 2030

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(42% of new-build generation). It was indicated at the time that the IRP should be a “living plan” which would be revised by the Department of Energy (DoE) every two years i.e., an update was done in 2013 [20]. Since the promulgation of the Integrated Resource Plan (IRP) 2010-30, there have been a number of developments in the energy sector in South and Southern Africa. In addition the electricity demand outlook has changed markedly from that expected in 2010 [6]. In order to enable independent power producers to supply electricity into the grid, the Independent System and Market Operator (ISMO) legislation has been developed and has been taken through the legislative process of government. The South African Department of Energy (DoE) Vision 2014 is of a transformed and sustainable energy sector with universal access to modern energy carriers for all by 2014 (not yet achieved), while the aim of Vision 2025 is to improve the energy mix by including 30% clean energy by 2025. South Africa’s long term energy blue print, the Integrated Resource Plan (IRP), gives wind power a significant allocation, about 8,400 MW (later revised to 9.1 GW ) of new capacity in the period up to 2030. There are expectations that this can be exceeded by a wide margin [5-6, 34]. To attract independent renewable generation, South Africa’s renewable energy feed-in tariffs (REFITs) was first announced in 2008. NERSA approved the REFIT policy in 2009. However, in 2011, the REFIT was abandoned for a competitive bidding process for renewable energy, known as the Renewable Energy Independent Power Producer Procurement (REIPPP) program. REIPPPP projects are procured on a competitive tender basis with 70% of the scoring going to price and 30% to socio-economic factors. Up to this point all these projects have been licensed by NERSA. Wind projects under the REIPPPP sell electricity to the national utility on a 20 year Power Purchase Agreement backed by the national government, with dispatch priority [36]. This program has been very successful in channeling substantial private sector expertise and investment into grid-connected renewable energy in South Africa at competitive prices [30, 36]. According to [36], “To date, a total of 64 projects have been awarded to the private sector, and the first projects are already on line. Private sector investment totaling US$14 billion has been committed, and these projects will generate 3922 megawatt (MW) of renewable power. Prices have dropped over the three bidding phases with average solar photovoltaic (PV) tariffs decreasing by 68 percent and wind dropping by 42 percent, in nominal terms. Most impressively, these achievements all occurred over a two-and-a-half year

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period. Finally, there have been notable improvements in the economic development commitments, primarily benefiting rural communities. One investor characterized REIPPPP as “the most successful public-private partnership in Africa in the last 20 years.” The REIPPP program has already gone through four rounds of bidding. In the first round, there were 53 bids for 2128 MW of generating power. But only 28 preferred bidders were selected totaling 1416 MW of power for a total investment of approximately US$6 billion. In that round 634 MW has allocated to wind power [9]. During 2014, the first projects under the REIPPP Program were commissioned. Several large wind farms of up to 138 MW came online [36]. In the second round of bidding (2012), some changes were made to tighten the procurement process and increase competition. In addition, the amount of power to be acquired was reduced. In total, 79 bids for 3233 MW were received and19 bids were ultimately selected for a total of 2457 MW [36]. The total of power allocated to wind was 563 MW [9, 34]. In the third round of bidding (2013), 93 bids were received totaling 6023 MW. Only, 17 preferred bidders were selected for 1456 MW [34]. The total of power allocated to wind was 787 MW [9, 34]. From the first three rounds, government had procured around 4000 MW from over 60 projects. In the fourth round (2015), about 13 projects were selected, with a collective capacity of about 1121 MW [34, 37]. The total of power allocated to wind was 676 MW. As of 2015, just over 1500 MW of renewable energy from more than 30 projects were connected to the grid [37]. At present, the government is seeking an additional 1,800 MW from previously unsuccessful bidders in the first four rounds. Meanwhile, the original target set by the White Paper on Renewable Energy to achieve 10,000GWh (equivalent to 1667 MW) of renewable energy by 2013 was achieved in the second bidding round in late 2012 [9]. Many of the leading international turbine manufacturers were involved in partnership with local developers in the first four rounds [5-6]. The original structure of the REIPPP program entailed five bidding rounds, with the last and final round due for submission in May 2015. The procurement rules include a strong government ambition to create a high level of local content, with an incentive to boost employment and to support local communities [5]. In 2013, the price for wind energy dropped well below the cost of new coal power in South Africa. The average selling price of electricity was around ZAR 63 cent/kWh (EUR 4.8/USD 5.4 cent), compared to ZAR100 cent/kWh

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in round two and ZAR 74cent/kWh in round three. This corresponds to a decrease of about 45% compared to round 1. New coal based power is likely to cost ZAR 1.05/kWh (EUR 8 /USD 9 cent) if there is no cross-subsidised from existing plants [5-6]. The wind industry in the country has established itself as a major new infrastructure sector and is now worth of about ZAR 44 billion (EUR 3.4/USD 3.7bn) [36]. Steel and cement towers are now made locally and a local blade manufacturing facility is likely to be set up with an international partner in the next few years. In recent years, many associations and programmes such as the South African Wind Energy Association (SAWEA), and the South Africa wind energy programme (SAWEP) have been created to promote the sustainable use of commercial wind energy in South Africa [34]. According to [5], “South Africa is moving towards a large wind industry with a domestic installed capacity in excess of 5,000 MW within eight years. South Africa is evolving into the hub for manufacturing and development that the industry has been looking forward to for many years.”

INTEGRATION WIND POWER IN THE SOUTH AFRICAN GRID: CHALLENGES In less than four years, South Africa has already added a total of 4,322MW of renewable energy capacity. As the cost per kWh of wind energy continues to drop, it is expected that in the future, more wind energy will be connected into the South African grid. However, the integration of renewable energy sources such as wind power, solar power, etc., into the grid will bring new challenges. Key issues related to the reliability, adequacy, and security still need to be addressed. Wind power is different from conventional generation sources such coal, nuclear, etc. It cannot be dispatched on demand, and it is nearly impossible to forecast with precisison. Because of these characteristics, wind power presents a number of challenges for system operators. South Africa is currently facing a very serious energy crisis. Existing power generation infrastructure is not able to keep pace with the growing national demand. In addition, the methods of power delivery to consumers are outdated and extremely inefficient. The transition to renewable energy will result in a dependence on stochastic resources, including both generation and consumption. As the penetration of renewable power plants increases (in particular wind power plants), it becomes

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important for these plants to also participate in frequency, voltage and reactive power control. Several countries worldwide have grid codes requiring that Renewable Power Plants (RPPs) connected to the transmission or distribution systems have these capabilities. The application of wind turbine generators (WTGs) in modern wind power plants (WPPs) requires an understanding of different aspects related to the design and capabilities of the machines involved. Voltage control capabilities of wind turbine generators depend on the wind turbine type. The South African Renewable Power Plant (RPP) Grid Connection Code has defined the voltage and frequency of renewable power plants under normal and abnormal operating conditions [32, 41]. One of the main challenges facing South Africa is that most the utilityscale wind farms tend to be built far from the customers (load centers) because the best wind resources are typically remote from load centers. Therefore, large amount of power generated by the wind farms has to be transmitted over relatively long distances to the customers. However, given the ageing state of transmission and distribution infrastructure in South Africa, the transmission of bulk power over these lines may be difficult to achieve. In the past, power plants based on synchronous machines were used to maintain the voltage at the load centers. As these old plants are retired and displaced by wind generators based asynchronous machine, the ability to maintain adequate voltage at the load centers may be reduced [25]. Moreover, if the wind farms that use asynchronous generators and are not equipped with power electronics (this is the case of SCIG), they will generally draw a large amount of reactive power from the system during faults as the voltage falls, therefore instead of contributing to the reactive power needed into the faults, these generators will do the opposite [1]. This could challenge the grid’s reactive power reserves and the ability to maintain voltage stability [22, 25]. In the case of asynchronous generators connected to the grid via power electronic devices (i.e., DFIG), they may be able to provide the necessary dynamic reactive power to grid to support voltage under normal operating conditions and faulted conditions [22, 25]. However, they are not able to create the same level of voltage stiffness during deep grid disturbance as conventional generators. In addition, they do contribute very little to short-circuit strength [25]. Another problem with displacing the old conventional plants based synchronous generators with new wind farms based on asynchronous generators is that the inertia of the system will be reduced which could affect the ability of the system to regulate frequency [40]. With wind generation that is variable and somewhat unpredictable, the need for ancillary services will grow and thereby increasing their value.

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Studies have shown that when the penetration of wind generation into the grid is low (i.e., about 20%-30%) issues of intermittence of wind can be resolved easily using the system’s available reserve margins. However, under high penetration scenario, these issues cannot be underestimated. A sudden decrease in wind energy could have a devastating consequence on the stability of the system if there is no adequate reserve to cope with supply demand unbalance. One possibility to deal with supply unbalance could be to use energy storage to better manage variations in wind power. However, at present the size of energy storage is relatively small, and may not be able to provide the utility-scale energy required during emergency. Apart from pumped storage, large-scale energy storage of electricity has generally been too expensive or too demanding of specific site requirements. Another biggest challenge is “overgeneration” which occurs when total energy supply exceeds the system’s ability to absorb it. This issue can arise in a system that supplies substantial wind energy output during periods of low demand generally at night. This may cause operational problems and increase costs [42, 43]. Other problems are related to power quality issues such voltage dips which are due to the variability of the wind, harmonics which are due to the power electronics used in wind conversion and flicker which can be caused by a power oscillation at three times the blade turning speed [1]. It should be mentioned that these problems will be exasperated in weak grids.

THE WAY FOREWARD Renewable energy will contribute significantly to the production of electricity in the future. Integration of these highly variable, widely distributed resources will call for new approaches to power system operation and control. To achieve the objectives of Integrated Resource Plan (IRP) 2010 for electricity which set a target of 42% penetration of renewable energy into the grid by 2030, South Africa should continue strengthening the REIPPP program which has shown great success in the past. To cope with high penetration of renewable energy in the future, the current transmission and distribution infrastructure in South Africa should be upgraded. In addition, the efficiency of the whole grid needs to be improved and DSM implemented in a larger scale. With the abundant renewable energy resources, South Africa can improve access to electricity services by adapting smart grid technologies to meet the electricity demand of the future [40]. Smart grid has been proposed as a radical

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change to the design of electric power network that has the potential to greatly improve the efficiency, reliability, security, and interoperability of the electrical grid. The goals of smart grid include the establishment of advanced digital technologies (i.e., microprocessor-based measurement and control, communications, computing, and information systems), the enabling of greater integration of renewable energy sources and energy storages, and the promotion of economic growth. The introduction of smart meters will make it possible for energy suppliers to charge variable electric rates to users so that charges would reflect the large differences in cost of generating electricity during peak or off peak periods. Such capabilities allow load control switches to control large energy consuming devices such as hot water heaters so that they consume electricity when it is cheaper to produce. To fully realize the benefits of smart grid, the utility industry will need to integrate a vast number of smart device systems and overcome a number of technical issues. In particular, the introduction of micro-grid (a group of interconnected load and distributed energy resources with clearly defined electrical boundaries that acts as a single controllable entity with respect to grid) to maintain grid resiliency should take priority [7, 40]. This will improve the efficient and reduce the current technical and non-technical losses in the South African grid which is estimated to be above 15% (19% in the Southern African Development Community-SADAC) region [9]. The implementation of smart grid technology in South Africa should be accelerated. In addition, the lack of skilled manpower should be addressed and collaboration with other African countries should be encouraged. Strong interconnections with other African countries will facilitate the development of their enormous renewable energy sources – not only wind, but hydro, geothermal and solar, creating clean power for economic growth, energy security, and increased access to energy for the roughly 600 million Africans who currently do not have access to electricity.

CONCLUSION Wind energy is playing an important and growing role in modern energy supply. In recent years, wind energy has become one of the most seasoned, efficient and economically competitive renewable energy. Wind energy is the least cost option when adding new generation capacity to the grid. It is an excellent energy source since it is widely available and does not produce pollution during generation. After taking a decade for the first 10 MW of wind power to be installed, the South African wind industry commissioned more

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than 500 MW wind projects to the country’s electricity grid in 2014. Currently, the South African wind industry is looking to develop about 5 GW of wind power by 2019, of which 562 MW is under construction and a further 787 MW has reached financial close. Moreover, it is expected that more than 600 MW from the preferred bidders for the fourth round under the REIPPP program will be added to the existing pipeline. This is good news in a country which has been plagued by serious power shortages for years. It is evident that renewable energy will contribute significantly to the production of electricity in South Africa in the future as set out in the IRP 2010. So far, the REIPPP program has shown great success. This programme should be strengthened with the view of expanding it. However, the integration of highly variable, widely unpredictable wind energy will call for new approaches to planning and operation of the power grid. The current transmission and distribution infrastructure in South Africa cannot cope with a high penetration of renewable energy. There is a need to upgrade the transmission and distribution infrastructure and to look at new ways at improving the efficiency of the whole grid. Flexibility is the key requirement for planning and operating the power system with a large share of variable renewable energy. Flexibility can be provided by interconnections, storage, Demand Side Management (DSM) and flexible generation. The government should continue meeting the needs of a fast growing economy without compromising its commitment to sustainable development. There is a need to accelerate the implementation of smart grid technologies while at the same time focusing on human resource capacity development and the strengthening of institutions.

REFERENCES [1] Fox, B; Flynn, D; Bryans, L; Jenkins, N; Milborrow, D; O’Malley, M; Watson, R; Anaya-Lara, O. Wind Power Integration-connection and system operational aspects. IET. Power and Energy series, 50, (2007) [2] Overview, https://energypedia.info/wiki/Wind_Energy_-_Introduction [3] Wind Power, http://www.acciona.com/business-divisions/energy/windpower-energy. [4] Khan, I. “Initial Feasibility Studies of a Micro-grid Implementation.” Final year project, University of Cape Town 2015. [5] Global wind energy outlook, 2014 at http://www.gwec.net/wp-content/ uploads/2014/10/GWEO2014_WEB.pdf.

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[7]

[8] [9] [10] [11] [12] [13] [14] [15] [16]

[17]

[18]

[19] [20] [21] [22]

Komla Agbenyo Folly Global wind report: Annual market update, (2014). http: www.gwec. net/wpcontent/uploads/2015/03/GWEC_Global_Wind_2014_Report_ LR.pdf01. Ainah, PK; Folly, KA. “Development of Micro-grid in Sub-Saharan Africa: An Overview,” Accepted for publication in Int. Review of Electrical Eng., (2015). Word Energy Outlook, 2013 at http://www.africa.com/blog/africasrenewable-energy-potential/. REN21, “SADAC Renewable Energy and Energy Efficiency: Status Report,” Paris: RENI Secretary, (2015). Wind Atlas for South Africa (WASA), (2014) at http://www. wasaproject.info/docs/final_reports/WASAPSCreportMar2015.pdf. Davis, K. Africa’s renewable energy potential, at http:// www.africa. com/ blog/africas-renewable-energy-potential/. Freris, L; Infield, D. “Renewable energy in power systems. Wiley (2008) Jenkins, N; Allan, R; Crossley, P; Kirschen, D; Strbac, G. Embedded Generation. IET Power and Energy series, 30, (2008). Khennas, S; Dunnett, S; Piggott, H. Small wind systems for rural energy services. ITDG Publishing, 2003. Wagner, HJ; Mathu, J. Introduction to wind energy systems: Basics, Technology and operation. Springer-VerVlag Berlin Heidelberg, (2009). Louie, H; Dauenhauer, P; Wilson, M; Zomers, A; Mutale, J. Eternal Light. IEEE Power and Energy magazine, July/August. 2014 (Tokyo, Japan), 70-78. Unplugged, The Economist, 3rd Jan. 2015http:// www.economist.com/ news/middle-east-and-africa/21637396-rolling-power-cuts-are-frayingtempers-unplugged. Price of US Wind Power at ‘All-Time Low’ of 2.5 Cents per KilowattHour, Wind markets and policy, at http:// www.greentechmedia.com/ articles/read/Price-of-US-Wind-Power-at-All-Time-Low-of-2.5-CentsPer-Kilowatt-Hour. Wall Street Journal, Xcel Energy plans to grow wind power by 30 percent. July 30, 2013. Integrated resource plan for electricity 2010-2030 (updated report 2013) at http://www.doe-irp.co.za/content/IRP2010_updatea.pdf. The South African Department of Energy (DoE) at www.energy.gov.za. Seboka, LP; Folly, KA. Impact of Several small scale grid-connected wind generators on the distribution system. Clemson power system conference-PSC, 2014.

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[23] How low can wind energy go? 2.5¢ per Kilowatt-hour is just the beginning.http://cleantechnica.com/2014/08/23/cost-of-wind-energy-25per-mwh-and-falling/. [24] Wind energy basics at http://windeis.anl.gov/guide/basics/. [25] Manz, D; Walling, R; Miller, N; D’Aquila, R; Daryanian, B. The grid of the future, IEEE Power & energy Magazine, Vol. 12, No. 3, May/June (2014), 26-36. [26] Ban, D; Žarko, D; Maderčić, M; Čulig, Z; Petrinić, M; Tomičić, B; Študir, J. “Generator technology for wind turbines trends in application and in Croatia,” at http:// bib.irb.hr/datoteka/ 308666.5_ sovetuvanje. Drago_Ban2.Tekst_EN_R.pdf. [27] Belmans, R; Heylen, W. “Impact of wind generation in future grid,” Ph.D Thesis, Katholieke Univesietiet Leuven, (2005). [28] Folly, KA; Malapermal, S. Embedded generation, Eskom course, University of Cape Town, 2013. [29] Emmanuel, P. Investigation into transient stability of a nuclear power plant using Digsilent. MSc. dissertation, University of Cape Town, (2015). [30] Baker, L. Governing electricity in South Africa: wind, coal and power struggles. The Governance of Clean Development working paper series, (2011) at http://www.tyndall.ac.uk/sites/ default/files/gcd_working paper015.pdf. [31] Eskom blackouts cost South Africa R80 billion per month, Business tech, at http://businesstech.co.za/news/general/83429/eskom-blackoutscost-south-africa-r80-billion-per-month/. [32] Network planning guideline for embedded generator (steady state studies), Eskom distribution guide, part 1, 2013. [33] South Africa electricity pricing compared to the rest of the word. June 2015 at http://www.htxt.co.za/2015/06/26/south-africas-electricitypricing-compared-to-the-rest-of-the-world/. [34] Hagemann, K. South Africa’s wind power potential. SANEA Lecture series (2009) at http://www.sanea.org.za/CalendarOfEvents/2013/ SANEALecturesCT/Feb13/KilianHagemannG7RenewableEnergiesAnd SAWEA.pdf. [35] Energy in South Africa, From Wikipedia at https://en.wikipedia.org/ wiki/Energy_in_South_Africa. [36] Eberhard, A; Kolker, J. South Africa’s renewable energy IPP procurement program: Success factor and lessons. Public-Private Infrastructure Advisory Facility, (2014).

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[37] Enel green power confirms three SA wind projects as round 4 bids are selected, Daily News, at http://www.engineeringnews.co.za/article/enelgreen-power-confirms-three-sa-wind-projects-as-round-4-bids-areselected-2015-04-14. [38] List of Wind farms in South Africa, The free Wikipedia at https:// en.wikipedia.org/wiki/List_of_wind_farms_in_South_Africa. [39] Eskom’s Sere wind farm in South Africa financed by the AfDB and CIF now in full commercial operation, African development bank, (2015) at http://www.afdb.org/en/news-and-events/article/eskoms-sere-wind-farmin-south-africa-financed-by-the-afdb-and-cif-now-in-full-commercialoperation-14420/. [40] Folly, KA. “Challenge in implementing smart grid technology in Africa, African utility week, Cape Town, (2013) at www.african-utility-week. com. [41] Grid Connection Code for renewable Power plants (RPP) Connected to the Electricity Transmission System (TS) or Distribution Systems (DS) in South Africa. Version 2.8, July 2014 at http://www.nersa.org.za/ Admin/Document/Editor/file/Electricity/TechnicalStandards/South. [42] National conference of state legislatures, Integrating Wind Power (2009), at http://www.uwig.org/ncsl-wind_integration.pdf [43] Olson, A; Mahone, A; Hart, E; Hargreaves, J; Jones, R; Schlag, N.; Kwok, G; Ryan N; Orans, R; Frowd, R. Halfway There. IEEE Power and Energy magazine, July/August 2015, 41-52.

In: Wind Energy Editor: Desiree Fleming

ISBN: 978-1-63484-229-7 © 2016 Nova Science Publishers, Inc.

Chapter 5

POLICY CHALLENGES FOR THE DEPLOYMENT OF WIND ENERGY PROJECTS IN THE EUROPEAN UNION Pablo del Río and Cristina Peñasco National Research Council of Spain (CSIC), Madrid, Spain

ABSTRACT Policy makers in the Member States (MS) of the European Union (EU) face a difficult task: how to support renewable electricity deployment successfully (i.e., effectively and efficiently) in the short and medium terms and, in particular, in a 2030 horizon. Within renewable electricity, wind energy is a crucial energy source, expected to substantially contribute to the 2030 EU target (27% of overall energy consumption should come from renewable energy sources). This task is directly and negatively affected by certain factors or challenges which have to be dealt with. The aim of this chapter is to provide an inventory of challenges for the deployment of wind energy in a 2030 timeframe in the EU. This chapter is circumscribed to policy challenges, understood as those challenges directly and indirectly related to factors affecting wind energy deployment in a 2030 timeframe in the EU and which can be influenced by policy. The list of challenges contained in this chapter is 

Corresponding author: Dr. Pablo del Río, Senior researcher, , Institute for Public Policies and Goods (IPP), Consejo Superior de Investigaciones Científicas (CSIC), C/Albasanz 26-28, 28037 Madrid, Spain, Tel: +0034916022560, [email protected]

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Pablo del Río and Cristina Peñasco based on an analysis of relevant literature,. The challenges are diverse, and include technological, macroeconomic, administrative, social acceptance and policy design aspects. In particular, the following specific challenges have initially been considered: how to adapt support levels to the trends in the costs of wind energy technologies and the uncertain evolution of resource potentials, how to cope with lower budgets for wind energy support, problems in accessing finance (credit restrictions), institutional challenges related to the implementation of market-based instruments (MBIs) in general and auctions in particular, making auctions and others MBIs effective and efficient, challenges related to target setting (an EU target without MS targets and an EU target with MS targets), merit order effect reducing wholesale prices and revenue for wind energy in particular, trade-offs between a greater stability and flexibility to adapt to new circumstances, delays in administrative procedures, trade-offs between not-in-my-back-yard (NIMBY) phenomena related to the concentration of wind energy projects and allocative efficiency, social rejection of high or escalating support costs and costs falling disproportionately on a given group of the population. There is no intention to rank the relevance of these challenges for different stakeholders and, in particular, for policy makers.

1. INTRODUCTION Within renewable electricity, wind energy is a crucial energy source, expected to substantially contribute to the 2030 EU target (27% of overall energy consumption should come from renewable energy sources). In the realm of RES-E deployment, particularly the deployment of wind energy, policy makers at both the EU and MS levels face a difficult task: how to support it successfully (i.e., effectively and efficiently) in the short and medium terms and, in particular, in a 2030 horizon. Indeed, the analysis of the European Commission suggest that this is already being the case, showing considerable difficulties in reaching the RES 2020 target (EC, 2013). Such main task is directly and negatively affected by certain factors or challenges which have to be dealt with. Some of these challenges which affect wind energy deployment might not be strictly circumscribed to the renewable energy realm or even to the functioning of electricity markets. For example, the economic crisis has put a greater pressure on governments around Europe to promote RES-E costefficiently. However, RES-E policies may still be adopted to cope with some of those challenges. Although challenges related to electricity markets and

Policy Challenges for the Deployment of Wind Energy Projects … 123 RES integration or those related to external developments are also very relevant, this chapter focuses its attention on challenges related to policy design, i.e., the choice of RES-E instruments and RES-E design elements, in the realm of the deployment of wind energy projects. Then, the aim of this chapter is to provide a concise inventory of challenges debated for future (2030) RES-E policy in Europe and, specifically, those affecting wind energy deployment. By identifying the challenges faced by policy-makers and possible policy options to tackle those challenges, the chapter will contribute to the design and guidance for practical implementation of suitable wind energy policy pathways towards 2030. RES-E policy challenges are understood as those challenges which comply with two conditions. First, they are directly and indirectly related to factors which affect RES-E deployment in a 2030 timeframe in the EU. Since we are interested in those challenges which can be tackled by RES-E support policies, a second condition applies: the challenges can be influenced by RESE policy. Those challenges which are related to the general functioning of electricity markets and, in particular, issues related to market integration and grid integration of wind energy, arguably very relevant for this technology, are not addressed in this chapter. This chapter will be structured as follows. The following section provides a broad justification for RES-E policy. The methodology for the identification of challenges is described in Section 3. Section 4 proposes a set of assessment criteria and highlights in what sense those challenges are relevant for policy makers. The list of wind energy policy challenges will be provided in Section 5. The integration of the different elements of the challenges is carried out in Section 6.

2. JUSTIFYING RES-E SUPPORT IN THE 2030 TIMEFRAME Some would argue that there are no challenges for RES-E policy at all, specifically in the case of wind energy that in the last decade has increased its world total capacity by more than 600% (REN21, 2015) A single CO2 price will renders dedicated RES-E support unnecessary1. However, this is not the 1

According to the European Commission (EC, 2013), in their replies to the Green Paper on a 2030 framework for climate and energy policies, general industry organizations, the energy sector and energy intensive industries typically are against renewable targets. In contrast, organizations and companies that provide low carbon equipment support renewables’ targets. This duality of positions also occurs in the academic world (see, e.g., del Río, 2014).

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position taken in this chapter. On the contrary, we do argue that such policy support is needed. Therefore, before entering into a discussion on RES-E policy challenges, it is important to provide a justification for dedicated RES-E support. RES-E is expected to contribute to different goals of policy-makers and RES-E policy could tackle different market failures, which, therefore, provides a rationale for the implementation of those policies.

2.1. An Environmentally Sustainable, Affordable and Secure Energy System Policy makers have different goals, which materialize in the adoption of targets for those goals, and the implementation of instruments and design elements within specific instruments to achieve those goals. The success in achieving those goals is mediated by the success of policies in triggering the implementation of renewable energy technologies. In the climate and energy realm, policy goals usually include the three traditional dimensions of environmental sustainability: CO2 mitigation and other pollutants, security of energy supply (diversification of energy sources) and competitiveness (i.e., affordable energy)2. But governments also have other relevant goals (and policies serving those goals), including employment and industry creation, regional and rural development and support for innovation. These goals are especially important in a post-crisis scenario where job creation is one the most relevant policy objectives. 7.7 million of people are estimated to worked directly or indirectly in renewable energy sector. In this sense, wind power industry deserves a great attention in some regions, representing almost 15% of the jobs in renewable energy industry around the world and more than 45% in Europe (IRENA, 2015a).

2.2. Multiple Market Failures In addition, policy intervention is justified in so far as policies are able to address market failures. The existence of externalities provides a justification for governments to implement policies to address them. Combinations of instruments may be justified if they address different goals. But even for the same goal, instruments may be used jointly if they tackle different market 2

See, for example, the Green Paper on a 2030 framework.

Policy Challenges for the Deployment of Wind Energy Projects … 125 failures. In the climate mitigation/renewable energy policy realms, we may envision the existence of a “three-externality problem.” Each may justify the implementation of an instrument which tackles each externality simultaneously, not sequentially3. 1. The environmental externality refers to firms not having to pay for the damages caused by their GHG emissions. 2. The innovation externality is related to spillover effects enabling copying of innovations, which would reduce the gains from innovative activity for the innovator without full compensation. Thus, firms are unable to fully appropriate their R&D. Basic research has especially high spill-over rates. This “innovation externality” does not only relate to R&D, but also to demonstration4 (del Río, 2010). As a result, there is less R&D than what is needed overall, i.e., what is optimal from a social point of view. Unfortunately, total public renewable energy RD&D budget in IEA member countries has decreased over the last 3 years (2010-2014). 3. The increased deployment of a technology which results in cost reductions and technological improvements due to learning effects and dynamic economies of scale may result in a positive deployment externality (Stern, 2007)5. The increased deployment of a technology results in cost reductions and technological improvements due to learning effects and dynamic economies of scale (Stern, 2007)6. Even companies that did not initially invest in the new technologies may benefit and produce or adopt the new technology at lower costs. Although investors can partially capture these learning benefits, e.g., using patents or their dominant position in the market (Neuhoff et al. 2009), they do not capture all these learning benefits. Thus, investments in the new technology will stay below socially optimal levels. Of course, learning is certainly a source of innovation and cost 3

Other market failures may exist, including informational problems and market power. However, we have focused on the three which we consider to be the most relevant to justify the coexistence of CO2 mitigation (ETS) and RES-E support policies. 4 The size and complexity of demonstrating these technologies, which often includes complex planning and infrastructural support, make it difficult for the private sector to independently finance demonstration (Lee et al., 2009). 5 Since the 1970s, the costs of energy production from all technologies have fallen systematically through innovation and economies of scale in manufacture and use (apart from nuclear power). 6 Technologies such as solar energy and off-shore wind have potential for further innovation and cost-reduction (Andersen, 2006; Lee et al., 2009).

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In the past, some authors have argued that a carbon price is a necessary and sufficient condition to achieve the type of energy technology revolution that will be required to change the energy system along more sustainable directions. For example, one of the most renowned climate change economist argues that “raising the price of carbon is a necessary and sufficient step for tackling climate change” (Nordhaus, 2008, p.22)8. It is usually argued that a credible long-term carbon price (whether in the form of a carbon tax or a capand-trade scheme), which internalizes the negative environmental externality from CO2 emissions, will increase the costs of using polluting technologies and, thus, encourage innovation and diffusion of cleaner ones (Newell, 2008). The carbon price is claimed to be better than command-and-control regulation (either in the form of technology or emissions standards) both with respect to economic efficiency and innovation effects. The carbon price encourages lowcarbon innovations since firms subject to a carbon price have an inherent incentive to either develop or adopt technologies in order to comply with their CO2 targets at lower costs. This incentive/pressure is transmitted to all the stages of the innovation process and to actors involved in this process, notably equipment suppliers (see, for example, among others, Chameides and Oppenheimer, 2007; Jung et al. 1996; Milliman and Prince, 1989; Downing and White, 1986). While a carbon price is an appropriate instrument to internalize the negative environmental externalities related to CO2 emissions, there are other externalities (i.e., market failures) in the innovation process, i.e., an innovation and a deployment externality, as mentioned above. These two externalities provide a rationale for complementing the carbon price with additional instruments which tackle those externalities. R&D can be encouraged directly with R&D subsidies, tax credits and rebates. Demonstration can be supported with funding of demonstration projects. Finally, deployment of low-carbon 7

8

In addition, there are other failures (some of them sector-specific) that might contribute to under-investment in innovation in market-only environments. These include constrained access to credit for small innovative firms, informational problems and costs and agency issues (split incentives) (Newell, 2008; McKinsey, 2009). They can vary across different technologies and sectors (Edenhofer et al., 2009). This author, however, recently justifies direct subsidies to fund necessary research on lowcarbon energy (p.24). Later, on page 29, the author is softer about the “sufficient condition”: “placing a near universal and harmonized price or tax on carbon is a necessary and perhaps even a sufficient condition for reducing the future threat of global warming.”

Policy Challenges for the Deployment of Wind Energy Projects … 127 technologies, renewable energy technologies and wind technologies in particular, can be promoted directly with a wide array of instruments, including feed-in tariffs, tradable green certificates (TGCs), tendering and investment subsidies, among others. The importance of each externality differs along the stages of the innovation process. For example, the innovation externality is particularly important in the first stages (i.e., research and development), and decreases as we move downstream in the innovation process, i.e., the diffusion stage where technologies are already mature (Figure 1). In contrast, the environmental externality is relatively more important in the diffusion stage. Thus, it seems clear that in the initial and final stages, instruments should predominantly tackle the innovation and environmental externalities, respectively. The deployment externality usually plays a major role in the intermediate stages of the innovation process, i.e., for technologies which have passed demonstration but are in the pre-commercialisation, and even in the initial phase of commercialization, and for which a large cost reduction potential with increased diffusion exists. Therefore, the specific characteristics of the technologies have to be taken into account, i.e., their maturity level, costs, different degrees of risk and market exposure, potentials for cost reductions and main sources of technological change (whether R&D or learning effects from deployment dominate). The implementation of instruments should take into account two main issues: 1) given the existence of different features of the technologies along the innovation process, a different combination of RD&D and deployment instruments is required in different stages of such process. RD&D plays a critical role in the initial stages, and its importance decreases for more mature technologies. Deployment support is highly relevant in the intermediate stages in order to allow technologies to advance along their learning curve. The potential improvements in the mature technologies which can be achieved through RD&D investments are rather limited. 2) Within the deployment instruments themselves, more technology-neutral instruments are justified towards the final stages of the innovation process, whereas technology-specific deployment instruments are more suitable in previous stages in order to allow technologies to benefit from economies of scale and learning effects and exploit their cost-reduction potential.

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Source: Adapted from IEA (2008). Figure 1. Degrees of maturity of renewable electricity technologies.

Currently wind on-shore technologies can be considered as a source of low-cost electricity that can compete with hydropower or biomass energy and even with non-renewable sources. Wind on-shore and also off-shore to some extent, are now in the last phases of the innovation process and are excellent technologies in those areas with good resources (Figure 1). In those zones wind power projects can achieve costs just USD 0.05/kWh without additional financial support (IRENA, 2015b). Then the policy challenges face by wind energy are mostly based on technology-neutral instruments and also on the existence of carbon prices. Therefore, the environmental and the diffusion externalities take precedence over the innovation externality. Of course, it could be argued that a carbon price can promote the development and diffusion of technologies with different maturity levels. However, this statement would not stand empirical scrutiny. The empirical literature shows that the impact of the carbon price in the European Union Emissions Trading Scheme (EU ETS) on radical innovation is likely to be very limited (Rogge et al., 2011; Rogge and Hoffmann, 2010; Bailey and Ditti, 2009; Pontoglio, 2008). Taylor et al. (2005), Taylor (2008) and Gagelmann and Frondel (2005) also show that the US Acid Rain program did not encourage significant innovation. Taylor et al. (2005) did not find evidence that the ETS within the Acid Rain program promoted innovation in a more

Policy Challenges for the Deployment of Wind Energy Projects … 129 effective manner than other instruments. Malueg (1989) and Driesen (2003) have criticized the common argument that emissions trading schemes promote innovation to a greater extent than other instruments. They claim that, while potential sellers of permits (i.e., those with the lowest abatement costs) have an incentive to innovate in order to sell permits and earn a profit, an emissions trading scheme reduces the incentive to innovate compared to standard regulation for those polluters with high abatement costs. Under traditional regulation these firms would have an incentive to innovate in order to comply with regulation. Under an ETS, this incentive would be lost since they just would buy the permits. Of course, in the case of the EU ETS, it might be argued that the EU ETS allowance price has been too low and volatile. But this is exactly the point: it has been too low because, apart from the economic crisis, countries were relatively generous in the allocation of allowances in order to mitigate the economic burden on their own firms (Ellerman and Buchner, 2008; Ellerman, 2013). Political economy considerations and the institutional path dependency approaches9 suggest that CO2 prices are unlikely to be set at a sufficiently high level to trigger (radical) low-carbon innovation10, as shown by the low EU ETS allowance price in the first and second commitment periods11. High carbon prices are unlikely to be politically feasible since a national hard climate policy is politically unprofitable, i.e., it does not help to win votes and may lead to loss of competitiveness and leakage by the country adopting such a policy. The above does not argue against the use of a carbon price to trigger the development and diffusion of low-carbon technologies, above all for those ones like wind energy technologies, almost competitive in the electricity market. However, although necessary, it is not, by itself, a sufficient element in the required policy mix. A carbon price cannot cover everything and cannot 9

In the realm of public policy, path dependence can be interpreted as policy outcomes being dependent on the (sometimes coincidental) starting point and specific course of an historical decision-making process (Woerdman, 2004). Due to increasing returns (self-reinforcing processes), institutional path dependency may lead to institutional lock-in, i.e., to the dominance of inefficient policy instruments, in the presence of superior institutional arrangements (op.cit.). See del Río and Labandeira (2009) for further details on the institutional path-dependency approach applied to energy and climate change issues. 10 For an overview of these approaches applied to climate change mitigation and low-carbon technologies, see del Río and Labandeira (2009). 11 During the first compliance period (2005-2007), the EU allowance price reached a peak near 30€ in mid 2006, declining gradually to near 0€ in February 2007 and remaining at such level for the rest of 2007. In the second compliance period (2008-2012), the price reached a peak near 30€ in early 2008 and then stabilized at around 15€ for most of the rest of the period, declining to around 8€ by the end of it.

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address all relevant externalities. Policies to specifically support the innovation and deployment of wind energy technology projects might be justified taking into account the aforementioned three externalities. However, policy combinations are not a panacea and bring problems of their own. They may lead to policy interactions between them, which might be positive or negative. One example of a negative interaction (conflict) might be the case of ETS and renewable energy support (see del Río, 2014 for further details).

3. METHODOLOGY The identification of challenges for RES-E policy in general and for wind energy projects in particular in the horizon 2030 is a challenge in itself. The perspective adopted here is that of the policy-maker. For this, written sources which state those perceptions are deemed very useful for the purposes of this chapter. The direct opinion of policy makers are obviously also valuable and should be considered, however this will be addressed in future works. More specifically, three steps have been followed to identify those challenges (Figure 2).

Source: Own elaboration. Figure 2. The steps for the identification of the challenges considered in this chapter.

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Step 1. Identification of Relevant Recent Literature Our starting point is that the perspectives of policy makers are reflected in policy documents, both European and MS. In order to restrict the search to the most relevant sources of information, we have only considered renewable energy policy and energy policy documents. In particular, the following publicly available EU documents have been consulted:     

The European Commission Guidance for the Design of Renewables Support Schemes published on November 5th 2013 (European Commission, 2013) The Communication from the Commission on January 22nd 2014 on a policy framework for climate and energy in the period from 2020 to 2030 (European Commission, 2014a). The Guidelines on State aid for environmental protection and energy 2014-2020 (European Commission, 2014b). Green Paper on “A 2030 framework for climate and energy policies.” The Renewable energy progress report in 2013.

In addition, the following information sources at MS level have been used:  

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The National Renewable Energy Action Plans (NREAPs). The comments from Member States to the Commission’s aforementioned Green Paper in the context of the public consultation launched on 27th March 201312. Either the “government” itself or the Ministries (usually Energy or Environmental ones) have responded in 14 Member States, stating the “official position” of the country: Austria, Cyprus, Czech Republic, Denmark, Spain, U.K., France, Estonia, Finland, Poland, Lithuania, Portugal, Romania and Slovenia. Other documents from institutions in some MS have been taken into account from those countries without an official response, including the German Federal Environment Agency, the Royal Swedish Academy of Sciences, the Netherlands Environmental Assessment Agency, the Nordic Council.

The documents related to the public consultation are publicly available http://ec.europa.eu/energy/consultations/20130702_green_paper_2030_en.htm.

at:

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In addition to those official documents, country case studies provide a relevant source of information on the challenges perceived by policy-makers. Case studies are carried out by academics, but usually policy-makers are consulted. Therefore, relevant opinions are collected in this manner. Thus, articles in energy and energy policy journals have been searched, including Energy Policy, Energy and Renewable and Sustainable Energy Reviews. Within these journals, a search for relevant articles has been made by including terms such as “wind energy,” “2030” “support policies” or “instruments.” Furthermore, the grey literature may provide valuable information in this context and has also been considered. Thus, a google search combining the following terms has been carried out: “energy policies towards 2030,” “wind energy capacity 2030,” “renewable energy post-2020,” “renewables beyond 2020.” Finally, reports from other EU-funded projects have been taken into account, including RE-SHAPING13, BEYOND202014, CECILIA205015 and KEEP-ON-TRACK16. In particular, for the identification of relevant assessment criteria, instruments and design elements the Beyond2020 project has been considered.

Step 2. Identification of Challenges from the Literature The resulting list of documents and articles has allowed us to identify relevant challenges for wind energy projects. This has been straightforward regarding the official documents from the Commission and MS. In the case of articles and the grey literature, we have identified those challenges and included them in a table, according to the following procedure. Potentially useful articles have passed a first filter. An initial analysis of the titles of those articles has been performed and the abstracts of those articles relating to renewable energy in a 2030 horizon have been selected. We have read those articles looking specifically for those challenges in wind energy technologies. Articles dealing with renewable energy in a 2020 or 2050 horizon have also been considered.

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See http://www.reshaping-res-policy.eu/ See http://www.res-policy-beyond2020.eu/ 15 See http://cecilia2050.eu/ 16 See http://www.keepontrack.eu/publications/ 14

Policy Challenges for the Deployment of Wind Energy Projects … 133

Step 3. Classification According to Categories Finally, we have classified those challenges in different categories: technology-related, macroeconomic-related, related to current policy discussions, administrative barriers and social acceptance. However, it is not the aim of this chapter to provide a prioritization of challenges. In other words, we do not try to identify which are the “most relevant” ones. This would be a rather subjective endeavor, since it would depend on the specific perspective of the policy-maker.

4. ASSESSMENT CRITERIA Policy makers in the RES-E policy realm usually judge the functioning of RES-E policies according to several “assessment criteria” (del Río et al., 2012, IRENA, 2014, Mitchell et al., 2011). They refer to what it is ultimately aspired or wished, considering a policymaker’s perspective. For example an effective and efficient deployment of RES-E is traditionally mentioned as one of the end-goals of those in charge of RES-E policy. It can be said that this goal is near to be reach in regards wind energy technologies. Different documents dealing with the evaluation of RESE policies emphasize the relevance of different criteria. For example, IPCC (2011), IRENA (2014) use effectiveness, efficiency, equity, institutional feasibility, IEA (2011) only considers cost-effectiveness. The Beyond 2020 project broadened the discussion to include effectiveness, cost-effectiveness, dynamic efficiency, equity, environmental and economic effects, sociopolitical feasibility and legal feasibility (see del Río et al., 2012). Thus, the following “assessment criteria” are deemed relevant from a RES-E policy maker perspective in a 2030 horizon.

4.1. Ensuring Compliance with RES-E Targets Target attainment is certainly a goal of public authorities in the MS. This refers to the extent to which targets for the penetration of renewable energy are fulfilled and the trend towards the fulfillment of those targets over time. Currently, they are mostly related to compliance with the 2020 and the interim targets set in the RES Directive (Directive 28/2009/EC). The January 22nd European Commission communication has proposed a 27% target for RES in

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2030, although this is still in the negotiation procedure. In order to reach this target, the contribution of wind energy projects is essential. For example in the EU wind technology is achieving high levels of penetration in some areas: 39.1% and 27% of the electricity demand were covered by wind energy in 2014 in Denmark and in Portugal respectively (REN21, 2015). This Communication does not propose to set targets for MS directly, but only the aforementioned target for the EU as a whole. But each MS may have its own target for RES or, at least their own idea (established in their National Energy Plan) about what should be the contribution of RES to their national GHG emissions reduction target. In addition, Member States will have some degree of responsibility in complying with the overall EU target. Compliance with targets depends on sufficient support levels and low risks for investors. Note that this suggests the potential existence of conflicts between challenges. For example, a greater support level increases the likelihood that RES targets are reached, but possibly at greater support costs than necessary (see below). However, the figures show that wind power investments have been together with solar energy technologies one of the most attractive for investors. Since 1990 in OECD countries the average annual rate of RES has been 2.5% being especially high for solar PV (45.2%) and wind projects (22.2%) (IEA, 2015). The following figure (Figure 3) shows the evolution of wind on-shore and off-shore in the EU.

4.2. …at the Lowest Possible System Costs … According to Held et al. (2014), system-related effects encompass all benefits and direct and indirect costs of RE-deployment. While direct costs include all the costs that are directly related to electricity or heat generation such as installation, operation and maintenance of RE-technologies, indirect costs are caused by integrating RE into the existing generation system such as grid extension costs, balancing costs, etc. Benefits from RET-use arise e.g., as a result of avoided GHG emissions and air pollutants. The main characteristics of system-related costs and benefits are that they represent additional costs or benefits of a RE-based generation system compared to a reference system based on a nuclear and fossil fuels. These costs are identified from a system perspective without taking into account any policy-induced payments. The IEA disaggregates system costs into three components:

Policy Challenges for the Deployment of Wind Energy Projects … 135   

Adequacy costs: the cost of ensuring that the power system has sufficient capacity to meet peak loads. Balancing costs: the cost of ensuring that the power system can respond flexibly to demand changes at any given time. Interconnection costs: the cost of linking sources of supply to sources of demand.

Source: EWEA (2015). Figure 3. Annual on-shore and off-shore wind energy installations in the EU (MW).

Table 1 provides a detailed classification of those costs. Regarding wind energy technologies, wind power systems levelised costs of electricity (LEC) includes capital costs, financing costs, operation and maintenance (O&M) costs and the expected annual energy production (IRENA, 2015b). Wind turbines represent the main costs and almost 84% of the total installed cost in onshore wind farms. O&M can reach 20 to 25% of the total LEC with average values of around USD 0.02 to USD 0.03/kWh. Offshore projects show significantly higher costs in all the concepts. As stated by IRENA (2015b), in OECD wind markets similar costs exists. Total installed costs declined from 2010 to 2014 and can be around USD 1.780/kW for projects in 2015.

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4.3. … and the Lowest Possible Policy Support Costs Since some renewable energy technologies have higher generation costs than traditional power generation technologies on a levelised electricity cost basis (LEC), they need public support to penetrate the market, which is ultimately paid by consumers and/or taxpayers (Figure 4).

Source: IRENA (2015b). Figure 4. Levelised cost of electricity ranges and regional weighted averages by technology.

Yet, other technologies do not have higher generation costs on a LEC basis, but their cost structure is different and could make them unattractive vis a vis their conventional, fossil-fuel competitors. They face higher upfront, capital costs even though their variable costs (O&M and fuel costs) are much lower (see IRENA, 2013).

Policy Challenges for the Deployment of Wind Energy Projects … 137 While part of the literature has focused on the minimisation of the generation (system) costs and, in fact, only these costs are taken into account in several recent papers on the economics of renewable energy17, some have argued about the need to reduce the overall policy costs for consumers or taxpayers (Huber et al., 2004; Ragwitz et al., 2007; Steinhilber et al., 2011; EC, 2008; IEA, 2008; IEA, 2011). In fact, governments around the world are highly concerned about these costs. Thus, the costs of support should also be taken into account. Except for the case of investment subsidies and tax incentives, which are generally covered by the public budget, RES-E support is, in the end paid by electricity consumers in their electricity bill. Therefore, cost-effectiveness has been interpreted in this context as supporting a given amount of RES-E at the lowest possible consumer costs18. In this case, the aim should be to minimise the revenues for producers (to sufficient and appropriate levels). Thus, instruments should be designed in a way which ensures that transfers of payments from consumers to producers are minimised. This would imply a reduction in the producer surplus. Lower support costs are not only an issue of moderate support levels19, but also of moderate risks for investors. Unstable, unpredictable support schemes involve a “risk premium”, i.e., greater support levels to make them attractive for investors.

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18

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See, e.g., Schmalensse (2011), Green and Yatchew (2012), Borenstein (2011), Heal (2010). For example, Schmalensee (2011) argues that “The notion of ex-post efficiency, explored in this section and the next, involves taking detailed policy goals as given and asking whether they are likely to be attained at minimum cost or anything close to it. In the case of renewable energy this mainly requires production at the best sites, given the technologies required or allowed to be employed. Ex post efficiency as regards the top-line twenty percent target requires EU-wide equalization of the marginal cost of producing electricity from renewable energy.” There are other authors which take the other extreme, i.e., they only look at the costs of support and disregard the minimisation of generation costs (i.e., Verbruggen and Lauber, 2012), including the very influential IPCC report on renewables (Mitchell et al., 2011). See, e.g., Huber et al. (2004), EC (2008), Ragwitz et al. (2007), IEA (2008), IEA (2011), Mitchell et al. (2011), among others. Note, however, that policy costs mostly refer to distributional issues between RES-E generators, electricity consumers and, eventually, taxpayers. Costs for consumers due to RES-E support are defined as transfers from consumers to producers due to RES-E support with respect to the consumer costs due to the purchase of conventional electricity.

Table 1. Additional system costs in power generation Types of additional system cost Power or heat generation costs  Direct costs  Relevant for heat and electricity Balancing costs  Indirect costs  Focus on forecast errors  Relevant for electricity Profile costs  Indirect costs  Focus on back-up capacity  Relevant for electricity

Grid costs  Indirect costs  Relevant for electricity (may also be relevant for biogas grid in the heating sector) Transaction costs  Indirect costs  Relevant for heat and electricity Source: Held et al. (2014).

Description Costs arising from electricity and heat generation:  The costs of the RE generation technology reduced by the avoided costs of conventional generation  The costs of combinations of RE and conventional generation technologies reduced by the avoided costs of conventional generation Balancing costs occur due to deviations from schedule of variable RE power plants and the need for operating reserve and intraday adjustments in order to ensure system stability. Balancing services may either increase or decrease the electricity fed-into the grid, provided by positive or negative balancing capacity According to Ueckerdt et al. (2013) profile costs occur due to the following effects:  A potential increase of average generation costs of the residual load as a result of RESinduced decrease of utilization of conventional power  Additional capacity of dispatchable technologies required due to the lower capacity credit of non-dispatchable RES such as wind or solar to cover electricity demand at peak times and simultaneous low RES generation  Potential curtailment of electricity required in times of overproduction represents another cost component. Reinforcement or extension of transmission or distribution grids as well as congestion management including re-dispatch required to manage situation of high grid load

Market transaction costs: additional forecasting, planning, monitoring, procuring power, establishing trade, contracting, data exchange, etc. Policy implementation costs: administrative cost to implement RE policies or fulfil data provision requirements (accounting, approvals,…)

Policy Challenges for the Deployment of Wind Energy Projects … 139 €/MWh SUPPORT COSTS (dfhg)

MgC*

Pe

g

d

MgCres-e h

PRODUCER SURPLUS (chg)

c a

e

f

TOTAL GENERATION COSTS (abhc) b Q*

MWh

Source: Huber et al. (2004) and Resch et al. (2009). Figure 5. Illustrating different cost concepts.

Figure 5 illustrates the different cost elements and clarifies what is meant by “support costs”20. MgCres-e represents the marginal cost curve of RES-E generation. It is an upward sloping curve which can be drawn either as a stepped or a continuous line (as in this case). It plots the long-term marginal costs of renewable energy technologies, from the cheapest to the most expensive ones. If the government sets a target or quota (Q*), then reaching it at the lowest support costs would involve that only electricity generation for technologies up to Q* would be supported. MgC* represents the marginal costs of the last technology needed to comply with the RES-E target/quota. The total support costs are defined as the difference between the MgC* and the wholesale price of electricity (Pe) (area dfhg). The producer surplus for RES-E generators is the difference between MgC* and the marginal costs of electricity generation (MgCres-e), i.e., area abhc) (Figure 5). Obviously, those policy costs bring additional distributive issues if they are concentrated on certain actors. In other words, it is not only the amount of those costs which may raise the concern of policy-makers, but the extent to which they fall disproportionately on certain actors (whether consumers,

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See del Río and Cerdá (2014) for further explanations regarding the concept of minimization of generation costs and minimization of support costs.

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producers or taxpayers) and, from the point of view of the EU, on certain countries.

4.4. Encouraging Innovation In the responses to the Green Paper, many countries underline that a greater focus on innovation is essential to ensure the feasibility and security of the EU energy system and for the further development of a portfolio of costeffective and sustainable energy options21. RES-E policy in a broad sense should promote innovation. RES-E policy in this context encompasses not only support for diffusion (deployment), but also support for R&D. Most countries, however, argue that the later should be provided on an EU-wide basis, i.e., by EU policies. Nevertheless, it should be taken into account that deployment policies have innovation effects. As detailed elsewhere (see del Río et al., 2012), the impact of RES-E support schemes upon innovation in renewable energy technologies has several aspects or “dimensions”: diversity; R&D; learning effects; and competition. Diversity refers to the extent to which an instrument favours the deployment of different technologies. R&D refers to the extent to which a RES-E support instrument encourages private R&D by firms. In turn, this is the result of a supply-push and a demand-pull effect. The former occurs because the deployment instrument creates a producer surplus (profit margin) which might be reinvested in R&D. The demand-pull relates to the fact that the deployment instrument creates the perspective for a market for the technology, which investors in R&D need to sell their technologies. Learning effects are related to the effectiveness of the instrument, which allows technologies to advance along their learning curve. Finally, the extent to which an instrument favours competition between RES-E generators and renewable energy technology suppliers leading to greater innovation should also be considered. Along the last decades, these learning effects have favoured above all wind and solar technologies. Onshore wind energy is now a mature technology. However this does not mean that it has not a path for 21

As recently put by the European Commission itself, “support scheme design should also reflect the need to address longer term goals of fostering technological innovation, economies of scale, cost-reductions and spill-over effects that facilitate reaching 2020 targets and reaching 2050 decarbonisation goals sustainably. Member States may also have a clear objective of promoting technology innovation in renewables to ensure the cost effective medium term transition to a sustainable energy system” (European Commission, 2013, p.8).

Policy Challenges for the Deployment of Wind Energy Projects … 141 improvements. R&D current trends are focused also on the improvement of off-shore technology.

4.5. Ensuring the Social Acceptance and Political Feasibility of RES-E Support Policy-makers are more likely to prefer the implementation of policies which are as socially acceptable as possible, in short, politically feasible. Obviously, social acceptability depends on other assessment criteria. More specifically, large support costs for RES-E deployment are likely to trigger a social rejection against the support scheme. Europeans are more in favour of renewable energy than to other energy sources. Specifically, 89% of the population are in favour of using wind energy (EC, 2011). In general, citizens see wind energy projects as an opportunity for the improvement of local communities. The job creation, the economic growth of the zone, and fighting against climate change are among the benefits perceived by the population.

5. THE WIND ENERGY POLICY CHALLENGES This section is devoted to provide an inventory of challenges for wind energy policy in a 2030 perspective. These challenges will be described and organized by grouping them under different categories. While different alternatives are possible, we have decided to use a thematic classification with the following categories: technological, macroeconomic, administrative, social acceptance and policy design. The perspective adopted is mostly that of renewable energy policy-making at MS level in Europe. Although not considered in this chapter, stakeholders view would be useful to establish the relative importance of those challenges and additional ones.

5.1. Technology-Related Policy Challenges Technological development determines whether RES are available on the market (Boie et al. 2014). RES-E deployment critically depends on technology development and cost reductions (EC, 2013). A set of challenges are related to the evolution of the costs and maturity of the technologies. Most of these

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challenges have been overcome in the case of wind energy technology in the last years. Now onshore wind techs can be competitive in the electricity market without any kind of additional economic support although for off-shore some improvements are still necessary. However, technology related challenges are not confined solely to the technology at hand. Trends in both competing and complementary technologies (e.g., storage) should also be considered, although these fall outside the realm of RES-E policy.

Adapting Support Level to Trends in Technology Costs Renewable energy technologies (RETs) can be located along a continuum from ready mature to immature technologies. For simplicity, they can be grouped in two categories. Some are high-cost or relatively less mature (e.g., wind off-shore and CSP) whereas others are already mature and close to being able to compete with other conventional technologies. This is exactly the case for wind on shore. In principle, and according to innovation theory, the higher the investment costs of the technologies and the lower their maturity level, the greater the costs reductions and improvements in the technology that can be expected. This has been the case in the last decades (IEA, 2012). Adapting support costs to the evolution of technology costs in order to avoid overcompensation (or too low support) is a challenge for policy makers. Likewise, the costs of RES-E deployment may also be affected by changes in material prices. For example, variations in the price of silicon and steel were important in explaining those changes in the past in the case of wind energy but also for other technologies like solar PV (Panzer, 2012). There are also concerns that shortages of carbon fiber (for the offshore wind industry) are likely to drive up the prices and hence will escalate installation costs in the short to medium term (Zyadin et al. 2014). All these trends would certainly affect the installed wind energy capacity. Appropriate Combination of R&D Support and Deployment Support for Less Mature RETs Also according to innovation theory, the sources of cost reductions, technology improvements and, in short, innovation can be expected to differ per RET, depending on their maturity level. Wind energy is now in the last stages of the innovation pipeline, then incremental improvements and dynamic economies of scale are much more relevant in this step than in less maturity levels (see Section 2 and del Río et al. 2012). This represents a challenge for

Policy Challenges for the Deployment of Wind Energy Projects … 143 policy-makers in that they have to adapt the type of RES-E policy to the specifics of the technology in question in order to induce those cost reductions and innovation. This is very relevant for wind technologies (onshore and off shore) because both are in different stages of the innovation process. While offshore technologies will require more emphasis on R&D investments, for onshore technologies mass deployment will still make more sense. Some responses of countries to the Green Paper claim that technology neutrality should be aimed at. This neutrality means limiting the policy intervention to the introduction of a CO2 price. However, elsewhere it has been shown that a CO2 price without dedicated support for RES-E will not provide a sufficient push for the uptake of RES-E technologies (Resch et al. 2013). Obviously, innovation spillovers in RETs are likely. This means that those cost-reductions, improvements in the technology and innovation may be the result of policies and activities outside the EU region. If, as a result, costs do not evolve as expected, this represents a challenge for policy-makers in the EU, since the RES-E target will either be reached at higher costs than expected (inefficiency) or will not be fulfilled at all (ineffectiveness). If costs are higher than expected, then the RES-E target may not be achieved, or it may be achieved at greater costs than initially envisaged. If costs are lower than envisaged, then the RES-E target would be more easily reached, but, under certain instruments and design elements (FITs or FIPs), it could even be exceeded, which would lead to very high total support costs. This was the case, for example, in some countries with solar PV promotion. However, in regards to wind energy which is much more capital intensive and requires longer planning periods this over support is highly unlikely, i.e., the period of maturation of wind energy projects are around 6 to 8 years. It is highly unlikely that a similar case is given in the wind sector. Higher or lower, in both cases, RES-E policies will probably have to be adapted accordingly and it will certainly be a challenge for policy-makers to do so. Adaptation might be more likely to occur under some instruments and design elements than under others. The above suggests that the balance between R&D support and deployment support for less mature technologies represents a critical challenge, both at the EU and MS levels. There is a role to be played by national policy-makers. As mentioned above, improvements and cost reductions for these technologies are a result of, both, R&D support and deployment support. The latter is certainly in the hands of Member States. But countries also have budgets dedicated to R&D, although R&D programs at EU level would probably be more effective, given the economies of scale in

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research and innovation activities. This means that less mature technologies will need EU-wide research and innovation policies. However, in the short to medium terms, it is MS which have to decide on the appropriate combination of both types of support. It seems that, in the past, the balance has been clearly tilted towards deployment support, which has been orders of magnitude greater compared to R&D support. For example, in Spain total public R&D support for wind energy amounted to 210M€ in the whole 1974-2013 period, according to the IEA (2015). Wind deployment incentives in the same country only in 2013 amounted to almost 3000M€ (CNE, 2013).

Adapting to the Uncertain Evolution of Factors Affecting the Competitiveness of RETs: Resource Potentials, Fossil Fuel Prices and Costs of Competing Technologies Since EU RES targets are set as a percentage of final energy consumption, they are affected by variables either influencing RES-E generation or electricity demand. RES-E generation would be affected by resource endowments. A lower than expected quality of renewable energy resources (wind speeds in the case at hand), would entail a lower than expected electricity production, ceteris paribus. It is certainly a challenge to estimate with absolute precision the amount of those resources a long time ahead, although this is probably not a major factor, i.e., its relative importance is lower compared to other variables. An important variable, although also mostly beyond RES policy, are fossil fuel prices. The higher they are, the lower are the levels of electricity demand. However, their influence on RES-E deployment is substantial. Since this depends on their relative profitability, i.e., compared to those fossil fuel prices. While it is beyond the boundaries and scope of RES policy, those prices have to be taken into account when setting targets and policies for RES. It is certainly a challenge to predict the trends in those prices, so that RES support is neither too low nor too high. The challenge is compounded by the fact that different types of RES and fossil fuel sources are substitutable to different degrees regarding peak and base loads. A somehow related topic is the trends in the costs of competing technologies. While, again, clearly beyond the realm of RES policy, an accurate calculation and prediction of the evolution of these costs should be made in order to appropriately set support levels for RES, a challenge on its own. A particularly relevant factor for RES deployment is the price of gas, which is likely to be affected in the future by the shale gas phenomenon and the significant increase in gas supply as a result.

Policy Challenges for the Deployment of Wind Energy Projects … 145

5.2. Macroeconomic-Related Policy Challenges Providing Support under Strict Fiscal Conditions There is a widespread perception that the economic and financial crisis has negatively affected RES-E deployment. On the one hand, the slowdown in economic activity has led to greater public expenditures in some countries related, among others, to unemployment benefits and the debt service. This has particularly been the case in the South of Europe. The austerity programs adopted to cope with the public deficit have involved a reduction of budgetfinanced RES support, although in most countries RES are predominantly supported through a surcharge on electricity bills. For example, in Spain, a 7% electricity generation tax (for all electricity generation technologies, not only RES) was adopted in 2012 in order to increase revenues for the public budget, with a negative impact on investors of 0.7% on the internal rate of return (del Rio et al. 2014). The degree of the uncertain economic recovery in the short and medium terms will influence RES-E policy and, thus, RES-E deployment. A main challenge for RES-E policy makers is to provide support even under strict fiscal conditions. This will certainly increase pressure for policy-makers to adopt cost-effective policies. Difficulties in Access to Credit On the other hand, the financial crisis has led to credit restrictions which have affected all types of productive investments and, particularly, RES-E investments. The cost of capital has risen in several MS (EC, 2013). This has involved a difficulty to access loans in order to finance those investments and/or substantially increase total capital costs22. In some countries, and particularly in the South of Europe (Portugal, Spain…), capital is relatively more expensive. However, wind sector has been one of the strongest sectors during the crisis period and there have been advances mainly in Asian countries. In fact, China was in 2012 the country with the biggest rate in new installed capacity around 13000 new MW installed and near to USA. Far away are European Countries, where the economic crisis was especially hard with new installed capacities below 3000 MW (GWEC, 2013). A main challenge for RES-E policy is to mitigate these restrictions and facilitate access to affordable finance for RES-E investors. This is particularly relevant to wind projects which need large investments. Although currently 22

According to Bloomberg/NEF, investment in renewables reached a peak in 2011 of 123$Mlrd and decreased thereafter to 53$Mlrd in 2013.

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those investment costs are slightly lower, for example in Spain, in 2010 the initial investment on average for setting up a new wind onshore facility was about 1.3 Million € and almost 3 Million € for offshore facilities. Then, facilitating access to credit to RES developers can be part of RES-E policy e.g., by means of government-backed loans, although the financial environment/situation of a country does escape the boundaries of RES-E policy indeed. New financing instruments or arrangements might be required to trigger RES-E investments. The cost of capital is sensitive to risk perception by investors. Policy can influence risk perception through more stable regulatory frameworks, through loan guarantees, long-term power purchase agreements, contracts for differences, capacity markets and through cofounding by financial institutions such as the European Investment Bank (EIB).

5.3. Administrative-Related Policy Challenges Improving and Reducing the Duration of the Administrative Procedures Administrative barriers are a crucial factor affecting the uptake of RES-E. Their relevance has been highlighted by several EU-funded projects and by the European Commission itself23. For example, article 6 of Directive 2001/77/EC already highlighted the negative effect of administrative barriers on RES deployment and exhorted Member States to take action to reduce them. The European Commission (2005) assessed the (inadequate) progress made in reducing these barriers in most Member States and made five precise recommendations. These were for Member States to establish, among others: 1) One-stop authorisation agencies to take charge of processing authorisation applications and providing assistance to applicants; 2) Clear guidelines for authorization procedures with a clear attribution of responsibilities24 and; 3) Pre-planning mechanisms in which regions and municipalities are required to assign locations for the different renewable energies. In 2008, the European Commission stated that, with respect to administrative barriers, little progress had been made to date in most Member States, that the effectiveness of support schemes was affected by the existence of those barriers and that 23

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Two projects worth mentioning in this regard are ADMIRE-REBUS (see Uyterlinde et al., 2003) and OPTRES (see Ragwitz et al., 2007). Authorisation procedures must be based on objective, non- discriminatory criteria which are known in advance to the undertakings concerned, in such a way as to circumscribe the exercise of the national authorities´ discretion, so that it is not used arbitrarily.

Policy Challenges for the Deployment of Wind Energy Projects … 147 Member States should therefore continue to implement measures to reduce them (European Commission, 2008, p.16). Directive 2009/28/EC also requires Member States to take adequate measures to achieve national overall targets, including cooperation between local, regional and national authorities (art. 4) and lays down rules for administrative procedures (art. 13). In particular, article 13 states that, Member States shall take the appropriate steps to ensure that administrative procedures are streamlined and expedited at the appropriate administrative level and rules governing authorisation, certification and licensing are objective, transparent, proportionate, do not discriminate between applicants and take fully into account the particularities of individual renewable energy technologies. More recently, the renewable energy progress report of the European Commission (EC, 2013) stresses that at EU and MS level, further efforts are needed in terms of administrative simplifications and clarity of planning and permitting procedures. It is stated that progress in removing the administrative barriers is still limited and slow25. Since administrative procedures have been and are a barrier for the penetration of RES-E, there is no reason to think that they would not be a main determinant of RES-E in a 2030 horizon. Thus, a main challenge for policymakers is to improve and streamline those procedures in order to facilitate the uptake of RES-E.

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More specifically, it is mentioned that “There are concerns about slow progress regarding online applications, administrative time limits for planning and permitting decisions, and transparent approval processes. The availability of a single administrative body for dealing with renewable energy project authorizations and assistance to applicants is still limited. Only Greece and Portugal reported newly introduced "one-stop-shop-agencies" since the plans were published; a few Member States had them in place before for some technologies (e.g., wind) or in some parts of the country (e.g., in Germany or in Sweden). Only Denmark, Italy and the Netherlands have a single permit system for all projects. These concerns are particularly acute in the heating and cooling sector, where the disparate nature of the different possible technologies hinders the development of uniform administrative approaches. Sub optimal administrative arrangements clearly raise the costs of renewable energy and their removal normally has low fiscal implications: simplifying and speeding up administrative procedures does not need to cost public administrations more, and the reduction in uncertainty and regulatory risk for investors can significantly reduce the cost of capital. For energy transmission infrastructure, such measures have been addressed at European level through the regulation on guidelines for trans-European infrastructure which defines responsibilities for coordinating and overseeing the permit granting process, sets minimum standards for transparency and public participation and fixes the maximum allowed duration of the permit granting process. Such measures are urgently needed under Article 13 of the Renewable Energy Directive for energy installations” (EC, 2013, p.8).

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5.4. Social-Acceptance Related Policy Challenges Mitigating the NIMBY of RES-E Projects Public opinion is of particular importance for the deployment of RES (Boie et al. 2014). The social acceptance of RES-E in a 2030 perspective has two sides. On the one hand, it may refer to the not-in-my-backyard (NIMBY) phenomena for RES-E deployment. Renewable energy projects bring, both, benefits for the local population and costs. The former may refer to an increase in employment levels and rural/regional development opportunities. The latter is usually associated to negative environmental impacts, i.e., visual intrusion, soil occupancy etc… . This issue would become more problematic with an increasing penetration of RES-E, which would be the case in 2030 if there was a concentration of RES-E deployment in certain places and for certain technologies. The magnitude of the necessary changes will require public consent to a variety of policies, which in turn implies increased efforts to raise public awareness of renewable energy (Mitchell et al. 2011). Some case studies have recently stressed the role of social acceptance and the role that some policies may have in this context. For example, Mendonça et al. (2010) found that steady, sustainable growth of RES would require policies that ensure diverse ownership structures and broad support for RES and they argued that local acceptance will become increasingly important as renewable energy technologies continue to grow in both size and number (Mendonça et al. 2010). A key challenge for policy makers lies in developing effective public participation strategies, and in gaining a better understanding of local attitudes and how participatory approaches in RES-E planning can facilitate further deployment of RES-E. Of course, it could make sense to implement instruments and design elements that avoid a concentration of RES-E project in a given location, i.e., that lead to a dispersed deployment of RES-E in order to mitigate the risk of NIMBY. However, the downside of this is that allocative efficiency would be negatively affected since the places with the best renewable energy resources would not be exploited first and, thus, generation costs would not be minimized addressing social rejection to high or escalating support costs. On the other hand, social acceptance may be related to the costs of public support. If these are too high or experience a substantial increase in a given year, this would lead to a social backlash against the RES-E deployment support scheme. This suggests that a major challenge for policy-makers is to keep the costs of RES-E policy within reasonable levels. Social acceptance

Policy Challenges for the Deployment of Wind Energy Projects … 149 may not only be related to the total amount of policy costs, but on their distribution among different actors (i.e., equity). If those costs fall disproportionately on a given group of the population, social rejection is more likely, especially if this group is well-organised and has considerable negotiation power. Anyway, as stated in previous sections, at least in Europe, public acceptance of energy technologies is higher for renewable energy projects than for other energy sources, particularly in the case of solar energy and wind energy. For the latter 60% of the respondents in the Eurobarometer of the European Commission were strongly in favour of of wind energy and 29% were fairly in favour (EC, 2011). Moreover, 90% of the Europeans think that it is important for their government to establish targets to increase renewable energy consumption by 2030 (EC, 2014c). However, it is worth keeping in mind that perceptions may vary when citizens take costs into account.

5.5. Challenges for Policy Design Institutional Adaptation to the Implementation of Market-Based Instruments Three recent communications from the European Commission provide recommendations to MS on the use of RES-E support instruments and, thus, have an impact on the future of RES-E support in the EU. The European Commission Guidance for the Design of Renewables Support Schemes published on November 5th 2013 (EC, 2013) argues that RES-E support instruments should adjust support levels to the costs of renewable energy technologies. Costs to the consumers should be reduced and overcompensation and excessive demand for new installations should be avoided. Instruments should be market-based, avoiding the problem of asymmetric information and reducing the risks of regulatory instability for investors. The document explicitly argues in favour of tenders for RES, which can be used to allocate different instruments such as feed-in premiums, investment support or green certificates. The Communication from the Commission on January 22nd 2014 on a policy framework for climate and energy in the period from 2020 to 2030 (EC, 2014a) states that national support schemes need to be rationalised to become more coherent with the internal market, more cost-effective and provide greater legal certainty for investors. Subsidies for mature energy technologies, including those for renewable energy, should be phased out entirely in the

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2020-2030 timeframe. Most importantly, the Guidelines on State aid for environmental protection and energy 2014-2020 (EC, 2014b) mention that market-based instruments, including competitive bidding processes but also FIPs, should gradually replace existing renewable support schemes from 2015 onwards. Those instruments are expected to increase cost-effectiveness and mitigate the distortions on competition. Competitive auctions will have to be implemented in order to provide support to all new installations from 2017 onwards. Arguably, this move to market-based instruments in general and auctions in particular will represent a challenge for policy makers, i.e., the institutional adaptation to a new instrument.

How to Design Auctions to Lead to Effective and Cost-Effective Deployment of RES-E In addition, the choice of design elements for the new market-based instruments in general and auctions in particular, in order to ensure an effective and cost-effective deployment of RES-E represents a main challenge. This last point is highly relevant, since auctions for RES can be designed in many different ways (see del Río and Linares, 2014). Past experiences with auctions for RES show that auctions in the past have not always led to effective and cost-effective RES-E deployment. Tenders have been limited to promote less mature technologies and to encourage the participation of smaller actors (see del Río and Linares, 2014; Held et al. 2014). Indeed, the aforementioned European Commission Guidelines on State Aid envisages some exceptions to the use of auctions: 1) Small installations or technologies in an initial stage of development26; 2) that MS could show that auctions would lead to a non-satisfactory outcome because they only promote a few projects or sites, because they would result in higher support levels or because they would be ineffective. All in all, the success of tenders, as with other instruments, depends to a large extent on how they are designed (del Río and Linares, 2014). In addition, since the Guidelines for State Aid provide leeway for MS to continue to use FITs (e.g., for smaller installations and less mature technologies), it will be a challenge for national policy-makers not to replicate past negative experiences in this sense (see, e.g., the case of FITs for solar PV in Spain, del Río and Mir-

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According to the Guidelines, small renewable energy installations are those with an installed electricity capacity of less than 1 MW. The threshold for wind plants is 6 MW.

Policy Challenges for the Deployment of Wind Energy Projects … 151 Artigues, 2014). In addition, the shift to other market-based instruments is also associated with substantial design challenges (see, e.g., Held et al., 2014).

Target Setting On the other hand, different target setting options represent different types of challenges for RES-E policy design, mostly at the EU level. The January 22nd 2014 communication does not envisage the existence of national targets. The existence of an EU target without national targets in a context in which the responsibility for RES-E policy instruments remains solely at the MS level raises the issue of how those MS policies can be expected to contribute to the EU target when there is no responsibility for a national amount of RES-E deployment. Lack of national targets may increase uncertainty (at least initially) in relation to proportions of RES-E on the system (CEER, 2013). This would affect the whole value chain for renewable energy technologies, including technology providers and project developers. Obviously, the alternative (i.e., MS targets and an EU-wide target) also bring challenges, mostly in terms of cost-effective deployment across the EU if the RES-E targets are set only partially considering renewable energy resource potentials in MS (i.e., when they are mostly set according to the economic capacity of countries). The Impact of the Merit Order Effect on the Competitiveness of Wind Energy Furthermore, it is usually argued that past and future reductions in the costs of wind will make this technology cost-competitive with respect to their competing alternatives in the medium or long-terms (Piria et al. 2013). For some, this means that support schemes for wind should be phased out. However, there is still a challenge for policy makers to encourage RES-E investments with the expected move to market-based instruments, since part of the revenues are received through the wholesale price and these are reduced when a greater penetration of RES-E takes place (merit order effect). Therefore, even where the full costs of (variable) renewables are lower than average market prices, policy intervention may be needed to ensure that sufficient investment is attracted to RES-E projects (Piria et al. 2013). Balancing Stability and Flexibility in RES-E Support Finally, a fundamental challenge for policy-makers is to balance the tradeoff between greater stability for investors and flexibility of the support scheme to adapt to changing circumstances in order to avoid overcompensation to

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RES-E generators. RES-E investors usually demand predictability and stability of the support scheme. As mentioned in Section 2, this is critical to ensure lower risks for investors and, thus, the effectiveness and even the efficiency of the support scheme. On the other hand, unexpected trends in, e.g., technology costs may result in a greater support level than initially envisaged. This overcompensation may result in a greater deployment level and, thus, much greater support costs than expected and those needed to trigger a certain RESE capacity. Obviously, ex-post changes by the government in the support levels leading to retroactive cuts would increase risks for investors and lead to an unstable investment climate. As stated by EC (2013), changes that reduce the return on investments already made alter the legitimate expectations of business and discourage investments. This trade-off is possibly more likely under certain instruments and design elements than under others, i.e., under FITs without degression components. Other challenges cannot be tackled directly by RES-E policy makers in the MS, but could be mitigated at EU level. One of the most relevant is the interaction of multiple targets (GHG reductions, RES deployment and energyefficiency targets), which may lead to conflicts (see del Río, 2014). This is obviously a challenge for EU policy-makers. The setting of those targets should be coordinated, ensuring that they do not weaken each other. The above discussion has identified the main challenges which are common to renewable technologies in general and to wind energy projects in particular. Notwithstanding, challenges are likely to differ per region and country. For example, as mentioned above, some are likely to have more credit restrictions and budget constrains than others.

6. INTEGRATION OF CHALLENGES The previous sections have provided different types of elements in the discussion of challenges for RES-E and wind energy policy in a 2030 perspective. This section is devoted to their integration, i.e., to their interrelationship. We bring everything together, providing links between challenges, assessment criteria and policy options to address those challenges. The following table summarises the result of our analysis. The first and second columns refer to each type of challenge. The third column informs about the assessment criteria the particular challenge is associated to.

Table 2. Relating challenges and policy options Challenge

Type of challenge

Adapting support levels to trends in Technology-related wind costs Precise combination of R&D Technology-related support and deployment support for less mature wind technologies (wind off-shore technologies) Uncertain evolution of resource Technology-related potentials Lower budget for wind support Macroeconomic-related

Access to finance (credit Macroeconomic-related restrictions) Current policy discussion Implementation of market-based instruments in general and auctions in particular

Assessment criteria involved Policy options addressing the challenge (examples) Support costs Flexible degression (FITs), tenders Effectiveness Social acceptability Innovation and cost It depends on the wind technology reductions (independent technology assessments) Support costs Effectiveness Effectiveness Support costs

Effectiveness Support costs Effectiveness Systems costs (administrative costs)

Contract-for-differences Decouple support from changes in budget (consumer-financed support might be more stable than budgetbased support. Specific instruments facilitating access to credit. Use existing institutional structures to the extent possible

Table 2. (Continued) Challenge

Type of challenge

Making auctions and others MBIs effective and efficient

Current policy discussion

An EU target without MS targets Current policy discussion An EU target with MS targets Current policy discussion Merit order effect reducing wholesale Current policy discussion prices and revenue for RETs

Assessment criteria involved Policy options addressing the challenge (examples) Effectiveness Design elements (see text) System costs Policy costs Social acceptability Effectiveness Inherent trade-off System costs Inherent trade-off Effectiveness CfD, TGCs, FITs, auctions System costs Policy costs Effectiveness Flexible degression under FITs and Support costs FIPs

Balance the trade-off between a greater stability and flexibility to adapt to new circumstances. Delays in administrative procedures

Current policy discussion

Administrative-related

Effectiveness

Trade-off NIMBY related to wind projects concentration vs. allocative efficiency Social rejection of high or escalating support costs

Social-acceptance

Costs falling disproportionately on a given group of the population

Social-acceptance

Effectiveness System costs Social acceptability Support costs Effectiveness Social acceptability Policy costs Social acceptability

Social-acceptance

Streamline administrative procedures (see text) Stepped FITs (but inherent trade-offs) Information campaigns on the local benefits of RES-E deployment Provide compensations to losers.

Policy Challenges for the Deployment of Wind Energy Projects … 155 In other words, it tells in what sense this specific challenge is really a challenge, i.e., because it relates to at least one criteria which is relevant for policy-makers. The final column provides examples of how the challenges could be tackled with policy options. For example, the first one (adapting support levels in wind costs) is a technology-related challenge. It is relevant in order to avoid overcompensation to wind energy producers and keeping support at reasonable levels. This also affects the social acceptability of the scheme, since high or substantially escalating costs may induce a public backlash against the policy. And it is also relevant in order to comply with RES-E targets (in case there are any) and to increase wind deployment (effectiveness) since failure to adjust support levels to unexpected increases in technology costs may lead to revenues (support levels) being below those costs, which would not be attractive for potential investors. This potential problem can be circumvented by implementing instruments (such as auctions) or design elements (such as flexible degression in FITs) which allow governments to adjust support levels automatically over time (for new plants).

ACKNOWLEDGMENTS This paper builds on an analysis conducted in the Intelligent Energy Europe (IEE) project “Dialogue on a RES policy framework for 2030 (Towards2030-dialogue).” The TOWARDS2030-dialogue project is an initiative that could be established thanks to the financial and intellectual support offered by the Intelligent Energy Europe (IEE) Programme of the European Commission, operated by the Executive Agency for Small and Medium Enterprises. For more details on the project, see http:// towards2030.eu/

REFERENCES Andersen, M.M., 2006. Eco-Innovation Indicators. European Environmental Agency, Copenhagen, February. Bailey, I., Ditty, C., 2009. Energy markets, capital inertia and economic instrument impacts. Climate Policy, 9(1), 22-39.

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Boie, I., Fernandes, C., Frías, P., Klobasa, M., 2014. Efficient strategies for the integration of renewable energy into future energy infrastructures in Europe – An analysis based on transnational modelling and case studies for nine European regions. Energy Policy, 67, 170-185. Borenstein, S., 2011. The private and public economics of renewable electricity generation. National Bureau of Economic Research, No. w17695. Council of European Energy Regulators (CEER), 2013. Implications of nonharmonised renewable support schemes–Consultation Document. CEER, Ref: C11-SDE-25-04, Brussels. Chameides, W., Oppenheimer, M., 2007. Climate Change: Carbon Trading over Taxes. Science, 315, 1670. Del Rio, P., 2010. Analysing the interactions between renewable energy promotion and energy efficiency support schemes: The impact of different instruments and design elements. Energy Policy, 38(9), 4978–4989. Del Río, P., 2014. On evaluating success in complex policy mixes: the case of renewable energy support schemes. Policy Sciences, 47 (3), 267-287. Del Río, P., Cerdá, E. 2014. The policy implications of the different interpretations of the cost-effectiveness of renewable electricity support. Energy Policy, 64: 364-372. Del Río, P., Labandeira, X., 2009. Climate change at times of economic crisis. Economía, 5, 09. Del Río, P., Linares, P., 2014. Back to the future? Rethinking auctions for renewable electricity support. Renewable and Sustainable Energy Reviews, 35, 42-56. Del Río, P., Mir-Artigues, P., 2014. Combinations of support instruments for renewable electricity in Europe: A review. Renewable and Sustainable Energy Reviews, 40, 287-295. Del Río, P., Ragwitz, M., Steinhilber, S., Resch, G., Busch, S., Klessmann, C., De Lovinfosse, I., Van Nysten, J., Fouquet, D., Johnston, A., 2012. Assessment criteria for identifying the main alternatives. D2.2 report under the beyond 2020 project, funded by the Intelligent Energy—Europe program http://www.res-policy-beyond2020.eu/. Del Río, P., Tarancón, M.A., Peñasco, C., 2014. The determinants of support levels for wind energy in the European Union. An econometric study. Mitigation and Adaptation Strategies for Global Change, 19 (4), 391-410. Downing, P. B., White, L. J., 1986. Innovation in pollution control. Journal of Environmental Economics and Management, 13(1), 18-29.

Policy Challenges for the Deployment of Wind Energy Projects … 157 Driesen, D.M., 2003. Does emissions trading encourage innovation?. Environmental Law, 32. Edenhofer, O., Carraro, C., Hourcade, J.C., Neuhoff, K., Luderer, G., Flachsland, C., Jakob, M., Popp, A., Steckel,J., Strohschein, J., Bauer, N., Brunner, S., Leimbach, M., Lotze-Campen, H., Bosetti, V., De Cian, E., Tavoni, M., Sassi, O., Waisman, H., Crassous Doerfler, R., Monjon, S., Dröge, S., Van Essen, H., Del Río, P., Türk, A., 2009. The economics of decarbonization. Report of the RECIPE project. Potsdam Institute for climate impact research, Potsdam. Ellerman, A.D., 2013. Personal Communication. Madrid, January 11th 2013. Ellerman, A. D., Buchner, B. K., 2008. Over-allocation or abatement? A preliminary analysis of the EU ETS based on the 2005–06 emissions data. Environmental and Resource Economics, 41(2), 267-287. European Commission (EC), 2001. Directive 2001/77/EC of the European Parliament and of the Council. Official Journal of the European Communities, 283 (33), 33-40. European Commission (EC), 2005. Communication from the Commission The support of electricity from renewable energy sources {SEC(2005) 1571} /* COM/2005/0627 final */. European Commission (EC), 2008. The support of electricity from renewable energy sources. Accompanying document to the Proposal for a Directive of the European Parliament and of the Council on the promotion of the use of energy from renewable sources. {COM(2008) 19}, SEC(2008) 57, Brussels. European Commission (EC), 2009. Directive 28/2009/EC European Parliament and Council, 2009. Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. European Commission, 2011. SPECIAL EUROBAROMETER 364 Public Awareness and Acceptance of CO2 capture and storage. Wave 75.1. Available at: http://ec.europa.eu/public_opinion/archives/ebs/ebs_364_ en.pdf. European Commission (EC), 2013. European Commission Guidance for the Design of Renewables Support Schemes. Accompanying the Document Communication from the Commission. Delivering the Internal Market in Electricity and Making the Most of Public Intervention. Brussels. SWD (2013) 439 final 2013.

158

Pablo del Río and Cristina Peñasco

European Commission (EC), 2014c. Climate Change report. Special Eurobarometer 409. Available at: http://ec.europa.eu/clima/citizens/ support/docs/report_2014_en.pdf. European Commission (EC), 2014a. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the regions. A policy framework for climate and energy in the period from 2020 to 2030. COM/2014/015 final. European Commission (EC), 2014b. Guidelines on State aid for environmental protection and energy 2014-2020, COM (2014) 2322, 2014. EWEA, 2015. Wind in power 2014 European statistics. EWEA. Available at: http://www.ewea.org/fileadmin/files/library/publications/statistics/EWEAAnnual-Statistics-2014.pdf. Gagelmann, F., Frondel, M., 2005. The impact of emission trading on innovation-science fiction or reality?. European Environment, 15, 203. Green, R., Yatchew, A., 2012. Support schemes for renewable energy: an economic analysis. Economics of Energy & Environmental Policy, 1(2). GWEC, 2013. Global Wind Report. Annual Market update 2012. Heal, G., 2010. Reflections—the economics of renewable energy in the United States. Review of Environmental Economics and Policy, 4(1), 139-154. Held A., Ragwity, M., Gephart, M., de Visser, E., Klessmann, C., 2014. Design features of support schemes for renewable electricity. A report within the European project “Cooperation between EU MS under the Renewable Energy Directive and interaction with support schemes.” Ecofys Netherlands, Utrecht. Huber, C., Faber, T., Haas, R., Resch, G., Green, J., del Rio, P., et al. 2004. Green-X. Deriving optimal promotion strategies for increasing the share of RES-E in a dynamic European electricity market, Final report of the project GREEN-X, Energy Economics Group, Vienna University of Technology, Vienna. IEA, 2008. Deploying renewables. Principles for effective policies. IEA/OECD, Paris. IEA, 2011. World Energy Outlook 2011. IEA/OECD, Paris. IEA, 2012. Key world energy statistics. IEA/OECD, París. IEA, 2015. Key Renewables Trends. Excerpt from: Renewables Information 2015. IEA/OCDE, París. IPCC, 2011. IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation. Prepared by Working Group III of the Intergovernmental Panel on Climate Change [O. Edenhofer, R. PichsMadruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, T. Zwickel, P.

Policy Challenges for the Deployment of Wind Energy Projects … 159 Eickemeier, G. Hansen, S. Schlömer, C. von Stechow (eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1075 pp. IRENA, 2013. Renewable Power Generation Costs in 2012: An Overview. IRENA, Abu Dhabi. IRENA, 2014. Evaluating Renewable Energy Policy: A Review of Criteria and Indicators for Assessment. IRENA, Abu Dhabi. IRENA, 2015a. Renewable Energy and Jobs Annual Review 2015. IRENA, Abu Dhabi. IRENA, 2015b. Renewable Power Generation Costs in 2014. IRENA, Abu Dhabi. Jung C., Krutilla, K., Boyd, R., 1996. Incentives for Advanced Pollution Abatement Technology at the Industry Level: An Evaluation of Policy Alternatives. Journal of Environmental Economics and Management, 30, 95-111. Lee, B., Lliev, L., Preston, F., 2009. Who owns our low carbon future? Intellectual Property and Energy Technologies. A Chatham House Report, London. Malueg, D. A., 1989. Emission credit trading and the incentive to adopt new pollution abatement technology. Journal of Environmental Economics and Management, 16(1), 52-57. McKinsey, 2009. Pathways to a low-carbon economy. McKinsey & Company. Mendonça, M., Jacobs, D., Sovacool, B., 2010. Powering the green economy – the feed-in tariff handbook, Earthscan, London. Milliman, S.R., Prince, R., 1989. Firm incentives to promote technological change in pollution control. Journal of Environmental Economics and Management, 17 (3), 247-265. Mitchell, C.H.C., Swain, J., Pokharel, G.R., Kammen, D., Wang, Z., Fifita, S., Jaccard, M., Langniss, O., Lucas, H., Nadai, A. et al. 2011. Policy, Financing and Implementation, in: Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., Zwickel, T., Eickemeier, P., Hansen, G., Schlömer, S., Stechow, CV., (eds.), IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation, Cambridge, UK, Cambridge University Press. Neuhoff, K., Dröge, S., Edenhofer, O., Flachsland, C., Held, H., Ragwitz, M., Strohschein, J., Türk, A., Michaelowa, A., 2009. Translating model results into economic policies. RECIPE Working paper. Available online at www. pik-potsdam. de/recipe.

160

Pablo del Río and Cristina Peñasco

Newell, R.G., 2008. A U.S. Innovation Strategy for Climate Change Mitigation. Washington, DC: Brookings Institution. Nordhaus, W., 2008. A question of balance. Weighing the Options on Global Warming Policies. Yale University Press- New Haven & London. Panzer, C., 2012. Investment costs of renewable energy technologies under consideration of volatile raw material prices. PhD Thesis, TU Vienna. Piria, R., Lorenzoni, A., Mitchell, C., Timpe, C., Klessmann, C., Resch, G., & del Río Gonzalez, P., 2013. Ensuring renewable electricity investments. 14 policy principles for a post-2020 perspective. Ecofys. Pontoglio, S., 2008. The role of environmental policies in the eco-innovation process: evidences from the European Union Emission Trading Scheme. Paper presented at DIME International Conference Innovation, sustainability and policy, 11-13 September 2008, GREThA, University Montesquieu Bordeaux IV; France. Ragwitz, M., Held, A., Resch, G., Faber, T., Haas, R., Huber, C., Coenraads, R., Voogt, M., Reece, G., Morthorst, P.E., Jensen, S.G., Konstantinaviciute, I., Heyder, B., 2007. OPTRES – Assessment and optimisation of renewable energy support schemes in the European electricity market, Supported by the European Commission (D.G. Energy and Transport), Brussels. REN21, 2015. Renewables 2015 Global Status Report. REN21. Resch, G., Gephart, M., Steinhilber, S., Klessmann, C., Del Rio, P., Ragwitz, M., 2013. Coordination or harmonisation? Feasible pathways for a European RES strategy beyond 2020. Energy and Environment, 24 (1-2): 147-169. Resch, G., Ragwitz, M., Faber, T., Panzer, C., Haas, R., 2009. 20% RES by 2020. An assessment of the new EU RES policy framework, 10th IAEE European Conference, Viena. Rogge, K.S., Hoffmann, V.H., 2010. The impact of the EU ETS on the sectorial innovation system for power generation technologies-Findings for Germany. Energy Policy, 38 (12): 7639-7652. Rogge, K.S., Schneider, M., Hoffmann, V.H., 2011. The Innovation Impact Of The Eu Emission Trading System-Findings Of Company case studies in the German power sector. Ecological Economics, 70 (3): 513-523. Schmalensee, R., 2011. Evaluating policies to increase the generation of electricity from renewable energy. MIT Center for Energy and Environmental Policy Research. Steinhilber, S., Ragwitz, M., Rathmann, R., Klessmann, C., Noothout, P., 2011. Indicators assessing the performance of renewable energy support

Policy Challenges for the Deployment of Wind Energy Projects … 161 policies in 27 Member States, EU-funded RE-Shaping project, Intelligent Energy – Europe. www.reshaping-res-policy.eu. Stern, N. H., 2007. The economics of climate change: the Stern review. Cambridge University Press. Taylor, M., 2008. Beyond technology-push and demand-pull: Lessons from California's solar policy. Energy Economics, 30(6): 2829-2854. Taylor, M.R., Rubin, E.S., Hounshell, D., 2005. Control of SO2 Emission from Power Plants: A Case of Induced Technological Innovation in the U.S. Technological Forecasting and Social Change, 72(6), 697-718. Ueckerdt, F., Hirth, L., Luderer, G., Edenhofer, O., 2013. System LCOE: What are the costs of variable renewables?. Energy, 63 (0), 61-75. Uyterlinde, M., Daniels, B., De Noord, M., De Zoeten-dartenset, C., Skytte, K., Meibom, P., Lescot, D., Hoffman, T., Stronzik, M., Gual, M., Del Río, P., Hernández, F., 2003. Final report of the EU-funded project ADMIREREBUS Assessment and Dissemination of Major Investment Oppprtunities for Renewable Electricity in Europe using the REBUS tool, ECN, Petten, The Netherlands. Verbruggen, A., Lauber, V., 2012. Assessing the performance of renewable electricity support instruments. Energy policy, 45, 635-644. Woerdman, E., 2004. The Institutional Economics of Market-Based Climate Policy. Developments in Environmental Economics, Elsevier, Netherlands. Zyadin, A., Halder, P., Kähkönen, T., Puhakka, A., 2014. Challenges to renewable energy: A bulletin of perceptions from international academic arena. Renewable Energy, 69, 82-88.

INDEX # 20th century, 94

A abatement, 129, 157, 159 access, 95, 115, 126, 145, 153 accounting, 138 adaptation, 150 aerodynamic force, 10, 11, 13, 14, 16 aero-generator, vii, viii, 39, 42 Africa, ix, 93, 95, 103, 104, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120 age, 35 agencies, 146, 147 air mass motions, 2 air pollutants, 134 airfoil, 12, 13, 14, 16, 17 algorithm, 54 allocative efficiency, x, 122, 148, 154 angle of attack, 10, 11, 16 annual rate, 134 anxiety, 30 Asia, 95 Asian countries, 145 assessment, 27, 123, 132, 133, 141, 152, 160 asymmetric information, 149

atmosphere, 2, 3, 7, 96 attribution, 146 Austria, 131 authorities, 32, 133, 146, 147

B backlash, 148, 155 banks, 100 barriers, 133, 146, 147 base, 54, 104, 105, 144 batteries, 25, 102 benefits, 88, 116, 125, 134, 141, 145, 148, 154 Bernoulli principle, 12 biogas, 37, 138 biomass, 103, 107, 128 bird and bat mortality, 29 birds, 29, 33, 81 Bizerte wind farm, 32 blade rotation, 10 break-even, 96, 98

C cables, 23, 25, 99 calculus, 24 campaigns, 3, 154 capital intensive, 143 capsule, 16

164

Index

carbon, ix, 80, 94, 95, 104, 107, 123, 126, 128, 129, 142, 159 carbon dioxide (CO2), 22, 29, 30, 31, 32, 33, 80, 95, 104, 123, 124, 125, 126, 129, 143, 157 cascades, 36 case studies, 3, 34, 132, 148, 156, 160 certification, 147 challenges, vii, ix, 94, 96, 113, 114, 121, 122, 123, 128, 130, 132, 133, 134, 141, 142, 151, 152, 153, 155 chemical, 22 China, 95, 145 circulation, 7, 35 citizens, 141, 149, 158 classification, 135, 141 clean energy, 111 cleaning, 24 climate, ix, 35, 36, 93, 108, 123, 124, 125, 126, 129, 131, 141, 149, 152, 157, 158, 161 climate change, ix, 93, 126, 129, 141, 161 climate change issues, 129 coal, 95, 98, 103, 104, 105, 107, 112, 113, 119 collaboration, 116 combustion, 80 commercial, 27, 80, 97, 99, 103, 109, 113, 120 communication, 30, 48, 133, 151 communities, 30, 112, 141 community, 107, 110 compensation, 125 competition, 112, 140, 150 competitiveness, 124, 129 competitors, 136 complexity, 125 compliance, 129, 133 conception, 10, 16, 33 conditioning, 24 conference, 118, 120 conflict, 130 conservation, 37 construction, 27, 28, 32, 97, 104, 105, 109, 117

consumers, 3, 24, 27, 105, 113, 136, 137, 139, 149 consumption, 80, 104, 113 contingency, 100 convergence, 52, 53, 54, 57, 58, 61, 63 cooling, 147 cost, ix, 2, 19, 20, 21, 22, 23, 27, 28, 29, 30, 33, 35, 76, 88, 93, 94, 96, 97, 98, 104, 106, 109, 112, 113, 116, 119, 122, 125, 127, 128, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 145, 146, 147, 149, 150, 151, 153, 156 cost of CO2 avoided, 31 Council of Europe, 156 critical angle of attack, 10, 16 Croatia, 119 crude oil, 80, 103 culture, 104 cumulative distribution function, 5 customers, 95, 103, 114 Cyprus, 131 Czech Republic, 131

D damages, 125 data gathering, 87 debt service, 145 decision makers, viii, 3, 80, 89 decision-making process, 129 deficit, 31, 145 Denmark, 28, 131, 134, 147 Department of Energy, 37, 38, 107, 111, 118 deposits, 80 depression, 7 depth, 27 designers, vii, 1 detection, viii, 2, 3 detection of inefficiencies, viii, 2 developed countries, 95 developing countries, 95 diffusion, 126, 127, 128, 129, 140 digital technologies, 116 direct cost, 134

Index displacement, 3, 15 distortions, 150 distribution, 4, 5, 6, 34, 41, 44, 106, 114, 115, 117, 118, 119, 138, 149 diversification, 87, 98, 105, 124 diversity, 140 dizziness, 30 DME, 108 DOI, 37 double fed induction generator (DFIG), vii, viii, 39, 40, 41, 44, 46, 54, 55, 65, 66, 69, 74, 76, 100, 101, 114 drag force, 10 drag-based wind turbines, 13 DSM, 115

E economic activity, 145 economic crisis, 122, 129, 145, 156 economic development, 112 economic efficiency, 126 economic evaluation, 30 economic growth, ix, 93, 96, 105, 107, 116, 141 economic incentives, 84 economics, 98, 137, 156, 157, 158, 161 economies of scale, 97, 125, 127, 140, 142, 143 Egypt, 95 electric current, 98 electricity, ix, 2, 23, 27, 29, 32, 34, 37, 88, 90, 93, 95, 96, 97, 98, 100, 102, 103, 104, 106, 107, 111, 112, 115, 117, 118, 119, 121, 122, 123, 128, 129, 134, 135, 136, 137, 138, 139, 142, 144, 145, 150, 156, 157, 158, 160, 161 electromagnetic, 48, 98 emergency, 115 emission, 22, 31, 80, 95, 98, 104, 158 employment, 30, 112, 124, 148 employment levels, 148 endowments, 144 end-users, 3

165

energy, vii, viii, ix, 2, 3, 7, 10, 12, 13, 14, 19, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 37, 40, 42, 76, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112, 113, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 137, 140, 141, 142, 143, 144, 147, 148, 149, 151, 152, 155, 156, 157, 158 energy consumption, ix, 121, 122, 144, 149 energy efficiency, 156 energy security, 116 energy supply, 94, 105, 116, 124 energy transfer, 40 environment, viii, 22, 29, 31, 39, 87, 88, 146 environmental impact, viii, 2, 21, 23, 148 environmental organizations, 84, 87, 88 environmental protection, 131, 150, 158 environmental sustainability, 124 environments, 126 equality, 26 equipment, 20, 21, 22, 23, 27, 32, 97, 99, 123, 126 equity, 133, 149 Estonia, 131 ethanol, 35 EU ETS, 128, 129, 157, 160 Europe, 95, 122, 123, 124, 141, 145, 149, 155, 156, 161 European Commission, 122, 123, 131, 133, 140, 146, 147, 149, 150, 155, 157, 158, 160 European Parliament, 157, 158 European Union, ix, 121, 128, 156, 160 evolution, x, 88, 89, 122, 134, 141, 142, 144, 153 exploitation, vii, 1 Exponentiated Weibull distribution (EW), 4, 5, 34 exposure, 127 external costs, 29, 32 externalities, 124, 126, 127, 128, 130 extraction, 15, 64, 74, 80

166

Index

F fabrication, 11, 16, 21, 22, 25, 26 farmers, 30 farms, viii, 2, 29, 81, 97, 114, 120 financial, 90, 117, 128, 145, 146, 155 financial crisis, 145 financial institutions, 146 financial support, 90, 128 Finland, 131 flexibility, x, 122, 151, 154 fluid, 7, 11, 14, 36 force, 3, 10, 11, 13, 14, 15, 16, 17, 23, 26 forecasting, 7, 138 formula, 20, 24 fossil fuels, vii, 80, 98, 134 foundations, 29 France, 34, 131, 160 frequency distribution, 41 friction, 43, 74 fuel consumption, 22 fuel prices, ix, 93, 144 funding, 126

G geothermal heat, vii, 80 Germany, 28, 95, 147, 160 GHG, 125, 134, 152 global climate change, 88 global warming, 80, 81, 126 governments, 88, 122, 124, 137, 155 gravity, 12, 25 Greece, 147 greenhouse gas(es), ix, 29, 88, 93, 95, 104, 107, 109 greenhouse gas emissions, 29, 96, 107, 109 grid technology, 116, 120 grids, 102, 115, 138 grouping, 141 growth, vii, ix, 30, 93, 94, 96, 97, 104, 109, 116 growth rate, 94 guidelines, 146, 147

H habitat, 33 height, 97, 99 hemisphere, 7, 35 high wind potential, vii, 1, 2 homes, 29, 100, 109 House Report, 159 hub, 17, 27, 28, 97, 113 hybrid, 102

I Iceland, 37 identification, 123, 130, 132 IEA, 35, 94, 125, 128, 133, 134, 137, 142, 144, 158 Impact Assessment, 87 imports, 81, 98 improvements, 2, 112, 125, 127, 141, 142, 143 increased access, 116 increasing returns, 129 India, 95 induction, vii, viii, 14, 16, 17, 18, 39, 75, 76, 77, 100 industries, 32, 87, 123 industry, 88, 94, 97, 98, 99, 104, 105, 109, 113, 116, 123, 124, 142 ineffectiveness, 143 inefficiency, 143 inertia, 114, 155 inflation, 106 information technology, 39 infrastructure, 113, 114, 115, 117, 147 innovator, 125 insecurity, ix, 93 institutions, 117, 131 integration, ix, 21, 94, 96, 113, 116, 117, 120, 123, 152, 156 interference, 22, 29, 98 internal rate of return, 145 interoperability, 116 intervention, 124, 143, 151

Index investment(s), viii, 19, 20, 21, 23, 27, 32, 80, 87, 88, 90, 104, 107, 111, 112, 125, 126, 127, 134, 137, 142, 145, 146, 149, 151, 152, 160 investors, 34, 125, 134, 137, 140, 145, 147, 149, 151, 155 Italy, 147

J Japan, vii, viii, 79, 80, 81, 82, 83, 84, 87, 88, 89, 90, 91, 95, 118 job creation, 124, 141 justification, 123, 124

K

167

marginal cost curve, 139 marginal cost of wind energy, 2 market failure, 124, 125, 126 mass, 2, 3, 4, 8, 14, 15, 16, 24, 26, 27, 98, 143 materials, 25, 29, 98 matrix, 46, 87 maximum lift coefficient., 10 Mediterranean, 36 meteor, 2 methodology, 123 Mexico, 35, 95 Middle East, 95 modelling, 37, 41, 44, 45, 59, 74, 156 models, viii, 2, 3, 7, 24, 34 Morocco, 95 mortality, 29 most efficient wind farms, vii, 1

Kenya, 107

N L landscape(s), 81, 98 leakage, 129 learning, 125, 127, 140 legislation, 105, 111 lift based wind turbine, 13 Lift force, 10 Limpopo, 108 Lithuania, 131 load of the wind farms, 25 loan guarantees, 146 loans, 145, 146 Lyapunov function, 53, 54

M machinery, 35 magnet, 77, 101 magnetization, 100 magnitude, 54, 144, 148 management, 104, 105, 138 manpower, 116 manufacturing, vii, 2, 29, 113

national policy, 143, 150 National Research Council, 121 natural gas, 97, 103 Navier Stocke’s equations, 4 negative effects, 29 negative experiences, 150 negotiation, 134, 149 Netherlands, 131, 147, 158, 161 neutral, 127, 128 nonlinear systems, 53, 77 non-renewable resources, ix, 93 North America, 95 NREL, 35

O OECD, 134, 135, 158 oil, 30, 80 operations, 27, 97, 101, 106 opportunities, 148 optimal aerodynamic design, 23 optimal design of wind propellers, 4 optimal structural design, 23

168

Index

optimization, 19, 35, 36 optimum angle of attack, 10 oscillation, 115 overproduction, 138 ownership structure, 148

P parallel, 7, 10, 100 parity, 109 Parliament, 157 patents, 125 pathways, 123, 160 permit, viii, 2, 147 petroleum, 34, 80, 95, 104 physics, 34 PI Controller, vii, viii, 39, 40, 50, 51, 69, 70, 72, 74, 77 pipeline, 117, 142 pitch, 13, 64 plants, 32, 37, 105, 113, 114, 120, 150, 155 Poland, 131 polar, 7, 8 policy, vii, viii, x, 1, 80, 81, 84, 86, 87, 88, 89, 90, 91, 106, 110, 111, 118, 121, 122, 123, 124, 128, 129, 130, 131, 132, 133, 134, 137, 139, 140, 141, 142, 143, 144, 145, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 158, 160, 161 policy instruments, 129, 151 policy makers, vii, x, 1, 122, 123, 130, 131, 142, 145, 148, 150, 151, 152 policy options, 123, 152, 153, 155 pollutant(s), 22, 124 polluters, 129 pollution, ix, 93, 94, 116, 156, 159 population, x, 95, 122, 141, 148, 149, 154 population growth, 95 portfolio, ix, 94, 96, 107, 140 Portugal, 131, 134, 145, 147 power generation, viii, ix, 2, 32, 40, 80, 81, 82, 88, 90, 93, 96, 97, 104, 107, 110, 113, 136, 138, 160 power plants, 101, 113, 114, 138 predictability, 152

principles, 19, 160 private sector, 111, 125 probability, 5, 6, 41 probability density function, 5, 6 procurement, 112, 119 producers, 104, 111, 137, 140, 155 profit, 129, 140 profit margin, 140 profitability, 107, 144 project, 2, 27, 32, 88, 97, 109, 117, 132, 133, 147, 148, 151, 155, 156, 157, 158, 161 proliferation, 81 promote innovation, 129, 140 propeller global efficiency, 13 protection, 27, 30 prototype, 110 public administration, 147 public awareness, ix, 94, 96, 107, 148 public expenditures, 145 public opinion, 82 public policy, 129 public support, 136, 148

Q quantification, viii, 2 questionnaire, 84, 87

R R&D investments, 143 racing, 34 radar, 98 radiation, 1, 3, 4 radio, 22, 98 radius, 7, 17, 18, 74 rain, vii, 80, 128 rate of change, 14 reality, 23, 158 recovery, 145 reference system, 134 regulatory framework, 146 rejection, x, 122, 141, 148, 154

Index reliability, viii, ix, 40, 94, 96, 97, 113, 116 renewable energy, vii, viii, ix, 1, 3, 35, 37, 80, 81, 87, 88, 90, 93, 94, 95, 97, 98, 104, 106, 107, 109, 110, 111, 112, 113, 115, 116, 118, 119, 121, 122, 124, 125, 127, 130, 131, 132, 133, 136, 137, 139, 140, 141, 142, 144, 147, 148, 149, 150, 151, 156, 157, 158, 160, 161 renewable energy technologies, 107, 124, 127, 136, 139, 140, 147, 148, 149, 151, 160 repair, 27 requirement(s), 100, 115, 117, 138 RES, 37, 122, 123, 125, 130, 133, 134, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 158, 160 researchers, vii, 1, 5, 6, 7, 30 reserves, 80, 103, 114 resistance, 26, 75 resolution, 10, 108 resources, 80, 95, 97, 98, 107, 113, 114, 115, 128, 144, 148 response, 51, 52, 88, 131 response time, 51, 52 restrictions, x, 2, 122, 145, 152, 153 RETs, 142, 143, 144, 154 revenue, x, 122, 154 risk(s), 29, 127, 137, 146, 147, 148, 149, 152 risk perception, 146 ROC, 79 Romania, 131 roughness, 102 rowing, 34, 36 rules, 112, 147 rural development, 124

S safety, 31, 80 saturation, 44 savings, 29 second generation, 35

169

security, ix, 94, 96, 104, 107, 113, 116, 124, 140 services, 114, 115, 118, 138 shape, 5, 6, 11, 13, 16, 107 shortage, 81 showing, 122 Sidi Daoud wind farm, 32 signals, 67, 98 silicon, 142 simulation(s), viii, 36, 39, 41, 73, 74 sleep deprivation, 30 Sliding Mode Controller (SMC), vii, viii, 39, 40, 41, 52, 54, 59, 69, 70, 71, 72, 73, 74, 77 small business, 100 social acceptance, x, 122, 133, 141, 148 social effect of wind energy, 30 society, ix, 94, 96, 107 solution, 9, 19 South Africa, vii, ix, 93, 95, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 118, 119, 120 South African grid, vii, ix, 94, 96, 113, 116 Southern African Development Community, 116 Spain, 28, 75, 95, 121, 131, 144, 145, 146, 150 specialists, vii, 2, 3 speculation, 107 spillover effects, 125 spillovers, 143 stability, ix, x, 41, 52, 53, 76, 94, 96, 114, 115, 119, 122, 138, 151, 154 stabilization, 73 stakeholders, x, 122, 141 standard deviation, 84 state(s), 47, 52, 53, 57, 58, 60, 62, 98, 114, 119, 120, 130, 147, 149 state legislatures, 120 steel, 142 storage, 23, 27, 103, 115, 117, 142, 157 stroke, 34 structure, viii, 54, 80, 84, 85, 89, 112, 136 subsidies, 149 sugarcane, 35

170

Index

sunlight, vii, 80 suppliers, 116, 126, 140 supply chain, 109 surplus, 137, 139, 140 sustainability, 160 sustainable development, 87, 88, 117 sustainable energy, 111, 140 sustainable growth, 148 Sweden, 147 synthesis, 50

trade, x, 122, 126, 138, 151, 154 trade-off, x, 122, 151, 154 trajectory, 52 transaction costs, 138 transformation, 42 transmission, 16, 106, 114, 115, 117, 138, 147 transparency, 147 transport, vii, 2, 21, 22, 23, 25, 27, 29 transportation, 28, 95, 104 turbine performance, 2, 97

T U Taiwan, vii, viii, 79, 80, 81, 82, 83, 84, 87, 88, 89, 90, 91 target, ix, 37, 106, 110, 112, 115, 121, 122, 133, 137, 139, 143, 151, 154 tariff, 159 tax base, 30 tax credits, 126 tax incentive, 137 taxes, 27 taxpayers, 136, 137, 140 techniques, vii, viii, 39, 41, 74, 88, 102 technological change, 127, 159 technological progress, 31 technologies, x, 34, 97, 100, 115, 117, 122, 125, 126, 127, 128, 129, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 147, 148, 149, 150, 152, 153, 160 technology, 2, 10, 20, 33, 97, 100, 103, 105, 119, 123, 125, 126, 127, 128, 130, 133, 134, 136, 138, 139, 140, 141, 142, 143, 151, 152, 153, 155, 159, 161 temperature, 9 tension, 76 testing, 37, 87 Thala wind farm, 32 theoretical basic equations, viii, 2, 3 thermo economic modeling, vii, 37 thermoeconomic analysis, 35 tides, vii, 80 tonne CO2 avoided, 29 total energy, 12, 115

United Kingdom, 159 United States (USA), 37, 95, 98, 145, 158, 159 universal access, 111

V validation, 3 valorization, 2, 37 variable costs, 136 variables, 53, 144 variations, 115, 142 vector, 4, 26, 53, 60, 62 velocity, 4, 10, 12, 15, 16, 17, 18, 24 vertigo, 30 vision, 10, 33

W Washington, 37, 38, 160 waste water, 37 water, 3, 9, 35, 94, 99, 105, 116 water heater, 116 wave number, 9 waves, vii, 30, 80, 106 Weibull distribution, 4, 5, 6, 34, 41 West Africa, 107 wholesale, x, 122, 139, 151, 154 wind energy penetration, 29, 31

Index wind farm, vii, 1, 2, 3, 4, 10, 15, 16, 19, 22, 23, 25, 27, 29, 30, 31, 32, 33, 37, 81, 97, 98, 107, 109, 110, 112, 114, 120, 135 wind installation, vii wind intensity, 23, 27 wind maps, 10 wind market, 30, 95, 109, 118, 135 wind power, vii, viii, ix, 2, 3, 4, 13, 15, 27, 28, 29, 40, 41, 45, 75, 80, 81, 83, 87, 88, 89, 90, 94, 95, 97, 102, 107, 109, 111, 112, 113, 115, 116, 118, 119, 124, 128, 134, 135 wind power plant models, 2 wind projects, 30, 111, 117, 120, 134, 145, 154 wind sites, vii, 1, 4

171

wind speed(s), viii, 5, 6, 7, 8, 9, 18, 24, 25, 26, 34, 35, 39, 41, 43, 74, 96, 98, 101, 102, 103, 144 wind turbine blades, 2 wind turbine efficiency, 2, 15, 24 wind turbines, 2, 3, 10, 13, 16, 17, 19, 21, 25, 27, 28, 29, 30, 32, 35, 37, 75, 81, 88, 98, 99, 102, 119 World Bank, 105, 107

Y Yale University, 160 yield of a wind turbine, 24, 25