Review on methodological and normative advances in assessment and estimation of wind energy

Introduction

Energy is a very complex issue, since, on the one hand, there is a global need to systematically reduce environmental pollution ¹ ; at the same time, the world population and energy demand grow exponentially. As a result of that growth, greenhouse gases (GHG) emissions contribute to global warming directly and indirectly. Global warming also increases the frequency of extreme weather events, which causes both human losses and damage to public and private infrastructure of all kinds.^2–4 To help counteract the effects of global warming and the factors that produce it, citizens around the world have started to promote and harness cleaner and sustainable energies, especially renewable energies (RE). Kaivo-oja et al. ⁵ discussed the policies of the impact of the GHG in primary countries regarding the alterations in the electrical generation-demand trends. These trends have been analyzed using databases from the World Bank covering the period between 1961 and 2011. These databases indicate that energy mixes are changing. The World Bank noted that the production of nuclear energy decreased during that period and that this reduction has been replaced by RE and the trend towards replacement of conventional energies continues to increase. REN 21 et al. ⁶ reported that in 2014 consumption of all RE was 19.2%, 2.5% Nuclear Energy and 78.3% Fossil Fuels. In this study, the RE considered were as follows: bioenergy (biomass, biofuels, biogas); geothermal; hydropower (small scale); wind and solar energy (solar PV, CSP, solar heating/cooling). Investments in RE have multiplied; for example in 2015, global investments totaled $286 trillion ⁷ six times more than in 2004. In addition, wind power produced 8.1 million jobs around the world. ⁸ Although RE are not dispatchable and are intermittent, investments continue to grow to contribute to reducing the effects of global warming. Globally, new RE power capacity additions were 147 GW, while levelized cost of electricity (LCOE) dropped across sectors, stimulating a larger number and scale of projects. ⁶ Simultaneously, strategies with energy storage systems use electrolytic systems and other technologies for energy storage in the form of heat, including the hybridization of energy systems.^9,10 For example, Cuce et al. ¹¹ successfully applied the hybridization of thermo-solar and photovoltaic energy simultaneously to greenhouses in cold weather. The results are consistent with the trends of survival and well-being of the population in the countryside or the city; REN 21 et al. ⁶ reported that in 2015 heat capacity additions were 38 GW-Thermal.

Qolipur ¹² and Jebaraj and Iniyan ¹³ reviewed different types of RE sources to address emerging issues related to energy modelling, finding that the RE discussed are intermittent, non-storable and non-transportable or non-dispatchable, which agrees with the information reported by REN 21 et al. ⁶ Also, the authors found that the wind energy has become one of the most important sources of RE and to achieve energy demand balance globally on the electrical grid (EG), the RE require conventional energies that are more stable and controllable to balance them. Wind energy converts the kinetic energy from the wind into mechanical power, which is transformed into electricity. This transformation is possible, thanks to the wind turbines, which are organized in groups to form a wind farm. As will be further detailed in the following sections, it is essential to evaluate every aspect of the wind farm design, modelling electrical energy delivery to avoid problems in its electrical production system, reliability and security in electric-energy-grid penetration. This is important because of the extreme weather events, caused by global warming, not only causes global variability of the magnitude and distribution of the kinetic energy of the wind but also in extraordinary precipitation events and a global temperature increase, alters the electrical generation-demand, among other effects. These phenomena impact pollution, the economy, livestock activities and agriculture, ¹⁴ among others. Based on their impact, governments and international organizations must continually reconfigure and adjust their global and local economic and environmental policies (UNFCCC: United Nations Framework Convention on Climate Change, 1992, New York, signed by 185 countries; Kyoto Protocol 1997, Kyoto, Japan, signed by 129 countries; Montreal Protocol, 1989, Helsinki, Finland, signed by 154 countries) to face the eventual reduction of the consumption and availability of hydrocarbons used as conventional energy. These policies must fulfill international agreements that address issues related to the environment, the greenhouse effect, the ozone layer, among others.

Therefore, in order to contribute to the environmental impact and meet the needs of energy demand, studies on wind energy are available in the literature from various aspects such as its effect on CO₂ emissions, policies of the impact of the GHG, reliability assessment, forecasting and prediction methodologies, among other topics developed in this study, such as exploration of wind resources, advances in standards, limits of the exploitation, extreme destructive wind speed, reliability and costs. With respect to the emissions of CO₂, De la Rue du Can et al. ¹⁵ propose a methodology to allocate CO₂ emissions that result from combusted fuel to produce electricity and heat to the end-use sectors (industry, buildings, transport, agriculture, etc.), where the electricity and heat are actually consumed. The authors base their arguments on the Fifth Assessment Report of Working Group III of the Intergovernmental Panel on Climate Change (IPCC). The current situation makes the need for governments and their organizations to resolve the economic-energy-balance dilemma of direct and indirect emissions that produce GHG in a timely and coherent manner especially urgent. More precise assessment and sector allocation methodologies for indirect emissions must be developed, which could be used as a basis for consensus on mitigation policies without detriment to their respective populations’ economy and quality-of-life indices. ¹⁶ Mardfekri and Gardoni ¹⁷ developed a reliability assessment of offshore wind turbines. The authors proposed a probabilistic framework to assess the structural performance of offshore wind turbines under multiple hazards. They used a database of virtual experiments to generate a three-dimensional finite element analysis of two identical turbines. One was tested with virtual data subject to extreme wind speeds in the Gulf of Mexico (on the Texas coast, prone to hurricanes) and the other of the California coast subject to earthquake ground motions (a high-seismic region). The authors used the model developed to estimate the fragility of the support structure of a given wind turbine. Diverse configurations in the placement of wind turbine, such as the Fish Schooling Concept on Horizontal Axis Wind Turbines and Vertical Axis Wind Turbines were suggested by Islam et al., ¹⁸ as being more economical and efficient with respect to the land use and the wake effect.

On the other hand, within the aspects of interest of wind energy on forecasting and prediction methodologies, several models, methodologies and advances in wind power forecasting and prediction were found and reported by Foley et al. ¹⁹ in an in-depth review. The authors first discuss numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes, followed by a detailed description of statistical and machine learning methods to reduce the need for additional energy demand balance and reserve power. These predictions offer a high degree of reliability on the availability of wind resources. Ko et al. ²⁰ proposed a method to correct wind power forecasting by considering wind speed forecast error, which is represented in normal distribution. The authors calculated the biased mean of wind power forecast error using the analytical approach for wind speed forecast. Due to the variability and stochastic nature of wind power, a variant Gaussian Process for time series forecasting was introduced to address these issues. ²¹ This new method has proven to be capable of reducing computational complexity and increasing prediction accuracy at the same time. These authors also used the teaching-learning-based optimization method to train the model and to accelerate the learning rate. Similarly, an adaptive ensemble model for probabilistic wind speed forecasting (WSF) was suggested by Peng et al. ²² based on a combination of the adaptive ensemble of online sequential outlier robust extreme learning machine and the time-varying mixture copula function to perform multi-step WSF. More sophisticated automatic controllers for wind turbines require short and very short-term availability forecasts and monitoring, while estimation and assessments of wind resources should be made by specialists technicians following standards specific to wind energy assessment and control. These tools are also necessary to increase the grid-penetration and competitiveness of the RE in the conventional energies mix currently injected into the world’s regional power grids.

Modelling is necessary for each type of RE as a consequence of its demand and the increase in industrial activities, agriculture, transportation, population growth, and also by the transition of these activities towards a greater percentage of the demand of each region to contribute to its penetration in the energy mix. The advantages of these models are that they allow for the forecasting, planning and reduction of emissions with greater precision and certainty for the operators and the market. Jebaraj and Iniyan ¹³ reviewed emerging models such as energy planning models, supply-demand models, forecasting models, RE models, emission reduction models, and optimization models based on neural network and fuzzy theory for solar, wind, bioenergy and small hydropower energy. Addressing energy models with an econometric and macro-statistical approach, the authors identified efficiency and cost factors as critical parameters in the objective function formulation, while useful life, reliability, intermittent supply, site selection and social acceptance, among others, were recognized as prime factors for the long-term utilization of RE.

Having discussed some of the literature on RE, we will focus primarily on a review of the literature on wind energy. The objectives of this review are as follows:

To assess research with results that address important topics and new technology used in the field for: Assessment, wind energy production, estimation and prediction of wind speed and power, and wind energy penetration, among other applications.

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Table 1.

Overview of methods, tools, means, advantages and disadvantages used on estimations, assessments and modelling of wind resource.

Methods	Tools/means	(A)dvantages/(D)isadvantages
Method 1: Folklore	From interviewing people with local knowledge; identifying tilted trees on landscape; from onomatological practice such as: ‘Windy Wellington’ (New Zealand); ‘Windy City’ (Chicago); ‘Windeby’ (Denmark); ‘La Ventosa and La Venta’ (México); ‘Windy Standard’ (Scotland). 40	(A) Very inexpensive and fast. (D) There is almost always a tendency to overestimate, primarily because the wind in windy areas is usually evaluated in terms of the chill factor: when it is cold, even moderate winds feel strong. Secondly, there is no certainty that the area has been surveyed in its entirety. Should be used only if no other method is available. 40
Method 2: Measurements Only	Sensors: anemometers 44 ; vanes; thermometers; atmospheric pressure; Relative Humidity; masts and booms; data loggers; SODAR; LIDAR. Through measurements from third parties like: airport observations, regional meteorological services observations; Standards on wind resources assessment of a specific site IEC-61400-15 (available in 2018), or guidelines.40,41,43,45	(A) When ad-hoc measurements are made directly in the exact area, height and point. (D) There is a substantial risk of either severely over-determining if the study is based mainly on observations in built-up or under-determining the resource if the observations are mainly from low-roughness very exposed areas. (A) Using the standards on wind resource evaluation or AWESS. (D) Must be done for each specific site in three-dimensional HUB position. 40
Method 3: Measure – Correlate - Predict, (MCP) and Computational Fluid Dynamics CFD modelling	WA S P 46 ; MS-Micro 47 and Raptor 48 ; Raptor-NL.6 Even MCP-CFD. 49 Using a short measuring campaign at the site correlating to climatological time series representativity of 5 to 10 years of the specific site (SS). 40	(A) Advantages include the fact that only short-term on-site measurements are required. (D) Often the correlation between the on-site and the climatological time series is quite low, consequently producing more errors; (A) Cost of assessing the wind resource of a particular site. For many purposes these tools are adequate. 40
Method 4: Global Databases	From National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and European Centre for Medium-range Weather Forecasting (ECMWF). Using global databases of winds in the free atmosphere have made it possible to obtain data from the past 4 to 5 years according to geostrophic draw law: G = u * k ( u * z 0 + A ) 2 + B 2 , where G is geostrophic wind value 50 ; u * is friction velocity; k = 0.4 is von Karman constant; f: Coriolis parameter; z0 is surface roughness; A = 1.8; B = 4.5. 40	(A) Insofar as these databases are globe-spanning; the method may can be applied to any area; since high-level winds are used; local effects are not introduced in the first place; databases usually have a range of 10 years or more, the estimations are stable climatologically. (D) Resolution is in degrees of Geographical Coordinates (GC), (i.e., 100 km each GC degree near equator); the winds aloft are not representative of local sites; the Coriolis parameter is small or even zero in equatorial regions. 40
Method 5: Wind Atlas Methodology (CFD Models)	Scatterometers and other Satellite tools 51 guidelines are important sources for best practices 52 ; MERRA-NASA is a good source of datasets 53 ; used in countries with dense networks of observations 54 ; CFD. 49	(A) Enhances prediction accuracy compared to WA S P model where flow separation occurs or thermal stability effects become important in complex terrain. The CFD wind modelling software being used makes it necessary for modules to be calibrated against data captured from on-site measurements. 40
Method 6: Site Data-Based Modelling	LINCOM, MS-Micro, CFD, WA S P could also be used. If some kind of on-site measurement exists, either from a short-term campaign MCP calculation or both. 40	(A) It is free of extrapolation errors (in the case of non-MCP data) and no non-on-site data are required. (D) Possible lack of climatological representativity (in the case of non-MCP) and a risk of enhancing any measurement or correlation errors. 40
Method 7: Mesoscale Modelling (MS)	Weather Research and Forecasting (WRF) model 55 ; WRF-derived time series; Karlsruhe Atmospheric Mesoscale Model (KAMM) 52 ; Fifth-Generation Penn State/NCAR Mesoscale Mode (MM5); Mesoscale Compressible Community Model (MC2); Modern-Era Retrospective Analysis for research and Applications (MERRA). 56 The grid size of the models is of the order of a few kilometers. 40	(A) MS models are good capturing mesoscale processes; (D) Grid resolution of MS models is greater than 5 δ x , ( δ x ≅ 1 : 15 k m ) ; cannot describe topographic effect smaller than the grid; cannot explicitly resolve turbulence; rely on initial conditions, boundary conditions and sometimes extra local observations; MERRA-MS cannot provide wind speeds measurements at different heights on land directly 56 ; cannot explicitly describe roughness changes; an ensemble of mesoscale is needed to diagnose the likelihood of certain weather outcomes. 57
Method 8: Combined Mesoscale Microscale Modelling	Mesoscale KAMM combined with microscale WA S P KAMM/ WA S P, (resolution ∼ 5 km).40,58,59	(A) Microscale is complemented by WA S P, local prediction details < 5 km are solved by WA S P or WindScape, Raptor or Raptor NL. (D) Two or more software applications are needed. 40

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Table 2.

Standards IEC for WTG, for further information and standards. ⁹⁰

Item	Part	Title	Reference, edition (date)
1	WT – Part 1	Design requirements	IEC 61400-1:2005+AMD1:2010 CSV, Ed. 3.1 (2014-04-15)
2	WT – Part 1	Design requirements	IEC 61400-1:2005+AMD1:2010 CSV/COR1:2016, Ed. 3.1 (2016-02-03), Corrigendum 1
3	WT – Part 1	Design requirements	IEC 61400-1:2005/AMD1:2010, Ed. 3.0 (2010-10-13), Amendment 1
4	WT – Part 1	Design requirements	IEC 61400-1:2005, Ed. 3.0 (2005-08-31)
5	WT – Part 2	Small WT	IEC 61400-2:2013, Ed. 3.0 (2013-12-12).
6	WT – Part 3	Design requirements for offshore WT	IEC 61400-3:2009, Ed. 1.0 (2009-02-11)
7	WT – Part 4	Design requirements for WT gearboxes	IEC 61400-4:2012, Ed. 1.0 (2012-12-04)
8	WT – Part 11	Acoustic noise measurement techniques	IEC 61400-11:2012+AMD1:2018 CSV, Ed. 3.1 (2018-06-15)
9	WT – Part 11	Acoustic noise measurement techniques	IEC 61400-11:2012, Ed. 3.0 (2012-11-07)
10	WT – Part 11	Acoustic noise measurement techniques	IEC 61400-11:2012/AMD:2018, Ed. 3.0 (2018-06-15), Amendment 1
11	WEGS – Part 12-1	Power performance measurements of electricity producing WT	IEC 61400-12-1:2017, Ed. 2.0 (2017-03-03)
12	WEGS – Part 12-1	Power performance measurements of electricity producing WT	IEC 61400-12-1:2017 RLV, Ed. 2.0 (2017-03-03)
13	WT – Part 12-2	Power performance of electricity- producing WT based on nacelle anemometry	IEC 61400-12-2:2013, Ed. 1.0 (2013-03-28)
14	WT – Part 12-2	Power performance of electricity producing WT based on nacelle anemometry	IEC 61400-12-2:2013/COR1:2016, Ed. 1.0 (2016-09-29), Corrigendum 1
15	WT – Part 13	Measurement of mechanical loads	IEC 61400-13:2015, Ed. 1 (2015-12-21)
16	WT – Part 14	Declaration of apparent sound power level and tonality values	IEC TS 61400-14:2005, Ed.1 (2005-03-22)
17	WT – Part 21	Measurement and assessment of power quality characteristics of grid connected WT	IEC 61400-21:2008, Ed. 2.0 (2008-08-13)
18	WT – Part 22	Conformity testing and certification WT	IEC 61400-22:2010, Ed. 1.0 (2010-05-31)
19	WT – Part 23	Full-scale structural testing of rotor blades	IEC 61400-23:2014, Ed. 1.0 (2014-04-08)
20	WT – Part 24	Lightning protection	IEC 61400-24:2010, Ed. 1.0 (2010-06-16)
21	WEGS – Part 25-1	Communications for monitoring and control of wind power plants – Overall description of principles and models	IEC 61400-25-1:2017, Ed. 2.0 (2017-07-20)
22	WEGS – Part 25-1	Communications for monitoring and control of wind power plants – Overall description of principles and models.	IEC 61400-25-1:2017 RLV, Ed. 2.0 (2017-07-20)
23	WT – Part 25-2	Communications for monitoring and control of wind power plants – Information models	IEC 61400-25-2:2015, Ed. 2.0 (2015-06-30)
24	WT – Part 25-3	Communications for monitoring and control of wind power plants – Information exchange models	IEC 61400-25-3:2015 RLV, Ed. 2.0 (2015-06-30)
25	WT – Part 25-3	Communications for monitoring and control of wind power plants – Information exchange models	IEC 61400-25-3:2015, Ed. 2.0 (2015-06-30)
26	WEGS – Part 25-4	Communications for monitoring and control of wind power plants – Mapping to communication profile	IEC 61400-25-4:2016 RLV, Ed. 2.0 (2016-11-30)
27	WEGS – Part 25-4	Communications for monitoring and control of wind power plants – Mapping to communication profile	IEC 61400-25-4:2016, Ed. 2.0 (2016-11-30)
28	WEGS – Part 25-5	Communications for monitoring and control of wind power plants – Compliance testing	IEC 61400-25-5:2017, Ed. 2.0 (2017-09-20)
29	WEGS – Part 25-6	Communications for monitoring and control of wind power plants- Logical node classes and data classes for condition monitoring	IEC 61400-25-6:2016, Ed. 2.0 (2016-12-16)
30	WT – Part 26-1	Timed-based availability for WTGS	IEC TS 61400-26-1:2011, Ed. 1.0 (2011-11-14)
31	WT – Part 26-2	Production-based availability for WT	IEC TS 61400-26-2:2014, Ed. 1.0 (2014-06-04)
32	WTGS – Part 26-3	Availability for wind power stations	IEC TS 61400-26-3:2016, Ed. 1.0 (2016-08-09)
33	WT – Part 27-1	Electrical simulation models – WT	IEC 61400-27-1:2015, Ed. 1.0 (2015-02-13)

Wind energy and its implications

Wind energy represents a significant alternative, along with other RE, to mitigate the causes of global warming. Wind energy has economic and environmental advantages; however, global warming can affect its use. Pryor and Barthelmie ²³ discuss some parameters and climatological mechanisms of Northern Europe in which global warming influences the penetration and efficient use of wind energy. The authors consider the operating conditions of wind turbines and wind farms, as well as the methodological tools for evaluation of wind resources in specific areas to quantify the effects of global warming on the use of wind energy. The magnitude of global warming effects could adversely modify energetic availability. ²⁴ In this line, Brown et al. ²⁵ and Greene and Geisken ²⁶ discuss socioeconomic issues, while the relation between wind energy and environment and social–environmental impact are considered by Greene and Geisken ²⁶ and Bergmann et al., ²⁷ respectively. These factors also affect the availability of wind energy on the basis of environmental impact. ²⁸ The life cycle of wind turbine and wind farm technologies is studied by IRENA, ⁸ D’Souza et al. ²⁹ and Ghenai. ³⁰ Land use and possession around the world is addressed by Teixeira et al., ³¹ while Juárez-Hernández and León ³² and Owens and Driffill ³³ discuss its social acceptance. At this point, it seems reasonable to submit the following hypothesis: depending on the site of interest with probable wind energy globally, the effects of global warming could be a factor in favour or against its exploitation.

Standards for wind energy applications

Standards and normative references are an important issue that must be considered long before considering a WESSA and must invariably be applied during the study and design process. However, not all development path(s) in wind energy are supported by international and national standards. During this documentary review, we corroborated that in México, there are no national standards, standards analogous to international standards regarding these issues, significant regulations for its practice or guidelines for the use of wind speed records. Most records are typically obtained from measurements for meteorological purposes, agricultural or hydrological purposes ⁷⁵ such as airport activity, among others which have a substantially different reach and purpose from records that must be obtained specifically to evaluate the wind resources for the specific site. In many cases, records originally used for other purposes from the outset neither correspond to the exact site nor to the orographic or topographic conditions of the location, where the estimation and prognosis of the wind resources is needed.^76,77 Gaps in technical contents have also been identified in Mexican national law. ⁶⁶ The Law for the use of RE and the Financing of the Energy Transition, last reform Official Federal Gazette (DOF) 12-01-2012, ⁶⁷ has no regulations or recommendations, on subjects ranging from the measurement campaigns to the manufacture of the wind turbine and construction of wind farm. Canada is using its own standards, based on others like International Electrical Code (IEC), published by Canadian Standard Association. ⁷⁸ With regard to best practice recommendations for measuring wind resources in Argentina, the written guide by Mattio and Tilca ⁷⁹ is based on compilations of MEASNET ⁴⁴ and Dahlberg et al. ⁸⁰ and other references that are very useful for such tasks. ⁸¹ Internationally, there are a considerable number of standards applicable to testing turbines, generators, gearboxes, electrical and electronic equipment among others, related to the use and validation of the rated wind turbine specifications. There are also detailed recommendations for wind speed measurements and for the implementation of meteorological stations. ⁴³ However, the current standards of the IEC 61400 series were not addressed the Assessment of Wind Energy conditions for a specific site or a WESSA.

Existing standards are used to verify and certify the electrical and mechanical characteristics of wind turbine in all sizes, primarily of horizontal axle type. The results obtained from the evaluation, estimation and prognosis of wind resource of an specific site typically vary according to the techniques employed by specialists in the subject.^76,82 These differences can be attributed to the factors such as selection of measurement points, equipment quality, practical implementation of measurement campaigns, methodologies used, among others, including their presentation. Other reasons for such differences are due to the different climatic and orographic conditions of a specific site, e.g., mainland or coastal, as well as between the warm and very cold specific site. IEA WIND ⁸³ reported that measurement of wind speed in very cold locations reduces the quality and reliability of the logs, since the anemometers may stop, slow down or sustain damaged due to freezing or falling ice and snow, (see Figure 1), even if the sensors are equipped with heaters. Sensors affected by physical or environmental problems can neither be easily detected in the sensors nor in the registers, which makes them difficult to be identified (see Figure 2, green and red circles). Even more critical is when they consume electrical energy for heating. This energy may be absent at the site for periods not originally taken into account.^83,84 Standard IEC 60721–3-3 defines limits related to climatic conditions for electrical equipment, such as transformers and switch-gears, among others used in wind turbine and wind farm.

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Figure 1.

Frozen instrumentation in very cold climates.^83,84

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Figure 2.

Sensors with freezing or sticking failure. (Adapted from [^83,84]).

There are several reasons why specific standards for wind resources evaluation of a specific site are necessary: first due to the conditions mentioned above; and second, because it is common for the methodologies applied by different entities to have variations or different approaches as indicates. ⁸² Disparate results may be due to the lack of uniformity in the details of the evaluation, from the time the site is selected; installing measuring stations; the configuration of the sensors; data loggers; the frequency of sampling and saving of records; treatment of records; analysis and interpretation of records and results; conclusions and report presentations. The above was obtained from the revision of the development of wind assessments methods taking into account missing data and/or short measurement time like MCP.^85–89 Uncertainties have origins as diverse as the combinations of the conditions of each case, in each WESSA. MEASNET ⁴³ are developing standard IEC 61400–15 (IEC/TC 88/WG 15) to assess the characteristics of the wind resource, energy yield and the suitability of specific site of entry to the wind farm with the intention of defining a standard framework for the presentation of reports. This standard, primarily dedicated to standardizing calculation, reporting results and comparing these results within a suitable common framework; its publication will be released until September, 2020.

Standard IEC 61400–15 ⁹⁰ will include the following:

Definition, measurement and prediction of long-term meteorological characteristics and wind flow at the site.

Integration of long-term meteorological characteristics and wind flow with wind turbine and balance of plant characteristics to predict net energy yield.

Characterization of environmental extremes and other relevant plant design drivers.

Evaluation of the uncertainty associated with each of these steps.

Addressing documentation and reporting requirements to help ensure the traceability of evaluation processes.

A new IEC Collaboration platform user guide was published for the Standard IEC 61400–15 Version 1.6 (13-06-2019). ⁹¹ Currently International Standard IEC 61400–12-1:2005(E), part 12–1 ⁹² is used in combination with others as reference for the measurements and performance tests of wind turbine generators (WTGs) for the generation of electrical energy. Other methodologies typically found in the literature on matters of reliability of the electrical power output of the WTG and wind farms include autoregressive moving-average (ARMA models, Markov chain model, moving-average models, normal distribution and other indices that will be discussed in more detail below, ⁹³ ARMA, Institute of Electrical and Electronics Engineers-Reliability Test System (IEEE-RTS) and Monte Carlo simulation (MCS) were used by Billinton and Gao, ⁹⁴ for power supply to the distribution grid by means of the capacity credit evaluation methodology from P2/6 (standard in Great Britain), ⁹⁵ and using a hierarchical framework analysis of system security and adequacy, ⁹⁶ among others. However, in IEC, ⁹² there is no information about specific standards for the assessment or estimation of the wind resource in a specific site prior to the construction of a wind farm. In the forecast of wind energy penetration, there are previous works dedicated to estimate the reliability of WTGs and wind farms based on their ‘steady state’. Records such as wind speed, failure rates and wind turbine repair, as well as load demands for calculation and comparison of short and long-term reliability are critical. Kahrobaee and Asgarpoor ⁹⁷ use a Markovian method to study the reliability of power energy based on time series.

Standard ⁹² is directly associated with measuring and validating the performance of commercial WTG power output with rotor areas >40 m², see standard IEC 61400–13:2001(E). Currently a subcommittee of IEC/TC 88/WG 27 is working on a standard ⁹⁸ to define dynamic simulation models of WTG/Wind Farm generic typologies and configurations on the market. These models can be used to analyze the effect of the interconnection of a WTG/Wind Farm, on its stability related to EG to which they are connected. They should also be applicable to events such as a short circuit, loss of power or load changes, and WTG/Wind Farm separation systems from the grid. In Part 1 of this standard, the specifications of WTG electrical characteristics relating to their terminal connections are established. It includes specifications of a metrology to create models of future designs in WTG, whose validation procedures will include the tests as specified in the IEC 61400–21. In Part 2, the specifications of the electrical characteristics of a wind farm referring to their terminal connections, including its control and auxiliary equipment, are established. In regard to the Wind Turbines Generator System (WTGS), Standard IEC 61400:2015 OC, Ed. 1.0 (23-04-2015), IEC 61400-online Collection is a non-limiting and always updatable series consisting of the parts described by Table 2.

Peesapati and Cotton ⁹⁹ discuss the relevancy of the test levels set forth in 61400–24 in comparison to actual lightning strike data gathered from different wind farms around the world. The test levels (at which components should be tested) stated in the standards are based on those presented in IEC 62035. Although there are works that do not refer to the use of standard 61400–23, Rachidi et al. ¹⁰⁰ identified new challenges for lightning protection due to the geometry (height and number of blades) and materials (carbon fiber and plastic) with which rotor blades are currently being built; Zhou et al. ¹⁰¹ approached on full-scale testing of rotor blades. Göksu et al. ¹⁰² and Sørensen et al. ¹⁰³ study electrical simulation models for wind power plants (WPPs) based on the IEC 61400–27-1 standard. The use of the wind energy for implementation intersects with other disciplines and spheres in its scope of action; thus, it is necessary to identify the specific standard and its proper and current references for a better practice and to identify which standard or issues remain to be defined, see Table 2.

There are entities dedicated to the manufacture, construction and certifications of both wind turbine and wind farm. DNV GL the result of a merger between Det Norske Veritas and Germanischer Lloyd citeGL2013 is an internationally accredited registrar and classification society that began certifications in 1986; Bureau Veritas UK and Ireland ¹⁰⁴ apply a recognized certification scheme based on the IEC 61400–22 standard, where the main steps of wind energy Projects Certification are defined, including site assessment, wind farm design, manufacturing, installation and operation. Reliability has increased as a result of applying standards and certifications for WEG, but the industry still needs to improve the quality and quantity of both wind turbine and wind farm to reduce the cost of wind energy onshore and offshore, making this an unfinished task with many issues still unresolved, including having the standards for wind resource assessment.

Many countries are establishing electrical power grid interconnection and grid integration rules to facilitate EG stability, as RE increases penetration, especially at the local level. In general, EG operators have been able to ensure that the next generation of new offerings meet their interconnection requirements and can operate in accordance with new integration standards. Denmark often generates >100% of its electrical energy per hour of wind, exporting the excess of it and balancing through interconnections with Norway, Sweden and Germany. In the business environment, wind energy is becoming the cleanest, most profitable RE source. Many articles demonstrate that wind energy has the lowest cost of any newly installed energy.^35,105,106 It experienced a declaration in its growth in 2013 with a global value of 35,289 MW, compared to 40,564 MW and 44,799 MW in 2012; nonetheless the wind industry showed an annual growth between 2014 and 2015 from 51,473 MW in 2014 to 63,467 in 2015. Globally, 51,752 MW of new wind generation capacity was added in 2014, 63,467 MW in 2015 and 54,600 MW in 2016, which represents a slight reduction according to the global wind energy market statistics of the Global Wind Energy Council ¹⁰⁷ reaching a cumulative capacity of 486,749 MW by the end of 2016 and 539,581 MW by the end of 2017, see Table 3. Currently, the five countries with the largest onshore installed capacity are PR China, the United States, Germany, India and Spain.

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Table 3.

Top 10 cumulative capacity December 2016 and December 2017. ¹⁰⁷

Country	MW 2016	% Share 2016	MW 2017	% Share 2017
PR Chinaa	168,690	34.7	188,232	35
USA	82,184	16.9	89,077	17
Germany	50,018	10.3	56,132	10
India	28,700	5.9	32,848	6
Spain	23,074	4.7	23,170	4
United Kingdom	14,543	3.0	18,872	3
France	12,066	2.5	23,759	3
Canada	11,900	2.4	12,239	2
Brazila	10,740	2.2	12,763	2
Italy	9,257	1.9	9479	2
Rest of the world	75,577	15.5	83,008	15
Total top 10	411,172	84	456,572	85
World total	486,749	100	539,581	100

The accumulated offshore installed capacity for the periods 2011–2016 was 14,384 MW and 2011–2017 was 18,814 MW, since for the 2016 and 2017 were installed 2,318 MW and 4,431 MW, respectively (which represent 16.00% and 23.02% offshore global growth, see Table 4), with UK, Germany, PR China, Denmark, Netherlands and Belgium being the five countries with more wind farms in the sea; it represents 14.85% of global installed capacity in 2016 and 22.60% in 2017. Marine wind energy is installed predominantly in Europe, where >90% of the wind is located off the coast (offshore wind). Despite being an emerging market, marine wind energy currently accounts for a small percentage of installed capacity worldwide. China, Japan and South Korea have launched plans for significant offshore wind development in the near future. ¹⁰⁷

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Table 4.

Global Cumulative Offshore Wind Capacity in 2011–2017, ¹⁰⁷ see GWEC statistics 2017.

Country	Total 2015	New 2016	New 2017	Total 2016	Total 2017
UK	5100	56	1680	5156	6836
Germany	3295	813	1247	4108	5355
PR China	1035	592	1161	1627	2788
Denmark	1271	0	0	1271	1271
The Netherlands	427	691	0	1118	1118
Belgium	712	0	165	712	877
Sweden	202	0	0	202	202
Vietnam a	0	99 a	0	99	99
Finland	32	0	60	32	92
Japan	53	7	5	60	65
S Korea	5	30	3	35	38
USA	0.02	30	0	30	30
Ireland	25	0	0	25	25
Taiwan a	0	0	8 a	0	8
Spain	5	0	0	5	5
Norway	2	2	0	2	2
France a	0	0	2 a	0	2
Portugal	2	−2	0	0	0
World total	12,167	2,318	4,331	14,483	18,814

^aProvisional number.

The commissioning, operation and integration of wind farms into a conventional electrical generation consumption grid involves a series of phases and their respective stages. Three phases can be identified a priori: assessment, deployment, and operation. The assessment phase (prospection) covers from: The location of a WESS; selection of instrumentation and appropriate equipment for the measurement of wind variables; implementation of measurement campaigns; configuration of equipment and deployment of automatic weather stations (AWS); recovery and processing of records; identification and adjustment of the models ¹⁰⁸ ; the adjustment ranges from the estimation of parameters of the same models by regression or some other method; validation of models and wind resource estimation among others. The deployment phase starts from the selection of wind turbine; design of placement of wind turbine on the WESS or wind farm^109–111; design and construction of wind farm from its foundations to start-up; design and construction of the electrical substation; design and construction of high-voltage transmission lines (HVTLs) for interconnection to the regional or national EG. The operation phase consists: the commissioning of the interconnected wind farm to the EG; full operation of the wind farm; wind turbine and wind farm maintenance; operation optimization; electric generation efficiency; short-term wind farm output power prediction to improve the penetration of the wind energy in the EG. Costa et al. ¹¹² conducted a review of the state of the art in wind power short-term prediction, concluding that they had identified, on-line tools: the adoption of reference standards for the measurement of model performance; mathematical tools and prediction statistics; integration between mathematical/statistical and physical/meteorological models; development of more accurate upscaling/downscaling methods and new approaches on complex terrain among others. They also proposed more further research on adaptive parameter estimation, since the models change as each wind farm and its surroundings change and must be updated automatically. Further exploring the parabolic nature of many atmospheric boundary layer (ABL) flows and the CFD, Da Costa and Palma ¹¹³ conducted computational modelling of a large dimension wind farm cluster using domain coupling ABL flow, in particular, using a precursor simulation as a source of data flow to improve the target domain’s inlet flow description over the standard synthetic boundary conditions, one-directionally coupling the solutions to two simulations. Using this approach, they modelled the case of flow over a two-offshore wind farm cluster using two small coupled simulations. The authors matched the results of a single simulation including the full cluster, concluding on a significant computational saving time, on the order of 70%. The performance of the wind farm is evaluated by reliability indices of the results, considering it a power system (PS) for the electrical conversion and penetration of wind energy in the EG. These indices are often employed in conventional electrical PS, the standard reliability test system is the IEEE-RTS, as a reference and simulation model. The most common indices used are as follows^94,114–116: loss of load expectation (LOLE), measured in hours per year; capacity outage probability table; load duration curve; forced outage rate (FOR); loss of energy expectation, measured in MWh/year; frequency of loss of load (FLOL), measured occurrence/year; duration per interruption, measured hour/occurrence; load not supplied per interruption, MW per occurrence; energy not supplied interruption, MWh/occurrence,^93,95, time to fail (TTF) and Bayesian Network (BN),^117,118, yearly interruption cost, interruption energy assessment rate;^96,119,120 expected generated wind energy^120,121 among others: wind generation interrupted energy benefit, wind generation interrupted cost benefit, equivalent capacity rate and load carrying capacity benefit ratio.

Previous reviews on wind energy

Joselin-Herbert et al. ³⁹ conducted a broad-spectrum review, from models and methodologies for wind energy evaluation; WESSA selection models; wind turbine technology; aerodynamic models and wake effect among other aspects; discussing the WTG performance and reliability models; the problems related to its components (blade, gearbox, generator, transformer) and grid, etc. The authors review several papers that reported techniques for designing structural loads on wind turbine; control systems and energy conversion systems related to wind energy technology, where each decision should be selected according to actual wind energy conditions. For his part, Ahmet Duran Sahin ¹²² reviewed the topic of wind energy discussing issues such as the history of wind energy; meteorology for wind energy; energy–climate relations; wind energy technology; wind energy formulation and measurements; wind economics; wind and hybrid applications and current state of installed wind power capacity to date. In 2002, Ackermann and Lennart Söder ¹²³ published an updated version of their earlier article ‘Wind Energy Technology and Current Status: A Review’, providing an overview of the historical development of wind energy technology and discussing the status of grid-connected and stand-alone WTG as well. Since these reviews were published, the results and technology reported have changed, as this paper demonstrates. The review concludes with standards and regulations for the manufacture, sale, tests and maintenance of small or large wind turbine.

As a review of wind energy standards (see Table 2) demonstrates, this study concluded that there is no one standard released for the measurement evaluation and assessment of wind resource in a specific site (IEC stated the IEC 61400–15 standard might be ready by 2018).

Likewise, this study proposes that the new standard establish in detail four crucial stages:

Actions and procedures prior to the wind measurement campaign (selection of location of a WESS and instrumentation for short, very short and ultra-short sampling time logs, including wind measurement representativity, spatial mounting position, reliable measurement equipment, and data integrity classes, among others).

At this point, Standard 61400–12-1^92,98 is specified for WTG tests (related to the power performance measurement of the wind turbine and according to Chapter 6.8 (Data acquisition system) of that standard. As in the aforementioned standard, in order to minimize the loss of wind speed and wind energy dynamic (which changes output quickly with changes in wind speed), the authors of this study propose that a digital data acquisition system with a sampling rate per channel of at least 1 Hz also be considered to collect measurements and store pre-processed data at this rate (a burst of measurements). This mandatory (duty cycle) request should be included in Standard IEC 61400–15 for a WESSA.

It is important not to lose sight of the fact that if every detail is not specified at each stage, beginning from the current and future standards, every deviation not only directly impacts the exploitation of wind resources and the useful life of wind turbines but indirectly the economy and competitiveness of wind energy.

Methodologies to forecast the wind power/intermittency

Because wind power is intermittent and uncontrollable, there are several methodologies to estimate the wind resources and its stochastic dynamics. The subject is very broad and it may be analyzed from different points of view and at different stages before developing a wind farm, during operation or after the wind farm has already been in operation. Cheng et al. ¹²⁴ proposed that existing methodologies be reviewed and refined to obtain a more appropriate probabilistic model to determine the wind power. Ramakumar and Albretcht Naeter ¹²⁵ proposed evaluating wind energy reliability in the PS in an integral way, measuring the input of a wind turbine and simultaneously measuring the energy it delivers. Determining wind power and WTG energy output continues to be a challenge, as this can range from simple aspects such as determining the probability density function (PDF) to very complex calculations such as the dynamic modelling of wind flow passing through a wind turbine, wake effect among other variables. Breakdowns and repairs of wind turbines in wind farms interconnected to an EG may also be examined based on their design, manufacturer, operation, maintenance, etc. The reliability of wind energy as it interacts with EC has been approached through probabilistic models as discussed in Deshmukh and Ramakumar. ¹²⁶ Reliability is very important to the continuity of delivery and incorporation of wind energy on the EG. Wind speed is represented using hourly observed data, hourly mean wind speed data, ARMA time series, moving average (MA) time series, normal distribution, and Markov chain models using indices. ⁹³ Considering the hierarchical level within the energy system (ES), in offshore wind farm, Barberis Negra et al. ¹¹⁷ reviewed nine of the most relevant factors that influence on wind PS reliability, considering system adequacy and security by simulating different conditions and faults of interconnection grids using the Monte Carlo method to improve energy demand balance. When the geographic location of a WESSA with good and sufficient wind resources is not close to a HVTL or an electrical sub-station it is necessary to construct both subsystems. Ahmed and Abul Wafa ¹²⁷ concluded that the possibility of reconfiguring the EG should be evaluated in terms of cost-benefit if one already exists or installing a new electrical power transmission line or HVTL to deliver energy from the wind farm to the EG. This also brings with it the need to use a probabilistic method that takes into account the dynamics of a HVTL connected remotely between a wind farm and its conventional EG system by associating the cost and risk to the entire system. Atwa et al. ¹²⁸ used MCS and an analysis technique that includes a clearness index PDF to model the solar irradiation model for a solar–wind hybrid system. The authors concluded that MCS requires a much longer computational time effort compared to PDF using the Markov method to model the power generated by the wind, the model requires a stochastic process for Nemes and Munteanu ¹²⁹ ; Zhou et al. ¹³⁰ used Weibull and Rayleigh distributions, respectively, both obtained the wind parks first-order statistical average with the PDF. Singh and Lago-Gonzalez ¹³¹ used wind power statistics for a reliability assessment in systems interconnected with unconventional energy sources. Singh and Kim ¹³² analyzed reliability using combinatorial states and sub-classifying the entire EG into subsystems of conventional and non-conventional generating units, treating the loads as random variables connected to the output of the nonconventional correlated unit, to calculate the LOLE and for the expected undelivered energy. Soleymani et al. ¹³³ developed a methodology to model WPPs whose variation of the power curve is stochastic in nature. The method proposed by the authors is a combination of analytical and simulation methods, where the mechanical behaviour of each WTG is modelled through a sequential MCS method. This work uses the site’s wind speed and the Markov model, which is used to model the electrical power output of the wind farm. In this same study the effects of different parameters, such as LOLE, FLOL, loss of load duration, expected energy not supplied, the repair rate and the number of output power levels of the wind turbines system reliability were analyzed. Unit unavailability is also known as the unit FOR and may be calculated with equation (1), where MTTF denotes mean time to failure and MTTR denotes mean time to repair

Unavailability ( FOR ) = MTTR ( MTTF + MTTR )

(1)

Billinton and Huang ⁹³ used an ARMA model with time series to simulate the wind speed, obtaining a model of wind speed. Papaefthymiou and Klockl ¹³⁴ discuss the use of Monte Carlo Markov chains using synthetic wind speed series as an appropriate representation of wind speed and power considering the power curve of the EG. The WTG generates between zero and nominal power region, wind speed data are considered independent in the electrical power domain as the simplest model that can be obtained by reducing the states of the Markov model. IEEE Committee Report 1989 ¹³⁵ used IEEE-RTS for educational propose to test practical electrical PS on early reliability studies. This work was a precursor to the newest approaches to WTG reliability and adequacy performance time on EGs. Real and synthetic time series were used subsequently in ARMA in conjunction with Weibull and normal distribution among other models, and compared to the real system data in other approaches. Wangdee and Billinton ¹³⁶ used the concepts of the effective load carrying capability and the generation replacement capability to consider both generation system adequacy and security domains to identify the reliability of a wind turbine or a wind farm connected later on to The Roy Billinton Test System) ¹³⁷ and the IEEE-RTS ¹³⁸ in combination with synthetic wind speed time series simulation. ¹¹⁷

For the proper discussion, just to make causes and terminology clear, wind speed intermittency is related to weather and its disturbances (short time duration effects) and wind speed variability is related to climate change (long time duration effects). Sansavini et al. ¹³⁹ developed a stochastic model to simulate the operations and the line disconnection and reconnection events of the EG due to overloads beyond the rated capacity. The authors represented and propagated the uncertainties related to consumption variability, ambient temperature variability, wind speed variability and WTG variability. Chen et al. ¹⁴⁰ also addressed the correlation between the wind speed measured and WTG outage as two factors that affect the reliability model of a wind farm. To consider the uncertainties and dependencies between wind speed and WTG failure, the Copula method was applied to simulate the correlated random variables representing the wind speed and the number of outages in WTG units. In addition, the authors used the linear apportioning technique of its behaviour to create multistate reliability models of wind farm from hourly wind power models. The authors also present case studies in which the generation adequacy indices increase with the correlation of wind speed and WTG forced outage rate. The proposed multistate reliability models of wind farm provide the theoretical basis for the reliability assessment of electric PS with integrated wind power. Rajesh Karki et al. ¹⁴¹ applied Cholesky decomposition techniques on a WESS and ARMA to evaluate the effect of correlation between several wind farms on the adequacy indices of the wind-integrated systems. The authors conclude that for low-wind penetration, the correlation is insignificant, while high penetration is important in determining the minimum number of states to simplify the wind capacity model. Xie and Billinton ¹⁴² applied time shifting techniques to determine a new site and wind speed time series (WSTS) from a two-site correlation with wind energy conversion system. The authors applied linear interpolation techniques to estimate the optimal time series of a new wind generation system of WSTS, from their means and statistical deviations. Gao and Billinton ¹⁴³ assumed that wind speeds at different sites are correlated to some extent if the distances between sites are <12 km. The authors applied genetic algorithm methods to adjust the parameters of ARMA time series models in order to simulate correlated hourly wind speed with specified cross-correlation coefficients of two wind sites. In addition, they proposed a method to generate random numbers with specified correlation coefficients for application in a state-sampling MCS technique. This study demonstrated that the proposed method can be used in the adequacy assessment of a generating system incorporating partially dependent wind farms. In conclusion, wind speed intermittency should not be confused with wind power intermittency, even though wind speed change from minute to minute, it does not happen abruptly on a wind farm; wind energy changes gradually, a wind turbine does not start and stop at irregular intervals because the automatic regulation of each wind turbine and wind farm stabilizes the electrical energy output injected to the grid continuously, because its intermittency dynamic takes a very short time or short time (milliseconds, seconds, minutes even hours). When there is a pause or stoppage wind inflow to a wind farm, generally these are forecast previously, i.e. before the wind farms construction (minutes, hours, days and months); before its operation (monitoring and predicted ad-hoc, measured minutes and hours) and continuously (wind turbine self-control, measured in milliseconds and seconds). Although wind power is sometimes erroneously considered an intermittent energy source, the wind speed intermittency is not a problem for its harvest. In extreme conditions, such as strong storms, it will take hours for a wind turbine to shut down. Moreover, periods with zero wind power production are predictable and the transition to zero power is gradual. Each methodology reviewed in this paper has evolved from simple analysis to more complex and detailed methodologies and computational tools to increase the penetration of wind energy in the grid.

Methodologies to forecast the wind power/variability

In addition to being intermittent, wind speed can change its average intensity over the long term, this is known as ‘Inter-Annual Variability of Wind Speed’ (IAVWS). Watson et al. ¹⁴⁴ used 57 UK weather stations from the British Atmospheric Data Center (BADC-57) and a regional wind index calculated for the 1983–2011 period. The authors also considered a smaller group of seven stations called BADC-7 and an additional index for the 1957–2011 period. Both indices showed an IAVWS of 4% (this corresponds to a typical variation of 7% of the Capacity Factor). These indices were compared with indices from other sources, such as European Re-Analysis (ERA-40), calculated with bilinear interpolation showing declines in the indices of the BACD-57 and BACD-7 groups while those obtained by ERA-40 show an increase according to Brower et al. ¹⁴⁵ The differences founded between the above indices are important because they reveal the uncertainty of the prediction of energy production from the wind. Typically the estimated values of wind variability are made from AWS records, but their use has disadvantages such as: 1) WESSAs are not located near existing AWS; 2) AWSs are frequently protected near buildings, while WESSAs tend to be located in the upper reaches of steep terrain and other types of exposed terrain; 3) In most AWS, the configuration of the instruments, measurement protocols used, surroundings, locations and height of the towers change over the years, sometimes resulting in significant discontinuities in trends in their historical records. If the above disadvantages are presented with the existing data in the WESS, it is possible to use numeric models for climate prediction in Mesoscale, as do the National Center for Atmospheric Research; The National Center for Environmental Prediction, and The National Global Re-Analysis Network (Versions 1 and 2). These methods use data from the historical observations satellites, airplanes, balloons and surface stations. With these models, it is possible to generate time series on an atmospheric grid and to extend them back for several decades, including other variables, such as temperature, atmospheric pressure, humidity and precipitation among others. ⁴⁵ New versions of the climate forecast system reanalysis are available. As indicated by Saha et al., ¹⁴⁶ the NCEP model has a resolution of ∼38 km in the period from 1979 to date. Most of the parameters of this model are available every six hours, although the variables may be selected every hour. A wind data simulations’ comparison between the weather research and forecasting (WRF) and the SAR model was performed in two case studies of intense lee waves. It was generated with the interaction of the atmospheric flow and the orography over an area in the eastern Mediterranean according to Zecchetto et al. ¹⁴⁷ Soares et al. ¹⁴⁸ obtained data using the WRF model and evaluated them against wind resources assessment campaigns to better represent a blind test. The authors validated Mesoscale wind data quantifying the statistics of error between observed and simulated wind data to better understand the possible sources of deviations. The U.S. National Aeronautics and Space Administration (NASA), has a global data assimilation system (the Modern-Era Retrospective Analysis for Research and Applications) with a resolution of ∼55 km (0.5° latitude, 0.66° longitude), and records since 1979 to date, most of its parameters are also available every six hours and the variables can be selected every hour. ⁵³ The ERA was derived from the main model of the European Center for Medium-Range Climate Forecasts. ¹⁴⁹ The satellites of this center are equipped with scatterometers which are instruments with a microwave radar that provides high-precision radiometric measures of normalized radar cross section of the ocean surface from multiple viewing angles. ¹⁵⁰ The intensity of the signal returned from the surface of the ocean depends mainly on its roughness. This system includes the following models: ERA-15, ERA-40 and ERA-Interim, based on the integrated forecast system, with a horizontal resolution of ∼80 km (0.85° latitude) with records from 1979 to date, most variables are available every three hours. ⁵¹ Also, another wind data resource is available on the Internet like NESDIS. ¹⁵¹

In conclusion, the variability of wind energy needs to be examined in the wider context, not only from the perspective of the electrical stability and reliability of the PS, the individual wind farm, the wind turbines or their respective models but also in a broad context that includes every subsystem mentioned above and the dynamics of global or regional climate combined. The wind derives energy indirectly from the sun (it is from this process that the principal causes of the variability of wind speed and wind power originate); the wind does not blow continuously at any WESS. The lack of wind inflow at any wind farm belonging to a group of wind farms interconnected to the regional or national grid has little of the overall WE impact but may be visualized as a train. Just as each train car moves from one latitude to the next as the train moves down the track, the wind has periods with a specific quantity of energy. Thus, where there are many wind farms, the wind works in a sequence; the wind is always blowing somewhere. The breakdown of a single unit has a negligible effect on overall wind farm output availability. It is necessary to correlate the output of all wind farms of a region with the overall wind energy entering all wind farms in that region or system sequentially. This correlation means that throughout the entire interacting system (climate-weather-wind energy-wind turbine-wind farm-PS-load-other sources), the wind can be harnessed or harvested to provide stable output even if the wind is not available all the time at any WESS.

Exploration of wind resources using traditional and modern models (three cases)

The differences in the AWS heights and their respective differences in orography configurations, terrain and ruggedness imply that there are differences among the records available to any given WESS. For a quick approach to explore wind resources, it is common to use a priori wind resources, estimating its density at the same time. Because this type of models entails a certain degree of uncertainty (see Folklore and Measurement methods 1 and 2 in Table 1), it should always be corroborated with models such as Weibull, Rayleigh or others, using available records. In Veracruz, Mexico authors ¹⁵² statistically analyzed five meteorological stations distributed across the state with typical heights of 10 m and two anemometric stations (Punta Delgada and Perote) with measurements at 20 and 40 m, with power exponent values of α = 0.16 and 0.12 for, respectively. The authors used equation (2), known as the profile-Powers Law, with a six-year availability in measurements, and concluded that the wind speed begin to increase 8:00 h and decline in intensity after 18:00 h, while the direction of the wind prevails from the North-Northeast to South-East, only two stations recorded average an annual velocity above 5 m/s, Perote and Punta Delgada

v v 0 = [ h h 0 ] α

(2)

Alaydi ¹⁵⁴ also applied the Power Law Profile equation to estimate the wind power density extrapolated to a height of 50 m from measurements made at a height 10 m and used a two-parameter Weibull distribution model at two sites of Palestine. In the Gaza Strip, reporting an average wind speed of 4.2 m/s and maximum wind speed of 5 m/s for 60% of the time with a power density of 186.5 W/m², (Form factor λ = 3.52, scale factor κ = 1.9). For Rafah airport, the average wind speed is 3.01 m/s and 4.0 m/s as the maximum speed with a power density of 69.6 W/m² (Form factor λ = 2.85, scale factor κ = 1.3). A study in India used geographic information systems and spatial multi-criteria decision analysis for the most appropriate selection of a WESS for wind farms development projects using wind speed records stretching back of almost 20 years in stations 20-m high with 142-mm anemometers, classifying these sites into four-category development. ¹⁵⁵ These are not the only articles that have considered wind speed assessment and the estimation of wind power at heights different than those that have been measured using the power law of equation (2); those mentioned are only to illustrate how it has been applied.

Advances in standards, methodologies, procedures and training

Analysts around the world are using various tools and technologies for the assessment and estimation of wind resources. Rodrigo ⁸² conducted a study based on 72 questions asked to 48 different organizations in the wind energy evaluator community. From this survey, the author concluded that (1) The duration of the training received by analysts varied from one month to one year, with significant differences in the homogenization of technical criteria as well as in the valuation of uncertainties; (2) 50% of the organizations use conventional land stations and the rest are trying to use instruments with remote metering; (3) There is some consensus that the use of CFD may be more accurate than using linear models in the estimation of the wind resource in complex terrain; (4) Mesoscale models are being used by wind energy developers, 75% of them have generated regional atlases. This sector shows that it lacks methodologies to adapt data from mesoscale wind conditions to microscale and to estimation of extreme wind values, involving uncertainties; (5) The top research priorities in wind resources’ estimation focus on four methodologies: (I) Development of instruments with remote measurement and post processing techniques; (II) Validation of wind resources estimation models based on measurement campaigns dedicated to complex terrain, high seas, etc.; (III) Development of turbulence models for wind turbine startup; (IV) Development of models for inbound flow and around forested areas; (6) Scale reduction and microscale, non-neutral stability effects, modelling and CFD specifications are specific needs from developers, manufacturers and consultants respectively. The report concluded that the recipe for success is as follows: (1) wind resources –assessment specific training programmes; (2) verification and validation of methodologies with dedicated measurement campaigns and (3) definition of quality check procedures. To date, no articles have been found with a focus on procedures and standards except for MEASNET^43,44 and IEC, ⁹⁸ but these are for WTG operation tests and validation of small and large wind turbines (see Table 2).

Limits of the exploitation wind energy

The wind turbine converts kinetic energy (KE) into electric energy; from this conversion, a part of the energy is returned to the atmosphere in the form of heat and KE. Jacobson and Archer ¹¹¹ point out that as number of wind turbine increases over large geographic region, the power extraction first increases linearly, but then converges to a saturation potential not identified previously from physical principles or wind turbine properties. In this case, the authors used the WRF regional atmospheric model in which the wind farm interacts with the atmospheric flow to derive the maximum wind power generation rate. These authors proposed a new method to determine the maximum theoretical wind power potential on Earth, based on the concept saturation described by the saturation wind power potential (SWPP). SWPP is the maximum wind power that can be extracted from the wind upon increasing the number of wind turbines over a large geographic region, independently of social, climatic or environmental factors and considerations. The authors also defined the fixed WPP as the maximum power that can be extracted by a fixed number of wind turbines at decreasing installed density and increasing geographic area. The SWPP is calculated at 100 m.a.g.l., because the wind turbines extract (KE) from atmospheric (wind) flow. According to Miller et al., ¹⁵⁶ this extraction reduces wind speed output as it limits it to the rated electric generation of large wind farms. These interactions can be approximated using the vertical KE flux method (VKEFM). The authors of this study conclude that while the VKEFM predicts the time series, the maximum generation rate is underestimated by about 50%, using Betz limit and taking into account the losses during the wind conversion. The author estimates that the maximum power generation is between 20% and 26% of the instantaneous downward transport of KE contained in the wind from the previous wind turbine in the wind farm by a factor of 4 · ( 3 ) 1 2 · ( 27 ) − 1 regardless of the weather conditions of inflow to the wind farm. According to the authors, this method only required the information of wind speeds and friction velocity of the control climate to provide an estimate of a maximum wind power-generation rate. Halminton and Cal ¹⁵⁷ carried out experiments to investigate the development of a wind turbine wake in a fully developed standard Cartesian wind turbine array, concluding that the wake dynamics of wind turbines become independent of location in the wind farm after the fourth row. At this point, the velocity fields oriented normally to the mean convective flow. Statistics indicate that the mean flow is nearly axisymmetric in the near wake ( x D ≤ 3.0 ), then showed trends vertically upward, convected by the mean vertical velocity. The authors measured the speed of the wind using stereo particles images velocimetry in forward scatter.

Extreme destructive wind speed and others

Extreme wind speeds have a return period and it is necessary to know both their magnitudes and their period of return. Lakshmanan et al. ¹⁵⁸ performed analyses on 70 meteorological stations in India, using the daily gust wind data in km/h for each site. Using the Gumbel probability approach, the extreme values were derived from scattered wind speeds, considering a 50-year return period of wind speed with a value of 126 km/h (35–38 m/s) from 1969 to 2005. Razali ¹⁵⁹ used records from 12 meteorological stations in Malaysia in the 1975–2008 period to obtain the extreme value of wind speed using the Gumbel distribution method for the return periods of 10, 30, 50 and 100 years. The authors found that in the short term, the extreme value does not exceed 15 m/s; they determined that there is no risk of harm to the wind turbine in the vicinity of the AWSs that served as a reference; however, the risk of damage increases as it extend to the periods of 50 and 100 years. In the case of installations where electricity is generated from the wind speed, according to Hamilton and Cal, ¹⁶⁰ more wind farms would be at risk, including those dedicated to agriculture and applications such as irrigation among others, also because policies should always allow to provide higher percentages of wind energy to the energy mix of each nation, based on demand. For example, in the United States, 20% of its demand will be met by wind energy by 2030. This makes it very necessary to have records of the extreme wind values of each WESS. Extreme wind speeds are not only important for wind farm installations but also for other power generation facilities such as nuclear power plants; since as El-Shanshoury and Ramadan ⁴ reminds us, accidents could occur during the construction and operation of these nuclear plants. Larsén et al. ¹⁶¹ examined various sources of the uncertainties in the application of two widely used extreme value distribution functions: The generalized extreme value distribution (GEVD) and the generalized Pareto distribution (GPD) and propose their contributions as a guideline to applying GEVD and GPD to WSTS of limited length. The authors suggested that these series could be extrapolated from global or mesoscale models, but the lack of climatological representativity is a major source of uncertainty in the use of both models.

Reliability and economic policies

From the economic point of view, in the one hand, there are detractors that consider the cost of wind energy investments very high in the United States ¹⁶² argues, for example that the intermittency or lack of constancy of the wind compared to conventional energies as a weakness. The authors further state that it is always necessary to install transmission lines to transport energy from the wind farm to consumer centers. They mention that the use of superconductors would be an option, but recognize that this type of conductors is extremely expensive. In summary, the authors assert that the costs of the initial investment; the operation; environmental damage; the effects on birds and the maintenance of wind machinery outweigh the benefits that wind energy generates; however, they do not provide an example that compares them in technical and economic terms to other conventional energies. Their main argument involves the economic impact in terms of federal tax credits through the production tax credit granted to these investments and the accelerated authorization of their depreciation,^163,164 claiming taxes and subsidies from the macroeconomic balance point of view lead to wind energy being an energy mainly dependent on U.S. federal budget. On the other hand, there are others such as Nagashima et al., ¹⁶⁵ who consider that, compared to conventional energies, the environmental and economic benefits contributed by the entire value chain associated with wind energy to replace conventional energies are more positive than the possible environmental and economic damages produced. The authors’ approach uses the life-cycle Input–Output ⁶² and I-O analysis methods. The amount of positive induced production and added value outweigh the negatives induced by substituting part of a conventional power generation of electricity with wind power generation. In recent years, another new law of property called ‘wind rights’ has appeared. Although a handful of state legislatures in many countries around the world have enacted laws seeking to clarify the nature and scope of wind rights, significant uncertainties surrounding these rights remains according to Rule et al. ¹⁶⁶

Costs of wind energy

Generally, the indices LCOE representing the net present value of the unit cost of electricity over the resource generated are used to compare the level of energy costs between different energy sources or technologies.^167,168 It is necessary to establish a methodology that includes such costs as: capital, fuel, fixed costs, and variables of operation and maintenance. U.S. EIA-DOE ¹⁶⁹ used levelized avoided cost of electricity, which conceptually represents the cost of using or installing an HVTL properly to avoid costs by not needing them, costs compared to other energy sources where it is possible to determine their competitiveness in the energy market at least in the United States. In México, there is no similar methodology defined to estimate these costs; all are referred to European or U.S. procedures and guidelines, although documents can be found related to land tenure ⁶⁵ that cite international costs to estimate their economic and market dynamics. ¹⁷⁰

Analysis of wind energy state of the art

As the analysis of the works discussed in this articles indicates, the topic of wind energy is very broad because it involves researchers from many academic disciplines focusing on issues related to wind from the point of view of meteorological, mathematical, economic, energy models, among others. The literature recognizes the nature and characteristics of intermittence, variability, turbulence, extreme values and wind energy power, its limitations, reliability, including the benefits and negative effects that the presence of the wind power represents in the WESS, places where the authors have studied it or where wind energy is being harvested. There are also works that have analyzed the impact of the wind on building structures and its relationship to the environment. In addition, it is recognized that the wind undergoes changes due to global warming including other technical aspects related to the difficulty involved, such as uncertainty in the probabilities of quantifiable forecasts, based on various models, parameters and tools very useful in other topics, but subtly adapted to carry out the wind prospecting and estimation of the KE contained in the wind that must be converted into electrical power. There are studies from other disciplines that have expressed the need to visualize the political aspect, the social aspect, as well as the technical, economic and environmental aspects to properly guide efforts towards the use of wind energy, although these approaches are usually undertaken separately, it would be advantageous to have literature that incorporates all these aspects for a global understanding of the issue. From a technical-practical point of view, many of the registers or meta data such as those mentioned in the articles come from 10-m high earth stations or ‘scatterometers’ installed on orbital satellites that measure directly on the surface of seas and oceans (high seas) with radar technology from space but do not measure wind speed on the ground due to technical limitations preventing them from achieving the necessary accuracy and reliability. This clearly demonstrates the nature of the problem and the need for a review of the methodologies for the modelling, parameterization and estimation of the wind resources in WESS, as well as of the clear definition of procedures in terms of standards, normative references, equipment and tools to estimate and predict the energy content of the wind in the various WESS.

Conclusions

In this review, we have considered articles whose state of the art report practical experimentation, comparative reliability in the different methodological applications, based on the needs and environmental conditions presented by the WESS. Typically, it is carried out based on some classification or temporal range, defined as ultra-short, short, medium and long-term. Specifically, we have discussed articles focused on wind energy penetration and assessment and the competitive advance in the total energy mix coexisting in the EG. This article identified various topics of interest that merit further research, including the development of reliable technology for wind energy storage needs; forecasts and prediction of wind energy from short to long term; the Intermittent and Variable nature of Wind; climatic disturbances; the nonlinear characteristic of wind energy; the diversity of orographic conditions; variability in the operational conditions of wind turbines; wind farms design; the lack of homogeneity or reliability of the registers given different ranges of representativity and identification of WESS. Modelling for the forecasting and prediction of wind energy represents an even greater challenge.

From the analysis of wind energy state of the art, it may be observed that although the variety of works in relating to wind power is extensive, there is very little literature focused on directed to the standardization of the campaigns of measurement of the meteorological variables and modelling of specific sites to evaluate wind resources. On the one hand, this is because the vast majority of records come from very low-altitude stations surrounded by trees, buildings and other obstacles, at sites other than the most propitious locations for wind power, because the number of years of records available is reduced or incomplete and because records of satellite measurements on the surface of bodies of water or oceans are often distant many distant from the WESS. On the other hand, we were unable to find standards of specific methodologies and models for RE assessment since most of the existing standards are focused on land tenure, lease and use. Other elements related to the exploitation of wind energy that continue to merit further consideration include: The standards necessary for the manufacture of blades, towers, generators, controls, inverters and some electronic devices, including the calibration standards of sensors for meteorological stations; the need for further research on specific standards of measurement campaigns where the equipment is specified; technological development related to wind energy; wind farm configuration, heights, wake effects and deployment according to the type of terrain or WESS; compatibility of meta-data with mesoscale datasets. In addition, although there are studies comparing both models and tools, for the estimation of model adjustment parameters using wind speed registers, to date there are no specific standards or standards related to the recommended methodologies to present the wind resource assessment modelling and estimation results. There are many computational tools and technical groups that could help establish these standards to further drive the development of wind energy.