A review on the complementarity between grid-connected solar and wind power systems

Journal of Cleaner Production 257 (2020) 120617

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Review

A review on the complementarity between grid-connected solar and wind power systems Franciele Weschenfelder a, b, *, Gustavo de Novaes Pires Leite b, c, Alexandre Carlos Araújo da Costa a, d, Olga de Castro Vilela a, d, Claudio Moises Ribeiro e, Alvaro Antonio Villa Ochoa b, c, Alex Maurício Araújo a, b a

UFPE - Federal University of Pernambuco, Centre for Renewable Energy (CER), Brazil UFPE - Federal University of Pernambuco, Department of Mechanical Engineering (DEMEC), Brazil IFPE - Federal Institute of Pernambuco, Academic Department of Industrial Control (DACI), Brazil d UFPE-Federal University of Pernambuco, Department of Nuclear Energy (DEN), Brazil e UFES -Federal University of Espírito Santo, Centre for Exact, Natural and Health Sciences, Brazil b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 October 2019 Received in revised form 6 February 2020 Accepted 15 February 2020 Available online 17 February 2020

Renewable energy has been used as an alternative solution to fossil fuels aiming to supply the increasing energy demand while reducing greenhouse gas emissions. Solar and wind energy are prominent renewable energy sources, and many countries around the globe are investing in these sources, which makes their installed capacity to overgrow. Both sources present an inherent variable, partly complex and unpredictable nature what imposes challenges to national grid operators to determine the available amount of power at a given moment. The spread use of both solar and wind energy could engender a complementarity behavior reducing their inherent and variable characteristics what would improve predictability and operability of the electrical grid. The study of the combined use of wind and solar power is a fundamental aspect of large-scale grid integration. Therefore, the goal of this work is to make a critical review of the state-of-the-art approaches to understand and assess the complementarity between grid-connected solar and wind power systems through the analysis of different methodologies and locations. The literature survey revealed 41 papers that were analyzed in the manuscript. The combined use of wind and solar in many places results in a smoother power supply, which is crucial for the operability and safety of electrical grids worldwide. © 2020 Elsevier Ltd. All rights reserved.

Handling editor: Prof. Jiri Jaromir Kleme
s Keywords: Wind power Solar power Complementarity Grid-connected system

Contents 1. 2. 3. 4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Literature survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Conceptual framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Synthesis of the literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 4.1. Complementarity analysis for different regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 4.2. Critical analysis of different complementarity database and metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion and recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Declaration of competing interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

* Corresponding author. UFPE, Federal University of Pernambuco, Av. da Arquiria, Recife, PE, CEP: 50740-550, Brazil. tetura, s/n , Cidade Universita E-mail addresses: [email protected] (F. Weschenfelder), [email protected] (G. de Novaes Pires Leite), alexandre.acosta@ ufpe.br (A.C. Araújo da Costa), [email protected] (O. de Castro Vilela), claudio.ribeiro@ ufes.br (C.M. Ribeiro), [email protected] (A.A. Villa Ochoa), ama@ ufpe.br (A.M. Araújo). https://doi.org/10.1016/j.jclepro.2020.120617 0959-6526/© 2020 Elsevier Ltd. All rights reserved.

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1. Introduction Solar and wind are free, renewable, and geographically spread sources of energy. They are a technical and economically viable choice to substitute fossil fuel-based sources, contributing to the reduction of greenhouse gas emissions. Many countries around the world are investing hardly on these renewable sources to meet the increasing energy demand. In 2017, 98 GW of solar energy were installed worldwide, totaling a cumulative capacity of 402 GW, while 52 GW of wind energy systems were added in 2017, totaling 539 GW (REN21, 2018). Wind and solar are intermittent sources at different time scales ranging from minutes to years due to the dependence on weather conditions (Jerez et al., 2013; Zhou et al., 2018), which impose challenges to the national electrical grid operators. The variations of both sources do not present the same characteristics, and usually, wind and solar sources changes are not in tune concerning the phase-frequency structure and amplitude of variations (Carvajalromo et al., 2019; Silva-Leon et al., 2019). Therefore, a proper mix of both sources can be a way to attain, for instance, a partially smoothed total output power (Carvalho et al., 2019; Keeley and Matsumoto, 2018). Variability and intermittency of both sources can be better managed and predicted when those systems are used together (Arribas et al., 2010; Lehtola and Zahedi, 2019; Sovacool, 2009). In power systems with a significant share of solar and wind power, it is crucial to study correlations between power sources to match consumers’ requirements and optimize the spinning reserve n, 2011). Some essential points addressed in this paper are (i) (Wide which methods are used to quantify the complementarity between solar and wind power and (ii) how the geographic dispersion of the power plants impacts on the complementarity (Han et al., 2019; Hoicka and Rowlands, 2011; Jerez et al., 2013; Sun and Harrison, 2019). Some review papers exclusively covering combined solar photovoltaic (PV) and wind power have been published over the past few years. Hart et al. (2012) overviewed the analytical techniques used to measure the capability of wind and solar power to supply the electrical demand. Sinha and Chandel (2015) reviewed optimization techniques used for the design and development of solar PV-wind-based hybrid energy systems. Khare et al. (2016) have reviewed methods to analyze prefeasibility, sizing, modeling, control, and reliability aspects of solar PVewind energy hybrid systems. Engeland et al. (2017) focused the literature review on the space-time variability of climate variables driving the intermittency of wind, solar, and hydropower generation. These review papers provide a basis for understanding the use of solar PV-wind hybrid systems, mainly with a focus on sizing, modeling, and control. However, it was not found in literature an updated review paper targeting precisely the issues of the complementarity between solar and wind sources. The main aim of this article is to make a critical review of state-of-the-art approaches to determine the complementarity between gridconnected solar and wind power systems, which is a fundamental aspect for large scale grid integration. This study is structured as follows: Section 2 (Literature research) descripts how the paper research was carried out. Section 3 (Conceptual framework) provides the technical background to subsidize

the discussion on the complementarity topic. Section 4 (Synthesis of the literature review) summarizes the main results and a discussion of the selected articles. Finally, Section 5 (Conclusions) concludes the paper and suggests paths for future research. 2. Literature survey The search for papers was carried out on scientific repository and the papers were categorized based on the following criteria: (i) localization, (ii) chronological order. The first evaluation considered the geographical localization of the selected studies. Fig. 1 presents the results for the studies examining wind, solar, and other sources, such as hydropower, which shows that complementarity is a topic that has been studied in all continents except Africa. This fact is intriguing because a considerable potential can be found on the African continent for both wind (Bandoc et al., 2018) and solar sources (Pravalie et al., 2019), which certainly creates opportunities for future works. The other evaluation focused on the chronological order of the selected complementarity studies. Fig. 2 presents a summary of the results of the research in which the first study about complementarity found in the survey was published in 2009, and the number of publications increased rapidly and consistently in the following years, which demonstrates that it is still a hot topic. 3. Conceptual framework Wind and solar are important sources of energy in the worldwide scenario. However, they operate intermittently, imposing challenges to grid operators. In some cases, the concomitant combined production use of both sources flattens the production output, mitigating risks and increasing reliability for the electrical grid operators. The main objectives of the combined use of wind and solar sources are depicted in Fig. 3. The two main points of view of a complementarity analysis between renewable sources are: i. Smoothed total output power (Fig. 3a): both sources are combined to produce a steady total output to be adopted as a primary source (Heide et al., 2010; Jerez et al., 2013), being the small fluctuations (around the steady level) compensated by storage or fast power plants such as gas units. On the other hand, in a hydro-dependent electricity matrix, the combined (wind

Fig. 1. Complementarity studies localization for wind, solar, and other sources.

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Fig. 2. Chronological order of the selected complementarity studies.

Fig. 3. Hypothetical examples of a complementarity situation (a) ideal smoothing of total output power and (b) attending a specific load demand.

and solar) steady level could partially substitute the primary hydro source preserving the water reservoirs as massive storage for the whole system. Note that the above situations could be attained only in exceptional cases in which the contributions from both (solar and wind) sources are notable, being the wind capable of compensating the absence of the solar source during the night; ii. To attend a specific load demand (Fig. 3b): the total output can have significant variations, but the combined production of solar and wind sources needs to match a particular demand load profile. An optimal complementary scenario might concern any combination of, for example, the relative size of solar and wind systems, or to compare site options within a geographic region (Hoicka and Rowlands, 2011). Another important aspect is the definition of performance metrics, which are essential to evaluate a complementarity study. For the case of smoothed total output, a high complementarity index is characterized when input sources are out of phase. For the case of load-following, to have a high-level metric, the sum of the different sources’ inputs should be in phase with the load input.

4. Synthesis of the literature review A critical analysis was carried out on the papers found in the literature review, and a synthesis is provided in Table 1. The papers were examined focusing on the following features: year of publication, author, region, sources, metric and significant results.

4.1. Complementarity analysis for different regions The integration of wind and solar power into the electric power grid has significantly grown over the last years and is relied upon to develop to high levels in the next years. While electrical grid operators have satisfactorily dealt with the large degree of intermittency of wind and solar power, the increase of these sources in the grid would pose new difficulties. Specifically, fluctuations that wind and solar may prompt new electrical grid operation and planning procedures (Shaker et al., 2016). The issue of complementarity is addressed with a broad range of strategies and methods. Despite the geographic location or analysis approach, benefits can be linked to the integration of wind and solar power generation. The combined use of wind and solargenerated power is effective when they are integrated into a large number of geographically dispersed locations. The big challenge is

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Table 1 List of reviewed articles. Author

Region

Sources

Metric

Li et al. (2009)

Australia

Denault and Dupuis (2009)

Quebec (Canada)

Wind and solar Wind and hydropower

Correlogram, correlation coefficient and crosscorrelation Coefficient of variation and Kendall’s correlation coefficient

Moura (2010)

Portugal

(Lew et al., 2013; Lew and Piwko, 2010; Miller et al., 2014; National Renewable Energy Laboratory (NREL), 2010) Heide et al. (2010)

Arizona, Colorado, Wind and Nevada, New solar Mexico, and Wyoming (USA) Europe Wind and solar

Li et al. (2011)

Oklahoma (USA)

Halamay et al. (2011)

Pacific Northwest Wind, solar and ocean wave

Hoicka and Rowlands (2011)

Ontario (Canada) Wind and solar

n (2011) Wide

Sweden

Gerlach et al. (2011)

World

Beluco et al. (2012)

e

Palfi and Zambon (2013)

Brazil

Wind and hydropower

Jong et al. (2013)

Brazil

Liu et al. (2013)

China

Solar, wind, and hydropower Wind and solar

Jerez et al. (2013)

Iberian Peninsula Wind and solar

Standard deviation

Monforti et al. (2014)

Italy

Correlation coefficient

Chao et al. (2014)

Bohai Bay (China) Wind and solar

Plots of Supply guarantee rate

Santos-Alamillos et al. (2015)

Spain

Principal component analysis and canonical correlation analysis

Sales et al. (2015)

Fernando de Noronha Island (Brazil) The southern part Wind power and of the Iberian Concentrated Peninsula Solar Europe Hydropower, wind and solar Brazil

Thomaidis et al. (2016)

François et al. (2016b)

Schmidt et al. (2016)

Solar, wind, and hydropower

Wind and solar

Wind and solar Wind and solar Hydropower and solar

Wind and solar

Wind and concentrated solar and solar PV Wind and solar

Significant results

There is a possibility that wind and solar resources in Australia could be used to supply base loads The intermittency of wind power imposes a significant operational disadvantage in the face of hydro’s great flexibility when it comes to providing capacity at each moment in time Correlation coefficient The multi-objective model enables the optimization of the renewable mix, ensuring a minimum level of intermittency, a minimum nonguaranteed peak load Statistical analysis, hourly production It is feasible for the WestConnect region to simulation analysis, and sub-hourly analysis accommodate 30% wind and 5% solar energy using minute-to-minute simulations and source penetration adequacy analysis Minimum standard deviation of the mismatch The optimal seasonal mix is found to be 55% wind energy used to find the optimal seasonal mix and 45% solar power generation for a European 100% wind and solar only scenario Complementarity Index of Wind and Solar The average level of complementarity between Radiation (CIWS) wind and solar for Oklahoma is 46% of the theoretical maximum CIWS value Variance, mean absolute error and root mean Utilizing an equal mix of wind, solar, and wave square error power generation, the overall reserve requirements are reduced compared to the wind alone Graphical representation, percentile ranking, The combination of solar and wind, in the same theoretical maximum as a proxy for capacity area and dispersed geographically, improves smoothness in power production output, as compared to each source alone Sample correlation coefficient The smoothing effect on combined solar plants is higher than on wind plants Estimation of the ratio of overlap and critical Solar and wind power complement each other overlap of full load hours The smallest failure indexes measuring the energy Phase difference between hydro and PV time series, relation between the average values over supply to the consumers are associated with the both time series and the amplitudes of variation best complementarity in time indexes of the sources of both time series Coefficient of correlation The seasonal inverse correlation between hydropower and wind behavior shows that the complementarity of these sources can contribute to the reliability and efficiency of the electrical system, mostly based on renewable energy Pearson correlation coefficient In the months of the dry season, there is greater complementarity of wind and solar energy Correlation coefficient and Standard deviation

Combining different resources improves smoothness in power output when compared to each source. In the hourly time scale, when the dispersion of sites is large enough, there is also a smoothing effect of combining the dispersed wind power alone The Iberian Peninsula shows strong potential in terms of complementarity between solar and wind power Wind and solar energy potential production have shown complementary time behavior, favorably supporting their integration in the energy system Coupling the utilization of wind and solar energy can improve the guaranteed use of renewable energy The results reveal that renewable energy can be used as base energy in the study region by locating wind and CSP plants

Detrended fluctuation analysis and detrended cross-correlation analysis

Existence of a certain level of complementarity between wind and solar

Standard deviation of the generating capacity and measure the average output delivered

It is generally much more advantageous to think in terms of power mixes rather than single-site installations

Coefficient of variation, Pearson correlation coefficient and penetration rate

For all regions, including hydropower in the mix allows increasing the penetration rate of solar and wind (from 1 to 8% points)

Optimization model

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Table 1 (continued ) Author

Region

Sources

Metric

Solar, wind, and hydropower

Solar and wind can contribute to stabilizing the daily, monthly, and annual combined hydro-windPV output compared to a hydro-thermal system only and could substantially decrease the need for thermal power generation Correlation coefficient The proposed methodology supports estimations on the complementarity between smallhydropower and solar systems Standard deviation At a short temporal scale (hourly), a high share of hydropower allows minimizing the energy balance variability. At a larger temporal scale (daily and monthly), the opposite occurs because of the lower variability of solar power Pearson correlation coefficient, coherence, and Brazilian wind resource is significant and presents cluster analysis high complementarity with hydropower resources Pearson correlation coefficient and standard In all months, daily variability in total power is deviation always reduced by incorporating solar. The scenario with the least seasonal variability is 70% of solar and 30% of wind. Kendall correlation coefficient The northwestern and northern regions present significant wind and solar complementarity. Relative coefficient of variation and interThe variability and intermittency caused by the quartile range. single-source generation can be mitigated strongly by the combined use with other sources The use of hybrid wind-solar power systems could Amplitude related partial complementarity be more effective than single photovoltaic or wind index, time and energy-related partial systems complementarity index and total complementarity index Correlation coefficient A degree of local complementarity between wind and solar sources is observed in many regions within the hourly time scale Optimization model For the same site, it is possible to obtain varied results, depending on the design considered demonstrating that it is not possible to generalize results Correlation coefficient It was found a high level of complementarity in the ~o Francisco River region of Sa Standard deviation and coefficient of variation The result indicates that small hydropower can compensate for the variability of solar PV Net revenue and annual solar curtailment rates The combined use of solar and hydropower could solve the problems of the variability of solar Ramp rate The results show that it is possible to smooth the solar power generation by the combined use with hydropower Coefficient of variance and Pearson correlation Complementary peaks of solar and wind coefficient production appear on an annual and daily level Coefficient of variation, synergy rate and profit Combinations of sub-regions could be selected as coefficient alternatives to make full use of those energies due to the complementarity behavior between them Optimization method as a linear least squares A penetration rate of 82% (of which 74% wind plus regression 26% solar) of renewable sources is found as the optimum scenario

Ioannis et al. (2016)

Hungary

Hydropower and solar

François et al. (2016a)

Italy

Solar and hydropower

Silva et al. (2016)

Brazil

Bett and Thornton (2016)

Britain

Hydropower and wind Wind and solar

Xu et al. (2017)

China

Prasad et al. (2017)

Australia

Pianezzola et al. (2017)

Brazil

Wind and solar

Miglietta et al. (2017)

Europe

Wind and solar

Amorim et al. (2017)

Brazil

Wind and solar

Cant~ ao et al. (2017)

Brazil

Jurasz et al. (2017)

Poland

Fang et al. (2017)

China

Jurasz and Ciapała (2017)

Poland

Wind and hydropower Solar and hydropower Solar and hydropower Solar and hydropower

(Slusarewicz and Cohan, 2018)

United States

Zhang et al. (2018)

China

Zappa and Broek (2018)

Europe

Wind and solar Wind and solar

Wind and solar Wind and solar Wind and solar

not the variability or intermittency of these sources, but how these characteristics can best be solved (Brouwer et al., 2014). In Europe, with the method of the minimum standard deviation of the mismatch energy, it was found that 55% wind and 45% solar power generation for a European 100% wind and solar scenario (Heide et al., 2010). In a study done in Sweden, with correlation coefficient as a metric, solar and wind power were negatively correlated on all time scales indicating high complementarity. The smoothing effect on n, 2011). combined solar units is higher than on wind units (Wide In Britain all over the year, daily variability in total power is reduced by incorporating solar. The correlation and standard deviation were used for observing the relationship between average wind speeds and solar irradiances. The scenario with the least seasonal variability is 70% solar to 30% wind (Bett and Thornton, 2016). Monforti et al. (2014) have confirmed the existence of complementarity behavior between the two sources in Italy, with the correlation coefficient as a metric.

Significant results

Jerez et al. (2013) developed a method to identify the optimal spatial distribution of wind and solar power plants across the Iberian Peninsula, where the combined wind-plus-solar power production met the condition of minimum temporal variability under the constraint of specific efficiency. The proposed algorithm found the combination of solar and wind power to guarantee that the generated power is larger than the minimum demand, and the standard deviation of the power generation series is the minimum among all the possible combinations of solar and wind plants. Another analysis performed with the method of optimization method as a linear least squares (LLSQ) regression in Europe concluded that, without storage, it is possible to set a penetration rate of 82% (of which 74% wind plus 26% solar) of renewable sources with the target of minimizing residual demand (Zappa and Broek, 2018). When compared solar, wind and hydro with the correlation coefficient in Portugal the conclusion was that the multi-objective model enables the optimization of the renewable mix, ensuring a

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minimum level of intermittency, a minimum non-guaranteed peak load share, and a minimum global cost, using the complementarity between renewable sources and the cost-effective impact (Moura, 2010). In Poland, the analysis of combined solar and hydro, using the standard deviation and the coefficient of variation, has concluded that a small lake of water storage of the hydro plant could minimize the solar PV variations (Jurasz and Ciapała, 2017). In Spain, the evaluation of wind and concentrating solar power showed that the two sources together reduces power fluctuation (Santos-Alamillos et al., 2015). Liu et al. (2013) carried out a study to determine the behavior of hourly wind speed and global solar radiation data, using the correlation coefficient, the standard deviation, and a theoretical maximum as a proxy for the capacity to evaluate several indexes to compare the effect of various source-combining scenarios in China. Results show that the combination of solar and wind energy within a specific area can reduce the zero-power hour. Zhang et al. (2018) also analyzed the complementary between solar and wind energies in China. The following methods were used: variation coefficient, ramp rate, synergy coefficient, and profit coefficient. Throughout the results, it was determined that the rational combinations of sub-regions could be selected as alternatives to make full use of those energies, and also, it was demonstrated that the proposed technique and tools developed can help to enhance the planning uses of renewable energy sources. In the USA, it is feasible for the West Connect region to accommodate 30% wind and 5% solar energy penetration (Lew et al., 2013; Lew and Piwko, 2010; Miller et al., 2014; National Renewable Energy Laboratory (NREL), 2010). In Texas (USA), considering the metrics coefficient of variance and the Pearson correlation coefficient, complementary peaks of solar and wind production appears on an annual and daily level (Slusarewicz and Cohan, 2018). In Oklahoma (USA), using the Complementary Index of Wind and Solar Radiation (CIWS) which is the total area between the two curves (wind and solar) it was concluded that the average level of complementarity between wind and insolation is 46 percent of the theoretical maximum CIWS value (Li et al., 2011). In Canada, the combination of solar and wind within and amongst two locations improves smoothness in power production, as compared to each source alone. This conclusion was based on three distinct approaches: graphical representation, percentile ranking, and theoretical maximum as a proxy for capacity (Hoicka and Rowlands, 2011). Using numerical and mathematical techniques, Sales et al. (2015), carried out a study to evaluate correlations in wind speed and solar radiation time series at the Island Fernando de Noronha in northeastern Brazil throughout of the uses of detrended fluctuation analysis (DFA) and detrended cross-correlation analysis (DCCA) method. Real data was used recorded, and from the results achieved along the periods analyzed, it was observed that when a decrease in correlation exponent for wind speed occurs solar radiation shows an increased persistence, which indicates the existence of complementarity between those energies sources as presented in Fig. 4. The Brazilian Ministry of Mines and Energy (MME) published a report about wind and hybrid solar systems (Amorim et al., 2017). Four locations in Brazil were analyzed using the following methodology: a hybrid system was considered with the wind and solar systems sharing the same electrical substation in a radius of 20 km. The objective of the study was to define which percentage of solar could be injected into the grid using the same substation of the wind power system. Wind and solar production were estimated, and the substation energy flux limit defined. After that, the solar percentage was set (on an annual, seasonal, and hourly scale using the substation idle capacity). The results support the idea that the

Fig. 4. Temporal variation of DFA exponent for wind speed and solar radiation data at Fernando de Noronha island (Sales et al., 2015).

two sources could share the same electrical substation. By using the coefficient of variation and inter-quartile range, Prasad et al. (2017) investigated the spatial-temporal synergy between solar and wind sources in Australia to verify the variability and intermittency associated with them, as presented in Fig. 5. The study has shown several results for different areas of the country and has concluded that assessing synergy characteristics of solar and wind are crucial in deciding future hybrid solar-wind power generating systems. Although focused on Australia, the methodology could be applied to investigate the complementarity of solar and wind sources in any region of the world. A study carried out in the coastal area in Australia, using three different methods for analyzing the relationship between the resources and the load demand: (i) Correlogram, (ii) Correlation coefficient, and (iii) Cross-correlation function, has concluded that wind and solar resources in Australia could be used to supply base loads (Li et al., 2009). 4.2. Critical analysis of different complementarity database and metrics All the results are deeply related to the method used and the database considered. Regarding wind and solar complementarity metrics, the two most used methods are correlation coefficient and standard deviation. The correlation coefficient quantifies the similarity between two-time series, and the standard deviation of time series describes how close the output data are clustered around the mean value. All methods listed in Table 1 presented engaging results as they statistically demonstrated complementarity behaviors. Some papers, such as Li et al. (2009), Bett and Thornton (2016), Hoicka and Rowlands (2011), and Li et al. (2011) revealed more than one complementarity assessment method, which leads to more substantiated and reliable results. As can be noticed in Table 1, there is no standard metric for the complementarity analysis that does not allow researchers to compare the results in a standard way. A crucial point here is pinpointed and included in the suggested future works in the conclusion section. Concerning the input data, the following two bases were identified: (i) measured data and (ii) reanalysis data. A study of windsolar complementarity based on measurements presents a risk of either overrating or underrating the resource, especially for short period observational data. It will be overrated if the study is mainly based on observations in developed regions, such as big cities.

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Fig. 5. Relationship of hourly averaged solar and wind resources with peak demand for the year 2010 for a reference grid point in the state of Victoria (Prasad et al., 2017).

Conversely, the resource will be underestimated if the observation data are from locations with extremely open areas. On the other side, a study built on reanalysis data presents a few disadvantages, being the most important, the low resolution, typically of the order of kilometers (14 km between two data points, for example) (Landberg et al., 2003). The best solution to solve these problems is to estimate the resource at a site with a statistical downscaling method, which involves developing a quantitative relationship between large-scale atmospheric variables (predictors), and local surface variables (predictands). Another important aspect regarding the input data is the necessity to implement a methodology of quality assurance in the nez measured data to avoid errors associated with the record (Jime et al., 2010). Data validation routines guarantee that the input vez et al., 2011). variables have been appropriately measured (Este Concerning the articles that combine solar and wind with other energy resources to assess the complementarity, the most commonly used source for these assessments is hydro. The methods used for these analyses are similar to those used for evaluating solar and wind resources, such as correlation coefficient and graphical representation. Adding hydropower to the assessment of complementarity can help obtain a more smoothed output of the power generation curve since large hydro plants can act as batteries. At last, almost all solar-wind complementarity papers mentioned the importance of increasingly using renewable energies to avoid climate change, and no article considers the climate change effect on the complementarity of the resources. Both solar and wind resources are susceptible to climatic fluctuations and, hence, vulnerable to climate change, as it has been shown in the studies presented by Fant et al. (2016) and Wachsmuth et al. (2013). Several studies investigate the potential impacts of climate change on wind speeds and, hence, on wind power. Breslow and Sailor (2002) presented a study to evaluate the effects of climate change on wind speeds in the continental USA. As a main result, the models proportionate consistent prediction, in where it was verified that the USA would see reduced wind speeds up to 3% in the next 50 years and up to 4.5% over the next 100 years. In the same context, Johnson and Erhardt (2016) investigated the impact of climate changes on wind power production focusing on the density of this resource and how the power generation

prediction will evolve in the next years for USA locations. Sailor presented two studies using the same model. Downscaling data was used in Sailor et al. (2000) to predict the wind speeds to verify its influence in the climate conditions for the next years. Sailor et al. (2008) analyzed different scenarios for climate change impacts on wind power generation potential in five American states. The main result shows that climate change could harm wind power generation by 40%. In the South America Continent, Lucena et al. (2010) evaluated the significant climate change impact that can modify the wind power production in Brazil. In the European continent, Pryor presented various studies to explain how climate change would affect wind power production. Pryor et al. (2005) verified the impacts of climate changes on the wind speed in Northern Europe through the use of General Circulation Models. Pryor et al. (2006) described in more detail the influence of the climate changes on power production, linking the results from the first work presented in 2005. Finally, Pryor and Barthelmie (2010) reviewed the climate change impact on real wind power production. Many authors investigated the climate change impacts affecting solar power generation as presented in Lima et al. (2016) and Panagea et al. (2014). Patt et al. (2013) also reviewed the potential impacts of climate change on solar energy systems, and concluded that the results could not identify extreme vulnerabilities to harm the development of those technologies. However, it was found a potential value to explore PV cells more heat-resilient and also to improve the cooling systems design for concentrating solar power. Some studies measured the impact of climate change on Solar PV power production. Wild et al. (2015) examined climate models to evaluate potential changes in surface solar radiation over the coming decades in combination with the greenhouse gas emissions that can be affecting PV production. The results indicate statistically significant decreases in PV production in large parts of the world, with few exceptions with positive trends in large parts of Europe, and also South-East of North America and the South-East of China. In the same context, but applying to the whole world, Crook et al. (2011) examined how changes in temperature and irradiation would affect photovoltaic and concentrated solar power production. The results verify that the PV output for the next 60 years is likely to increase slightly in Europe and China, and also in Algeria and Australia, and decreasing by a small percentage in the western

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USA and also Saudi Arabia. An increasing number of studies were conducted in the European Continent aiming to predict the renewable resources to produce energy. Burnett et al. (2014) investigated the solar irradiation resource in the United Kingdom aiming for the prediction of the solar source and the increasing risks on energy generation due to the climate change effects. The method and results were compared with the solar irradiation levels to present solar irradiance measurements. Panagea et al. (2014) carried out a study to evaluate the effect of changes in parameters, such as; irradiance and temperature, and how they can change with climate changes and their influence on the performance of PV systems in Greece. The results showed a negative linear dependence on the performance of photovoltaic systems when temperature increases, which are outweighed by the expected growth in total radiation resulting in a 4% increase in total energy output. Gaetani et al. (2014) presented a viability study aiming at the feasibility of the future of the PV energy source and production in Europe and Africa, focusing on the sensitivity of solar energy sources to different concentrations of anthropogenic aerosols. The influence of the aerosol concentrations on the performance of the PV energy was assessed by using climate simulations considering different emission scenarios. The results demonstrate the importance of climate modeling as a crucial tool for predicting future changes in PV productivity.

 Adoption of quality assurance methodologies for observational data used in the studies to avoid that measurement errors interfere in the complementarity analysis;  Implementing statistical downscaling, mixing the input data with observational and reanalysis data to obtain the local aspects and the resource variability aspects;  Definition of a reference metric to allow comparison of different outputs from complementarity analysis;  The impacts of climate change may directly affect the complementarity between the wind and sources. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The first author thanks the PPGEM/UFPE and the CAPES for the development of her doctorate. The second and sixth authors thank the IFPE for its financial support throughout the Call 10/2019/Propesq and also for financial support for the research project - Universal 402323/2016e5. The sixth author thanks the CNPq for the scholarship of Productivity nº 309154/2019-7. References

5. Conclusion and recommendations The increase in the installed capacity of solar and wind power in the world is a good signal for future sustainable development and is helpful for decarbonization. An important point is to know how the high level of renewable energy could impact the electrical grid safety due to the variability of the sources. It was found that the combined use of different levels of wind and solar, also in different locations, could improve the smoothing of the total output power of these sources, bringing security to the grid. From the 41 papers analyzed in this study, 15 focused in Europe, 17 in the Americas, 7 in Asia, and two others had a global focus. The review of the techniques that have been used to evaluate the complementarity of solar and wind energy systems shows that traditional statistical methods are mostly applied to assess complementarity of the resources, such as correlation coefficient, variance, standard deviation, percentile ranking, and mean absolute error. The two most commonly used methods are based on the correlation coefficient (between solar and wind signals, and the combined output with load demand) and standard deviation (variability of the hybrid output). The most crucial point is the quality of the input data, which could affect the results obtained in the analysis. From the 41 papers, 17 used the correlation coefficient as a metric, and 6 used standard deviation. The other 18 papers used different techniques to evaluate the complementarity between wind and solar. An ideal proposal for complementarity studies would include: the adoption of quality assurance methodologies for observational data, implementing statistical downscaling, mixing input data with observational and reanalysis data to obtain local and resource variability aspects. Also, a fusion of different methods for complementarity analysis, correlation coefficient, and mean absolute error and percentile ranking, for example, to achieve a more reliable result. Some unsolved topics, poorly, or even, unexploited points that could improve the research about wind and solar complementarity are highlighted as follows:

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