Main Trends and Criteria Adopted in Economic Feasibility Studies of Offshore Wind Energy: A Systematic Literature Review

: Offshore wind energy has been identiﬁed as one of the most promising and increasingly attractive sources of energy. This technology offers a long-term power-generation source, less environmental impact, and fewer physical restrictions. However, given the complexity of this technology, economic feasibility studies are essential. Thus, this study aims to identify the main trends and criteria or the methods used in the economic feasibility studies of offshore wind energy, providing a review of the state of the art in this literature. For this, a Systematic Literature Review was carried out. The article shows the growing interest in offshore wind power generation and highlights how recently the interest in the studies that assess the technical–economic feasibility of this source has grown; it presents the main milestones of the topic. Based on a structured literature review, this article identiﬁes the main trends in this topic: ( i ) wind farms, ( ii ) risk, ( iii ) ﬂoating offshore wind farms, ( iv ) decommissioning and repowering, ( v ) net present value, ( vi ) life cycle cost, and ( vii ) multi-criteria decision-making; it provides a broad view of the methodological possibilities and speciﬁcities for investors and researchers interested in conducting studies on the economic feasibility of offshore wind generation. In addition, ﬁnally, a research agenda is proposed.


Introduction
The rampant production growth since the industrial revolution has made societies use natural resources, renewable or not, without worrying about the impacts for future generations [1]. However, this behavior has been changing, and it can be seen that most countries are seeking to reduce dependence on fossil energy sources, with renewable energy sources (RES) being identified as more sustainable solutions with less environmental impact [2]. There is an urgent need for the development and use of renewable sources, with wind energy being pointed out as one of the most promising due to its low cost and the possibility of using it on an industrial scale [3].
Wind energy sources are divided into onshore (structures on land) and offshore (structures at sea) [4]. In recent years, offshore wind energy has been identified as one of the potential sources and has become increasingly attractive. This technology offers a long-term power-generation source, with a lower environmental impact and fewer physical restrictions compared to onshore wind farms [5]. According to the Global Wind Energy In the first stage (Review planning), the research problem was defined, clarified, and refined. The relevance and dimension of the literature were identified, with the aim of delimiting the researched area. After that, the protocol was determined, which is basically a document that explicitly unites all the steps that are performed in the SLR [17]. In the research protocol, the database used was defined, which in this case was the Web of Science (WOS). The WOS is a more consolidated and older citation database, having a strong coverage of citation data and bibliographic data and considered to be the base with the greatest depth and quality [18,[23][24][25].
The string from round (i) returned a large number of results (9340 articles), and it was observed that many of the studies were more related to the technical perspectives of offshore wind farms. In order to refine the search, the string from round (ii) included the variations of the terms of the offshore wind energy theme as the paper title in the WOS. In this case, there was a return of a smaller number of results (4943 articles). Finally, the last string was based on a literature review article [18] that conducted an SLR on the financial and economic feasibility studies of the use of Battery Energy Storage Systems, or simply BESS. The reason for the choice was due to the article in question, which approached an SLR on aspects of economic feasibility studies and had a great adherence to the objectives outlined in this research. Then, from round (iii), the initial sample was defined (341 articles). For better refinement [18], some exclusion criteria were applied in this sample, such as: (a) All studies that are not scientific and/or review articles. (b) Articles that are not related to energy generation. (c) Articles that do not address methods, economic and financial analysis models, and tools related to risk or uncertainty or at least mention some financial indicator.
The second stage refers to Conduct the review. The database was extracted from WOS on May 2021. The database was first ordered by year and then by total number of citations in order to start processing the data by the most cited and most recent studies. By reading the title and abstract, the first information about the studies, such as the authors, objective, methodology, results, and conclusions, was extracted. This information was entered into an MS Excel file, allowing the storage of the history of decisions throughout the entire review study.
The third stage (Report and dissemination) was explained in the results topic. It is known that a good SLR should make it easier for interested parties to understand a given topic [17], synthesizing a large number of the primary research articles from which it was derived. The flow of exclusions is performed as shown in Figure 1, where a final sample of 83 articles is obtained.
First, to answer RQ1 (what are the main characteristics of the literature on the economic feasibility of offshore wind energy generation?), a descriptive analysis of the sample was performed in order to provide an initial and general understanding of this literature. In addition, this analysis was used to assess the number of publications in recent years, to identify which of the main journals are publishing articles related to search strings, and to know what content is presented in the most cited articles in the sample and the behavior of their citations over time. Finally, the Vosviewer software was used to generate a network graph of the countries of the first author's institution, allowing the identification of the countries that had the most developed research on the topic addressed in this study, as will be presented in Section 3.1. First, to answer RQ1 (what are the main characteristics of the literature on the economic feasibility of offshore wind energy generation?), a descriptive analysis of the sample was performed in order to provide an initial and general understanding of this literature. In addition, this analysis was used to assess the number of publications in recent years, to identify which of the main journals are publishing articles related to search strings, and to know what content is presented in the most cited articles in the sample and the behavior of their citations over time. Finally, the Vosviewer software was used to gen- Next, in order to answer RQ2 (what are the scenarios and the main methods and criteria used in the economic feasibility analysis of offshore wind farms?), the information regarding the research methods, the countries where the case studies were carried out, and the specifics adopted in these studies was extracted, as will be seen in Section 3.2.
Finally, to answer RQ3 (what are the main trends and research opportunities for this topic?), Vosviewer software was used again. The authors' keywords network chart allowed us to identify which of the main related terms are in greater evidence, indicating possible trends within the studied topic. Moreover, through a critical analysis derived from the Energies 2022, 15, 12 5 of 24 answers to the previous research questions, a research agenda was proposed, as will be seen in Section 3.3.

Characteristics of the Literature on Economic Feasibility in Offshore Wind Energy Generation
In order to answer RQ1 (what are the main characteristics of the literature on the economic feasibility of offshore wind energy generation?), 341 articles were identified through the search strings, of which 83 were considered, as shown in Figure 1. Through this final sample, it was possible to identify a significant growth since 2015, pointing to the strong growth of interest in the topic addressed. It was observed that 2019 was an atypical year and presented a small reduction compared to the observed growth, as seen in Figure 2 (blue bars). This same figure also shows the representativeness of the sample when compared to the general literature on offshore wind energy (red line).
Next, in order to answer RQ2 (what are the scenarios and the main methods and criteria used in the economic feasibility analysis of offshore wind farms?), the information regarding the research methods, the countries where the case studies were carried out, and the specifics adopted in these studies was extracted, as will be seen in Section 3.2.
Finally, to answer RQ3 (what are the main trends and research opportunities for this topic?), Vosviewer software was used again. The authors' keywords network chart allowed us to identify which of the main related terms are in greater evidence, indicating possible trends within the studied topic. Moreover, through a critical analysis derived from the answers to the previous research questions, a research agenda was proposed, as will be seen in Section 3.3.

Characteristics of the Literature on Economic Feasibility in Offshore Wind Energy Generation
In order to answer RQ1 (what are the main characteristics of the literature on the economic feasibility of offshore wind energy generation?), 341 articles were identified through the search strings, of which 83 were considered, as shown in Figure 1. Through this final sample, it was possible to identify a significant growth since 2015, pointing to the strong growth of interest in the topic addressed. It was observed that 2019 was an atypical year and presented a small reduction compared to the observed growth, as seen in Figure 2 (blue bars). This same figure also shows the representativeness of the sample when compared to the general literature on offshore wind energy (red line). Analyzed in the same sample were the main journals with articles related to the defined strings. Figure 3 indicates the five journals with the most articles in the sample. The journal that presented the highest number of articles in the analyzed sample was Renewable Energy, followed by Energies.   Analyzed in the same sample were the main journals with articles related to the defined strings. Figure 3 indicates the five journals with the most articles in the sample. The journal that presented the highest number of articles in the analyzed sample was Renewable Energy, followed by Energies.   Table 1 identifies the twelve most cited articles in the analyzed sample, and a summary of these studies is presented. In addition, in a complementary way, the total number of citations and their average citations per year are shown.    Table 1 identifies the twelve most cited articles in the analyzed sample, and a summary of these studies is presented. In addition, in a complementary way, the total number of citations and their average citations per year are shown. A cash-flow-based model was proposed to calculate the Levelized Cost of Energy (LCOE) and the break-even price. 35 projects in Europe, the United States, and China were analyzed, considering data appropriate from policies and electricity markets.
Hou et al. [27] 74 (12.3) The authors proposed a mathematical model that includes the variation of wind direction and wake deficit for selecting the location arrangement of wind turbines in large-scale farms. A Particle Swarm Optimization Algorithm in which the LCOE was defined as an objective function was applied in a German case study.
Kim et al. [28] 53 (6.6) This study deals with the ideal selection for a wind farm in South Korea. The economic technical feasibility was tested, considering among other parameters, a cost-benefit ratio (LCOE), the convenience of grid connection, and the power capacity to be installed.
Astariz et al. [29] 50 (7.1) The authors carry out an economic analysis of tidal, wave, and offshore wind energy. The LCOE is adopted in comparison, considering specific costs and investments for each type of project. Wave energy had the highest costs, while at the other extreme is offshore wind energy.
Ioannou et al. [30] 46 (11.5) The study developed a technical-economic lifecycle assessment framework for predicting the life cycle costs of offshore wind parks, which was later applied to a realistic case study in the UK.
Shafiee et al. [31] 43 (7.2) A cost breakdown structure for offshore wind farms is proposed. For this, a combined multivariate regression and neural network approach is developed to identify key cost drivers and evaluate the costs associated with the project's phases, until the end of their useful life. Net Present Value (NPV) is used to test the feasibility of the project.
Gonzalez et al. [32] 40 (4.4) An improved genetic algorithm model is proposed for optimizing wind turbine installations in large offshore wind power plants with the aim of maximizing the economic profitability of the project. The proposed model is defined as complete and realistic for evaluating the economic behavior of this type of project.
Castro-Santos et al. [33] 39 (6.5) A methodology to assess the economic viability of a floating offshore wind farm is proposed and applied to a case study in Spain. The methodology considers that the project's life cycle cost is composed of the total costs in all phases of the installation's life cycle.
Chaouachi et al. [34] 37 (7.4) A multi-criteria approach is proposed for the evaluation of locations for the installation of offshore wind farms, considering economic and technical aspects. Analytic Hierarchy Process (AHP) is used. The proposed model is applied in three Baltic States: Estonia, Latvia, and Lithuania. The article presented a preliminary study on the technical and economic feasibility of installing an offshore wind farm in Italy. The Weighted Average Cost of Capital (WACC) and Capital Asset Pricing Model (CAPM) were used to calculate the discount rate, and then the economic viability was evaluated through the NPV and the Internal Rate of Return (IRR).
In order to complement the information presented in Table 1, Figure 4 shows the distribution of the citations of the most cited articles in the sample over time. It can be observed that studies such as those of Levitt et al. [26] and Moller Bernd [35] have a high absolute number of citations; however, their influences have reduced over the years. At the same time, Kim et al. [28] also remain influential in the most recently published studies. Finally, Ioannou et al. [30], with the most recent publication among the twelve most cited in the sample, have their influence highlighted. Finally, using Vosviewer software, a network graph was created based on the country of the institution or research center to which the first author belongs. In the network graph, the parameters for its elaboration were: (i) number of documents equal to one, and (ii) number of citations greater than zero. Of the 27 countries listed, 11 were included in the created network, as seen in Figure 5. Levitt et al. [26] Hou et al. [27] Kim et al. [28] Astariz et al. [29] Ioannou et al. [30] Shafiee et al. [31] Gonzalez et al. [32] Castro-Santos et al. [  Finally, using Vosviewer software, a network graph was created based on the country of the institution or research center to which the first author belongs. In the network graph, the parameters for its elaboration were: (i) number of documents equal to one, and (ii) number of citations greater than zero. Of the 27 countries listed, 11 were included in the created network, as seen in Figure 5. Finally, using Vosviewer software, a network graph was created based on the country of the institution or research center to which the first author belongs. In the network graph, the parameters for its elaboration were: (i) number of documents equal to one, and (ii) number of citations greater than zero. Of the 27 countries listed, 11 were included in the created network, as seen in Figure 5. Levitt et al. [26] Hou et al. [27] Kim et al. [28] Astariz et al. [29] Ioannou et al. [30] Shafiee et al. [31] Gonzalez et al. [32] Castro-Santos et al. [  In the analysis of the network of the countries of the authors' institutions, Spain and England stood out as the two countries of institutions within this sample which have the largest number of publications. England, in addition to being prominently featured in the institutions' network of countries, is the country with the largest installed capacity in the world in offshore wind energy [6]. The dominance of countries (based on institutions) on the European continent was expressive. Among the 15 countries that appeared in the chart, ten countries are from the European continent. In this identification, Turkey was considered as divided between the European continent and the Asian continent.
Different from Figure 5, which indicated the country based on the first author's institution, the data in Figure 6 indicate the places used for the case studies. Notably, Spain appears in first place with ten published articles, followed by South Korea, with five articles published, and the United States, Portugal, the United Kingdom, and Greece, with four case studies published in each one. In the analysis of the network of the countries of the authors' institutions, Spain and England stood out as the two countries of institutions within this sample which have the largest number of publications. England, in addition to being prominently featured in the institutions' network of countries, is the country with the largest installed capacity in the world in offshore wind energy [6]. The dominance of countries (based on institutions) on the European continent was expressive. Among the 15 countries that appeared in the chart, ten countries are from the European continent. In this identification, Turkey was considered as divided between the European continent and the Asian continent.
Different from Figure 5, which indicated the country based on the first author's institution, the data in Figure 6 indicate the places used for the case studies. Notably, Spain appears in first place with ten published articles, followed by South Korea, with five articles published, and the United States, Portugal, the United Kingdom, and Greece, with four case studies published in each one. In addition, analyzing the content of the selected articles, it was also possible to identify that, with the exception of one of the studies in the final sample, the others used the modeling and simulation methods.

Evaluation Criteria and Methods
In addition, in order to answer RQ2 (what are the scenarios and the main methods and criteria used in the economic feasibility analysis of offshore wind farms?) the criteria, approaches, and tools used to support the decisions related to investments in offshore wind farms were identified. Not only that, but the study also presents other relevant information, such as the country where the technology was evaluated, the type of offshore wind farm platforms (fixed or floating), and whether the study considered the end-of-life scenarios (Decommissioning and Repowering), as shown in Tables 2-5. It is noteworthy that these tables include all the articles in the sample. Tables 2-5  Offshore wind farm platforms can be basically classified into two types: fixed and floating [37]. Fixed structures are generally located in shallower regions of the sea and In addition, analyzing the content of the selected articles, it was also possible to identify that, with the exception of one of the studies in the final sample, the others used the modeling and simulation methods.

Evaluation Criteria and Methods
In addition, in order to answer RQ2 (what are the scenarios and the main methods and criteria used in the economic feasibility analysis of offshore wind farms?) the criteria, approaches, and tools used to support the decisions related to investments in offshore wind farms were identified. Not only that, but the study also presents other relevant information, such as the country where the technology was evaluated, the type of offshore wind farm platforms (fixed or floating), and whether the study considered the end-of-life scenarios (Decommissioning and Repowering), as shown in Tables 2-5. It is noteworthy that these tables include all the articles in the sample. Tables 2-5  Offshore wind farm platforms can be basically classified into two types: fixed and floating [37]. Fixed structures are generally located in shallower regions of the sea and floating structures are located in deeper waters (from 60 m deep) [19]. Thus, Table 2 presents the economic feasibility studies for the fixed platforms, while Table 3 presents those for the floating platforms. Complementarily, Table 4 presents the studies that deal with both the fixed and the floating platforms. Moreover, and finally, Table 5 presents studies in which the type of platform was not identified. In addition, the articles listed in Tables 2-5 are presented in ascending order of publication date, that is, starting from the oldest to the current date.     [106] Not specified Yes • In Table 2, referring to the fixed platforms, the United Kingdom and South Korea stood out for the number of case studies on this type of platform, with four and three case studies, respectively. Regarding methods and criteria, the LCOE and NPV criteria were the most used. Finally, only 56.67% of these studies specifically considered end-of-life scenarios for the feasibility results.
In Table 3, referring to the floating platforms, there was an evident dominance in the number of case studies in Spain, with 10 case studies. Regarding the methods and criteria, again the LCOE and NPV criteria were the most used, with 18 and 11 uses, respectively. Another important fact to emphasize is that the publication dates of the studies that consider the type of floating platform are more recent compared to the studies that considered the fixed platforms. This may indicate the reason for the increase in the number of articles that considered the end-of-life scenarios (70%), which ended up making the models more realistic and even allowed the possibility of maximizing the return of investors [13].
In Tables 4 and 5, there was again the domain of the LCOE and NPV criteria. Regarding the case studies, unlike in Tables 2 and 3, there was no country that stood out in the number of case studies. Finally, most of the studies did not consider the end-of-life aspects, with Table 4 with only 25% and Table 5 with 38%. Figure 7 shows the criteria mapped in each article analyzed, although the studies may have used one or more approaches, tools, or methods for their analyses. This figure was created from the year of publication of the studies, in order to identify a trend in the use of the criteria.  In general, among the various criteria identified, the use of the LCOE, or simply the Levelized Cost of Energy, should be highlighted as the most used criterion. In addition, the NPV, IRR, and PB or DPB criteria were widely used, and the simultaneous use of the three criteria occurred in twelve studies. Over the years, the LCOE and NPV criteria were the most used, proving to be the most relevant in the recent years of the historical series.
The NPV is the method that allows the evaluation of the feasibility of an investment, through the sum of its cash flows, considering the discounts since the beginning of the investment [31]. A financial option to justify investment in offshore wind farms is when the NPV value is positive [42,49,69]. Cash flows are composed of revenues and investment and the operation and maintenance costs and can be expressed by the Equation (1)    In general, among the various criteria identified, the use of the LCOE, or simply the Levelized Cost of Energy, should be highlighted as the most used criterion. In addition, the NPV, IRR, and PB or DPB criteria were widely used, and the simultaneous use of the three criteria occurred in twelve studies. Over the years, the LCOE and NPV criteria were the most used, proving to be the most relevant in the recent years of the historical series. The NPV is the method that allows the evaluation of the feasibility of an investment, through the sum of its cash flows, considering the discounts since the beginning of the investment [31]. A financial option to justify investment in offshore wind farms is when the NPV value is positive [42,49,69]. Cash flows are composed of revenues and investment and the operation and maintenance costs and can be expressed by the Equation (1) [49]: where Capex is total cost of spending capital, FC t is cash flow for a period t, r is the discount rate, and t is the project lifetime. Also taken as an exact indicator, the IRR measures the expected future returns on a given investment and the higher this criterion, the greater the profitability [33,69]. This criterion can be calculated using the NPV formula, when the NPV value is equal to zero [33,55,69], according to Equation (2): On the other hand, seen as less robust and suitable for further analysis, the PB is the criterion used to analyze the time needed to return the amount invested [54]. Basically, there are two variations of payback indicators, which can be simple Payback (PB) or Discounted Payback (DPB). The big difference is that DPB takes into account discounts over time, which makes the analysis more realistic compared to the traditional method [54,91].
One of the main financial indicators in power-generation projects is the LCOE. This is a model proposed by the National Renewable Energy Laboratory (NREL) and defines the cost of electricity production per unit [107]. This makes it possible to carry out the comparison between different types of power-generation technology according to the economic aspect, which can be considered as a global benchmark criterion [69,71,92]. The LCOE can be calculated using the Equation (3) [65]: where I t is the investment in period t, M t is the operation and maintenance costs in period t, E t is the total energy produced in period t, r is the discount rate, and t is the project duration.
LCC is another method used to analyze the economic viability of a project. Its implementation helps to identify unlikely and potential areas that can be overlooked by traditional methods [9]. The LCC is an effective tool for identifying and assessing cost impacts throughout its lifecycle, allowing the mapping of different cost stages, from predevelopment to the decommissioning phase [108].
Fleeing the most traditional, the Monte Carlo simulation (MCS) is one of the methods used in economic feasibility analyses, which allows, through simulations, to quantify the risk linked to the input parameters inserted in the analyzed model [19]. Unlike deterministic modeling, where estimates are made on a fixed value, in the MCS a range of values is obtained. The method generates thousands of scenarios, making it possible to identify, for example, the probability of success of a given investment. This is very useful for managing the variability and uncertainty present in investments [19].
Another method identified in the literature that deserves to be highlighted is Real Options Theory. This is a method associated with considering the opportunity to make a decision after uncertainties. Uncertainty and the agent's ability to respond to it (flexibility) are sources of value for an option. Unlike traditional approaches that generally use the expected cash flows to value a given investment, the RO considers the entire distribution of cash flows, allowing the investor to have the possibility to react over the investment's longtime horizon. There are basically three types of solving for RO: (i) dynamic programming, (ii) partial differential equations, and (iii) simulation, with simulation pointed out as the most useful tool [109].
Finally, a method that stood out for having been identified among the trends of interest in the economic feasibility studies of offshore wind generation was the multi-criteria decision-making method (MCDM), as seen in answering RQ1. MCDM is a method that, unlike traditional methods, allows dealing with the complexity of the current environment, providing a management tool that is flexible, and allowing different variables to be aggregated and analyzed in different ways [110]. Offshore wind energy projects present a high degree of complexity in their technological, economic, environmental, and social aspects, which are characterized as risks and need to be considered simultaneously in order to increase the probability of project success [9]. Offshore wind energy projects present a high degree of complexity in their technological, economic, environmental, and social aspects, which are characterized as risks and need to be considered simultaneously, in order to increase the probability of project success [9].

Offshore Wind Power Trends and Research Agenda
To answer RQ3 (what are the main trends and research opportunities for this topic?), through a keyword network graph created using the Vosviewer software, it was possible to identify the most current trends and topics. In addition, it was possible to propose a research agenda (Section 3.3.1), in which possibilities for future studies were identified.
In the keyword network graph, three was considered the minimum number of occurrences of a keyword among the 227 keywords identified in this sample. It is noteworthy that the keywords were extracted and treated so that there was a standardization of words written in a different way, but which had the same meaning. This was possible through the use of the Thesaurus text file, which was inserted in the VOSviewer software for the generation of networks.
In the network analysis of the authors' keywords, it was possible to identify the keywords that were in more evidence in recent years. This is a strong indication that the topic linked to the keyword has a high degree of interest in the scientific community [18]. From that, it was possible to identify some of the themes, such as the trends in the offshore wind energy area in terms of economic and financial feasibility. Among the highlighted words, those that had greater evidence were: "Wind Farms", "Risk", "Floating Offshore Wind Farms", "Decommissioning and Repowering", "NPV", "Life Cycle Cost", and "Multi-criteria-Decision-Making (MCDM)", as can be seen in Figure 8. unlike traditional methods, allows dealing with the complexity of the current environment, providing a management tool that is flexible, and allowing different variables to be aggregated and analyzed in different ways [110]. Offshore wind energy projects present a high degree of complexity in their technological, economic, environmental, and social aspects, which are characterized as risks and need to be considered simultaneously in order to increase the probability of project success [9]. Offshore wind energy projects present a high degree of complexity in their technological, economic, environmental, and social aspects, which are characterized as risks and need to be considered simultaneously, in order to increase the probability of project success [9].

Offshore Wind Power Trends and Research Agenda
To answer RQ3 (what are the main trends and research opportunities for this topic?), through a keyword network graph created using the Vosviewer software, it was possible to identify the most current trends and topics. In addition, it was possible to propose a research agenda (Section 3.3.1), in which possibilities for future studies were identified.
In the keyword network graph, three was considered the minimum number of occurrences of a keyword among the 227 keywords identified in this sample. It is noteworthy that the keywords were extracted and treated so that there was a standardization of words written in a different way, but which had the same meaning. This was possible through the use of the Thesaurus text file, which was inserted in the VOSviewer software for the generation of networks.
In the network analysis of the authors' keywords, it was possible to identify the keywords that were in more evidence in recent years. This is a strong indication that the topic linked to the keyword has a high degree of interest in the scientific community [18]. From that, it was possible to identify some of the themes, such as the trends in the offshore wind energy area in terms of economic and financial feasibility. Among the highlighted words, those that had greater evidence were: "Wind Farms", "Risk", "Floating Offshore Wind Farms", "Decommissioning and Repowering", "NPV", "Life Cycle Cost", and "Multi-criteria-Decision-Making (MCDM)", as can be seen in Figure 8.   The highlighted words (in yellow) in Figure 8 are briefly discussed below in order to understand the possible reason why these keywords are on the rise.

(i) Wind farms
The concern of nations in developing alternatives that bring solutions to global climate change and environmental problems is increasingly evident [111]. Among possible alternative energy sources, it is highlighted that wind energy has the potential to reduce the problems generated by other non-renewable sources [112,113].
Wind energy has very impressive potential. Currently the combined technical potential of solar and wind energy sources is practically 100 times greater than the global demand [114]. The term technical potential is used to determine the amount of energy that can be captured with the current technology [20]. Thus, with technological advances, the technical potential tends to increase, further increasing this proportion.
Two benefits of using wind energy can be cited [115]. The first benefit is the very low generation of polluting gases, such as CO 2 . Basically, the generation of polluting gases linked to the generation of wind energy is associated with the construction, maintenance, and decommissioning stages. The second benefit is less water consumption during the lifetime of the wind plant. It is known that wind energy consumes a much smaller amount of water compared to other sources [116]. One of the most expressive examples is the difference between it and hydroelectric power, which has an extremely high volumetric water footprint.

(ii) Risk
There are high expectations that offshore wind energy can significantly contribute to renewable energy generation [117]. The offshore wind energy industry is considered recent and is still in the learning-curve stage [118] and, in addition, offshore wind energy plants have a large number of uncertainties related to their technical and financial analysis [119]. Thus, there is a significant risk between the planned costs and the costs incurred for an operation and maintenance (O and M) of an offshore wind farm, as it is complex to identify the parameters over the useful life of the structure [120].
Investments in wind farms require a large initial financial contribution, which makes a correct and accurate approach to analyzing the feasibility of these projects essential [112]. This emphasizes the need to use methods that mitigate the risks associated with this type of project, increasing the possibility of viability for investors [121].
Some recent studies used risk as a criterion for technical and economic evaluations. Yeter et al. [57] developed a framework that makes it possible to translate the structural risks of an offshore wind project into a Risk Premium applied to the initial investment. Thus, it was identified that an effective maintenance planning can positively impact the capital and operating costs of the project. Jadali et al. [62] used risk as a factor to analyze different end-of-life scenarios of offshore wind energy platforms. The risk-aversion economic model was used and through the analysis it was identified in hypothetical scenarios in the UK that the lowest Risk Premium is linked to the Repowering scenario.

(iii) Floating Offshore Wind Farms
Approximately 70% of planet Earth is composed of deep-water seas, which indicates a strong trend in the offshore wind energy industry to use floating platforms [19]. In addition to the potential for the physical space in the seas that can be explored [62,71,76], floating platforms bring advantages, such as the possibility of installation in locations with greater wind speed [19,62,71], lower social impact compared to onshore structures [62], and lower visual impact and sound [71].
However, floating platforms have the disadvantage of presenting a greater degree of complexity and higher installation, maintenance, and decommissioning costs. This is due to restricted access to the site due to possible adverse weather conditions, expensive installation procedures, and high grid connection costs [71]. Such characteristics reinforce the need to develop economic feasibility models for this specific technology.
In the study by Cordal-Iglesias et al. [79], a framework was developed to economically analyze floating offshore platforms. In this, platforms built in concrete were considered and economic analyses were carried out using Arcwind software in a case study conducted in the Canary Islands, Spain. In the study developed by Jung et al. [81] the economic feasibility of floating offshore platforms was analyzed, comparing the use of two types of generators in floating platforms, specifically the semi-submersible ones. Castro-Santos et al. [80] conducted an economic feasibility analysis of floating offshore wind energy in the oceans of the countries of Spain and Portugal. The economic analysis considered current scenarios and future scenarios, including the climate changes that are expected to affect the region.

(iv) Decommissioning and Repowering
The offshore wind energy industry is recent, with the first wind farm being built in 1991. As such, its first installations are only now reaching or close to reaching the project's nominal useful life time [62]. Basically, there are three strategies adopted for the end-of-life management of wind farms: service-life extension, repowering, and decommissioning [31].
The extension of the useful life is the decision to extend it for a few more years, in relation to the period of years that was initially determined, as long as there are minimum conditions for the operation to continue satisfactorily and safely. The decision related to repowering can be divided into partial and total. In partial repowering, it involves the replacement of smaller project components, such as the power train or the rotor, aiming, if possible, to maintain the tower, foundations, and cables. Total repowering, on the other hand, consists of trying to use most of the original electrical system by installing more powerful turbines on existing foundations [122]. Finally, decommissioning is the final phase decision that can be considered as the opposite of the installation phase [123].
Project end-of-life decisions have great relevance and directly impact the viability of investments as they have the potential to increase profitability and reduce costs [62]. Therefore, it is necessary to accurately and reliably estimate the costs involved in these steps [60] as this final step has a relevant impact and, if not considered from the beginning of the project, the impacts generated due to this can be serious, and costs may be higher than expected [124].
The discussion about the selection of the ideal end-of-life scenario has become very relevant as such decisions can increase profitability, potentially reducing costs and directly impacting the viability of the investment. Operators need to assess the current condition of their assets and the state of the technology that was originally acquired and maximize the value of their initial investment [62]. In order to ensure the financial viability of decommissioning, investors need to make an accurate and reliable estimate of the decommissioning costs [60] as end-of-life strategies are a significant part of any project and require consideration from an early stage of design. If this is not done, the impacts could be severe and the costs could be higher than expected [124].
Adedipe and Shafiee [60] have developed an economic valuation framework for the decommissioning of offshore wind farms, using a cost breakdown structure (CBS) approach. The framework was tested and simulated on jacket-type offshore foundation platforms, which are fixed-type foundations. Zuo et al. [106] performed a technical and economic analysis of the end-of-life scenarios, considering the possibilities of repowering and scaleexpansion combined. The article analyzed different types of wind energy collector system topologies, evaluating the economic and reliability advantages. Finally, an optimization framework was proposed and tested in order to maximize the gains and reliability of the system. Jadali et al. [62] carried out a technical and economic feasibility study of offshore wind farms in a case study in the UK. Two decision scenarios related to the platforms' end of life, repowering, and decommissioning were considered and compared.

(v) Net Present Value (NPV)
The Net Present Value (NPV) is the net value of a project's cash flows, taking into account its discount at a certain rate since the beginning of the investment [33]. Specifically in offshore wind farms, cash flows are related to revenues from the sale of energy, and costs are investments and operating and maintenance costs throughout the project's useful life [86].
The NPV is a criterion said to be exact and consolidated in the literature and has been used specifically in economic analyses in the area of wind power generation in recent decades [125][126][127][128]. Although NPV has been used for many decades, it remains one of the main criteria in economic feasibility analysis studies in offshore wind farms [58,62,72,79,81,106].

(vi) Life Cycle Cost (LCC)
The Life Cycle Cost Analysis or Life Cycle Cost (LCC) is a method of evaluating the costs of a given system or project throughout its useful life [31]. Specifically in an offshore wind farm, the life cycle starts in the project development phase until decommissioning, and the sum of all the costs over this period is considered as the life cycle cost [57]. The LCC method started to gain greater visibility due to the uncertainties present in wind energy projects, mainly offshore [51]. The main benefits of the tool are linked to the fact that it allows the identification, analysis, evaluation, and reduction in the overall cost of the main operations. The analyses provide essential information for decision makers, allowing for greater depth of knowledge related to the economic life of the assets and also for the support of stakeholders in decision making regarding the best investments [31].
However, developing a realistic analysis of wind farms is quite complex [129]. There are few models that focus on the installation or decommissioning phases [55], with simplified models generally being developed and/or with an emphasis on the O and M phases [51].
Maienza et al. [71] developed an LCC model for the evaluation of floating offshore platforms, as applied in a case study in Italy. The key parameters of the model were capital expenditure (CAPEX), operational expenditure (OPEX), decommissioning expenditure (DECEX), and LCOE. Ioannou et al. [58] developed an LCC model through the technical and economic aspects, with a probabilistic approach, applied in a case study in the UK. The methods of the Artificial Neural Networks (ANN) and the Auto-Regressive Integrated Moving Average (ARIMA) combined with the Monte Carlo Simulation (MCS) method were used to assess the impact of uncertainties on the technical and economic performance of the project. Adedipe and Shafiee [60] developed an economic analysis framework for offshore wind farms using a cost decomposition structure (CBS) approach. The cost breakdown structure approach was used to assess the life cycle costs of an offshore wind energy platform, with an emphasis on the decommissioning phase.

(vii) Multi-Criteria Decision-Making Methods (MCDM)
Multi-criteria decision-making methods (MCDM) analyze a decision process by dividing it into different stages and assigning relative importance to specific decision criteria [130]. The method allows the decision maker to deal with specific problems by comparing and ranking alternatives based on an assessment of multiple, sometimes conflicting, criteria [131].
The use of this method has grown a lot in recent years [132,133]. The increased use of this method is related to the complexity of offshore wind energy. The multiplicity of uncertain factors that can conflict with each other makes project evaluation a multi-criteria problem. The MCDM is one of the methods used to select the most viable energy alternative based on different environmental, economic, and social sustainability indicators [132].
Deveci et al. [84] developed an MCDM method that considered the technical, economic, environmental, and social factors in a case study applied in Ireland. This model was specifically developed for optimal site selection for offshore wind farms. Spyridonidou and Vagiona [61] developed a methodology for planning offshore wind farms in a case study applied in Greece. Initially, the Geographic Information System (GIS) was used to locate the appropriate locations and configure the location of the wind turbines. Finally, two MCDM methods were used through the Statistical Design Institute software: the Analytic Hierarchy Process (AHP) to assess the relative importance of the evaluation criteria and then the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritize the most suitable locations for offshore wind energy platforms. Ziemba [105] analyzed the planned investments in Poland according to the degree of financial potential. The MCDM method used is a modified version of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which allowed the ranking of the investments that should be treated as a priority.

Research Agenda
Through the analyses adopted in this study, an increase in the number of studies that assess the economic and financial feasibility of offshore wind generation was evidenced, reaching the highest number of publications in 2020. More than that, in the same year the number of publications on the subject reached its highest percentage in the general literature on offshore wind power. This fact demonstrates the need for discussion on the most appropriate financial approaches and for more studies that evaluate such projects that are subject to different climatic and environmental conditions, technologies (e.g., comparisons of technologies), regions and countries (e.g., feasibility studies in specific localities or decision to location), public policies and regulation, and macroeconomic scenarios, among other factors seen as critical to the project's feasibility. It is worth noting that terms such as NPV and LCC, consolidated methodologies used in economic feasibility studies, stand out as being of recent use in the wind offshore power literature.
Furthermore, it was possible to identify some trends related to economic feasibility studies for offshore wind energy. Three major themes were highlighted, "Floating Offshore Wind Farms", "Decommissioning", and "Multi-criteria Decision-Making (MCDM)".
Floating offshore wind farms are considered the future of the offshore wind energy industry as this technology allows the exploration of deeper ocean locations. However, this technology is still in the prototyping phase and has a greater degree of complexity compared to fixed structures. Economic feasibility analysis models that can accurately analyze these situations will be needed so that the risks associated with these investments are mitigated.
Decommissioning is a phase/decision related to the end-of-life of projects. The offshore wind energy industry is new, and the first structures are just now reaching that stage. Thus, it is identified as essential to include and analyze this phase very carefully, taking this type of decision from the beginning of project planning. If this type of decision is not given due importance, unexpected costs can directly affect the economic viability of offshore wind energy. Studies related to the end of life of offshore wind farms can provide greater returns to investors, increase their profitability, and reduce their risks.
Regarding the most used criteria and methods, the LCOE, NPV, and IRR can be highlighted. However, Multi-criteria Decision-Making (MCDM) is identified as a method with great potential to support decision makers in economic and technical feasibility analyses. It was observed that this criterion was used infrequently, indicating a possible opportunity for future studies as such tools are able to support decisions from more than one perspective.
It was noted that another of the trend terms is "Risk". Thus, methods such as the Real Options Theory can be applied to feasibility studies for offshore wind generation as it ensures flexibility for managers to change the direction of projects, providing a better relationship between return and risk. From the same perspective of dealing with risks, MCS can be used more, especially in obtaining probabilities of economic and financial viability. Moreover, Value at Risk (VaR) can be used to assess the financial risks of this type of project.
Finally, it was observed that there was no study of this type of technology in countries such as South Africa, Morocco, the Philippines, Sri Lanka, and Vietnam, which have great potential for this energy source, and are considered opportunities for studies to be developed. In addition to these, for India and Brazil, countries that make up the group with great potential, only one study was identified for each country.

Conclusions
This article proposes a review of the state of the art of the literature on economic feasibility studies in offshore wind generation and, based on gaps identified in the literature, three research questions were proposed (RQ1: what are the main characteristics of the literature on the economic feasibility of offshore wind energy generation? RQ2: what are the scenarios and the main methods and criteria used in the economic feasibility analysis of offshore wind farms? RQ3: what are the main trends and research opportunities for this topic?). For that, adopting a methodological rigor, an SLR was carried out. Then, a sample of 83 articles extracted from the WOS database were analyzed qualitatively and quantitatively (through bibliometric techniques).
The contribution of this research is to summarize the main tools and criteria used in the economic feasibility analysis of offshore wind power, facilitating access to information for researchers and investors in this type of technology. Furthermore, through the analysis carried out, it was possible to identify some trends in the offshore wind energy area, providing an overview of the characteristics of this literature.
The novelty of this article is evident as no articles were identified that are intended for trend studies and feasibility analysis in offshore wind energy. In addition, based on the trends evidenced in this research, a research agenda was proposed.
Although consolidated in the literature, the tools used in the feasibility studies, such as NPV, LCC, and even IRR, have been seen as trending words in this area of knowledge. MCDM has been used to support investment decisions in such sources, as such tools support decisions from more than one perspective. Analyses that allow for risk consideration are also in evidence with the growing interest in studies on the economic viability of large farms. Issues such as repowering and decommissioning have shown greater relevance with the need and interest in studies that assess the extension of the useful life of these investments.
Finally, with the growing interest in this renewable source, regional studies under specific conditions will continue to be demanded. In addition, in general, there are few studies that are dedicated to an assessment of economic viability, considering that most of them carry out a technical economic analysis in which they use only a financial or cost variable, highlighting the large gap to be filled.
The limitations of the research may be related to the methodological options in SLR development. A limitation of the research is the fact that only the WOS is used; however, this is attenuated as it is one of the largest databases for academic publications. Second, the decision on the search strings used may exclude relevant publications in the analyzed literature. For this, several attempts at combinations of search strings were tested. Another way of validating the search strings was the analysis of the most cited articles to verify if they were relevant enough to be included in the final sample of the article. Finally, another limitation is related to the inclusion and exclusion criteria of each article to form the final sample, which we sought to mitigate with the participation of three different researchers.