1. Introduction
As a result of adopting renewable energies, which has accelerated due to the need to minimize the impact of climate change, the energy sector is experiencing changes in the composition of the energy matrix globally. According to Ud-Din Khan et al. [
1], the incorporation of these sources of energy has been driven by the implementation of policies and regulations in developed economies, the improvement of technologies, the decrease in the costs of non-conventional renewable energies (mainly wind and photovoltaic), and the Paris Agreement in force since 2015 and ratified in 2016.
The share of non-conventional renewable energies within the global energy matrix has been growing and, for the first time, exceeded 10% in 2021 [
2], and since the end of this year (due to the increase in fossil fuel price) and from 2022 (derived from the war in Ukraine), renewable energies have been spotted not only to combat climate change but also for their role in the improvement of energy security and sovereignty; this constitutes a synergy between energy transition and supply security [
2]. Indeed, high levels of renewable energy and greater efficiency in power use improve energy sovereignty, security, and diversification. At the same time, this reduces the exposure to fluctuations in energy prices and contributes to the sector’s decarbonization [
2,
3,
4].
Globally, renewable energy capacity additions grew 17% in 2021 and reached a new high point of more than 314 GW of additional capacity; from it, 100 GW corresponds to utility-based photovoltaic energy additions, which represents a 20% growth of the additions of this technology [
2]. This increase is explained by its economic competitiveness [
2], rapid technological progress, and low maintenance requirements [
5]. Consequently, the global weighted average levelized energy cost of utility-scale PV generation projects fell 88% between 2010 and 2021, while that of onshore wind energy fell 68% and offshore 60% [
6].
IRENA [
7] has indicated that transforming the electricity system towards one that is based on renewable energy causes some challenges due to its intermittent nature. These risks can be mitigated by providing some degree of complementarity to the energy matrix using more than one power source [
8] and using energy storage systems for the matrix’s primary sources [
4]. Along these lines, Li et al. [
9] argued that solar and wind power generation can help decarbonize the electricity system if their variable output is cost-effectively remodeled from large-scale energy storage. Hence, it allows for satisfying demand and offers system reliability.
Jacobson et al. [
10] and Aghahosseini et al. [
11], cited by Canales et al. [
12], concluded that it was possible to have an electrical system based entirely on renewable energies if the generation and storage systems are carefully planned. For the World Bank [
4], generating energy from renewable sources, particularly non-conventional sources such as solar and wind, allows progress towards compliance with Sustainable Development Goal 7 (SDG 7), which is related to accessing affordable, reliable, sustainable, and modern energy by 2030 [
13], and could be a change driver for developing countries since these sources are abundant, profitable, and reliable when combined with energy storage.
The importance of energy generation projects with non-conventional renewable sources that incorporate storage systems justifies the need for a financial valuation that analyses the relevance of allocating resources to these initiatives. Therefore, it must be taken into account that these projects face financial challenges that are associated with their characteristics and that, according to Santos et al. [
14], influence the choice of the best method to evaluate them financially: (i) they are carried out in competitive markets; (ii) they demand investments of significant amounts; (iii) with high irreversibility levels; and (iv) they are exposed to multiple factors of uncertainty, so the associated investment decisions deserve to be analyzed holistically to compare projects, support investment decisions, and, thus, achieve a sustainable, viable and profitable energy future [
15]. Hence, studying and developing valuation methods for these projects is essential to encourage these investment initiatives, not only due to the technical advances anticipated in these technologies but also because their assessment must consider the multiplicity of variables that influence their viability [
16].
The methods that have been used to financially evaluate investment projects range from the traditional one, based on the construction of deterministic cash flows and the calculation of financial indicators such as the net present value (NPV), the internal rate of return of the project (IRR), payback period (PR), return on investment (ROI), and, for energy projects, levelized cost of energy (LCOE) [
14,
16], to more advanced methods such as that of Real Options (ROs), Games Theory (GT), and Option Games (OGs), which allow us to overcome the limitations of the traditional one (it does not consider uncertainty and the effect of interactions among competitors) on the value of investment initiatives [
16,
17].
In fact, real options allow the incorporation of inherent volatility factors in investment projects; this is based on the premise that the decision to invest can be strongly modified by the degree of irreversibility, uncertainty, and the decision-maker’s room for maneuver [
18], thus quantifying the operational flexibility of projects [
19]. On the other hand, OGs explicitly recognize that competitive forces can provide an incentive to exercise options early and emphasize the advantages of the competitor who makes the first move. This method highlights the fact that long-term strategic decisions involve establishing an appropriate balance between strategic commitments and flexibility and postponing or organizing investments facing competition [
17].
Considering the above, this study intends to carry out a bibliometric analysis through Bibliometrix, VOSViewer, and Tree of Science to understand the methods that have been used to financially evaluate energy generation projects with non-conventional renewable resources such as photovoltaics, which is one of the most promising in the framework of the energy transition, with energy storage systems, as a solution to the intermittency of these sources. This is pertinent to define the most convenient investment strategies under the characteristics of irreversibility, uncertainty, and competition that these projects have and, thus, to contribute to the decision-making of project planners, policymakers, and researchers.
Knowledge Gap, Objectives, and Contributions
The growing interest in generating energy from renewable sources has motivated us to identify the methods used to financially evaluate these projects and those related to energy storage systems. Indeed, in the last five years, several literature review articles have been published for this purpose. Delapedra-Silva et al. [
16] reviewed (from 2011 to 2020 in the Scopus database) the publications that addressed the investment valuation models of renewable energy projects with the objective of analyzing whether the used methods have changed and identifying the factors that have motivated the development of new approaches. For their analysis, they grouped the methods into four categories: (i) indicators based on the traditional discounted cash flow method; (ii) the levelized cost of energy (LCOE); (iii) the return on investment (ROI); and (iv) real options (ROs).
The reviews by Kozlova [
20] and Lazo et al. [
19] focused on studies that used the real options method in renewable energy projects [
20] and, specifically, photovoltaic energy [
19]. Kozlova [
20] considered 101 articles from the Scopus and Web of Science databases from 2002 to 2017 that used this method and analyzed them, considering three possible uses: (i) to value renewable energy generation projects; (ii) to analyze the effects of hedging strategies on uncertainty factors; and (iii) to evaluate the policies that are formulated to encourage these investments and that generate flexibility in the projects. The aim of this review was to describe the design of the model used, addressing four components of real options analysis: (i) the identification of the sources of uncertainty; (ii) modeling of uncertainty variables; (iii) recognition of the type of real options used; and (iv) the analysis of the real options valuation method.
Lazo et al. [
19] included 92 articles from the Scopus and Web of Science databases from 2003 to 2022 that used the real options method for evaluating photovoltaic (PV) generation projects. The researchers classified these studies into 11 categories, according to their area of application: (i) behavior of PV projects under various market conditions such as: technological changes, volatility analysis on investment timing, subsidies, climate change initiatives, price schemes, and financing mechanisms; (ii) PV projects with operational flexibility, (iii) PV systems with energy storage systems; (iv) the evaluation of public policies; (v) design of energy generation portfolios; (vi) design of microgrids; (vii) PV projects in buildings; (viii) consideration of climatic conditions in the evaluation of PV projects; (ix) evaluation of PV panel production and recycling projects; and (x) integration of PV systems with commercial satellites and (xi) prosumers and local energy markets.
Regarding the investment valuation models of storage systems, Rotella Junior et al. [
3] developed a review of the state-of-the-art to identify the most commonly used methods to perform an economic analysis of battery energy storage systems (BESSs) as an alternative to improve the techno-economic viability of renewable energy systems. In this review, the researchers considered 92 articles from the Scopus and Web of Science databases from 2008 to 2020. They identified three clusters in the literature: (i) photovoltaic systems with energy storage systems in residential areas, (ii) comparison between storage system technologies, and (iii) services to improve network quality and reliability.
Based on the reviews described, Lazo et al. [
19] indicated that photovoltaic energy generation projects addressed the use of real options, which is one of the methods available in financial theory. They concluded that most studies used this method to determine the optimal investment timing under different market conditions. Regarding projects with energy storage, the review by Rotella Junior et al. [
3] considered the methods to financially evaluate generation projects with renewable resources (not only photovoltaics), which included batteries; it is one of the available storage technologies. In this way, it is evident that no reviews have been carried out in the literature that address the investment valuation models of photovoltaic energy generation projects with storage systems as a strategy to mitigate the variability of this source and guarantee its reliability and supply.
This research intends to help overcome this gap by developing a bibliometric analysis of the available literature on the investment valuation models used in photovoltaic energy generation projects with storage systems. The importance of this study is based on the fact that it offers project planners, policymakers, and researchers information on the trends and future lines of research associated with the topic and that it is relevant to the investment decision-making process of these projects. Likewise, it allows identifying potential gaps in the investment valuation process of these generation projects that are found when analyzing whether the methods used allow the incorporation of the characteristics of irreversibility, competition, and uncertainty inherent to these projects and that, due to their importance, may affect the investment decisions. Finally, this study will jointly use sophisticated tools such as Bibliometrix, VOSViewer, and Tree of Science, which makes it innovative compared to the reviews described above. It also allows us to present the intellectual structure of the subject, its evolution in time, and the researchers who lead this field of research.
The structure of this paper is organized as follows: After this introduction section, the materials and methods are described in
Section 2, including the data source and data processing. In addition, the techniques of bibliometric analysis are explained, and the tools to be used are defined.
Section 3 presents the bibliometric analysis using a performance analysis in which the evolution of published studies, the most cited papers and authors on the topic, the analysis of keywords and trends, and mapping science to define the intellectual structure of the topic are discussed.
Section 4 presents the discussion to understand the advantages and limitations of each financial investment evaluation method used in PV power generation projects with storage systems. Finally,
Section 5 summarizes the main conclusions and highlights gaps, hot topics, and future trends.
2. Materials and Methods
The bibliometric analysis examines, through massive and objective data, the scientific literature on a specific field of study. It explores its intellectual structure and understands its evolution over time, trends, and emerging areas; it ensures that the scholars obtain an overview of the field, identify knowledge gaps, new research areas, and position their contributions to the field [
21]. According to Koseoglu et al. [
22] and Benckendorff et al. [
23], cited by Fabregat-Aibar et al. [
24], bibliometric studies can be classified into three groups:
- (i)
Review techniques allow knowledge creation using bibliographic data from published studies and statistical analyses. This group encompasses systematic literature reviews, meta-analyses, and qualitative studies.
- (ii)
Evaluation techniques assess the academic impact of scientific studies considering their relative influence based on productivity measures such as the number of publications per year and researchers, impact metrics such as the number of citations, and hybrid metrics such as the average number of citations and productivity and impact indexes.
- (iii)
Relational techniques offer information about the structure of a research topic, identify patterns between researchers and affiliations, and detect topics of interest and research methods through co-citation analyses.
For Donthu et al. [
21], bibliometric analyses are carried out under two categories: (i) performance analysis, which evaluates the contribution of research in the field of study, and (ii) scientific mapping, which focuses on the relationships among the research comprising the analysis. These categories can be built using the evaluation and relational techniques presented by Fabregat-Aibar et al. [
24].
In the present study, the evaluation and relational techniques of bibliometric analyses defined by [
24] will be used; it uses the grouping of [
21] to (i) identify the methods that have been used in the literature to evaluate photovoltaic energy projects with energy storage systems financially; (ii) to monitor the knowledge frontier in this research area and determine how the use of these methods has evolved, the type of projects in which it has been used, and its scope; (iii) to recognize leading researchers, institutions, countries, and key topics; and (iv) to infer research trends in the area. For this study, Bibliometrix, VOSViewer, and Tree of Science were used.
In order to search and select the relevant publications, the PRISMA methodology [
25] presented in
Figure 1 and the scientific databases Scopus and the Web of Science (WoS) were used.
In the identification phase, four sets of keywords were defined, each with their respective synonyms: the first set is associated with photovoltaic energy as a non-conventional renewable source; the second one is related to energy storage; the third includes methods for investing in financial valuations; and the last one considers keywords associated with investment, valuations, and decision-making. The search equation (used in both databases) was constructed based on these sets. No restriction was placed on the study’s start date; its end date was set to July of 2023. The equation used was as follows:
(((TITLE = (“Photovoltaic” OR “PV”)) AND TITLE-ABS-KEY = (“Energy storage”)) AND TITLE-ABS-KEY = (“Real Options” OR “Game Theory” OR “Option games” OR “Net present value” OR “NPV” OR “Internal rate of return” OR “IRR” OR “Payback” OR “PBP” OR “Return on Investment” OR “ROI” OR “Discounted cash flows” OR “DCF” OR “LCOE” OR “Levelized cost of energy” OR “Levelized cost of electricity”)) AND TITLE-ABS-KEY = ((“Investment*” OR “Investment appraisal” OR “Investment assessment” OR “Invesment feasibility” OR “Investment analys*” OR “Investment decision*” OR “Investment model*” OR “Strategic decision*” OR “Strategic investment*” OR “Decision-making” OR “Decision making” OR “Financ*” OR “Financial model*” OR “Financial feasibility” OR “Financial Assessment” OR “Economic*” OR “Economic Feasibility” OR “Economic assessment” OR “Valuing investment*” OR “Valuat*” OR “Assessment” OR “Feasibility”))
From this study, 329 studies were found in Scopus and 234 in the WoS in a time window between 2005 and July 2013. Subsequently, the process was limited to studies and reviews in English and Spanish. From these initial filters, 94 studies from Scopus and six from the WoS were excluded. Finally, using the Bibliometrix in R package, 176 duplicate studies from both databases were identified and excluded, leaving 287 records.
In the screening stage, nine duplicate studies that were not identified by the software tool were excluded, and when reviewing the titles of the studies, three additional ones were excluded because they were based on energy exergy analyses; four that focused on projects related to distribution networks; and one on transmission networks. Through this process, the final sample consisted of 270 studies.
4. Discussion
Assessing investment projects is central to analyzing their financial relevance and enabling the decision-making process, considering the scarcity of resources. The studies on photovoltaic energy generation projects with storage described in this paper have mainly performed techno-economic and optimization analyses, in which they have persistently incorporated traditional investment valuation methods (
Table 5 and
Table 6) that do not consider the uncertainty and competition that characterize these investments but offer deterministic decision criteria that facilitate the comparison of technologies. Among them are the LCOE, IRR, and NPV, and from 2022, some studies are evident, like those of [
93,
94], that are considered more advanced methods like the one of real options.
Regarding the LCOE, Petrollese et al. [
64] stated that it is a very useful method for assessing energy generation investments since it allows comparing different technologies, even when the investment costs differ. However, Branker et al. [
63] explained that this method does not consider risks nor any of the different financing alternatives available for each technology type; they stated that the LCOE is a static method to derive the price of the generated energy, but the real prices in the market are dynamic, and usually, the technical hypothesis used in this method is generalized for the equipment’s configuration.
Branker et al. [
63] indicated that the most relevant variables for calculating the LCOE in photovoltaic generation projects with storage are the system’s cost (considering the technological and geographic variability), its financing, life cycle, and the loan’s term. The authors concluded that, given the uncertainty of these variables, it is necessary to complement this investment assessment method in the energy sector with sensitivity analyses; this allows determining the variables’ real distributions. Mundada et al. [
68] agreed with Branker et al. [
63] regarding the variables that must be included to determine the LCOE in a hybrid system; they included the financing and operation costs, as well as the maintenance and the fuel’s.
The IRR and NPV methods are widely used by investors, project planners, and researchers to evaluate the financial viability of investment projects. According to Huang et al. [
99], the IRR is a practical method that allows calculating the economic profitability of the invested capital, and the ratio forms simplify the comparison of projects. Regarding the NPV, the authors argued that it offers four advantages: (i) it considers the value of money through time; (ii) it is calculated from the cashflows discounted from the project, which includes the income and outcome; (iii) it includes risk assessments based on the discount ratio used to calculate the actual cashflow value; and (iv) the acceptance or rejection criterion is simple so that it facilitates decision-making processes. Delapedra-Silva et al. [
16] argued that the return on investment (ROI) method is an extension of the NPV and is defined as the average annual net return on invested capital. However, since it does not consider the time value of money, it is unsuitable for valuing investment projects.
However, Mokhtari et al. [
100] argued that in the current context, investors face scenarios with multiple uncertainty factors due to information restraints, changes in the markets, technological advances, and unstable economic conditions, among others. Due to that, the use of deterministic methods to evaluate investment projects, such as the discounted cashflow (on which the IRR and NPV are based), may lead to inappropriate decisions because they do not consider changes in cash flows associated with project uncertainty; they assume that the rate at which cash flows are reinvested does not vary and that investors keep their expectations of profitability and investment alternatives constant. In this sense, Huang et al. [
99] also stated that the cashflows’ variability is a disadvantage of the traditional methods, and they add other drawbacks such as the selection of discount ratios (which should consider the systematic and non-systematic risk of the investment project), the NPV’s sensitivity, and the investment decision-making’s static nature of these methods. Regarding the latter, Andreolli et al. [
94] agreed with the fact that the NPV assesses the projects at a moment in time, but it does not consider the possibility of reacting against the changes in external and internal conditions.
Mascareñas et al. [
18] argued that, through the real options method, it is possible to assess investment projects, including the changes in cash flows, when they adapt to the conditions prevailing in the market during the project’s lifecycle; this is known as operational flexibility, and it can add value to the investment project. According to the authors, this method establishes that the decision to invest can be strongly modified by the decision-maker’s degree of irreversibility, uncertainty, and room for maneuvering. Lazo et al. [
19] stated that real options allow assessing the strategic flexibility (in investment and operation) that projects have to adapt to contingencies aiming at increasing profits and minimizing risk.
Photovoltaic generating projects with energy storage systems are carried out on long-term planning horizons; these are affected by technological innovation processes, changes in electrical and economic market conditions, and the mechanisms and incentives defined by governments. This fact implies that the parameters used to build cashflows (and under which decisions are made) are uncertain. This makes it necessary to incorporate the value of strategic flexibility within the evaluation of these projects. Li et al. [
93] and Andreolli et al. [
94] used the real options method, and both considered the price of energy as one of the uncertainty factors. The authors of [
93] also considered the price of CO
2. Andreolli et al. [
94] incorporated flexibility in the investment phase, and Li et al. [
93] also considered flexibility in the operation phase.
Finally, Smit et al. [
17] argued that companies make decisions under the conditions of uncertainty, rivalry in the competitive environment, and partial information; therefore, long-term strategic decisions involve establishing an appropriate balance between strategic commitments (irreversible, visible, understandable, and credible decisions that generate reactions from competitors [
101]) and strategic flexibility. In this sense, the authors proposed the option games method, which explicitly accepts that competitive forces can provide an incentive to early exercise options and also emphasizes the advantages that the competitor who makes the first move has. In the review carried out, it was found that this method has not yet been explored in assessing photovoltaic projects with energy storage systems. Thus, it would be relevant to address whether the decision to invest in this type of project is affected by the reaction of competitors in the energy market and study the convenience of using the method in these projects.
5. Conclusions
This study presents a bibliometric analysis to understand the methods used in the literature to evaluate photovoltaic energy generation projects with energy storage systems as a solution to the intermittent nature of this source. The analysis considered the evaluation and relational techniques presented by Fabregat-Aibar et al. [
24] using the clustering of Donthu et al. [
21] and tools such as Bibliometrix, VOSViewer, and Tree of Science. The search for the studies was carried out in the Web of Science and Scopus databases, and the PRISMA methodology was followed for the selection of publications, achieving a sample of 268 studies between 2013 and 2023.
Compared to previously published literature reviews on the subject, this study constitutes a contribution from the following perspectives:
- (i)
It focuses on understanding the methods that have been used to analyze the financial viability of photovoltaic projects with storage. In this regard, the review carried out by Delapedra-Silva et al. [
16] considered the methods used for renewable energy generation projects in general, and Kozlova et al. [
20] and Lazo et al. [
19] considered photovoltaic projects but focused on the review of studies that used real options, which is one of the methods that can be used.
- (ii)
It presents a novel approach based on the use of tools such as Bibliometrix and VOSViewer that allows for a complete overview of the evolution of the research topic based on the evolution of publications, the analysis of journals, authors, countries, institutions, studies, keywords, and trends. The reviews in [
16,
19,
20] used systematic literature review techniques, but they did not analyze the thematic structure of the topic using ToS analyses and co-citation analyses, which can be used in the literature review and can identify trends in research development in a specific area.
- (iii)
It offers the intellectual structure of the research topic based on the Tree of Science’s exposition that allows identifying the seminal studies on the topic (root), those that shape the theory (trunk), and new trends (branches and leaves).
The results of the bibliometric analysis allow concluding that in the last 10 years, the interest of researchers in addressing the generation of photovoltaic energy has increased, which, according to [
27], is one of the most widespread technologies due to its reduced cost, modularity, ease of maintenance, and storage systems to provide reliability to the supply. The three most relevant journals for disseminating this topic are
Applied Energy,
Renewable Energy, and
Energy Conversion and Management from Elsevier. Saudi Arabia is the country that has the highest proportion of publications in relation to the number of universities in the country; China is the country that leads in the number of citations; and North China Electric Power University is the institution that leads in terms of productivity on the subject.
The analysis of studies and ToS highlight important findings. The methods widely used to analyze the financial viability of photovoltaic generation projects with storage systems are the traditional methods, with the levelized cost of energy being the most used both for techno-economic studies and comparison of generation and storage technologies, as well as for those that determine optimal sizing of these systems according to defined conditions. Real options have been used by Li et al. [
93] to evaluate incentives that promote investment in photovoltaic systems with large-scale energy storage and by Andreolli et al. [
94] to model household investment decisions in PV systems with storage. The Game Theory was applied by Han et al. [
102], along with the Analytical Hierarchical Process (AHP), for storage battery selection.
Likewise, three trends were identified. The first is associated with residential photovoltaic systems with energy storage. The second is linked to hybrid energy systems with energy storage systems, and the third is to optimize hybrid energy systems with energy storage systems. In this sense, the need to carry out future research addressing the financial viability of hybrid energy generation projects with storage is evident. It would include the uncertainty, irreversibility, and competition that are characteristics of these investments, as well as all the qualitative and quantitative variables that can affect the projects in such a way that it is possible to determine if this positively or negatively impacts the investment decision of the economic agent.
On the other hand, the high capital cost of energy storage systems may limit their widespread use in PV power generation projects as a solution to intermittency [
87]. For this reason, governments are proposing energy policies, remuneration schemes, and incentives that provide a secure investment framework to promote PV power generation projects with storage systems for both prosumers and grid operators [
87,
98]. Studies have addressed the impact of energy policies and remuneration schemes on residential and commercial PV systems with energy storage. For example, Nousdilis et al. [
87] developed a techno-economic evaluation model that analyzes the financial viability of residential PV generation systems integrated with storage systems, considering different incentives. They concluded that, at current market prices, systems with storage are less cost-effective compared to stand-alone PV systems, which is expected to change with the cost reduction trends of storage systems. They showed that the net-billing scheme is the most cost-effective for the prosumer and can encourage using storage systems in the residential sector.
Zakeri et al. [
98] argued that energy policies can encourage residential consumers to combine PV systems with storage and increase self-consumption. However, they acknowledge that investments in energy storage are not cost-effective under current market conditions. They concluded that replacing incentives for PV generation with a self-consumption bonus offers a return on investment in household energy storage systems equivalent to a capital subsidy on these systems and improves the cost-effectiveness when these systems are combined with PV. D’Adamo et al. [
35] argued that incentives based on tax deductions and subsidies for energy produced and self-consumed by PV generation systems integrated with storage systems can facilitate a sustainable energy future in the residential sector. The authors suggested a combination of policies: (i) subsidized tax deduction and rebate for generated and self-consumed energy for PV plants; (ii) subsidized tax deduction for battery-based storage systems at a lower value than PV plants; and (iii) promotion of the recycling industry.
Li et al. [
93] indicated that in order to promote the development of PV systems with energy storage, it is indispensable for governments to formulate incentives for storage systems. They concluded that the combination of three proposed incentive policies—(i) investment cost subsidies; (ii) preferential taxation; and (iii) energy price subsidies—has a more significant effect on promoting investment in integrated systems than the policies alone. Finally, Ilham et al. [
103] addressed a techno-economic model of in-building PV systems with energy storage under different tariff structures (flat and dynamic tariff rate structures such as enhanced-time-of-use and real-time wholesale tariff) and compensation schemes (self-consumption and net-energy-metering). They concluded that incorporating energy storage can improve the financial viability of in-building PV systems, provided that governments establish compensation systems with different tariff structures. Rauf et al. [
104] addressed the financial viability for end-users of installing a small-scale PV system in residential and commercial buildings. They considered a sensitivity analysis to assess the impact of the net metering scheme, which allows users to generate and export surplus energy to the public grid.
In this context, it becomes relevant to develop financial models that allow the cost of the system of energy policies on these projects to be valued and compared with the benefit expected from them regarding their impact on profitability and financial viability. In addition, this evaluation must consider the moment, within the projects, in which the best use of these policies and incentives can be made, considering the uncertainty of these projects and the competition’s response. In addition to the above, it is proposed to advance the study of the financial viability of these projects at the utility scale in the analysis of alternative financing sources (such as green bonds and sustainability-linked bonds) and the inclusion of investment and remuneration schemes for both energy generation and non-conventional renewable resources, such as storage systems, so that project planners, policymakers, and researchers can have complete information for making investment decisions or incentive policies around these projects. Furthermore, they denote global importance since they are framed in the Sustainable Development Objectives.