Financial Investment Valuation Models for Photovoltaic and Energy Storage Projects: Trends and Challenges

Abstract

Energy production through non-conventional renewable sources allows progress towards meeting the Sustainable Development Objectives and constitutes abundant and reliable sources when combined with storage systems. From a financial viewpoint, renewable energy production projects withstand significant challenges such as competition, irreversibility of investments, high uncertainty levels, and considerable investment amounts. These facts make their financial valuation fundamental for all the agents involved. Using the Web of Science (WoS) and Scopus databases, a scientometric analysis was carried out to understand the methods that have been used in the financial appraisal of photovoltaic energy generation projects with storage systems. The present research project was developed from 268 studies published between 2013 and 2023; tools such as Bibliometrix 4.1.3, VOSViewer 1.6.19, and Tree of Science 0.0.1a9 were used. Two main findings stand out: (i) the most used methods in the literature are the traditional ones, and within them, the levelized cost of energy has been used with greater frequency; and (ii) there is an interest in analyzing the investments of these systems for residences within the framework of distributed energy generation. Two gaps were found in the literature: (i) the studies that were carried out have not comprehensively incorporated the financial challenges faced by these investments; and (ii) the evaluation of these projects has not been addressed from the perspective of a utility-based power generator.

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.

3. Bibliometric Analysis

3.1. General Information

The bibliometric analysis presented in this study includes 270 studies published since 2005. However, 99.26% of these (268 studies) were published between 2013 and July 2023. Thus, this time window will be considered for the respective bibliometric analysis. During this period, there was an annual 24.01% publication growth rate and an average of 32.63 citations per study. This echoes the importance of the research topic for the academic environment.

These studies were published across 70 scientific journals. From the considered sample, and according to the classification provided by the scientific databases, 263 studies, 5 reviews, and 913 researchers were identified. Most of the studies were co-authored; only 11 were written by a single author. On average, 3.98 researchers per study were found. This shows a collaborative structure among scholars in this field. It was found that 57% of the documents were financed by government entities or research centers attached to universities, which shows the interest of the public sector and academia in advancing research oriented towards the study of photovoltaic generation projects with storage systems.

Table 1. Summary of the obtained information. Source: Own elaboration using the Bibliometrix tool.

Description	Results	Description	Results
MAIN INFORMATION ABOUT THE DATA		AUTHORS
Timespan	2013:2023	Authors	913
Sources (journals, books, etc.)	70	Authors of single-authored docs	10
Documents	268	AUTHOR COLLABORATIONS
Annual growth rate%	24.01	Single-authored docs	11
Document average age	2.98	Co-suthors per doc	3.98
Average citations per doc	32.63	International co-authorships %	5.597
References	13,287	DOCUMENT TYPES
DOCUMENT CONTENTS		Article	263
Keywords plus (ID)	1791	5	5
Author’s keywords (DE)	919

presents a summary of the general information from the considered studies.

3.2. Performance Analysis

3.2.1. The Evolution of Published Studies and Citations

Figure 2 shows a significant increase in the published studies over the last ten years; it goes from 5 articles (published in 2013) to 43, as of July 2023. This allows us to conclude that there is a growing interest from the academic community in carrying out research related to photovoltaic energy projects with storage systems. This fact coincides with the global increase in investments in this type of energy, as one of the most promising ones within the energy transition framework, and also meets the need for the energy sector to incorporate storage systems that mitigate the variability of this energy source.

Figure 3 represents the average number of citations per year, indicating that 2014 was the year with the highest number of average citations (15.2%), with two published studies that, in addition, are part of the intellectual structure of the research topic associated with investment valuation of photovoltaic generation projects with storage systems. Both studies address these projects in residences, which is part of the distributed generation of energy that has been proposed as an alternative for sustainable development [26] and in which photovoltaic energy, due to its cost and modularity, has been conceived as a technology conducive to residential users [27].

3.2.2. Analysis of Sources

Figure 4 shows the ten journals with the highest number of studies related to the investment valuation of photovoltaic projects with storage systems. It is evident that this topic has been widely addressed and that 48% of the published studies focus on five journals: (i) Energies (36), (ii) Applied Energy (29), (iii) Energy (24), (iv) Renewable Energy (20), and Energy Conversion and Management (19). These journals are essential for the dissemination of results associated with this research topic. Among the topics considered in these journals is energy planning and management, which would include studies related to techno-economic analyses and the economic and financial viability of energy generation and storage projects.

Mohd Noor et al. [28] argued that the journal’s impact can vary, depending on the field of research. In particular, the number of total citations of a journal is a good indicator of the journal’s relative importance in a research field, and the h-index can also measure its importance according to its status in scientific communication. Considering the number of citations and the h-index (Figure 5), the journal Applied Energy is in first place, with 1567 citations and an h-index of 19, followed by Renewable Energy, with 1243 citations and an h-index of 15, Energy Conversion and Management, with 1210 citations and an h-index of 14, and Energy, with 980 citations and an h-index of 19.

3.2.3. Analysis of the Researchers

According to Mohd Noor et al. [28], the number of publications and citations per manuscript allows for the identification of the most productive scholars in the research area and the visibility of their research. Figure 6 shows the researchers who stand out for having the greatest number of studies published on the research topic addressed in this study. Researchers with four or more published studies were considered. According to the data, the researchers with the highest productivity are Idiano D’Adamo with seven studies, Massimo Gastaldi with five, and Federica Cucchiella, Tao Ma, David Parra, and Ali Sohani with four studies each.

Regarding the work carried out by the most relevant researchers on the subject, due to their number of publications and the fact that these are linked to the investment valuation of photovoltaic energy generation projects with storage, it was found that Idiano D’Adamo addressed in his publications the analysis and economic viability of photovoltaic systems with energy storage for the case of residences [29,30,31,32,33,34,35] and considered the impact of incentives and subsidies for these projects. The studies of this researcher were carried out in co-authorship with Massimo Gastaldi [33,34] and between Federica Cucchiella and Massimo Gastaldi [29,30,31,34].

Ali Sohani developed studies on the design of hybrid generation systems with wind–photovoltaic energy and hydrogen storage in residential complexes [36] and off-grid systems [37]. He also addressed the techno-economic evaluation of a power generation system that combines rooftop photovoltaic systems and building-based thermal-photovoltaic systems (BIPV/T) employing surplus energy in a hot and cold water storage system [38].

David Parra analyzed in [39] the techno-economic impact of adding a lithium ion battery system for various consumer applications to a photovoltaic system with different residential electrical load profiles. In [27], he carried out a techno-economic comparison of energy storage systems in photovoltaic systems of single-family homes, considering the levelized cost and profitability of lead–acid batteries (BPA), lithium ion batteries (Li-Ion), and hot water tanks. He studied in [40] the performance and economic benefits of adding BPA and Li ion battery systems to residential PV generation with different retail prices.

Tao Ma developed, through dynamic programing, an operation strategy for a photovoltaic system with battery energy storage in an office building, considering the economic viability of the system, the self-consumption rate, and factors such as energy price, cyclic aging battery, and the demand response’s features [41]. He also performed techno-economic analyses to evaluate and compare the battery storage systems of a grid-connected photovoltaic system [42].

Table 2. Most relevant researchers by citations. Notes: TGCs: total global citations, TCT: total citations in the topic, NP = number of publications; PY_Start: publication year start. Source: Own elaboration using the Bibliometrix tool and Scopus.

#	Researcher	Affiliation	h-Index	TGCs	TCT	NP	PY_Start
1	Chun Sing Lai	Brunel University London	27	2877	428	3	2017
2	David Parra	Université de Genève	31	2914	281	4	2015
3	Idiano D’Adamo	Sapienza Università di Roma	46	5454	274	7	2016
4	Massimo Gastaldi	Università degli Studi dell’Aquila	35	3108	241	5	2016
5	Federica Cucchiella	Università degli Studi dell’Aquila	35	3805	229	4	2016
6	Abdul Rauf Bhatti	Government College University Faisalabad	17	1304	212	3	2018
7	Ahmad Arabkoohsar	Technical University of Denmark	43	5078	197	3	2015
8	Ricardo Nicolau Nassar Koury	Universidade Federal de Minas Gerais	19	1516	197	3	2015
9	Luiz Henrique Jorge MacHado	Universidade Federal de Minas Gerais	24	2153	197	3	2015
10	Loiy Al-Ghussain	University of Kentucky	17	1255	188	3	2018

presents the most cited researchers on investment valuation of photovoltaic projects with energy storage between 2013 and 2023, their affiliation’s organization, total global citations (TGCs) obtained from Scopus, total citations in the topic (TCT) generated by Bibliometrix in R, and the h-index of each researcher according to Scopus. Chun Sing Lai is the researcher with the highest number of citations on the research topic with 428, followed by David Parra (281), Idiano D’Adamo (274), Massimo Gastaldi (241), and Federica Cucchiella (229), who also stood out for having the largest number of studies (Figure 6).

Chun Sing Lai’s studies focused on the levelized cost of energy (LCOE) of PV and biogas power generation systems with storage in an off-grid community [43] and on stand-alone and grid-connected PV systems with storage networks [44]. Furthermore, he developed a model to evaluate the financial performance of energy storage systems and applied it to lithium ion (Li-Ion) batteries within a photovoltaic and biogas system [45]. Within the framework of the investment valuation models of energy generation system projects with storage, the levelized energy cost is one of the most commonly used traditional methods, which would explain the high number of citations by the researcher.

Abdul Rauf Bhatti carried out in [46] a comparative techno-economic analysis of two systems that produce power from the sun: photovoltaic and concentration solar (CSP), and in [47,48], he addresses the study of photovoltaic systems with storage of energy connected to the grid for recharging electric vehicles that are part of distributed energy resources (DERs); he addresses these through different perspectives that include optimization and management schemes. Ahmad Arabkoohsar, Ricardo Nicolau Nassar Koury, and Luiz Henrique Jorge Machado developed three co-authored studies in which they analyzed the techno-economic viability of grid-connected photovoltaic systems with compressed air storage (CAES) and City Gate Station (CGS) under different technical particularities [49,50,51]. Finally, Loiy Al-Ghussain, in his studies, addressed the design of hybrid energy generation systems that include photovoltaics and other sources and analyzed the techno-economic viability of incorporating energy storage from lithium ion batteries in these systems [52,53] and the establishment of the optimal size of a hybrid power generation system with photovoltaic, wind, and biomass with and without energy storage [54]. These studies have been applied in factories and university campuses.

As a common element of the studies carried out by the most relevant researchers by the number of publications or citations, there are two aspects of interest: (i) the researchers have considered hybrid generation systems; and (ii) they have focused on photovoltaic systems with storage in buildings and residences. The first point highlights the academic community’s interest in studying the diversification of the energy matrix as a strategy to guarantee the reliability and security of supply. The second aspect is consistent with the current interest in evolving in the transition of the energy sector towards more decentralized structures and including renewable sources [55], with the global paradigm shift (in which more and more people consume the energy generated by these sources) [56], and with the accomplishment of the Sustainable Development Goal 11 (SDG 11): sustainable cities and communities [13].

In this way, the expansion and development of electrical systems that were traditionally achieved through large-scale generation and transmission have now migrated to decentralized, small schemes located near consumption centers, which take advantage of new technologies; in these, the distributed generation and self-generation as mechanisms within distributed energy resources (DERs) have a relevant role [56]. Consequently, studies of these mechanisms, which are built in residential units or buildings as single structures or in microgrids, are at the center of current debates and have included techno-economic and optimal design analyses of the operation of generation systems and energy storage and evaluating the impact of possible government incentives.

3.2.4. The Leading Countries and Institutions

Regarding the scientific production, according to the countries of the corresponding authors (Figure 7), it is found that Saudi Arabia has the highest proportion of publications in relation to the number of universities in the country (10.3%), followed by Italy (5.9%), Australia (4.8%), and Spain (3.6%); the other countries/regions have less than 3% of studies published in relation to their number of universities.

According to Mohammed [57], the geographic and climatic characteristics of Saudi Arabia allow it to depend on renewable energy sources and consider them financially desirable, so the government promotes initiatives that allow the diversification of its energy sources, which also explains the interest of researchers in addressing issues associated with the valuation of non-conventional renewable energy generation projects, such as photovoltaic energy, with storage systems. In the case of Italy and Spain, the ecological transition raised in the United Nations Agenda 2030 and the policies defined by the European Union have become the central issue in the policy options of most countries and a challenge to ensure sustainable development and solve the problem of energy supply [58]. This motivates the study of issues related to power generation, such as photovoltaics integrated with storage systems to ensure supply.

Within the framework of the Paris Agreement, Australia is committed to increasing its share of renewable energy sources and recognizes that electrical energy storage will be a crucial element to having a clean and secure electricity supply because it allows for minimizing the variability of these sources (especially wind and photovoltaic) [59]. This interest motivates researchers to conduct studies that address the issues associated with investments in photovoltaic projects as a renewable source with storage systems. Finally, although China and India have advanced decisively in the energy transition sector, the percentage of publications in relation to the number of universities is very low because they are countries with more than 2000 universities.

Concerning the countries with the highest number of total citations (Figure 8), it is evident that China has the highest number of citations (1050); the USA bears second place (661 citations), followed by Italy (657 citations), the United Kingdom (579 citations), and Malaysia (557 citations); the other countries have less than 500 citations.

Considering the indicator of average citations per study (TC/TP) presented in

Table 3. Most relevant countries. Notes: NP = number of publications; TCs = total citations. Source: Own elaboration using the Bibliometrix tool.

#	Country/Region	NP	TCs	TC/TP
1	China	1050	43	24.40
2	Italy	657	17	38.60
3	Iran	334	12	27.80
4	USA	661	11	60.10
5	India	281	11	25.50
6	Spain	217	11	19.70
7	United Kingdom	579	9	64.30
8	Australia	418	9	46.40
9	Malaysia	557	8	69.60
10	Saudi Arabia	395	8	49.40

, it is found that, of the ten countries with the highest level of productivity, Malaysia, the United Kingdom, and the USA are the ones with the highest number of average citations per study (more of 60), highlighting the impact of these publications on the academic community.

The top 10 organizations that led productivity in investment valuation of photovoltaic power generation projects with energy storage between 2013 and 2023 are shown in Figure 9. North China Electric Power University (China) ranks first with 11 studies, followed by Universiti Putra Malasya (Malaysia), contributing seven studies; Universidad de Jaen (Spain); Shanghai Jiao Tong University (China); Pontificia Universidad Católica de Chile (Chile); Majmaah University (Saudi Arabia); and King Fahd University of Petroleum and Minerals (Saudi Arabia), each contributing six studies.

3.2.5. Most Frequently Used Keywords

The analysis of the Author’s Keywords and Keywords Plus is relevant to understanding research trends from the authors’ perspective and what is extracted from the titles and keywords [60].

Table 4. Most frequently used keywords. Source: Own elaboration using the Bibliometrix tool.

	Author’s Keywords	Occurrences	Keywords Plus	Occurrences
1	Energy storage	160	Energy storage	645
2	Photovoltaic system	137	Photovoltaic system	497
3	Levelized Cost of Energy	29	Economic analysis	175
4	Techno-Economic analysis	26	Renewable energy resources	101
5	Economic analysis	24	Investments	99
6	Optimization	23	Power generation	79
7	Renewable energy resources	22	Costs	78
8	Self-Consumption	17	Optimization	73
9	Net Present Value	10	Techno-Economic analysis	58
10	Electric Vehicles	9	Buildings	48
11	Energy	8	Levelized Cost Of Energy	45
12	Payback Period	8	Energy	40
13	Wind	8	Sensitivity analysis	35
14	Microgrid	7	Cost Benefit analysis	32
15	Distributed Generation	5	Profitability	30

presents the most frequently used keywords, including the Author’s Keywords and Keywords Plus, between 2013 and 2023 (July). The Author’s Keywords show some of the methods that have been used to valuate photovoltaic energy generation projects with financial storage. Specifically, the levelized cost of energy (LCOE) ranks first with 29 occurrences, followed by the Net Present Value (NPV) and Payback Period (PBP) with 10 and 8 occurrences, respectively. These indicators are part of the traditional methods for the investment valuation of projects. The Keywords Plus analysis found that the levelized cost of energy (LCOE) is also the method with the most occurrences (45).

From Figure 10a, the co-occurrence of keywords is analyzed, which allows complementing that of most frequency words by identifying relationships among these words. The Author’s Keywords and Keywords Plus were used together for this analysis. In terms of the financial methods used for the investment valuation of photovoltaic generation projects with storage, three clusters of interest for this study are evident:

(i)

Green cluster (Figure 10b): It relates photovoltaic systems—energy storage systems—investments—techno-economic analysis, which are the keywords that stand out because of their frequency. The payback period is the investment valuation method that appears in this cluster, which allows us to infer its use as a criterion for the techno-economic comparison or investment decision of photovoltaic systems with storage.

(ii)

Yellow cluster (Figure 10c): The levelized cost of energy (LCOE) stands out as the most frequently used keyword. This is one of the traditional methods of investment valuation of energy projects. Its use is evident in optimization analysis, another keyword relevant to this cluster. Hybrid energy systems and microgrids are also highlighted. This shows that the analyses of photovoltaic systems with storage have frequently been carried out in systems with more than one energy generation or storage technology and have focused on microgrids within the framework of distributed energy generation. It explains the relationship between this cluster and red in the building keyword and reveals the gap regarding the study of these investments at the utility scale.

(iii)

Red cluster (Figure 10d): The economic analysis stands out because of its frequency. In this cluster, the Net Present Value appears to be the most recurrent investment valuation method and is part of the traditional methods. The sensitivity analysis is also highlighted as a complementary method to investment valuation when these methods (considered deterministic) are used. This cluster’s keywords relate to those in yellow, inferring that the economic analysis is integrated with the levelized cost of energy to evaluate photovoltaic power generation projects with storage.

3.2.6. Trending Topics over the Years

Figure 11 shows the evolution of the trends of the topics associated with the investment valuation of photovoltaic energy generation projects with storage from 2013 to 2023. For this analysis, the Author’s Keywords with a five-word frequency per study three times a year were considered. It was found that, in 2020, the trend was associated with the levelized energy cost, which is one of the indicators that allows for the evaluation of photovoltaic energy generation systems with energy storage, which is proposed as a solution to the intermittency of this source. In 2021, trends related to optimization, self-consumption, and renewable energy resources were highlighted. This allows an understanding of the interest of researchers in developing methods for optimizing power generation systems from renewable sources, considering the self-consumption rate, and, in general, addressing distributed energy generation. For 2022, the trend is associated with techno-economic analyses.

According to these results, it is evident that there is a gap in research regarding the incorporation of advanced methods, such as real options and option games, that allow uncertainty and competition to be included in the valuation investments of photovoltaic energy generation projects with storage that could improve investors’ decision-making and provide relevant information to policymakers.

3.3. Science Mapping

To understand the studies that define the intellectual structure of a topic, it is relevant to undertake the co-citation analysis of these to determine those that are cited most frequently and that, therefore, can represent the key concepts or methods in a field of research [24]. This analysis can be carried out using the Tree of Science (ToS) perspective, in which studies are evaluated considering three indicators: (i) degree of entry; (ii) intermediation; and (iii) degree of exit [61].

According to Robledo Giraldo et al. [61] and Landinez et al. [62], the classification of studies can be similar to the structure of a tree, dividing them into four categories: (i) the roots are those that have a high degree of input and zero output and correspond to the seminal studies that initially present an idea that manages to have high influence within a discipline; (ii) the trunks are the studies that have a high degree of intermediation and shape the theory of the research topic; (iii) the branches are those that determine the perspectives; and (iv) the leaves are those that have a high degree of exit and no degree of entry.

Figure 12 presents the ToS structure for the topic associated with the investment valuation of photovoltaic energy generation projects with energy storage that was obtained considering the initial search equation for Scopus and WoS from tools such as Tree of Science.

3.3.1. The Root

According to Robledo Giraldo et al. [61], the studies found at the root of the ToS tool represent those that support the investment valuation of photovoltaic energy generation projects with storage systems. Among these studies (Figure 12), there is that by Branker et al. [63], who reviewed the methodology used to correctly calculate the levelized cost (LCOE) of photovoltaic solar energy, correcting the conceptual errors in the assumptions found throughout the literature. Bortolini et al. [26] addressed a techno-economic study of a photovoltaic system connected to the grid with storage batteries (PV-BESS).

Petrollese et al. [64], Beck et al. [65], Zhang et al. [66], and Linssen et al. [67] presented optimization models for photovoltaic systems with batteries to store energy. The model by Petrollese et al. [64] addressed a hybrid generation system with concentrated solar thermal and photovoltaic plants; the one by Beck et al. [65] considered a grid-connected system that minimizes operation and investment costs to promote self-consumption; the one by Zhang et al. [66] sought to optimize the battery capacity and system operation strategy, and the one by Linssen et al. [67] minimized the costs of battery systems in photovoltaic systems.

On the other hand, Mundada et al. [68] presented a method to quantify the economic viability of off-grid PV, battery, and cogeneration systems by calculating the levelized cost of electricity (LCOE) of the technology compared to centralized grid electricity. Uddin et al. [69] performed a techno-economic analysis of the residential photovoltaic system with lithium batteries for energy storage, and Koskela et al. [70] analyzed how the price of electricity affects the profitability of photovoltaic and energy storage systems.

Table 5. Financial methods used by the root’s documents. Notes: NPV = Net Present Value, IRR = Internal Rate of Return, PBP = Payback Period, ROI = Return on Investment, and LCOE = Levelized Cost of Energy. Source: Own elaboration based on the cited authors.

Financial Methods		Year
		2011	2014	2016	2017	2019
Traditional	NPV		[71]		[66]
	IRR					[70]
	PBP
	ROI
	LCOE	[63]	[26]	[64,65,68]	[67]

shows the financial methods used by the studies found at the ToS’s root. It is evident that traditional methods, and within these, the levelized cost of energy (LCOE), are the most commonly used to financially evaluate these projects.

3.3.2. Trunk

According to Robledo Giraldo et al. [61], the studies shaping the theory that includes the investment valuation of photovoltaic energy generation projects with storage systems are represented by the ToS’s trunk. Within the studies that are in this position (Figure 12), research related to the calculation of the levelized cost of energy for different energy generation systems, having photovoltaics and storage in common, is addressed [43,44,72]. Models have been proposed to make comparisons of different energy storage systems considering technical and economic variables [27,73,74], and others, such as [38,75,76,77,78,79,80], carry out techno-economic evaluations of energy generation systems with storage. Within these studies, Aguilar-Jiménez et al. [78] and Zurita et al. [75] considered hybrid power generation systems. Lin et al. [76] and Bhayo et al. [77] included pumped hydroelectric storage systems, and [38,75,78] considered thermal storage.

Lahnaoui et al. [80] evaluated, in their model, the impact of the angle of inclination and orientation of a solar generator in homes on the system’s performance, and Khalilpour et al. [81] analyzed the impact of various techno-economic parameters such as the geographical location, meteorological conditions, energy price, feed-in price, cost of the photovoltaic system with battery (PV-B), and the technical characteristics of the system on the economic viability of grid-connected PV-B systems.

Other studies, such as those of [82,83,84,85,86,87,88,89], present models to size energy generation systems with storage. The one in Jiménez-Castillo et al. [84] considers parameters such as taxes, depreciation, and financing cost. Zheng et al. [89] took into account the electricity production and consumption profile of the installation, the costs of the PV and BES systems, and the energy rates. In [85], the model incorporates government incentives, changes in energy prices, and the amount of investment in both PV and storage systems.

On the other hand, Refs. [29,32,90,91] present models to evaluate the financial viability of residential photovoltaic generation systems with energy storage. Aketer et al. [90] included within their model energy market price structures the investment costs of both the PV system and storage. Da Silva et al. [92] performed a simulation of photovoltaic energy production and an economic analysis of grid-connected distributed generation systems with and without batteries for energy storage.

Table 6. Financial methods used by the trunk’s documents. Notes: NPV = Net Present Value, IRR = Internal Rate of Return, PBP = Payback Period, ROI = Return on Investment, LCOE = Levelized Cost of Energy, ROs = Real Options, GT = Game Theory, and OGs = Option Games. Source: Own elaboration based on the cited authors.

Financial Methods		Year
		2013	2014	2016	2017	2018	2019	2020	2021	2022	2023
Traditional	NPV	[85]		[29,81]	[32]	[31,88,92,95]		[84,87,91]		[83]
	IRR	[85]		[27,40]	[90]			[87]
	PBP	[85]			[90]	[92,95]		[76]		[42]	[38]
	ROI							[86]
	LCOE		[73]	[27,40]	[43,44,82]	[72,75,78,80,92]	[77,79]	[96]	[74,89,94]	[83]
Advanced	ROs								[94]	[93]
	GT
	OGs

shows the financial methods used by the studies found in the ToS’s trunk. In these studies, traditional methods are the most commonly used, and within them, the levelized cost of energy (LCOE) and the Net Present Value (NPV) are the most common. From all of these studies, only two use real options that are part of the methods that, for the purposes of this study, have been classified as advanced. Specifically, Li et al. [93] developed a model based on composite real options that consider the flexibility and uncertainty of the investment and operation phase to determine the incentives that promote investments in photovoltaic systems with large-scale energy storage. Andreolli et al. [94] provided a theoretical scheme to model investment decisions by households in photovoltaic power generation systems with battery storage, considering the value of the operational flexibility that storage provides.

3.3.3. Branches and Leaves

In the ToS’s branches and leaves lie the new perspectives and trends associated with the investment valuation of photovoltaic power generation projects with storage. These trends are built on the recurrence of the issues addressed in the most recent studies. The results of the analysis allow for the highlighting of three trends: (i) the residential photovoltaic systems with energy storage systems; (ii) the hybrid energy systems with energy storage systems; and (iii) the optimization of hybrid energy systems with energy storage systems (Figure 12, Figure 13, Figure 14 and Figure 15). Regarding the first trend (Figure 13), [97] indicated that decarbonizing buildings is one of the key ways to stop or reverse climate change. Within this trend, it can be mentioned the studies by [29,31,32,35,69,71,90,95] that addressed the techno-economic or optimization analysis of residential photovoltaic or hybrid systems with storage. Additionally, there are studies by [98] who proposed policies to encourage households to have photovoltaic systems with batteries (PV-B) and [33] in which they evaluate the impact on the Net Present Value of a tax deduction on the residential photovoltaic system with batteries as an energy storage system.

The second trend (Figure 14) is associated with the importance of integrating different energy generation sources into the energy matrix to guarantee reliability and security in supply, as well as the possibility of including energy storage systems. Within this trend, there are the studies by [38,43,74,75,83]. Finally, the third trend (Figure 15) incorporates studies that develop models that allow determining the sizing of a hybrid energy generation system with energy storage systems, considering a set of techno-economic variables that are expected to be optimized. In this trend, the studies by [26,86,89] can be mentioned.

Finally, the science mapping analysis based on the Tree of Science (ToS) makes it possible to detail the evolution of the research topic related to the investment evaluation of photovoltaic generation projects with storage systems. In particular, it was found that throughout the analysis period, the studies have addressed photovoltaic generation projects with storage in hybrid systems (with different generation or storage technologies) and homes and buildings in the framework of distributed energy generation, so it is found that the analysis of these projects at utility scale has not yet been sufficiently explored, and this is explained by the current trend of decentralizing the energy market.

Regarding the methods used to evaluate these projects, the studies found in the root use traditional methods. In turn, in the trunk, it was found that, although traditional valuation methods continue to be the most recurrent, the studies begin to incorporate the concept of operational and investment flexibility in these projects from the use of the real options method, and, with this, the uncertainty inherent to these investments is addressed, which is one of the financial challenges to be faced in their valuation. However, the impact, within the evaluation, of the response of energy market competitors when one of the actors invests in these projects has yet to be considered, which could be carried out using the game theory method, which has been used to analyze the strategic behavior of agents within the market and not as a method to make investment decisions. Even these investments have yet to be analyzed when competition and uncertainty converge, for which the game theory of options should be addressed.

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 CO₂. 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.