Content area
This study aims to conduct a bibliometric review of the performance analysis and optimization of renewable energy systems over the past 24 years. Using the Scopus database and the Biblioshiny (Bibliometrix R package), suitable keywords were combined for the articles published between 2000 and 2024, and 138 research papers were analyzed using the PRISMA framework. Of the selected articles, 72% focused on reliability and availability, and 58% focused on optimization techniques. On various aspects analysis was conducted such as annual scientific production, total citations per year, documents and authors citation, highlighted the relevant sources and authors, common key terms, etc. The growing focus on reliability and availability is highlighted in this analysis, indicating advancements in research and ever-more targeted research objectives. All these results help researchers in the future by giving them directions to explore more options to develop more efficient and reliable renewable energy solutions.
Article Highlights
This study highlights the top nations that are contributing to the studies on the reliability and optimization of renewable energy systems. India was found to be the largest contributor, with 100 articles published, followed by China (86 articles) and Iran (35 articles). This reflects India’s rising focus on sustainable energy technology and on upgrading the productivity of renewable energy systems. The study suggests that the global collaboration of countries like India, China, and Iran strengthens the progress in renewable energy studies. Therefore, this trend shows that international partnership is very important as it enhances renewable energy technologies around the world.
The term ‘optimization’ is the most frequently used term, having appeared 51 times. It shows the importance of this term in the research on renewable energy systems. The study identified that scientific publications have increased over time. The highest number of papers were published in 2023, and the lowest number of papers were published between 2000 and 2004. These results show the increasing interest in solving problems related to the performance, reliability, and availability of renewable energy systems. The results also proved that the improvement in technologies and methodologies gives a clear path for the researchers to focus on targeted objectives for their future studies in this domain.
Journals like Energy Conversion and Management play a significant role in publishing papers (7 articles) in this related area. The study also assessed the impact of top authors, institutions, and international collaborations and gave a clear outline of the research environment. Therefore, this analysis of top journals and thorough studies can help researchers and professionals to collect some information for their studies. This shows the important role of journals in sharing knowledge and methodologies for improving renewable energy systems.
Introduction
Renewable energy, also known as green energy, is the energy from renewable natural resources that are constantly replenished. In today’s world where climate change is a serious issue and the planet’s resources are depleting, renewable energy appears as not just a choice, but a necessity. It comes from natural sources that are constantly replenished. Solar energy, wind power, hydropower, bioenergy, and geothermal power are widely used renewable energy. Burning of coal, oil, and gas for energy can release harmful substances into the air. These harmful pollutants not only pollute the atmosphere but also accelerate global warming and climate change.
The use of renewable energy in secondary industries like manufacturing, production, process, and service sectors is very economically friendly, as it develops many job creations and contributes to sustainability. In manufacturing industries, the use of fossil fuels is reduced and lowering carbon emissions, as this industry can shift towards renewable energy sources such as solar and wind to power factories. Now the installation of solar panels on rooftops of factories is increasing and these factories can also utilize wind farms to power their operations. In manufacturing plants, the high demand for energy can be fulfilled by these renewable sources. Many car manufacturers use renewables to run assembly lines. Manufacturers also use energy-efficient technologies like energy storage systems and smart grids that reduce costs and increase sustainability.
The generation of electricity for large-scale operations from renewable energy comes from the production industry. Traditionally, power plants burn coal, oil, or gases to generate electricity. But now, power plants use renewable sources like solar farms, wind farms, hydroelectric dams, and geothermal power stations. For example, the flowing water is used to generate electricity in hydropower plants, wind turbines use wind which is absolutely a limitless resource. Nowadays, production industries also use hybrid energy systems. These systems are more reliable and less harmful to the environment. For example, a solar-wind hybrid power plant uses solar energy during the day and switches to wind power at night.
The process industry produces raw materials from chemical, physical, or biological methods. In industries like petrochemicals, and food processing, bioenergy (energy from organic materials) can be used. Bioenergy has proved to be a better option than fossil fuels. In industries like, chemical production, or metal refining, geothermal power can be used. It does not need the burning of fuels and provides a low-carbon heat source. Nowadays, process industries also make interest in Carbon Capture and Storage (CCS) technologies. This technology collects CO2 from industrial processes and stores it underground. Therefore, the overall environmental effect of CO2 can be reduced by this technology.
A shift from the traditional process of production of energy to renewable energy production also affects the service industry. Sectors like transportation, finance, IT, and healthcare are included in the service industry. All these sectors are now using renewable energy. For example, many major tech companies are now using renewable energy. They use renewable energy to power their data centers that provide cloud computing services. Because of renewable energy, the development of electric vehicles (EVs) is increasing. These vehicles are run by batteries that are charged using solar or wind energy. Therefore, a larger number of jobs are produced by the renewable energy sector in a service industry like maintenance, installation, research in renewable technologies, etc.
Therefore, all these sectors that are using renewable energy need careful concerns about factors like reliability, availability, maintainability, and dependability (RAMD). Energy storage systems, hybrid setups, predictive maintenance technologies, and advanced energy management systems enable renewable energy sources to fulfill the demands of industries. Due to the improvement of these technologies, industries will have lower operational costs, lower environmental impact, and a more sustainable energy future. RAMD factors ensure smooth and uninterrupted operations in these industries. By improving reliability, factors like consistent energy supply and smooth production can also be improved. Availability guarantees the accessibility of energy when needed. Maintainability decreases downtime. Timely repairs and predictive maintenance can make system operation smooth. Dependability integrates all these factors and makes sure that renewable energy systems and power supplies give stable and long-term performance which establishes both operational efficiency and sustainability goals. Kumar et al. [1] calculate and optimize the operational availability of solar photovoltaic systems. A stochastic model was developed based on the Markov birth–death process and the cuckoo search algorithm. Kushwaha and Bhattacharjee [2] presented a techno-economic-environmental (TEE) design of an off-grid microgrid (OGM) to improve power reliability in a rural village in India. The slime mold algorithm (SMA) is used to identify the most efficient OGM configuration that can enhance power reliability.
So, all these RAMD factors can be optimized by using machine learning tools. Unexpected downtimes are reduced by these tools and system reliability improves. These ML models can forecast energy demand and production needs. Statistical methods can also be used to improve the performance of industries. These methods can be used for the decision-making process. It helps to design more reliable systems and improve overall availability and efficiency. There are two types of statistical methods, traditional statistical methods and computational statistical methods. Traditional methods involve mutual calculations by formulas and techniques like regression, correlation, and hypothesis testing. It is used for small datasets, time-consuming due to manual processes, apply only to fixed models. On the other hand, computational statistical methods involve advanced computing techniques like machine learning, Monte Carlo simulations, nature-inspired algorithms, bio-inspired algorithms, and Bayesian methods. It can analyze large datasets, give more accurate predictions, and is less time-consuming and more efficient. Wen et al. [3] highlighted the effect of artificial intelligence in increasing the efficiency and reliability of solar energy systems integrated with smart grids. To improve solar power forecasting and grid stability, two advanced AI techniques Support Vector Regression (SVR) and Artificial Neural Networks (ANN) are used [4, 5, 6, 7, 8, 9–10]. Bhagat and Anwer [11] suggested a study to improve reliability and reduce cost of Hybrid Renewable Energy System (HRES) using feed forward neural networks and chemical reaction optimization. (Elazab et al. [12] focused on energy management, modeling methodologies, and strategies for handling uncertainties of renewable energy microgrids using machine learning and robust optimization.
Now let’s focus on three important terms-reliability, availability, and optimization, and see how trends in these three terms changed over time and how they matter for renewable energy systems. Reliability is the ability of the component or system to perform its required function under specific conditions without failure for a given period of time. Due to dependency on weather conditions like sun or wind, renewable energy systems like solar and wind were seen as unreliable. However, with the improvement in technology and predictive models, reliability has increased. Due to high reliability, renewable energy sources become more consistent, and due to this grid operators can plan better for energy needs. This results in a stable power supply from renewable sources. Availability is defined as the portion of time a component or system is in an operational state. In the past, the availability of renewable energy sources is very low. Therefore, the availability of a renewable energy system was enhanced by modeling better storage solutions (like batteries) and improved grid systems. Now more energy can be stored and distributed when it is required. Due to this stored energy, we can use renewables even when the sun isn’t shining, or the wind isn’t blowing. Optimization is the process that involves making systems as effective as possible by reducing waste, minimizing waste, and increasing productivity. In the early days, renewable energy systems were not as effective as they are now. But now there are better strategies to make energy production, storage, and distribution more valuable. For example, according to energy demands and weather forecasts, many algorithms can be used to predict the best time to store or release energy. To build a clean and ecological energy future, the improvement of these three trends is very important. It will make renewable energy a beneficial option for switching traditional energy sources [13, 14, 15, 16, 17, 18, 19–20].
Ghaffari and Askarzadeh [21] presented an efficient optimization approach for sizing the hybrid power generation systems and reducing the total net present cost. The hybrid system was composed of a photovoltaic (PV) panel, diesel generator, and fuel cell and the results show that this system enhances both reliability and cost-effectiveness [22, 23–24]. Kother et al., [25] suggested that for meeting the electricity needs of remote rural areas in southern Iraq, using a hybrid renewable energy system (HRES), which was optimized through the particle swarm optimization (PSO) techniques, was the most effective solution. The optimal configuration includes 10 photovoltaics, 5 wind turbines, and 33 batteries. A cost of energy (COE) of 0.518US$/kWh, high-reliability rate of 99.927%, and complete utilization of renewable energy sources was achieved by this configuration. Therefore, HRES can be able to meet the energy challenges in many regions, promoting sustainability and reliability [26, 27, 28–29]. Blaabjerg et al. [30] concluded that power electronics are essential for renewable energy systems. It helps to reduce costs, increase efficiency, and improve power density. To minimize failure rates, and ensure cost-effective operations, the main factor is to achieve high reliability in these electronics. The shift in Design for Reliability (DfR) towards a physics-of-failure approach was highlighted. How DfR can be used to predict the lifetime of components and systems based on their working conditions was determined through a case study of a wind turbine and a photovoltaic system. Shi and Chew [31] concluded that for improving the performance of sustainable buildings, the important factor is to optimize the design of renewable energy systems. Many factors affect these systems, like solar-based systems, factors like the selection of the right system, the building’s orientation, tilt angle, and surface temperature. For day-lighting systems, many components need to be designed carefully like windows, dimensions, orientations, and shading devices. Season condition, operating condition, mode of the system, and ground heat exchanger are the main factors to enhance the system’s performance and reduce cost for ground-based energy systems. Mashilo [32] highlighted the importance of integrating various renewable energy sources through hybrid systems. The main aim was to increase the performance and reliability of standalone systems. Broad recommendations were presented for hybrid renewable energy systems, highlighting the use of an interleaved multiple-input DC-DC converter [33, 34–35]. Mohammed et al. [36] focused on the optimal design and energy management of a hybrid wind/tidal/PV power generation system. Linear programming technique is used to find optimal combinations of these renewable energy sources, by considering high reliability as a main factor, with various conditions like location, solar radiation, and temperature. Ouessant Island in Brittany, France, was the place where the proposed model was tested, with a load demand of 16 GWh/year. Results highlighted that the optimization algorithm successfully designed the hybrid power system. The energy needs of the site were met properly, with high efficiency and reliability [37, 38]. Senthilkumar et al. [39] proposed a hybrid method, combining the golden jackal optimization (GJO) and white shark optimization (WSO), and found that it improved the reliability of power systems using hybrid renewable energy sources like photovoltaic and wind turbines. The GJO-WSO was found to be more effective as it reduces the power outage costs, a provides a high accuracy of its performance with a 99.68%. Therefore, this technique was found to be more optimal in ensuring the reliability and cost-effectiveness of power distribution systems [40, 41]. Bose [42] found Artificial Intelligence (AI) as a powerful technique that makes a big impact on power electronics, which is very important for smart grids and renewable energy systems. AI makes these systems more reliable, secure, and efficient by designing, controlling, and fixing these systems. As day-by-day AI continues to develop, it will play a key role in enhancing how we generate and use electricity, making it more cost-effective and better for consumers [43, 44, 45, 46, 47–48]. Somasundaram et al. [49] utilized machine learning techniques to predict energy production in renewable systems. To make reliable prediction models, the study analyzed many factors like wind speed, sunshine, and air temperature. For this modeling, many algorithms were used such as linear regression, decision trees, random forests, and support vector machines (SVM). The strengths and weaknesses of renewable energy forecasting were highlighted by comparing these algorithms. The results proved to be valuable for researchers, and policymakers in sustainable energy development. Sharma et al. [50] found that due to continuous increases in energy demand and fossil fuel costs, renewable energy resources become more attractive. So, perfectly sizing hybrid renewable energy systems (HRES) is necessary. To predict weather data like solar irradiation, temperature, and wind speed, advanced forecasting models like Gaussian Process Regression (GPR) are used. The forecasts used to optimize the size of HRES were provided by GPR. Many optimization algorithms were tested, from which the Tunicate Swarm Algorithm (TSA) performed best with a 0.42% reduction in energy costs. So, the importance of precise weather forecasting and the use of advanced optimizations was highlighted in the study to improve the efficiency and cost of HRES. Talukdar et al. [51] found improved reliability of power supply by integrating multiple renewable energy sources, like solar, wind, and tidal into electrical distribution systems. A reliability estimation model was developed using a combination of Markov and well-being frameworks to calculate an active distribution network (ADN) connected to these renewable sources. For countries with long coastlines, the integrated sources enhance the grid reliability and provide the greatest cost benefits. The financial impact of power outages on consumers was also supported by this approach and overall supports sustainable energy goals [52, 53, 54, 55, 56, 57, 58–59].
Now, the comparison of renewable energy systems with non-renewable energy systems is a good option to find the progress and challenges for renewable energy systems to grow fast. When comparing renewable and non-renewable energy, the renewable energy process shows a rapid growth in publications due to global efforts to handle climate change and reduce carbon footprints while for non-renewable energy, the research is only limited to existing systems like oil, coal, etc. Storage problems, efficiency, and integration into grids are also the main challenges for renewables. This bibliometric analysis can help to highlight the areas with advanced technologies and where they still lag.
This paper aims to conduct a bibliometric analysis of performance analysis and optimization of renewable energy sources. Many exciting publications are explored and provide a comprehensive overview. A performance analysis of appropriate publications is conducted. Hence characteristics of these publications are described as highly cited publications, annual indicators, etc. Highly productive research contributions of countries, institutions, and authors are also identified with the study of their collaborative relationships. To identify emerging research trends over time, authors, sources, countries, and institutions are analyzed. This study provides a data-driven overview, which is different from other reviews that only summarize technical developments. It provides a detailed and wide range of articles, highly cited publications, and contributions from countries, authors, and institutions which is not present in other common reviews. There is more guidance for future research in this type of bibliometric review. Basically, it combines both the quantitative and qualitative findings of the research. The evolution of the field of renewable energy systems can be clearly shown in this study.
Research methodology
Scopus is one of the most widely used source-neutral abstract and citation databases. It is used by researchers, institutions, and professionals to access and analyze literature in many fields like science, technology, medicine, social sciences, arts, and humanities. It includes peer-reviewed journals, conference papers, books, patents, and trade publications. It was launched by Elsevier in 2004. It helps researchers to understand the impact of their work and that of others. To find any specific articles, authors, or topics, it provides an advanced search filter. Based on the content of the search area, it also suggests many related articles. It creates a profile for authors, in which their publication history, citations, co-authors, and research areas are shown. It provides metrics like SCImago Journal Rank (SJR), Source-Normalized Impact per Paper (SNIP), and Citescore, which helps to find the impact and quality of a journal. It provides high-quality and reliable information. Therefore, it is used as a data source in this bibliometric analysis. Biblioshiny (R bibliometrix package) is used for this bibliometric analysis. It imports bibliometric data from various databases like Scopus, Web of Science, and PubMed. Then it cleans and organizes the dataset. It generates the annual scientific production, citation trends, word cloud for author’s keywords, most cited published documents, co-occurrence networks, bar charts, scatterplots, thematic maps, etc.
Bibliometric analysis is performed in the field of renewable energy sources. All journals in the Scopus collection were eligible for inclusion. The “reliability”, “availability”, “optimization”, and “renewable energy sources” are the search terms that are used for analysis. Many inclusion criteria were used the study should be articles, review papers, and conference papers, and the study should be in the field of renewable energy systems. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is chosen for this analysis because it provides systematic selection criteria, accepts all the related literature, it provides a flow diagram that shows the number of studies included and excluded for a particular reason. It will help to enhance the quality of analysis and provide a systematic study [60, 61, 62, 63, 64, 65, 66–67].
A total of 400 systematic papers were identified based on the search terms. Then 120 studies (thesis, dissertation, workshop paper, technical report, etc.) were extracted. Only 280 studies were included which were articles, conference papers, and publication stage papers. The next extraction was based on language, only 200 English language documents were included, and 80 documents from other languages were excluded. This bibliometric analysis was conducted by the study between 2000 and 2024. So, finally, 138 papers were included that were published between 2000 and 2024, and the remaining 62 papers were excluded.
Initially, 400 papers were found. After removing 262 papers that didn’t meet eligibility criteria, 138 papers were included in the bibliometric analysis (Fig. 1). Of all the included papers, original articles get the majority with 62.31%, followed by conference papers with 28.26%, review papers with 8.69%, and publication stage with 0.72%. These 138 research papers were studied using the Biblioshiny application through the Bibliometrix R package for the bibliometric analysis. Based on the gathered information, bibliometric analysis shows a more suitable literature review that includes many types of observing patterns. Therefore, analysis of these 138 documents shows the workflow of the annual scientific production, total citations per year, documents and authors citations, highlighted the relevant sources and authors, common key terms, etc.
Fig. 1 [Images not available. See PDF.]
Flow diagram of selection of bibliometric analysis for study
Results and analysis
In the existing collection of literature, the most important key topics, gaps, and emerging trends in research were studied by bibliometric analysis. This type of analysis gives a quantitative basis for the necessity and applicability of the current study, within the broader research landscape. It uses data from published works to show the importance of research in a particular area. The latest research styles, collaboration patterns, and thematic developments observed in the literature were presented in each section of the analysis. The current research networks within each country were also highlighted. After performing all eligibility criteria 138 records arrive for final studies. Figure 2 shows the flow diagram of the selection of bibliometric analysis with an annual growth rate of 13.75%. The results of the proposed studies were also analysed successively according to the publication years. There is a total of 507 authors from which 6 articles are of single authors, 38 are of international authorships.
Fig. 2 [Images not available. See PDF.]
Main information distribution
The data in Fig. 2, provides valuable information about the existing research highlighting the nature of the field, the importance of collaboration, etc. The annual growth rate of 13.75% shows a good growth rate which indicates the interest of researchers in this field but in this rapid growth rate, the maintenance of standards of research quality is quite challenging. The international co-authorship of 27.54% shows the sharing of ideas and research across borders. Collaborating with international authors can give a wide range of ideas, approaches, and methodologies. But sometimes it creates challenges like communication gaps, different languages, etc. Similarly, the average citation per doc shows the impact of research in a particular field. The author’s keywords (489) and references (6012) give a valuable insight of the field. By studying these keywords and references researchers can find the trending areas and research gaps of the field.
Scientific publication and citation trend
The scientific publication trends show the number of publications over time. It shows the evolution of a particular activity in any field of research. From the scientific publication plot, the increase and decrease in research activity can be observed. The citation trend shows the citations received by the publication over time. The impact and effect of research in a particular field are evaluated by this trend. The plot of citation trend shows the citation count over the years for a set of publications and how their impact has increased or decreased.
In the past 24 years, annually published documents that fulfil the authors’ selection criteria are shown in Fig. 3. It shows clear evidence of increasing publication trend with 2000–2004 being the lowest and 2023 being the highest (n = 34). On the other hand, the annual mean citation trend increased in an inconsistent manner, where the mean citation in 2010 was the maximum at 19.20. (Fig. 4). In 2023, the major interest of research is the optimal designing of renewable energy-based hybrid systems using machine learning techniques [42, 68, 69–70] battery optimization for power systems [51, 71, 72, 73, 74, 75, 76–77], grid-connected photovoltaic systems [78, 79, 80–81] optimal sizing of renewable energy storage [82]. In 2010, the publication with the highest mean citation was off-grid rural electrification of remote areas using integrated renewable energy systems.
Fig. 3 [Images not available. See PDF.]
Annual Scientific Production
Fig. 4 [Images not available. See PDF.]
Average citation per year
These trends help researchers to find the development in a particular field, to choose relevant research topics, to improve research strategy, to find research gaps in the literature, etc. The research field which gains more attention, increases the interest in researchers to collaborate with different institutions (universities, research labs, industries). Collaboration improves the quality of work.
Word cloud of the authors’ keywords
The word cloud of the author’s keywords is a visual representation of the most frequently used keywords in the publications. Authors’ keywords enclosed in the word cloud are shown in Fig. 5. The most frequent word that appears in the analysis was shown by the word of the biggest size in the word cloud. The term “optimization (with frequency 51)” is the most frequent word used by the researchers. Similarly, “renewable energy resources” has a frequency of 48, and “reliability” has 34. Table 1 shows the frequency of happening of the top 10 most productive author keywords as identified among the published documents. This word cloud helps researchers find prominent topics, themes, or research trends in a particular set of publications [83, 84, 85, 86, 87, 88, 89, 90–91].
Fig. 5 [Images not available. See PDF.]
Word cloud analysis of reliability research literature
Table 1. Most frequent words of the authors’
S/No | Terms | Frequency |
|---|---|---|
1 | Optimization | 51 |
2 | Renewable energy resources | 48 |
3 | Reliability | 34 |
4 | Wind power | 34 |
5 | Renewable energy sources | 32 |
6 | Renewable energies | 28 |
7 | Stochastic systems | 25 |
8 | Electric power transmission networks | 20 |
9 | Optimizations | 19 |
10 | Uncertainty analysis | 19 |
Researchers can use these keywords for their future research, for example, as we know that “optimization”, “renewable energy resources”, and “reliability” are frequently used keywords. So more advanced techniques can be explored in these areas, and optimization algorithms can be improved which can be used for performance optimization of renewable energy plants, and operating systems in different industries. Researchers can use keywords that are less often used to do research in under-research areas. These keywords can also be used with other techniques like machine learning, data analytics, and computing applications to produce useful research. These optimization techniques can also be applied to real-world problems to increase the efficiency of renewable energy resources and enhance the reliability of systems in many industries [92, 93, 94, 95, 96, 97, 98, 99–100]. For example, keywords like “stochastic systems”, and “uncertainty analysis” can be used to model variable factors like weather conditions, developing strong optimization techniques for uncertainties. To optimize systems with uncertainty, stochastic modeling is always a better option. Adaptive algorithms can also be formed to face the challenges from unpredictable factors like market dynamics, fluctuating energy demands, etc. [101, 102, 103, 104, 105–106].
Most cited published documents
The quality of published documents is shown in this section by taking two major quantities of information: the number of citations collected by a publication and the studies cited in published documents. The top 10 research publications as per citations related to the reliability and optimization of renewable energy systems in the Scopus database, are shown in Table 2. And the name of the corresponding publishing journal is also included. It is visible from the results that the journal “Renewable Energy Sustainable Review” has the highest number of citations of the articles titled “A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off-grid applications” authored by Siddaiah R, et al. [107]. It receives about 319 total citations (TC), 35.44 TC per Year (TCPY), and 5.18 Normalized TC (NTC). This work on Hybrid Renewable Energy (HRE) shows the importance of optimal design in minimizing costs. Several mathematical models and optimization techniques are reviewed that have been utilized to plan, configure, and model HRE systems. These models were compared to provide valuable insights for researchers. To develop cost-effective and customized designs for HRE, this study helps to identify the most suitable model that can fulfill the energy needs of remote areas.
Table 2. Top 10 most cited published documents
S/No | TITLE | OBJECTIVE | TC | TCPY | NTC |
|---|---|---|---|---|---|
1 | A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications [108]. | The study focuses on planning, configurations, optimization techniques, and cost-effective designs for off-grid hybrid renewable systems | 319 | 35.44 | 5.18 |
2 | Integrated renewable energy systems for off grid rural electrification of remote area [109]. | The study proposed and evaluated an Integrated Renewable Energy System (IRES) to optimize reliability, cost, and sustainability | 288 | 19.20 | 1.00 |
3 | Characterization of PV panel and global optimization of its model parameters using genetic algorithm [110] | The study used genetic algorithm to develop an improved PV solar model for parameter optimization, reliability predictions | 285 | 23.75 | 1.96 |
4 | Identification of optimal strategies for energy management systems planning under multiple uncertainties [111] | The study developed a fuzzy-random interval programming (FRIP) model to optimize energy management systems | 258 | 16.13 | 1.89 |
5 | Stochastic Performance Assessment and Sizing for a Hybrid Power System of Solar/Wind/Energy Storage [112] | The study developed a stochastic model for optimal sizing and reliability analysis of hybrid power systems | 206 | 18.73 | 1.91 |
6 | Reliability Analysis and Cost Optimization of Parallel-Inverter System [113] | The study evaluated the reliability and cost optimization of parallel inverters under various strategies | 136 | 10.46 | 1.30 |
7 | An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand [114] | The study created a machine learning-based energy management approach for renewable microgrids. Support vector machine and modified dragonfly optimization were used | 127 | 31.75 | 6.91 |
8 | Modeling and multi-objective optimization of a stand-alone PV-hydrogen-retired EV battery hybrid energy system [107] | The study optimizes the design and size of a PV-hydrogen-reusing retired electric vehicle batteries (REVB) to improve its reliability and efficiency | 116 | 19.33 | 3.11 |
9 | Dynamic, Stochastic, Computational, and Scalable Technologies for Smart Grids [115] | The study developed technologies to manage complexity, optimize energy storage and tackles the challenges of modeling, and optimization in smart grids | 99 | 7.07 | 1.28 |
10 | Optimal stochastic scheduling of CHP-PEMFC, WT, PV units and hydrogen storage in reconfigurable micro grids considering reliability enhancement [116] | The study used firefly algorithm to increase profit, reduce emissions, and optimize the scheduling of microgrids | 91 | 11.38 | 3.75 |
After observing all these existing studies, it was found that more advanced algorithms and machine learning methods can be introduced to improve efficiency, minimize cost, and optimize energy distribution in hybrid renewable energy systems. To increase reliability and minimize cost, these HRE systems can be integrated into smart grids. Most of the existing studies use general models, for future research new HRE models can be made for specific areas having unique climatic, geographic, and economic conditions. Research on advanced energy storage models is also a better study to improve the reliability of HRE models.
Co-occurrence network
The analysis of the author’s keyword co-occurrence is shown in Fig. 6. It shows the frequency of occurrence of these keywords in literature and how they correlate with other keywords. The degree of co-occurring is also found by this. The frequency or repetition of keywords in the related document is found by the size of the node’s size. The bigger the node’s size, the more frequency of occurrence of that author’s keyword. The observation of the co-occurrence mapping gives a sequential order that optimization, reliability, renewable energy sources, energy storage, energy management, particle swarm optimization, availability, Markov model, modeling, and solar PV are the top 10 author keywords, all of which have a high frequency of occurrence [47, 117, 118, 119, 120, 121–122].
Fig. 6 [Images not available. See PDF.]
Co-occurrence of author’s keywords
Table 3 shows the interval between any of the author’s keywords. These intervals reflect how frequently these keywords were used in journal articles. From the mapping analysis, the keywords that are closest together have been more commonly used in many research publications as compared to those that are far apart. The closeness of keywords shows the interconnection of keywords in particular research. For example, “optimization” and “reliability” have many practical applications like enhancing availability, improving energy efficiency, and minimizing operational costs. The closeness between these keywords shows the link between them. Optimization techniques are used to enhance the system performance by increasing efficiency, minimizing costs, and reliability focusing on ensuring stable performance by reducing downtime. The collaborating study focuses on improving the reliability of systems by using optimization algorithms [48, 123, 124, 125, 126, 127, 128–129].
Table 3. Top 10 authors keywords in clusters
S/No | Node | Cluster | Betweenness | Closeness | PageRank |
|---|---|---|---|---|---|
1 | Optimization | 1 | 129 | 0.027 | 0.141 |
2 | Reliability | 1 | 60.737 | 0.024 | 0.083 |
3 | Renewable energy sources | 1 | 0 | 0.013 | 0.021 |
4 | Energy storage | 1 | 0 | 0.018 | 0.023 |
5 | Energy management | 1 | 0 | 0.018 | 0.018 |
6 | Particle swarm optimization | 1 | 20 | 0.017 | 0.035 |
7 | Availability | 1 | 0 | 0.016 | 0.017 |
8 | Markov model | 1 | 0 | 0.018 | 0.018 |
9 | Modeling | 1 | 0 | 0.018 | 0.018 |
10 | Solar PV | 1 | 0 | 0.018 | 0.018 |
Based on the keyword co-occurrence analysis, many new areas can be explored in the future. To improve the reliability of renewable energy systems in industries, the use of Markov models can be explored. This research gives valuable solutions to improve energy efficiency, increase reliability, and minimize the costs of renewable energy systems in industries. Integration of optimization techniques with energy management strategies can also be explored in the future to minimize operational costs and increase efficiency. Computational intelligence techniques and simulation techniques can be used to improve decision-making in dynamic environments [130, 131, 132–133].
Co-citation network
The co-citation network analysis related to the reliability and optimization of renewable energy systems is shown in Fig. 7. This network visualization shows how repeatedly the pair of authors are cited together due to related ideas of the research area. This type of structure shows the relationship and clusters of authors who are often co-cited. Maleki a (cumulative degree-1), wang l (cumulative degree-0.737), and Yang h (cumulative degree-0.737) have the three topmost co-citations which are greater than any other co-authorship.
Fig. 7 [Images not available. See PDF.]
Co-citation network of authors
From these results, there are many future research opportunities. Most of the authors focus on renewable energy optimization. With the help of these studies, we can use optimization algorithms in the systems of other industries to improve system reliability and enhance system performance. Renewable energy optimization can be studied in policy, economics, and sustainability studies. Research on how government policies can help optimize systems and enhance energy access, research on how renewable energy costs are reduced, and the most important factor is research on how these optimized systems can reduce carbon footprints and support a healthy environment.
Bradford’s law
Bradford, S.C [134] proposed Bradford’s law. It states that the journals which have small ranks, publish most of the significant research, while many journals contribute less relevant articles. Basically, in a specific field of study, the “core sources” describe a small group of journals or publications that are most important and effective. The criteria behind them are frequently cited publications and the high impact factor of the journal. “Zone 1” refers to the core group of journals in a particular field of study. The significance of Zone 1 is very high as it improves the research efficiency, it gives the knowledge for the field so future research can be done to improve that field.
It helps in identifying the most important journals to focus on while conducting literature research. Figure 8 shows the top 10 sources of publications. In this research field, most of the articles have been published by the Journal of Energy Conversion and Management, Applied Energy, Energies, Energy, and Journal of Energy Storage with the article frequency of occurrence at 7, 5, 5, 5, and 5 respectively, in line with the guided law. The 10 topmost journals for the core sources by Bradford’s law with rank, frequency, cumulative frequency, and zone are shown in Table 4. It helps researchers to make their research more effective by finding the most productive journal. It takes less time and effort to find suitable literature [127, 135, 136, 137, 138, 139, 140, 141, 142–143].
Fig. 8 [Images not available. See PDF.]
Bradford’s Law for core sources
Table 4. Most prolific publications for the core sources by Bradford’s law
Source | RANK | FREQ | CUMFREQ | ZONE |
|---|---|---|---|---|
Energy conversion and management | 1 | 7 | 7 | Zone 1 |
Applied energy | 2 | 5 | 12 | Zone 1 |
Energies | 3 | 5 | 17 | Zone 1 |
Energy | 4 | 5 | 22 | Zone 1 |
Journal of energy storage | 5 | 5 | 27 | Zone 1 |
Renewable energy | 6 | 5 | 32 | Zone 1 |
IEEE access | 7 | 4 | 36 | Zone 1 |
IEEE transaction on sustainable energy | 8 | 3 | 39 | Zone 1 |
International journal of electrical power and energy | 9 | 2 | 41 | Zone 1 |
International journal of hydrogen energy | 10 | 2 | 43 | Zone 1 |
World research production and collaborations
World research production shows the geographical distribution of academic publications across different countries. It shows the total number of research articles published by each country, the color intensity on the world map shows the comparison of research production between countries, gives information about the leading country in a specific field over time, and highlights the international research collaborations based on co-authorship. The countries’ scientific production is shown in Fig. 9. The results show that India was in the lead with 100 articles, followed by China and Iran with 86 and 35 articles, respectively. The total number of publications over the research period is shown in Table 5. It was found that some of the articles are a collaboration between two or more countries and have multiple country publications that are counted for the individual countries involved. Therefore, from Table 5, the sum of all articles concerning the various countries exceeds the total number of publications studied in this work. There are several reasons for the leading position of countries like India, China, and Iran. In India growing population demands more energy, so India has more focus on achieving more energy from renewable sources. Natural resources like solar and wind are the main basis for research in renewable energy for India. China always prioritizes clean energy innovation and made large investments in renewable energy research. Renewable energy is an alternative for Iran which is dependent on fossil fuels. Countries like India and China have well-established universities and research centers that use more advanced techniques for energy management.
Fig. 9 [Images not available. See PDF.]
Country scientific production
Table 5. Country scientific production showing frequency of collaborations as depicted in Fig. 9
Region | FREQ | Region | FREQ |
|---|---|---|---|
India | 100 | Singapore | 4 |
China | 86 | Azerbaijan | 3 |
Iran | 35 | Bangladesh | 3 |
Australia | 29 | Cameroon | 3 |
USA | 23 | Colombia | 3 |
Saudi Arabia | 22 | Ethiopia | 3 |
UK | 22 | Finland | 3 |
Canada | 17 | France | 3 |
South Africa | 17 | Netherlands | 3 |
Egypt | 14 | Romania | 3 |
Germany | 13 | United Arab Emirates | 3 |
South Korea | 12 | Denmark | 2 |
Brazil | 11 | Ecuador | 2 |
Malaysia | 11 | Italy | 2 |
Peru | 10 | Kenya | 2 |
Morocco | 7 | Algeria | 1 |
Spain | 7 | Indonesia | 1 |
Iraq | 6 | Jordan | 1 |
Austria | 5 | Nigeria | 1 |
Greece | 5 | Norway | 1 |
Ghana | 4 | Poland | 1 |
Japan | 4 | Qatar | 1 |
New Zealand | 4 | 4 |
Figure 10 shows countries’ scientific production over time from the year 2000–2024. The country collaboration map (Fig. 11) shows the international research collaborations between different countries. The lines connecting countries show partnerships (countries that have co-authored research articles), the strength or frequency of collaborations is shown by the thickness or colour of the lines, and most international collaborations of countries are highlighted by different colours or node sizes. Figure 12 shows the collaboration network of countries. How countries collaborate in publications is shown by a visual representation. The connection between the countries based on co-authorship is shown by this network. Each node in this network represents a country. Collaboration between countries based on co-authorship is represented by the connecting lines. Analysis of global cooperation and identification of leading countries and their partnership were examined by this network.
Fig. 10 [Images not available. See PDF.]
Countries production over time
Fig. 11 [Images not available. See PDF.]
Country collaboration map
Fig. 12 [Images not available. See PDF.]
Collaboration network of countries
Trend topics
Figure 13 visualizes the trend topics of keywords. It analyses how the usage of specific keywords in research articles has changed over time. It also analyses the most used topic within a research field by finding the frequency of keywords in publications over different periods. It helps to understand which topic is gaining or losing interest in the research field, find emerging research areas, and guide future research directions. “Optimization” was found to be the most focused topic in the year 2018. After two years, in 2020, the focus shifted to “renewable energy resources”, “wind power”, and “reliability”. Again, after two years, in 2022, “renewable energies”, “uncertainty analysis”, and “solar energy” are the most discussed topics. All the trend topics by year and frequency are shown in Table 6 [144, 145, 146, 147, 148, 149, 150, 151, 152–153].
Fig. 13 [Images not available. See PDF.]
Trend topics of keywords on performance analysis and optimization of renewable energy plants
Table 6. Trend topics by year and frequency
Term | Frequency | Year (Q1) | Year (Median) | Year (Q3) |
|---|---|---|---|---|
Optimization | 51 | 2015 | 2018 | 2022 |
Renewable energy resources | 48 | 2016 | 2020 | 2023 |
Reliability | 34 | 2016 | 2020 | 2023 |
Wind power | 34 | 2014 | 2020 | 2023 |
Renewable energies | 28 | 2016 | 2022 | 2024 |
Electric power transmission networks | 20 | 2016 | 2019 | 2021 |
Uncertainty analysis | 19 | 2018 | 2022 | 2023 |
Optimisations | 19 | 2021 | 2023 | 2024 |
Particle swarm optimization (PSO) | 17 | 2020 | 2021 | 2023 |
Solar energy | 17 | 2016 | 2022 | 2024 |
Alternative energy | 15 | 2020 | 2023 | 2023 |
Uncertainty | 14 | 2022 | 2023 | 2024 |
Wind | 13 | 2015 | 2019 | 2022 |
Costs | 12 | 2015 | 2016 | 2019 |
Electric utilities | 11 | 2014 | 2016 | 2020 |
Energy efficiency | 11 | 2015 | 2018 | 2022 |
Photovoltaic cells | 11 | 2014 | 2019 | 2019 |
Fossil fuels | 10 | 2018 | 2021 | 2023 |
Power generation | 9 | 2008 | 2016 | 2023 |
Energy policy | 8 | 2009 | 2021 | 2023 |
Renewable energy | 8 | 2024 | 2024 | 2024 |
Smart power grids | 8 | 2019 | 2024 | 2024 |
Renewable resources | 7 | 2010 | 2012 | 2023 |
Electric load dispatching | 7 | 2020 | 2024 | 2024 |
Energy storage systems | 6 | 2017 | 2018 | 2020 |
Commerce | 5 | 2017 | 2017 | 2019 |
System reliability | 5 | 2010 | 2017 | 2018 |
As we see from the above results, “optimization” was a focus in 2018. Now future research can be conducted to find out how optimization methods can improve uncertainties in systems in industries. With “reliability” which gains attention in 2020, new research can be conducted to improve the reliability of renewable energy systems under fluctuating wind or solar power output. Similarly, after knowing the trending topics many new and useful research can be studied. To manage risks in energy systems or any other system in industries, advanced AI or machine learning tools can be used, new optimization strategies can be developed for uncertainties like the effect of climatic change on renewable energy resources, and multiple energy systems can be combined to increase system reliability and efficiency, a new solution for energy storage can be found by optimization and grid management strategies. Research on microgrids (small, local energy systems) is also a better option for rural and remote areas. It can aim to optimize microgrids for enhanced reliability, and better efficiency. Due to these microgrids, these areas become independent from large power grids and more self-sufficient.
Discussion and limitations
For this bibliometric analysis, 138 papers were considered, and it was possible to separate them by year of publication. This reveals the annual growth rate of publications and the journals with the most published articles on performance analysis and optimization of renewable energy systems. In analyzing the publications between 2000 and 2004, clearly, a constant trend was shown and there was no increase in the number of papers. Between 2004 and 2014, a slight increase was found in the number of papers as compared to previous years. This increase in the number of papers was only due to an increase in concern for renewable energy and renewable energy systems. This increase motivates researchers to collect more useful information about the performance of renewable energy systems. In the years 2023 and 2024, there is the highest number of publications, which shows that the performance of renewable energy systems is now considered a strong field of research. This increase is very important as there is a global attempt to promote the use of renewable energy technologies. Consequently, this has promoted renewable energy and reduced greenhouse gas emissions. It was also found that the mean citation has been declining in recent years (2021–2024). It shows that older publications are cited more than newly published papers. The majority of papers on this topic were from India and China, as India and China are in the top ten countries to produce renewable energy. Top journals based on the number of papers published, not all of them have a high impact factor. Therefore, it was proved that the quality of papers does not depend on the impact factor of paper publication. Many researchers have considered factors like socio-tecno-economic-environmental (STEE) factors in optimizing hybrid renewable energy systems [154, 155, 156–157]. The findings of the marine predators algorithm (MPA) for optimizing reliability have a high impact on hybrid renewable energy systems [38, 158]. In Ref. [154], the configuration, modeling, design, and sensitivity analysis are studied. It will help designers to select optimal hybrid renewable energy system configurations.
The results of this analysis are very beneficial for real-world policies and practices. For example, better decisions for energy policies can be made by the government by analyzing these results. By finding the reliability of renewable energy systems like solar or wind, policymakers can develop a consistent and dependable energy supply. They can also set up some regulations for energy generation, distribution, and grid management. The use of renewable energy technologies by the industries can be improved by the research on optimization. For example, if an industry can optimize the performance of a solar panel or a wind turbine, then they can make these technologies more effective, cost-friendly, and reliable. All these findings show a clear picture of how this research can help to make better policies or technological improvements in the renewable energy area.
There are some limitations also, such as the ranking of authors, institutions, and journals being based on the number of publications, not on the quality or impact of the research. The larger number of published papers by authors and institutions does not mean that their work has a greater significance in the field. Similarly, total citations and citations per document per year were used to assess the impact and quality of the research papers but using citations has its limitations. Only citations may not accurately show the quality of the study. Many times, studies were cited because of being part of ongoing discussions, not always for their quality or overall significance. This study may have missed some valuable information, as only the Scopus database was used in this study. For example, some countries do not speak English, maybe they use their local language. So, they have their database and publish their work in their local language. Therefore, these studies might not be in Scopus or any other known databases. This will result in publication bias where studies on performance analysis and optimization of renewable energy systems were not included in other bibliometric analyses. The bibliometric approach is data-driven, but sometimes, it overlooks some significant studies, even if the study is highly cited. This may be due to dataset selection (the dataset may exclude some studies due to search term restrictions, etc.), sometimes the study is undervalued or missed during the dataset selection, or sometimes manual data filtering can also exclude some useful studies.
For renewable energy systems and hybrid renewable energy systems, the factors like socio-techno-economic-environmental (STEE) can be examined for supplying reliable electricity.
Social Factors: Renewable energy systems, like solar and wind farms, play a big role in shaping communities and society. They often need a lot of land, and the cost of that land depends on things like location, availability, and local rules. Cheaper land makes these projects easier to afford and more acceptable to the community. These energy systems also improve people’s lives by providing clean, reliable power that helps schools, hospitals, and industries grow, which contributes to overall human development. On top of that, they create jobs at every stage—building, running, and maintaining the systems—helping local economies thrive, especially in rural areas where jobs can be harder to find.
Technical Factors: Technical factors help us understand how well renewable energy systems work and how reliable they are. They use measures like unmet load, renewable energy share, duty factor, excess energy factor, and loss of power supply probability (LPSP). Unmet load happens when energy demand is higher than what’s available, often because of weather changes, and reducing this ensures a steady energy supply. The renewable energy share shows how much of the total energy comes from renewables—higher percentages mean more sustainability and less reliance on fossil fuels. The duty factor looks at how often a system, like solar panels, runs near its full potential, especially during sunny hours. The excess energy factor highlights how much energy gets wasted because it’s not used, showing where we could add storage or connect better to the grid. Lastly, LPSP tells us how likely it is to have energy shortages due to resource unpredictability, with lower numbers meaning the system is more dependable.
Economical Factors: Economic factors look at how cost-effective and financially sustainable renewable energy systems are. They focus on things like the annualized cost, cost of energy (CoE), and total net present cost (TNPC). The annualized cost breaks down all expenses—like installation, maintenance, and operation—over the system’s lifetime, aiming to keep costs low while ensuring the system works reliably. CoE is basically the cost to produce each unit of energy, which depends on factors like the initial investment and how efficiently the system runs. A lower CoE makes renewable energy more competitive with fossil fuels. TNPC is the total cost of the project, including all upfront and ongoing expenses, adjusted for inflation and the value of money over time. A lower TNPC means the project is more financially viable and a smarter choice for long-term investment.
Environmental Factors: Environmental factors look at how renewable energy systems affect the planet and how they help the environment. Solar and wind energy produce almost no carbon emissions compared to fossil fuels, which helps fight climate change. They also don’t release harmful particles, like soot, into the air, which means cleaner air and better health for everyone. On top of that, renewable energy has little to no extra costs for the damage it prevents to the environment, making it a much cleaner and more sustainable option.
Conclusion
Scope of study: This study aims to examine the series of articles on performance analysis and optimization of renewable energy systems from 2000 to 2024. So, a bibliometric analysis was conducted by a total of 138 research papers sourced from the Scopus database. A total of 400 published documents were selected from the Scopus database but later only 138 documents were selected by the inclusion and exclusion criteria from the PRISMA flow diagram with original articles (62.31%), conference papers (28.26%), review papers (8.69%), and publication stage (0.72%). The bibliometric analysis of 138 papers presents quantitative and qualitative evaluations of the top countries, authors, institutions, leading journals, and global collaborations.
Key findings: Outcomes from the word cloud show that “optimization (with frequency 51)” is the most frequently used word by researchers. From the scientific publication trend, it was found that 2000–2004 has the lowest number of publications, and 2023 has the highest number of publications. “Energy Conversion and Management” was found to be the top journal to publish articles with the frequency of occurrence at 7. Results from world research production show that India was in the lead with 100 articles, followed by China and Iran with 86 and 35 articles, respectively.
Future research ideas: Based on the discussed findings, it is possible to recommend some areas of future research in the domain of renewable energy systems. Future research should not only depend on the reliability of systems but also include availability, maintainability, dependability, and safety or other performance analyses. Few studies use the Markovian-birth–death process for performance analysis of renewable energy systems. But many other methods can also be used in the future to improve system efficiency and reliability like Fault Tree Analysis (FTA), Reliability Block Diagrams (RBD), Monte Carlo Simulation, Bayesian Networks, Weibull Analysis, Semi-Markov Processes, Artificial Neural Networks (ANN), etc. This will predict failures and perform maintenance for renewable energy systems. Few studies use metaheuristic techniques (genetic algorithm, particle swarm optimizations, ant colony optimization), etc. for optimization. But many other nature-inspired and bio-inspired optimizations can also be used. This will help engineers or system makers to make enhanced predictive models (weather forecasting integration and failure prediction models) and smart grid technologies.
Advanced applications: Advanced storage technologies can be made to develop new types of energy storage systems, like solid-state batteries to improve reliability and availability of energy. Other methods can be used to evaluate the cost-effectiveness of reliability improvements and maintenance strategies. Researchers can also check the environmental and social impact of renewable energy systems by conducting studies including manufacturing, operation, and disposal. The use of artificial intelligence for optimizing the performance of systems and predicting energy demand is not fully explored yet. So, this can also be explored in the future to solve renewable energy challenges.
More technologies: Researchers can also show their interest in combining renewable and non-renewable energy sources to make the system more reliable. However, research on decentralized grids (small energy systems) is also needed, so it can be used in remote areas to reduce dependency on centralized systems. The methods and technologies for solar tracking like sensors and control algorithms can be explored in future studies. Solar tracking systems are used to maximize the efficiency of solar systems. The methods and technologies for solar tracking include measurement of sunlight’s intensity to adjust the tracker at its best position, in cloudy weather it uses infrared radiation to locate the sun, etc. There are many control algorithms like proportional-integral-derivative (PID) controllers, fuzzy logic controllers, machine learning algorithms, astronomical algorithms, etc. The designing of solar tracking systems improves the productivity of solar power plants by optimizing factors like cost-effectiveness, efficiency, accurate tracking, and reliability.
Author contribution
Kanak Saini (KS): Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing—original draft preparation. Monika Saini (MS): Formal analysis, Writing—review and editing, Supervision, Project administration. Ashish Kumar (AK): Conceptualization, Software, Validation, Formal analysis, Data curation, Writing—original draft preparation, Writing—review and editing, Visualization. and Dinesh Kumar Saini (DKS): Conceptualization, Methodology, Supervision, review final draft.
Funding
Open access funding provided by Manipal University Jaipur.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Competing interests
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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