Review

A review on the classifications and applications of solar photovoltaic technology

  • Received: 28 May 2023 Revised: 18 September 2023 Accepted: 23 October 2023 Published: 13 November 2023
  • Our aim of this work is to present a review of solar photovoltaic (PV) systems and technologies. The principle of functioning of a PV system and its major components are first discussed. The types of PV systems are described regarding the connections and characteristics of each type. PV technology generations are demonstrated, including the types, properties, advantages and barriers of each generation. It was revealed that the first generation is the oldest among the three PV generations and the most commonly utilized due to its high efficiency in spite the high cost and complex fabrication process of silicon; the second generation is characterized by its low efficiency and cost and flexibility compared to other generations; and the third generation is not commercially proven yet in spite the fact that it has the highest efficiency and relatively low cost, its raw materials are easy to find and its fabrication process is easier than the other generations. It was shown that the target of all the conducted studies is to study the PV technology to enhance its performance and optimize the benefit from solar energy by reducing conventional energy dependence, mitigating CO2 emissions and promote the economic performance.

    Citation: Amal Herez, Hassan Jaber, Hicham El Hage, Thierry Lemenand, Mohamad Ramadan, Mahmoud Khaled. A review on the classifications and applications of solar photovoltaic technology[J]. AIMS Energy, 2023, 11(6): 1102-1130. doi: 10.3934/energy.2023051

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    Autism Spectrum Disorder (ASD) is a disease described as strongly heterogeneous due to the large number of symptoms which may appear in the child's functioning [1], as well as the variable response of the body to the treatment process [1],[2]. In spite of the fact that the symptoms are multiple and occur with changing intensity, every person with autism presents abnormalities in communication and social interaction [2], exhibits repetitive behaviours, and a limited scope of interests [3],[4]. The onset of the disease occurs in early childhood [3]. Only a minor percentage of people with diagnosed Autism Spectrum Disorder, with mild symptoms (ex. Difficulty in social communication, problematic with adaptation to change, planning difficulty) , are able to live a relatively independent life as adults [2],[3],[5]. The majority (with symptoms of moderate and severe intensity) need the help of their families or social welfare to the end of their lives [2]. Their functioning in adult life depends on the early introduction of intensive therapeutic programmes, modifying the undesirable behaviours, and aimed at teaching social and communication skills [6][8].

    Scientific literature stresses a constant growth in the incidence of the disorder under discussion. For example, data from the Autism and Developmental Disabilities Monitoring Network shows that, in 2012, in the USA, there were twice as many eight-year-old children with diagnosed ASD as only two years earlier, in 2010 [9]. Taking into consideration the whole population of children, in 2000, ASD was reported as occurring in one in every 150 children, and in 2010, in one of every 68 [10]. The causes of this situation are unknown. The scientists believe it is related to greater public awareness of the symptoms of autism, new diagnostic criteria, and possibility of diagnosis at a younger age [11][14]. These are only hypotheses, but they undoubtedly encourage various agencies-medical, social, educational and other to search for effective solutions for supporting people with autism and their families [4].

    The symptoms of autism are recognised in the child's environment quite quickly. Usually, it is the parents who first realise that their child does not achieve the expected milestones in development; his or her development is retarded or stopped [15][17]. At that time, parents observe that their child does not react to their physical affection, does not want to express emotions, often avoids hugging (which is very hard for parents, especially for mothers) and eye contact, and does not want to communicate in any way [15],[16]. Moreover, the child may present atypical behaviours, movements related to a strong need of isolation from its surroundings, which are incomprehensible to the parents [16]. Usually, these include destructive, socially unacceptable behaviours [18]. These symptoms arouse anxiety and feelings of helplessness in the parents and make them seek professional help [15].

    The problems affecting the autistic child affect also the parents. Therefore, it may be said that the autism of a child has considerable implications for its parents [19]. Caring for a child with autism is associated with emotional consequences [20][23]. It has been proved that parents of atypical children experience parental stress much more frequently than the general population [24], as the moment of the child's diagnosis generates strong uncertainty about the future life of the child and the whole family [25],[26].

    The study by Bitsik and Sharpley, conducted on the basis of an analysis of fathers and mothers of ASD children, showed that women are more preoccupied and prone to depression than men caring for their disabled children [27]. Similar results were obtained by Dąbrowska, who indicated that mothers are much more frequently exposed to stress [28][30]. Moreover, it was proved that parents of ASD children are three to five times more vulnerable to depression than parents of neurotypical children [31]. The most commonly used assessment tools for preoccupation and depression [27] of parents include:

    • Self-Rating Depression Scale—SDS [32].
    • Self-Rating Anxiety Scale—SAS [33].
    • Connor-Davidson Resilience Scale—CD-RISC [34].

    The aim of the paper is to evaluate the functioning of families with an ASD child and compare it to the functioning of families with neurotypical children. The degree of flexibility, cohesion and level of communication enables the family to be classified either as healthy or dysfunctional.

    The study was approved by Bioethics Committee of the Poznan Univeristy of Medical Sciences (approval number: 1223/17) and Australian New Zealand Clinical Trials Registry (ANZCTR) number ACTRN12618000598280.

    The study was performed using (Flexibility and Cohesion Evaluation Scales, FACES-IV) questionnaire by David H. Olson, in its Polish form, developed by Andrzej Margasiński. The questionnaire consists of sixty-two statements, to which the subject responds in a 5-degree scale, from strongly disagree to strongly agree. The statements are divided into eight sub-scales. Six of them are the main sub-scales of David H. Olson's Circumplex Model of the two dimensions of family functioning: cohesion and flexibility (Balanced Cohesion, Disengaged, Unmeshed, Balanced Flexibility, Rigid, Chaotic). The two remaining sub-scales measure family communication (which is the third dimension of the Circumplex Model) and family satisfaction. Apart from sub-scale results, it is possible to calculate three complex ratios: Cohesion Ratio, Flexibility Ratio and Total Circumplex Ratio, which reflects the degree to which family functioning is healthy [35].

    The tool used is based on the Circumplex Model, which focuses on three crucial dimensions of family functioning: cohesion, flexibility and communication. Cohesion means the emotional bonding that family members have towards one another. Flexibility of relationships is defined as the quality and expression of leadership and organization, role relationships, and relationships rules and negotiations [36]. The communication dimension is viewed as a facilitating dimension that helps families alter their levels of cohesion and flexibility. The intensity of cohesion and flexibility of family relationships may have two basic levels: balanced or unbalanced. Unbalanced cohesion may mean extremely high cohesion level (unmeshed relationships) or an extremely low cohesion level (disengaged relationships, lack of bonding). On the other hand, unbalanced flexibility may mean extremely high (chaotic family relationships) or extremely low (rigid family relationships) flexibility levels. The main hypothesis of the model says that there is a positive relationship between a balanced cohesion level, balanced flexibility level, and healthy family functioning, as well as a positive relationships between unbalanced cohesion level, unbalanced flexibility level and problematic family functioning [36].

    The third basic dimension of D. H. Olson's Circumplex Model, which influences both flexibility and cohesion, is communication [37]. This refers to the skill of providing the family members with information, plans and emotions. This dimension is also defined as the positive communication skills utilized in the couple or family system[38].

    Cluster analysis of data obtained from studies, using Flexibility and Cohesion Evaluation Scales, resulted in distinguishing six family types: Balanced, Cohesively Rigid, Flexibly Disengaged, Mid-range, Rigidly Disengaged and Unbalanced [35]. The Balanced type is characterised by the highest scores on the balanced sub-scales and the lowest scores on the remaining sub-scales. The Cohesively Rigid type is characterised by high scores in the balanced cohesion and rigid sub-scales, moderate enmeshed scores, and low disengaged and chaos scores. The Flexibly Disengaged type is characterised by high scores on the Balanced Flexibility and Disengaged sub-scales, and low scores on the Rigid sub-scale The Mid-range type is characterised by moderate scores on all of the sub-scales, with the exception of the disengaged sub-scale, where the score is usually low. The Rigidly Disengaged type is characterised by high scores on all of the sub-scales other than Cohesion, where moderate to low scores are characteristic. The Unbalanced type is characterised by high scores on all four of the unbalanced scales: Disengaged, Unmeshed, Rigid and Chaotic, and low scores on the two balanced scales: Balanced Cohesion and Balanced Flexibility. These families are assumed to experience the greatest difficulties and be the most problematic in terms of their functioning. It is estimated that this is the family type most often looking for therapy [35].

    The study with Flexibility and Cohesion Evaluation Scales, by David H. Olson, in its Polish adaptation by Andrzej Margasiński, included 70 parents of ASD children, and 70 parents with children without diagnosed ASD, as the control group. The study was performed in January and February 2018. The study used inclusion criteria: (1). parents aged 25–45; (2). children without comorbidities; (3). diagnosis of autism in children.

    In order to compare FACES IV results obtained by the parents of ASD children and the control group, an independent samples t-test for equality of means was performed, and the statistical significance of the obtained differences was assessed.

    The analysis of the Balanced Cohesion sub-scale indicated that the parents of children with autism achieve lower FACES-IV results in the Balanced Cohesion sub-scale than the control group. The study covered 140 observations. The significance level of Levene's test indicates that the results should be interpreted with the assumed equality of variance. The p-value for the t-test for difference of means is 0.002; therefore, the means in both groups differ in a statistically significant way. The results are presented in Table 1.

    Table 1.  The sub-scales in the group of parents of children with ASD vs. parents of neurotypical children.
    Group N Average P-value
    Balanced Cohesion sub-scale (STEN) Autism 70 5.2000 21,843
    Control group 70 6.3571 22,135
    Balanced Flexibility sub-scale (STEN) Autism 70 5.6857 20,820
    Control group 70 6.2143 19,478
    Disengaged sub-scale (STEN) Autism 70 7.2857 18,583
    Control group 70 6.4143 18,217
    Unmeshed sub-scale (STEN) Autism 70 6.8857 20,610
    Control group 70 5.4857 18,077
    Rigid sub-scale (STEN) Autism 70 6.9143 17,672
    Control group 70 6.6857 17,573
    Chaotic sub-scale (STEN) Autism 70 6.7143 18,893
    Control group 70 6.0143 19,597
    Family Communication sub-scale (STEN) Autism 70 5.3857 24,215
    Control group 70 6.1714 24,846
    Family Satisfaction sub-scale (STEN) Autism 70 6.3143 24,586
    Control group 70 7.2857 21,274

     | Show Table
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    • Balanced flexibility sub-scale The p-value for the t-test for difference of means is 0.123; therefore, the means in both groups do not differ in a statistically significant way.
    • Disengaged sub-scale The p-value for the t-test for difference of means is 0.006; therefore, the means in both groups differ in a statistically significant way.
    • Unmeshed sub-scale The p-value for the t-test for difference of means is 0.000; therefore, the means in both groups differ in a statistically significant way.
    • Rigid sub-scale The p-value for the t-test for difference of means is 0.444; therefore, the means in both groups do not differ in a statistically significant way.
    • Chaotic sub-scale The p-value for the t-test for difference of means is 0.033; therefore, the means in both groups differ in a statistically significant way.
    • Family communication sub-scale The p-value for the t-test for difference of means is 0.060; therefore, the means in both groups do not differ in a statistically significant way.
    • Family satisfaction sub-scale The p-value for the t-test for difference of means is 0.014; therefore, the means in both groups differ in a statistically significant way.

    The analyses within the group of parents of ASD children did not show any statistically significant differences in FACES-IV due to socio-demographic variables.

    Research into parental stress levels showed that parents of children with ASD have greater uncertainty, stress and depression levels than parents of neurotypical children [39][43] and also parents of children with other disabilities [44],[45]. Similar results can be observed in the comparison between the stress levels of parents of ASD children and the general population [21],[43],[46][49].

    The most significant factor generating parenting stress are the ASD symptoms in their children [31]. Among the most frequent symptoms contributing to parental stress, scientists enumerate impaired cognitive functions and impaired social reactions, which directly correspond to the emergence of parental stress, anxiety and depression [50][53]. Other aspects of autism which may induce parental stress are: the level of functioning of the child, the child's age, the dysfunction of adaptive behaviours, agammaession, tantrums, and self-inflicted injuries [21],[54][57].

    However, it is emphasized that there is no social understanding of the characteristics of ASD, due to which, both the parents and the ASD children themselves, are subject to more severe social criticism. The specific behaviours of ASD patients are often perceived as parenting errors [31]. What is even more important, it is considered that parental stress factors come exclusively from outside of this social group and not from the personality and behavioural models of the parents themselves [31],[58]. Important factors influencing the development of parental stress and burn-out include lack of activity of mothers of ASD children outside the home in comparison to mothers of neurotypical children, who can spend much more of their free time outside the family, in a stress-free environment [59]. Similar conclusions were made in other studies, which, apart from isolation factors, also identified the phenomenon of “self-blaming” mothers, who burden themselves with blame for their child's difficulties [60],[61]. Another aspect of parental stress, described in the literature, is escaping from the problems related to the child's disability, visible as its difficult behaviours [62].

    The assessment of parental stress showed that over a half (55.8%) of fathers feel overwhelming helplessness one to five times a month. On the other hand, over 70% of mothers experience this feeling one to five times a month. The results of this study confirmed earlier research into anxiety and depression in parents, conducted by the same authors on a group of parents in Australia [27],[63].

    Another study focused on the parents of children with diagnosed ASD. The research into parental stress showed that the majority of the subjects agreed to the statements that “caring for a child takes a lot of time and energy” and “the behaviour of my child embarrasses and stresses me”. In the area of social support, the majority of the subjects agreed with the statements “the members of my family rely on me” and “I cannot rely on the members of my family”. As far as the area of self-efficacy is concerned, the majority marked the answer “try another solution if the first one did not bring expected results”. The study described showed that there are multiple sources of parental stress and that its level is influenced by all members of the ADS child's family, including parents, siblings, and grandparents. It was also shown that, despite the difficulties and problems, the caregivers of ASD children have social support and can cope with difficult situations [64].

    The scientific literature also includes works devoted to the role of stress resilience and self-efficacy in parents of ASD children. One of them analyses the group under discussion. The study was conducted using the Satisfaction With Life Scale (SWLS) [65], the Coping Strategy Inventory (CSI) [66], and the Coping with Stress Self-Efficacy Scale (CSSES) [67]. The results confirm that bringing up a child influences coping strategies and the sense of self-efficacy. Therefore, stress has an impact on the level of satisfaction with life of parents of ASD children. The scientists found differences depending on the parent's sex, stating that the primary goal of a woman is the sense of self-efficacy, while men put problem solving in the first place [67][72]. It was also shown that, together with the ageing of ASD parents, the social support for these families decreases, as does cognitive restructuring [69],[73],[74].

    The results of many studies prove that the sense of self-efficacy contributes to higher life satisfaction. Moreover, the sense of self-efficacy correlates positively with resilience strategies (problem solving and cognitive restructuring) and negatively with dysfunctional strategies (social isolation, wishful thinking, self-criticism) [60],[69],[75][79].

    It is worth mentioning the study conducted by Bitsik et al. (2017), analysing daily cortisol levels in parents of ASD children. Cortisol is called the neurohormone of stress [57],[80]. Cortisol levels were measured via the analysis of the subjects' saliva. It is estimated that cortisol is present in this material for about 10 minutes from the occurrence of the stress factor [81]. It was proved that, in 129 subjects, the levels of cortisol drop in accordance with the circadian rhythm. At the same time, the studies proved that self-inflicted injuries in children with ASD may be a stress-provoking factor in parents [57],[82].

    In order to reduce parental stress, parents of ASD children are recommended to introduce effective mitigation of autism symptoms [83]. It is emphasized that only successful reduction of ASD symptoms in the child may improve the well-being of the whole family [84]. Long-term stress may have drastic health consequences on parents of ASD children [31]. Support groups for parents of ASD children are one of the forms of therapy aimed at coping with stress and preventing burn-out [85].

    (1). It has been established that the parents of children with autism achieve lower results in the balanced cohesion sub-scale than the control group.

    (2). The parents of ASD children obtained higher scores in the disengaged sub-scale than the control group.

    (3). Furthermore, in the unmeshed sub-scale, their scores were higher than in the control group.

    (4). In the chaotic sub-scale, the parents of ASD children obtained higher scores than the control group.

    (5). It was found out that the family satisfaction level in parents of ASD children is lower than in the control group.

    (6). In the balanced flexibility, rigid and family communication sub-scales, there were no statistically significant differences between the parents of ASD children and the parents from the control group.

    (7). In parents of ASD children, the scores in all unbalanced sub-scales were higher than in families with children without autism (even if in some of differences were not statistically significant) while the scores in the balanced sub-scales were lower.

    (8). The STEN analysis of mean results of the parents of ASD children does not show extreme results in the scales studied, their results remain in the mid-range values (with the assumption that the middle of the STEN scale is 5.5 and the standard deviation is 2).

    (9). In families with ASD children, there is a higher risk of the unbalanced or rigidly disengaged family type than in families with neurotypical children.

    This may be a significant result, suggesting the risk of the occurrence of a disturbed family system, functioning in families with children with ASD, which should be a trigger for providing these families with early family functioning diagnosis and consequent support and therapy.



    [1] Khaled M, Harambat F, El Hage H, et al. (2011) Spatial optimization of an underhood cooling module—Towards an innovative control approach. Appl Energy 88: 3841–3849. https://doi.org/10.1016/j.apenergy.2011.04.025 doi: 10.1016/j.apenergy.2011.04.025
    [2] Showers SO, Raji AK (2022) State-of-the-art review of fuel cell hybrid electric vehicle energy management systems. AIMS Energy 10: 458–485. https://doi.org/10.3934/ENERGY.2022023 doi: 10.3934/ENERGY.2022023
    [3] Elweddad M, Güneşer M, Yusupov Z (2022) Designing an energy management system for household consumptions with an off-grid hybrid power system. AIMS Energy 10: 801–830. https://doi.org/10.3934/ENERGY.2022036 doi: 10.3934/ENERGY.2022036
    [4] Jaber H, Ramadan M, Lemenand T, et al. (2018) Domestic thermoelectric cogeneration system optimization analysis, energy consumption, and CO2 emissions reduction. Appl Therm Eng 130: 279–295. https://doi.org/10.1016/j.applthermaleng.2017.10.148 doi: 10.1016/j.applthermaleng.2017.10.148
    [5] Jaber H, Khaled M, Lemenand T, et al. (2017) Effect of exhaust gases temperature on the performance of a hybrid heat recovery system. Energy Proc 119: 775–782. https://doi.org/10.1016/j.egypro.2017.07.110 doi: 10.1016/j.egypro.2017.07.110
    [6] Jaber H, Khaled M, Lemenand T, et al. (2015) Short review on heat recovery from exhaust gas. Case Stud Therm Eng 119: 95–110. https://doi.org/10.1016/J.RSER.2017.08.016 doi: 10.1016/J.RSER.2017.08.016
    [7] Jaber H, Lemenand T, Ramadan M, et al. (2019) Hybrid heat recovery system applied to exhaust gases-thermal modeling and case study. Heat Transf Eng 42: 106–119. https://doi.org/10.1080/01457632.2019.1692495 doi: 10.1080/01457632.2019.1692495
    [8] Faraj A, Jaber H, Chahine K, et al. (2020) New concept of power generation using TEGs: Thermal modeling, parametric analysis, and case study. Entropy 22. https://doi.org/10.3390/E22050503
    [9] Sahoo SK (2016) Solar photovoltaic energy progress in India: A review. Renewable Sustainable Energy Rev 59: 927–939. https://doi.org/10.1016/j.rser.2016.01.049 doi: 10.1016/j.rser.2016.01.049
    [10] Armendariz-Lopez JF, Luna-Leon A, Gonzalez-Trevizo ME, et al. (2016) Life cycle cost of photovoltaic technologies in commercial buildings in Baja California, Mexico. Renewable Energy 87: 564–571. https://doi.org/10.1016/j.renene.2015.10.051 doi: 10.1016/j.renene.2015.10.051
    [11] Humada AM, Hojabri M, Hamada HM, et al. (2016) Performance evaluation of two PV technologies (c-Si and CIS) for building-integrated photovoltaic based on tropical climate conditions: A case study in Malaysia. Energy Build 119: 233–241. https://doi.org/10.1016/j.enbuild.2016.03.052 doi: 10.1016/j.enbuild.2016.03.052
    [12] Herez A, El Hage H, Lemenand T, et al. (2021) Parabolic trough photovoltaic/thermal hybrid system: Thermal modeling and parametric analysis. Renewable Energy 165: 224–236. https://doi.org/10.1016/j.renene.2020.11.009 doi: 10.1016/j.renene.2020.11.009
    [13] Alhousni FK, Ismail FB, Okonkwo et al. (2022) A review of PV solar energy system operations and applications in Dhofar, Oman. AIMS Energy 10: 858–884. https://doi.org/10.3934/ENERGY.2022039 doi: 10.3934/ENERGY.2022039
    [14] Herez A, Hage H El, Lemenand T, et al. (2021) Parabolic trough photovoltaic/thermal hybrid system: Thermal modeling, case studies, and economic and environmental analysis. Renewable Energy Focus 38: 9–21. https://doi.org/10.1016/j.ref.2021.05.001 doi: 10.1016/j.ref.2021.05.001
    [15] Van D, Gerardo D (2014) Carbon dioxide as working fluid for medium and high-temperature concentrated solar thermal systems. AIMS Energy 1: 99–115. https://doi.org/10.3934/ENERGY.2014.1.99 doi: 10.3934/ENERGY.2014.1.99
    [16] Herez A, El Hage H, Lemenand T, et al. (2020) Review on photovoltaic/thermal hybrid solar collectors: Classifications, applications, and new systems. Sol Energy 207: 1321–1347. https://doi.org/10.1016/j.solener.2020.07.062 doi: 10.1016/j.solener.2020.07.062
    [17] Herez A, El Hage H, Lemenand T, et al. (2021) Parabolic trough photovoltaic thermoelectric hybrid system: Thermal modeling, case studies, and economic and environmental analyses. Sustainable Energy Technol Assess 47: 101368. https://doi.org/10.1016/j.seta.2021.101368 doi: 10.1016/j.seta.2021.101368
    [18] Masson G, Latour M, Biancardi D (2012) Global market outlook for photovoltaics until 2016. Available from: https://www.scirp.org/(S (i43dyn45teexjx455qlt3d2q))/reference/ReferencesPapers.aspx?ReferenceID = 1124597.
    [19] Kamarzaman NA, Tan CW (2014) A comprehensive review of maximum power point tracking algorithms for photovoltaic systems. Renewable Sustainable Energy Rev 37: 585–598. https://doi.org/10.1016/j.rser.2014.05.045 doi: 10.1016/j.rser.2014.05.045
    [20] Voudoukis NF (2018) Photovoltaic technology and innovative solar cells. Eur J Electr Eng Comput Sci 2. https://doi.org/10.24018/EJECE.2018.2.1.13 doi: 10.24018/EJECE.2018.2.1.13
    [21] Mundo-Hernández J, De Celis Alonso B, Hernández-Álvarez J, et al. (2014) An overview of solar photovoltaic energy in Mexico and Germany. Renewable Sustainable Energy Rev 31: 639–649. https://doi.org/10.1016/j.rser.2013.12.029 doi: 10.1016/j.rser.2013.12.029
    [22] Lin JX, Wen PL, Feng CC, et al. (2014) Policy target, feed-in tariff, and technological progress of PV in Taiwan. Renewable Sustainable Energy Rev 39: 628–639. https://doi.org/10.1016/j.rser.2014.07.112 doi: 10.1016/j.rser.2014.07.112
    [23] Paiano A (2015) Photovoltaic waste assessment in Italy. Renewable Sustainable Energy Rev 41: 99–112. https://doi.org/10.1016/j.rser.2014.07.208 doi: 10.1016/j.rser.2014.07.208
    [24] Khan HA, Pervaiz S (2013) Technological review on solar PV in Pakistan: Scope, practices, and recommendations for optimized system design. Renewable Sustainable Energy Rev 23: 147–154. https://doi.org/10.1016/j.rser.2013.02.031 doi: 10.1016/j.rser.2013.02.031
    [25] Khatib T, Mohamed A, Sopian K (2013) A review of photovoltaic systems size optimization techniques. Renewable Sustainable Energy Rev 22: 454–465. https://doi.org/10.1016/j.rser.2013.02.023. doi: 10.1016/j.rser.2013.02.023
    [26] Makki A, Omer S, Sabir H (2015) Advancements in hybrid photovoltaic systems for enhanced solar cells performance. Renewable Sustainable Energy Rev 41: 658–684. https://doi.org/10.1016/j.rser.2014.08.069 doi: 10.1016/j.rser.2014.08.069
    [27] Jordehi AR (2016) Parameter estimation of solar photovoltaic (PV) cells: A review. Renewable Sustainable Energy Rev 61: 354–371. https://doi.org/10.1016/j.rser.2016.03.049 doi: 10.1016/j.rser.2016.03.049
    [28] Awasthi A, Shukla AK, Murali Manohar SR, et al. (2020) Review on sun tracking technology in solar PV system. Energy Rep 6: 392–405. https://doi.org/10.1016/j.egyr.2020.02.004 doi: 10.1016/j.egyr.2020.02.004
    [29] Wong J, Lim YS, Tang JH, et al. (2014) Grid-connected photovoltaic system in Malaysia: A review on voltage issues. Renewable Sustainable Energy Rev 29: 535–545. https://doi.org/10.1016/j.rser.2013.08.087 doi: 10.1016/j.rser.2013.08.087
    [30] Khan J, Arsalan MH (2016) Solar power technologies for sustainable electricity generation—A review. Renewable Sustainable Energy Rev 55: 414–425. https://doi.org/10.1016/j.rser.2015.10.135 doi: 10.1016/j.rser.2015.10.135
    [31] Tao J, Yu S (2015) Review on feasible recycling pathways and technologies of solar photovoltaic modules. Sol Energy Mater Sol Cells 141: 108–124. https://doi.org/10.1016/j.solmat.2015.05.005 doi: 10.1016/j.solmat.2015.05.005
    [32] Zarmai MT, Ekere NN, Oduoza CF, et al. (2015) A review of interconnection technologies for improved crystalline silicon solar cell photovoltaic module assembly. Appl Energy 154: 173–182. https://doi.org/10.1016/j.apenergy.2015.04.120 doi: 10.1016/j.apenergy.2015.04.120
    [33] Sugathan V, John E, Sudhakar K (2015) Recent improvements in dye sensitized solar cells: A review. Renewable Sustainable Energy Rev 52: 54–64. https://doi.org/10.1016/j.rser.2015.07.076 doi: 10.1016/j.rser.2015.07.076
    [34] El-Khozondar HJ, El-Khozondar RJ, Matter K (2015) Parameters influence on MPP value of the photo voltaic cell. Energy Proc 74: 1142–1149. https://doi.org/10.1016/j.egypro.2015.07.756 doi: 10.1016/j.egypro.2015.07.756
    [35] Ramli MAM, Hiendro A, Sedraoui K, et al. (2015) Optimal sizing of grid-connected photovoltaic energy system in Saudi Arabia. Renewable Energy 75: 489–495. https://doi.org/10.1016/j.renene.2014.10.028 doi: 10.1016/j.renene.2014.10.028
    [36] Hernández-Moro J, Martínez-Duart JM (2015) Economic analysis of the contribution of photovoltaics to the decarbonization of the power sector. Renewable Sustainable Energy Rev 41: 1288–1297. https://doi.org/10.1016/j.rser.2014.09.025 doi: 10.1016/j.rser.2014.09.025
    [37] Dean AD (2015) Review on Photovoltaic Technology Based. Int J Adv Technol Eng Sci 3: 215–20. Available from: http://www.ijates.com/images/short_pdf/1424872115_P215-220.pdf.
    [38] Guerrero-Lemus R, Vega R, Kim T, et al. (2016) Bifacial solar photovoltaics—A technology review. Renewable Sustainable Energy Rev 60: 1533–1549. https://doi.org/10.1016/j.rser.2016.03.041 doi: 10.1016/j.rser.2016.03.041
    [39] Kow KW, Wong YW, Rajkumar RK, et al. (2016) A review on performance of artificial intelligence and conventional methods in mitigating PV grid-tied related power quality events. Renewable Sustainable Energy Rev 56: 334–346. https://doi.org/10.1016/j.rser.2015.11.064 doi: 10.1016/j.rser.2015.11.064
    [40] Sengupta D, Das P, Mondal B, et al. (2016) Effects of doping, morphology, and film thickness of photo-anode materials for dye sensitized solar cell application—A review. Renewable Sustainable Energy Rev 60: 356–376. https://doi.org/10.1016/j.rser.2016.01.104 doi: 10.1016/j.rser.2016.01.104
    [41] Forcan M, Durišić Ž, Mikulović J (2016) An algorithm for elimination of partial shading effect based on a theory of reference PV string. Sol Energy 132: 51–63. https://doi.org/10.1016/j.solener.2016.03.003 doi: 10.1016/j.solener.2016.03.003
    [42] Teffah K, Zhang Y (2017) Modeling and experimental research of a hybrid PV-thermoelectric system for high concentrated solar energy conversion. Sol Energy 157: 10–19. https://doi.org/10.1016/j.solener.2017.08.017 doi: 10.1016/j.solener.2017.08.017
    [43] Singh R, Banerjee R (2017) Impact of large-scale rooftop solar PV integration: An algorithm for hydrothermal-solar scheduling (HTSS). Sol Energy 157: 988–1004. https://doi.org/10.1016/j.solener.2017.09.021 doi: 10.1016/j.solener.2017.09.021
    [44] Qureshi TM, Ullah K, Arentsen MJ (2017) Factors responsible for solar PV adoption at household level: A case of Lahore, Pakistan. Renewable Sustainable Energy Rev 78: 754–763. https://doi.org/10.1016/j.rser.2017.04.020 doi: 10.1016/j.rser.2017.04.020
    [45] Jayaraman K, Paramasivan L, Kiumarsi S (2017) Reasons for low penetration in the purchase of photovoltaic (PV) panel systems among Malaysian landed property owners. Renewable Sustainable Energy Rev 80: 562–571. https://doi.org/10.1016/j.rser.2017.05.213 doi: 10.1016/j.rser.2017.05.213
    [46] Vasel A, Iakovidis F (2017) The effect of wind direction on the performance of solar PV plants. Energy Convers Manage 153: 455–461. https://doi.org/10.1016/j.enconman.2017.09.077 doi: 10.1016/j.enconman.2017.09.077
    [47] Quansah DA, Adaramola MS, Appiah GK, et al. (2017) Performance analysis of different grid-connected solar photovoltaic (PV) system technologies with a combined capacity of 20 kW located in a humid tropical climate. Int J Hydrogen Energy 42: 4626–4635. https://doi.org/10.1016/j.ijhydene.2016.10.119 doi: 10.1016/j.ijhydene.2016.10.119
    [48] Prasanth Ram J, Rajasekar N (2017) A new global maximum power point tracking technique for solar photovoltaic (PV) systems under partial shading conditions (PSC). Energy 118: 512–525. https://doi.org/10.1016/j.energy.2016.10.084 doi: 10.1016/j.energy.2016.10.084
    [49] Rezaee Jordehi A (2018) Enhanced leader particle swarm optimisation (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Sol Energy 159: 78–87. https://doi.org/10.1016/j.solener.2017.10.063 doi: 10.1016/j.solener.2017.10.063
    [50] Dhanalakshmi B, Rajasekar N (2018) Dominance square based array reconfiguration scheme for power loss reduction in solar PhotoVoltaic (PV) systems. Energy Convers Manage 156: 84–102. https://doi.org/10.1016/j.enconman.2017.10.080 doi: 10.1016/j.enconman.2017.10.080
    [51] Palm J, Eidenskog M, Luthander R (2018) Sufficiency, change, and flexibility: Critically examining the energy consumption profiles of solar PV prosumers in Sweden. Energy Res Soc Sci 39: 12–18. https://doi.org/10.1016/j.erss.2017.10.006 doi: 10.1016/j.erss.2017.10.006
    [52] Honrubia-Escribano A, Ramirez FJ, Gómez-Lázaro E, et al. (2018) Influence of solar technology in the economic performance of PV power plants in Europe. A comprehensive analysis. Renewable Sustainable Energy Rev 82: 488–501. https://doi.org/10.1016/j.rser.2017.09.061 doi: 10.1016/j.rser.2017.09.061
    [53] Andenaes E, Jelle BP, Ramlo K, et al. (2018) The influence of snow and ice coverage on the energy generation from photovoltaic solar cells. Sol Energy 159: 318–328. https://doi.org/10.1016/j.solener.2017.10.078 doi: 10.1016/j.solener.2017.10.078
    [54] Moslehi S, Reddy TA, Katipamula S (2018) Evaluation of data-driven models for predicting solar photovoltaics power output. Energy 142: 1057–1065. https://doi.org/10.1016/j.energy.2017.09.042 doi: 10.1016/j.energy.2017.09.042
    [55] Dehghani E, Jabalameli MS, Jabbarzadeh A (2018) Robust design and optimization of the solar photovoltaic supply chain in an uncertain environment. Energy 142: 139–156. https://doi.org/10.1016/j.energy.2017.10.004 doi: 10.1016/j.energy.2017.10.004
    [56] Xu L, Zhang S, Yang M, et al. (2018) Environmental effects of China's solar photovoltaic industry during 2011–2016: A life cycle assessment approach. J Clean Prod 170: 310–329. https://doi.org/10.1016/j.jclepro.2017.09.129 doi: 10.1016/j.jclepro.2017.09.129
    [57] Saxena A, Deshmukh S, Nirali S, et al. (2018) Laboratory-based experimental investigation of Photovoltaic (PV) thermo-control with water and its proposed real-time implementation. Renewable Energy 115: 128–138. https://doi.org/10.1016/j.renene.2017.08.029 doi: 10.1016/j.renene.2017.08.029
    [58] Hoffmann FM, Molz RF, Kothe JV, et al. (2018) Monthly profile analysis based on a two-axis solar tracker proposal for photovoltaic panels. Renewable Energy 115: 750–759. https://doi.org/10.1016/j.renene.2017.08.079 doi: 10.1016/j.renene.2017.08.079
    [59] Jakica N (2018) State-of-the-art review of solar design tools and methods for assessing daylighting and solar potential for building-integrated photovoltaics. Renewable Sustainable Energy Rev 81: 1296–1328. https://doi.org/10.1016/j.rser.2017.05.080 doi: 10.1016/j.rser.2017.05.080
    [60] Ram JP, Manghani H, Pillai DS, et al. (2018) Analysis on solar PV emulators: A review. Renewable Sustainable Energy Rev 81: 149–160. https://doi.org/10.1016/j.rser.2017.07.039 doi: 10.1016/j.rser.2017.07.039
    [61] Belarbi M, Haddouche K, Sahli B, et al. (2018) Self-reconfiguring MPPT to avoid buck-converter limits in solar photovoltaic systems. Renewable Sustainable Energy Rev 82: 187–193. https://doi.org/10.1016/j.rser.2017.09.019 doi: 10.1016/j.rser.2017.09.019
    [62] Hyder F, Sudhakar K, Mamat R (2018) Solar PV tree design: A review. Renewable Sustainable Energy Rev 82: 1079–1096. https://doi.org/10.1016/j.rser.2017.09.025 doi: 10.1016/j.rser.2017.09.025
    [63] Shayestegan M, Shakeri M, Abunima H, et al. (2018) An overview of the prospects of new-generation single-phase transformerless inverters for grid-connected photovoltaic (PV) systems. Renewable Sustainable Energy Rev 82: 515–530. https://doi.org/10.1016/j.rser.2017.09.055 doi: 10.1016/j.rser.2017.09.055
    [64] Novaes Pires Leite G de, Weschenfelder F, Araújo AM, et al. (2019) An economic analysis of the integration between air-conditioning and solar photovoltaic systems. Energy Convers Manage 185: 836–849. https://doi.org/10.1016/j.enconman.2019.02.037 doi: 10.1016/j.enconman.2019.02.037
    [65] Rahnama E, Aghbashlo M, Tabatabaei M, et al. (2019) Spatio-temporal solar exergoeconomic and exergoenvironmental maps for photovoltaic systems. Energy Convers Manage 195: 701–711. https://doi.org/10.1016/j.enconman.2019.05.051 doi: 10.1016/j.enconman.2019.05.051
    [66] Zhao BY, Zhao ZG, Li Y, et al. (2019) An adaptive PID control method to improve the power tracking performance of solar photovoltaic air-conditioning systems. Renewable Sustainable Energy Rev 113: 109250. https://doi.org/10.1016/j.rser.2019.109250 doi: 10.1016/j.rser.2019.109250
    [67] Fernández R, Ortiz C, Chacartegui R, et al. (2019) Dispatchability of solar photovoltaics from thermochemical energy storage. Energy Convers Manage 191: 237–246. https://doi.org/10.1016/j.enconman.2019.03.074 doi: 10.1016/j.enconman.2019.03.074
    [68] Rosas-Flores JA, Zenón-Olvera E, Gálvez DM (2019) Potential energy savings in urban and rural households of Mexico with solar photovoltaic systems using geographical information systems. Renewable Sustainable Energy Rev 116: 109412. https://doi.org/10.1016/j.rser.2019.109412 doi: 10.1016/j.rser.2019.109412
    [69] Rajvikram M, Sivasankar G (2019) Experimental study conducted for the identification of the best heat absorption and dissipation methodology in solar photovoltaic panels. Sol Energy 193: 283–292. https://doi.org/10.1016/j.solener.2019.09.053 doi: 10.1016/j.solener.2019.09.053
    [70] Troncoso N, Rojo-González L, Villalobos M, et al. (2019) Economic decision-making tool for distributed solar photovoltaic panels and storage: The case of Chile. Energy Proc 159: 388–393. https://doi.org/10.1016/j.egypro.2018.12.071 doi: 10.1016/j.egypro.2018.12.071
    [71] Trindade A, Cordeiro L (2019) Automated formal verification of stand-alone solar photovoltaic systems. Sol Energy 193: 684–691. https://doi.org/10.1016/j.solener.2019.09.093 doi: 10.1016/j.solener.2019.09.093
    [72] Liu J, Chen X, Cao S, et al. (2019) Overview on hybrid solar photovoltaic-electrical energy storage technologies for power supply to buildings. Energy Convers Manage 187: 103–121. https://doi.org/10.1016/j.enconman.2019.02.080 doi: 10.1016/j.enconman.2019.02.080
    [73] Kiyaninia A, Karimi H, Madadi Avargani V (2019) Exergoeconomic analysis of a solar photovoltaic-based direct evaporative air-cooling system. Sol Energy 193: 253–266. https://doi.org/10.1016/j.solener.2019.09.068 doi: 10.1016/j.solener.2019.09.068
    [74] Sow A, Mehrtash M, Rousse DR, et al. (2019) Economic analysis of residential solar photovoltaic electricity production in Canada. Sustainable Energy Technol Assess 33: 83–94. https://doi.org/10.1016/j.seta.2019.03.003 doi: 10.1016/j.seta.2019.03.003
    [75] Zafrilla JE, Arce G, Cadarso MÁ, et al. (2019) Triple bottom line analysis of the Spanish solar photovoltaic sector: A footprint assessment. Renewable Sustainable Energy Rev 114: 109311. https://doi.org/10.1016/j.rser.2019.109311 doi: 10.1016/j.rser.2019.109311
    [76] Zieba Falama R, Hidayatullah, Doka SY (2019) A promising concept to push efficiency of pn-junction photovoltaic solar cell beyond Shockley and Queisser limit based on impact ionization due to high electric field. Optik 187: 39–48. https://doi.org/10.1016/j.ijleo.2019.04.136 doi: 10.1016/j.ijleo.2019.04.136
    [77] Sadanand, Dwivedi DK (2020) Numerical modeling for earth-abundant highly efficient solar photovoltaic cell of non-toxic buffer layer. Opt Mater (Amst) 109: 110409. https://doi.org/10.1016/j.optmat.2020.110409 doi: 10.1016/j.optmat.2020.110409
    [78] Ren FR, Tian Z, Liu J, et al. (2020) Analysis of CO2 emission reduction contribution and efficiency of China's solar photovoltaic industry: Based on input-output perspective. Energy 199: 117493. https://doi.org/10.1016/j.energy.2020.117493 doi: 10.1016/j.energy.2020.117493
    [79] Qi L, Jiang M, Lv Y, et al. (2020) A celestial motion-based solar photovoltaics installed on a cooling tower. Energy Convers Manage 216: 112957. https://doi.org/10.1016/j.enconman.2020.112957 doi: 10.1016/j.enconman.2020.112957
    [80] Jan I, Ullah W, Ashfaq M (2020) Social acceptability of solar photovoltaic system in Pakistan: Key determinants and policy implications. J Clean Prod 274: 123140. https://doi.org/10.1016/j.jclepro.2020.123140 doi: 10.1016/j.jclepro.2020.123140
    [81] Janardhan K, Mittal A, Ojha A (2020) Performance investigation of stand-alone solar photovoltaic system with single phase micro multilevel inverter. Energy Reps 6: 2044–2055. https://doi.org/10.1016/j.egyr.2020.07.006 doi: 10.1016/j.egyr.2020.07.006
    [82] Kumar P, Pal N, Sharma H (2020) Performance analysis and evaluation of 10 kWp solar photovoltaic array for remote islands of Andaman and Nicobar. Sustainable Energy Technol Assess 42: 100889. https://doi.org/10.1016/j.seta.2020.100889 doi: 10.1016/j.seta.2020.100889
    [83] Ali MM, Ahmed OK, Abbas EF (2020) Performance of solar pond integrated with photovoltaic/thermal collectors. Energy Reps 6: 3200–3211. https://doi.org/10.1016/j.egyr.2020.11.037 doi: 10.1016/j.egyr.2020.11.037
    [84] Yang Y, Campana PE, Stridh B, Yan J (2020) Potential analysis of roof-mounted solar photovoltaics in Sweden. Appl Energy 279: 115786. https://doi.org/10.1016/j.apenergy.2020.115786 doi: 10.1016/j.apenergy.2020.115786
    [85] Anand B, Shankar R, Murugavelh S, et al. (2021) A review on solar photovoltaic thermal integrated desalination technologies. Renewable Sustainable Energy Rev 141: 110787. https://doi.org/10.1016/j.rser.2021.110787 doi: 10.1016/j.rser.2021.110787
    [86] Syahputra R, Soesanti I (2021) Renewable energy systems based on micro-hydro and solar photovoltaic for rural areas: A case study in Yogyakarta, Indonesia. Energy Reps 7: 472–490. https://doi.org/10.1016/j.egyr.2021.01.015 doi: 10.1016/j.egyr.2021.01.015
    [87] Alipour M, Salim H, Stewart RA, et al. (2021) Residential solar photovoltaic adoption behaviour: End-to-end review of theories, methods and approaches. Renewable Energy 170: 471–486. https://doi.org/10.1016/j.renene.2021.01.128 doi: 10.1016/j.renene.2021.01.128
    [88] Kazemian A, Parcheforosh A, Salari A, et al. (2021) Optimization of a novel photovoltaic thermal module in series with a solar collector using Taguchi based grey relational analysis. Sol Energy 215: 492–507. https://doi.org/10.1016/j.solener.2021.01.006 doi: 10.1016/j.solener.2021.01.006
    [89] Bhavsar S, Pitchumani R (2021) A novel machine learning based identification of potential adopter of rooftop solar photovoltaics. Appl Energy 286: 116503. https://doi.org/10.1016/j.apenergy.2021.116503 doi: 10.1016/j.apenergy.2021.116503
    [90] De RK, Ganguly A (2021) Modeling and analysis of a solar thermal-photovoltaic-hydrogen-based hybrid power system for running a standalone cold storage. J Clean Prod 293: 126202. https://doi.org/10.1016/j.jclepro.2021.126202 doi: 10.1016/j.jclepro.2021.126202
    [91] Wang Y, He J, Chen W, et al. (2021) Distributed solar photovoltaic development potential and a roadmap at the city level in China. Renewable Sustainable Energy Rev 141: 110772. https://doi.org/10.1016/j.rser.2021.110772 doi: 10.1016/j.rser.2021.110772
    [92] Rodziewicz T, Rajfur M, Teneta J, et al. (2021) Modelling and analysis of the influence of solar spectrum on the efficiency of photovoltaic modules. Energy Reps 7: 565–574. https://doi.org/10.1016/j.egyr.2021.01.013 doi: 10.1016/j.egyr.2021.01.013
    [93] Li Q, Zhang Y, Liu W, et al. (2022) Analysis of output coupling characteristics among multiple photovoltaic power stations based on correlation coefficient. Energy Reps 8: 908–915. https://doi.org/10.1016/J.EGYR.2022.10.031 doi: 10.1016/J.EGYR.2022.10.031
    [94] Yang H, Wang H (2022) Numerical simulation of the dust particles deposition on solar photovoltaic panels and its effect on power generation efficiency. Renewable Energy 201: 1111–1126. https://doi.org/10.1016/J.RENENE.2022.11.043 doi: 10.1016/J.RENENE.2022.11.043
    [95] Liu J, Sun J, Yuan H, et al. (2022) Behavior analysis of photovoltaic-storage-use value chain game evolution in a blockchain environment. Energy 260: 125182. https://doi.org/10.1016/J.ENERGY.2022.125182 doi: 10.1016/J.ENERGY.2022.125182
    [96] Yuan H, Ye H, Chen Y, et al. (2022) Research on the optimal configuration of photovoltaic and energy storage in a rural microgrid. Energy Reps 8: 1285–1293. https://doi.org/10.1016/J.EGYR.2022.08.115 doi: 10.1016/J.EGYR.2022.08.115
    [97] Bisognin Garlet T, Duarte Ribeiro JL, de Souza Savian F, et al. (2022) Competitiveness of the value chain of distributed generation of photovoltaic energy in Brazil. Energy Sustainable Dev 71: 447–461. https://doi.org/10.1016/J.ESD.2022.10.019 doi: 10.1016/J.ESD.2022.10.019
    [98] Peters IM, Hauch JA, Brabec CJ, et al. (2022) The role of innovation for the economy and sustainability of photovoltaic modules. IScience 25: 105208. https://doi.org/10.1016/J.ISCI.2022.105208 doi: 10.1016/J.ISCI.2022.105208
    [99] Micheli L, Talavera DL, Marco Tina G, et al. (2022) Techno-economic potential and perspectives of floating photovoltaics in Europe. Sol Energy 243: 203–214. https://doi.org/10.1016/J.SOLENER.2022.07.042 doi: 10.1016/J.SOLENER.2022.07.042
    [100] Kijo-Kleczkowska A, Bruś P, Więciorkowski G, et al. (2022) Profitability analysis of a photovoltaic installation—A case study. Energy 261: 125310. https://doi.org/10.1016/J.ENERGY.2022.125310 doi: 10.1016/J.ENERGY.2022.125310
    [101] Majewski P, Dias PR, et al. (2023) Product stewardship scheme for solar photovoltaic panels. Curr Opin Green Sustainable Chem 44: 100859. https://doi.org/10.1016/J.COGSC.2023.100859 doi: 10.1016/J.COGSC.2023.100859
    [102] Sun Y, Zhu D, Li Y, et al. (2023) Spatial modelling the location choice of large-scale solar photovoltaic power plants: Application of interpretable machine learning techniques and the national inventory. Energy Convers Manage 289: 117198. https://doi.org/10.1016/J.ENCONMAN.2023.117198 doi: 10.1016/J.ENCONMAN.2023.117198
    [103] Yu Y, Bai X, Li S, et al. (2023) Review of silicon recovery in the photovoltaic industry. Curr Opin Green Sustainable Chem 2023: 100870. https://doi.org/10.1016/J.COGSC.2023.100870 doi: 10.1016/J.COGSC.2023.100870
    [104] Lv S, Zhang M, Lai Y, et al. (2023) Comparative analysis of photovoltaic thermoelectric systems using different photovoltaic cells. Appl Therm Eng 235: 121356. https://doi.org/10.1016/J.APPLTHERMALENG.2023.121356 doi: 10.1016/J.APPLTHERMALENG.2023.121356
    [105] Liao Q, Li S, Xi F, et al. (2023) High-performance silicon carbon anodes based on value-added recycling strategy of end-of-life photovoltaic modules. Energy 281: 128345. https://doi.org/10.1016/J.ENERGY.2023.128345 doi: 10.1016/J.ENERGY.2023.128345
    [106] Gao L, Zhang X, Hua W (2023) Recent progress in photovoltaic thermal phase change material technology: A review. J Energy Storage 65: 107317. https://doi.org/10.1016/J.EST.2023.107317 doi: 10.1016/J.EST.2023.107317
    [107] Yasmeen R, Wang B, Shah WUH, et al. (2023) Adequacy of photovoltaic power on provincial and regional levels of income inequality in China. Sol Energy 262: 111906. https://doi.org/10.1016/J.SOLENER.2023.111906 doi: 10.1016/J.SOLENER.2023.111906
    [108] Al Miaari A, Ali HM (2023) Technical method in passive cooling for photovoltaic panels using phase change material. Case Stud Therm Eng 49: 103283. https://doi.org/10.1016/J.CSITE.2023.103283 doi: 10.1016/J.CSITE.2023.103283
    [109] Yao Y, Wang Y, Jia H, et al. (2023) An analytical approach based on coupled multi-physics model for photovoltaic arrays performance simulation. Electr Power Syst Res 224: 109773. https://doi.org/10.1016/J.EPSR.2023.109773 doi: 10.1016/J.EPSR.2023.109773
    [110] Wang Y, Cui X, Huang H (2023) Spatial patterns and environmental benefits of photovoltaic poverty alleviation programs in China. Environ Impact Assess Rev 103: 107272. https://doi.org/10.1016/J.EIAR.2023.107272 doi: 10.1016/J.EIAR.2023.107272
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