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[Display omitted] • Dye-sensitized solar cells (DSSCs) • Using DFT and TD-DFT, to investigate the impact of the diphenylamine moieties on the planarity, photochemical and photophysical properties of a modeled dyes. • The dyes all have donor and acceptor groups linked by a π-linker group (D-π-A). • Designed dyes have smaller energy band-gap, larger λ max , higher electron-donating power, and higher electron-accepting power. • All the dyes have the potential to be used as photosensitizers in DSSCs. Dye-sensitized solar cells (DSSCs) play a significant role in the development of affordable, safe, and efficient solar energy technology. In this research, we have designed nine new dyes (J01-09) by modifying an experimentally reported thiophene-based dye (DSS) in the literature. The diethylaniline in the DSS as the electron donating group was replaced with diphenylamine to reduce planarity in the dye. The designed dyes all have donor and acceptor groups linked by a π-linker group (D-π-A). Using DFT and TD-DFT, we investigated the impact of the diphenylamine moieties on the planarity, photochemical and photophysical properties of the modeled dyes in isolation. The optimized structures, UV–Vis absorption, photochemical, and photovoltaic parameters were computed to study the photosensitizing capability of the modeled dyes. The photochemical and photovoltaic parameters of the study include; open-circuit voltage (V OC), light-harvesting efficiency (LHE), driving force of electron injection (ΔGinj), driving force of regeneration (ΔGreg), excited-state lifetime (τ), ionization potential (IP), adiabatic ionization potential (AIP), vertical ionization potential (VIP), electron affinity (EA), chemical hardness (η), electrophilicity index (ω), electron-donating power (ω-) and electron-accepting power (ω+) were calculated. Our findings indicate that only J03 and J08 absorb at longer wavelengths (719 and 753 nm respectively) indicating lower absorption energy bandgap compared to the DSS dye, attributed to the extension of conjugation between the thiophene and donor groups. Also, only J03 and J08 exhibited lower ΔGinj values (−0.985 and −0.667 eV respectively) and ΔGreg values (0.245 and 0.179 eV respectively) implying the dyes can be regenerated spontaneously compared to the DSS dye. Therefore, J03 and J08 are more promising to be developed as a superior photosensitizer in DSSCs. [ABSTRACT FROM AUTHOR]

Solar energy is one of the cleanest forms of energy sources and considered as a green source of energy. Solar energy benefit ranges from low carbon emission, no fossil fuel requirement, long term solar resources, less payback time and other. However like other power generation sources, solar energy has also some Safety, Health and Environmental (SHE) concerns. This paper presents the overview of solar energy technologies and addresses the SHE impact of solar energy technologies to the sustainability of human activities. This paper will also recommend the possible ways to reduce the effect of potential hazards of widespread use of solar energy technologies. [ABSTRACT FROM AUTHOR]

• A novel enhanced MPA algorithm has been proposed for effective PV modelling. • Static and dynamic PV model parameters have been estimated. • The estimated parameters have been validated via experimental data-sets. • The statistical analysis has been performed for effectiveness of the EMPA method. • The results confirm the EMPA efficiency comparing with state-of-the-art algorithms. Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301 e - 04 and 5.9135 e - 07 . Similarly for double diode are 7.4396 e - 04 and 3.1849 e - 05 , respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505 ± 1.0971 e - 17 , which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505 ± 9.6235 e - 14 and 0.0084505 ± 2.5235 e - 9). The results show the proposed model's superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563. [ABSTRACT FROM AUTHOR]

This paper aims to provide an overview of the innovation and evolution of global solar energy technology (SET) and further analyze the driving forces. It addresses the evolution from a longitudinal perspective throughout more than 150 years, and discusses the multiple and interactive driving forces in the process of SET innovation. Our study encompasses three theories, including demand-driven theory, science advancement and technology catch-up theory. We retrieve patent data from PatSnap, a global patent platform, and obtain 313,530 hits in total worldwide, published from 1867 to 2018. Through employing the functions of cell diagram analysis and geographic territory mapping embedded in PatSnap platform, we conduct a relatively comprehensive analysis of SET evolution. Results disclose that the evolutionary trajectory analysis of SET reveals different characteristics in different stages, which involves spatial, main companies, IPCs and hot topics evolution, etc.. Multiple interactive driving forces play a significant role in various development phases. Finally, we give the research contributions and put forward related policy implications. • From the perspective of solar energy technology innovation and evolution, this paper focuses on the analysis, discussion and study of its change rules. • We develop this paper following a framework that combines three driving forces, including science advancement, demand-driven theory, and technology catch-up theory. • This paper uses different kinds of visualization scheme and levels to provide evidence of our argument. [ABSTRACT FROM AUTHOR]