Solar photovoltaic (PV) systems have drawn significant attention over the last decade. One of the most critical obstacles that must be overcome is distributed energy generation. This paper presents a comprehensive quantitative bibliometric study to identify the new trends and call attention to the evolution within the research landscape concerning the integration of solar PV in power networks. The research is based on 7146 documents that were authored between 2000–2021 and downloaded from the Web of Science database. Using an in-house bibliometric tool, Bibliometrix R-package, and the open-source tool VOSviewer we obtained bibliometric indicators, mapped the network analysis, and performed a multivariate statistical analysis. The works that were based on solar photovoltaics into power networks presented rapid growth, especially in India. The co-occurrence analysis showed that the five main clusters, classified according to dimensions and significance, are (i) power quality issues that are caused by the solar photovoltaic penetration in power networks; (ii) algorithms for energy storage, demand response, and energy management in the smart grid; (iii) optimization, techno-economic analysis, sensitivity analysis, and energy cost analysis for an optimal hybrid power system; (iv) renewable energy integration, self-consumption, energy efficiency, and sustainable development; and (v) modeling, simulation, and control of battery energy storage systems. The results revealed that researchers pay close attention to “renewable energy”, “microgrid”, “energy storage”, “optimization”, and “smart grid”, as the top five keywords in the past four years. The results also suggested that (i) power quality; (ii) voltage and frequency fluctuation problems; (iii) optimal design and energy management; and (iv) technical-economic analysis, are the most recent investigative foci that might be appraised as having the most budding research prospects.

This study developed a novel folding light shelf system that integrates reflectors, photovoltaic (PV) modules, and adaptive louvers that adjust based on solar altitude, aiming to improve daylight distribution, minimize glare, and reduce energy consumption in office buildings. The research employed an advanced optimization approach, utilizing Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Latin Hypercube Sampling, a highly effective method suitable for managing complex multi-objective scenarios involving numerous variables, to efficiently identify high-performance configurations with increased precision. Key design variables across all three components of the system included angle, width, distance, and the number of folds in the light shelf, along with the number of louvers. The proposed method successfully integrates PV technology into light shelves without compromising their functionality, enabling both daylight control and energy generation. The optimization results demonstrate that the system achieved up to a 15% improvement in useful daylight illuminance (UDI) and a 16% reduction in cooling energy consumption. Furthermore, the PV modules generated 509.5 kWh/year, ensuring improved efficiency and sustainability in building performance.