Considering the increasing interest in the realization of carbon-neutral and RE100 systems, and the expansion of the supply of renewable energy through large-scale floating photovoltaic systems (FPVs), this study designed large-scale bifacial FPVs for maximum power density. Moreover, the estimated power generation was compared and analyzed according to the installation methods of the conventional monofacial PV module facing south (Mono-S), bifacial PV module facing south (Bi-S), and bifacial PV module facing east and west (Bi-EW). Using the proposed design method, the power generation per unit area for FPVs was 17.87% to 36.08% higher than that of the conventional installation method. In addition, this method can contribute to gird stability by decreasing the peak power around noon and increasing power generation during low irradiation.

The performance evaluation (PE) of the Photovoltaic(PV) system is an index representing the efficiency and reliability of the system. Most PE indicators evaluate the ratio of theoretically calculated power generation to actually measured power generation. The closer the ratio is to 1, the more ideal the PV system is. PV system varies depending on weather conditions and regional characteristics, especially on the types of sensors and measuring variables. Floating Photovoltaics(FPVs) and Marine photovoltaics(MPVs) vary with the environmental variables more as it is installed on the water and sea. In this paper, on the contrary to the existing PE methods, the most accurate regression model considering ambient temperature, relative humidity, and wind speed was used to predict the power in order to improve the accuracy. The optimal PE method for the PV system to easily and accurately detect failures of the PV system is proposed. Data from three FPVs in the same environment were analyzed for 1 year. The PV power prediction model including the wind speed and relative humidity was used to improve accuracy. The quality diagnosis was performed with an improved PE and the impact of various events can be represented through this. In this paper, the distribution of the corresponding Power Performance Index (PPI) values was analyzed using a descriptive statistic method, and indicators in terms of quality control were presented. The PV power generation state can be determined by the location of the average and median values of the boxplot. The fluctuation of PV power generation was identified using the size change of Inter Quartile Range (IQR), which represents the degree of data scattering. As a result, it was confirmed that failure occurred in the system when the IQR value is 2 times bigger than the normal IQR value.

Globally, large-scale photovoltaic (PV) systems are being installed to achieve maximum power generation efficiency. However, this often results in severe power fluctuations, impacting the stability of the power system. To enhance the power system stability, it is crucial to effectively distribute the times of maximum power output. This can be achieved by making changes to the inclination angle and azimuth of PV modules at power plants. The modeling results indicate a prediction error of 3.65% relative to the actual measurements. To assess system stability based on each installation method, new concepts called demand gradient (DG) and demand gradient average (DGA) were introduced and analyzed using the load pattern in Metropolitan Seoul. The electricity usage was categorized into four divisions: residential (dwelling), public, service, and manufacturing. The analysis revealed that optimal inclination angles for the dwelling, public, service, and manufacturing sectors were 60°, 30°, 30°, and 15°, respectively. As for installation azimuths, the optimal values were 0°, 80°, 160°, and 230°, respectively. When both the inclination angle and azimuth were varied simultaneously, their optimal installation methods included inclination angles of 60°, 60°, 35°, and 50°, and azimuths of 330°, 210°, 230°, and 190° for the dwelling, public, service, and manufacturing sectors, respectively. Among the various scenarios, the most significant improvement in grid stability was observed in the existing south-facing installation of DGA in the dwelling sector, demonstrating a remarkable 77% reduction from 0.1 to 0.057.

The escalating demand for environmentally sustainable energy sources in response to climate change necessitates the expansion of renewable power generation, particularly in the field of photovoltaic (PV) technology. Continuous advancements in PV power generation have substantially reduced production costs and chronic initial defects in PV modules. Effective PV power generation requires meticulous consideration of the installation structures and site selection processes. However, the identification of suitable power-generation locations often encounters challenges associated with civil complaints and environmental impediments. Floating Photovoltaic (FPV) systems positioned on water bodies have emerged as a promising solution for mitigating the environmental concerns associated with ground-based installations. This study endeavors to scrutinizes the reliability of PV modules within water-based FPV systems by leveraging climate data for analysis. Module deformation is forecasted through material simulations, and the output values are estimated. An empirical pressurization experiment validates the actual alterations in the output values during a defect, thereby explaining the potential defect types. Considering the escalating investments in FPV power generation facilities, this study anticipates and assesses the potential defects and problems arising from natural disasters. The objective is to provide a robust predictive model for FPV power generation, encompassing dynamic simulations based on material properties and the outcomes of mechanical design of experiments on PV modules. The proposed methodology aims to provide scholarly insights into the judicious selection of FPV installation structures and optimal sites in placid water conditions and in circumstances characterized by strong winds and high oceanic waves. The proactive analytical framework enhances the decision-making processes concerning the installation and operation of FPV systems.

Globally, renewable energies are indispensable resources on account of RE100 and the Paris Agreement. The most developed renewable energies are photovoltaics (PV) and wind energy, and they are continuously expanding. This study aims to optimize and analyze the nuclear power plant (NPP) load-following operation in various PV conditions in a metropolitan region. With theoretically estimated power demand and PV power, a mixed-integer problem (MIP) with ramping cycle constraint (RCC) was constructed for a safe load-following operation and simulated through duck curves under various NPP load-following regions (the extreme, normal, and safe regions). The simulation showed two major results for NPP load-following. Technically, RCC successfully controlled the NPP ramp cycle and was assured to be an optimization tool for NPP operation. Numerically, NPP load-following alleviated PV intermittency to almost 50%, 30%, and 15% depending on the load-following region. However, these effects were restricted when the PV capacity rate was high, especially when it exceeded 60%. Thus, PV system capacity is recommended to be 63% of the maximum power demand in the metropolitan region with NPP load-following, and larger PV systems need more flexibility.

Environmental factors that influence marine photovoltaic (PV) systems differ considerably from those affecting overland PV systems. The bird dropping, which is one of them, is a main cause of marine PV power reduction. The power prediction of a PV module contaminated by bird droppings is hard because light could pass through the bird dropping depending on the thickness of it unlike hard shading condition. This article shows differences between a bird-dropping shading and hard shading of the contaminated modules, and suggests a prediction model considering a transmittance of a bird dropping using image processing (IP). First, the shading rate (SR), which is the PV module surface ratio covered by bird droppings, is calculated using IP technologies. The accuracy of the SR is verified through an experiment creating artificial hard shading on the module using black masking tape. It has an error rate of 3% compared to our targeted benchmark SRs. Subsequently, an equivalent step methodology, which combines IV curves from individual cells to yield an IV curve for the entire PV module, was adopted to predict the power. The accuracy of this methodology is confirmed, and it showed a 5% error. However, in the case of real bird-dropping shades, discrepancies up to 15% are observed. In this article, the light penetration through bird droppings by a transmittance function was considered to predict the output of modules contaminated by the bird droppings. Errors fluctuate between 1%–20% based on the quantification of light interferences.