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The global photovoltaic (PV) installed capacity, vital for the electric sector's decarbonation, reached 1552.3 GW p in 2023. In France, the capacity stood at 19.9 GW p in April 2024. The growth of the PV installed capacity over a year was nearly 32% worldwide and 15.7% in France. However, integrating PV electricity into grids is hindered by poor knowledge of rooftop PV systems, constituting 20% of France's installed capacity, and the lack of measurements of the production stemming from these systems. This problem of lack of measurements of the rooftop PV power production is referred to as the lack of observability. Using ground-truth measurements of individual PV systems, available at an unprecedented temporal and spatial scale, we show that by estimating the PV power production of an individual rooftop system by combining solar irradiance and temperature data, the characteristics of the PV system inferred from remote sensing methods and an irradiation-to-electric power conversion model provides accurate estimations of the PV power production. We report an average estimation error (measured with the pRMSE) of 10% relative to the system size. Our study shows that we can improve rooftop PV observability, and thus its integration into the electric grid, using little information on these systems, a simple model of the PV system, and weather data. More broadly, this study shows that limited information is sufficient to derive a reasonably good estimation of the PV power production of small-scale systems. [ABSTRACT FROM AUTHOR]

As a result of global efforts to combat the rise in global climate change and carbon dioxide emissions, there has been a substantial increase in renewable energy investment for both residential and utility power generation. Solar power facilities are estimated to be among the major contributors to global decarbonization in terms of capacity by 2050. Consequently, the majority of economically significant countries are progressively implementing utility-scale photovoltaic (U-PV) systems. Nevertheless, a major obstacle to the expansion of U-PV generation is the identification and assessment of direct current (DC) faults in the extensive array of PV panels. In order to address this obstacle, it is imperative to provide an evaluation method that can accurately and cost-effectively identify and locate potential DC faults in PV arrays. Therefore, many studies attempted to utilize thermal cameras, voltage and current sensors, power databases, and other detecting elements; however, some of these technologies provide extra hurdles in terms of the quantity and expense of the utilized hardware equipment. This work presents a sophisticated system that aims to diagnose and locate various types of PV faults, such as line-to-ground, line-to-line, inter-string, open-circuit, and partial shading events, within a PV array strings down to a module level. This study primarily depends on three crucial indicators: precise calculation of the PV array output power and current, optimal placement of a limited number of voltage sensors, and execution of specifically specified tests. The estimation of PV array power, along with selectively placed voltage sensors, minimizes the time and equipment required for fault detection and diagnosis. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate that the proposed approach can accurately diagnose and localize faults with only NS/2 voltage sensors, where NS is the number of PV array parallel strings. [ABSTRACT FROM AUTHOR]

The global photovoltaic (PV) installed capacity, vital for the electric sector’s decarbonation, reached 1552.3 GWp in 2023. In France, the capacity stood at 19.9 GWp in April 2024. The growth of the PV installed capacity over a year was nearly 32% worldwide and 15.7% in France. However, integrating PV electricity into grids is hindered by poor knowledge of rooftop PV systems, constituting 20% of France’s installed capacity, and the lack of measurements of the production stemming from these systems. This problem of lack of measurements of the rooftop PV power production is referred to as the lack of observability. Using ground-truth measurements of individual PV systems, available at an unprecedented temporal and spatial scale, we show that by estimating the PV power production of an individual rooftop system by combining solar irradiance and temperature data, the characteristics of the PV system inferred from remote sensing methods and an irradiation-to-electric power conversion model provides accurate estimations of the PV power production. We report an average estimation error (measured with the pRMSE) of 10% relative to the system size. Our study shows that we can improve rooftop PV observability, and thus its integration into the electric grid, using little information on these systems, a simple model of the PV system, and weather data. More broadly, this study shows that limited information is sufficient to derive a reasonably good estimation of the PV power production of small-scale systems.

Photovoltaic (PV) energy development has increased in the last years mainly based on large utility-scale plants. These plants are characterised by a huge number of panels connected to high-power inverters occupying a large land area. An accurate estimation of the power production of the PV plants is needed for failure detection, identifying production deviations, and the integration of the plants into the power grid. Various studies have used Machine Learning estimation techniques developed on very small PV plants. This paper deals with large utility-scale plants and uses all the available information to represent the non-uniform radiation over the whole studied solar field. Variables measured in up to four meteorological stations and distributed across the plant are used. Three PV plants with 1, 2 and 4 meteorological stations have been used to develop Machine Learning models. The hyperparameters were systematically optimised, demonstrating the improvements by comparing with a simple model based on Multiple Linear Regression. The best results were obtained with the Random Forest technique for the three PV plants, providing a RMS error value ranging from 1.9% to 5.4%. The final models were compared with those found in the literature for tiny PV plants showing in general much better performance. • Use of distributed met-station data improves production estimations in large PV plants. • Systematic optimisation of hyperparameters of ML models is a must for improving performance. • The estimations obtained with tree-based models outperform those provided by ANN. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) models are essential for energy planning and grid integration applications. The models used for PV power conversion typically adopt physical, data-driven, or hybrid approaches. Different hybrid techniques, combining elements of physical and data-driven methods, have been effectively developed for PV power estimation and forecasting, leveraging the strengths of both native methods. The data-driven approach allows models to account for unmodeled uncertainties and nonlinearities that purely physical models cannot capture. Meanwhile, the guidance of scientific theory makes the models less dependent on data, thereby improving generalization, interpretability, and accuracy. This review paper provides a comprehensive overview of hybrid methodologies for PV power estimation and forecasting. The main available hybridization methods are summarized and discussed under a novel methodological classification into three primary categories: physics-informed machine learning models, optimized physical models, and physics-guided models. Furthermore, these hybrid models are compared in terms of methodology, applications, and elucidating the merits and demerits of different techniques. By offering insights into these hybrid models, this review lays a foundation for researchers in this field and contributes to the advancement of PV power estimation and forecasting methodologies. • Hybrid models outperform traditional physics and data-driven methods by merging them. • Physics rules prevent illogical prediction and overfit, and improve generalization. • Data-driven methods offer flexibility, addressing unmodeled/seasonal phenomenon. • Hybrid approaches span collaborative to physics- or data-driven-centric methods. • Current models exploit more ML than physics techniques, and fair benchmark is crucial. [ABSTRACT FROM AUTHOR]

This article proposes a novel method to estimate the optimal parameters and power outputs for photovoltaic (PV) power generation. Accurate estimation for PV power generation allows efficient scheduling to meet the load demand and reduces the effect of uncertainty for a microgrid. The parameters that are provided by the PV manufacturer have a nonlinear relationship with power output and may vary with the aging of the PV cells. To allow finer and more accurate estimation for PV power output, the parameters of the single-diode $R_{p}$ model are transformed into 13 parameters under various weather conditions. The principal component analysis (PCA) and an assessment index are used to delete the parameters that have little effect on the output. Using the actual input/output data, a hybrid charged system search (HCSS) algorithm is then used to estimate the optimal parameters. When the parameters are optimized, the estimation for PV power output can be produced as long as the inputs are given. The proposed method is tested on two different PV power generation systems. To verify the performance of the proposed method, the results are compared with the results for the application of the traditional differential evolution (DE) and particle swarm optimization (PSO) methods. [ABSTRACT FROM AUTHOR]

• A multi-parameter model is established to adapt to different climatic conditions. • An online model parameter optimization scheme is developed. • An adaptive LM method is tailored to search for the optimum of PV parameters. • GSA is utilized to identify critical parameters in the multi-parameter model. This paper proposes a novel method to estimate the optimal parameters of a single-diode photovoltaic (PV) module model as well as the active power of PV generation. First, a multi-parameter PV module model is established considering the changes of temperature and solar irradiance; thus, the model is adaptive to different environmental conditions. Second, the model parameter estimation is designed as an optimization problem. An online optimization scheme that only requires the normal operation information is proposed, which avoids the need for an offline experiment to determine the current–voltage (I-V) curve as in conventional PV modeling methods. Third, the adaptive Levenberg-Marquardt (ALM) method with an adaptative damping factor selection strategy is developed to solve the parameter optimization problem, which overcomes the singular issue of traditional LM methods. Moreover, the global sensitivity analysis (GSA) is leveraged to identify critical parameters affecting the PV generation output power, which provides information on model simplification. Finally, the proposed method is tested on data from real-world PV systems in Bend, Oregon, USA, under diverse conditions and scenarios. It is revealed that compared with commonly used methods, the proposed method obtains more accurate PV module parameters and offers PV generation outputs closer to the true outputs. [ABSTRACT FROM AUTHOR]