The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.

The paper presents a comparative study of two solar string inverters based on the Quasi-Z-Source (QZS) network. The first solution comprises a full-SiC two-level QZS inverter, while the second design was built based on a three-level neutral-point-clamped QZS inverter with Silicon based Metal–Oxide–Semiconductor Field-Effect Transistors (Si MOSFETs). Several criteria were taken into consideration: the size of passive elements, thermal design and size of heatsinks, voltage stress across semiconductors, and efficiency investigation. The Photovoltaic (PV)-string rated at 1.8 kW power was selected as a case study system. The advantages and drawbacks of both solutions are presented along with conclusions.

Focus is on small signal modeling and analysis of a single-phase interleaved quasi-z-source inverter (QZSI) with active-power decoupling (APD) circuit. The QZSI-APD is featured by low input current ripple and low power ripple. Equivalent circuits for the main operation modes are presented and a dynamic model in the form of matrix equations is derived for describing the behavior of the converter. The model is used for adjustment of the passive components values and for the control system tuning. The simulation results that confirm efficient performance of the converter are presented and discussed.

The integration of lithium-ion (Li-ion) battery energy storage systems (LiBESSs) with photovoltaic (PV) generation offers a promising solution for powering auxiliary services (ASs) in high-voltage power stations. This study conducts a systematic literature review (SLR) to evaluate the feasibility, benefits, and challenges of this integration. The proposed SLR complies with the PRISMA 2020 statement, and it is also registered on the international PROSPERO platform (ID 1073599). The selected methodology includes the following key steps: definition of the research questions; search strategy development; selection criteria of the studies; quality assessment; data extraction and synthesis; and discussion of the results. Through a comprehensive analysis of scientific publications from 2013 to 2024, trends, advancements, and research gaps are identified. The methodology follows a structured review framework, including data collection, selection criteria, and evaluation of technical feasibility. From 803 identified studies, 107 were eligible in accordance with the assessed inclusion criteria. Then, a custom study impact factor (SIF) framework selected 5 out of 107 studies as the most representative and assertive ones on the topics of this SLR. The findings indicate that Li-ion BESSs combined with PV systems enhance reliability, reduce reliance on conventional sources, and improve grid resilience, particularly in remote or constrained environments. The group of reviewed studies discuss optimization models and multi-objective strategies for system sizing and operation, along with practical case studies validating their effectiveness. Despite these advantages, challenges related to cost, regulatory frameworks, and performance variability remain. The study concludes that further experimental validations, pilot-scale implementations, and assessment of long-term economic impacts are necessary to accelerate the adoption of BESS-PV systems in high-voltage power substations. This study was funded by the R&D program of the Brazilian National Electric Energy Agency (ANEEL) via project number PD-07351-0001/2022.

This work presents a comparative study of the effects of soiling and shading of two photovoltaic systems with the same module and inverter technology with the same brand, model, power, and the same installation conditions, at the same residential house with similar but different nominal powers, installed on a roof in the urban environment of the city of Goiânia – GO. The energy productions of both systems were compared, keeping one of them periodically clean, called clean grid-connected photovoltaic system (Clean – GCPS), and another without periodic cleaning, called soiled grid-connected photovoltaic system (Soiled – GCPS) in order to assess the influence of soiling deposition on the modules during the period of their installation until the month of November 2021. The work proposes two unprecedented indices to measure the comparative effect of soiling between clean and dirty photovoltaic systems, namely, the relative soiling index, SR; and the percentage of relative degradation due to dirt, DRS%. These two indices are a way to normalize the effect of soiling on photovoltaic systems under the same installation conditions (orientation, inclination, shading) and technical characteristics (technologies, brands, models of photovoltaic modules and inverters). The values of relative degradation due to dirt indicate that the effect of reducing photovoltaic production was more accentuated in the dry period (May to September) with degradation values between 7.7% and 13%; and lower degradation values in photovoltaic production in the rainy season (November to April), with rates ranging from –0.2% to 3.5%. This work also determined the comparative effect of shading between two photovoltaic systems with similar characteristics based on simulations of the computational tool PVsyst. The monthly mean irradiation data was edited and changed to the values from the Brazilian Solar Energy Atlas and the monthly mean temperatures were changed to the values from the INMET database in order to improve the PVsyst results. The value of photovoltaic production calculated by simulation was 1.37% higher than the value measured in Clean – SFCR during the period of analysis from September 2020 to August 2021. Este trabalho apresenta um estudo comparativo dos efeitos da sujidade e do sombreamento de dois sistemas fotovoltaicos com mesma tecnologia de módulos e inversores com mesma marca, modelo e potência e mesmas condições de instalação, na mesma unidade consumidora com potências nominais próximas, porém distintas, instalados em telhado no ambiente urbano da cidade de Goiânia – GO. Comparou-se a produção de energia dos sistemas, mantendo-se um deles limpo periodicamente, denominado sistema fotovoltaico conectado à rede (SFCR) limpo (SFCR – Limpo), e outro sem limpeza periódica, denominado sistema fotovoltaico conectado à rede sujo (SFCR – Sujo), a fim de avaliar a influência da deposição de sujidade sobre os módulos durante o período de sua instalação até o mês de novembro de 2021. O trabalho propõe dois índices inéditos para mensurar o efeito comparativo da sujidade entre sistemas fotovoltaicos limpos e sujos, nomeadamente, o índice de sujidade relativa, SR; e o percentual de degradação relativa por sujidade, DRS%. Estes dois índices são uma forma de normalizar o efeito da sujidade sobre sistemas fotovoltaicos sob as mesmas condições de instalação (orientação, inclinação, sombreamento) e características técnicas (tecnologias, marcas e modelos de módulos fotovoltaicos e inversores). Os valores de degradação relativa por sujidade indicam que o efeito de redução na produção fotovoltaica foi mais acentuado nos meses do período seco (maio a setembro) com valores de degradação entre 7,7% e 13%; e menores valores de degradação na produção fotovoltaica no período chuvoso (outubro a abril), com índices variando de –0,2% a 3,5%. Este trabalho determinou também o efeito comparativo do sombreamento entre dois sistemas fotovoltaicos de características semelhantes com base em simulações da ferramenta computacional PVsyst. Os dados de irradiação média mensal foram editados e alterados para os valores do Atlas Brasileiro de Energia Solar e as temperaturas médias mensais foram alteradas para os valores da base de dados do INMET a fim de aprimorar os resultados do PVsyst. O valor da produção fotovoltaica calculada por simulação foi 1,37% superior ao valor medido no SFCR – Limpo durante o período de um ano de setembro de 2020 a agosto de 2021.