PSS (Photovoltaic Solar Systems) are a key technology in energy transition, and their efficiency depends on multiple interrelated factors. This study uses a systematic review based on the PRISMA methodology to identify four main categories affecting performance: technological, environmental, design and installation, and operational factors. Notably, technological advances in materials such as perovskites and emerging technologies like tandem and bifacial cells significantly enhance conversion efficiency, fostering optimism in the field. Environmental factors, including solar radiation, temperature, and contaminants, also substantially impact system performance. Design and installation play a crucial role, particularly in panel orientation, solar tracking systems, and the optimization of electrical configurations. Maintenance, material degradation, and advanced monitoring systems are essential for sustaining efficiency over time. This study provides a comprehensive understanding of the field by reviewing 113 articles and analyzing three key areas—materials, application of sizing technologies, and optimization—from 2018 to 2025. The paper also explores emerging trends, such as the development of energy storage systems and the integration of smart grids, which hold promise for enhancing photovoltaic module (PM) performance. The findings highlight the importance of integrating technological innovation, design strategies, and effective operational management to maximize the potential of PM systems, providing a solid foundation for future research and applications across residential, industrial, and large-scale contexts.

Agribusiness ranks second as the sector with the highest greenhouse gas emissions linked to methane, constituting a crucial challenge for global sustainability. Although its impact on climate change is considerable, small rural farmers do not have effective technologies to manage the organic waste derived from their daily activities. In this context, anaerobic digestion is an innovative solution that converts waste into biogas and biofertilizers, promoting a sustainable and circular approach. However, its implementation faces significant barriers due to inadequate designs and poor operational practices, which makes its adoption difficult in rural areas. This applied theoretical research seeks to overcome these barriers by improving the design and operation of small-scale biogas plants. The system studied operates at 70% of its capacity, with a hydraulic retention time of 20 days and a feed of 4 kg organic matter. The substrates considered were 30% organic waste and 70% bovine manure, achieving an average production of 63.75 L CH₄/kg of organic matter, which exceeded the usual yields of small biodigesters. A mathematical model was created and applied to the case study with an R² correlation of 98% and a pseudo-R² of 89.5%, evidencing a remarkable predictive capacity. This biogas plant model is efficient and sustainable, and it is presented as a viable solution for small rural farmers.

The linear economy model in the sugar agroindustry has generated multiple impacts due to the underutilization of by-products and reliance on fossil fuels. Through predictive modeling and anaerobic digestion, the circular bioeconomy of sugarcane biomass enables the generation of biogas and electricity in an environmentally sustainable manner. This theoretical-applied research proposes a predictive model to estimate the energy potential of by-products such as bagasse, vinasse, molasses, and filter cake, based on historical production data and validated technical coefficients. The model uses milled sugarcane as a baseline and projects its energy conversion under three scenarios through 2030. In its most favorable configuration, the model estimates energy production of up to 15.5 billion Nm³ of biogas in Cuba and 9.9 billion in Peru. The model’s architecture includes four residual biomass flows and bioenergy conversion factors applicable to electricity generation. It is validated using national statistical series from 2000 to 2018 and presents relative errors below 5%. Cuba, with a peak of over 13,000 GWh of electricity from bagasse, and Peru, with a stable output between 6500 and 7500 GWh, reflect the highest and lowest projected energy utilization, respectively. Bagasse accounts for over 60% of the total estimated energy contribution. This modeling tool is fundamental for advancing a transition toward a circular economy, as it helps mitigate environmental impacts, improve agroindustrial waste management, and guide sustainable policies in sugarcane-based contexts.

The agro-industry is among the largest methane emitters, posing a critical challenge for sustainability. In rural areas, producers lack effective technologies to manage daily organic waste. Anaerobic digestion (AD) offers a circular pathway by converting waste into biogas and biofertilizers; however, its adoption is limited by inappropriate designs and insufficient operational control. Theoretical-applied research addresses these barriers by improving the design and operation of small-scale biodigesters, elevating pH and Electrical Conductivity (EC) from passive indicators to first-order control variables. Based on the design of a compact biodigester previously validated in the Chillón Valley and replicated in Huaycán under a utility model patent process (INDECOPI, Exp. 001087-2025/DIN), a stoichiometric NaHCO${}_3$ strategy with joint pH–EC monitoring was formalized, defining operational windows (pH 6.92–6.97; EC 6200–6300 μS/cm and dose–response curves (0.3–0.4 kg/day for 3–4 day) to buffer VFA shocks and preserve methanogenic ionic strength. The system achieved stable productions of 370–462 L/day, surpassing the theoretical potential of 352.88 L/day calculated by Buswell’s equation. A multivariable predictive model (linear, quadratic, interaction terms pH × EC, temperature, and loading rate) was developed and validated with field data: R² = 0.78; MAPE = 2.7%; MAE = 11.2 L/day; RMSE = 13.8 L/day; r = 0.89; residuals normally distributed (Shapiro–Wilk p = 0.79). The proposed approach enables daily decision-making in low-instrumentation environments and provides a replicable and scalable pathway for the safe valorization of organic waste in rural areas. The design consolidates the shift from reactive to proactive and co-optimized pH–EC control, laying the foundation not only for standardized protocols and training in rural systems but also for improved environmental sustainability.

The feed industry is characterized by high energy consumption during the grinding stage, where hammer mills can account for up to 50% of total electricity usage; furthermore, efficiency analyses are based only on the classical equations reported in the literature. In this context, the present theoretical-applied research aimed to improve the efficiency of a plant operating below its nominal capacity. To achieve this, a comprehensive mathematical model was developed, integrating power and grain disintegration equations while overcoming the limitations of classical comminution theories. The model incorporates key factors such as feed rate, moisture content, absorbed power and hammer wear. Additionally, specific correction factors for temperature ($K_t$) and mechanical degradation ($K_d$) were introduced to accurately represent real operating conditions. The study was based on extensive measurements of electrical current, power factor, energy consumption, particle size distribution and thermal variations under different load conditions. The statistical analysis, which included ANOVA, ANCOVA and multiple regressions, demonstrated a predictive accuracy of 98% (R²) and a pseudo-R² of 89%. This high correlation allowed for an 18% reduction in energy consumption equivalent to 4 kWh/t and up to a 30% improvement in particle size uniformity, surpassing typical factory performance. The findings highlight that integrating operational, thermodynamic and wear-related factors enhances the robustness of the model, promoting more reliable energy-management practices in hammer mills. Consequently, the results confirm that the developed model serves as a scientifically robust, efficient and applicable tool for improving energy efficiency and reducing environmental impacts in the agri-food industry.

Multi-criteria methods are highly attractive tools to address the inherent complexity of evaluating problems in various scientific areas. The combination of methods such as Delphi/AHP is emerging as a robust alternative to evaluate the sustainability of renewable energy sources. In this theoretical-descriptive research, the use of the Delphi method is proposed to select criteria and sub-criteria to obtain a high level of reliability, while the AHP method is used to establish an order of preference among the alternatives analyzed. This process requires the support of a committee of experts, whose role is to identify the various biomass alternatives that can be used in the sugar industry, considering aspects related to sustainability. The selected experts have identified energy, exergy, and emergetic indicators, in which economic, environmental, and social aspects are integrated. The multi-criteria analysis shows that the V1 variant was the most satisfactory in terms of biomass sustainability, representing 45% and 53% of the overall priorities in the evaluated case studies. In addition, the sensitivity analysis under an equal-weighted scenario for both study cases evidenced that variant V1 acquired the highest score (38.17%) among all alternatives. Variant V4 achieved the second highest score (31.79%), while alternative V2 achieved only 29.04%, respectively. The integration of Delphi/AHP methods emerges as a novel tool to assess sustainability in different industries of the energy sector.