The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun’s path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm.

Solar coatings have become increasingly relevant as a means to enhance the performance and efficiency of photovoltaic (PV) panels, playing a critical role in advancing sustainable solar energy solutions. This study employs the life cycle assessment (LCA) methodology to quantify the greenhouse gas (GHG) emissions associated with the production process of a coating used on solar PV panels. Actual data were collected for the manufacture of the solar coating, constituted by two layers: (i) tetraethyl orthosilicate (TEOS) and (ii) titanium isopropoxide (TTIP). Data on energy and material flows were compiled. The GHG emissions for the TEOS and TTIP coatings were 1.8977 and 6.3204 g CO₂-eq/mL, respectively. With experimental data demonstrating a 4.5% increase in panel efficiency from the coatings, a simulation was carried out to verify the impact of the solar coating on a 16.4 MW solar power plant. The results indicate lifetime avoided emissions of 98,029,294 kg CO₂-eq over 25 years. Sensitivity assessments verified the impact of shorter lifetimes of the coatings, and even with frequent reapplication—down to monthly intervals—the coating continues to provide net environmental benefits. This robustness reinforces the potential of solar coatings as a complementary strategy for decarbonizing PV systems.

Combined-cycle gas turbine (CCGT) power plants play an important role in reducing greenhouse gas emissions while maintaining high thermal efficiency. Accurate thermodynamic models are essential for evaluating plant performance under different operating conditions and supporting improvements in energy resource management. This study presents a Python-based thermodynamic model of an isolated natural gas-fired CCGT power plant, applied to 1027 steady-state operating points recorded over one month of operation. The model integrates energy, exergy, and exergoeconomic analyses based on the Specific Exergy Costing (SPECO) methodology to assess plant operation with and without supplementary firing. The model was validated against the measured steam production, with a mean absolute percentage error of 2.8%. The proposed methodology enables the evaluation of component efficiencies, exergy destruction, cost formation, and the influence of different operating modes on electricity production. The plant achieved median gross first- and second-law efficiencies of 46.6% and 44.6%, respectively (45.1% and 43.2% on a net basis). The combustion chamber exhibited the highest exergy destruction in both operating modes, while the supplementary burner became one of the major sources of irreversibility when supplementary firing was activated. The specific exergy cost of electricity decreased from about 21.0 to 14.2 $/GJ as the electrical output increased from 11.3 to 20.9 MW, with a median of 16.47 $/GJ in the period. The supplementary burner presented the highest relative cost difference (median of 1.06), whereas the combustion chamber had the highest exergy destruction rate and cost rate of exergy destruction. The proposed model provides a flexible and reproducible framework for evaluating CCGT power plants and can support operational assessment and decision-making aimed at improving thermodynamic and economic performance.