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Due to decrease of oil reserves, the international commitment for the reduction of pollutant emissions and environmental protection, renewable energy sources are intensively studied, including solar energy applications. However, solar energy is not constant and one possible alternative are solar hybrid thermosolar power plants. A hybrid solar gas turbine has three subsystems: a solar concentrator with heliostat field and central tower receiver, a combustion chamber, and a regenerative gas turbine. A previous thermodynamic analysis allowed shows an energy and exergy study of the plant, from a thermodynamic model of the system that has a method of solar resource estimation. However, this analysis did not allow evaluation of the energy losses in the system components, although the original model considered the typical irreversibilities of these cycles. This work aimed to develop a thermodynamic model that estimates the energy losses in the subsystems and the solar hybrid gas turbine components from a few parameters. The model estimated the energy losses for a Brayton cycle hybrid solar thermal plant throughout the day July 20 in Barranquilla, Colombia. A Dymola compiler in Modelica language was used to evaluate the model, which facilitates the estimation of the results at different times of the day. In this case, the computations were performed hourly throughout the day. In the results, energy losses were 16 % in the solar concentrator when the solar resource was the maximum at noon and close to 1 % in the combustion chamber. Therefore, the hybrid solar Brayton cycle system is technically feasible and reduces fuel consumption. Consequently, it is important to continue developing concentration systems and reduce their energy losses. [ABSTRACT FROM AUTHOR]

In this paper, we present a strategy for integrating photovoltaic systems into power distribution networks to improve the technical, economic, and environmental aspects of these networks. Taking into account variations in photovoltaic generation and energy demand, the proposed strategy uses a mathematical model that is subject to the technical and operational constraints of the system under analysis. The proposed strategy employs a master–slave methodology based on metaheuristic algorithms, in which the master stage determines the optimal locations for the photovoltaic generators and the slave stage estimates the optimal power injection. We proposed three different configurations of the methodology, each using the Particle Swarm Optimization (PSO) algorithm for the slave stage and the following algorithms for the master stage: the Chu and Beasley's Genetic Algorithm (CBGA), the Monte Carlo (MC) algorithm, and the PSO algorithm. These proposed configurations were evaluated in two test scenarios tailored to the characteristics of two regions in Colombia. This allowed us to analyze the impact of the methodologies on grid-connected (urban network) and off-grid systems (rural network). According to the results, the configuration that used two PSO algorithms achieved the most favorable outcomes in the urban scenario, reducing energy losses by 32.88%, operating costs by 42.41%, and pollutant gas emissions by 40.37%. Similarly, the same configuration obtained the best results in the rural scenario, reducing energy losses by 22.12%, operating costs by 43.35%, and pollutant gas emissions by 41.94%. These results were obtained in a typical day of operation for each system. • A strategy for integrating photovoltaic energy into distribution electrical grids. • The strategy assesses distributed generation's impact on urban and rural grids. • 32.88% urban, 22.12% rural electrical loss reductions. • 42.41% urban, 43.35% rural operating cost reductions achieved • 40.37% urban, 41.94% rural CO 2 emission reductions achieved. [ABSTRACT FROM AUTHOR]