This paper presents an Energy Management System (EMS) for solving the problem regarding the optimal daily operation of Photovoltaic (PV) distributed generators in Alternate Current (AC) distribution grids. To this effect, a nonlinear programming problem (NLP) was formulated which considered the improvement of economic (investment and maintenance costs), technical (energy losses), and environmental (${CO}_2$ emission) grid indices as objective functions, considering all technical and operating constraints for the operation of AC networks with the presence of PV sources. To solve this mathematical formulation, a master–slave methodology was implemented, whose master stage employed the antlion optimizer to find the power dispatch of PV sources in each period of time considered (24 h). In the slave stage, an hourly power flow based on the successive approximations method was used in order to obtain the values of the objective functions and constraints associated with each possible PV power configuration proposed by the master stage. To evaluate the effectiveness and robustness of the proposed methodology, two test scenarios were used, which included three installed PV sources in an urban and a rural network, considering the PV power generation and demand located reported for Medellín and Capurganá, respectively. These systems correspond to connected and standalone grids located in two different regions of Colombia. Furthermore, the proposed methodology was compared with three optimization methodologies reported in the literature: the Chu and Beasley genetic algorithm, the particle swarm optimization algorithm, and the vortex search optimization algorithm. Simulation results were obtained via the MATLAB software for both test scenarios with all the optimization methodologies. It was demonstrated that the proposed methodology yields the best results in terms of solution quality and repeatability, with shorter processing times.

This research explores the application of metaheuristic algorithms to refine parameter estimation in dynamic systems, with a focus on the inverted pendulum model. Three optimization techniques, Particle Swarm Optimization (PSO), Continuous Genetic Algorithm (CGA), and Salp Swarm Algorithm (SSA), are introduced to solve this problem. Through a thorough statistical evaluation, the optimal performance of each technique within the dynamic methodology is determined. Furthermore, the efficacy of these algorithms is demonstrated through experimental validation on a real prototype, providing practical insights into their performance. The outcomes of this study contribute to the advancement of control strategies by integrating precisely estimated physical parameters into various control algorithms, including PID controllers, fuzzy logic controllers, and model predictive controllers. Each algorithm ran 1000 times, and the SSA algorithm achieved the best performance, with the most accurate parameter estimation with a minimum error of 0.01501 N m and a mean solution error of 0.01506 N m. This precision was further underscored by its lowest standard deviation in RMSE (1.443 99 × 10⁻⁶ N m), indicating remarkable consistency across evaluations. The 95% confidence interval for error corroborated the algorithm’s reliability in deriving optimal solutions.

Hydropower is an important source of energy in Latin America. Many countries in the region, including Brazil, Peru, Colombia, and Chile, rely heavily on hydropower plants to meet their energy needs. However, there are also challenges related to the use of hydropower in the region, such as the construction of dams that can have negative impacts on ecosystems and local communities. A new alternative is the production of energy through hydrokinetic turbines because they are a clean and renewable energy source that does not emit greenhouse gases. In addition, its production is predictable and can be generated in a variety of environments, from coasts to rivers and canals. Within the hydrokinetic turbines are the H-Darrieus turbines although they are still under development, they are seen as an important opportunity to diversify the energy matrix and reduce dependence on fossil fuels. The main purpose of this study is to determine and compare the efficiency of three Darrieus H-type vertical axis hydrokinetic turbines numerically. The turbines were configured with different solidities. The NACA 0018 profile was used for the turbine design. The study was carried out using the ANSYS® Fluent 2022R2 software, two-dimensional (2D) simulations set up constant operating conditions. Rotation speed variations have been set between 21 and 74 RPM with 10 rpm increments. Furthermore, the General Richardson extrapolation method is used for the analysis of mesh convergence, monitoring the turbine power coefficient as a convergence parameter. The numerical results show that the turbine H-Darrieus with a solidity of 1.0, a wider operating range, and lower power and torque coefficient. At low TRS, the largest solidity provided the best efficiency and the greatest self-starting capability, but it also had the smallest operating range

The energy consumption estimation of a locomotive for a particular route is important for the selection of a locomotive technology, the improvement of the energy management system, the evaluation of the locomotive’s potential energy generation, among others. The methodologies reported in the literature usually assume that the information of the railway track is available; however, in some cases, the track information is incomplete, not available, or the route is still in a planning stage. Therefore, this paper proposes a methodology to estimate the energy consumption and the potential energy generation of a locomotive when the railway track information is not available or incomplete. The methodology begins by extracting the main technical information of the locomotive to be analyzed. Then, the route is traced on Google Earth with steps of 100 m and the obtained information is processed to generate the longitude, latitude, elevation, and distance of the points along the route. From such information, it is possible to generate the slope and curvature profiles, while the speed profile can be obtained from the track operator or the regulations of a specific country. With that information, it is possible to estimate the equivalent power of the locomotive at each point of the route to finally calculate the consumed energy. The proposed methodology is validated with two case studies. The first one compares the results with a methodology available in the literature for the same route and locomotive, while the second case shows the applicability of the proposed methodology for a route without information.

Traditional optimization approaches for power transmission systems often optimize gear and shaft designs separately, rely on conventional materials, and face challenges handling mixed-variable problems involving discrete and continuous variables. These limitations hinder the potential for significant weight reduction and performance improvements in modern mechanical systems. This research presents an innovative optimization approach for reducing the gear pitch in a power transmission system comprising a shaft with two gears. By employing bio-inspired metaheuristic algorithms and utilizing materials common in additive manufacturing and composite materials, the study aims to minimize system weight while ensuring mechanical integrity. The optimization integrates gear and shaft design variables, accurately models mechanical constraints through penalty functions, and leverages the unique properties of advanced materials to enhance performance. In this work, we developed an integrated optimization model that overcomes the limitations of previous studies by combining advanced materials with bio-inspired algorithms to achieve significant weight reduction in gear systems. This approach effectively addresses the complexities of mixed-variable optimization and mechanical constraints, providing a practical and innovative solution for modern engineering applications.

In industry, the search for reducing energy consumption directly impacts the manufacturing sectors due to the high power consumption required for their processes. Thus, studies on machining centers that identify factors impacting this demand, while maintaining the quality of the surface finish on...