This work proposes a homogeneous methodological framework for the evaluationof metaheuristics in the power dispatch of an Energy ManagementSystem (EMS) for an autonomous hybrid microgrid modeled as a DC bus,using real data recorded in Laboratory 6 of CIESOL (Almería, Spain). Themicrogrid integrates photovoltaic generation, wind turbines, a backup dieselgenerator, and battery-based energy storage, using real time-series data forirradiance, wind, and demand at a 10 min resolution. The problem is formulatedas a single-objective optimization that minimizes the total cost ofthe diesel generator through a quadratic function, considering a power balancepenalty and operational constraints. The evaluation framework considersa phased protocol: (i) sensitivity analysis to define a common evaluationbudget, (ii) configuration exploration, and (iii) final benchmarkingthrough multiple independent runs. In addition, a feasibility verificationscheme based on a deterministic reference solution (interior-point method) isincorporated. Under this framework, the performance of Circulatory SystemBased Optimization (CSBO), Flow Direction Algorithm (FDA), MountainGazelle Optimizer (MGO), and Particle Swarm Optimization (PSO) is analyzed,highlighting CSBO, whose use in this type of application is limited.The results show that CSBO and FDA achieve 100 % feasible runs; CSBOattains the lowest mean cost with near-zero variability, whereas FDA standsout for its lower computation time. In the weekly evaluation, CSBO maintainsfull feasibility and a renewable fraction close to 93 %, confirming itsrobustness for prolonged operation.

The growing energy demand in rural areas such as the ejido Boca del Cerro, located in Tenosique, Tabasco (Mexico), near the Usumacinta River, calls for sustainable energy solutions such as microgrids. This study proposes an energy management system combining renewable energy forecasting and fuzzy control for a simulated small autonomous rural microgrid scenario designed to supply a fixed priority load of 5 kW and a variable flexible load ranging from 1 to 10 kW. Three LSTM architectures (vanilla, stacked, and bidirectional) are compared for predicting solar irradiance, wind speed, and river flow. The vanilla model is optimized using Hyperband to improve prediction accuracy, particularly for flow rate, which is rarely addressed in similar studies. Forecasts feed into models of photovoltaic, wind, and hydro systems within the microgrid. Energy dispatch is managed through fuzzy logic control. The fuzzy controller supports load prioritization, battery charge/discharge management, and surplus energy redirection to an absorbing load. The final vanilla LSTM achieved RMSE values of 25.741, 0.302, and 12.644 for solar irradiance, wind speed, and river flow, respectively, with NSE values above 0.949 in all cases. These results indicate high forecasting accuracy for solar irradiance and river flow, with limited improvement for wind speed. Overall, the proposed EMS enables effective energy flow management, while the integration of hydrokinetic turbines with AI-based forecasting represents a novel contribution.

This work establishes and applies a common evaluation framework for the evaluation of metaheuristics in the power dispatch of an Energy Management System (EMS) for an autonomous hybrid microgrid modeled as a directcurrent (DC) bus, using real data recorded in Laboratory 6 of CIESOL (Almería, Spain). The microgrid integrates photovoltaic generation, wind turbines, a backup diesel generator, and battery-based energy storage, with a 10 min temporal resolution. The problem is formulated as a single-objective optimization that minimizes diesel operating cost, considering a balance penalty and operational constraints. Circulatory System Based Optimization (CSBO), Flow Direction Algorithm (FDA), Mountain Gazelle Optimizer (MGO), and Particle Swarm Optimization (PSO) are evaluated through budget sensitivity, parameter selection, and daily and five-day comparisons using 20 000 evaluations per interval and 30 runs per algorithm in the comparative phases. Physical feasibility is verified directly, while statistical cost comparisons and avoidable diesel energy complement the performance assessment. MGO achieved the lowest daily mean cost, 1.48% below CSBO, with greater variability and avoidable diesel energy. In the five-day comparison, CSBO achieved full feasibility, the lowest mean cost and variability, practically zero avoidable diesel energy, and a renewable fraction of 93.46%. FDA provided similar central costs with approximately 63% less computation time than CSBO, whereas PSO achieved 80% feasibility. These findings support CSBO as a consistent option for the evaluated EMS and FDA as a computationally efficient alternative, while indicating that economic advantages depend onthe operating scenario.

This work presents the experimental validation of proportional–integral (PI) controller gains for a DC–DC boost converter obtained using two metaheuristic optimization algorithms: Particle Swarm Optimization (PSO) and Lungs Performance Optimization (LPO). The controller gains were previously determined through a simulation-based optimization procedure in which the proportional and integral gains were selected by minimizing a performance-based objective function incorporating settling time and overshoot. In the present study, the optimized gains are implemented in a physical boost converter prototype and experimentally evaluated under varying operating conditions. Their performance is compared with that of a conventional PI controller tuned using the Root Locus method. The experimental results demonstrate stable output-voltage regulation for all evaluated controllers. Under the investigated conditions, the controllers tuned using the metaheuristic approaches exhibit dynamic responses comparable to those obtained with the conventional tuning method. These results provide experimental evidence supporting the applicability of metaheuristic optimization for PI controller tuning in power electronic converters.