This paper investigates the analysis of the energy management system for a DC microgrid. The microgrid consists of a photovoltaic panel and a batteries system that is connected to the microgrid through a bidirectional power converter. The optimization problem is solved by the hybrid internal point method with the genetic algorithms method and particle swarm optimization (PSO) method, by considering forecasting demand and generation for all the elements of the microgrid. The analysis includes a comparison of energy optimization of the microgrid for solar radiation data from two areas of the world and a comparison the efficiency and effectiveness of optimization methods. The efficiency of the algorithm for energy optimization is verified and analyzed through experimental data. The results obtained show that the optimization algorithm can intelligently handle the energy flows to store the largest amount in the batteries and thus have the least amount of charge and discharge cycles for the battery and prolong the useful life.

An Energy Management System (EMS) that uses a Model Predictive Control (MPC) to manage the flow of the microgrids is described in this work. The EMS integrates both wind speed and solar radiation predictors by using a time series to perform the primary grid forecasts. At each sampling data measurement, the power of the photovoltaic system and wind turbine are predicted. Then, the MPC algorithm uses those predictions to obtain the optimal power flows of the microgrid elements and the main network. In this work, three time-series predictors are analyzed. As the results will show, the MPC strategy becomes a powerful energy management tool when it is integrated with the Double Exponential Smoothing (DES) predictor. This new scheme of integrating the DES method with an MPC presents a good management response in real-time and overcomes the results provided by the Optimal Power Flow method, which was previously proposed in the literature. For the case studies, the test microgrid located in the CIESOL bioclimatic building of the University of Almeria (Spain) is used.

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 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.