Microgrids have emerged as a solution to address new challenges in power systems with the integration of distributed energy resources (DER). Inverter-based microgrids (IBMG) need to implement proper control systems to avoid stability and reliability issues. Thus, several researchers have introduced multi-objective control strategies for distributed generation on IBMG. This paper presents a review of the different approaches that have been proposed by several authors of multi-objective control. This work describes the main features of the inverter as a key component of microgrids. Details related to accomplishing efficient generation from a control systems’ view have been observed. This study addresses the potential of multi-objective control to overcome conflicting objectives with balanced results. Finally, this paper shows future trends in control objectives and discussion of the different multi-objective approaches.

Dual Active Bridge (DAB) converters are efficient energy conversion systems that require robust control algorithms. The wide implementation of DAB converters in electrical applications such as power generation, power distribution and energy management shows the great potential in electrical energy conversion. The DAB converter topology consists in the interface of two single-phase power converter bridges through a high frequency transformer, which allows DAB converter to reach different power rates. DAB converters have several advantages respect to conventional electromechanical transformers, such as: electrical energy efficiency, low size and weight. This paper proposes a Sliding Mode Controller (SMC) with integral linear quadratic surface to regulate power distribution by changing the phase shift between the primary and secondary commutations of the semiconductors in each bridge. In order to test the SMC performance on robustness, load variation and reference tracking tests are applied. The SMC operation is evaluated through the criterion of the Integral of the Absolute value of the Error (IAE) and conclusions are developed from analysis of results.

The adoption of wind energy is pivotal for advancing sustainable power systems, particularly in off-grid microgrids where infrastructure limitations hinder conventional energy solutions. The inherent variability of wind generation, however, challenges grid reliability and demand–supply balance, necessitating accurate forecasting models. This study proposes a hybrid framework for short-term wind speed prediction, integrating deep learning (Long Short-Term Memory, LSTM) and ensemble methods (random forest, Extra Trees) to exploit their complementary strengths in modeling temporal dependencies. A multivariate approach is adopted using meteorological data (including wind speed, temperature, humidity, and pressure) to capture complex weather interactions through a structured time-series design. The framework also includes a feature selection stage to identify the most relevant predictors and a hyperparameter optimization process to improve model generalization. Three wind speed variables, maximum, average, and minimum, are forecasted independently to reflect intra-day variability and enhance practical usability. Validated with real-world data from Cuenca, Ecuador, the LSTM model achieves superior accuracy across all targets, demonstrating robust performance for real-world deployment. Comparative results highlight its advantage over tree-based ensemble techniques, offering actionable strategies to optimize wind energy integration, enhance grid stability, and streamline renewable resource management. These insights support the development of resilient energy systems in regions reliant on sustainable microgrid solutions.