In the revolution of green energy development, microgrids with renewable energy sources such as solar, wind and fuel cells are becoming a popular and effective way of controlling and managing these sources. On the other hand, owing to the intermittency and wide range of dynamic responses of renewable energy sources, battery energy-storage systems have become an integral feature of microgrids. Intelligent energy management and battery sizing are essential requirements in the microgrids to ensure the optimal use of the renewable sources and reduce conventional fuel utilization in such complex systems. This paper presents a novel approach to meet these requirements by using the grey wolf optimization (GWO) technique. The proposed algorithm is implemented for different scenarios, and the numerical simulation results are compared with other optimization methods including the genetic algorithm (GA), particle swarm optimization (PSO), the Bat algorithm (BA), and the improved bat algorithm (IBA). The proposed method (GWO) shows outstanding results and superior performance compared with other algorithms in terms of solution quality and computational efficiency. The numerical results show that the GWO with a smart utilization of battery energy storage (BES) helped to minimize the operational costs of microgrid by 33.185% in comparison with GA, PSO, BA and IBA.

We introduce an adaptive sampling method that has been developed to support the Backseat Driver control architecture of the Memorial University of Newfoundland (MUN) Explorer autonomous underwater vehicle (AUV). The design is based on an acoustic detection and in-situ analysis program that allows an AUV to perform automatic detection and autonomous tracking of an oil plume. The method contains acoustic image acquisition, autonomous triggering, and thresholding in the search stage. A new biomimetic search pattern, the bumblebee flight path, was designed to maximize the spatial coverage in the oil plume detection phase. The effectiveness of the developed algorithm was validated through simulations using a two-dimensional planar plume model and a 90-degree scanning sensor model. The results demonstrate that the bumblebee search design combined with a genetic solution for the Traveling Salesperson Problem outperformed a conventional lawnmower survey, reducing the AUV travel distance by up to 75.3%. Our plume detection strategy, using acoustic sensing, provided data of plume location, distribution, and density, over a sector in contrast with traditional chemical oil sensors that only provide readings at a point.

In today's underwater environment, complex missions, such as underwater repair and docking operations, require precise control to maneuver unmanned underwater vehicles (UUVs) in extremely demanding operating conditions. Although numerous control methodologies have been used for UUVs, adaptive control is considered a promising solution due to its inherent ability to adapt to uncertainty and parameter variations. Nevertheless, it is handicapped by the tradeoff between low adaptive gains and tracking performance. Low gains are preferred to maintain stability and obtain smooth control signals. However, the resulting tracking performance, especially during transients operations, does not allow for precise maneuvering. A possible solution is model reference adaptive control (MRAC) with composite adaptation modification, which uses a prediction error in addition to the tracking error to improve learning without increasing the adaptive gains. Even though this is not a new modification to adaptive control, there is little evidence in the public domain of extensive experimental validations and quantitative analysis under low adaptive gains, especially for underwater operations. Furthermore, newer versions, such as composite MRAC (CMRAC) and predictor-based MRAC (PMRAC), offer several additional advantages. In previous publications, the authors have verified CMRAC and PMRAC for UUVs through computer simulations. Thus, this paper focuses on the experimental validation of CMRAC and PMRAC fitted to a UUV, comparing their performance under normal operations, partial thruster failure, and external disturbances. The results indicate that, while both CMRAC and PMRAC show improvements over MRAC, PMRAC has a substantial advantage over CMRAC and is recommended for future UUV applications.