The Smart Grid (SG) is considered as an imminent future power network because of its fault identification and self-healing capabilities. Energy sustainability, renewable energy integration and an efficient control system are the key factors to be considered in developing SG system. Among various SG concepts, the term virtual power plant (VPP) integrates renewable energy to the grid and provides higher operational flexibility, but it requires extra capital costs for control system and software. The operational activities of a smart grid largely depend on the active customer demands. This paper defines and discusses various SG system concepts such as virtual power plant, and active demand in consumer networks, and also presents drivers and roadmaps for development of smart grids worldwide. Furthermore, this work provides an insight into present research and development on smart grids around the world, and sheds light on developing and establishing SG for the Sultanate of Oman.

To enhance the fuel efficiency of fuel cell hybrid electric vehicles (FCHEVs), we propose a hierarchical energy management strategy (HEMS) to efficiently allocate power to a hybrid system comprising a fuel cell and a battery. Firstly, the upper-layer supervisor employs a fuzzy fault-tolerant control and prediction strategy for the battery and fuel cell management system, ensuring vehicle stability and maintaining a healthy state of charge for both the battery and fuel cell, even during faults. Secondly, in the lower layer, dynamic programming and Pontryagin’s minimum principle are utilized to distribute the necessary power between the fuel cell system and the battery. This layer also incorporates an optimized proportional-integral controller for precise tracking of vehicle subsystem set-points. Finally, we compare the economic and dynamic performance of the vehicle using HEMS with other strategies, such as the equivalent consumption minimization strategy and fuzzy logic control strategy. Simulation results demonstrate that HEMS reduces hydrogen consumption and enhances overall vehicle energy efficiency across all operating conditions, indicating superior economic performance. Additionally, the dynamic performance of the vehicle shows significant improvement.

Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE), a convolutional neural network (CNN), and a recurrent neural network (RNN), called LE-CRNN. In the proposed model architecture, LE, with its high ability in dimensional reduction, transfers the salient features and specific values of power consumption curves (PCCs) of household electrical appliances (HEAs) to a low-dimensional space. Then, the combined model of CRNN significantly improves the structure of CNN in fully connected layers so that the process of identification and separation of the HEA type can be performed without overfitting problems and with very high accuracy. In order to implement the suggested model, two real-world databases have been used. In a separate scenario, a conventional CNN is applied to the data for comparing the performance of the suggested model with the CNN. The designed networks are trained and validated using the PCCs of HEAs. Then, the whole energy consumption of the building obtained from the smart meter is used for load disaggregation. The trained networks, which contain features extracted from PCCs of HEAs, prove that they can disaggregate the total power consumption for houses intended for the Reference Energy Disaggregation Data Set (REDD) and Almanac of Minutely Power Dataset (AMPds) with average accuracies (Acc) of 97.59% and 97.03%, respectively. Finally, in order to show the accuracy of the developed hybrid model, the obtained results in this study are compared with the results of similar works for the same datasets.

Bipedal humanoid robots are expected to play a major role in the future. Performing bipedal locomotion requires high energy due to the high torque that needs to be provided by its legs’ joints. Taking the WABIAN-2R as an example, it uses harmonic gears in its joint to increase the torque. However, using such a mechanism increases the weight of the legs and therefore increases energy consumption. Therefore, the idea of developing a mechanism with adjustable stiffness to be connected to the leg joint is introduced here. The proposed mechanism would have the ability to provide passive and active motion. The mechanism would be attached to the ankle pitch joint as an artificial tendon. Using computer simulations, the dynamical performance of the mechanism is analytically evaluated.

In this work, a new nonlinear robust fuzzy decentralized constrained controller (NRFDCC) based on robust fuzzy anti-windup is proposed to improve the overall robustness and tracking ability of nonlinear dynamic systems subject to a wide range of parameter uncertainties and disturbances. Saturations on both control and the states are also taken into account. The studied system is considered as composed of several overlapping nonlinear subsystems. The nonlinear interaction between the several subsystems is considered a disturbance which is compensated by the proposed strategy. Sufficient conditions are derived for robust stabilization in the sense of Lyapunov stability and are formulated with linear matrix inequalities (LMIs) to obtain the controller gains and anti-windup gains. First, the proposed controller is designed subject to a disturbance with parameter uncertainties. Next, an additional anti-windup is designed to mitigate the adverse effects due to state and control constraints. The main contributions of this article are: 1) stabilizes the nonlinear system over a wide range of operating conditions, disturbance, and parameter uncertainties; 2) robustness is improved to provide good responses with input and/or state constraints; and 3) improves the performances of the stability of the power nonlinear system, for instance, short circuits with expected robust transient stability. The proposed control system is compared with alternative frameworks existing in the literature based on robust control via simulations performed with MATLAB software, to illustrate the effectiveness and robustness of the proposed method.