This paper presents a decentralized and automated framework for microgrid management using blockchain and smart contract technologies. More precisely, a decentralized energy trading protocol using smart contracts to automate transaction processing with energy tokens is proposed to monitor and complete energy delivery between producers and consumers. We defined an energy token, WattCoin, for payments and a dynamic price negotiation algorithm between producers and consumers in microgrid (PN-MG), which, via multiple rounds of price negotiations, should converge to a transaction price that optimizes their profits and energy savings. Conducted simulations show that the energy transactions between producers and consumers completed under our framework increased producers’ profits by 5.26% and decreased consumers’ costs by 6.73% compared to fixed-price transactions.

Battery storage systems (BSS) are critical for maintaining stability and flexibility in renewable energy microgrids. However, the generation and load profiles introduce challenges for optimal energy management. This study presents a deep reinforcement learning (DRL) approach to real-time battery scheduling in a grid-connected microgrid. The control problem is modeled as a Markov decision process (MDP), and a Deep Q-Network (DQN) agent is trained to output optimal charging and discharging actions based on system states. Results indicate that the proposed DQN-based EMS achieves superior performance in balancing economic cost, energy self-sufficiency, and battery health compared to conventional rule-based strategy. This study contributes to advancing reinforcement learning-based approaches for real-time battery scheduling in microgrid energy systems.

Metal hydride hydrogen tank (MHHT) presents a promising approach for achieving safe and environmentally sustainable hydrogen storage, particularly in embedded systems such as electric vehicles. Precise monitoring of the hydrogen content is critical for operational safety; however, direct measurement of the stored hydrogen remains a significant challenge. This work proposes a novel approach for estimating the state-of-charge (SoC) of an MHHT using a custom-developed model in MATLAB and integrating an unscented Kalman filter (UKF) for state estimation. Real-time pressure and temperature measurements are used to validate the accuracy of the UKF-based SoC estimation. To further enhance the filter’s performance and robustness, a particle swarm optimization (PSO) algorithm is employed to optimize UKF hyperparameters. The results demonstrate that the hybrid PSO-UKF framework offers a viable and precise method for real-time SoC estimation in MHHTs, achieving RMSE values of 5.1862 × +10−5and 6.1445 × +10−5at hydrogen flow rates of 4 and 8 Nl/min, repectively.

This paper investigates the time-varying (TV) resource management problem (RMP) for microgrids (MGs) under a multi-agent system (MAS) framework. A fully distributed algorithm is developed by integrating a dynamic event-triggered (DET) mechanism with a fixed-time (FXT) consensus control. The proposed method guarantees fast convergence of TV incremental costs and supply-demand balance within a FXT, with settling time independent of initial conditions. In addition, the designed DET mechanism avoids global coordination and significantly reduces communication overhead. Simulation results are presented to validate the effectiveness of the proposed approach in multi-agent TV optimization scenarios.

The study introduces a novel Radial Basis Function Neural Network-based Super-Twisting Sliding Mode Collective Blade Pitch Control (RBFNN-STSM-CBPC), designed specifically for semi-submersible platform-based Floating Offshore Wind Turbines (FOWTs) operating above rated speed (Region III). The proposed composite controller is developed using a refined nonlinear Control-Oriented Model, including lumped unmodeled dynamics and external disturbances. To our knowledge, this is the first time that a neural network STSM-CPBC approach is designed for this application. The RBFNN operates as an adaptive observer for the lumped disturbance, enhancing the robustness and performance of the standard STSM-CBPC for the same gains. Its adaptive law, formulated through the Lyapunov method, ensures stability and convergence by adjusting the adaptive weight. Simulation results demonstrate the superiority of the RBFNN-STSM-CBPC over the standard STSM-CBPC method in regulating rotor speed and mitigating platform motion.

The floating offshore wind turbine (FOWT) technology has great energy potential, however, minimizing the movement of the structure, under the combined effect of wind and waves, while ensuring maximum power extraction on all operating ranges remains a challenge. This paper proposes the design of a deep reinforcement learning (DRL) controller for FOWTs in the operating area III. To our knowledge, this is the first time that DRL-based control approach is used for this application. The proposed DRL controller is based on trust region policy optimization (TRPO) algorithm, composed of two neural networks, the actor and the critic networks, for the learning of the optimal control law. Simulation results and comparison study are provided to validate the proposed DRL controller for the 5-MW baseline ITI Barge wind turbine model on OpenFAST.

International audience

With increasingly serious environmental issues such as global warming, fossil fuels are gradually being replaced by clean energy. Fuel cells (FC) combining with hydrogen as a sort of clean energy have already been employed in many applications. Due to the nonlinear current-voltage property of FC, a DC-DC boost converter and a suitable controller are necessary for the FC system. In this paper, an observer-based adaptive sliding mode control is designed by considering the actual vehicle applications with unknown load and variable FC voltage for interleaved boost converter (IBC). The designed controller shows strong robustness in output voltage because of the adaptive laws. The system stability is proofed by Lyapunov functions. Last, the simulations were carried out to further verify the effectiveness of the designed controller.

With the drastic inclination towards reduction of atmospheric issues, fuel-cell vehicles are becoming the major alternative for internal combustion. Due to the characteristics of the fuel cell, multiple-input converter is more attractive in redundancy and safety. In this paper, a multi-input three-phase interleaved boost converter (IBC) is designed by analyzing every state of its work to obtain its averaged state-space equation. Two control methods based on PI and super-twisting (ST) are designed and applied to the converter. A fault-tolerant method is proposed for the IBC. The simulation results of fault-tolerant based on two control methods are compared. The results show that it has better performance in fluctuation of DC bus voltage and inductor current when using ST method.

A homogeneous continuous sliding mode control scheme based on finite time stability is developed in this paper for an upper limb exoskeleton robot dedicated for rehabilitation. Indeed this type of robot interacts directly with human limbs whose dynamics are unknown and different for users. Therefore, the main idea is to provide a robust motion control of the exoskeleton robot using a continuous higher order sliding mode controller despite parameters variations, uncertainties and external disturbances. The proposed controller combines the homogeneity concept and the super-twisting algorithm. Finite time stabilization is achieved using the first part of the controller while disturbance rejection is granted by the second part of the controller. Performance of the controller and its robustness are illustrated through simulations of trajectory tracking tests corresponding to passive rehabilitation exercises.