This paper deals with the experimental validation of a Matlab-Simulink simulation tool of marine current turbine (MCT) systems. The developed simulator is intended to be used as a sizing and site evaluation tool for MCT installations. For that purpose, the simulator is evaluated within the context of speed control of a permanent magnet synchronous generator-based (PMSG) MCT. To increase the generated power, and therefore the efficiency of an MCT, a nonlinear controller has been proposed. PMSG has been already considered for similar applications, particularly wind turbine systems using mainly PI controllers. However, such kinds of controllers do not adequately handle some of tidal resource characteristics such as turbulence and swell effects. Moreover, PMSG parameter variations should be accounted for. Therefore, a robust nonlinear control strategy, namely second-order sliding mode control, is proposed. The proposed control strategy is inserted in the simulator that accounts for the resource and the marine turbine models. Simulations using tidal current data from Raz de Sein (Brittany, France) and experiments on a 7.5-kW real-time simulator are carried out for the validation of the simulator.

The current development of fuel cell hybrid electric vehicles is facing many technical challenges, evolving the power sources, the power electronic configuration, the energy management strategy, and the control techniques. Among these challenges rises the optimal sizing issue as fuel cell hybrid electric vehicle efficiency is highly dependent on the on-board energy storage system. Thus, this paper focuses on the optimal sizing of the hybrid energy storage systems by considering the energy management strategy based on frequency separation. Best solutions are computed for different load profiles through a multi-objective grey wolf optimizer. Then, the obtained results are presented and discussed.

The global need to solve pollution problems has conducted automotive engineers to promote the development and the use of electric vehicle technologies. This paper focuses on the fuel cell hybrid electric vehicle which uses a proton exchange membrane fuel cell as a main source associated to hybrid storage device: lithium ion battery and ultracapacitors. A common interest in such technology is to spread out the energy flow between its different sources in order to satisfy the power demand for any requested mission. However, the challenging task stills the optimization of this split to reduce hydrogen consumption and respect, at the same time, the system limitations such as admissible limits of storage system capacities and battery current variation. An adaptive filtering-based energy management strategy is proposed in this paper to ensure an optimum distribution of the energy between the sources taking into account dynamic and energetic constraints of each device. For more performance, a fuzzy logic system is used to adapt the frequency of separation with the system state evolution. A sliding mode control is applied to control electric characteristics (voltage and currents) in the considered hybrid power supply. Simulation results, obtained under MATLAB^®/SimPowerSystems^® for four driving cycles are presented. The proposed strategy achieved good performances by respecting the ultracapacitors state of charge while preserving the battery lifetime under various driving missions.

High penetration of distributed generation and renewable energy sources in power systems has created control challenges in the network, which requires the coordinated management of these resources. Using virtual power plants (VPPs) on a large scale has solved these challenges to a significant extent. VPPs can be considered systems consisting of distributed generations, energy storage, controllable loads, electric vehicles (EVs), and other types of resources to provide energy and ancillary services. VPPs face various challenges such as energy management, operation, resource uncertainty, participation in electricity markets, etc. This paper discusses an overview of the basic challenges of VPPs, including control and communication issues, electricity markets, its different models, and energy management issues. The main purpose is to investigate the performance of VPP in different markets, energy management of VPP in different operating conditions and strategies, and compare different planning methods for VPP. Note that the application of blockchain to control and improve VPP performance has been investigated, taking into account the different layers of this technology.

Recently, the integration of distributed generation and energy systems has been associated with new approaches to plant operations. As a result, it is becoming increasingly important to improve management skills related to distributed generation and demand aggregation through different types of virtual power plants (VPPs). It is also important to leverage their ability to participate in electricity markets to maximize operating profits. The present study focuses on VPP concepts, its different potential services, various control methodologies, distinct optimization approaches, and some practical implemented real cases. To this end, a comprehensive review of the most recent scientific literature is conducted. The paper concludes with remained challenges and future trends in the topic.

The high penetration of renewable energy systems with fluctuating power generation into the electric grids affects considerably the electric power quality and supply reliability. Therefore, energy storage resources are used to deal with the challenges imposed by power variability and demand-supply balance. The main focus of this paper is to investigate the appropriate storage technologies and the capacity needed for a successful tidal power integration. Therefore, a simplified sizing method, integrating an energy management strategy, is proposed. This method allows the selection of the adequate storage technologies and determines the required least-cost storage capacity by considering their technological limits associated with different power dynamics. The optimal solutions given by the multi-objective evolutionary algorithm are presented and analyzed.