• Energy Research

In this paper, a new modified model predictive control is proposed to improve the performance of the model predictive control for two-level voltage source inverters by alleviating computational burden and the disadvantages associated with the conventional model predictive control strategy. The objective of the proposed method is to reduce the number of candidate voltage vectors in each sector, thereby improving the overall performance of the control system, as well as achieving common-mode voltage reduction for two-level voltage source inverters. Two strategies are introduced to achieve this objective. Firstly, an algorithm is developed based on statistical computational processes to pre-define the candidate voltage vectors. This strategy involves ranking and considering the most frequently used voltage vectors. Consequently, by reducing the computational burden, the search space for the optimal voltage vectors is reduced. Furthermore, based on statistical results, a strategy is proposed to divide the sectors into three sectors instead of the six sectors in the conventional method. This approach effectively reduces the number of candidate voltage vectors. The modified model predictive control strategy aims to improve the efficiency of the control system by reducing the computational burden, and suppressing the common-mode voltage. The simulation and experiments are carried out to verify the effectiveness of the proposed strategy under various operational conditions. The results demonstrate that the modified model predictive control approach significantly reduces the computational burden and complexity of the control system while effectively suppressing the common-mode voltage; this contributes to improving the performance of two-level voltage source inverters and enhancing their applicability for connecting the renewable energies to the grid.

Hybrid renewable energy systems are a promising technology for clean and sustainable development. In this paper, an intelligent algorithm, based on a genetic algorithm (GA), was developed and used to optimize the energy management and design of wind/PV/tidal/ storage battery model for a stand-alone hybrid system located in Brittany, France. This proposed optimization focuses on the economic analysis to reduce the total cost of hybrid system model. It suggests supplying the load demand under different climate condition during a 25-years interval, for different possible cases and solutions respecting many constraints. The proposed GA-based optimization approach achieved results clear highlight its practicality and applicability to any hybrid power system model, including optimal energy management, cost constraint, and high reliability.

Abstract This paper deals with the sizing and rough optimization of a renewables-based farm devoted to energy production in a stand-alone marine context. The studied hybrid farm consists in combining wind turbines, tidal turbines, diesel generators, and pumped hydroelectric storage. The main sizing and optimization constraint is to achieve a compromise between the hybrid system overall cost, for a period of 15 years, and the CO2 emission minimization. The proposed sizing and optimization study is first based on the different available renewable sources, wind and tidal currents, characterization. This preliminary study is essential. Indeed, it will provide the renewables sources basic information in terms of wind and tidal speed potential for the hybrid farm components choice. After presenting the hybrid farm different components, the simulation constraint is defined (energy conservation at the grid) in order to form a power exchange network at the grid level but in a stand-alone context. The optimization results are analyzed to explore wind turbines, tidal turbines, and the proposed pumped hydroelectric storage sizes on the targeted objectives.

Tidal stream energy is acquiring more attention as a future potential renewable energy source. Considering the harsh submarine environment, the main challenges that face the tidal stream turbine (TST) industry are cost and reliability. Hence, simple and reliable technologies, especially considering the drivetrain, are preferred. The multibrid drivetrain configuration with only a single stage gearbox is one of the promising concepts for TST systems. In this context, this paper proposes the design optimization of a multibrid permanent magnet generator (PMG), the design of a planetary gearbox, and afterwards analyzes the multibrid concept cost-effectiveness for TST applications. Firstly, the system analytical model, which consists of a single-stage gearbox and a medium speed PMG, is presented. The optimization methodology is afterwards highlighted. Lastly, the multibrid system optimization results for different gear ratios including the direct-drive topology are discussed and compared where the suitable gear ratio (topology) is investigated. The achieved results show that the multibrid concept in TST applications seems more attractive than the direct-drive one especially for high power ratings.

This paper presents a newly designed switching linear feedback structure of sliding mode control (SLF-SMC) plugged with an model reference adaptive system (MRAS) based sensorless field-oriented control (SFOC) for induction motor (IM). Indeed, the performance of the MRAS depends mainly on the operating point and the parametric variation of the IM. Hence, the sliding mode control (SMC) could be considered a good control alternative due to its easy implementation and robustness. Simulation and experimentation results are presented to show the superiority of the proposed SLF-SMC technique in comparison with the classical PI controller under different speed ranges and inertia conditions.

Conventional energy generation sources mainly provide energy supply to remote areas nowadays. However, because of growing concerns over greenhouse gas emissions, the integration of renewable energy sources is mandatory to meet power demands and reduce climatic effects. The advancements in renewable generation sources and battery storage systems pave the way for microgrids (MGs). As a result, MGs are becoming a viable solution for power supply shortage problems in remote-area applications, such as oceanic islands. In this paper, an islanded MG, which consists of PV system, tidal turbine (TT), diesel generator (DG), and Li-ion battery, is considered for Ouessant island in Brittany region in France. The economic operation of the MG is achieved by including battery degradation cost, levelized costs of energy of the PV system and TT, operating and emission costs of DG, and network constraints. The developed model leads to a non-linear and non-convex problem, which unfortunately can converge to a local optimum solution. The problem has, therefore, been relaxed and converted to a convex second-order cone model to achieve an optimal decision strategy for islanded MG operations with a global or near-global solution. Numerical simulations are carried out to prove the effectiveness of the proposed strategy in reducing the operating and emission costs of the islanded MG. It is shown that the developed convex energy management system formulation has an optimality gap of less than 1% with reduced computational cost.

In recent years, the growing demand for efficient and sustainable energy management has led to the development of innovative solutions for embedded systems. One such solution is the integration of hybrid nanogrid energy management systems into various applications. There are currently many energy management systems in different domains, such as buildings, electric vehicles, or even naval transport. However, an embedded nanogrid management system is subject to several constraints that are not sufficiently studied in the literature. Indeed, such a system often has a limited energy reserve and is isolated from any energy supply for a long time. This paper aims to provide a comprehensive overview of the current state of research, advancements, and challenges in the field of hybrid nanogrid energy management systems. Furthermore, it offers a comparative analysis between hybrid nanogrids and microgrids and the implications of their integration in embedded systems. This paper also discusses the key components, operation principles, optimization strategies, real-world implementations, challenges, and future prospects of hybrid nanogrid energy management systems. Moreover, it highlights the significance of such systems in enhancing energy efficiency, reducing carbon footprints, and ensuring reliable power supply.

This article deals with the techno-economic optimal sizing of a tidal stream turbine (TST)–battery system. In this study, the TST system consists of a turbine rotor and a permanent magnet synchronous generator (PMSG) associated with a three-phase converter coupled to a DC bus. A battery is used within the system as an energy storage system to absorb excess produced power or cover power deficits. To determine the optimal sizing of the system, an iterative approach was used owing to its ease of implementation, high accuracy, and fast convergence speed, even under environmental constraints such as swell and wave effects. This technique is based on robust energy management, and the recursive algorithm includes the deficiency of power supply probability (DPSP) and the relative excess power generation (REPG) as technical criteria for the system reliability study, and the energy cost (EC) and the total net present cost (TNPC) as economic criteria for the system cost study. As data inputs, the proposed approach used the existing data from the current speed profile, the load, and economic parameters. The desired output is the system component optimal sizing (TST power, and battery capacity). In this paper, the system sizing was studied during a one-year time period to ensure a more reliable and economical system. The results are compared to well-known methods such as genetic algorithms, particle swarm optimization, and software-based (HOMER) approaches. The optimization results confirm the efficiency of the proposed approach in sizing the system, which was simulated using real-world tidal velocity data from a specific deployment site.