Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
As renewable energy becomes increasingly dominant in the energy mix, the power system is evolving towards high proportions of renewable energy installations and power electronics-based equipment. This transition introduces significant challenges to the grid’s safe and stable operation. On the one hand, renewable energy generation equipment inherently provides weak voltage support, necessitating improvements in the voltage support capacity at renewable energy grid points. This situation leads to frequent curtailments and power limitations. On the other hand, the output of renewable energy is characterized by its volatility and randomness, resulting in substantial power curtailment. The joint intelligent control and optimization technology of “renewable energy + energy storage + synchronous condenser” can effectively enhance the deliverable capacity limits of renewable energy, boost its utilization rates, and meet the demands for renewable energy transmission and consumption. Initially, the paper discusses the mechanism by which distributed synchronous condensers improve the short-circuit ratio based on the MRSCR (Multiple Renewable Energy Station Short-Circuits Ratio) index. Subsequently, with the minimum total cost of system operation as the optimization objective, a time-series production simulation optimization model is established. A corresponding optimization method, considering the joint configuration of “renewable energy + energy storage + synchronous condenser,” is proposed. Finally, the effectiveness of the proposed method is verified through common calculations using BPA, SCCP, and the production simulation model, considering a real-world example involving large-scale renewable and thermal energy transmission through an AC/DC system. The study reveals that the joint intelligent control and optimization technology can enhance both the sending and absorbing capacities of renewable energy while yielding favorable economic benefits....
This paper presents the results of the design process focused on the development of the energy subsystem (ES) of a wave energy converter (WEC). The ES is an important electrical part that significantly affects the energy reliability and energy efficiency of the entire WEC device. The designed ES was intended for compact WECs powering IoT network devices working in the distributed grid. The developed ES is an electronic circuit consisting of three cooperating subsystems used for energy conversion, energy storage, and energy management. The energy conversion subsystem was implemented as a set of single-phase bridge rectifiers. The energy storage subsystem was a battery-less implementation based on the capacitors. The energy management subsystem was implemented as a supervisory circuit and boost converter assembly. The designed ES was verified using the physical experiment method. The model experiment reflected the operation of the designed ES with a piezoelectric PZT-based WEC. The experimental results showed a 41.5% surplus of the energy supplied by ES over the energy demanded by the considered load at a duty cycle of ca. 6 min—37.2 mJ over 26.3 mJ, respectively. The obtained results have been evaluated and discussed. The results confirmed the designed ES as a convenient solution, which makes a significant contribution to the compact WECs that can be applied among others to a distributed grid of autonomous IoT network devices powered by free and renewable energy of sea waves. Finally, it will also enable sustainable development of mobile and wireless communication in those maritime areas where other forms of renewable energy may not be available....
This paper presents a comparative study of different energy management strategies and technologies of fuel-cell hybrid electric vehicles integrating a proton exchange membrane fuel cell device in addition to a lithium-ion battery as a secondary energy source. Therefore, an experimental analysis is carried out to seek the successful hybrid powertrains considering the hydrogen utilization in fuel cells and state-of-charge regulation in Li-ion batteries. Different approaches were simulated using a developed vehicle simulator in MATLAB/Simulink. The simulations were performed using three standard driving cycles in which a second study based on energy management strategies tested was presented and analyzed. Simulation’s results show the superiority and economic success of the proposed technology and method, especially the FSBS and MEPT management strategies due to the successful use of the sources and the significant optimization in terms of hydrogen consumption while maintaining optimal Li-ion battery usage....
The supporting position and role of smart grids in the construction of smart cities have not been fully explored. Based on systematizing the system architecture of smart cities, we first analyze the facilitating and constraining roles of smart grids and smart cities with each other and make a quantitative analysis of the coordination and supporting roles between them; in the smart grid environment, we propose a framework of energy management system based on particle swarm optimization (PSO) dispatching model. The algorithm optimizes the operation of dispatchable loads, electric vehicles, and energy storage systems based on outdoor temperature forecasts, renewable energy power output forecasts, day-ahead tariff signals, and customer preferences to minimize customer electricity costs. The performance of the algorithm is verified through simulation experiments, and the results show that the proposed algorithm significantly reduces electricity consumption costs by 32.54% compared to algorithms that only optimize the scheduling of loads or some components of the home energy management system....
As the penetration level of solar power generation increases in smart cities and microgrids, an automatic energy management system (EMS) without human supervision is most communly deployed. Therefore, assuring safe and reliable data against cyber attacks such as false data injection attacks (FDIAs) has become of utmost importance. To address the aforementioned problem, this paper proposes detecting FDIAs considering visual data. The aim of visual state estimation is to enhance the resilience and security of renewable energy systems. This approach provides an additional layer of defense against cyber attacks, ensuring the integrity and reliability of solar power generation data and facilitating the efficient and secure operation of EMS. The proposed approach uses a modified VGG-16 neural network model to obtain an intermediate representation that provides textual and numerical explanations about the visual weather conditions from sky images. Numerical results and simulations corroborate the validity of our proposed approach. The performance of the modified VGG-16 neural network model is also compared with previous state-of-the-art machine learning models in terms of accuracy....
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