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![State machine control [30]](publication/343594959/figure/fig5/AS:923527758770187@1597197584802/State-machine-control-30.jpg)
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![FIGURE 1: Global scenario of energy storage adoption [13].](publication/343594959/figure/fig1/AS:923527758749709@1597197584566/Global-scenario-of-energy-storage-adoption-13_Q320.jpg)
![FIGURE 2: Applications of energy storage [13].](publication/343594959/figure/fig2/AS:923527758745611@1597197584641/Applications-of-energy-storage-13_Q320.jpg)
![FIGURE 12: State machine control [30]](publication/343594959/figure/fig5/AS:923527758770187@1597197584802/State-machine-control-30_Q320.jpg)
The ever increasing trend of renewable energy sources (RES) into the power system has increased the uncertainty in the operation and control of power system. The vulnerability of RES towards the unforeseeable variation of meteorological conditions demands additional resources to support. In such instance, energy storage systems (ESS) are inevitable...
Citations
... It facilitates the energy management plan's utilization of HPS and the hybrid energy system's (HES's) complementary characteristics. Moreover, it facilitates the implementation of a vast array of control methods [37]. The chosen EV includes battery and SC storage systems connected in parallel to the DC-bus using bidirectional DC-DC converters (BBC), and a SynRM is fed directly from the inverter. ...
In this paper, an optimal energy management system (EMS) for an electric vehicle (EV)microgrid made of a battery-supercapacitor hybrid power system is proposed. Through bidirectional DC-DC converters, the storage systems are coupled in parallel to the DC-bus and fed via an inverter, a synchronous reluctance motor (SynRM). The driving factor behind the suggested EMS is using the complementing properties of two techniques: the Slap Swarm optimization Algorithm (SSA) and State Machine Control (SMC). The SSA’s fast optimization method makes real-time adaption of the SMC improvements possible, maximizing system performance. The primary objective of the proposed EMS is to provide DC-bus stability, respect source dynamics, and meet SynRM motor power requirements. Additionally, the algorithm lessens the impact of the motor’s harmonics, hence improving battery lifetime. To test the control design and assess the efficacy of the proposed EMS, extensive simulations of the advocated optimal EMS of the hybrid power system of an EV are carried out in a Matlab/Simulink environment.
... 3 Electricity storage systems have emerged as crucial elements of the global energy transition by providing a wide range of grid services throughout the electricity system, 4,5 such as providing ancillary services, facilitating the integration of renewable energy resources, and unlocking multiple revenue streams. [6][7][8][9][10][11][12][13] Battery energy storage system (BESS) is an electrochemical type of energy storage technology where the chemical energy contained in the active material is converted into electrical energy through electrochemical reactions. 14,15 BESSs possess substantial advantages such as extremely fast response, quick deployment time, high efficiency, flexibility, scalability, modularity, etc., and present themselves as promising assets for grid services. ...
A battery energy storage system (BESS), due to its very fast dynamic response, plays an essential role in improving the transient frequency stability of a grid. The performance of the BESS varies with the system's installation site. Hence, the optimal location of the BESS is of utmost importance for improving transient frequency stability. Therefore, this paper presents a hierarchical approach for optimizing the BESS placement to improve a grid's transient frequency stability. In most research, frequency nadir and rate of change of frequency (ROCOF) have been considered for studying frequency stability. This paper considers two more parameters, along with frequency nadir and ROCOF, to study the transient frequency stability, settling time, and decay ratio. A novel frequency stability index (FSI) using the four transient frequency parameters has been developed. After a significant disturbance in a benchmarked test system, the FSI was used to identify the optimal location of the BESS for stabilizing the frequency. It has been observed that, after a sudden generator outage, the ROCOF and the frequency nadir improve the best when the BESS is located at the bus closest to the generator experiencing the outage. However, considering the other two parameters as well, the value of the FSI is the minimum; that is, the optimum solution is when the BESS is located at the bus that is the second closest to the generator experiencing the outage. Results of similar studies validate the proposed FSI in indicating the optimal location of the BESS in improving the transient frequency behavior of the system.
... Based on the findings of this paper [15], it is determined that asset managers must assess the effects of each device with regard to the reliability of the assessment. Babu et al. [16] analysed various controlling strategies used in a hybrid energy storage systems. The key benefits of using hybrid sources are reduced initial cost, better system efficacy, minimized stress, and better storage capacity. ...
Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided.
... Based on the findings of this paper [14], it is determined that asset managers must assess the effects of each device with regard to the reliability of the assessment. Babu, et al [15] analyzed various controlling strategies used in a hybrid energy storage systems. ...
Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, we provide insights into the transformative potential of ML in shaping the future of power system asset management.
... Many other battery models with differing levels of complexity have been created by researchers throughout the world (Babu et al., 2020). They can be categorized as electrical circuit equivalent models, electrochemical models, and mathematical models (Chen and Rincón-Mora, 2006;Bhide and Shim, 2011). ...
The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10 − 3 , and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10 − 3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency.
... While these sources offer a more environmentally friendly solution, their integration into existing energy infrastructures faces formidable challenges due to inherent variability and intermittent energy generation [5]. Mitigating these challenges necessitates the implementation of advanced energy storage devices, such as batteries and supercapacitors [6]. Applications heavily reliant on electricity, such as smart home energy systems and electric vehicles (EVs), underscore the critical need for reliable and efficient energy storage solutions [7]. ...
Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.
... It allows to associate each source to a BDDC, which enables to control the current of each source independently and to limit the power sizing of the onboarded storage system. More details about each topology can be found in [4]. ...
The integration of supercapacitors as hybrid energy storage systems in electric vehicles has attracted the attention of many researchers and has been considered as a promising solution. Bidirectional DC/DC converters (BDDCs) play a fundamental role in HESS, as they manage the power flow by controlling currents and regulating the DC bus voltage. However, they encounter the challenge of uncertainties and high fluctuation power loads, necessitating the fast dynamics, stability, and high robustness of the controller. This paper proposes a novel hybrid proportional–integral and backstepping cascade controller to regulate the DC‐bus voltage under uncertainties and load variations, and to control the current references of the on‐boarded sources. To confirm the asymptotic stability of the whole system, a nonlinear stability analysis is conducted using the Lyapunov theorem. A power management strategy is applied to distribute the power loads and generate reference currents for the BDDCs controller. Simulations results under various driving cycles using MATLAB/Simulink demonstrate the superiority of the proposed controller compared to conventional proportional–integral and backstepping controllers. A real‐time controller‐hardware‐in‐the‐loop test bench is developed to validate the effectiveness of the proposed strategy.
... By symbiotically merging energy storage techniques with renewable assets, these systems can counteract the capriciousness inherent to renewables. They adeptly capture surplus energy during peak production times, releasing it during lulls, thereby bestowing the grid with a more constant and dependable energy flux [3]. ...
In the face of escalating global energy demand, the shift towards renewable energy sources has emerged as a sustainable solution. However, the integration of renewable energy into the electrical grid introduces challenges such as intermittent and instability. The concept of energy-storage-based hybrid systems, which combines renewable energy systems with energy storage, presents a promising approach to overcome these hurdles. These hybrid systems enhance grid stability by ensuring a consistent energy supply, compensating for the variable output of renewable energy sources, and providing ancillary services to the grid. Furthermore, they pave the way for a more resilient and reliable energy infrastructure, fostering the seamless integration of a substantial share of renewable energies. This paper offers a comprehensive exploration of energy-storage-based hybrid systems, discussing their structure, functioning, and the pivotal role they play in bolstering grid stability and promoting the unobstructed integration of renewable energy sources.
... Combining different storage technologies to leverage their respective strengths. [166] Scalability: Designing systems that can scale to meet varying energy demands. ...
Integrating wind power with energy storage technologies is crucial for frequency regulation in modern power systems, ensuring the reliable and cost-effective operation of power systems while promoting the widespread adoption of renewable energy sources. Power systems are changing rapidly, with increased renewable energy integration and evolving system architectures. These transformations bring forth challenges like low inertia and unpredictable behavior of generation and load components. As a result, frequency regulation (FR) becomes increasingly important to ensure grid stability. Energy Storage Systems (ESS) with their adaptable capabilities offer valuable solutions to enhance the adaptability and controllability of power systems, especially within wind farms. This research provides an updated analysis of critical frequency stability challenges, examines state-of-the-art control techniques, and investigates the barriers that hinder wind power integration. Moreover, it introduces emerging ESS technologies and explores their potential applications in supporting wind power integration. Furthermore, this paper offers suggestions and future research directions for scientists exploring the utilization of storage technologies in frequency regulation within power systems characterized by significant penetration of wind power.
... for fixed solar systems and the provision of uninterrupted electrical power 82,83 . To choose the best battery type, a number of technological factors are taken into account, including maturity, weight, size, discharge rate, temperature sensitivity, upkeep, and the effectiveness of costs 84,85 . In this paper, we use the lithium-ion battery for the advantages mentioned above and summarized in Table 1 below, and the supplementary appendix shows the statistics for the Li-ion battery, the DC/DC boost converter, and the buck converter's specifications. ...
This article offers a PV-PEMFC-batteries energy management strategy (EMS) that aims to meet the following goals: keep the DC link steady at the standard value, increase battery lifespan, and meet power demand. The suggested multi-source renewable system (MSRS) is made to meet load demand while using extra power to fill batteries. The major energy source for the MSRS is photovoltaic, and fuzzy logic MPPT is used to guarantee that the PV operates at optimal efficiency under a variety of irradiation conditions. The suggested state machine control consists of 15 steps. It prioritizes the proton exchange membrane fuel cell (PEMFC) as a secondary source for charging the battery when power is abundant and the state of charge (SOC) is low. The MSRS is made feasible by meticulously coordinating control and power management. The MSRS is made achievable by carefully orchestrated control and electricity management. The efficacy of the proposed system was evaluated under different solar irradiance and load conditions. The study demonstrates that implementing the SMC led to an average improvement of 2.3% in the overall efficiency of the system when compared to conventional control techniques. The maximum efficiency was observed when the system was operating under high load conditions, specifically when the state of charge (SOC) was greater than the maximum state of charge (SOCmax). The average efficiency achieved under these conditions was 97.2%. In addition, the MSRS successfully maintained power supply to the load for long durations, achieving an average sustained power of 96.5% over a period of 7.5 s. The validity of the modeling and management techniques mentioned in this study are confirmed by simulation results utilizing the MATLAB/Simulink (version: 2016, link: https://in.mathworks.com/products/simulink.html) software tools. These findings show that the proposed SMC is effective at managing energy resources in MSRS, resulting in improved system efficiency and reliability.
