A comprehensive state‐of‐the‐art review of power conditioning systems for energy storage systems: Topology and control applications in power systems

Rafaq, Muhammad Saad; Basit, Bilal Abdul; Mohammed, Sadeq Ali Qasem; Jung, Jin‐Woo

doi:10.1049/rpg2.12498

Cited by 16 publications

(7 citation statements)

References 151 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fault self-check is carried out by diagnostic algorithm to prevent excessive charging and discharging from damaging vehicle battery, and timely alarm is given when necessary to ensure safe operation. BMS comprehensively monitors battery performance from all aspects, transmits data to charging and discharging devices (EV-PCS) with CAN bus, and then reports it to the background management system to make response strategies, and transmits charging and discharging instructions back to the charging and discharging devices, thus realizing the bidirectional flow of information [10,16]. 3.1 is the user terminal, and users can intuitively understand the status information of electric vehicles on the dashboard.…”

Section: V2g System Components and Information Flowmentioning

confidence: 99%

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

Xu,

Chang,

Sun

2024

SCPE

View full text Add to dashboard Cite

In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.

show abstract

Section: V2g System Components and Information Flowmentioning

confidence: 99%

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

Xu,

Chang,

Sun

2024

SCPE

View full text Add to dashboard Cite

show abstract

“…Hence, the accuracy tends to reduce with the increase in the real-time utilization of the battery's capacity, constraining the effective capacity of the storage systems to maintain balance. Without artificial capacity constraints, the circulating current and temporary overloading of some storage systems in a network are existing drawbacks that upset the optimization and steadiness of the control, reduce the balance and sustainability of the power flow, deteriorate the health and life of the batteries, and limit the introduction and buffering capability of renewable energy [14][15][16]. DC infrastructure influences are a crucial magnifier for raising the impact of these drawbacks in real operation, potentially leading to the disparity of load participation due to the impact of the influence of the power electronics and transmission lines.…”

Section: Statement Of Problemmentioning

confidence: 99%

“…Therefore, the infrastructure, primarily the transmission lines and power electronics, has a potentially major impact on the management of power-storage flow in the real-world operation of the MARL-based control for the following influences [16][17][18].…”

Section: Statement Of Problemmentioning

confidence: 99%

“…Since the operational methodology of each decentralized BESS in the microgrid experiences different instantaneous operational conditions and scenarios, three scenarios were constructed to emulate the real operation in the simulations and hardware-in-the-loop (HiL)-based experiments. These scenarios were taken into consideration when measuring and compensating for the DC infrastructure impact on the controller regarding the discharging participation, charging participation, and plug-and-play off-participation, as described below [16,36,37].…”

Section: The Compensation Of the DC Infrastructure Influence On The C...mentioning

confidence: 99%

See 1 more Smart Citation

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

Al-Saadi,

Short

2023

Batteries

View full text Add to dashboard Cite

The influence of the DC infrastructure on the control of power-storage flow in micro- and smart grids has gained attention recently, particularly in dynamic vehicle-to-grid charging applications. Principal effects include the potential loss of the charge–discharge synchronization and the subsequent impact on the control stabilization, the increased degradation in batteries’ health/life, and resultant power- and energy-efficiency losses. This paper proposes and tests a candidate solution to compensate for the infrastructure effects in a DC microgrid with a varying number of heterogeneous battery storage systems in the context of a multiagent neighbor-to-neighbor control scheme. Specifically, the scheme regulates the balance of the batteries’ load-demand participation, with adaptive compensation for unknown and/or time-varying DC infrastructure influences. Simulation and hardware-in-the-loop studies in realistic conditions demonstrate the improved precision of the charge–discharge synchronization and the enhanced balance of the output voltage under 24 h excessively continuous variations in the load demand. In addition, immediate real-time compensation for the DC infrastructure influence can be attained with no need for initial estimates of key unknown parameters. The results provide both the validation and verification of the proposals under real operational conditions and expectations, including the dynamic switching of the heterogeneous batteries’ connection (plug-and-play) and the variable infrastructure influences of different dynamically switched branches. Key observed metrics include an average reduced convergence time (0.66–13.366%), enhanced output-voltage balance (2.637–3.24%), power-consumption reduction (3.569–4.93%), and power-flow-balance enhancement (2.755–6.468%), which can be achieved for the proposed scheme over a baseline for the experiments in question.

show abstract

“…National and international scholars have proposed many control strategies, such as activefrequency, reactive-voltage, droop control, model predictive control, sliding mode control, etc. The above control strategies have the advantages of fast response speed and simple and flexible control structure, but they lack the regulation of inertia and damping and cannot actively follow the voltage and frequency changes of the microgrid (Rafaq et al, 2022). To address the aforementioned issues, certain academics have suggested using the virtual synchronous generator (VSG) technique.…”

Section: Introductionmentioning

confidence: 99%

Power stability control of wind-PV-battery AC microgrid based on two-parameters fuzzy VSG

Zhou,

Wang,

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

Virtual synchronous generator (VSG) control addresses the issue of decreasing microgrid standby inertia caused by the rise in wind turbines and photovoltaic (PV) penetration. However, various types of perturbations occur frequently making the traditional constant parameter VSG control unable to meet the system performance requirements, and thus a two-parameters fuzzy VSG control is proposed to ensure that microgrid inertia and damping. Firstly, the device-level control of each generation unit in the microgrid is designed based on the wind-PV-battery alternating current (AC) microgrid architecture. Secondly, fuzzy VSG control uses fuzzy rules written in plain language to represent the relationship between the main VSG factors and the power and frequency. Then, the influence of virtual inertia and damping coefficient on the dynamic performance of the system is analyzed through the theory of small-signal model, and a reasonable variation range of VSG parameters are given. Finally, the simulation model of wind-PV-battery AC microgrid is built in MATLAB/Simulink, and compared with other improved VSG control strategies, the fuzzy VSG control proposed in this paper has better dynamic performance and safety stability. This research emphasizes the practicality and importance of utilizing fuzzy control to adjust VSG techniques for developing microgrid configurations incorporating more renewable energy sources to guarantee the reliability and efficiency of microgrid.

show abstract

A comprehensive state‐of‐the‐art review of power conditioning systems for energy storage systems: Topology and control applications in power systems

Cited by 16 publications

References 151 publications

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

Power stability control of wind-PV-battery AC microgrid based on two-parameters fuzzy VSG

Contact Info

Product

Resources

About