Energy management and control system for microgrid based wind-PV-battery using multi-agent systems

Azeroual, Mohamed; Boujoudar, Younes; Iysaouy, Lahcen El; Aljarbouh, Ayman; Fayaz, Muhammad; Qureshi, Muhammad Shuaib; Rabbi, Fazle; Markhi, Hassane El

doi:10.1177/0309524x221075583

Cited by 17 publications

(6 citation statements)

References 18 publications

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“…The improved PSO algorithm is faster and more efficient. This shows that the particle swarm optimization algorithm is more efficient [2].…”

Section: Literature Reviewmentioning

confidence: 81%

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

Xu,

Chang,

Sun

2024

SCPE

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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.

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“…The improved PSO algorithm is faster and more efficient. This shows that the particle swarm optimization algorithm is more efficient [2].…”

Section: Literature Reviewmentioning

confidence: 81%

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

Xu,

Chang,

Sun

2024

SCPE

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“…Furthermore, as the number of DG units in MGs increases, so does the complexity of fine-tuning the system's control settings. Many complex strategies have also been employed to control such parameters in MGs, considering economic load dispatch in MG systems (Azeroual et al, 2022). EMS solutions based on artificial intelligence have been used to increase the efficiency and performance of MG systems, allowing them to fulfill demand while generating the most energy Frontiers in Energy Research frontiersin.org (Chaudhary et al, 2021).…”

Section: Microgrid Intelligent Techniquesmentioning

confidence: 99%

Microgrid energy management and monitoring systems: A comprehensive review

J.Albarakati

Boujoudar

Azeroual³

et al. 2022

Front. Energy Res.

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Microgrid (MG) technologies offer users attractive characteristics such as enhanced power quality, stability, sustainability, and environmentally friendly energy through a control and Energy Management System (EMS). Microgrids are enabled by integrating such distributed energy sources into the utility grid. The microgrid concept is proposed to create a self-contained system composed of distributed energy resources capable of operating in an isolated mode during grid disruptions. With the Internet of Things (IoT) daily technological advancements and updates, intelligent microgrids, the critical components of the future smart grid, are integrating an increasing number of IoT architectures and technologies for applications aimed at developing, controlling, monitoring, and protecting microgrids. Microgrids are composed of various distributed generators (DG), which may include renewable and non-renewable energy sources. As a result, a proper control strategy and monitoring system must guarantee that MG power is transferred efficiently to sensitive loads and the primary grid. This paper evaluates MG control strategies in detail and classifies them according to their level of protection, energy conversion, integration, benefits, and drawbacks. This paper also shows the role of the IoT and monitoring systems for energy management and data analysis in the microgrid. Additionally, this analysis highlights numerous elements, obstacles, and issues regarding the long-term development of MG control technologies in next-generation intelligent grid applications. This paper can be used as a reference for all new microgrid energy management and monitoring research.

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“…With the rapid development of processors, intelligent technology has become part of all aspects of our lives, in addition to PV systems [23]. Intelligent PV plants will be an important part of the next generation of intelligent power grids with the application of big data, cloud computing, etc.…”

Section: Introductionmentioning

confidence: 99%

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Zhang,

Zhai,

Mao

et al. 2024

Energies

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PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system.

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Energy management and control system for microgrid based wind-PV-battery using multi-agent systems

Cited by 17 publications

References 18 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

Microgrid energy management and monitoring systems: A comprehensive review

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

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