Optimal Control and Communication Strategies in Multi-Energy Generation Grid

Khan, Muhammad Waseem; Li, Guojie; Wang, Keyou; Numan, Muhammad; Xiong, Linyun; Khan, Muhammad Azam

doi:10.1109/comst.2023.3304982

Cited by 8 publications

(2 citation statements)

References 263 publications

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“…Supervisory, predictive, and adaptive control strategies form the backbone of control systems in renewable energy. Supervisory control ensures the coordinated operation of various components, optimizing the overall system performance (Khan et al, 2023). Predictive control algorithms enhance efficiency by anticipating changes in renewable energy generation.…”

Section: Literature Reviewmentioning

confidence: 99%

Control systems in renewable energy: A review of applications in Canada, USA, and Africa

Onyinyechukwu Chidolue,

Cosmas Dominic Daudu,

Valentine Ikenna Illojianya

et al. 2024

World J. Adv. Eng. Technol. Sci.

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This research explores the applications of control systems in renewable energy across Canada, the United States, and Africa. It underscores their pivotal role in optimizing efficiency and reliability by examining supervisory, predictive, and adaptive control strategies. The literature review delves into global and regional renewable energy landscapes, emphasizing unique challenges and opportunities. Technological innovations, including advanced monitoring, artificial intelligence, and blockchain, are investigated, highlighting their transformative impact. The paper anticipates prospects such as quantum computing, decentralized systems, and heightened cybersecurity measures. The findings contribute to understanding the nuanced interplay between control systems and renewable energy, offering insights for policymakers, researchers, and industry stakeholders as they navigate the evolving landscape of sustainable energy solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Control systems in renewable energy: A review of applications in Canada, USA, and Africa

Onyinyechukwu Chidolue,

Cosmas Dominic Daudu,

Valentine Ikenna Illojianya

et al. 2024

World J. Adv. Eng. Technol. Sci.

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“…Moreover, the energy management system control of microgrids enables managing the uncertainties of RES sources effectively and simultaneously controls the balance between energy demand and consumption. Khan et al [11] provided insight into the operations and impacts of VOLUME 11, 2023 multi-source-based microgrids by discussing their various architectures, control strategies for their reliable operations and the solutions for meeting the requirements of their control and communication. Anvari-Moghaddam et al [12] suggested an energy management control structure for monitoring and controlling microgrid operation in real-time through MAS based on defined standards.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Smart Energy Management in Hybrid Microgrids: Evaluating Operational Modes, Resources Optimization, and Environmental Impacts

Billah,

Yousif,

Numan

et al. 2023

IEEE Access

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Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system's performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids.INDEX TERMS Renewable energy sources, particle swarm optimization, energy management system, multi agent system, microgrid. NOMENCLATURE RES Renewable energy sources. DER Distributed energy resources. PV Photovoltaic. MAS Multi agent system. PSO Particle swarm optimization. AI Artificial intelligence. MILP Mixed integer linear programming.The associate editor coordinating the review of this manuscript and approving it for publication was Xiwang Dong.

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A tri-level control framework for carbon-aware multi-energy microgrid cluster considering shared hydrogen energy storage

Armghan,

Xu,

Bai

et al. 2024

Applied Energy

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Optimal Control and Communication Strategies in Multi-Energy Generation Grid

Cited by 8 publications

References 263 publications

Control systems in renewable energy: A review of applications in Canada, USA, and Africa

Control systems in renewable energy: A review of applications in Canada, USA, and Africa

Decentralized Smart Energy Management in Hybrid Microgrids: Evaluating Operational Modes, Resources Optimization, and Environmental Impacts

A tri-level control framework for carbon-aware multi-energy microgrid cluster considering shared hydrogen energy storage

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