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Hierarchical Control and Economic Optimization of Microgrids Considering the Randomness of Power Generation and Load Demand

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  • Yinghao Shan

    (College of Information Science and Technology, Donghua University, Shanghai 201620, China)

  • Liqian Ma

    (College of Information Science and Technology, Donghua University, Shanghai 201620, China)

  • Xiangkai Yu

    (College of Information Science and Technology, Donghua University, Shanghai 201620, China)

Abstract

Hierarchical control has emerged as the main method for controlling hybrid microgrids. This paper presents a model of a hybrid microgrid that comprises both AC and DC subgrids, followed by the design of a three-layered control method. An economic objective function is then constructed to account for the uncertainty of power generation and load demand, and the optimal power guidance value is determined using the particle swarm optimization algorithm. The optimized power output is subsequently used to guide the tertiary control in the microgrid, mitigating potential safety and stability issues. Finally, the performance of each control layer is compared under dynamic changes in AC and DC loads, as well as stochastic variations in power generation and load consumption. Simulation results demonstrate that the hybrid microgrid can function stably, ensuring reliable and cost-effective AC and DC bus voltage supply despite the randomness of power generation and load demand.

Suggested Citation

  • Yinghao Shan & Liqian Ma & Xiangkai Yu, 2023. "Hierarchical Control and Economic Optimization of Microgrids Considering the Randomness of Power Generation and Load Demand," Energies, MDPI, vol. 16(14), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5503-:d:1198614
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    References listed on IDEAS

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    2. David W. Puma & Y. P. Molina & Brayan A. Atoccsa & J. E. Luyo & Zocimo Ñaupari, 2024. "Distribution Network Reconfiguration Optimization Using a New Algorithm Hyperbolic Tangent Particle Swarm Optimization (HT-PSO)," Energies, MDPI, vol. 17(15), pages 1-13, August.
    3. Falah Noori Saeed Al-dulaimi & Sefer Kurnaz, 2023. "Optimized Distributed Cooperative Control for Islanded Microgrid Based on Dragonfly Algorithm," Energies, MDPI, vol. 16(22), pages 1-24, November.
    4. Xueyang Wu & Yinghao Shan & Kexin Fan, 2024. "A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids," Sustainability, MDPI, vol. 16(3), pages 1-22, January.
    5. Gao, Jinling & Maalla, Allam & Li, Xuetao & Zhou, Xiao & Lian, Kong, 2024. "Comprehensive model for efficient microgrid operation: Addressing uncertainties and economic considerations," Energy, Elsevier, vol. 306(C).
    6. Pabel Alberto Cárdenas & Maximiliano Martínez & Marcelo Gustavo Molina & Pedro Enrique Mercado, 2023. "Development of Control Techniques for AC Microgrids: A Critical Assessment," Sustainability, MDPI, vol. 15(21), pages 1-28, October.

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