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A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

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  • Xiaomin Wu

    (School of Automation, China University of Geosciences, Wuhan 430074, China
    Hubei KEY Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China)

  • Weihua Cao

    (School of Automation, China University of Geosciences, Wuhan 430074, China
    Hubei KEY Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China)

  • Dianhong Wang

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China)

  • Min Ding

    (School of Automation, China University of Geosciences, Wuhan 430074, China
    Hubei KEY Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China)

Abstract

With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

Suggested Citation

  • Xiaomin Wu & Weihua Cao & Dianhong Wang & Min Ding, 2019. "A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters," Energies, MDPI, vol. 12(11), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2160-:d:237587
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    References listed on IDEAS

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    2. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
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    5. Akhtar Hussain & Hak-Man Kim, 2020. "Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids," Sustainability, MDPI, vol. 12(19), pages 1-18, October.
    6. Yang, Qiangda & Dong, Ning & Zhang, Jie, 2021. "An enhanced adaptive bat algorithm for microgrid energy scheduling," Energy, Elsevier, vol. 232(C).
    7. Abdelfettah Kerboua & Fouad Boukli-Hacene & Khaldoon A Mourad, 2020. "Particle Swarm Optimization for Micro-Grid Power Management and Load Scheduling," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 71-80.
    8. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    9. Wenshuai Bai & Dian Wang & Zhongquan Miao & Xiaorong Sun & Jiabin Yu & Jiping Xu & Yuqing Pan, 2023. "The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    10. Wenhao Zhuo & Andrey V. Savkin, 2019. "Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting," Energies, MDPI, vol. 12(15), pages 1-17, July.
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