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Multi-stage optimal scheduling of multi-microgrids using deep-learning artificial neural network and cooperative game approach

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  • Alizadeh Bidgoli, Mohsen
  • Ahmadian, Ali

Abstract

This article proposes a two-stage system for the daily energy management of micro-grids (MGs) in the presence of wind turbines, photovoltaic (PV) panels, and electrical energy storage systems (ESSs). Each MG uses historical data to predict its consumers' load demand, wind speed, and solar irradiance in the first stage. In the second stage, the cooperative game method is used to determine the MG's daily dispatch and energy transaction. The paper develops a prediction model using artificial neural network (ANN) and rough neuron water cycle (RNWC) algorithms, called deep learning artificial neural network (DLANN), which is a combination of technology from the artificial neural network and WC algorithm in order to predict uncertain parameters. The above model is implemented in the 33bus power distribution system; the simulation results show that the DLANN method provides more accurate predictions than the ANN method. The results also show that a MG can achieve energy cost savings through an alliance of MGs using the cooperative game approach. Furthermore, analysis of the impact of the ESS on the operation of the MG shows that the absence of the ESS will reduce the power output of the wind turbine.

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  • Alizadeh Bidgoli, Mohsen & Ahmadian, Ali, 2022. "Multi-stage optimal scheduling of multi-microgrids using deep-learning artificial neural network and cooperative game approach," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221022842
    DOI: 10.1016/j.energy.2021.122036
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    5. Zhao, Leilei & Xue, Yixun & Sun, Hongbin & Du, Yuan & Chang, Xinyue & Su, Jia & Li, Zening, 2023. "Benefit allocation for combined heat and power dispatch considering mutual trust," Applied Energy, Elsevier, vol. 345(C).
    6. Sana Qaiyum & Martin Margala & Pravin R. Kshirsagar & Prasun Chakrabarti & Kashif Irshad, 2023. "Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    7. Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).
    8. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    9. Jing Yu & Jicheng Liu & Jiakang Sun & Mengyu Shi, 2023. "Evolutionary Game of Digital-Driven Photovoltaic–Storage–Use Value Chain Collaboration: A Value Intelligence Creation Perspective," Sustainability, MDPI, vol. 15(4), pages 1-30, February.
    10. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).

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