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Stochastic energy scheduling in microgrid with real-time and day-ahead markets in the presence of renewable energy resources

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Abstract

Renewable resources are a crucial component in power system research within contemporary intelligent power systems. It is imperative that they are given meticulous consideration. Microgrids (MGs) have emerged as a solution to facilitate the integration of renewable energy sources on a large scale. The incorporation of power production innovations that exhibit high levels of unpredictability in their generation would have a notable impact on the management of power resource planning within microgrids. Therefore, it is imperative to implement efficient power management mechanisms in microgrids. This study introduces a scheduling arrangement for a microgrid (MG) that includes a solar power unit (PV), which operates on a day-ahead basis. This paper explores the effects of various weather variables on the power generated by photovoltaic units and the optimum scheduling of microgrids. The present study employs the modified fluid search optimization algorithm to address the challenge of optimum management of energy in a microgrid connected to the grid that is characterized by a high level of unpredictability. The simulation results of the study indicate that the implementation of a photovoltaic model in an actual setting enhances the precision of the system for managing energy and reduces the overall operational costs of the interconnected microgrid. The algorithm under focus has been evaluated on a standard MG. The method under consideration has been executed on the MATLAB/Simulink computational environment and its efficacy has been evaluated.

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Acknowledgements

This work was supported by Foundation of State Key Laboratory of Public Big Data(No.2023004), National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No. ZK[2022]549), the Natural Science Foundation of Education of Guizhou province (No. [2019]203, No. KY[2019]067), and the Funds of Qiannan Normal University for Nationalities (No.qnsy2019rc09).

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This study was not funded any institution and organization.

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Authors and Affiliations

  1. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China

    Tao Hai & Jincheng Zhou

  2. Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, China

    Tao Hai

  3. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, Guizhou, China

    Tao Hai

  4. Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun, 558000, China

    Jincheng Zhou

  5. Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan, Duyun, China

    Jincheng Zhou

  6. Solar Energy and Power Electronic Co, Ltd, Tokyo, Japan

    Mohsen Latifi

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  1. Tao Hai
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Correspondence to Jincheng Zhou or Mohsen Latifi.

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Hai, T., Zhou, J. & Latifi, M. Stochastic energy scheduling in microgrid with real-time and day-ahead markets in the presence of renewable energy resources. Soft Comput 27, 16881–16896 (2023). https://doi.org/10.1007/s00500-023-09021-y

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