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Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast

Author

Listed:
  • Tianrui Zhang

    (School of Mechanical Engineering, Shenyang University, Shenyang 110044, China)

  • Weibo Zhao

    (School of Mechanical Engineering, Shenyang University, Shenyang 110044, China)

  • Quanfeng He

    (School of Mechanical Engineering, Shenyang University, Shenyang 110044, China)

  • Jianan Xu

    (School of Mechanical Engineering, Shenyang University, Shenyang 110044, China)

Abstract

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

Suggested Citation

  • Tianrui Zhang & Weibo Zhao & Quanfeng He & Jianan Xu, 2025. "Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast," Sustainability, MDPI, vol. 17(2), pages 1-30, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:648-:d:1567930
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    References listed on IDEAS

    as
    1. Ranran Cao & He Tian & Dahua Li & Mingwen Feng & Huaicong Fan, 2023. "Short-Term Photovoltaic Power Generation Prediction Model Based on Improved Data Decomposition and Time Convolution Network," Energies, MDPI, vol. 17(1), pages 1-18, December.
    2. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    3. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
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