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Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting

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  • Wen, Lulu
  • Zhou, Kaile
  • Yang, Shanlin
  • Lu, Xinhui

Abstract

A deep recurrent neural network with long short-term memory units (DRNN-LSTM) model is developed to forecast aggregated power load and the photovoltaic (PV) power output in community microgrid. Meanwhile, an optimal load dispatch model for grid-connected community microgrid which includes residential power load, PV arrays, electric vehicles (EVs), and energy storage system (ESS), is established under three different scheduling scenarios. To promote the supply-demand balance, the uncertainties of both residential power load and PV power output are considered in the model by integrating the forecasting results. Two real-world data sets are used to test the proposed forecasting model, and the results show that the DRNN-LSTM model performs better than multi-layer perception (MLP) network and support vector machine (SVM). Finally, particle swarm optimization (PSO) algorithm is used to optimize the load dispatch of grid-connected community microgrid. The results show that EES and the coordinated charging mode of EVs can promote peak load shifting and reduce 8.97% of the daily costs. This study contributes to the optimal load dispatch of community microgrid with load and renewable energy forecasting. The optimal load dispatch of community microgrid with deep learning based solar power and load forecasting achieves total costs reduction and system reliability improvement.

Suggested Citation

  • Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:1053-1065
    DOI: 10.1016/j.energy.2019.01.075
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    as
    1. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
    2. Pedro G. Lind & Luis Vera-Tudela & Matthias Wächter & Martin Kühn & Joachim Peinke, 2017. "Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach," Energies, MDPI, vol. 10(12), pages 1-14, November.
    3. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    4. Djørup, Søren & Thellufsen, Jakob Zinck & Sorknæs, Peter, 2018. "The electricity market in a renewable energy system," Energy, Elsevier, vol. 162(C), pages 148-157.
    5. Kovacic, Zora & Giampietro, Mario, 2015. "Empty promises or promising futures? The case of smart grids," Energy, Elsevier, vol. 93(P1), pages 67-74.
    6. Alham, M.H. & Elshahed, M. & Ibrahim, Doaa Khalil & Abo El Zahab, Essam El Din, 2016. "A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management," Renewable Energy, Elsevier, vol. 96(PA), pages 800-811.
    7. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico, 2014. "An integrated framework of agent-based modelling and robust optimization for microgrid energy management," Applied Energy, Elsevier, vol. 129(C), pages 70-88.
    8. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    9. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    10. Bucciarelli, Martina & Paoletti, Simone & Vicino, Antonio, 2018. "Optimal sizing of energy storage systems under uncertain demand and generation," Applied Energy, Elsevier, vol. 225(C), pages 611-621.
    11. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    12. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    13. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving," Applied Energy, Elsevier, vol. 147(C), pages 246-257.
    14. Wang, Luhao & Li, Qiqiang & Ding, Ran & Sun, Mingshun & Wang, Guirong, 2017. "Integrated scheduling of energy supply and demand in microgrids under uncertainty: A robust multi-objective optimization approach," Energy, Elsevier, vol. 130(C), pages 1-14.
    15. Zhang, Yue-Jun & Peng, Hua-Rong, 2017. "Exploring the direct rebound effect of residential electricity consumption: An empirical study in China," Applied Energy, Elsevier, vol. 196(C), pages 132-141.
    16. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    17. Kavousi-Fard, Abdollah & Abunasri, Alireza & Zare, Alireza & Hoseinzadeh, Rasool, 2014. "Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids," Energy, Elsevier, vol. 78(C), pages 904-915.
    18. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    19. de la Rue du Can, Stephane & Pudleiner, David & Pielli, Katrina, 2018. "Energy efficiency as a means to expand energy access: A Uganda roadmap," Energy Policy, Elsevier, vol. 120(C), pages 354-364.
    20. Kamankesh, Hamidreza & Agelidis, Vassilios G. & Kavousi-Fard, Abdollah, 2016. "Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand," Energy, Elsevier, vol. 100(C), pages 285-297.
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