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Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization

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  • Rahmani-Andebili, Mehdi

Abstract

In this study, the energy storage systems (ESS) integrated with renewable energy sources (RES), installed in a medium-voltage primary electrical distribution system, are controlled based on a proposed stochastic, adaptive, and dynamic approach to minimize the daily operation cost of system managed by the local distribution company (DISCO). A stochastic approach is applied in the operation problem to address the uncertainty of power of RESs. In addition, a model predictive control (MPC) technique is employed to deal with the variability of power of RESs. The daily operation cost of distribution system includes the hourly energy loss cost of electrical feeder, the hourly operation cost of ESSs, and the hourly switching cost of ESSs. The numerical study demonstrates a remarkable potential for reducing the operation cost of system by optimal control of ESSs and application of stochastic MPC. In addition, it is proven that applying MPC in the problem results in better outcomes. Moreover, it is shown that MPC increases the robustness of optimization procedure with respect to the prediction errors, due to dynamic and adaptability characteristics of MPC.

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  • Rahmani-Andebili, Mehdi, 2017. "Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization," Renewable Energy, Elsevier, vol. 113(C), pages 1462-1471.
    • Handle: RePEc:eee:renene:v:113:y:2017:i:c:p:1462-1471
    DOI: 10.1016/j.renene.2017.07.005
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    5. Li, Jinghua & Lu, Bo & Wang, Zhibang & Zhu, Mengshu, 2021. "Bi-level optimal planning model for energy storage systems in a virtual power plant," Renewable Energy, Elsevier, vol. 165(P2), pages 77-95.
    6. Wang, Jueying & Hu, Zhijian & Xie, Shiwei, 2019. "Expansion planning model of multi-energy system with the integration of active distribution network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Sheikhahmadi, P. & Bahramara, S. & Moshtagh, J. & Yazdani Damavandi, M., 2018. "A risk-based approach for modeling the strategic behavior of a distribution company in wholesale energy market," Applied Energy, Elsevier, vol. 214(C), pages 24-38.
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    9. Wang, Yongli & Ma, Yuze & Song, Fuhao & Ma, Yang & Qi, Chengyuan & Huang, Feifei & Xing, Juntai & Zhang, Fuwei, 2020. "Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response," Energy, Elsevier, vol. 205(C).
    10. Song, Xiaoling & Wang, Yudong & Zhang, Zhe & Shen, Charles & Peña-Mora, Feniosky, 2021. "Economic-environmental equilibrium-based bi-level dispatch strategy towards integrated electricity and natural gas systems," Applied Energy, Elsevier, vol. 281(C).
    11. Liang Zhang & Kang Chen & Ling Lyu & Guowei Cai, 2019. "Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm," Energies, MDPI, vol. 12(6), pages 1-17, March.
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    13. Correa-Florez, Carlos Adrian & Gerossier, Alexis & Michiorri, Andrea & Kariniotakis, Georges, 2018. "Stochastic operation of home energy management systems including battery cycling," Applied Energy, Elsevier, vol. 225(C), pages 1205-1218.
    14. Nikzad, Mehdi & Samimi, Abouzar, 2021. "Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems," Applied Energy, Elsevier, vol. 282(PA).
    15. Müller, C. & Hoffrichter, A. & Wyrwoll, L. & Schmitt, C. & Trageser, M. & Kulms, T. & Beulertz, D. & Metzger, M. & Duckheim, M. & Huber, M. & Küppers, M. & Most, D. & Paulus, S. & Heger, H.J. & Schnet, 2019. "Modeling framework for planning and operation of multi-modal energy systems in the case of Germany," Applied Energy, Elsevier, vol. 250(C), pages 1132-1146.

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