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Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction

Author

Listed:
  • Shi, Jie
  • Wang, Luhao
  • Lee, Wei-Jen
  • Cheng, Xingong
  • Zong, Xiju

Abstract

Incorporating Energy Storage System (ESS) with wind farm to establish Wind-Storage Combined Generation System is a promising solution to improve the dependability of integrated wind power. Hybrid Energy Storage System (HESS) is designed based on wind power fluctuation and ESS features. The optimization of system sizing and very short-term generation scheduling are the key points affecting system effectiveness and reliability of wind power. This paper proposes a novel real-time model prediction control (MPC) -multi objective cross entropy (MOCE) based energy management algorithm (MMEMA) to coordinate an HESS based on power output feature extraction. The proposed algorithm includes the SOC regulation strategies considering practical issues including charge/discharge power. Firstly, based on Wavelet Package Decomposition (WPD) and Hilbert Huang Transform (HHT) respectively, the fluctuation feature of real-time wind power output is studied to propose a HESS model aiming to obtain the economic capacity as well as maximum charging/discharging power in every generation scheduling period (10 min). Thus, according to case study, the sizing approach can reduce invest cost by 25.7–47.0%. Then, HESS data is taken as parameters and constraints for the proposed model prediction control (MPC) –multi objective cross entropy (MOCE) algorithm to minimize the deviation between generation scheduling plan and real-time integrated power. The optimization results from case wind farm show that the proposed MMEMA algorithm performs better in smoothing out the fluctuation and managing the SOC of HESS than Non-dominated Sorting Genetic Algorithm II based method (NEMA) by means of evaluating the indexes PD, HSOC and MSE.

Suggested Citation

  • Shi, Jie & Wang, Luhao & Lee, Wei-Jen & Cheng, Xingong & Zong, Xiju, 2019. "Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316022
    DOI: 10.1016/j.apenergy.2019.113915
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    4. Barra, P.H.A. & de Carvalho, W.C. & Menezes, T.S. & Fernandes, R.A.S. & Coury, D.V., 2021. "A review on wind power smoothing using high-power energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    5. Xinyu Zhao & Yunxiao Zhang & Xueying Cui & Le Wan & Jinlong Qiu & Erfa Shang & Yongchang Zhang & Haisen Zhao, 2023. "Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
    6. Song, Hui & Gu, Mingchen & Liu, Chen & Amani, Ali Moradi & Jalili, Mahdi & Meegahapola, Lasantha & Yu, Xinghuo & Dickeson, George, 2023. "Multi-objective battery energy storage optimization for virtual power plant applications," Applied Energy, Elsevier, vol. 352(C).
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