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Optimal sizing of a wind-energy storage system considering battery life

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  • Liu, Ye
  • Wu, Xiaogang
  • Du, Jiuyu
  • Song, Ziyou
  • Wu, Guoliang

Abstract

A battery energy storage system (BESS) can smooth the fluctuation of output power for micro-grid by eliminating negative characteristics of uncertainty and intermittent for renewable energy for power generation, especially for wind power. By integrated with lithium battery storage system the utilization and overall energy efficiency can be improved. However, this target could be obtained only if the BESS is optimal matched. For this issue, the degradation of battery capacity has a significant impact on the operating costs of Wind-ESS system. The research focus on the optimal method for components sizing of BESS in Wind-ESS system with independent system operators. We present an operating cost model for the hybrid energy storage system considering capacity fading of lithium battery in the cycle life. For the optimal objective of component sizing, the global optimization method of dynamic programming (DP) is adopted by setting operating costs and capacity degradation as optimal objectives under the constrains of performance for lithium battery and requirement for grid operation. Based on the DP algorithm and capacity degradation of battery model, the optimal output of the wind power is obtained. The rule based method and genetic algorithm are also be used for simulation. The simulation results show that compared with other two optimal approaches, capacity degradation and operation cost of energy storage for wind power generation system are significantly reduced.

Suggested Citation

  • Liu, Ye & Wu, Xiaogang & Du, Jiuyu & Song, Ziyou & Wu, Guoliang, 2020. "Optimal sizing of a wind-energy storage system considering battery life," Renewable Energy, Elsevier, vol. 147(P1), pages 2470-2483.
  • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:2470-2483
    DOI: 10.1016/j.renene.2019.09.123
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    References listed on IDEAS

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    1. Song, Ziyou & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Lu, Languang & Ouyang, Minggao & Hofmann, Heath, 2014. "Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 135(C), pages 212-224.
    2. Diouf, Boucar & Pode, Ramchandra, 2015. "Potential of lithium-ion batteries in renewable energy," Renewable Energy, Elsevier, vol. 76(C), pages 375-380.
    3. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    4. Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
    5. Burgholzer, Bettina & Auer, Hans, 2016. "Cost/benefit analysis of transmission grid expansion to enable further integration of renewable electricity generation in Austria," Renewable Energy, Elsevier, vol. 97(C), pages 189-196.
    6. Li, Guang & Weiss, George & Mueller, Markus & Townley, Stuart & Belmont, Mike R., 2012. "Wave energy converter control by wave prediction and dynamic programming," Renewable Energy, Elsevier, vol. 48(C), pages 392-403.
    7. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    8. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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