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Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts

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  • Kou, Peng
  • Gao, Feng
  • Guan, Xiaohong

Abstract

The limited dispatchability of wind energy poses a challenge to its increased penetration. One technically feasible solution to this challenge is to integrate a battery energy storage system (BESS) with a wind farm. This highlights the importance of a BESS control strategy. In view of this, a stochastic model predictive control scheme is proposed in this paper. Based on the forecasted wind power distributions, the proposed scheme ensures the optimal operation of BESS in the presence of practical system constraints, thus bringing the wind-battery combined power output to the desired dispatch levels. The salient feature of the proposed scheme is that it takes into account the non-Gaussian wind power uncertainties. In this scheme, a probabilistic wind power forecasting model is employed as the prediction model, which quantifies the non-Gaussian uncertainties in wind power forecasts. Using chance constraints, the quantified uncertainties are incorporated into the controller design, thus forming a chance constrained stochastic programming problem. Using warping function, this problem is recast as a convex quadratic optimization problem, which is tractable both theoretically and practically. This way, the proposed control scheme handles the non-Gaussian uncertainties in wind power forecasts. The simulation results on actual data demonstrate the effectiveness of the proposed scheme. The data used in the simulation are obtained in the real operation of a wind farm in China.

Suggested Citation

  • Kou, Peng & Gao, Feng & Guan, Xiaohong, 2015. "Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts," Renewable Energy, Elsevier, vol. 80(C), pages 286-300.
    • Handle: RePEc:eee:renene:v:80:y:2015:i:c:p:286-300
    DOI: 10.1016/j.renene.2015.02.001
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    Cited by:

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    13. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    14. Appino, Riccardo Remo & González Ordiano, Jorge Ángel & Mikut, Ralf & Faulwasser, Timm & Hagenmeyer, Veit, 2018. "On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages," Applied Energy, Elsevier, vol. 210(C), pages 1207-1218.
    15. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Liu, Zhou & Liu, Wen & Chen, Zhe & Blaabjerg, Frede, 2020. "Scheduling of wind-battery hybrid system in the electricity market using distributionally robust optimization," Renewable Energy, Elsevier, vol. 156(C), pages 47-56.
    16. Hemmati, Reza & Saboori, Hedayat & Saboori, Saeid, 2016. "Assessing wind uncertainty impact on short term operation scheduling of coordinated energy storage systems and thermal units," Renewable Energy, Elsevier, vol. 95(C), pages 74-84.
    17. İskeceli, Bilge Dilara & Kayakutlu, Gulgun & Daim, Tugrul U. & Shaygan, Amir, 2020. "Optimization of battery and wind technologies: Case of power deviation penalties," Technology in Society, Elsevier, vol. 63(C).
    18. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "Dual-meta pool method for wind farm power forecasting with small sample data," Energy, Elsevier, vol. 267(C).
    19. Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1-16, December.
    20. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    21. Duchaud, Jean-Laurent & Notton, Gilles & Darras, Christophe & Voyant, Cyril, 2018. "Power ramp-rate control algorithm with optimal State of Charge reference via Dynamic Programming," Energy, Elsevier, vol. 149(C), pages 709-717.

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