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Stochastic search for a parametric cost function approximation: Energy storage with rolling forecasts

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  • Ghadimi, Saeed
  • Powell, Warren B.

Abstract

Rolling forecasts have been almost overlooked in the renewable energy storage literature. In this paper, we provide a new approach for handling uncertainty not just in the accuracy of a forecast, but in the evolution of forecasts over time. Our approach shifts the focus from modeling the uncertainty in a lookahead model to accurate simulations in a stochastic base model. We develop a robust policy for making energy storage decisions by creating a parametrically modified lookahead model, where the parameters are tuned in the stochastic base model. Since computing unbiased stochastic gradients with respect to the parameters require restrictive assumptions, we propose a simulation-based stochastic approximation algorithm based on numerical derivatives to optimize these parameters. While numerical derivatives, calculated based on the noisy function evaluations, provide biased gradient estimates, an online variance reduction technique built in the framework of our proposed algorithm, will enable us to control the accumulated bias errors and establish the finite-time rate of convergence of the algorithm. Our numerical experiments show the performance of this algorithm in finding policies outperforming the deterministic benchmark policy.

Suggested Citation

  • Ghadimi, Saeed & Powell, Warren B., 2024. "Stochastic search for a parametric cost function approximation: Energy storage with rolling forecasts," European Journal of Operational Research, Elsevier, vol. 312(2), pages 641-652.
  • Handle: RePEc:eee:ejores:v:312:y:2024:i:2:p:641-652
    DOI: 10.1016/j.ejor.2023.08.003
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    References listed on IDEAS

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    1. Liu, Heping & Shi, Jing & Erdem, Ergin, 2010. "Prediction of wind speed time series using modified Taylor Kriging method," Energy, Elsevier, vol. 35(12), pages 4870-4879.
    2. Jurate saltyte Benth & Fred Espen Benth & Paulius Jalinskas, 2007. "A Spatial-temporal Model for Temperature with Seasonal Variance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(7), pages 823-841.
    3. Powell, Warren B., 2019. "A unified framework for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 795-821.
    4. Daniel R. Jiang & Warren B. Powell, 2015. "Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 525-543, August.
    5. Xiaomin Xi & Ramteen Sioshansi, 2016. "A dynamic programming model of energy storage and transformer deployments to relieve distribution constraints," Computational Management Science, Springer, vol. 13(1), pages 119-146, January.
    6. Akarslan, Emre & Hocaoğlu, Fatih Onur & Edizkan, Rifat, 2014. "A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting," Energy, Elsevier, vol. 73(C), pages 978-986.
    7. Yurii NESTEROV & Vladimir SPOKOINY, 2017. "Random gradient-free minimization of convex functions," LIDAM Reprints CORE 2851, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Chen, Xi & Li, Kaiwen & Lin, Sidian & Ding, Xiaosong, 2024. "Technician routing and scheduling with employees’ learning through implicit cross-training strategy," International Journal of Production Economics, Elsevier, vol. 271(C).

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