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Look-ahead decision making for renewable energy: A dynamic “predict and store” approach

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  • Wang, Jingxing
  • Chung, Seokhyun
  • AlShelahi, Abdullah
  • Kontar, Raed
  • Byon, Eunshin
  • Saigal, Romesh

Abstract

This paper presents an integrative methodology for managing and stabilizing the output of a wind/solar farm using storage devices in a cost effective and real-time manner. We consider the problem where a renewable farm should decide the amount of energy charged into, or withdrawn from, the battery given the stochastic and time-varying nature in the renewable energy power output. Our methodology features a seamless integration of a non-myopic decision framework and a sequential non-parametric predictive model based on functional principal component analysis. A key feature of our algorithm is that it quantifies costs over a rolling horizon where both predictions and decisions are updated on the fly as new data is acquired. Our technology is tested on the California ISO dataset. The case study provides a proof-of-concept that highlights both the benefits and ease of implementation of our forward looking framework.

Suggested Citation

  • Wang, Jingxing & Chung, Seokhyun & AlShelahi, Abdullah & Kontar, Raed & Byon, Eunshin & Saigal, Romesh, 2021. "Look-ahead decision making for renewable energy: A dynamic “predict and store” approach," Applied Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:appene:v:296:y:2021:i:c:s0306261921005225
    DOI: 10.1016/j.apenergy.2021.117068
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    References listed on IDEAS

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    Cited by:

    1. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
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    3. Junhui, L.I. & Pan, Yahui & Mu, Gang & Chen, Guohang & Zhu, Xingxu & Yan, Ganggui & Li, Cuiping & Jia, Chen, 2024. "A hierarchical demand assessment methodology of peaking resources in multi-areas interconnected systems with a high percentage of renewables," Applied Energy, Elsevier, vol. 367(C).
    4. Tian, Beile & Mao, Songbo & Guo, Feiqiang & Bai, Jiaming & Shu, Rui & Qian, Lin & Liu, Qi, 2022. "Monolithic biochar-supported cobalt-based catalysts with high-activity and superior-stability for biomass tar reforming," Energy, Elsevier, vol. 242(C).
    5. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    6. Jeong, Cheoljoon & Byon, Eunshin, 2024. "Calibration of building energy computer models via bias-corrected iteratively reweighted least squares method," Applied Energy, Elsevier, vol. 360(C).

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