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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 We consider the problem where a renewable farm should decide the amount of energy charged into, or withdrawn from, the battery given the stochastic and time-varying nature in the renewable energy power output.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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