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

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

Applied Energy, 2021, vol. 296, issue C, No S0306261921005225

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 We consider the problem where a renewable farm should decide the amount of energy charged into, or withdrawn from, the battery given the stochastic and time-varying nature in the renewable energy power output.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

Keywords: Renewable energy; Battery storage; Look-ahead optimization; Joint prediction and prescription; Functional principal component analysis; Bayesian inference (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921005225
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:296:y:2021:i:c:s0306261921005225

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2021.117068

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().