Look-ahead decision making for renewable energy: A dynamic “predict and store” approach
Look-ahead decision making for renewable energy: A dynamic “predict and store” approach
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DOI：
10.1016/j.apenergy.2021.117068
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发表时间：
2021-08
期刊：
影响因子：
11.2
通讯作者：
Jingxing Wang;Seokhyun Chung;Abdullah AlShelahi;R. Kontar;E. Byon;R. Saigal
中科院分区：
文献类型：
--
作者：
Jingxing Wang;Seokhyun Chung;Abdullah AlShelahi;R. Kontar;E. Byon;R. Saigal
本文提出了一种综合方法，用于管理和稳定使用存储设备的风能/太阳能发电场的输出，具有成本效益和实时的方式。我们考虑的问题是，一个可再生能源农场应该决定多少能量充电，或从电池，给定的随机和时变性质的可再生能源的输出。我们的方法以非短视决策框架和基于功能主成分分析的顺序非参数预测模型的无缝集成为特点。我们算法的一个关键特征是，它量化了一个滚动视界上的成本，随着新数据的获取，预测和决策都在动态更新。我们的技术在加州ISO数据集上进行了测试。案例研究提供了一个概念验证，突出了我们的前瞻性框架的优点和实现的便利性。
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 We consider the problem where a renewable farm should decide the amount of energy charged into, or withdrawn from, the battery given the stochastic and time-varying nature in the renewable energy power output.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.