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Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region

机译：评估峰值用电负荷对气候的敏感性，以进行弹性电源系统的规划和运营：一项针对德克萨斯州地区的研究

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摘要

Accurate forecasting of peak electricity load has long been an active area of research in electricity markets, and power systems planning and operation. Unanticipated climate-induced surges in peak load can lead to supply shortages causing frequent brownouts and blackouts, and large-scale socioeconomic impacts. In this paper, the climate sensitivity of daily peak load is characterized by leveraging advanced statistical machine learning algorithms. More specifically, a rigorously tested and validated predictive model based on the Bayesian additive regression trees algorithm is proposed. Results from this study revealed that maximum daily temperature followed by mean dew point temperature are the most important predictors of the climate-sensitive portion of daily peak load. Among the non-climatic predictors, electricity price was found to have a strong positive association with the daily peak load. Economic growth was observed to have an inverse association with the daily peak load. While the proposed framework is established for the state of Texas, one of the most energy-intensive states with geographic and demographic susceptibility to climatic change, the methodology can be extended to other states/regions. The model can also be used to make short-term predictions of the climate-sensitive portion of daily peak load. (C) 2019 Elsevier Ltd. All rights reserved.
机译：准确预测峰值用电负荷一直是电力市场以及电力系统规划和运营研究的活跃领域。意外的气候导致的高峰负荷激增会导致供应短缺，从而导致频繁的电力不足和停电，并给社会经济带来大规模影响。本文利用先进的统计机器学习算法来表征每日峰值负荷的气候敏感性。更具体地说，提出了一种基于贝叶斯加性回归树算法的经过严格测试和验证的预测模型。这项研究的结果表明，最高每日温度和平均露点温度是每日峰值负荷中气候敏感部分的最重要预测指标。在非气候预测因素中，发现电价与每日峰值负荷有很强的正相关性。观察到经济增长与每日高峰负荷成反比。虽然提议的框架是针对得克萨斯州建立的，得克萨斯州是最耗能的州之一，对气候变化具有地理和人口敏感性，但该方法可以扩展到其他州/地区。该模型还可用于对每日峰值负荷的气候敏感部分进行短期预测。 （C）2019 Elsevier Ltd.保留所有权利。

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