On the long-term density prediction of peak electricity load with demand side management in buildings
On the long-term density prediction of peak electricity load with demand side management in buildings
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DOI：
10.1016/j.enbuild.2020.110450
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发表时间：
2020-09
影响因子：
6.7
通讯作者：
Youngchan Jang;E. Byon;E. Jahani;Kristen S. Cetin
中科院分区：
文献类型：
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作者：
Youngchan Jang;E. Byon;E. Jahani;Kristen S. Cetin
长期的日高峰需求预测对电力系统的有效、经济运行和规划具有重要作用。然而，与气候和社会经济变化相关的许多不确定性和建筑需求变化，使需求预测复杂化，并使电力系统运营商面临无法满足电力需求的风险。这项研究为提供日高峰需求的长期密度预测提供了一种新的方法。具体地说，我们利用基于物理的全球气候模型的温度预测，并校准这些预测，以解决可能的偏差。此外，还考虑了人口增长和建筑物需求侧管理工作的影响。最后，用非齐次广义极值分布对日高峰需求进行建模，允许参数随预测的温度和人口而变化。使用德克萨斯州中南部地区的实际建筑使用数据的案例研究表明，所提出的方法可以量化综合框架中的不确定性，并为峰值需求密度的长期演变提供有用的见解。一个完善的建筑需求节约策略预计将缓冲长期高峰电力需求的日益增长的需求。
Long-term daily peak demand forecast plays an important role in the effective and economic operations and planning of power systems. However, many uncertainties and building demand variability, which are associated with climate and socio-economic changes, complicate demand forecasting and expose power system operators to the risk of failing to meet electricity demand. This study presents a new approach to provide the long-term density prediction of the daily peak demand. Specifically, we make use of temperature projections from physics-based global climate models and calibrate the projections to address possible biases. In addition, the effects of population growth and demand side management efforts in buildings are taken into consideration. Finally, the daily peak demands are modeled with the nonhomogeneous generalized extreme value distribution where the parameters are allowed to vary, depending on the predicted temperature and population. A case study using actual building use data in the south-central region in Texas demonstrates that the proposed approach can quantify the uncertainties in an integrative framework and provide useful insights into the long-term evolution of peak demand density. A well-established building demand saving strategy is predicted to buffer against the growing needs of long-term peak electricity demand.