Data-Driven Approach to Transactive Energy Systems with Commercial Buildings

被引：0

作者：

Ramesh, Meghana ^{[1
]}

Xie, Jing ^{[1
]}

McDermott, Thomas E. ^{[1
]}

Mukherjee, Monish ^{[1
]}

Diedesch, Michael ^{[2
]}

Bose, Anjan ^{[3
]}

机构：

[1] Pacific Northwest Natl Lab, Richland, WA 99354 USA

[2] Avista Corp, Spokane, WA USA

[3] Washington State Univ, Pullman, WA USA

来源：

2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM | 2023年

关键词：

Buildings; data-driven modeling; deep learning; energy storage; distributed power generation; transactive energy;

D O I：

10.1109/PESGM52003.2023.10253057

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A microgrid with solar, storage, and responsive load resources has been implemented and tested on an urban academic campus. Through modeling and simulation, a consensus transactive energy mechanism has been implemented, with each resource participating as a virtual battery. Most owners of large buildings do not have the information and expertise to develop and validate suitable models of their buildings using available tools. To mitigate this adoption barrier, a data -driven building model has been implemented and validated. It uses 5 -minute weather data, 10-second feeder data, 3 -second revenue meter data, energy audit information, and a load reduction test conducted by the building owner. The model tracks daily and seasonal variations, along with changes in grid voltage and feeder load.

引用

页数：5

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