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Title: Coordination of Behind-the-Meter Energy Storage and Building Loads: Optimization with Deep Learning Models*

Abstract

With the increasing penetration of renewable energy systems and energy storage systems in buildings, it is critical to optimize system operation. In this paper, we develop an optimization strategy to minimize the operation cost as well as maintain indoor thermal comfort, for a building integrated with battery and PV. A Recurrent Neural Network (RNN) is used to predict building thermal load and zone temperatures. A black-box optimization algorithm known as nonlinear optimization by mesh adaptive direct search (NOMADS) is employed in simulation to provide look-ahead optimal battery dispatch and zone temperature set-point schedules so that the operation cost is minimized. Field data collected from a medium sized office building at Pacific Northwest National Laboratory (PNNL) integrated with Photovoltaic (PV) system model and battery storage system model is used to demonstrate the proposed methodology. Compared with rule based methods, the optimization strategy obtained a lower cost of operation while satisfying comfort constraints.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1532525
Report Number(s):
PNNL-SA-138381
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy 2019), June 25-28, 2019, Phoenix, AZ
Country of Publication:
United States
Language:
English

Citation Formats

Chen, Yimin, Chandan, Vikas, Huang, Yunzhi, Alam, Md Jan E., Ahmed, Osman, and Smith, Lane D. Coordination of Behind-the-Meter Energy Storage and Building Loads: Optimization with Deep Learning Models*. United States: N. p., 2019. Web. doi:10.1145/3307772.3331025.
Chen, Yimin, Chandan, Vikas, Huang, Yunzhi, Alam, Md Jan E., Ahmed, Osman, & Smith, Lane D. Coordination of Behind-the-Meter Energy Storage and Building Loads: Optimization with Deep Learning Models*. United States. doi:10.1145/3307772.3331025.
Chen, Yimin, Chandan, Vikas, Huang, Yunzhi, Alam, Md Jan E., Ahmed, Osman, and Smith, Lane D. Fri . "Coordination of Behind-the-Meter Energy Storage and Building Loads: Optimization with Deep Learning Models*". United States. doi:10.1145/3307772.3331025.
@article{osti_1532525,
title = {Coordination of Behind-the-Meter Energy Storage and Building Loads: Optimization with Deep Learning Models*},
author = {Chen, Yimin and Chandan, Vikas and Huang, Yunzhi and Alam, Md Jan E. and Ahmed, Osman and Smith, Lane D.},
abstractNote = {With the increasing penetration of renewable energy systems and energy storage systems in buildings, it is critical to optimize system operation. In this paper, we develop an optimization strategy to minimize the operation cost as well as maintain indoor thermal comfort, for a building integrated with battery and PV. A Recurrent Neural Network (RNN) is used to predict building thermal load and zone temperatures. A black-box optimization algorithm known as nonlinear optimization by mesh adaptive direct search (NOMADS) is employed in simulation to provide look-ahead optimal battery dispatch and zone temperature set-point schedules so that the operation cost is minimized. Field data collected from a medium sized office building at Pacific Northwest National Laboratory (PNNL) integrated with Photovoltaic (PV) system model and battery storage system model is used to demonstrate the proposed methodology. Compared with rule based methods, the optimization strategy obtained a lower cost of operation while satisfying comfort constraints.},
doi = {10.1145/3307772.3331025},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Conference:
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