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Title: Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics

Abstract

A distributed energy management system for community microgrids is developed here. Unlike a centralized optimization-based energy management system, it schedules distributed energy resources and energy storage systems, as well as residential appliances, indirectly through iterative interaction between the microgrid central controller and home energy management systems, based on price signals. In each iteration, the microgrid central controller adjusts the scheduling of distributed energy resources and energy storage systems at the microgrid level. Meanwhile, the home energy management system of each house adjusts the scheduling of residential appliances. Then, the energy price at each bus is updated according to the unbalanced power between generation and demand. The optimization converges when the unbalanced power of all buses is close to zero, i.e., the microgrid central controller and home energy management systems reach an agreement on the energy price and generation/consumption. In particular, a detailed thermal dynamic model of the house is integrated into the HEMS scheduling for intelligent control of the heating, ventilation, and air-conditioning system by customers. Lastly, the distribution network is also considered to make the energy price signal locational. Results of case studies validate the efficacy of the proposed distributed energy management system.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1];  [3];  [4]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Northeast Electric Power University, Jilin (China)
  3. Global Energy Interconnection Research Institute North America (GEIRINA), San Jose, CA (United States)
  4. Univ. of Tennessee, Knoxville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Electricity (OE)
OSTI Identifier:
1494905
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 239; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 24 POWER TRANSMISSION AND DISTRIBUTION; Distributed optimization; Community microgrids; Energy management; Distribution network; Building thermal dynamics

Citation Formats

Liu, Guodong, Jiang, Tao, Ollis, Thomas Ben, Zhang, Xiaohu, and Tomsovic, Kevin. Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics. United States: N. p., 2019. Web. doi:10.1016/j.apenergy.2019.01.210.
Liu, Guodong, Jiang, Tao, Ollis, Thomas Ben, Zhang, Xiaohu, & Tomsovic, Kevin. Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics. United States. https://doi.org/10.1016/j.apenergy.2019.01.210
Liu, Guodong, Jiang, Tao, Ollis, Thomas Ben, Zhang, Xiaohu, and Tomsovic, Kevin. Fri . "Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics". United States. https://doi.org/10.1016/j.apenergy.2019.01.210. https://www.osti.gov/servlets/purl/1494905.
@article{osti_1494905,
title = {Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics},
author = {Liu, Guodong and Jiang, Tao and Ollis, Thomas Ben and Zhang, Xiaohu and Tomsovic, Kevin},
abstractNote = {A distributed energy management system for community microgrids is developed here. Unlike a centralized optimization-based energy management system, it schedules distributed energy resources and energy storage systems, as well as residential appliances, indirectly through iterative interaction between the microgrid central controller and home energy management systems, based on price signals. In each iteration, the microgrid central controller adjusts the scheduling of distributed energy resources and energy storage systems at the microgrid level. Meanwhile, the home energy management system of each house adjusts the scheduling of residential appliances. Then, the energy price at each bus is updated according to the unbalanced power between generation and demand. The optimization converges when the unbalanced power of all buses is close to zero, i.e., the microgrid central controller and home energy management systems reach an agreement on the energy price and generation/consumption. In particular, a detailed thermal dynamic model of the house is integrated into the HEMS scheduling for intelligent control of the heating, ventilation, and air-conditioning system by customers. Lastly, the distribution network is also considered to make the energy price signal locational. Results of case studies validate the efficacy of the proposed distributed energy management system.},
doi = {10.1016/j.apenergy.2019.01.210},
journal = {Applied Energy},
number = C,
volume = 239,
place = {United States},
year = {Fri Feb 01 00:00:00 EST 2019},
month = {Fri Feb 01 00:00:00 EST 2019}
}

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