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Title: Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics

Abstract

Here, this paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed optimization model optimizes not only the operating cost, including fuel cost, purchasing cost, battery degradation cost, voluntary load shedding cost and the cost associated with customer discomfort due to room temperature deviation from the set point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, the detailed thermal dynamic model of buildings is integrated into the distribution optimal power flow (D-OPF) model for the optimal operation of community microgrids. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of the proposed model and significant saving in electricity cost could be achieved with network operational constraints satisfied.

Authors:
 [1]; ORCiD logo [1];  [1];  [2];  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. The Univ. of Tennessee, Knoxville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1399988
Grant/Contract Number:
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energies (Basel)
Additional Journal Information:
Journal Name: Energies (Basel); Journal Volume: 10; Journal Issue: 10; Journal ID: ISSN 1996-1073
Publisher:
MDPI AG
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; community microgrids; distribution optimal power flow; multiobjective optimization; thermal dynamic model; HVAC

Citation Formats

Liu, Guodong, Ollis, Thomas B., Xiao, Bailu, Zhang, Xiaohu, and Tomsovic, Kevin. Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics. United States: N. p., 2017. Web. doi:10.3390/en10101554.
Liu, Guodong, Ollis, Thomas B., Xiao, Bailu, Zhang, Xiaohu, & Tomsovic, Kevin. Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics. United States. doi:10.3390/en10101554.
Liu, Guodong, Ollis, Thomas B., Xiao, Bailu, Zhang, Xiaohu, and Tomsovic, Kevin. 2017. "Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics". United States. doi:10.3390/en10101554. https://www.osti.gov/servlets/purl/1399988.
@article{osti_1399988,
title = {Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics},
author = {Liu, Guodong and Ollis, Thomas B. and Xiao, Bailu and Zhang, Xiaohu and Tomsovic, Kevin},
abstractNote = {Here, this paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed optimization model optimizes not only the operating cost, including fuel cost, purchasing cost, battery degradation cost, voluntary load shedding cost and the cost associated with customer discomfort due to room temperature deviation from the set point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, the detailed thermal dynamic model of buildings is integrated into the distribution optimal power flow (D-OPF) model for the optimal operation of community microgrids. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of the proposed model and significant saving in electricity cost could be achieved with network operational constraints satisfied.},
doi = {10.3390/en10101554},
journal = {Energies (Basel)},
number = 10,
volume = 10,
place = {United States},
year = 2017,
month =
}

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