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Title: A New Distributed Optimization for Community Microgrids Scheduling

Abstract

This paper proposes a distributed optimization model for community microgrids considering the building thermal dynamics and customer comfort preference. The microgrid central controller (MCC) minimizes the total cost of operating the community microgrid, including fuel cost, purchasing cost, battery degradation cost and voluntary load shedding cost based on the customers' consumption, while the building energy management systems (BEMS) minimize their electricity bills as well as the cost associated with customer discomfort due to room temperature deviation from the set point. The BEMSs and the MCC exchange information on energy consumption and prices. When the optimization converges, the distributed generation scheduling, energy storage charging/discharging and customers' consumption as well as the energy prices are determined. In particular, we integrate the detailed thermal dynamic characteristics of buildings into the proposed model. 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 proposed model.

Authors:
 [1];  [2]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1325471
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: The 50th Huwaii International Conference on System Science (HICSS-50), Waikoloa, HI, USA, 20170104, 20170107
Country of Publication:
United States
Language:
English
Subject:
Community microgrids; scheduling; thermal dynamic model; decentralized optimization; alternating direction method of multipliers (ADMM)

Citation Formats

Starke, Michael R, and Tomsovic, Kevin. A New Distributed Optimization for Community Microgrids Scheduling. United States: N. p., 2017. Web.
Starke, Michael R, & Tomsovic, Kevin. A New Distributed Optimization for Community Microgrids Scheduling. United States.
Starke, Michael R, and Tomsovic, Kevin. 2017. "A New Distributed Optimization for Community Microgrids Scheduling". United States.
@article{osti_1325471,
title = {A New Distributed Optimization for Community Microgrids Scheduling},
author = {Starke, Michael R and Tomsovic, Kevin},
abstractNote = {This paper proposes a distributed optimization model for community microgrids considering the building thermal dynamics and customer comfort preference. The microgrid central controller (MCC) minimizes the total cost of operating the community microgrid, including fuel cost, purchasing cost, battery degradation cost and voluntary load shedding cost based on the customers' consumption, while the building energy management systems (BEMS) minimize their electricity bills as well as the cost associated with customer discomfort due to room temperature deviation from the set point. The BEMSs and the MCC exchange information on energy consumption and prices. When the optimization converges, the distributed generation scheduling, energy storage charging/discharging and customers' consumption as well as the energy prices are determined. In particular, we integrate the detailed thermal dynamic characteristics of buildings into the proposed model. 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 proposed model.},
doi = {},
url = {https://www.osti.gov/biblio/1325471}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {1}
}

Conference:
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