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Energies 2017, 10(10), 1554; https://doi.org/10.3390/en10101554

Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics

1
Power & Energy Systems Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
2
Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
*
Author to whom correspondence should be addressed.
Received: 16 August 2017 / Revised: 19 September 2017 / Accepted: 27 September 2017 / Published: 10 October 2017
(This article belongs to the Section Electrical Power and Energy System)
Full-Text   |   PDF [971 KB, uploaded 10 October 2017]   |  

Abstract

This paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed multi-objective optimization model optimizes not only the operating cost, including fuel cost, electricity purchasing/selling, storage degradation, voluntary load shedding and the cost associated with customer discomfort as a result of the room temperature deviation from the customer setting point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, we integrate the detailed thermal dynamic model of buildings into the distribution optimal power flow (D-OPF) model for the optimal operation. Thus, the proposed model can directly schedule the heating, ventilation and air-conditioning (HVAC) systems of buildings intelligently so as to to reduce the electricity cost without compromising the comfort of customers. Results of numerical simulation validate the effectiveness of the proposed model and significant savings in electricity cost with network operational constraints satisfied. View Full-Text
Keywords: community microgrids; distribution optimal power flow; multiobjective optimization; thermal dynamic model; HVAC community microgrids; distribution optimal power flow; multiobjective optimization; thermal dynamic model; HVAC
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, G.; Ollis, T.B.; Xiao, B.; Zhang, X.; Tomsovic, K. Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics. Energies 2017, 10, 1554.

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