Liu, G.; Ollis, T.B.; Xiao, B.; Zhang, X.; Tomsovic, K. Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics. Preprints2017, 2017090060. https://doi.org/10.20944/preprints201709.0060.v1
APA Style
Liu, G., Ollis, T.B., Xiao, B., Zhang, X., & Tomsovic, K. (2017). Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics. Preprints. https://doi.org/10.20944/preprints201709.0060.v1
Chicago/Turabian Style
Liu, G., Xiaohu Zhang and Kevin Tomsovic. 2017 "Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics" Preprints. https://doi.org/10.20944/preprints201709.0060.v1
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, 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. 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 savings in electricity cost with network operational constraints satisfied.
Keywords
community microgrids; distribution optimal power flow; multiobjective optimization; thermal dynamic model; HVAC
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
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.
