Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41526

A New Distributed Optimization for Community Microgrids Scheduling

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Title: A New Distributed Optimization for Community Microgrids Scheduling
Authors: Liu, Guodong
Starke, Michael
Xiao, Bailu
Zhang, Xiaohu
Tomsovic, Kevin
Keywords: Community microgrids
scheduling
thermal dynamic model
decentralized optimization
alternating direction method of multipliers (ADMM).
Issue Date: 04 Jan 2017
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.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41526
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.369
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Markets, Policy, and Computation Minitrack



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