Open AccessDistributed energy management for community microgrids considering phase balancing and peak shaving
Author(s) -
Liu Guodong,
Ollis Thomas B.,
Xiao Bailu,
Zhang Xiaohu,
Tomsovic Kevin
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.5881
Subject(s) - microgrid , distributed generation , energy management , peaking power plant , energy storage , computer science , energy consumption , load balancing (electrical power) , electricity , energy management system , power (physics) , energy (signal processing) , distributed power , automotive engineering , distributed computing , control (management) , electrical engineering , engineering , renewable energy , mathematics , voltage , statistics , physics , geometry , grid , quantum mechanics , artificial intelligence
In this study, a distributed energy management for community microgrids considering phase balancing and peak shaving is proposed. In each iteration, the house energy management system (HEMS) installed in each house minimises its electricity costs and the costs associated with the discomfort of customers due to deviations in indoor temperature from customers’ set points. At the community level, the microgrid central controller (MCC) schedules the distributed energy resources (DERs) and energy storage based on the received load profiles from customers and the forecast energy price at the point of common coupling. The MCC updates the energy price for each phase based on the amount of unbalanced power between generation and consumption. The updated energy price and unbalanced power for each phase are distributed to the HEMSs on corresponding phases. When the optimisation converges, the unbalanced power of each phase is close to zero. Meanwhile, the schedules of DERs, energy storage systems and the energy consumption of each house are determined by the MCC and HEMSs, separately. In particular, the phase balancing and peak shaving are considered in the proposed distributed energy management model. The effectiveness of the proposed distributed energy management has been demonstrated by case studies.