Fair cost distribution among smart homes with microgrid

• Published in: Energy conversion and management 2014-04, Vol.80, p.498-508
• Main Authors: Zhang, Di, Liu, Songsong, Papageorgiou, Lazaros G.
• Format: Article
• Language: English
• Subjects: Applied sciences; Cost engineering; Distributed generation; Electric power distribution; Energy; Energy costs; Energy distribution; Exact sciences and technology; Fair planning/scheduling; Households; Lexicographic minimax method; Mathematical models; Microgrid; Mixed integer linear programming; Real-time pricing; Smart buildings; Smart homes
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2014.01.012

•Work aims at fair cost distribution among smart homes with microgrid.•An MILP-based approach is adopted based on lexicographic minimax method.•Domestic appliances from multiple smart homes are scheduled.•Results from two illustrative examples indicate fair cost distribution. Microgrid is composed of a set of distributed energy resources (DER) and is considered as an alternative energy providing system to the current centralised energy generation. Smart homes equipped with smart grid technology, such as smart meter and communication system, are becoming popular for their lower energy cost and provision of comfort. Flexible energy-consuming household tasks can be scheduled coordinately among multiple homes which share the common microgrid. When local DERs cannot fulfill the whole demand, smart homes will compete with each other to obtain energy from local DERs and achieve their respective lowest energy cost. In this paper, a mathematical programming formulation is presented for the fair cost distribution among smart homes with microgrid. The proposed model is based on the lexicographic minimax method using a mixed integer linear programming (MILP) approach. One-day forecasted energy cost of each smart home is minimised under fairness concern. DER operation, DER output sharing among smart homes and electricity consumption household tasks are scheduled. Two numerical examples with 10 and 50 smart homes are studied. The computational results illustrate that the proposed approach can obtain obvious cost savings (30% and 24% respectively) and fair cost distribution among multiple homes under given fairness scenario.