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A Home Energy Management System With Renewable Energy and Energy Storage Utilizing Main Grid and Electricity Sellingopen access

Authors
Dinh, Huy TruongYun, JaeseokKim, Dong MinLee, Kyu-HaengKim, Daehee
Issue Date
2020
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Optimization; Renewable energy sources; Meteorology; Energy storage; Convergence; Peak to average power ratio; Home energy management systems; electricity selling; renewable energy sources; energy storage systems; day-ahead price; meta-heuristic algorithms
Citation
IEEE Access, v.8, pp 49436 - 49450
Pages
15
Journal Title
IEEE Access
Volume
8
Start Page
49436
End Page
49450
URI
https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/3729
DOI
10.1109/ACCESS.2020.2979189
ISSN
2169-3536
Abstract
With the development of new technologies in the field of renewable energy and batteries, increasing number of houses have been equipped with renewable energy sources (RES) and energy storage systems (ESS) to reduce home energy cost. These houses usually have home energy management systems (HEMS) to control and schedule every electrical device. Various studies have been conducted on HEMS and optimization algorithms for energy cost and peak-to-average ratio (PAR) reduction. However, none of papers give a sufficient study on the utilization of main grid's electricity and selling electricity. In this paper, firstly, we propose a new HEMS architecture with RES and ESS where we take utilization of the electricity of the main grid and electricity selling into account. With the proposed HEMS, we build general mathematical formulas for energy cost and PAR during a day. We then optimize these formulas using both the particle swarm optimization (PSO) and the binary particle swarm optimization (BPSO). Results clearly show that, with our HEMS system, RES and ESS can help to drop home energy cost significantly to 19.7%, compared with the results of previous works. By increasing charge/discharge rate of ESS, energy cost can be decreased by 4.3% for 0.6 kW and 8.5% for 0.9 kW. Moreover, by using multi-objective optimization, our system can achieve better PAR with an acceptable energy cost.
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