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Energies 2017, 10(9), 1326; doi:10.3390/en10091326

Automated Energy Scheduling Algorithms for Residential Demand Response Systems

School of Computer Science and Engineering, Chung-Ang University, 221 Heukseok, Dongjak, Seoul 156-756, Korea
Author to whom correspondence should be addressed.
Received: 6 July 2017 / Revised: 26 August 2017 / Accepted: 1 September 2017 / Published: 2 September 2017
(This article belongs to the Special Issue Innovative Methods for Smart Grids Planning and Management)
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Demand response technology is a key technology for distributing electricity tasks in response to electricity prices in a smart grid system. In the current demand response research, there has been much demand for an automated energy scheduling scheme that uses smart devices for residential customers in the smart grid. In this paper, two automated energy scheduling schemes are proposed for residential smart grid demand response systems: semi-automated scheduling and fully-automated scheduling. If it is possible to set the appliance preference, semi-automated scheduling will be conducted, and if it is impossible, fully-automated scheduling will be operated. The formulated optimization problems consider the electricity bill along with the user convenience. For the fully-automated scheduling, the appliance preference can automatically be found according to appliance type from the electricity consumption statistics. A performance evaluation validates that the proposed scheme shifts operation to avoid peak load, that the electricity bill is significantly reduced, and that user convenience is satisfied. View Full-Text
Keywords: residential demand response; smart grid; fully-automated scheduling residential demand response; smart grid; fully-automated scheduling

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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. (CC BY 4.0).

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MDPI and ACS Style

Park, L.; Jang, Y.; Bae, H.; Lee, J.; Park, C.Y.; Cho, S. Automated Energy Scheduling Algorithms for Residential Demand Response Systems. Energies 2017, 10, 1326.

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