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iGreen: green scheduling for peak demand minimization

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Abstract

Home owners are typically charged differently when they consume power at different periods within a day. Specifically, they are charged more during peak periods. Thus, in this paper, we explore how scheduling algorithms can be designed to minimize the peak energy consumption of a group of homes served by the same substation. We assume that a set of demand/response switches are deployed at a group of homes to control the activities of different appliances such as air conditioners or electric water heaters in these homes. Given a set of appliances, each appliance is associated with its instantaneous power consumption and duration, our objective is to decide when to activate different appliances in order to reduce the peak power consumption. This scheduling problem is shown to be NP-Hard. To tackle this problem, we propose a set of appliance scheduling algorithms under both offline and online settings. For the offline setting, we propose a constant ratio approximation algorithm (with approximation ratio \(\frac{1+\sqrt{5}}{2}+1\)). For the online setting, we adopt a greedy algorithm whose competitive ratio is also bounded. We conduct extensive simulations using real-life appliance energy consumption data trace to evaluate the performance of our algorithms. Extensive evaluations show that our schedulers significantly reduce the peak demand when compared with several existing heuristics.

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Acknowledgements

This research is supported by NSFC (61222201,11531011).

Funding was provided by National Natural Science Foundation of China (Grant No. 11531011), Talent Youth Project (Grant No. 2013711011).

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Authors and Affiliations

  1. University of Texas at Dallas, Richardson, TX, USA

    Shaojie Tang, Jing Yuan & Ding-zhu Du

  2. Zhejiang Normal University, Jinhua, China

    Zhao Zhang

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  1. Shaojie Tang
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  2. Jing Yuan
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  3. Zhao Zhang
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  4. Ding-zhu Du
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Correspondence to Zhao Zhang.

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Tang, S., Yuan, J., Zhang, Z. et al. iGreen: green scheduling for peak demand minimization. J Glob Optim 69, 45–67 (2017). https://doi.org/10.1007/s10898-017-0524-y

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