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Optimal scheduling of resources and appliances in smart homes under uncertainties considering participation in spot and contractual markets

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  • Mohammadi Rad, Amin
  • Barforoushi, Taghi

Abstract

In this paper, a new framework is proposed for optimal energy resource management in a smart home comprising of controllable and non-controllable appliances and local renewable resources. Here, the smart home can procure the required energy through both the spot and contractual markets under Real-Time Pricing (RTP) mechanism. Uncertainties in the production of renewable resources, spot market price and non-controllable appliances, modeled by sets of scenarios, are considered. Controllable appliances are classified into two groups of continuous and interruptible, while the non-controllable appliances are classified into elastic and inelastic groups. Both RTP and Inclining Block Rate (IBR) tariffs are used to prevent the consumption in certain hours in addition to a reflection of spot market fluctuations. First-order Markov chain and Multi-Layer Perceptron (MLP) neural networks are used to predict the production of renewable resources. The optimization problem is designed to minimize the consumer’s expected net cost in the form of a two-stage stochastic problem and is formulated as a MILP problem. The model is applied to smart home to illustrate the impact of the proposed model. Simulation results show the positive impact of the proposed scheduling method on reducing consumer costs and network Peak to Average Ratio (PAR).

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  • Mohammadi Rad, Amin & Barforoushi, Taghi, 2020. "Optimal scheduling of resources and appliances in smart homes under uncertainties considering participation in spot and contractual markets," Energy, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219322431
    DOI: 10.1016/j.energy.2019.116548
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    Cited by:

    1. Wang, Fei & Ge, Xinxin & Yang, Peng & Li, Kangping & Mi, Zengqiang & Siano, Pierluigi & Duić, Neven, 2020. "Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing," Energy, Elsevier, vol. 213(C).
    2. Kühnbach, Matthias & Bekk, Anke & Weidlich, Anke, 2022. "Towards improved prosumer participation: Electricity trading in local markets," Energy, Elsevier, vol. 239(PE).
    3. Ghayour, Sepideh Saravani & Barforoushi, Taghi, 2022. "Optimal scheduling of electrical and thermal resources and appliances in a smart home under uncertainty," Energy, Elsevier, vol. 261(PA).
    4. Zhang, Lizhi & Kuang, Jiyuan & Sun, Bo & Li, Fan & Zhang, Chenghui, 2020. "A two-stage operation optimization method of integrated energy systems with demand response and energy storage," Energy, Elsevier, vol. 208(C).
    5. Xiong, Binyu & Wei, Feng & Wang, Yifei & Xia, Kairui & Su, Fuwen & Fang, Yingjia & Gao, Zuchang & Wei, Zhongbao, 2024. "Hybrid robust-stochastic optimal scheduling for multi-objective home energy management with the consideration of uncertainties," Energy, Elsevier, vol. 290(C).
    6. Neeraj Bokde & Bo Tranberg & Gorm Bruun Andresen, 2020. "A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications," Papers 2009.03160, arXiv.org.

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