IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v192y2020ics0360544219322431.html
   My bibliography  Save this article

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

Author

Listed:
  • 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).

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219322431
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, Elsevier, vol. 45(C), pages 671-679.
    2. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    3. Shirazi, Elham & Jadid, Shahram, 2017. "Cost reduction and peak shaving through domestic load shifting and DERs," Energy, Elsevier, vol. 124(C), pages 146-159.
    4. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    5. Rastegar, Mohammad & Fotuhi-Firuzabad, Mahmud & Aminifar, Farrokh, 2012. "Load commitment in a smart home," Applied Energy, Elsevier, vol. 96(C), pages 45-54.
    6. Yahia, Z. & Pradhan, A., 2018. "Optimal load scheduling of household appliances considering consumer preferences: An experimental analysis," Energy, Elsevier, vol. 163(C), pages 15-26.
    7. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    8. Liu, Yang & Yu, Nanpeng & Wang, Wei & Guan, Xiaohong & Xu, Zhanbo & Dong, Bing & Liu, Ting, 2018. "Coordinating the operations of smart buildings in smart grids," Applied Energy, Elsevier, vol. 228(C), pages 2510-2525.
    9. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:192:y:2020:i:c:s0360544219322431. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.journals.elsevier.com/energy .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.