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Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method

Author

Listed:
  • Masoud Alilou

    (Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran)

  • Gevork B. Gharehpetian

    (Electrical Engineering Department, Amirkabir University of Technology, Tehran 1591634311, Iran)

  • Roya Ahmadiahangar

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
    Smart City Center of Excellence (Finest Twins), Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Argo Rosin

    (Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
    Smart City Center of Excellence (Finest Twins), Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Amjad Anvari-Moghaddam

    (Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark)

Abstract

The two-way communication of electricity and information in smart homes facilitates the optimal management of devices with the ability to charge and discharge, such as electric vehicles and electrical storage systems. These devices can be scheduled considering domestic renewable energy units, the energy consumption of householders, the electricity tariff of the grid, and other predetermined parameters in order to improve their efficiency and also the technical and economic indices of the smart home. In this paper, a novel framework based on decision vectors and the analytical hierarchy process method is investigated to find the optimal operation schedule of these devices for the day-ahead performance of smart homes. The initial data of the electric vehicle and the electrical storage system are modeled stochastically. The aim of this work is to minimize the electricity cost and the peak demand of the smart home by optimal operation of the electric vehicle and the electrical storage system. Firstly, the different decision vectors for charging and discharging these devices are introduced based on the market price, the produce power of the domestic photovoltaic panel, and the electricity demand of the smart home. Secondly, the analytical hierarchy process method is utilized to implement the various priorities of decision criteria and calculate the ultimate decision vectors. Finally, the operation schedule of the electric vehicle and the electrical storage system is selected based on the ultimate decision vectors considering the operational constraints of these devices and the constraints of charging and discharging priorities. The proposed method is applied to a sample smart home considering different priorities of decision criteria. Numerical results present that although the combination of decision criteria with a high rank of electricity demand has the highest improvement of technical and economic indices of the smart home by about 12 and 26%, the proposed method has appropriate performance in all scenarios for selecting the optimal operation schedule of the electric vehicles and the electrical storage system.

Suggested Citation

  • Masoud Alilou & Gevork B. Gharehpetian & Roya Ahmadiahangar & Argo Rosin & Amjad Anvari-Moghaddam, 2022. "Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11773-:d:919093
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    References listed on IDEAS

    as
    1. Arshad Mohammad & Mohd Zuhaib & Imtiaz Ashraf & Marwan Alsultan & Shafiq Ahmad & Adil Sarwar & Mali Abdollahian, 2021. "Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability," Energies, MDPI, vol. 14(24), pages 1-27, December.
    2. Hafiz Majid Hussain & Nadeem Javaid & Sohail Iqbal & Qadeer Ul Hasan & Khursheed Aurangzeb & Musaed Alhussein, 2018. "An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid," Energies, MDPI, vol. 11(1), pages 1-28, January.
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