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A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

Author

Listed:
  • Zubair Khalid

    (Department of Technology, The University of Lahore, Lahore 54000, Pakistan
    All authors contributed equally to this work.)

  • Ghulam Abbas

    (Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan
    All authors contributed equally to this work.)

  • Muhammad Awais

    (Department of Technology, The University of Lahore, Lahore 54000, Pakistan
    All authors contributed equally to this work.)

  • Thamer Alquthami

    (Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    All authors contributed equally to this work.)

  • Muhammad Babar Rasheed

    (Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54000, Pakistan
    All authors contributed equally to this work.)

Abstract

In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.

Suggested Citation

  • Zubair Khalid & Ghulam Abbas & Muhammad Awais & Thamer Alquthami & Muhammad Babar Rasheed, 2020. "A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid," Energies, MDPI, vol. 13(5), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1062-:d:326418
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    References listed on IDEAS

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    1. Zafar Iqbal & Nadeem Javaid & Syed Muhammad Mohsin & Syed Muhammad Abrar Akber & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling," Energies, MDPI, vol. 11(10), pages 1-31, October.
    2. Sofana Reka. S & Tomislav Dragičević & Pierluigi Siano & S.R. Sahaya Prabaharan, 2019. "Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review," Energies, MDPI, vol. 12(11), pages 1-17, June.
    3. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    4. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving," Applied Energy, Elsevier, vol. 147(C), pages 246-257.
    5. Noor, Sana & Yang, Wentao & Guo, Miao & van Dam, Koen H. & Wang, Xiaonan, 2018. "Energy Demand Side Management within micro-grid networks enhanced by blockchain," Applied Energy, Elsevier, vol. 228(C), pages 1385-1398.
    6. Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
    7. Christian Borgs & Ozan Candogan & Jennifer Chayes & Ilan Lobel & Hamid Nazerzadeh, 2014. "Optimal Multiperiod Pricing with Service Guarantees," Management Science, INFORMS, vol. 60(7), pages 1792-1811, July.
    8. Shaojie Tang & Jing Yuan & Zhao Zhang & Ding-zhu Du, 2017. "iGreen: green scheduling for peak demand minimization," Journal of Global Optimization, Springer, vol. 69(1), pages 45-67, September.
    9. Hyun-soo Ahn & Mehmet Gümüş & Philip Kaminsky, 2007. "Pricing and Manufacturing Decisions When Demand Is a Function of Prices in Multiple Periods," Operations Research, INFORMS, vol. 55(6), pages 1039-1057, December.
    10. Luthander, Rasmus & Widén, Joakim & Munkhammar, Joakim & Lingfors, David, 2016. "Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment," Energy, Elsevier, vol. 112(C), pages 221-231.
    11. Macedo, M.N.Q. & Galo, J.J.M. & de Almeida, L.A.L. & de C. Lima, A.C., 2015. "Demand side management using artificial neural networks in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 128-133.
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    Cited by:

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