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An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

Author

Listed:
  • Thamer Alquthami

    (Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ahmad H. Milyani

    (Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Muhammad Awais

    (Department of Technology, The University of Lahore, Lahore 54000, Pakistan)

  • Muhammad B. Rasheed

    (Department of Electronics and Electrical Systems, The University of Lahore, Lahore 54000, Pakistan
    Computer Engineering Department, University of Alcalá, 28805 Alcalá de Henares, Spain)

Abstract

Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process.

Suggested Citation

  • Thamer Alquthami & Ahmad H. Milyani & Muhammad Awais & Muhammad B. Rasheed, 2021. "An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6066-:d:563862
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    References listed on IDEAS

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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Muhammad Babar Rasheed & Nadeem Javaid & Ashfaq Ahmad & Mohsin Jamil & Zahoor Ali Khan & Umar Qasim & Nabil Alrajeh, 2016. "Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing," Energies, MDPI, vol. 9(8), pages 1-25, July.
    3. Urooj Asgher & Muhammad Babar Rasheed & Ameena Saad Al-Sumaiti & Atiq Ur-Rahman & Ihsan Ali & Amer Alzaidi & Abdullah Alamri, 2018. "Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources," Energies, MDPI, vol. 11(12), pages 1-26, December.
    4. Das, Ridoy & Wang, Yue & Putrus, Ghanim & Kotter, Richard & Marzband, Mousa & Herteleer, Bert & Warmerdam, Jos, 2020. "Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services," Applied Energy, Elsevier, vol. 257(C).
    5. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
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    Cited by:

    1. Bugaje, Bilal & Rutherford, Peter & Clifford, Mike, 2022. "Convenience in a residence with demand response: A system dynamics simulation model," Applied Energy, Elsevier, vol. 314(C).
    2. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    3. Dan Zhou & Xiaodie Niu & Yuzhe Xie & Peng Li & Jiandi Fang & Fanghong Guo, 2022. "An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response," Sustainability, MDPI, vol. 14(7), pages 1-17, March.

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