Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

Ussama Assad, Muhammad Arshad Shehzad Hassan, Umar Farooq, Asif Kabir, Muhammad Zeeshan Khan, S. Sabahat H. Bukhari, Zain ul Abidin Jaffri, Judit Oláh and József Popp
Additional contact information
Ussama Assad: Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan
Muhammad Arshad Shehzad Hassan: Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan
Umar Farooq: Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan
Asif Kabir: Department of CS & IT, University of Kotli, Azad Jammu and Kashmir 11100, Pakistan
Muhammad Zeeshan Khan: Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan
S. Sabahat H. Bukhari: School of Computer Science, Neijiang Normal University, Neijiang 641100, China
Zain ul Abidin Jaffri: College of Physics and Electronic Information Engineering, Neijiang Normal University, Neijiang 641100, China
Judit Oláh: Faculty of Economics and Business, University of Debrecen, 4032 Debrecen, Hungary
József Popp: College of Business and Economics, University of Johannesburg, Johannesburg 2006, South Africa

Energies, 2022, vol. 15, issue 6, 1-36

Abstract: In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smart grid.

Keywords: renewable energy resources; demand response; intelligent algorithms; machine learning; quantum computing; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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