Dynamic Price-Based Demand Response through Linear Regression for Microgrids with Renewable Energy Resources

Muhammad Arshad Shehzad Hassan, Ussama Assad, Umar Farooq, Asif Kabir, Muhammad Zeeshan Khan, S. Sabahat H. Bukhari, Zain ul Abidin Jaffri, Judit Oláh and József Popp
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Muhammad Arshad Shehzad Hassan: Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan
Ussama Assad: 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 & Kashmir, 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 4, 1-17

Abstract: The green innovations in the energy sector are smart solutions to meet the excessive power requirements through renewable energy resources (RERs). These resources have forwarded the revolutionary relief in control of carbon dioxide gaseous emissions from traditional energy resources. The use of RERs in a heuristic manner is necessary to meet the demand side management in microgrids (MGs). The pricing scheme limitations hinder the profit maximization of MG and their customers. In addition, recent pricing schemes lack mechanistic underpinning. Therefore, a dynamic electricity pricing scheme through linear regression is designed for RERs to maximize the profit of load customers (changeable and unchangeable) in MG. The demand response optimization problem is solved through the particle swarm optimization (PSO) technique. The proposed dynamic electricity pricing scheme is evaluated under two different scenarios. The simulation results verified that the proposed dynamic electricity pricing scheme sustained the profit margins and comforts for changeable and unchangeable load customers as compared to fixed electricity pricing schemes in both scenarios. Hence, the proposed dynamic electricity pricing scheme can readily be used for real microgrids (MGs) to grasp the goal for cleaner energy production.

Keywords: renewable energy resources; linear regression; dynamic electricity pricing scheme; demand response; particle swarm optimization (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 (4)

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