A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

Emad M. Ahmed, Rajarajeswari Rathinam, Suchitra Dayalan, George S. Fernandez, Ziad M. Ali, Shady H. E. Abdel Aleem and Ahmed I. Omar
Additional contact information
Emad M. Ahmed: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Rajarajeswari Rathinam: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
Suchitra Dayalan: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
George S. Fernandez: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
Ziad M. Ali: Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
Shady H. E. Abdel Aleem: Department of Electrical Engineering, Valley High Institute of Engineering and Technology, Science Valley Academy, Qalyubia 44971, Egypt
Ahmed I. Omar: Electrical Power and Machines Engineering, The Higher Institute of Engineering at El-Shorouk City, El-Shorouk City 11837, Egypt

Mathematics, 2021, vol. 9, issue 18, 1-24

Abstract: In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

Keywords: smart grid; demand-side management; dynamic pricing; time of use; real-time pricing; strawberry algorithm; particle swarm optimization; peak-to-average ratio (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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