Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

Suchitra Dayalan, Sheikh Suhaib Gul, Rajarajeswari Rathinam, George Fernandez Savari, Shady H. E. Abdel Aleem, Mohamed A. Mohamed () and Ziad M. Ali
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Suchitra Dayalan: Department of EEE, SRMIST, Kattankulathur 603203, India
Sheikh Suhaib Gul: Department of EEE, SRMIST, Kattankulathur 603203, India
Rajarajeswari Rathinam: Department of EEE, SRMIST, Kattankulathur 603203, India
George Fernandez Savari: Department of EEE, SRMIST, Kattankulathur 603203, India
Shady H. E. Abdel Aleem: Department of Electrical Engineering, Valley High Institute of Engineering and Technology, Science Valley Academy, Qalyubia 44971, Egypt
Mohamed A. Mohamed: Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt
Ziad M. Ali: Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia

Sustainability, 2022, vol. 14, issue 17, 1-25

Abstract: Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters.

Keywords: demand response; energy; smart-grid; grid operator; industrial customer; Stackelberg–particle swarm optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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