Distributed Price-Based Demand Response Program for Peak Shaving and Customer Satisfaction Maximization in Presence of Distributed Generation

  • Ahmed Mohamadain Elbadwi

Student thesis: Master's Thesis

Abstract

As the world's energy requirement grows and renewable energy sources gain traction, researchers and scientists are investigating and producing novel control and management strategies to provide robust, efficient, and sustainable solutions. The intermittent nature of Renewable Energy Sources (RES), along with the uncertainty of consumer consumption behavior, has a considerable impact on power system stability and flexibility. The purpose of this thesis is to investigate the advantages of participating in the Demand Response (DR) program, which is regarded as one of the most efficient mechanisms for managing and controlling the load side. Based on a review of the literature on various DR programs, a distributed residential price-based DR program was developed to achieve peak reduction, as well as minimization of electricity consumption and cost for customers and utility companies, protection of user satisfaction and privacy, and integration of Distributed Energy Resources (DER) such as Battery Energy Storage System (BESS) and Rooftop Photovoltaic (PV). The IEEE 13 node feeder with residential consumers is being used as a case study, and the key findings of the proposed algorithm encompass a 42% peak reduction and a 34% drop in Peak to Average Ratio (PAR) from 1.91 to 1.25. The deployed algorithm not only minimized peaks and consumption but also secured users' anonymity and maintained a desirable degree of comfort and satisfaction. Furthermore, the algorithm effectively regulated BESS charging and discharging, resulting in 23 % reduction in electricity bills. When executed in the stated case study with 90 residences with various types of appliances, the suggested method converged in 16 iterations and in 27 seconds.
Date of AwardJul 2022
Original languageAmerican English

Keywords

  • DR program
  • Peak Shaving
  • Peak to Average Ratio
  • Utility maximization
  • Distributed Algorithm
  • Privacy.

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