A sustainable approach for demand side management considering demand response and renewable energy in smart grids

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To addr...

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Main Authors: Syed Yasir Ahmad, Ghulam Hafeez, Khursheed Aurangzeb, Khalid Rehman, Taimoor Ahmad Khan, Musaed Alhussein
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-09-01
Series:Frontiers in Energy Research
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2023.1212304/full
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author Syed Yasir Ahmad
Ghulam Hafeez
Khursheed Aurangzeb
Khalid Rehman
Taimoor Ahmad Khan
Musaed Alhussein
author_facet Syed Yasir Ahmad
Ghulam Hafeez
Khursheed Aurangzeb
Khalid Rehman
Taimoor Ahmad Khan
Musaed Alhussein
author_sort Syed Yasir Ahmad
collection DOAJ
description The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.
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spelling doaj.art-d7b2ec7b4b3c4a06a844ca3cae32c7862023-09-11T14:27:19ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-09-011110.3389/fenrg.2023.12123041212304A sustainable approach for demand side management considering demand response and renewable energy in smart gridsSyed Yasir Ahmad0Ghulam Hafeez1Khursheed Aurangzeb2Khalid Rehman3Taimoor Ahmad Khan4Musaed Alhussein5Department of Electrical Engineering, CECOS University of IT and Emerging Sciences, Peshawar, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Mardan, PakistanDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Electrical Engineering, CECOS University of IT and Emerging Sciences, Peshawar, PakistanSchool of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, United KingdomDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaThe development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1212304/fullsmart gridrenewable energy sourcesdemand responseday-ahead schedulingenergy management controllerelectric vehicles
spellingShingle Syed Yasir Ahmad
Ghulam Hafeez
Khursheed Aurangzeb
Khalid Rehman
Taimoor Ahmad Khan
Musaed Alhussein
A sustainable approach for demand side management considering demand response and renewable energy in smart grids
Frontiers in Energy Research
smart grid
renewable energy sources
demand response
day-ahead scheduling
energy management controller
electric vehicles
title A sustainable approach for demand side management considering demand response and renewable energy in smart grids
title_full A sustainable approach for demand side management considering demand response and renewable energy in smart grids
title_fullStr A sustainable approach for demand side management considering demand response and renewable energy in smart grids
title_full_unstemmed A sustainable approach for demand side management considering demand response and renewable energy in smart grids
title_short A sustainable approach for demand side management considering demand response and renewable energy in smart grids
title_sort sustainable approach for demand side management considering demand response and renewable energy in smart grids
topic smart grid
renewable energy sources
demand response
day-ahead scheduling
energy management controller
electric vehicles
url https://www.frontiersin.org/articles/10.3389/fenrg.2023.1212304/full
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