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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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2023-09-01
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Series: | Frontiers in Energy Research |
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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. |
first_indexed | 2024-03-12T01:33:39Z |
format | Article |
id | doaj.art-d7b2ec7b4b3c4a06a844ca3cae32c786 |
institution | Directory Open Access Journal |
issn | 2296-598X |
language | English |
last_indexed | 2024-03-12T01:33:39Z |
publishDate | 2023-09-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Energy Research |
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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