Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm
Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823005288 |
| _version_ | 1797733065898852352 |
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| author | Abdul Mateen Muhammad Wasim Abdul Ahad Tehreem Ashfaq Muddesar Iqbal Amjad Ali |
| author_facet | Abdul Mateen Muhammad Wasim Abdul Ahad Tehreem Ashfaq Muddesar Iqbal Amjad Ali |
| author_sort | Abdul Mateen |
| collection | DOAJ |
| description | Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. |
| first_indexed | 2024-03-12T12:23:40Z |
| format | Article |
| id | doaj.art-64285292e2b24c9a86a0385ccd1d5486 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-12T12:23:40Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-64285292e2b24c9a86a0385ccd1d54862023-08-30T05:49:57ZengElsevierAlexandria Engineering Journal1110-01682023-08-0177593611Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithmAbdul Mateen0Muhammad Wasim1Abdul Ahad2Tehreem Ashfaq3Muddesar Iqbal4Amjad Ali5Department of Computer Science, University of Management and Technology, Sialkot Campus, PakistanDepartment of Computer Science, University of Management and Technology, Sialkot Campus, PakistanDepartment of Computer Science, University of Management and Technology, Sialkot Campus, Pakistan; School of Software, Northwestern Polytechnical University, Xian, Shaanxi 710072, PR China; Corresponding authors at: Division of Computer Science and Informatics, School of Engineering, London South Bank University, London SE1 0AA, U.K. and Department of Computer Science, University of Management and Technology, Sialkot Campus, Pakistan (M. Iqbal and A. Ahad).City, University of London, EC1V 0HB, United KingdomRenewable Energy Laboratory, Communications and Networks Engineering Department, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia; Division of Computer Science and Informatics, School of Engineering, London South Bank University, London SE1 0AA, U.K; Corresponding authors at: Division of Computer Science and Informatics, School of Engineering, London South Bank University, London SE1 0AA, U.K. and Department of Computer Science, University of Management and Technology, Sialkot Campus, Pakistan (M. Iqbal and A. Ahad).Information and Computing Technology (ICT) Division, College of Science and Engineering (CSE), Hamad Bin Khalifa University, Doha, QatarDemand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC.http://www.sciencedirect.com/science/article/pii/S1110016823005288Smart gridFlower pollinationDemand responseHome energy managementCritical peak pricingReal time pricing |
| spellingShingle | Abdul Mateen Muhammad Wasim Abdul Ahad Tehreem Ashfaq Muddesar Iqbal Amjad Ali Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm Alexandria Engineering Journal Smart grid Flower pollination Demand response Home energy management Critical peak pricing Real time pricing |
| title | Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm |
| title_full | Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm |
| title_fullStr | Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm |
| title_full_unstemmed | Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm |
| title_short | Smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm |
| title_sort | smart energy management system for minimizing electricity cost and peak to average ratio in residential areas with hybrid genetic flower pollination algorithm |
| topic | Smart grid Flower pollination Demand response Home energy management Critical peak pricing Real time pricing |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823005288 |
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