A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid
In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management control...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-03-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/10/3/319 |
| _version_ | 1798042083312795648 |
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| author | Nadeem Javaid Sakeena Javaid Wadood Abdul Imran Ahmed Ahmad Almogren Atif Alamri Iftikhar Azim Niaz |
| author_facet | Nadeem Javaid Sakeena Javaid Wadood Abdul Imran Ahmed Ahmad Almogren Atif Alamri Iftikhar Azim Niaz |
| author_sort | Nadeem Javaid |
| collection | DOAJ |
| description | In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics. |
| first_indexed | 2024-04-11T22:30:33Z |
| format | Article |
| id | doaj.art-ca94fd10273b4fe1b3768a7e41e92288 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:30:33Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ca94fd10273b4fe1b3768a7e41e922882022-12-22T03:59:26ZengMDPI AGEnergies1996-10732017-03-0110331910.3390/en10030319en10030319A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart GridNadeem Javaid0Sakeena Javaid1Wadood Abdul2Imran Ahmed3Ahmad Almogren4Atif Alamri5Iftikhar Azim Niaz6COMSATS Institute of Information Technology, Islamabad 44000, PakistanCOMSATS Institute of Information Technology, Islamabad 44000, PakistanResearch Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi ArabiaInstitute of Management Sciences (IMS), Peshawar 25000, PakistanResearch Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi ArabiaResearch Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi ArabiaCOMSATS Institute of Information Technology, Islamabad 44000, PakistanIn recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.http://www.mdpi.com/1996-1073/10/3/319Demand side managementpriority schedulinguser comfortheuristic optimization |
| spellingShingle | Nadeem Javaid Sakeena Javaid Wadood Abdul Imran Ahmed Ahmad Almogren Atif Alamri Iftikhar Azim Niaz A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid Energies Demand side management priority scheduling user comfort heuristic optimization |
| title | A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid |
| title_full | A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid |
| title_fullStr | A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid |
| title_full_unstemmed | A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid |
| title_short | A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid |
| title_sort | hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid |
| topic | Demand side management priority scheduling user comfort heuristic optimization |
| url | http://www.mdpi.com/1996-1073/10/3/319 |
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