Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units
With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/3/611 |
| _version_ | 1828151819107303424 |
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| author | Ghulam Hafeez Nadeem Javaid Sohail Iqbal Farman Ali Khan |
| author_facet | Ghulam Hafeez Nadeem Javaid Sohail Iqbal Farman Ali Khan |
| author_sort | Ghulam Hafeez |
| collection | DOAJ |
| description | With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption. |
| first_indexed | 2024-04-11T22:02:56Z |
| format | Article |
| id | doaj.art-19c6790b2949414d9c8afb11f03c947b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:02:56Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-19c6790b2949414d9c8afb11f03c947b2022-12-22T04:00:51ZengMDPI AGEnergies1996-10732018-03-0111361110.3390/en11030611en11030611Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic UnitsGhulam Hafeez0Nadeem Javaid1Sohail Iqbal2Farman Ali Khan3COMSATS Institute of Information Technology, Islamabad 44000, PakistanCOMSATS Institute of Information Technology, Islamabad 44000, PakistanSEECS, National University of Science and Technology, Islamabad 44000, PakistanCOMSATS Institute of Information Technology, Attock 43600, PakistanWith the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.http://www.mdpi.com/1996-1073/11/3/611rooftop photovoltaic unitsdemand-side managementheuristic techniquesreal-time pricing tariffinclined block rateenergy storage systemload scheduling |
| spellingShingle | Ghulam Hafeez Nadeem Javaid Sohail Iqbal Farman Ali Khan Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units Energies rooftop photovoltaic units demand-side management heuristic techniques real-time pricing tariff inclined block rate energy storage system load scheduling |
| title | Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units |
| title_full | Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units |
| title_fullStr | Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units |
| title_full_unstemmed | Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units |
| title_short | Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units |
| title_sort | optimal residential load scheduling under utility and rooftop photovoltaic units |
| topic | rooftop photovoltaic units demand-side management heuristic techniques real-time pricing tariff inclined block rate energy storage system load scheduling |
| url | http://www.mdpi.com/1996-1073/11/3/611 |
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