Utility pricing scheme.
<div><p>Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling contr...
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| منشور في: |
2024
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| _version_ | 1852025411430514688 |
|---|---|
| author | Hisham Alghamdi (20114096) |
| author2 | Lyu-Guang Hua (17811102) Ghulam Hafeez (20114099) Sadia Murawwat (20114102) Imen Bouazzi (17811105) Baheej Alghamdi (20114105) |
| author2_role | author author author author author |
| author_facet | Hisham Alghamdi (20114096) Lyu-Guang Hua (17811102) Ghulam Hafeez (20114099) Sadia Murawwat (20114102) Imen Bouazzi (17811105) Baheej Alghamdi (20114105) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hisham Alghamdi (20114096) Lyu-Guang Hua (17811102) Ghulam Hafeez (20114099) Sadia Murawwat (20114102) Imen Bouazzi (17811105) Baheej Alghamdi (20114105) |
| dc.date.none.fl_str_mv | 2024-11-05T18:25:45Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0307228.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Utility_pricing_scheme_/27614789 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Pharmacology Biotechnology Ecology Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified smart power grids reducing power costs primary problems faced preserving customer satisfaction inclined block tariff every appliance runs load scheduling simulation load scheduling controller based load scheduling power price uncertainties intermittent renewable energy implementing demand response home appliances based average demand ratios whale optimization algorithm optimal adaptive wind oawdo works better fly optimization algorithm varying price system energy optimization varying price scheduling issue price hours oawdo algorithm genetic algorithm driven optimization varying pricing household appliances appliances run woa ), utility companies storage battery several benefits results indicate rebound peaks oawdo technique minimize peak manage supply important topics ideal schedule grid constraints ga ), ffoa ), electricity bills currently one could result |
| dc.title.none.fl_str_mv | Utility pricing scheme. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_d44dfc97798a5ddaf4e35f03bef31bf7 |
| identifier_str_mv | 10.1371/journal.pone.0307228.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27614789 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Utility pricing scheme.Hisham Alghamdi (20114096)Lyu-Guang Hua (17811102)Ghulam Hafeez (20114099)Sadia Murawwat (20114102)Imen Bouazzi (17811105)Baheej Alghamdi (20114105)PharmacologyBiotechnologyEcologyScience PolicyPlant BiologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsmart power gridsreducing power costsprimary problems facedpreserving customer satisfactioninclined block tariffevery appliance runsload scheduling simulationload scheduling controllerbased load schedulingpower price uncertaintiesintermittent renewable energyimplementing demand responsehome appliances basedaverage demand ratioswhale optimization algorithmoptimal adaptive windoawdo works betterfly optimization algorithmvarying price systemenergy optimizationvarying pricescheduling issueprice hoursoawdo algorithmgenetic algorithmdriven optimizationvarying pricinghousehold appliancesappliances runwoa ),utility companiesstorage batteryseveral benefitsresults indicaterebound peaksoawdo techniqueminimize peakmanage supplyimportant topicsideal schedulegrid constraintsga ),ffoa ),electricity billscurrently onecould result<div><p>Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort.</p></div>2024-11-05T18:25:45ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0307228.g002https://figshare.com/articles/figure/Utility_pricing_scheme_/27614789CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/276147892024-11-05T18:25:45Z |
| spellingShingle | Utility pricing scheme. Hisham Alghamdi (20114096) Pharmacology Biotechnology Ecology Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified smart power grids reducing power costs primary problems faced preserving customer satisfaction inclined block tariff every appliance runs load scheduling simulation load scheduling controller based load scheduling power price uncertainties intermittent renewable energy implementing demand response home appliances based average demand ratios whale optimization algorithm optimal adaptive wind oawdo works better fly optimization algorithm varying price system energy optimization varying price scheduling issue price hours oawdo algorithm genetic algorithm driven optimization varying pricing household appliances appliances run woa ), utility companies storage battery several benefits results indicate rebound peaks oawdo technique minimize peak manage supply important topics ideal schedule grid constraints ga ), ffoa ), electricity bills currently one could result |
| status_str | publishedVersion |
| title | Utility pricing scheme. |
| title_full | Utility pricing scheme. |
| title_fullStr | Utility pricing scheme. |
| title_full_unstemmed | Utility pricing scheme. |
| title_short | Utility pricing scheme. |
| title_sort | Utility pricing scheme. |
| topic | Pharmacology Biotechnology Ecology Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified smart power grids reducing power costs primary problems faced preserving customer satisfaction inclined block tariff every appliance runs load scheduling simulation load scheduling controller based load scheduling power price uncertainties intermittent renewable energy implementing demand response home appliances based average demand ratios whale optimization algorithm optimal adaptive wind oawdo works better fly optimization algorithm varying price system energy optimization varying price scheduling issue price hours oawdo algorithm genetic algorithm driven optimization varying pricing household appliances appliances run woa ), utility companies storage battery several benefits results indicate rebound peaks oawdo technique minimize peak manage supply important topics ideal schedule grid constraints ga ), ffoa ), electricity bills currently one could result |