{"title":"需求侧燃料电池和可控负荷通过优化机组承诺降低微电网运行成本","authors":"H. Howlader, A. Saber, T. Senjyu","doi":"10.1109/IC4ME247184.2019.9036589","DOIUrl":null,"url":null,"abstract":"Nowadays, the price of photovoltaic (PV) has been reducing drastically, that has been increasing the installation of PVs in both power supply-side and demand-side. Definitely, it is a positive development, but huge penetration of PVs in the daytime changes the load demand of thermal generators (TGs) and increases the peak and off-peak gap. This change may create duck curve. Duck curve increases the TG’s fuel cost and start-up cost(SUC) because of frequently start-up and shutdown, and ramping up and down of generators reduce the efficiency. Therefore, load leveling is essential, but it is a challenging issue. This research proposes a smart micro-grid operation and management system for a large island. The micro-grid considers PVs, TGs in supply-side and demand-side considers smart houses which include rooftop gird connected PVs, fuel-cells(FCs), controllable loads and other loads. Real-time pricing (RTP) based demand response (DR) has been introduced for shifting the usages of controllable loads and FCs for leveling the loads. Neural Networks predicts the PVs output power and MATLAB® INTLINPROG optimization toolbox determines simulation results.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Demand-side Fuel-cells and Controllable Loads to Reduce Operational Costs of Micro-grid through Optimal Unit Commitment\",\"authors\":\"H. Howlader, A. Saber, T. Senjyu\",\"doi\":\"10.1109/IC4ME247184.2019.9036589\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, the price of photovoltaic (PV) has been reducing drastically, that has been increasing the installation of PVs in both power supply-side and demand-side. Definitely, it is a positive development, but huge penetration of PVs in the daytime changes the load demand of thermal generators (TGs) and increases the peak and off-peak gap. This change may create duck curve. Duck curve increases the TG’s fuel cost and start-up cost(SUC) because of frequently start-up and shutdown, and ramping up and down of generators reduce the efficiency. Therefore, load leveling is essential, but it is a challenging issue. This research proposes a smart micro-grid operation and management system for a large island. The micro-grid considers PVs, TGs in supply-side and demand-side considers smart houses which include rooftop gird connected PVs, fuel-cells(FCs), controllable loads and other loads. Real-time pricing (RTP) based demand response (DR) has been introduced for shifting the usages of controllable loads and FCs for leveling the loads. Neural Networks predicts the PVs output power and MATLAB® INTLINPROG optimization toolbox determines simulation results.\",\"PeriodicalId\":368690,\"journal\":{\"name\":\"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC4ME247184.2019.9036589\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC4ME247184.2019.9036589","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Demand-side Fuel-cells and Controllable Loads to Reduce Operational Costs of Micro-grid through Optimal Unit Commitment
Nowadays, the price of photovoltaic (PV) has been reducing drastically, that has been increasing the installation of PVs in both power supply-side and demand-side. Definitely, it is a positive development, but huge penetration of PVs in the daytime changes the load demand of thermal generators (TGs) and increases the peak and off-peak gap. This change may create duck curve. Duck curve increases the TG’s fuel cost and start-up cost(SUC) because of frequently start-up and shutdown, and ramping up and down of generators reduce the efficiency. Therefore, load leveling is essential, but it is a challenging issue. This research proposes a smart micro-grid operation and management system for a large island. The micro-grid considers PVs, TGs in supply-side and demand-side considers smart houses which include rooftop gird connected PVs, fuel-cells(FCs), controllable loads and other loads. Real-time pricing (RTP) based demand response (DR) has been introduced for shifting the usages of controllable loads and FCs for leveling the loads. Neural Networks predicts the PVs output power and MATLAB® INTLINPROG optimization toolbox determines simulation results.