Hayder O. Alwan, Hamidreza Sadeghian, S. Abdelwahed
{"title":"典型住宅离网可再生能源系统的最优能源调度","authors":"Hayder O. Alwan, Hamidreza Sadeghian, S. Abdelwahed","doi":"10.1109/CICN.2019.8902414","DOIUrl":null,"url":null,"abstract":"Demand side management (DSM) is the conventional load scheduling method aimed at minimizing electricity costs This paper aims to present an approach for demand side management for a group of residential homes which can be used in response to day-ahead electricity price signal, and to maximize the usage of the power generated. This work is a dual-scenario case study of four households that are participants in a DSM program on a single feeder line. In the first scenario, each of the four households has local PV generation. In the second scenario, PV generation is provided only by other non-participant households on the same feeder. Simulation results confirm that the proposed scheduling algorithm can effectively reflect and affect user’s energy consumption behavior and achieve the optimal time of electricity usage. For practical consideration, we have also taken into consideration the impact of PV generation on the total electricity cost. Analysis shows that application of higher penalty factors can significantly improve the PV utilization efficiency while reducing fluctuations in the voltage profile for the entire system. The impact of applying a DSM algorithm on the total power losses of the feeder is also studied in this paper. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA).","PeriodicalId":329966,"journal":{"name":"2019 11th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimal Energy scheduling of an off-grid renewable system used for typical residential households\",\"authors\":\"Hayder O. Alwan, Hamidreza Sadeghian, S. Abdelwahed\",\"doi\":\"10.1109/CICN.2019.8902414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Demand side management (DSM) is the conventional load scheduling method aimed at minimizing electricity costs This paper aims to present an approach for demand side management for a group of residential homes which can be used in response to day-ahead electricity price signal, and to maximize the usage of the power generated. This work is a dual-scenario case study of four households that are participants in a DSM program on a single feeder line. In the first scenario, each of the four households has local PV generation. In the second scenario, PV generation is provided only by other non-participant households on the same feeder. Simulation results confirm that the proposed scheduling algorithm can effectively reflect and affect user’s energy consumption behavior and achieve the optimal time of electricity usage. For practical consideration, we have also taken into consideration the impact of PV generation on the total electricity cost. Analysis shows that application of higher penalty factors can significantly improve the PV utilization efficiency while reducing fluctuations in the voltage profile for the entire system. The impact of applying a DSM algorithm on the total power losses of the feeder is also studied in this paper. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA).\",\"PeriodicalId\":329966,\"journal\":{\"name\":\"2019 11th International Conference on Computational Intelligence and Communication Networks (CICN)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 11th International Conference on Computational Intelligence and Communication Networks (CICN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICN.2019.8902414\",\"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 11th International Conference on Computational Intelligence and Communication Networks (CICN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICN.2019.8902414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Energy scheduling of an off-grid renewable system used for typical residential households
Demand side management (DSM) is the conventional load scheduling method aimed at minimizing electricity costs This paper aims to present an approach for demand side management for a group of residential homes which can be used in response to day-ahead electricity price signal, and to maximize the usage of the power generated. This work is a dual-scenario case study of four households that are participants in a DSM program on a single feeder line. In the first scenario, each of the four households has local PV generation. In the second scenario, PV generation is provided only by other non-participant households on the same feeder. Simulation results confirm that the proposed scheduling algorithm can effectively reflect and affect user’s energy consumption behavior and achieve the optimal time of electricity usage. For practical consideration, we have also taken into consideration the impact of PV generation on the total electricity cost. Analysis shows that application of higher penalty factors can significantly improve the PV utilization efficiency while reducing fluctuations in the voltage profile for the entire system. The impact of applying a DSM algorithm on the total power losses of the feeder is also studied in this paper. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA).