{"title":"安置与光伏发电和电池存储相结合的电动汽车充电站","authors":"Bei Zhang, Qin Yan, M. Kezunovic","doi":"10.1109/EVER.2017.7935870","DOIUrl":null,"url":null,"abstract":"This paper introduces an optimal way to configure a grid-connected charging station for electric vehicles (EVs), in which the photovoltaic (PV) generation and local battery storage are integrated. The proposed method aims at generating the optimum combination of EV chargers, PV panels and local batteries, while considering: 1) EV mobility and uncertainties (PV generation, electricity price, etc.); 2) the impact on the grid; and 3) stochastic charging demand from EVs. The Erlang-loss system and the stochastic programming are adopted to model the EV mobility and uncertainties. Besides, the attribute sets as well as the clustering-based scenario generation method are proposed to generate necessary scenarios for stochastic programming. Finally, numerical experiments are conducted to validate the effectiveness of the proposed approach and show the impact of different factors on the final placement scheme.","PeriodicalId":395329,"journal":{"name":"2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Placement of EV charging stations integrated with PV generation and battery storage\",\"authors\":\"Bei Zhang, Qin Yan, M. Kezunovic\",\"doi\":\"10.1109/EVER.2017.7935870\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces an optimal way to configure a grid-connected charging station for electric vehicles (EVs), in which the photovoltaic (PV) generation and local battery storage are integrated. The proposed method aims at generating the optimum combination of EV chargers, PV panels and local batteries, while considering: 1) EV mobility and uncertainties (PV generation, electricity price, etc.); 2) the impact on the grid; and 3) stochastic charging demand from EVs. The Erlang-loss system and the stochastic programming are adopted to model the EV mobility and uncertainties. Besides, the attribute sets as well as the clustering-based scenario generation method are proposed to generate necessary scenarios for stochastic programming. Finally, numerical experiments are conducted to validate the effectiveness of the proposed approach and show the impact of different factors on the final placement scheme.\",\"PeriodicalId\":395329,\"journal\":{\"name\":\"2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EVER.2017.7935870\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EVER.2017.7935870","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Placement of EV charging stations integrated with PV generation and battery storage
This paper introduces an optimal way to configure a grid-connected charging station for electric vehicles (EVs), in which the photovoltaic (PV) generation and local battery storage are integrated. The proposed method aims at generating the optimum combination of EV chargers, PV panels and local batteries, while considering: 1) EV mobility and uncertainties (PV generation, electricity price, etc.); 2) the impact on the grid; and 3) stochastic charging demand from EVs. The Erlang-loss system and the stochastic programming are adopted to model the EV mobility and uncertainties. Besides, the attribute sets as well as the clustering-based scenario generation method are proposed to generate necessary scenarios for stochastic programming. Finally, numerical experiments are conducted to validate the effectiveness of the proposed approach and show the impact of different factors on the final placement scheme.