F. Wolfertstetter, S. Wilbert, Felix Terhag, N. Hanrieder, A. Fernández-García, C. Sansom, P. King, L. Zarzalejo, A. Ghennioui
{"title":"Modelling the soiling rate: Dependencies on meteorological parameters","authors":"F. Wolfertstetter, S. Wilbert, Felix Terhag, N. Hanrieder, A. Fernández-García, C. Sansom, P. King, L. Zarzalejo, A. Ghennioui","doi":"10.1063/1.5117715","DOIUrl":null,"url":null,"abstract":"Concentrating solar power (CSP) plants are often located in dusty environments. Soiling depends strongly on location, time, weather conditions and mirror orientation and is characterized by the soiling rate: the loss of the specular reflectance due to soiling per time interval. The average soiling rate can reach 2%/day on sites with heavy dust loads such as the Arabian Peninsula. On some days (for example during a sandstorm) the soiling rate can be significantly higher than that. Measurement campaigns for the soiling rate are of interest for the CSP plant site selection and the plant design, but they are time consuming and costly. In this study, a soiling model is presented that describes particle deposition processes based on physical equations from where the soiling rate can be derived. The model uses easily measureable meteorological parameters such as aerosol particle number concentration, wind speed and direction at 10 m height, relative humidity, temperature and precipitation as input parameters. The model has been optimized and validated using measurement data from two sites in Morocco and Spain. The measurement data have been divided into two parts. One was used to find optimum model parameters by parameterization. The second dataset was used to validate the model. The model reaches a bias of 0.1%/d and a root mean square deviation of 0.4 %/d. Days with weak soiling (<1%/d) rates are identified with an accuracy of more than 90 %, the question whether or not the soiling rate is above 1%/d is answered correctly in 85 % of the cases.Concentrating solar power (CSP) plants are often located in dusty environments. Soiling depends strongly on location, time, weather conditions and mirror orientation and is characterized by the soiling rate: the loss of the specular reflectance due to soiling per time interval. The average soiling rate can reach 2%/day on sites with heavy dust loads such as the Arabian Peninsula. On some days (for example during a sandstorm) the soiling rate can be significantly higher than that. Measurement campaigns for the soiling rate are of interest for the CSP plant site selection and the plant design, but they are time consuming and costly. In this study, a soiling model is presented that describes particle deposition processes based on physical equations from where the soiling rate can be derived. The model uses easily measureable meteorological parameters such as aerosol particle number concentration, wind speed and direction at 10 m height, relative humidity, temperature and precipitation as input parameters. Th...","PeriodicalId":21790,"journal":{"name":"SOLARPACES 2018: International Conference on Concentrating Solar Power and Chemical Energy Systems","volume":"57 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SOLARPACES 2018: International Conference on Concentrating Solar Power and Chemical Energy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/1.5117715","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
Concentrating solar power (CSP) plants are often located in dusty environments. Soiling depends strongly on location, time, weather conditions and mirror orientation and is characterized by the soiling rate: the loss of the specular reflectance due to soiling per time interval. The average soiling rate can reach 2%/day on sites with heavy dust loads such as the Arabian Peninsula. On some days (for example during a sandstorm) the soiling rate can be significantly higher than that. Measurement campaigns for the soiling rate are of interest for the CSP plant site selection and the plant design, but they are time consuming and costly. In this study, a soiling model is presented that describes particle deposition processes based on physical equations from where the soiling rate can be derived. The model uses easily measureable meteorological parameters such as aerosol particle number concentration, wind speed and direction at 10 m height, relative humidity, temperature and precipitation as input parameters. The model has been optimized and validated using measurement data from two sites in Morocco and Spain. The measurement data have been divided into two parts. One was used to find optimum model parameters by parameterization. The second dataset was used to validate the model. The model reaches a bias of 0.1%/d and a root mean square deviation of 0.4 %/d. Days with weak soiling (<1%/d) rates are identified with an accuracy of more than 90 %, the question whether or not the soiling rate is above 1%/d is answered correctly in 85 % of the cases.Concentrating solar power (CSP) plants are often located in dusty environments. Soiling depends strongly on location, time, weather conditions and mirror orientation and is characterized by the soiling rate: the loss of the specular reflectance due to soiling per time interval. The average soiling rate can reach 2%/day on sites with heavy dust loads such as the Arabian Peninsula. On some days (for example during a sandstorm) the soiling rate can be significantly higher than that. Measurement campaigns for the soiling rate are of interest for the CSP plant site selection and the plant design, but they are time consuming and costly. In this study, a soiling model is presented that describes particle deposition processes based on physical equations from where the soiling rate can be derived. The model uses easily measureable meteorological parameters such as aerosol particle number concentration, wind speed and direction at 10 m height, relative humidity, temperature and precipitation as input parameters. Th...