HelioSoil:用于 Heliostat 染色分析和清洁优化的 Python 库

Giovanni Picotti, Michael E. Cholette, Ye Wang, Cody B. Anderson, Theodore A. Steinberg, J. Pye, G. Manzolini
{"title":"HelioSoil:用于 Heliostat 染色分析和清洁优化的 Python 库","authors":"Giovanni Picotti, Michael E. Cholette, Ye Wang, Cody B. Anderson, Theodore A. Steinberg, J. Pye, G. Manzolini","doi":"10.52825/solarpaces.v1i.719","DOIUrl":null,"url":null,"abstract":"Soiling losses and their mitigation via cleaning operations represent important challenges for Solar Tower (ST) plants. Yet soiling losses are not well considered in existing CSP software, likely due to the lack of tools for soiling estimation and cleaning optimization. In this paper, a Python-based heliostat soiling library, called HelioSoil, is introduced which allows for the assessment of heliostats’ soiling state and the optimization of the solar field cleaning schedule to maximize plant profit. The library is freely available on GitHub under a LGPL license, which enables extensions via other Python APIs (e.g. CoPylot) and integration with other CSP plant simulation packages to consider soiling losses. This latter capability is demonstrated in this study through an LCOE assessment and cleaning optimization of a hypothetical Australian ST plant with SolarTherm. Hence, HelioSoil provides the CSP community with a package for soiling assessment and cleaning resource optimization, which can be integrated with available software for high-level, long-term simulations. HelioSoil facilitates the inclusion of soiling and cleaning costs in CSP economics and ultimately aim to de-risk the deployment of ST plants.","PeriodicalId":506238,"journal":{"name":"SolarPACES Conference Proceedings","volume":"137 36","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HelioSoil: A Python Library for Heliostat Soiling Analysis and Cleaning Optimization\",\"authors\":\"Giovanni Picotti, Michael E. Cholette, Ye Wang, Cody B. Anderson, Theodore A. Steinberg, J. Pye, G. Manzolini\",\"doi\":\"10.52825/solarpaces.v1i.719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Soiling losses and their mitigation via cleaning operations represent important challenges for Solar Tower (ST) plants. Yet soiling losses are not well considered in existing CSP software, likely due to the lack of tools for soiling estimation and cleaning optimization. In this paper, a Python-based heliostat soiling library, called HelioSoil, is introduced which allows for the assessment of heliostats’ soiling state and the optimization of the solar field cleaning schedule to maximize plant profit. The library is freely available on GitHub under a LGPL license, which enables extensions via other Python APIs (e.g. CoPylot) and integration with other CSP plant simulation packages to consider soiling losses. This latter capability is demonstrated in this study through an LCOE assessment and cleaning optimization of a hypothetical Australian ST plant with SolarTherm. Hence, HelioSoil provides the CSP community with a package for soiling assessment and cleaning resource optimization, which can be integrated with available software for high-level, long-term simulations. HelioSoil facilitates the inclusion of soiling and cleaning costs in CSP economics and ultimately aim to de-risk the deployment of ST plants.\",\"PeriodicalId\":506238,\"journal\":{\"name\":\"SolarPACES Conference Proceedings\",\"volume\":\"137 36\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SolarPACES Conference Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52825/solarpaces.v1i.719\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SolarPACES Conference Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52825/solarpaces.v1i.719","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

污损以及通过清洁操作减少污损是太阳能塔(ST)发电厂面临的重要挑战。然而,现有的 CSP 软件并没有很好地考虑污损问题,这可能是由于缺乏污损估算和清洁优化工具。本文介绍了一个基于 Python 的定日镜污损库,名为 HelioSoil,可用于评估定日镜的污损状态和优化太阳能场清洁计划,以实现发电厂利润最大化。该库在 GitHub 上以 LGPL 许可免费提供,可通过其他 Python API(如 CoPylot)进行扩展,并与其他 CSP 工厂模拟软件包集成,以考虑污损损失。本研究通过对一个使用 SolarTherm 的假设澳大利亚 ST 工厂进行 LCOE 评估和清洁优化,展示了后一种功能。因此,HelioSoil 为 CSP 社区提供了用于污垢评估和清洁资源优化的软件包,该软件可与现有软件集成,用于高级长期模拟。HelioSoil 有助于将污垢和清洁成本纳入 CSP 经济学,并最终实现降低 ST 工厂部署风险的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HelioSoil: A Python Library for Heliostat Soiling Analysis and Cleaning Optimization
Soiling losses and their mitigation via cleaning operations represent important challenges for Solar Tower (ST) plants. Yet soiling losses are not well considered in existing CSP software, likely due to the lack of tools for soiling estimation and cleaning optimization. In this paper, a Python-based heliostat soiling library, called HelioSoil, is introduced which allows for the assessment of heliostats’ soiling state and the optimization of the solar field cleaning schedule to maximize plant profit. The library is freely available on GitHub under a LGPL license, which enables extensions via other Python APIs (e.g. CoPylot) and integration with other CSP plant simulation packages to consider soiling losses. This latter capability is demonstrated in this study through an LCOE assessment and cleaning optimization of a hypothetical Australian ST plant with SolarTherm. Hence, HelioSoil provides the CSP community with a package for soiling assessment and cleaning resource optimization, which can be integrated with available software for high-level, long-term simulations. HelioSoil facilitates the inclusion of soiling and cleaning costs in CSP economics and ultimately aim to de-risk the deployment of ST plants.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信