一个综合的地理空间建模框架,用于规划农村电气化的混合微电网尺寸
Berino Francisco Silinto1,2, Darlain Edeme3, Silvia Corigliano3
1Department of Planning, Faculty of Spatial Sciences, University of Groningen, Landleven 1.9747 AD, Groningen, Netherlands.
MethodsX
|February 3, 2025
概括
本研究介绍了一种开源的地理空间能源模型,GISEle,用于优化发展中国家农村电气化的混合系统. 它通过整合空间数据和改进对成本有效解决方案的需求评估来增强规划.
科学领域:
- 能源系统工程 能源系统工程
- 地理空间分析的研究.
- 可持续发展 可持续发展 可持续发展
背景情况:
- 发展中国家的农村电气化由于能源建模工具不足而面临挑战.
- 现有的工具往往缺乏与特定农村环境相关的关键地理和社会经济参数.
- 有限的开源工具可用于分析发展中国家农村地区的混合发电系统.
研究的目的:
- 为低成本的农村电气化提供一个综合的地理空间能源建模框架.
- 引入一个扩展的开源工具,GISEle (农村电气化的GIS),以实现最佳的混合微电网规划.
- 为分析发展中国家的分散式混合动力发电系统提供普遍适用的框架.
主要方法:
- 开发了一个扩展的GISEle模型,包括针对当地特点和未来需求的先进数据收集.
- 整合了远程区域多能系统负载配置文件 (RAMP) 以改进负载需求评估.
- 在GISEle框架内连接了土壤和水评估工具 (SWAT) 模型,以便在GISEle框架内准确地测量水电.
主要成果:
- 增强的GISEle框架为农村电气化规划提供了一个强大的方法.
- 改进负载需求评估考虑了电气化对未来能源需求的影响.
- 水力发电潜力可以被精确大小,有助于优化混合动力系统设计.
结论:
- 提出的地理空间能源建模框架有效地解决了当前农村电气化工具的局限性.
- 扩展的GISEle模型为规划去中心化混合动力发电系统提供了一个实用,开源的解决方案.
- 该框架支持开发农村地区的可持续和成本效益高的能源解决方案.
相关概念视频
01:29Levels of Use of a GIS
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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01:23GIS Software, Hardware, and Sources of GIS Data
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01:25Selected Data About Geographic Locations
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01:23Maximum Power Flow and Line Loadability
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01:17Design Example: Alignment of a Road Line Using GIS
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01:21Manipulation and Analysis
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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