技术
- 分析与建模 - 大数据分析
- 应用基础设施与中间件 - 数据可视化
适用行业
- 电网
- 可再生能源
适用功能
- 产品研发
用例
- 微电网
- 物体检测
服务
- 数据科学服务
关于客户
本案例研究中的客户是国家可再生能源实验室 (NREL),这是一家美国联邦实验室,专注于可再生能源、商业化、开发和研究。 NREL 致力于发展可靠、有弹性且负担得起的清洁能源。为了实现这一目标,他们必须胸怀大局,考虑大局,其中包括实现可再生能源的高渗透率,同时实现广泛的目标。 NREL 致力于克服可再生能源在空间和时间上的可变性和连续性带来的挑战。他们致力于确保可再生能源得到适当的表征和保护。
挑战
国家可再生能源实验室 (NREL) 是美国唯一一家专注于可再生能源、商业化、开发和研究的联邦实验室。 NREL 面临的挑战是如何实现可再生能源的高渗透率,同时实现可靠性、弹性和可负担性的广泛目标。能源网的复杂性及其众多的发电机和可变负载,需要复杂的工具和可视化来理解和管理。可再生能源的一个根本挑战是其在空间和时间上的可变性和连续性,这对传统模型提出了挑战。问题是如何采用本质上连续且可变的现象,并将其拟合到离散模型空间中,无论是节点的还是区域的。最大的问题是如何确保资源得到正确的描述和保存。
解决方案
NREL 采用空间分析和地理空间数据科学来应对这些挑战。这种方法结合了传统的 GIS、地理空间大数据、地理统计分析以及时空和多元建模和可视化。 NREL 的空间数据科学团队拥有广泛的研究重点,以金字塔形式表示,该金字塔应用技术和经济限制来削减资源,直到发现可开发的可再生能源潜力。该团队使用空间分析来了解障碍和法规、传输分析、风电场特征和资源分析。他们还使用发电机建模来了解广泛地区不同光伏技术的性能。所有这些不同的分析和研究都结合在一个时空技术经济模型中,称为可再生能源潜力模型或“reV”。该模型结合了资源信息、系统工程建模、经济学、技术空间约束和传输分析,得出地理空间供应曲线。
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