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Edge AI: Deploying AI Flexibility in a Virtualized LV/ MV Substation
技术
- 平台即服务 (PaaS) - 边缘计算平台
适用行业
- 电网
- 可再生能源
适用功能
- 商业运营
- 维护
用例
- 需求计划与预测
- 数字孪生
- 边缘计算与边缘智能
服务
- 软件设计与工程服务
挑战
西班牙 Grip 运营商 Cuerva 寻求通过实施人工智能能源预测模型来增强电网知识，以获得用户需求和能源发电的精确预测。
Cuerva 的电网涵盖超过 16,000 个不同的供应点，使得基于云的运营变得复杂，并且容易受到连接丢失、信息传输延迟以及对集中式基础设施的依赖等问题的影响，从而可能导致关键数据丢失。
为了应对这些挑战，边缘技术已被证明是能够有效解决这些问题的唯一替代方案。它确保实时数据访问并以分散的方式运行，最大限度地减少设备故障对网络整体功能的影响。
在这个成功案例中，我们展示了如何使用 Barbara DSO 直接在变电站中实施人工智能，以准确预测与部署了 Barbara 运行的边缘节点的转型中心相连的消费者的需求和生产价值。
客户
库埃尔瓦
关于客户
Cuerva 是一家 Spansih 能源运营商，为整个能源价值链提供服务，包括发电、配电、零售和客户能源服务。该公司拥有 140 多名员工，业务遍及 3 个国家：西班牙、秘鲁和巴拿马。
该公司正在进行大规模的数字化进程，旨在让最终用户更容易使用，以提高供应质量并提供更具价值的新服务。这涉及通过网络推出全面的控制系统，从变电站（由 Cuerva Distribución 运营）到最终用户，覆盖线路、变电站和分段，以便实时获取每分钟的测量结果，提供网络上的完全可见性。
解决方案
所提出的解决方案基于分布在各个转型中心 (TC) 的边缘计算节点。更准确地说，该解决方案由以下部分组成：
• 专为分布式边缘计算而设计的物联网节点，可安装在TC 中。
• 边缘节点管理平台可有效管理这些节点。
• 工业化计算算法用于：
- 预测用户端的有功功率和无功功率，以识别低压 (LV) 电网上潜在的拥塞和过压事件。
.- 评估 Cuerva 网格内的灵活性资产并为其创建模型。
.- 确定系统的灵活性要求
收集的数据
Energy Production, Energy Usage
运营影响
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