Gridsight is a cloud analytics platform that utilizes machine learning and data-driven models to enhance low voltage visibility and calculate hosting capacity for distributed energy resources. The platform enables electrical utilities to identify safety hazards and optimize network performance, facilitating the integration of residential solar, batteries, and electric vehicles into the grid.
Funding
$14.7K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Electrical utilities face challenges in maintaining low voltage (LV) network visibility and accurately assessing hosting capacity for distributed energy resources (DERs) like solar, batteries, and electric vehicles. Inaccurate network data and limited real-time monitoring can lead to safety hazards, inefficient grid operations, and delayed DER integration. Traditional methods for calculating hosting capacity often rely on outdated GIS models or complex impedance studies, hindering the rapid deployment of DERs.
Solution
Gridsight offers a cloud-based analytics platform that leverages machine learning and data-driven models to provide enhanced LV network visibility and streamline hosting capacity calculations. The platform enables utilities to proactively identify and address safety issues like degrading neutrals, automate customer-transformer mapping, and expedite complaint resolution. By providing real-time insights into network constraints and DER performance, Gridsight facilitates the safe and efficient integration of distributed energy resources into the grid. Its dynamic operating envelope (DOE) engine optimizes voltage and thermal constraints, while its model-free power flow analysis eliminates the need for complex GIS or impedance models.
Target Audience
Gridsight primarily targets electrical utilities seeking to enhance low voltage network visibility, optimize grid performance, and accelerate the integration of distributed energy resources.
Features
- Data-driven network data enrichment to improve the accuracy of DER hosting capacity modeling.
- Machine learning-derived dynamic hosting capacity calculations, eliminating reliance on GIS or impedance models.
- Real-time LV network visibility with safety and performance reports.
- Automated customer-transformer mapping to validate LV network connectivity.
- Dynamic operating envelopes for managing voltage and thermal constraints.
- Integration with Common Information Model (CIM) network data.
- DER performance monitoring to verify installed capacity, compliance, and location.