Graice Suite – AI-based software solutions for power distribution grids

The goal of this module is to create a consistent and load-flow-capable CIM (Common Information Model) grid model. Various checks are performed and, where possible, corrections and additions are made. This includes checking the logical relationships of the grid topology, checking the parameters of the grid elements, and the convergence of the load flow calculation.

Measurement data management is about integrating measurement data from various data sources and preparing and evaluating the measurement data for further processing in grid planning and operation. Where necessary and possible, the measurement data is corrected and supplemented. When evaluating the measurement data, the measurement errors of the measurement chain are taken into account. Analysis of the measurement data includes evaluating status messages from measuring devices, checking voltages with regard to defined voltage bands, checking compliance with limits regarding rated current and power, and much more.

For grid state monitoring with minimal measurement technology, the optimal placement of measurement technology in the grid is an important step. gridhound combines engineering approaches with AI algorithms to generate the optimal placement of measurement technology while considering existing technology and any limitations.

Grid monitoring is a solution for determining all electrical operating parameters (voltage, current, power) of electrical grid equipment (lines, busbars, transformers, consumers, generators) in a (partial) grid area of a distribution grid at medium and low voltage levels. gridhound's solution uses AI algorithms that reduce the need for measurement technology to a minimum while still delivering at least comparable results to classical methods (WLS state estimation).

gridhound has developed AI methods that can predict the power output for individual renewable energy systems (PV, wind) as well as for entire local grid stations. The prediction method is trained with historical master data, power measurements, and weather data, and can then predict power values using current master and weather data. This even works across different grid areas. The forecasts can be generated both short-term and long-term and are significantly influenced by the quality of the weather forecast.

Based on real-time grid monitoring and power forecasting for renewable energy systems and local grid stations, gridhound has developed AI methods that can predict the load for entire grid areas. These facilitate redispatch measures and grid connection planning, e.g., for renewable energy systems, charging stations, and heat pumps.

With the energy management system developed by gridhound, flexible generators and consumers can be controlled both for grid services and optimized for self-consumption. The basis is gridhound's grid monitoring and grid and power forecasts. Grid service use cases include, for example, avoiding and resolving bottlenecks and voltage deviations in the distribution grid. Self-consumption optimization includes use cases such as peak reduction, load/generation shifting, self-consumption optimization, etc.

gridhound has developed a simulation platform that combines all other Graice modules and enables playing through various scenarios. This allows generating data necessary for planning and implementing measures required for the energy transition.