Context
Electricity grids are facing increasing strain due to rising renewable energy penetration and growing electrification of consumption. Distribution system operators must process connection requests efficiently while balancing system security and network optimization.
Challenge
Maintaining reliability and accuracy in assessing network criticality is essential while preventing issues caused by excessive load demand. Ensuring granular visibility of electrical stress across time and network segments is critical to support effective decision-making.
AI Solution
The Load‑Flow Simulator provides a real‑time view of low‑voltage grid conditions, mapping load levels across all network components and enabling assessment of connection feasibility for new or upgraded customer demand.
Impact & evidence
The solution improves accuracy in assessing network criticalities while reducing manual analysis effort for connection requests, helping prevent overload risks on the low‑voltage network. It strengthens decision support for operators, enhancing grid stability, asset reliability, operational efficiency, and overall productivity while optimizing costs.
Implementation / Adoption
Implementation of the solution requires availability of low‑voltage network topology and asset data, along with access to customer connection and load data, supported by integration with billing, GIS, and asset management systems and a centralized load‑flow calculation engine. Deployment involves integrating data from these systems, configuring load‑flow models, deploying an interactive user interface, and progressively enabling operational use for connection assessments and power increase evaluations.
Data
Solution requires LV network topology and asset data from GIS systems, along with customer and connection data from billing systems, complemented by load and capacity data for network elements to enable accurate load flow analysis.
Future enhancements
Future enhancements are focused on extending simulation capabilities with additional scenarios and functionalities while improving user experience through greater automation. Further integration with operational planning tools and continuous refinement of calculation models will enhance accuracy and support more advanced decision-making.