Summary
Scalytics Federated strengthens utility operations by running analytics and AI directly where grid data lives: on SCADA networks, IoT gateways, and on-premise systems, with no data movement and no loss of control. For utilities under NIS2 and CER, that means modern grid intelligence without restructuring isolated or regulated environments.
The approach comes from direct experience. Scalytics CTO Alexander Alten was Chief Digital Architect at E.ON, where he led the move from legacy Hadoop to cloud-native data platforms and built IoT systems connecting distributed energy assets across Europe. That work with SCADA integration, grid digitization, and the operational realities of utility AI shaped Scalytics Federated from the start.
Use Case 1: Demand-side Management
Demand-side management shifts or reduces consumption during peak periods. AI-driven DSM uses smart meter, IoT, weather, market, and SCADA data to predict load and coordinate responses that hold grid stability, including flexible tariffs tuned to residential, commercial, or industrial profiles. Shifting consumption off-peak relieves transmission and distribution and avoids emergency interventions.
With Scalytics Federated, DSM and forecasting models run inside decentralized environments, including private SCADA networks. Utilities keep control of sensitive operational data while predicting load peaks, congestion, and demand swings at high accuracy.
Dunkelflaute, prolonged low-wind and low-solar periods, hits European grids several times a year. ENTSO-E now tracks it as a core flexibility indicator, and TSOs such as TenneT and RTE study its detection against the European Resource Adequacy Assessment. Accurate forecasting through these events is essential for balancing and procurement, and it is exactly the kind of model that has to run close to operational data.
Use Case 2: Renewable Energy Integration
Solar and wind output swings with the weather, and that variability is rising fast. German TSOs project renewable capacity climbing from 116 GW in 2020 toward 616 GW by 2045, with electricity demand roughly doubling over the same period. AI helps utilities forecast availability, flag stability risk, and optimize dispatch.
Scalytics Federated runs forecasting and operational models on distributed datasets, at edge sites, in operational systems, and on on-premise platforms, which suits utilities operating under strict network separation.
- Predictive maintenance: detect equipment anomalies and predict failures, cutting downtime and preventing system-wide disruption.
- Asset optimization: tune renewable installations. Wind-park wake optimization is reported to add 2 to 3 percent in output.
- Energy forecasting: sharper procurement and resource planning through high-demand or low-generation periods.
- Grid optimization: better use of generation and transmission, reducing congestion cost and improving resilience.
Strengthening Digital Operations
Scalytics Federated lets utilities build and deploy complex energy models across existing systems, cutting integration work and speeding up grid intelligence. Operators get real-time insight, automated workflows, and one decision framework across the value chain.
- Higher efficiency: less manual work, sharper operational decisions.
- Lower cost: models find avoidable losses and raise asset utilization.
- Better reliability: forecasting and anomaly detection cut disruption risk.
Benefits for grid operations
Higher efficiency: AI reduces manual workload and sharpens operational decision-making.
Lower operational costs: Models locate avoidable energy losses and increase asset utilization.
Improved reliability: Forecasting and anomaly detection increase resilience against disruptions and reduce the probability of blackouts.
Why Utilities Choose Scalytics Federated
Utilities run under strict security and operational rules. Core data sits in SCADA systems, isolated networks, and regulated on-premise environments. Centralizing it is costly, slow, or prohibited. Scalytics Federated runs analytics and AI where the data is, so operators modernize without rebuilding infrastructure.
The platform carries lessons from large utility modernization programs. Our founding team includes veterans of E.ON's digital transformation: migrating from on-premise Hadoop to cloud-native big data, building unified IoT systems across energy assets, and running GDPR-compliant processing at scale. That shapes a product built around real operational constraints.
For architecture guidance and next steps, see our Smart Grid Intelligence overview.
Sources
- ENTSO-E, System Flexibility Needs for the Energy Transition (2024): https://www.entsoe.eu
- Dunkelflaute detection methods for grid operators (TenneT, RTE): https://iopscience.iop.org/article/10.1088/2753-3751/adcf29
- German transmission system operators, grid development projections: https://www.bundesnetzagentur.de/EN/Areas/Energy/NEP/start.html
