Energy & Utilities
Data-driven control for critical energy infrastructure
Data as a strategic asset in critical energy infrastructure
The energy and utilities sector operates within critical infrastructure, where the continuity of networks, generation sources, and distribution systems must be ensured 24/7 – regardless of weather conditions, load, or market conditions. In this environment, data is a strategic resource: it enables precise system balancing, loss and reliability management, early detection and prediction of failures, and compliance with stringent regulatory and environmental requirements.
Connecting grid operations with energy market dynamics
Integration with energy markets is also becoming increasingly important – from real-time price monitoring and automatic retrieval of exchange quotes to analysis of the profitability of sources. Combining operational data with market data allows operators and producers to make decisions based on a complete picture of the situation: from the state of the grid to current and forecast energy prices.
The faster an organization can assess the actual state of its infrastructure and predict changes in it – in conjunction with market fluctuations – the safer, more stable, and more efficient it operates. This is particularly important in the context of the growing share of renewable energy sources, generation volatility, and dynamic energy prices on wholesale markets.
Read about the benefits achieved by PGE GiEK S.A.
Smart RDM – real-time outage management system
In the energy and utilities sectors, the key factors are: security, reliability, balancing, ESG reporting, and rapid response.
Smart RDM creates a central decision-making layer that:
- integrates data from SCADA, meters, forecasts, renewable energy sources, and the market,
- detects anomalies and deviations in real time,
- recommends field and dispatch activities,
- automates environmental and compliance reports,
- enables infrastructure transformation towards renewable energy sources and efficiency.
Key advantages of Smart RDM:
AI Reasoning
Analysis not only of exceedances, but also of trends, correlations, weather conditions, data quality, and failure risk.
Predictive networks
Prediction of losses, leaks, overloads, peak power, and energy balances.
Incident management
Automatic alerts, workflow, prioritization, event tracking.
RES + energy storage
Integration and prediction of operation, production and demand scenarios.
ESG and CSRD reporting
Operational data converted into ready-to-use environmental reports.
Any architecture
On-premises, edge, cloud, or hybrid – depending on regulatory requirements.