Digital Resilient Energy Systems (DRES) use integrated digital technologies, predictive analytics and real-time monitoring to strengthen the adaptability and reliability of power networks. By combining operational data with climate intelligence, automated decision support and cybersecure control systems, they improve outage prevention, optimize distributed-asset coordination and enhance long-term planning. 

DRES help distribution operators manage growing electrification, rising climate volatility and increasing distributed energy resources (DER) penetration by providing continuous situational awareness and risk forecasting down to the low-voltage level. Predictive models can identify abnormal operating patterns, estimate fault probabilities and highlight assets at elevated climate risk. Through digital workflows that connect vegetation and weather intelligence, operational analytics, cost–benefit assessment and resilience-based investment planning, DRES creates a data-driven foundation for more sustainable, reliable and climate-ready electricity networks. 

Main outcomes of the solution 

• Sustainability: Supports decarbonization by reducing technical losses, avoiding fossil-fuel backup generation during outages, and ensuring renewable resources remain connected even under stress conditions. 
• System Resilience: Improves the ability of electricity networks to anticipate, withstand and recover from extreme weather and other disruptive events by integrating predictive risk models, climate forecasting and continuous monitoring. 
• Institutional Efficiency & Transparency: Embeds data-driven decision-making into grid planning by integrating ESG criteria, quantifying resilience benefits and providing clear justification for investment prioritization and regulatory processes. 

Key deployment and scaling factors 

1. Integrate operational, meteorological, climate and vegetation datasets into a unified platform to provide consistent risk indicators, early-warning capabilities and near real-time situational awareness. 
2. Develop predictive-analytics and scenario-modelling tools to forecast faults, evaluate stress conditions, simulate DER interactions and assess climate impacts on grid assets. 
3. Implement interoperable modules (vegetation management, weather analysis, advanced grid operation, cost-benefit modelling and resilience planning) to support coordinated operational and investment decisions. 
4. Define resilience and ESG metrics for long-term planning, enabling prioritization of vulnerable assets and optimization of total expenditure across technical, environmental and social dimensions. 
5. Ensure scalability and repeatability through modular system design and interoperability with existing utility systems. 
6. Strengthen data governance and cybersecurity measures to safeguard sensitive operational data and maintain confidence in digital decision-support tools. 

Case Study 

Enel (2025), Brazil 

Enel’s DRES4BR initiative applies digital resilience tools to support distribution-network planning and operations in areas experiencing more frequent climate-related disruptions. The project, led by Enel Distribuição São Paulo together with AiDash, PSR, Tempo OK and academic partners, aims to ensure stable and continuous electricity service for communities and critical infrastructure. 

The system integrates five digital modules combining vegetation monitoring, high-resolution weather forecasting, advanced grid-operation analytics, cost–benefit modelling and long-term resilience planning. These tools allow operators to identify vulnerable assets, predict faults and evaluate which interventions will deliver the greatest technical, environmental and economic benefits. The platform uses satellite and drone imagery, predictive models and ESG criteria to prioritize maintenance and investment where climate risks are highest. 

By reducing outages – tracked through DEC/FEC reliability indicators2 – and improving the planning of network upgrades, DRES4BR helps lower operational costs and strengthens grid performance. It supports Brazil’s wider decarbonization efforts by reducing technical losses, limiting reliance on fossil-fuel backup during disruptions and enabling more stable renewable integration. The approach is designed for scalability to other regions subject to local regulatory requirements.[1]

National Grid and Rhizome (2025), USA 

National Grid is deploying Rhizome’s gridFIRM platform to strengthen wildfire-risk assessment and resilience planning across its transmission and distribution networks in the Northeastern United States. While the region has historically been considered lower risk for wildfires, New York and Massachusetts recorded over 2,600 wildfire incidents in 2024, more than double the number reported in the previous year, prompting the utility to adopt more proactive, data-driven planning tools. 

gridFIRM integrates climate data, geospatial analysis and long-term risk modelling to help identify assets exposed to elevated wildfire risk, quantify potential ignition pathways and prioritize mitigation options. The platform supports National Grid’s ability to assess vulnerabilities across its network, compare risk-reduction strategies and inform longer-term resilience planning decisions.[2]

References:

1. Information provided to World Economic Forum I Enel (November 2025)
2. As wildfire risks grow, predictive tech is powering grid resilience I National Grid (December 2025)