Context
Networks are experiencing growing levels of distributed energy resource (DER) connection and intermittency, which increases the likelihood of local network constraints such as congestion, voltage excursions, and overloads. The operational environment requires earlier visibility of where and when constraints may emerge, alongside more coordinated planning and operational decision-making to maintain stable and flexible system performance.
Challenge
The core challenge is to anticipate, with sufficient lead time and location specificity, when and where congestion, voltage violations, and overloads will occur as DER volumes increase and net load becomes more variable. This anticipation needs to be timely enough to support proactive operational action and planning, rather than reacting after constraints materialise.
AI Solution
The solution applies grid topology and forecasting real-time data for net load and flexibility needs, enabling proactive congestion management and optimised DER dispatch.
Impact & evidence
Evidence and measurement approaches include forecast accuracy tracked using Mean Absolute Percentage Error (MAPE), SAIDI/SAIFI (System Average Interruption Duration Index / System Average Interruption Frequency Index) linked to preventing congestion on the grid, and a KPI for the number of newly accepted DER connections.
Implementation / Adoption
“Replication prerequisites include a connected digital foundation, DER management capabilities, access to DER and network data, and integration with operational and enterprise systems. Data/systems required include DER and network data from GIS (Geographic Information System), SCADA (Supervisory Control and Data Acquisition) historical data, and weather data.
The implementation and deployment timeline depends on DER scope, system integration complexity, deployment model, and utility-specific requirements; where DERMS (Distributed Energy Resource Management System) can be hosted in the cloud, deployment timeline is accelerated to about 8 months.”
Data
The solution requires DER operational data, SCADA historical data, weather data. In addition to this, it requires network context, enterprise and operational system integration, and software components supporting forecasting and flexibility workflows.
Additional information
ENWL .- MazimizeDER Adoption under dynamic network conditions
Forrester TEI study: Unlock ROI with EcoStruxureADMS with DERMS: Forrester Report