CGI: Drone‑based photovoltaic (PV) plant defect detection
Context Large photovoltaic plants require repeatable, standardised inspection approaches to maintain performance and availability at scale. Challenge Traditional drone‑imagery analysis is slow and difficult to standardise at scale. Reporting must also comply with IEC‑TS‑62446 requirements, increasing the need for faster, accurate, repeatable and cost‑efficient inspections while reducing manual analysis. AI Solution The solution combines drone-based […]
CGI: Electric vehicle (EV) charging demand forecasting
Context Electric vehicle (EV) charging demand is becoming an increasingly important input for electricity procurement and grid planning for utilities and charge point operators (CPOs). Challenge Utilities and CPOs lack visibility on EV charging demand, leading to inefficient electricity procurement, grid stress, and inability to anticipate peak loads. AI Solution The solution uses ML for […]
CGI: Renewable asset performance optimisation using renewables management system (RMS)
Context Optimising the performance of renewable portfolios requires continuous monitoring and timely operational decisions across diverse asset types (e.g., wind, solar, and storage). Challenge Renewable operators face unplanned equipment failures, difficulty detecting anomalies early, and high downtime due to inefficient maintenance planning. AI Solution CGI RMS enables real‑time monitoring, predictive maintenance, and performance analytics across […]
A2A Group (Unareti): Load flow simulator
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 […]
A2A Group (Unareti): Load forecasting for critical support and flexibility
Context Accurate forecasting of the load on Milan’s electricity grid is necessary to anticipate critical situations and support the development of a local flexibility market. Challenge Urban distribution networks in Milan face increasing pressure to accurately anticipate grid load and avoid emerging risks, particularly as flexibility mechanisms begin to play a larger role in system […]
A2A Group (Unareti): AI-driven grid predictive maintenance
Context Milan’s utilities are shifting from reactive maintenance to data‑driven approaches to better manage asset health and system performance, leveraging dynamic failure rate modeling to support flexibility services. By integrating advanced AI technologies with heterogeneous data sources, the initiative aims to improve network operations. Challenge Challenges persist in improving the reliability and efficiency of Milan’s […]
Fortum: Flexibility portfolio optimiser
Context Markets utilities operate in have volatile energy prices and capacity constraints. Customers in these markets invest in distributed energy resources and seek solutions to optimise and monetise flexibility behind the meter. Challenge Customers with distributed energy assets require real‑time control to ensure assets deliver the highest value across multiple possible use cases. Assets can […]
Fortum: AI‑based condition monitoring for hydropower assets
Context Utilities operating hydro power plants require asset condition monitoring to support maintenance decisions across the hydro generation fleet. Challenge Asset maintenance in hydro power plants is reactive and fragmented, leading to unplanned downtime and higher costs. Maintenance operators require early identification of emerging equipment issues to enable timely intervention. AI Solution Applied AI to […]
Engie: Long‑term service agreement (LTSA) Performance Tracker
Context Utility company that manages wind assets under long-term service agreements (LTSA) often receives performance information both from maintenance providers and from its own measurements. Challenge Manual reconciliation of maintenance providers’ reports with internal measurements creates effort and can slow identification of contractual deviations and claims. AI Solution LTSA Performance Tracker leverages AI to automatically […]
Engie: AI-enabled operations and maintenance (O&M) optimisation for renewable assets
Context Improving productivity across a renewable energy asset portfolio increasingly depends on better planning and coordination of operations and maintenance activities. Challenge O&M decisions require balancing multiple, changing inputs (weather, asset condition, workforce capacity, supply constraints, and market signals), which makes it difficult to consistently choose the optimal maintenance actions. AI Solution AI agents integrate […]