Challenge:
Operational Risk Model
Transelec faces the challenge of transforming its traditional operational risk management model based on static photos, fragmented models and manual decisions to a dynamic, live approach, connected to the environment in real time.
We seek to identify and implement technological solutions that allow the company to integrate, analyze and visualize operational risk in real time, considering internal and external variables, facilitating more agile, efficient and secure decision-making in the operation of the network.
Transelec Context
As part of its journey towards transformation and long-term sustainability, Transelec has decided to diversify its business and drive a set of strategic challenges to do so in the most efficient way, with concrete, high-impact solutions. These challenges are part of an open innovation agenda aimed at connecting the company with startups, scaleups, and technology providers from around the world, capable of bringing new capabilities in a changing and competitive environment.
Accelerating the company’s ability to adapt, anticipate, and lead in the energy infrastructure segment is a key objective. However, the rules of the game are changing rapidly: the electricity market, water infrastructure, energy storage, and digital solutions are converging, and Transelec wants to be at the forefront of this technological and commercial transformation.
The current environment is dynamic, with a growing number of competitors, customers, suppliers, and other key players. The number of relevant actors has multiplied, making it necessary to rely on intelligence to find opportunities. To address this, Transelec is launching this open innovation challenge focused on Market Intelligence one of the key pillars enabling its diversification strategy, commercial growth, and competitive anticipation.
Context
Transelec faces the challenge of transforming its traditional operational risk management model based on static snapshots, fragmented models, and manual decisions into a dynamic, real-time, environment-connected approach.
This challenge arises in a context where:
• Network operation faces increasing risks from weather events, wildfires, social events, and new supply continuity restrictions.
• Fieldwork is becoming increasingly complex, with greater social and community pressure, limiting disconnections and forcing operations under higher risk levels.
• The company has multiple risk models, analyses, simulators, and databases, but there is no integration, standardization, or consolidated visibility.•
• There is a wealth of valuable information (lessons learned, RCA analyses, asset monitoring, maintenance records, etc.) that is not integrated to support agile and proactive decision-making.
The opportunity lies in evolving from reactive operational risk management to proactive, intelligent, and dynamic management by integrating multiple internal and external variables to anticipate and prioritize risk management actions.
Challenge Objective
Identify and implement technological solutions that enable Transelec to integrate, analyze, and visualize operational risk in real time, considering internal variables (asset health, maintenance, SCADA, protections) and external variables (weather, distributed generation ]small-scale distributed generation – PMGD], social events, consumption patterns), facilitating faster, more efficient, and safer decision-making in network operations.
This intelligence must enable Transelec to:
• Anticipate risk events.
• Prioritize maintenance and resource allocation.
• Better manage high-impact events.
• Systematize lessons learned.
• Dynamically visualize the network’s status.
Solution Focus Areas
Integration of Risk Models and Internal Data
• Consolidate data from SCADA, asset monitoring, maintenance records, failure analyses, among others.
• Common standards for risk classification and evaluation.
• Integrated visualization of critical operational information.
Incorporation of External Variables
• Weather, wildfires, humidity, temperature, distributed generation (small-scale distributed generation – PMGD), social or seasonal events.
• Predictive models for climate impact or demand shifts.
• Dynamic alerts considering multiple variables.
Dynamic Risk Visualization and Management
• Real-time operational risk dashboards.
• Automatic prioritization of activities or maintenance tasks.
• Scenario simulation and contingency planning management.
Knowledge Management and Lessons Learned
• Capture, systematization, and recommendation of actions based on past events.
• Smart search engine for previous experiences or similar incidents.
Guiding Questions
How could we visualize operational risk in real time by integrating multiple internal and external variables?
How could we better anticipate and prioritize maintenance, fieldwork, or interventions based on dynamic risk?
How could we automate alerts or action recommendations in response to critical changes in network conditions?
How could we systematize and leverage lessons learned from past events to prevent recurrence?
How could we better manage high-impact events and prepare the organization for complex emergencies?
How could we integrate climate, distributed generation (small-scale distributed generation – PMGD), and other external sources into risk analysis and visualization models?
Key Challenge Objectives
Develop or test a functional technological solution to dynamically visualize and manage operational risk.
• Reduce exposure to unforeseen critical events.
• Prioritize interventions and resource allocation based on actual, not just planned, risk levels.
• Integrate and automate internal and external variables in risk analysis.
• Systematize organizational learning regarding past event management.
• Enable agile, safe decision-making based on reliable information.
Success Milestone
The selected startup or solution must deliver a functional MVP that enables the dynamic visualization, analysis, and prioritization of operational risk in a pilot area of Transelec’s network. This solution must integrate external variables into risk analysis, reduce analysis time, and generate automatic alerts that are useful and validated by operational users.
We take your existing solution or co-create one, test it in our facilities and, if successful, scale it commercially.
