Red Eléctrica is implementing a new vegetation management model for its power lines. Until now, the identification of vegetation species was done through manual photo-interpretation techniques.

Through this project, alongside our partner Overstory, we have created a series of algorithms that automate the photointerpretation of tree and shrub species from satellite images and images from the National Aerial Orthophotography Plan (PNOA).

Enables the budgeting and planning of investment projects in the Transport Network using all the information available and specific to each project. These are feedback and automatic learning models to be applied during the life cycle of the projects.

Objective: to improve management of the investment activity of the company and decision-making in the DC and DGT based on the use of the information generated in the activity of the projects.

The goal of this project is to switch from a reactive/preventive model to a predictive model in the area of occupational health and safety. We make use of the traceability and digital footprint of the data and information provided by the management tools and applications of the Red Eléctrica Group to make analyses with descriptive (what happened), predictive (what could happen) and prescriptive models (what can we do to stop it happening) The first goal of the project is to use Artificial Intelligence methods to create a probability indicator of the risk of accidents and issues associated with any maintenance and construction work.

The CONPP project aims to develop a methodology for forecast probability intervals calculation, combining the intervals of the individual predictors of the suppliers with the best accuracy, which provide the hourly demand and production forecast curves at the level of each system. The main objective of this work is the development of a procedure for the construction of prediction intervals associated to forecasts obtained as combinations of independent model and supplier predictors.

Casandra is an innovative response to the changing needs of electrical planning which offers adaptable tools for carrying out electrical studies with which to address current and future challenges of the sector in an agile and efficient way. The project came about in order to unite these and other initiatives and provide a common response to the needs of the Electrical Planning process, offering an ambitious response and comprehensive solution to address three essential areas in the electrical planning process.

The project is of vital importance for the future of visual inspections, as it represents a significant advancement that will help technicians easily detect anomalies in a grid of over 45,000 km of high-voltage lines. The synthetic image project, within the DALIA framework, is making advancements in training artificial intelligence models with synthetic images to improve the software’s efficiency and scalability.

Generation and demand forecasts significantly affect the company's high-impact activities, such as guaranteeing the customers' electricity supply, ensuring appropriate planning of the transmission grid or explaining studies on electricity demand coverage to stakeholders. The main purpose of this project is to harmonise the forecasting models and methodologies used for the various processes and to conduct research to improve the different forecasting models and their implementation in order to better adapt to the current and future challenges of the energy sector.

The project involved the automatic creation of stakeholder profiles of Red Eléctrica's transmission planning team and the identification of their perception of the company based on the analysis of communications in text format, using Argilla's Biome tool.

Red Eléctrica currently has 1,200 kilometers of submarine cables on service that enable the transport of electricity under the sea or ocean, but their maintenance and potential repairs are very complex due to their difficult access. With the aim of developing a solution that facilitates the work of technicians and engineers, Elewit, through its fourth Venture Client program, launched a project with the startup Akselos, a company that has one of the most advanced engineering simulation software in the world.

The analysis of documentation involves a high workload given the extensive volume of information involved. As a consequence, Elewit, in collaboration with the Grid Access Department, has promoted this project in which we have explored how LLM (Large Language Models) used in generative AI can help streamline processes that require analysis and summarization of extensive and heterogeneous documentation. Specifically, a proof of concept has been developed to evaluate and validate the advantages offered by this technology compared to others which has demonstrated that generative AI is a potential key technology to streamline and simplify this type of processes.

The GridCal and Newton improvement project for electrical planning, developed by Elewit and Red Eléctrica’s electrical planning and system operation model teams, has represented a significant step forward in electrical grid simulation capabilities. It uses the open-source software GridCal as an interface for the design and simulation of electrical grids, complementing other tools in national planning.

The CounterCraft company has a cyber deception platform that consists of recreating realistic virtual environments to deceive potential attackers and extract as much information as possible about them. The CounterCraft pilot had a twofold objective: on the one hand, a mock-up of a power substation connected to the internet was created, emulating the data traffic and accessible functionality as if it were a real, physical substation.

The CounterCraft tool monitored this mock-up to obtain information and profile the attackers (locations, IPs, tools and scripts, vulnerabilities, etc.). On the other hand, the information emulated by the mock-up was collected to extend this tool and thus facilitate the deployment of new realistic virtual mock-ups while Countercraft completes its range of solutions. The project was successfully completed and has provided relevant information that will undoubtedly increase the resilience of the systems, leading to further proofs of concept.

The objective of the SLISE project is to mitigate the vulnerabilities that the new virtualization technologies adopted massively at the core of the 5G architecture (and which are already part of the technical drafts of the sixth generation) have dragged into the new paradigm of communications as a service. Specifically, research into new algorithms is proposed: incident analysis, encryption, radio attack detection identification and automated response; in a more flexible context to face the risks inherent to virtualization technologies: Network Function Virtualization (NFV), Software Defined Networks (SDN) and Network Slicing (NS). All this will be studied, defining demanding indicators that broadly cover these objectives, in a set of use scenarios that present different protection priorities and that include the use of communications in the context of critical infrastructure management, as well as the use of communications in the manufacturing industry.

The Kymatio project is a web-based training program for professionals, dedicated to the cyber-awareness and the assessment of their alertness in an unattended and personalized way, while providing a risk management tool associated with the human element with metrics, evolution over time and action plans.

Red Eléctrica has developed a project with the cybersecurity startup Radiflow leveraging the technology offered by its iSID industrial threat detection and management platform. This anomaly detection and OT (Operational Technology) visibility suite allows to improve the security of industrial networks through a complete visualization of the network, threat detection and management of communication policies between devices. It must be noted that the collaboration between Radiflow and Red Eléctrica was born thanks to the startup's participation in Elewit's IV Venture-Client program and that it will continue due to the fact that the solution they are proposing is being implemented in Red Eléctrica's infrastructure.

The project aims to develop a transmission grid based on local and remote monitoring and sensors, operating with transmission categories calculated in real time. These capacities are calculated using a thermal model of the line and data obtained from the monitoring of immediate atmospheric conditions and/or the physical parameters of the installation along its full length. This will enable us to access the capacity that has so far been blocked by the circuits, giving us better knowledge of their real conditions. The result is more flexible and safer operations.

Implementation of Process Mining for the analysis and identification of inefficiencies such as bottlenecks, degree of automation, rejections, approval flow times, and opportunities to optimise the business processes of the company. The incubation project will test different Process Mining tools and will design and optimise the application flow of this type of techniques in corporate processes.

Objective: To optimise business processes in the sales/purchasing areas in order to reduce costs.

At #Elewit, together with Accenture, we are developing a pilot project to predict the capacity of lines in future time horizons, as the calculation and prediction of the capacity of overhead electricity lines (DLR) is key, among other things, to improve the safety of the lines, to minimise the damage caused by breakdowns and failures and to contribute to guaranteeing supply and the balance between demand and generation.

Current automatic substation systems are distributed systems in which the software is associated with specific hardware. Therefore, the possibility of innovating with new algorithms is conditioned by the existing software-hardware assemblies.

Through this project, we intend to create an executable software platform on general hardware platforms, applying Edge technology concepts and microservices, creating a virtualised environment capable of implementing the functions of the automatic substation system and with a high capacity for integrating new functions into the system.

Project carried out together with partners NEARBY Computing, CIRCE, ZIV Automation, Indizen and INDRA-Minsait.

Its goal is to see the real level of small-scale self-supply (P <1MW) because there is no plan for real-time measurements or metering for the amount generated. The information, however, is available in the IoT and the cloud. The platform makes monitoring possible in real time, enabling us to estimate production in the system and to make forecasts.

This project is intended to ensure the safety of people and facilities in discharge operations, removing situations of risk for operators associated with their work in protected zones and accompanying tasks, as part of the Red Eléctrica Group’s strategic goal of “zero accidents”.

Strategos is a calculation tool that enables the optimisation of the portfolio of investment projects for the development and maintenance of the electricity transmission network. Through the implementation of Machine Learning techniques and Redeia's own know-how, the tool optimises decision-making when executing a set of investment projects.

In addition, it allows to simulate and estimate the risks associated with the implementation of projects, thus helping decision-making.

Barbara is developing a tool based on edge computing to obtain a real-time representation of the status of different elements at an electricity substation when an incident occurs (typically a short circuit). Therefore, technicians will be able to access the data for both monitoring and analysis more quickly than they can now.

Edge computing is a type of on-site computing that relies on collecting and processing data from the hardware itself rather than sending it to the cloud or somewhere else for analysis, as do most IoT-based systems (Internet of Things), which are more passive in nature.

The inter-area oscillation damping project is taking advantage of the boost in artificial intelligence and, more specifically, machine learning, to develop software which is capable of conducting efficient predictive analysis.