Digitalization of knowledge in electrical asset engineering and construction

By Guillermo Rodríguez Gaya

Head of Transmission Management, Red Eléctrica de España


The climate change and the decarbonization of the energy system drive the need to develop and introduce new sources of renewable energy into the electrical system. This challenge is providing a strong push for the development of new infrastructures for the transmission and distribution of electrical energy. Nowadays every country needs to reinforce their power grids.

The development of these new infrastructures is a challenge for electrical companies. Linear asset projects, such as electric transmission lines, require large investments and many years to become a reality. The development of these projects is always complex, due to technical issues but mainly because of the management of the stakeholders, and the permitting that ensures that it complies with all the legal, environmental and social requirements of the territory they pass through.

All that means that these are projects that may take a lot of time to conclude and that can also suffer several "stops and starts" along the way. This, traditionally, have made it difficult to develop a sound long-term planning because it is ... too complicated.

On the other hand, there is the difficulty that the necessary knowledge accumulated by companies to develop this type of infrastructure is not structured and, most of the time, it is a knowledge that companies keep in their key personnel (e.g. project managers, engineers) who have participated in previous projects. They have the knowledge to develop a long-term planning but that knowledge is inside their heads, and it is not easily accessible. It may seem common sense to all of us that the total length, the terrain through which it passes or the technical complexity may be relevant for a project... but it is a gut feeling because you can't make it tangible or explicit. In other words, this is a tacit knowledge, which can only be shared with the teams by socialization (SECI model of Nonaka & Takeuchi), so if we take into account the aging workforce, it is often lost or not transmitted in an effective way. And if we finally add what it is known as the "planning fallacy", that leads our subconscious to underestimate the time to complete a task (Kahneman), we conclude that having a realistic long-term planning seems ... too complicated.

Finally, utilities have historically relevant deficiencies in their technological capabilities; this means lots of data lost, data not used, data silos, internal segmentation, communication problems and the lack of a unified point of view. Again, getting the information for a long-term vision seems… too complicated.

But the technology available today has shown that it can help to address this problem effectively. As it has happened in other industries before (retail, music, renting, transportation...) what has been complicated until yesterday can now be achieved with new approaches.

As an example, Red Eléctrica de España, the Spanish TSO (Transmission System Operator) decided to tackle this problem and started an ambitious plan to transform the way they plan in advance from the inside, at the very core of their investment projects, where all the electric network is planned and built.

REE found that it could model the way the company was making the long-term planning, that could be found internal and external variables that would help REE to estimate costs and timelines with better precision and in an efficient way. These models that they were looking for, had to support and enhance the capabilities and knowledge of the engineering and planning teams and, why not, retain that knowledge for the future.

At the very beginning they decided to start using all the data available, both internal and external. The first step was to explore and store all relevant historical data, just to realize that there was less data than could be expected. Only after they decided to start using lots and lots of external data, like geospatial layers, weather historical and prediction models, building public records, altimetry... This large volume of external data, combined with their internal data, allowed them to identify the key variables that affect the development of the projects and, based on them, they began to build and refine the planning models.

Fig. 1: Example of variable introduction in models: % mountainous terrain


The result of this effort more than met the expectations that were generated and currently the company has a set of predictive scope models and budgeting tools integrated with the planning systems, which manage the day-to-day operations of the projects. The good results even allowed the development of predictive risk models in projects, which allows modelling the uncertainty associated with them.

Fig. 2: Example of use cases of the predictive planning models


These new technological capabilities, and the persistence of the team who pushed hard to transform this idea into reality, have made it possible to improve long-term project planning by more than 50% in time and by more than 20% in cost estimation, demonstrating that it is possible to make complex knowledge accessible and make easy what until yesterday seemed… too complex.