Analytics for Hydropower: ready for operational innovation
Utility companies are no strangers to the practical use of data analytics. For many years, utilities have been leading exponents of scientific data analysis – applying it to forecasting for generation and consumption, and for optimized trading.
Right now, we are seeing significant changes in both the operational models affecting hydropower, and in the ways in which analytics, cognitive computing and the Internet-of-Things can be used to create operational advantage. In this new landscape, open data becomes particularly important.
Let’s begin by looking at the changing role of hydropower in the utility mix – and then we’ll consider ways in which we can best create advantage through analytics and its associated technologies.
Hydropower and the changing renewable mix
In many parts of the world, we saw renewables delivering more power than fossil-fuelled generation on numerous occasions during 2017. The mix gets richer, with wind, wave and solar all combining with hydropower to create a socially and industrially viable alternative to fossil and nuclear. According to World Energy Council research, hydropower makes the largest contribution to the renewable mix, for now.
But the renewable alternative is not just about the means of generation. It’s also about the scale and the business model. Renewables have become an option for local control. This is evident at domestic, metropolitan and commercial level: homes, hospitals and supermarkets can now turn roof space and car parks into sustainable power plants.
Similarly, in hydropower, we see the major utility companies who operate multiple installations now being part of a distributed grid in which smaller local players can also make a viable contribution.
This mix can work only because it is data-driven. This is not just about managing the power grid – it’s also about being able to create operational models with multiple and multi-scale providers. Collaboration between entities that would once have seen each other as competitors will become the norm.
Now think analytics …
Until recently, industrial analytics in utilities existed only with the big hitters. Only the giant utility companies could afford the computing power and the scientific knowledge needed to develop and process serious analytical and physical models.
Well, that’s changed. Analytics is now available and affordable to even the most modest regional and municipal players in hydropower. But why should they care, and what can they do with it?
There are many applications, both within the operational perimeter of the individual hydropower operator, and across the wider ecosystem of other renewable sources and beyond.
For the individual operator, it is now possible to apply industrial-strength analytics to activities such as machine monitoring and maintenance planning. Taking advantage, for example, of low-cost sensors in the turbine hall, operators can put the Internet-of-Things to use, in anticipating the need for pre-emptive maintenance and optimizing routine service schedules.
Cognitive computing has a real impact too, as the outputs of continuous analytics become the inputs for active machine learning. Across the wider ecosystem, cognitive computing can aid hydropower operators in increasing the efficiency of collective planning and production with both the major grid suppliers and with the smaller independent producers.
These new levels of mutually profitable collaboration can draw on the wider sources of open data – and on their looser operational associations. The hydropower operator, for example, can now make both realtime and historic consumption forecasts part of routine next-day planning. To do this, they can draw on sources as diverse as the open data of meteorological experts and indeed on the production forecasts of local industry.
Taking advantage of multiple open data sources radically increases the value and granularity of the resulting insight. Where once, operators could only rely on more general experience (we need less power on a Sunday), they can now get down into the detail (how and why does one Sunday differ from another).
Access to intelligent analytics also delivers a natural boost to areas of collaboration which, until now, have been almost impossible to manage effectively. Knowing how to accurately predict reservoir levels, for example, has a direct impact on collaboration with those responsible for the upkeep of river banks and water courses.
Whenever we are faced with game-changing technological innovation, the first step is often the hardest to take. The easiest path is often to wait until somebody else established models of best practice and then to copy them.
When considering how best to exploit analytics, cognitive and the Internet-of-Things in hydropower, there is no reason to wait for sector leaders to emerge.
The technologies are immediately accessible and affordable, and thanks to the emphasis placed on use-cases by business technologists like Atos, there is a clear fast-track to experimentation and adoption.
Take time out to see how analytics, cognitive computing and the Internet-of-Things can boost operational efficiency, commercial performance, and environmental responsibility across your hydropower estate.