Data, the new fuel for the energy sector

McKinsey estimates that by 2035, 50% of the energy produced globally will be from wind and solar sources. A transformation that has a significant impact on the methods of production, distribution and sale, all along the supply chain.

The challenges facing the energy sector as a whole are numerous. Effective use of data is an essential factor in streamlining operations and remaining competitive in a changing market.

Data efficiency in energy production

Renewable energy producers face major challenges: their production is intermittent by nature and less predictable due to the natural phenomena that cause it. Auction buyers are more cautious and part of the production can be lost if it is not sold on the intraday market.

Detailed information in real time, however, makes it possible to accurately predict the course of supply and demand. In addition, good use of data allows informed decisions to be made, such as the possibility of going directly to the intraday market and obtaining a better return per watt generated.

Several studies predict that proper use of advanced data analytics can lead to savings in the range of 5-7,5%. This is due to several factors: an optimization of the operating time of the plants, predictive maintenance applications to increase the availability, a reduction in the consumption of fuel which feeds these plants and a fine control of the performances which eliminates overproduction.

This need to improve production costs becomes even more important if we consider that more and more energy is injected into the network by individual players, in particular from solar panels. These actors are at both ends of the chain, since they are both producers and consumers. This scenario suggests that in the fairly near future, there will be a large "long tail" of producers that will need to be accounted for in any production forecasting model. It is for this reason that a data platform allowing analysis and accurate forecasting of supply and demand becomes essential.

Smarter network management

The energy distribution market has changed little in recent years, but it has been challenged by various factors. Among these: the integration of a large number of small producers spread over the whole territory, the vagaries of the weather and the rise of electric vehicles.

For example, charging vehicles and injecting energy from solar panels can cause energy spikes in the middle of the day and create more volatile demand patterns. To cope with these changes and avoid overloading their network, distribution companies will have to invest in so-called “intelligent” networks, also called Smart Grid. This is how electricity distributors will benefit from the development of electric vehicles and their two-way charging mechanism (V2G), which will offer them controlled solutions for storing electricity at a lower cost.

Evolution being inevitable, the question becomes one of strategic investment. This is where data comes in. McKinsey estimates that using data-driven technologies can reduce operating and maintenance costs by more than 12%. For example, predictive maintenance, based on machine learning, allows companies to take preventive measures to avoid large-scale power outages and cost overruns. Today, the costs of sensors, data ingestion and storage have dropped considerably: they are 10 times lower than 10 years ago. This leads to a proliferation of data allowing for deeper analysis and efficiencies.

These efficiency gains are achieved through remote control solutions, which reduce resolution times, but also through predictive maintenance and optimizing the efficiency of asset management.

This is possible thanks to the combination of IoT solutions with technologies such as 5G. Thanks to these innovations, managers will be able to collect data and analyze it in real time, which will enable them to optimize operating costs and even define predictive maintenance policies.

Towards a better customer experience

In the retail market, competition is fierce. With market liberalization, analysts have observed customer churn rates of up to 25%.

The challenges to be met are different depending on the actors. On the one hand, large traditional suppliers often need to transform and innovate. On the other hand, the new players, although more agile thanks to digital technology, have fewer financial means and must take up the challenge of sustainability.

But for both of them, data is an essential tool for improving the customer experience.

Solutions such as automated voice analysis in call centers or automatic analysis of energy consumption and prices, allow companies to better understand their customers and reduce the rate of attrition by offering them personalized offers. In addition, to optimize their efficiency, retail companies can rely on a better assessment of customer creditworthiness and consumption variation, the reduction of payment defaults and the prevention of fraud. The impact of using different analytical techniques in this area increases the profitability of these companies by 5-10%, while increasing brand image through improved customer satisfaction.

The use of analytical aspects and data management has a direct impact on the entire value chain of the energy sector. In a changing economic environment as we know it today, with crises and peaks in demand, the agility and adaptability of companies in the energy sector are differentiating aspects. They will be reflected in companies' profitability, market share and shareholder value.

We are facing a paradigm shift where data analysis is a differentiating factor in the optimization of energy production, transport and distribution.

Tribune by Denis Fraval-Olivier, SEMEA Sales Engineering Director at Cloudera (LinkedIn).