How Renewables benefit from Data Science

Renewable energy is supported by tendentious governmental policies, technology development and booming innovative ideas, including production efficiency, precise prediction, control on both supply and demand end, and energy storage. The market growth of renewable energy is an irreversible trend not only ecologically but also economically. However, renewable energy is facing the following two main challenges during its growth.

Challenge 1: Uncertainty. Because you cannot predict how long or strong the sun will shine, and how strong the wind will blow at the level that is enough to predict the production of renewable energy. This causes a problem because energy is very difficult and costly to store, many redundant energy can be only wasted if the supply and demand amount is not well balanced. Thus, for the energy plants, it is very crucial to control the production close to usage level, in order to cut the cost and operate safely.

Challenge 2: Dispersiveness. There is massive amount of small and medium renewable energies suppliers: a factory which has its own energy supplying system can produce redundant renewable energy, and they can sell it to a big energy plant; or private houses with solar system can also join in energy trading market. This needs a well-built energy trading grid. But it is a challenge for dispersive renewable energy units to merge into the traditional grid and to be distributed effectively under a well-balance system between power suppliers and users.

Luckily, with the development of digital technologies including advanced data analytics, AI (Artificial Intelligence), and IoT (Internet of Things), some very innovative solutions have derived. I will name 3 very interesting solutions in this article.

Solution 1: Prediction of Renewable energy, this will help with avoiding unexpected fluctuation of energy production, and so power plants can correspond in advance to cope with the changes. A French startup company Stedysun developed a 3-classes system to predict the solar energy.

In addition, there is also a solution for predicting the wind energy. Enercast, for example, has developed and continue to train a much more complicated power production prediction physics model that is based on artificial neuro network. Because wind energy is influenced not only by atmosphere data, but also by the geographic data at that location and other features from surrounding wind power generators.

Solution 2: Control on the energy users’ end. Especially for the industrial energy users, some of the industry process can be flexible within a certain range with the usage of energy, and not to be affected by a lower efficiency. A Belgium start-up company, Restore, has created a solution to analyse the factory’s habit of using energies by collecting the data from their industrial process. They have defined the process that factories can reduce the usage when supply end does not have enough power generated and has a full-run when the supply is sufficient. This way factories benefit from energy cost saving, especially with a dynamic pricing system form power plant.

Solution 3: Virtual Power Station. Next Kraftwerke GmbH was founded in Germany. It builds a virtual power station which solves the challenge of dispersiveness by coordinating all the small and medium power supply units through IT technology. With access to more than 4000 new energy power units, Next — Kraftwerke’s power generation has reached 2.7GW in 2016, equal to the amount of energy that a large traditional power plant could generate. The operation model is to install Next Box, a remote-control unit at every power supply unit, and all the Next Box connect to the central server through an encoded GPRS channel, transferring real-time data and receive control orders from the server. A virtual power station has a big amount of data on the production of the energy, the trade of the energy, the prices, and so on. It integrates small power suppliers to the large ones based on the virtual grid they created, and offers peaking service based on its quick responsive capability. But most importantly in my opinion, with all the data, Next Kraftwerke can create important insights for energy trading in the power market, and gain profit from energy trading.

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