Energy

Crunching the numbers on renewable energy

November 06, 2015

Global

November 06, 2015

Global
Our Editors

The Economist Intelligence Unit

_____________________

The costs of generating energy from renewable sources remain high but the application of analytics is helping to boost efficiency.

The international community is committed to renewable energy. The EU aims for 20% of its energy to come from renewable sources by the end of this decade, and the US hopes to do the same by 2030.

However, meeting these targets will require energy providers, businesses and consumers to switch to renewable sources. And while the costs of renewable energy generation are falling steadily, they are still greater than conventional power – especially following the sharp drop in oil prices seen in 2015.

Even if customers prefer the concept of renewable electricity sources, it is very hard for the industry to supply green energy at the same price. One solution to this is to improve the efficiency of the generation technologies themselves. But another, complementary approach is to optimise their use and application through sophisticated analytics.

Data analysis can boost renewable power in at least three ways. Firstly, analysing weather patterns and geographic data can help to locate the ideal location for facilities such as a wind farm or wave generator. Secondly, it can be used to find the optimal orientation: which way should the solar panels point, for example. The third area is calibration – monitoring performance data from equipment can reveal which equipment is under-performing, and how it can be optimised for its precise environment.   

The potential impact of this approach becomes all the greater as the speed of analysis increases. Combining real-time data with historical analysis allows systems to become predictive. This means operators can re-orient and re-calibrate their equipment based on live weather data, for example, to ensure the maximum possible yield.

Equally, weather data can be used to predict when generation will be least effective, so that any maintenance can be carried out. And the power of predictive analytics goes beyond generation: if a producer can predict its output more effectively, it can negotiate with distribution partners to get the best price. This is turn will make renewable generation more lucrative.

Thus, powerful effectiveness and efficiency gains are on offer to the renewables industry by aligning equipment to incoming conditions.

This kind of analysis is not without challenges. It requires its own investment, of course, although the cost will most likely fall as the practice is more widely adopted. And the sheer amount of information required can result in paralysis instead of steady improvement. Renewable energy businesses must stick to their key questions, choosing the right systems they need to inform essential, cost-affecting decisions.

But the gains on offer could tempt energy generation firms to make the investment. In one example, industrial firm GE claims that its own smart wind turbines take temperature data, misalignment and vibrational information to make adjustments that boost energy production by up to 20%.

Of course, many of the techniques described here can equally be applied to non-renewable energy sources, so their impact on the cost differential between green and conventional energy remains to be seen.

Nevertheless, they could certainly help to bring renewable energy sources down to a cost that is low enough to convince more producers and consumers to switch.

Is analytics the key to making renewable energy affordable? Share your thoughts on the , sponsored by Dassault Systèmes.

Enjoy in-depth insights and expert analysis - subscribe to our Perspectives newsletter, delivered every week