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Renewable Energy Market Predictions – Pick a Scenario, Any Scenario…

Sean Salleh 27 Sep 2013 Energy and environment

Modelers have been busy recently making renewable energy market predictions. However their forecasts vary, sometimes significantly. This may be a reflection of the different assumptions being made: for instance, in terms of programs to support renewable energy being started or stopped. Modelers just starting in renewable energy market predictions have a choice. They can build their own models using their chosen sources of data. They can also reverse engineer existing models to make educated guesses about how they have been built, to then improve them with new versions. Models have already been built with Analytica for the simulation of renewable energy projects.

Renewable energy market predictions differ according to who is making them Image source:

Bloomberg’s Bright Future Scenario

In a recent publication from Bloomberg New Energy Finance, three different scenarios are offered: ‘New Normal’, ‘Barrier Busting’ and ‘Traditional Territory’. In its renewable energy market predictions, Bloomberg sees renewable energy providing 69-74 per cent of the total additional capacity for power that is projected for 2030. Given that the worldwide increase in demand for energy is estimated to be about an additional one-third of today’s levels, renewable energy would account for an extra 20 percent of the total demand by 2030 – 2035. While that figure is already impressive, plans elsewhere include providing all of the world's energy with wind, hydroelectric, and solar power, also by the year 2030.

Not So Fast is the Naysayer’s Response

Not everybody believes that renewable energy market predictions are so rosy. Another forecast shows fossil fuels still providing around 80 per cent of energy for the world, including both transportation and electricity requirements. The overall increase in demand for energy is not in question – a figure of a 56 per cent increase by 2040 is not incompatible with a figure of 33.33 per cent above by 2030. Neither is the source of the increase in dispute: Asian countries, and in particular India and China, are driving the total energy consumption figures upwards. However, the modeling difference is in how such an demand will be met – whether for example China will continue on its current trajectory of coal-fired power generation or whether it will bring in more ecologically-friendly solutions.

Modeling the Factors at Work in These Markets

Which way the wind blows in terms of power costs, national policies and power network capability is what will determine the reality beyond the renewable energy market predictions. The International Energy Agency points out a number of renewable energy market phenomena that could push forecasts one way or the other. Positive factors include: the popularity of wind-generation power plants compared to fossil fuel electricity in Brazil, New Zealand and Turkey; and the cost of solar-generated electricity in Australia, Italy and Spain below retail prices. Negatives include: the reversal on renewable energy programs in Bulgaria, Czech Republic and Spain; and the need for the power grid in a number of markets to be revamped before significantly higher levels of renewable energy can be accepted.

Collateral Market Effects (Driven or Driving?)

The changes in the market suggested by different renewable energy market predictions could have side effects as well. Renewable energy solutions for electricity generation could stimulate a market for high voltage direct current (HVDC) transmission technology. With a potential of $90 billion in revenues, vendors in this market might also push for moves towards renewable energy. In other words, HVDC as well as being driven by the renewable energy market could also become a driver of that market in its own right.

If you’d like to know how Analytica, the modeling software from Lumina, can help you to forecast energy markets of any kind, then try a thirty day free evaluation of Analytica to see what it can do for you.

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Sean Salleh

Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.

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