Renewable energy potential

Some consequences of our way of life sustainment by using fossil biofuels have made renewable energy become one of the focus issues in our society. However, the arguments presentation is often based on powerfull images, categorical sentences, and universally assumed principles that in a superficial analysis are turned into social and technically sound arguments.

The result is that many citizens perceive that renewable energies are the difficult free solution, often living carefree about the energy problem.

While it is indisputable that renewable energies should, and will have an increasing contribution to total primary energy consumption, the analysis and its potential social impact, and the study of deployment policies should be supported in a study of potential costs and impacts using rigorous methods and well-founded assumptions.

Such quantification is not straightforward for several reasons, including:

  • Renewable energies have, by its nature, several procedences, and each one requires a specific processing.
  • The required data are different (statistical, geographical, economic), with different procedences, and its reliability is not always reported or appropriate.
  • Quantifying the potential is needed at global, regional or national scales; the amount of data to handle is considerable, and may be the case that the same kind of data is not centralized, but must be collected from several resources depending on the geographic location (eg autonomous communities).
  • Many needed data require modeling with a high degree of uncertainty (for example, the available roof area), or speculative long-term predictions (eg, changes in a technology specific cost or the energy demand).

These pages present the results of a study conducted by the Numerical Fluidynamics Group from the University of Zaragoza, using a rigorous methodology, which has the following general characteristics:

  • Rationality: the assumptions and models have to be well documented and justified.
  • Verifiability: where possible, the quality of the self-made data, and the model results has been contrasted with results from external sources.
  • Modularity: the models or parts that compose the study have to be easily replaceable by another, either for improvements or to sensitivity or parametric studies.