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Technology Assessment: Energy Storage Technologies for Wind Power Generation

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Book cover Technology Development

Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

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Abstract

The problems in generation imbalance for wind power require multi-criteria analysis for the decision makers. In addition to the required multi-criteria analysis, there is also a problem of uncertainty inherent in future changes as a result of interdependence among these criteria. To counter this two problems, this paper describes a systematic approach of Bayesian causal maps and systematic probability generation method. Bayesian causal maps, which is built from causal maps, is used to develop a proposed framework on scenario-based assessment of energy storage technologies for wind power generation. Causal maps provides a rich representation of ideas, through the modeling of complex structures, representing the chain of arguments, as networks.

A prior version of this paper was included in the conference Proceedings of PICMET 2013.

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Author information

Authors and Affiliations

  1. Portland State University, Portland, OR, USA

    Yulianto Suharto

  2. Engineering and Technology Management Department, Portland State University, Portland, OR, USA

    Tugrul U. Daim

Authors
  1. Yulianto Suharto
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  2. Tugrul U. Daim
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Corresponding author

Correspondence to Yulianto Suharto .

Editor information

Editors and Affiliations

  1. Engineering and Technology Management, Portland State University, Portland, Oregon, USA

    Tugrul U. Daim

  2. Intel Corporation, Hillsboro, Oregon, USA

    Ramin Neshati

  3. Nike, Inc., Beaverton, Oregon, USA

    Russell Watt

  4. Tri Quint Semiconductos, Hillsboro, Oregon, USA

    James Eastham

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© 2014 Springer International Publishing Switzerland

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Suharto, Y., Daim, T.U. (2014). Technology Assessment: Energy Storage Technologies for Wind Power Generation. In: Daim, T., Neshati, R., Watt, R., Eastham, J. (eds) Technology Development. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-05651-7_5

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