An Exploratory Analysis of the Dutch Electricity System in Transition

详细信息    查看全文

• 作者：Jan H. Kwakkel (1)
  G?nen? Yücel (2)
  
• 关键词：Electricity supply ; Transitions ; Exploratory modeling and analysis ; Uncertainty ; Agent ; based modeling
• 刊名：Journal of the Knowledge Economy
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：5
• 期：4
• 页码：670-685
• 全文大小：747 KB
• 参考文献：1. Agusdinata, DB (2008). / Exploratory modeling and analysis: a promising method to deal with deep uncertainty. Ph.D. thesis, Delft University of Technology.
  2. Ascher, W. (1978). / Forecasting: an appraisal for policy makers and planners. Baltimore: Johns Hopkins University Press.
  3. Axelrod, R. (1997). Advancing the art of simulation in the social sciences. / Complexity, 3, 16-2. href="http://dx.doi.org/10.1002/(SICI)1099-0526(199711/12)3:2<16::AID-CPLX4&gt;3.0.CO;2-K" target="_blank" title="It opens in new window">CrossRef
  4. Bankes, S. (1993). Exploratory modeling for policy analysis. / Operations Research, 4, 435-49. href="http://dx.doi.org/10.1287/opre.41.3.435" target="_blank" title="It opens in new window">CrossRef
  5. Bankes, S. (1994). Exploring the foundations of artificial societies: experiments in evolving solutions to N-player Prisoner’s dilemma. In R. Brooks & P. Maes (Eds.), / Artifical life IV. Cambridge: MIT Press.
  6. Bankes, S., & Margoliash, D. (1993). Parametric modeling of the temporal dynamics of neuronal responses using connectionist architectures. / Journal of Neurophysiology, 69, 980-91.
  7. Ben Haim, Y. (2001). / Information-gap decision theory: decision under severe uncertainty. Waltham: Academic Press.
  8. Ben Haim, Y. (2004). Uncertainty, probability and information-gaps. / Reliability Engineering and System Safety, 85, 249-66. href="http://dx.doi.org/10.1016/j.ress.2004.03.015" target="_blank" title="It opens in new window">CrossRef
  9. Ben-Akiva, M. E., & Lerman, S. R. (1985). / Discrete choice analysis: theory and application to travel demand. Cambridge: MIT Press.
  10. Breeze, P. (2005). / Power generation technologies. Oxford: Newnes.
  11. Brooks, A., Bennet, B., & Bankes, S. (1999). An application of exploratory analysis: the weapon mix problem. / Military Operations Research, 4, 67-0. href="http://dx.doi.org/10.5711/morj.4.1.67" target="_blank" title="It opens in new window">CrossRef
  12. Bryant, B. P., & Lempert, R. J. (2010). Thinking inside the box: a participatory computer-assisted approach to scenario discovery. / Technological Forecasting and Social Change, 77, 34-9. href="http://dx.doi.org/10.1016/j.techfore.2009.08.002" target="_blank" title="It opens in new window">CrossRef
  13. Cambell, D., Crutchfield, J., Farmer, D., & Jen, E. (1985). Experimental mathematics—the role of computation in nonlinear science. / Communications of the ACM, 28, 374-84. href="http://dx.doi.org/10.1145/3341.3345" target="_blank" title="It opens in new window">CrossRef
  14. Chong, I. G., & Jun, C. H. (2008). Flexible patient rule induction method for optimizing process variables in discrete types. / Expert Systems with Applications, 34, 3014-020. href="http://dx.doi.org/10.1016/j.eswa.2007.05.047" target="_blank" title="It opens in new window">CrossRef
  15. Davison, J. (2007). Performance and costs of power plants with capture and storage of CO₂. / Energy, 32, 1163-176. href="http://dx.doi.org/10.1016/j.energy.2006.07.039" target="_blank" title="It opens in new window">CrossRef
  16. de Vries, L. J. (2004). / Securing the public interest in electricity generation markets: / the myths of the invisible hand and the copper plate. PhD thesis, Delft University of Technology.
  17. Dessai, S., Hulme, M., & Lempert, R. (2009). Do we need better predictions to adapt to a changing climate? / Eos, 90, 111-12. href="http://dx.doi.org/10.1029/2009EO130003" target="_blank" title="It opens in new window">CrossRef
  18. Enserink, B., Hermans, L., Kwakkel, J. H., Thissen, W., Koppenjan, J. F. M., & Bots, P. W. G. (2010). / Policy analysis of multi-actor systems. Lemma: Utrecht.
  19. Epstein, J. M., & Axtell, R. (1996). / Growing artificial societies: social science from the bottom up. Washington D.C: Brookings Institution Press.
  20. Friedman, J. H., & Fisher, N. I. (1999). Bump hunting in high-dimensional data. / Statistics and Computing, 9, 123-43. href="http://dx.doi.org/10.1023/A:1008894516817" target="_blank" title="It opens in new window">CrossRef
  21. Garcia, R. (2005). Uses of agent-based modeling in innovation/new product development research. / The Journal of Product Innovation Management, 22, 380-98. href="http://dx.doi.org/10.1111/j.1540-5885.2005.00136.x" target="_blank" title="It opens in new window">CrossRef
  22. Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case study. / Research Policy, 1257-274.
  23. Geels, F. W. (2005). / Technological transitions and system innovations: a co-evolutionary and socio-technical analysi
• 作者单位：Jan H. Kwakkel (1)
  G?nen? Yücel (2)
  
  1. Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands
  2. Industrial Engineering Department, Bo?azi?i University, ?stanbul, Turkey
  
• ISSN：1868-7873

文摘

Recent contextual developments constitute a backdrop of change for the Dutch electricity system. Institutional change driven by liberalization, changing economic competitiveness of the dominant fuels, new technologies, and changing end-user preferences regarding electricity supply are some examples of these developments. This paper explores plausible transition trajectories in the face of these developments given technological uncertainty about investment and operating costs, and fuel efficiency of various alternative technologies; political uncertainty about future CO2 abatement policies such as emission trading; and socio-economic uncertainty about fuel prices, investment decisions of suppliers, and load curves. Various alternative developments for these uncertainties are specified. The consequences of each of these alternative developments are assessed using an agent-based model of the Dutch electricity system. The outputs are analyzed using various data-mining and data visualization techniques in order to reveal arch-typical transition trajectories and their conditions for occurring. Policy recommendations are derived from this. The results indicate that most transition trajectories point towards a future energy supply system that is reliant on clean coal and gas. Under the explored uncertainties, only rarely a transition to renewables occurs. The various sustainable energy support programs appear to be ineffective in steering the energy supply system towards a more sustainable mode of functioning across the various uncertainties.