Skip to main content

Advertisement

Log in

An Exploratory Analysis of the Dutch Electricity System in Transition

  • Published:
Journal of the Knowledge Economy Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Electricity generated from renewable sources such as wind, solar, biomass, etc.

References

  • Agusdinata, DB (2008). Exploratory modeling and analysis: a promising method to deal with deep uncertainty. Ph.D. thesis, Delft University of Technology.

  • Ascher, W. (1978). Forecasting: an appraisal for policy makers and planners. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Axelrod, R. (1997). Advancing the art of simulation in the social sciences. Complexity, 3, 16–22.

    Article  Google Scholar 

  • Bankes, S. (1993). Exploratory modeling for policy analysis. Operations Research, 4, 435–449.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Bankes, S., & Margoliash, D. (1993). Parametric modeling of the temporal dynamics of neuronal responses using connectionist architectures. Journal of Neurophysiology, 69, 980–991.

    Google Scholar 

  • Ben Haim, Y. (2001). Information-gap decision theory: decision under severe uncertainty. Waltham: Academic Press.

    Google Scholar 

  • Ben Haim, Y. (2004). Uncertainty, probability and information-gaps. Reliability Engineering and System Safety, 85, 249–266.

    Article  Google Scholar 

  • Ben-Akiva, M. E., & Lerman, S. R. (1985). Discrete choice analysis: theory and application to travel demand. Cambridge: MIT Press.

    Google Scholar 

  • Breeze, P. (2005). Power generation technologies. Oxford: Newnes.

    Google Scholar 

  • Brooks, A., Bennet, B., & Bankes, S. (1999). An application of exploratory analysis: the weapon mix problem. Military Operations Research, 4, 67–80.

    Article  Google Scholar 

  • 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, 34–49.

    Article  Google Scholar 

  • Cambell, D., Crutchfield, J., Farmer, D., & Jen, E. (1985). Experimental mathematics—the role of computation in nonlinear science. Communications of the ACM, 28, 374–384.

    Article  Google Scholar 

  • Chong, I. G., & Jun, C. H. (2008). Flexible patient rule induction method for optimizing process variables in discrete types. Expert Systems with Applications, 34, 3014–3020.

    Article  Google Scholar 

  • Davison, J. (2007). Performance and costs of power plants with capture and storage of CO2. Energy, 32, 1163–1176.

    Article  Google Scholar 

  • 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.

  • Dessai, S., Hulme, M., & Lempert, R. (2009). Do we need better predictions to adapt to a changing climate? Eos, 90, 111–112.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: social science from the bottom up. Washington D.C: Brookings Institution Press.

    Google Scholar 

  • Friedman, J. H., & Fisher, N. I. (1999). Bump hunting in high-dimensional data. Statistics and Computing, 9, 123–143.

    Article  Google Scholar 

  • Garcia, R. (2005). Uses of agent-based modeling in innovation/new product development research. The Journal of Product Innovation Management, 22, 380–398.

    Article  Google Scholar 

  • Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case study. Research Policy, 1257–1274.

  • Geels, F. W. (2005). Technological transitions and system innovations: a co-evolutionary and socio-technical analysis. Cheltenham: Edward Elgar Publishing, Inc.

    Book  Google Scholar 

  • Gensch, D. H., & Recker, W. W. (1979). The multinomial, multiattribute logit choice model. Journal of Marketing Research, 16, 124–132.

    Article  Google Scholar 

  • Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. Berkshire: Open University Press.

    Google Scholar 

  • Goodwin, P., & Wright, G. (2010). The limits of forecasting methods in anticipating rare events. Technological Forecasting and Social Change, 77, 355–368.

    Article  Google Scholar 

  • Green, R. (2004). Did English generators play Cournot? Capacity withholding in the electricity pool. Cambridge Working Papers in Economics. Cambridge: University of Cambridge.

  • Grimm, V., & Railsback, S. F. (2005). Individual-based modeling and ecology. Princeton: Princeton University Press.

    Google Scholar 

  • Grin, J., Rotmans, J., & Schot, J. (Eds.). (2010). Transitions to sustainable development. New directions in the study of long term transformative change. New York/London: Routledge.

    Google Scholar 

  • Groves, D. G., & Lempert, R. J. (2007). A New analytic method for finding policy-relevant scenarios. Global Environmental Change, 17, 73–85.

    Article  Google Scholar 

  • Hamarat, C., Kwakkel, J. H. & Pruyt, E. (2012). Energy transitions: adaptive policy making under deep uncertainty. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2012.10.004.

  • Hodges, J. S. (1991). Six (or so) things you can do with a bad model. Operations Research, 39, 355–365.

    Article  Google Scholar 

  • Hodges, J. S., & Dewar, J. A. (1992). Is it you or your model talking? A framework for model validation. Santa Monica: RAND.

    Google Scholar 

  • Hughes, T. P. (1983). Networks of power: electrification in western society, 1880–1930. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Jones, E., Oliphant, T., Peterson, P. & Others, A. (2001). SciPy: open source scientific tools for Python.

  • Keeney, R., & Gregory, R. (2005). Selecting attributes to measure the achievement of objectives. Operations Research, 53, 1–11.

    Article  Google Scholar 

  • Keeney, R., & Raiffa, H. (1993). Decisions with multiple objectives: preferences and values tradeoffs. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Kemp, R., Schot, J., & Hoogma, R. (1998). Regime shifts to sustainability through processes of niche formation: the approach of strategic niche management. Technology Analysis and Strategic Management, 10, 175–195.

    Article  Google Scholar 

  • Kwakkel, J. H., Auping, W. & Pruyt, E. (2012). Dynamic scenario discovery under deep uncertainty: the future of copper. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2012.09.012.

  • Kwakkel, J. H. & Pruyt, E. (2012). Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty. Technological Forecasting and Social Change. doi:10.1016/j.techfore.2012.10.005.

  • Kwakkel, J. H., Walker, W. E., & Marchau, V. A. W. J. (2012). Assessing the efficacy of adaptive airport strategic planning: results from computational experiments. Environment and Planning B: Planning and Design, 39, 533–550.

    Article  Google Scholar 

  • Lako, P., & Seebregts, A. (1998). Characterisation of power generation options for the 21st century: report on behalf of macro task E1. Petten: ECN.

    Google Scholar 

  • Lempert, R. J. (2002). A new decision sciences for complex systems. Proceedings of the National Academy of Sciences of the United States of America, 99, 7309–7313.

    Article  Google Scholar 

  • Lempert, R. J., Popper, S., & Bankes, S. (2002). Confronting surprise. Social Science Computer Review, 20, 420–439.

    Article  Google Scholar 

  • Lempert, R. J., Popper, S., & Bankes, S. (2003). Shaping the next one hundred years: new methods for quantitative, long term policy analysis. Santa Monica: RAND.

    Google Scholar 

  • Lempert, R. J., Bryant, B. P., & Bankes, S. (2008). Comparing algorithms for scenario discovery. Santa Monica: Rand.

    Google Scholar 

  • Loorbach, D. (2007). Transition management: new mode of governance for sustainable development. Utrecht: International Books.

    Google Scholar 

  • McInerney, D., Lempert, R., & Keller, K. (2012). What are robust strategies in the face of uncertain climate threshold responses. Climate Change, 112, 547–568.

    Article  Google Scholar 

  • Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: an introduction to computational models of social life. Princeton: Princeton University Press.

    Google Scholar 

  • North, M. J., Collier, N. T., & Vos, J. R. (2006). Experiences creating three implementations of the repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation, 16, 125.

    Article  Google Scholar 

  • Orrel, D., & McSharry, P. (2009). System economics: overcoming the pitfalls of forecasting models via a multidisciplinary approach. International Journal of Forecasting, 25, 734–743.

    Article  Google Scholar 

  • Park, G., & Lempert, R. (1998). The class of 2014: preserving access to california higher education. Santa Monica: RAND.

    Google Scholar 

  • Pepermans, G., Driesen, J., Haeseldonckx, D., Belmans, R., & D’Haeseleer, W. (2005). Distributed generation: definition, benefits and issues. Energy Policy, 33, 787–798.

    Article  Google Scholar 

  • Pilkey, O. H., & Pilkey-Jarvis, L. (2007). Useless arithmetic: why environmental scientists can’t predict the future. New York: Columbia University Press.

    Google Scholar 

  • Pirog, R. L., Stamos, S., Cohn, S., Knapp, R. H., & Simon, R. M. (1987). Energy economics: theory and policy. Prentice-Hall: Englewood Cliffs.

    Google Scholar 

  • Rödel, J. G. (2008). Ecology, economy and security of supply of the dutch electricity supply system: a scenario based future analysis. PhD thesis, Delft University of Technology.

  • Rotmans, J., Kemp, R., & Asselt, M. V. (2001). More evolution than revolution: transition management in public policy. Foresight, 3, 15–31.

    Article  Google Scholar 

  • Schwartz, P. (1991). The art of the long view. Chichester: Wiley.

    Google Scholar 

  • Schwarz, N., & Ernst, A. (2009). Agent-based modeling of the diffusion of environmental innovations - an empirical approach. Technological Forecasting and Social Change, 76, 497–511.

    Article  Google Scholar 

  • Seebregts, A. (2005). Appendix B: description of the POWERS model (in Dutch). In: A. J. Seebregts, M. J. J. Scheepers, R. Jansma, & J. F. A. V. Hienen (eds.) Kerncentrale Borssele na 2013. Gevolgen van beëindiging of voortzetting van de bedrijfsvoering. Petten: ECN.

  • Sterman, J. D. (2002). All models are wrong: reflections on becoming a systems scientist. System Dynamics Review, 18, 501–531.

    Article  Google Scholar 

  • Timmermans, J., de Haan, H., & Squazzoni, F. (2008). Computational and mathematical approaches to societal transitions. Computational and Mathematical Organization Theory, 14, 391–414.

    Article  Google Scholar 

  • van Damme, E. (2005). Liberalizing the Dutch electricity market: 1998–2004. Tilburg: CentER - Tilburg University.

    Google Scholar 

  • van den Broek, M., Faaij, A., & Turkenburg, W. (2008). Planning for an electricity sector with carbon capture and storage: case of the Netherlands. International Journal of Greenhouse Gas Control, 2, 105–129.

    Article  Google Scholar 

  • van der Heijden, K. (1996). Scenarios: the art of strategic conversation. Chichester: Wiley.

    Google Scholar 

  • van der Pas, J. W. G. M., Walker, W. E., Marchau, V. A. W. J., van Wee, B., & Agusdinata, B. (2010). Exploratory MCDA for handling deep uncertainties: the case of intelligent speed adaptation implementation. Journal of Multicriteria Decision Analysis, 17, 1–23.

    Article  Google Scholar 

  • van Rossum, G. (1995). Python reference manual. CWI Report CS-R9525.

  • Vogstad, K.-O. (2004). A system dynamics analysis of the Nordic electricity market: the transition from fossil fuelled toward a renewable supply within a liberalised electricity market. PhD thesis, Norwegian University of Science and Technology.

  • Voorspools, K. (2004). The modelling of large electricity-generation systems with applications in emission-reduction scenarios and electricity trade. PhD thesis, Leuven KU.

  • Voorspools, K., & D’Haeseleer, W. (2003). The impact of the implementation of cogeneration in a given energetic context. IEEE Transactions on Energy Conversion, 18, 135–141.

    Article  Google Scholar 

  • Wittmann, T. (2008). Agent-based models of energy investment decisions. Heidelberg: Physicrra.

    Google Scholar 

  • Yücel, G. (2010). Analyzing transition dynamics: the actor-option framework for modelling socio-technical systems. PhD thesis, Delft University of Technology.

  • Yücel, G., & van Daalen, C. (2012). A simulation-based analysis of transition pathways for the Dutch electricity system. Energy Policy, 42, 557–568.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan H. Kwakkel.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kwakkel, J.H., Yücel, G. An Exploratory Analysis of the Dutch Electricity System in Transition. J Knowl Econ 5, 670–685 (2014). https://doi.org/10.1007/s13132-012-0128-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13132-012-0128-1

Keywords

Navigation