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.
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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.
Axelrod, R. (1997). Advancing the art of simulation in the social sciences. Complexity, 3, 16–22.
Bankes, S. (1993). Exploratory modeling for policy analysis. Operations Research, 4, 435–449.
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.
Bankes, S., & Margoliash, D. (1993). Parametric modeling of the temporal dynamics of neuronal responses using connectionist architectures. Journal of Neurophysiology, 69, 980–991.
Ben Haim, Y. (2001). Information-gap decision theory: decision under severe uncertainty. Waltham: Academic Press.
Ben Haim, Y. (2004). Uncertainty, probability and information-gaps. Reliability Engineering and System Safety, 85, 249–266.
Ben-Akiva, M. E., & Lerman, S. R. (1985). Discrete choice analysis: theory and application to travel demand. Cambridge: MIT Press.
Breeze, P. (2005). Power generation technologies. Oxford: Newnes.
Brooks, A., Bennet, B., & Bankes, S. (1999). An application of exploratory analysis: the weapon mix problem. Military Operations Research, 4, 67–80.
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.
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.
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.
Davison, J. (2007). Performance and costs of power plants with capture and storage of CO2. Energy, 32, 1163–1176.
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.
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.
Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: social science from the bottom up. Washington D.C: Brookings Institution Press.
Friedman, J. H., & Fisher, N. I. (1999). Bump hunting in high-dimensional data. Statistics and Computing, 9, 123–143.
Garcia, R. (2005). Uses of agent-based modeling in innovation/new product development research. The Journal of Product Innovation Management, 22, 380–398.
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.
Gensch, D. H., & Recker, W. W. (1979). The multinomial, multiattribute logit choice model. Journal of Marketing Research, 16, 124–132.
Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. Berkshire: Open University Press.
Goodwin, P., & Wright, G. (2010). The limits of forecasting methods in anticipating rare events. Technological Forecasting and Social Change, 77, 355–368.
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.
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.
Groves, D. G., & Lempert, R. J. (2007). A New analytic method for finding policy-relevant scenarios. Global Environmental Change, 17, 73–85.
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.
Hodges, J. S., & Dewar, J. A. (1992). Is it you or your model talking? A framework for model validation. Santa Monica: RAND.
Hughes, T. P. (1983). Networks of power: electrification in western society, 1880–1930. Baltimore: Johns Hopkins University Press.
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.
Keeney, R., & Raiffa, H. (1993). Decisions with multiple objectives: preferences and values tradeoffs. Cambridge: Cambridge University Press.
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.
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.
Lako, P., & Seebregts, A. (1998). Characterisation of power generation options for the 21st century: report on behalf of macro task E1. Petten: ECN.
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.
Lempert, R. J., Popper, S., & Bankes, S. (2002). Confronting surprise. Social Science Computer Review, 20, 420–439.
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.
Lempert, R. J., Bryant, B. P., & Bankes, S. (2008). Comparing algorithms for scenario discovery. Santa Monica: Rand.
Loorbach, D. (2007). Transition management: new mode of governance for sustainable development. Utrecht: International Books.
McInerney, D., Lempert, R., & Keller, K. (2012). What are robust strategies in the face of uncertain climate threshold responses. Climate Change, 112, 547–568.
Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: an introduction to computational models of social life. Princeton: Princeton University Press.
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.
Orrel, D., & McSharry, P. (2009). System economics: overcoming the pitfalls of forecasting models via a multidisciplinary approach. International Journal of Forecasting, 25, 734–743.
Park, G., & Lempert, R. (1998). The class of 2014: preserving access to california higher education. Santa Monica: RAND.
Pepermans, G., Driesen, J., Haeseldonckx, D., Belmans, R., & D’Haeseleer, W. (2005). Distributed generation: definition, benefits and issues. Energy Policy, 33, 787–798.
Pilkey, O. H., & Pilkey-Jarvis, L. (2007). Useless arithmetic: why environmental scientists can’t predict the future. New York: Columbia University Press.
Pirog, R. L., Stamos, S., Cohn, S., Knapp, R. H., & Simon, R. M. (1987). Energy economics: theory and policy. Prentice-Hall: Englewood Cliffs.
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.
Schwartz, P. (1991). The art of the long view. Chichester: Wiley.
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.
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.
Timmermans, J., de Haan, H., & Squazzoni, F. (2008). Computational and mathematical approaches to societal transitions. Computational and Mathematical Organization Theory, 14, 391–414.
van Damme, E. (2005). Liberalizing the Dutch electricity market: 1998–2004. Tilburg: CentER - Tilburg University.
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.
van der Heijden, K. (1996). Scenarios: the art of strategic conversation. Chichester: Wiley.
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.
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.
Wittmann, T. (2008). Agent-based models of energy investment decisions. Heidelberg: Physicrra.
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.
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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
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DOI: https://doi.org/10.1007/s13132-012-0128-1