An Exploratory Analysis of the Dutch Electricity System in Transition

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.

[1]  Yakov Ben-Haim,et al.  Uncertainty, probability and information-gaps , 2004, Reliab. Eng. Syst. Saf..

[2]  Jan H. Kwakkel,et al.  Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty , 2013 .

[3]  D. Finegold.  Book and Resource Reviews , 2005 .

[4]  L. Hermans,et al.  Policy Analysis of Multi-Actor Systems , 2010 .

[5]  Nicholas I. Fisher,et al.  Bump hunting in high-dimensional data , 1999, Stat. Comput..

[6]  G. Nigel Gilbert,et al.  Simulation for the social scientist , 1999 .

[7]  John Davison,et al.  Performance and costs of power plants with capture and storage of CO2 , 2007 .

[8]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[9]  Vincent Marchau,et al.  Assessing the Efficacy of Dynamic Adaptive Planning of Infrastructure: Results from Computational Experiments , 2012 .

[10]  L. J. De Vries,et al.  Securing the public interest in electricity generation markets. The myths of the invisible hand and the copper plate , 2004 .

[11]  K. R. Voorspools,et al.  The Impact of the Implementation of Cogeneration in a Given Energetic Context , 2002, IEEE Power Engineering Review.

[12]  B. Agusdinata,et al.  Exploratory modeling and analysis: a promising method to deal with deep uncertainty , 2008 .

[13]  Jan H. Kwakkel,et al.  Dynamic scenario discovery under deep uncertainty: The future of copper , 2013 .

[14]  George Wright,et al.  The limits of forecasting methods in anticipating rare events , 2010 .

[15]  Ralph L. Keeney,et al.  Selecting Attributes to Measure the Achievement of Objectives , 2005, Oper. Res..

[16]  Benjamin P. Bryant,et al.  Thinking Inside the Box , 2010 .

[17]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[18]  Robert J. Lempert,et al.  The Class of 2014: Preserving Access to California Higher Education , 1998 .

[19]  Richard Green,et al.  Did English Generators Play Cournot? Capacity withholding in the Electricity Pool , 2004 .

[20]  Nina Schwarz,et al.  Agent-based modeling of the diffusion of environmental innovations — An empirical approach , 2009 .

[21]  Erik Pruyt,et al.  Essential Skills for System Dynamics Practitioners: A Delft University of Technology Perspective , 2010 .

[22]  F. Geels.  Technological Transitions And System Innovations: A Co-evolutionary And Socio-technical Analysis , 2005 .

[23]  David G. Groves,et al.  A New Analytic Method for Finding Policy-Relevant Scenarios: , 2007 .

[24]  Klaus-Ole Vogstad,et al.  A system dynamics analysis of the Nordic electricity market : the transition from fossil fuelled toward a renewable supply within a liberalised electricity market , 2005 .

[25]  J. G. Rödel.  Ecology, Economy and Security of Supply of the Dutch Electricity Supply System: A Scenario Based Future Analysis , 2008 .

[26]  Ronnie Belmans,et al.  Distributed generation: definition, benefits and issues , 2005 .

[27]  T. P. Hughes,et al.  Networks of Power: Electrification in Western Society , 1984 .

[28]  Gönenç Yücel,et al.  A Simulation-based Analysis of Transition Pathways for the Dutch Electricity System , 2011 .

[29]  Chi-Hyuck Jun,et al.  Flexible patient rule induction method for optimizing process variables in discrete type , 2008, Expert Syst. Appl..

[30]  James S. Hodges,et al.  Is It You or Your Model Talking?: A Framework for Model Validation , 1992 .

[31]  T. P. Hughes.  Networks of power : electrification in Western society, 1880-1930 , 1984 .

[32]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[33]  André Faaij,et al.  Planning for an electricity sector with carbon capture and storage , 2008 .

[34]  Robert J. Lempert,et al.  Comparing Algorithms for Scenario Discovery , 2008 .

[35]  Wilfred W. Recker,et al.  The Multinomial, Multiattribute Logit Choice Model , 1979 .

[36]  Robert Axelrod.  Advancing the art of simulation in the social sciences , 1997 .

[37]  Lesley F. Wright,et al.  Information Gap Decision Theory: Decisions under Severe Uncertainty , 2004 .

[38]  Tobias Wittmann,et al.  Agent-Based Models of Energy Investment Decisions , 2008 .

[39]  John H. Miller,et al.  Complex adaptive systems - an introduction to computational models of social life , 2009, Princeton studies in complexity.

[40]  Flaminio Squazzoni,et al.  Computational and mathematical approaches to societal transitions , 2008, Comput. Math. Organ. Theory.

[41]  Peter Schwartz,et al.  The art of the long view , 1991 .

[42]  J. Grin,et al.  Transitions to Sustainable Development: New Directions in the Study of Long Term Transformative Change , 2010 .

[43]  Steve Bankes,et al.  An Application of Exploratory Analysis: The Weapon Mix Problem , 1999 .

[44]  Michael J. North,et al.  Experiences creating three implementations of the repast agent modeling toolkit , 2006, TOMC.

[45]  Kris Voorspools,et al.  The modelling of large electricity-generation systems with applications in emission-reduction scenarios and electricity trade , 2004 .

[46]  D Margoliash,et al.  Parametric modeling of the temporal dynamics of neuronal responses using connectionist architectures. , 1993, Journal of neurophysiology.

[47]  R. Lempert,et al.  What are robust strategies in the face of uncertain climate threshold responses? , 2012, Climatic Change.

[48]  F. Geels.  Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study , 2002 .

[49]  J. Schot,et al.  Regime shifts to sustainability through processes of niche formation : the approach of strategic niche management , 1998 .

[50]  James S. Hodges,et al.  Six (Or So) Things You Can Do with a Bad Model , 1991, Oper. Res..

[51]  Warren E. Walker,et al.  Exploratory MCDA for handling deep uncertainties: the case of intelligent speed adaptation implementation , 2010 .

[52]  Eric van Damme,et al.  Liberalizing the Dutch Electricity Market: 1998-2004 , 2005 .

[53]  D. Loorbach.  Transition Management: New Mode of Governance for Sustainable Development , 2007 .

[54]  R. L. Henshel,et al.  Forecasting: An Appraisal for Policy-Makers and Planners. , 1978 .

[55]  Rosanna Garcia.  Uses of Agent-Based Modeling in Innovation/New Product Development Research , 2005 .

[56]  Jan H. Kwakkel,et al.  Adaptive Robust Design under deep uncertainty , 2013 .

[57]  Robert J. Lempert,et al.  Do We Need Better Predictions to Adapt to a Changing Climate , 2009 .

[58]  Lutz E. Schlange.  Scenarios: The art of strategic conversation , 1997 .

[59]  Paul Breeze,et al.  Power Generation Technologies , 2005 .

[60]  Robert J. Lempert,et al.  Confronting Surprise , 2002 .

[61]  Patrick E. McSharry,et al.  System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach , 2009 .

[62]  Robert J Lempert,et al.  A new decision sciences for complex systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[63]  Steve Bankes,et al.  Exploratory Modeling for Policy Analysis , 1993, Oper. Res..

[64]  Guido Rossum,et al.  Python Reference Manual , 2000 .

[65]  John D. Sterman,et al.  All models are wrong: reflections on becoming a systems scientist† , 2002 .

[66]  M. V. Asselt,et al.  More evolution than revolution: transition management in public policy , 2001 .

[67]  Gönenç Yücel,et al.  Analyzing Transition Dynamics: The Actor-Option Framework for Modelling Socio-Technical Systems , 2010 .

[68]  Steven F. Railsback,et al.  Individual-based modeling and ecology , 2005 .

[69]  Linda Pilkey-Jarvis,et al.  :Useless Arithmetic: Why Environmental Scientists Cant Predict the Future , 2007 .