1. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Caner Hamarat & Erik Pruyt Delft University of Technology Faculty of Technology, Policy & Management Policy Analysis Section The 4th International Seville Conference on Future-Oriented Technology Analysis (FTA) 12 & 13 May 2011 E-mail: c.hamarat@tudelft.nl

2. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Introduction Decision making under deep uncertainty Adaptive policy making Exploratory Modeling and Analysis (EMA) Case Study: Energy Transitions EMA Methodology Conclusions Selected References

3. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Decision making under deep uncertainty Deep Uncertainty –Uncertainty about models, probability distributions, evaluation of outcomes. (Lempert et al, 2003) –Enumeration of alternatives without being able to rank order the alternatives in terms of how likely or plausible they are (Kwakkel, 2010) Models: Formal representations of real-world Different modelling paradigms –Spread-sheet modelling, Agent-Based, Econometrics, System Dynamics Under deep uncertainty, prediction can be misleading.

4. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Adaptive Policy Making Dynamic Complexity & Deep Uncertainty The trouble of static policies Instead of optimal, robust & adaptive In the presence of deep uncertainties, flexibility and adaptability should be aimed by the policy makers (Kwakkel et al, 2010).

5. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Exploratory Modeling & Analysis EMA can be used to –explore the influence of uncertainties –test effectiveness/robustness of policies given these uncertainties EMA is not a modelling technique! A methodology for using models in an explorative way. Procedure: –Development of (relatively) simple model(s) of system of interest –Design of Experiments –Specification of one or more policy options and calculate performance of options for experiments using the ensemble of fast and simple models –Analysis of performance of policy options across experiments –Iteration through previous steps until a satisfying policy emerges

6. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Case: Energy Transitions Energy Transitions: –deeply uncertain & dynamically complex Competition of existing and new sustainable technologies 4 different technologies –Technology 1 represents the existing dominant one. –Technology 2, 3 & 4 are new sustainable ones. A System Dynamics model about Energy Transitions competition

7. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Methodology Technical background –A shell written in Python language integrated with Vensim DSS software. 1000 runs using Latin Hypercube Sampling Time horizon: 2000 - 2100

8. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Uncertainties to be explored

9. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty -Total fraction of new technologies (2,3 and 4) -Installed capacity of Technology 1 -Installed capacity of Technology 2 -Installed capacity of Technology 3 -Installed capacity of Technology 4 -Total capacity installed Results without policy

10. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty A simple static policy –Forcing an upper limit of 500.000 Euros for the cost of new capacities for new technologies 2, 3 and 4. -Total fraction of new technologies (2,3 and 4) -Installed capacity of Technology 1 -Installed capacity of Technology 2 -Installed capacity of Technology 3 -Installed capacity of Technology 4 -Total capacity installed Results with static policy

11. -Total fraction of new technologies (2,3 and 4) -Installed capacity of Technology 1 -Installed capacity of Technology 2 -Installed capacity of Technology 3 -Installed capacity of Technology 4 -Total capacity installed Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Results with adaptive policy Adaptive policy: –Preferences about CO2 emissions and expected cost per MW produced adjusted according to the level of installed capacities. Lookup table for preferences

12. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Comparison of results Number of runs over certain levels of new technologies fraction over 1000 runs > 20%> 30%> 40%> 50% No Policy 926889803632 Static Policy 933898817657 Adaptive Policy 997976883725

13. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Conclusions Main purpose: –Presenting the use of EMA for adaptive policy making. Adaptive & robust policy making is crucial! EMA has a big potential for: –Dealing with deep uncertainty & dynamic complexity –Testing & comparing the performance policies –Being a successful tool for Technology Foresight & Future-oriented studies

14. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Future work The need for data mining/pattern analysis techniques! Better visualization techniques. Dealing with model uncertainty.

15. Energy Transitions: Adaptive Policy Making Under Deep Uncertainty Selected References AGUSDINATA, D. Exploratory Modelling and Analysis. A Promising Method to Deal with Deep Uncertainty, PhD dissertation, Delft University of Technology, Delft, 2008. BANKES, S. Exploratory Modelling for Policy Analysis, Operations Research, Vol. 41 No. 3: 435-449, 1993. KWAKKEL, J.H., WALKER, W.E. and MARCHAU, V.A.W.J.; Classifying and communicating uncertainties in model-based policy analysis, Int. J. Technology, Policy and Management, Vol. 10, No. 4, pp.299–315, 2010. PRUYT, E.; "System Dynamics Models of Electrical Wind Power," in The 22th International Conference of the System Dynamics Society, Oxford, England, 2004. PRUYT, E.; Dealing with uncertainty? Combining system dynamics with multi-criteria decision analysis or with exploratory modelling, Proceedings of the 25th International Conference of the System Dynamics Society, Boston, 2007. PRUYT, E. and C. HAMARAT; The concerted run on the DSB Bank: An Exploratory System Dynamics Approach, In Proceedings of the 28th International Conference of the System Dynamics Society, Seoul, Korea, System Dynamics Society, 2010. PRUYT, E. and C. HAMARAT; The Influenza A(H1N1)v Pandemic: An Exploratory System Dynamics Approach, In Proceedings of the 28th International Conference of the System Dynamics Society, Seoul, Korea, 2010a. Thanks for your attention. Any questions/suggestions are welcomed.