CASCADE Complex Adaptive Systems, Cognitive Agents and Distributed Energy The role of behaviour and learning in modelling pathways to smart grid active demand Author Richard Snape - rsnape@dmu.ac.uk Date 24th May 2011 Institute of Energy and Sustainable Development 1 www.iesd.dmu.ac.uk/~cascade The Smart Grid Bidirectional power flow Bidirectional information flow €, £, $ Image: Copyright 2006 by Hawaiian Electric Company, Inc., all rights reserved Institute of Energy and Sustainable Development 2 www.iesd.dmu.ac.uk/~cascade Why the Smart Grid? • Climate change objectives • Energy security • Avoidance of investment in (unnecessary?) infrastructure Institute of Energy and Sustainable Development 3 www.iesd.dmu.ac.uk/~cascade Key component – active demand Energy Deficiency Supply Demand Power Surplus Energy Daily Time Cycle Institute of Energy and Sustainable Development 4 www.iesd.dmu.ac.uk/~cascade Pathways to Smart Grid? • Unclear • Many future scenarios e.g. LENS scenarios (Ofgem, 2008), • Routemap produced for government (ENSG, 2010) • Smart Meters (enabling technology) will be installed (e.g. DECC, 2010) • Model needed Institute of Energy and Sustainable Development 5 www.iesd.dmu.ac.uk/~cascade Modelling approach Agent Based Model chosen Suitable for a Socio-Technical System with Heterogeneous agents Complex interactions Multiple networks (social, communications, physical) Investigating emergent patterns and practices Institute of Energy and Sustainable Development 6 www.iesd.dmu.ac.uk/~cascade PhD focus What type of learning / behaviour model is appropriate to represent individuals, firms and automata as agents in the model Prototype concerned with individuals as household decision makers Exploring the effect of different learning representations on potential for active demand Institute of Energy and Sustainable Development 7 www.iesd.dmu.ac.uk/~cascade Learning agents in an ABM Options: Zero learning Computationally lightweight – still have interesting results Basic learning Individual – e.g. rational choice or reinforcement Advanced learning Individual and / or social; “Rich” modelling of cognitive processes Computationally expensive Institute of Energy and Sustainable Development 8 www.iesd.dmu.ac.uk/~cascade Social learning Two schools of thought: Social Cognitive Theory - learning from observation of others’ behaviour and outcomes (Bandura, 1985) Communities of Practice - acquisition of knowledge as a social process using concepts of situated learning (Lave & Wenger, 1991; Wenger, 1998; Wenger 2007) Institute of Energy and Sustainable Development 9 www.iesd.dmu.ac.uk/~cascade Model runs Pre-seed the agents with consumption practices or “Energy cultures” (Stephenson, 2010) Overlay social networks on the physical and economic supplier / consumer network Observe emergent pathways that occur with greater probability over many runs Analyse these more likely pathways in light of the behaviour and learning representation used and currently proposed future scenarios and policy Institute of Energy and Sustainable Development 10 www.iesd.dmu.ac.uk/~cascade Any questions or comments? For further information see www.iesd.dmu.ac.uk/~cascade Institute of Energy and Sustainable Development 11 www.iesd.dmu.ac.uk/~cascade References ENSG (2010) Electricity Networks Strategy Group - A Smart Grid Routemap. United Kingdom Ofgem (2008) Long-Term Electricity Network Scenarios (LENS) - final report. Available from: <http://www.ofgem.gov.uk/Pages/MoreInformation.aspx? docid=5&refer=Networks/Trans/Archive/ElecTrans/LENS> [Accessed 1 July 2011]. DECC (2010) Smart Metering implementation programme: prospectus - Department of Energy and Climate Change [Internet]. Available from: <http://www.decc.gov.uk/en/content/cms/consultations/s mart_mtr_imp/smart_mtr_imp.aspx> [Accessed 1 July 2011]. Thøgersen, J. & Grønhøj, A. (2010) Electricity saving in households--A social cognitive approach. Energy Policy, 38 (12), pp.7732-7743. Zhang, T. & Nuttall, W.J. (2011) Evaluating Government's Policies on Promoting Smart Metering Diffusion in Retail Electricity Markets via Agent-Based Simulation. Journal of Product Innovation Management, 28 (2), pp.169-186. Verbong, G.P.J. & Geels, F.W. (2010) Exploring sustainability transitions in the electricity sector with socio-technical pathways. Technological Forecasting and Social Change, 77 (8), p.pp.1214-1221. Bandura, A. (1986) Social foundations of thought and action. Stanford University, Prentice-Hall, Inc., Eaglewood Cliffs, New Jersey. Lave, J. & Wenger, E. (1991) Situated learning: Legitimate peripheral participation. Cambridge University Press. Wenger, E. (1998) Communities of practice: Learning, meanings, and identity. Cambridge university press. Institute of Energy and Sustainable Development 12 www.iesd.dmu.ac.uk/~cascade Bibliography Learning Behavioural theories Roth, A.E. & Erev, I. (1995) Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term*. Games and Economic Behavior, 8 (1), pp.164-212. Ajzen, I. (1991) The theory of planned behavior. Organizational behavior and human decision processes, 50 (2), pp.179-211. Sutton, R. & Barto, A. (1998) Reinforcement Learning: An Introduction. Cambridge, MA, MIT Press. Argyris, C. & Schon, D.A. (1996) Organizational learning II: Theory, method and practice. Reading MA, AddisonWellesley March, J.G. (1991) Exploration and exploitation in organizational learning. Organization science, 2 (1), pp.7187. Vriend, N.J. (2000) An illustration of the essential difference between individual and social learning, and its consequences for computational analyses. Journal of Economic Dynamics and Control, 24 (1), pp.1-19. Institute of Energy and Sustainable Development Stern, P.C. (2000) New environmental theories: toward a coherent theory of environmentally significant behavior. Journal of social issues, 56 (3), pp.407-424. Triandis, H.C. (1977) Interpersonal behavior. Monterey, Brooks/Cole Pub. Co. 13 www.iesd.dmu.ac.uk/~cascade Prototype results (1 year run) Scenario 1 Scenario 3 Institute of Energy and Sustainable Development Scenario 2 Scenario 4 14 www.iesd.dmu.ac.uk/~cascade