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CASCADE
Author
Date
Complex Adaptive Systems, Cognitive Agents and Distributed Energy
BEHAVIOUR AND LEARNING IN AN AGENT BASED MODEL OF THE SMART GRID
Richard Snape
22nd March 2011
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CONTENT
• Context
• CASCADE model overview
• Agent description
• Behaviour and learning description
• Prototype implementation and results
• Conclusion and questions
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THE TEAM
Prof. M. Rylatt – PIDr. P. Boait – CIDr. M. Lemon – CIDr. V. PakkaB. Mahdavi ArdestaniJ. R. Snape – PhD student
Prof. P. Allen – CIProf. M. Savill – CIDr. L. VargaDr. M. Strathern
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ELECTRICITY NETWORK
Heterogeneous actors
Multiple posited future scenarios
Multiple (nested?) physical scales
Multiple economic scales
Multiple temporal scales
Multiple networks
Highly complex problem domain !!
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SMART GRID
€, £, $
Bidirectional power flow
Bidirectional information flow
Image: Copyright 2006 by Hawaiian Electric Company, Inc., all rights reserved
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WHY MODEL THE SMART GRID?“The UK Government’s low carbon strategy and Ofgem’s Low Carbon Network Fund and RPI-X@20 project are the
key context for the development of smart grid”ENSG,
A Smart Grid Routemap, 2010, p. 6
[The Routemap requires us to] “Demonstrate new business and revenue models and associated regulatory and
commercial frameworks that support demand reduction and better energy management”
ibid, p. 14
[including] “Development of new customer products and services and ESCO / VPP etc. business models – building
on existing models and relationships”ibid, p.17
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NOT JUST SMART METERS!
Smart meters
Smart grid
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MODEL OVERVIEW
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COMPONENTS AND INFORMATION FLOWS
Local physical model
Local aggregator agent (can be ESCO, retail supplier, virtual island)
Prosumer agent (can be generator, load, or both)
Agent based model
Forward pricebroadcast
Prosumer response
Prosumer energy flows
Distribution costs and constraints
Social interaction between prosumers
Grid & wholesale market agent
Agent environ-ment (policy, weather, etc)
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MODEL DIAGRAM
Context Model
Environmental (contextual) variables
E.g.
• Weather• Policy• Regulation• CO2 price
Market
Social network (household prosumers 4 -> N)
Aggregator 1
Aggregator 2
Aggregator M
Prosumer 1
Prosumer 2
Prosumer 3
Prosumer N+1
Prosumer N+2
...
Output – overall consumption
...
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AGENT DESCRIPTION
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PROSUMER
Prosumers
Pure consumer Pure generator
CHP
Net generatorNet consumer
Pmax -Pmax0
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AGGREGATOR
•Behaviour
•Objectives
•Learning•Decision
•£/kWh; or•CO2/kWh; or•Exergy loss;
or•?
•kWh/30 min•kWh/5 min•?
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AGENT ANALOGUES
Prosumers may represent:
•Automata e.g. A household smart controller
•Individuals e.g. A householder
•Collectives e.g. Generating companies
Aggregators generally represent:
•Collectives e.g. firms, communities, housing associations...
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POSSIBLE MODEL CONFIGURATIONS
Business as usual– Aggregators = Utility companies– Prosumers = Power stations, wind farms, households
Energy Service Company (ESCo) model– Aggregators = ESCos– Prosumers = as above plus meso level generation e.g.
Community wind farms or district level CHP
Microgrids– Aggregators = local scale agents with objective net
demand of zero– Prosumers = as above
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BEHAVIOUR AND LEARNING
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BEHAVIOUR AND LEARNING ALGORITHMS
Psychology based
Optimisation based
Organisation theory based
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PSYCHOLOGY BASED LEARNING 1
Does the name Pavlov ring a bell?References
•Sutton & Barto (1998)•Roth & Erev (1995)
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PSYCHOLOGY BASED LEARNING 2
• More elaborate models
References – theory•Triandis, H.C. (1977)•Bandura, A. (1986)•Ajzen, I. (1991)•Stern, P.C. (2000)
References – application in electricity
•Thøgersen & Grønhøj (2010)
•Zhang & Nuttall (2011)
Society
Attitude
Norms...
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OPTIMISATION BASED LEARNING
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ORGANISATIONAL LEARNING
References•Senge (1990) •March (1991)•Argyris & Schon (1996)
Explore vs exploit
Cyclical causality
Reinforcing vs balancing
Organisational rules
Drawn from Nancy Dixon’s The Organisational Learning Cycle - How we can learn collectively, McGraw-Hill, 1994.
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PROTOTYPE IMPLEMENTATION AND RESULTS
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PROTOTYPE IMPLEMENTATION
Selected RePast Simphony toolbox– Java, fully open source, well used, powerful
Extensibility
Scalability
Interface between scales – temporal and spatial
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PROTOTYPE MODEL
Stylised test case– 1000 household prosumers – 1 wind turbine– 1 aggregator
Only household prosumers learnAggregator transmits price signal to its
prosumers to encourage smoothing of demand
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HOUSEHOLD PROSUMER BEHAVIOURS TESTED IN
PROTOTYPE1. No adaptation2. Householders manually respond to signal
by reducing up to 15% of demand3. Householders learn to adopt automated
smart device which then time shifts up to 15% of demand within day
4. Householders learn to adopt automated smart device which then totally smoothes demand within day (stylised edge case)
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PROTOTYPE RESULTS (1 YEAR RUN)
Scenario 1 Scenario 2
Scenario 3 Scenario 4
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SUMMARY
ABM suited to the highly complex electricity system under transition to smart grid
Modelled using prosumer and aggregator abstraction
Behaviour and learning algorithms crucial to model behaviour
Challenges remain...
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Any questions or comments?
For further information seewww.iesd.dmu.ac.uk/~cascade
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REFERENCESENSG (2010) Electricity Networks Strategy Group
- A Smart Grid Routemap. United KingdomRoth, 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.
Sutton, R. & Barto, A. (1998) Reinforcement Learning: An Introduction. Cambridge, MA, MIT Press.
Thøgersen, J. & Grønhøj, A. (2010) Electricity saving in households--A social cognitive approach. Energy Policy, 38 (12), pp.7732-7743.
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.
Argyris, C. & Schon, D.A. (1996) Organizational learning II: Theory, method and practice. Reading MA, Addison-Wellesley
March, J.G. (1991) Exploration and exploitation in organizational learning. Organization science, 2 (1), pp.71-87.
Senge, P.M. (1990) The fifth discipline: Mastering the five practices of the learning organization. New York.
Ajzen, I. (1991) The theory of planned behavior. Organizational behavior and human decision processes, 50 (2), pp.179-211.
Bandura, A. (1986) Social foundations of thought and action. Stanford University, Prentice-Hall, Inc., Eaglewood Cliffs, New Jersey.
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.
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.