A presentation conducted by Dr Amineh Ghorbani, Faculty of Technology, Policy and Management, Delft University of Technology. Presented on Tuesday the 1st of October 2013. Infrastructure systems consist of many heterogeneous decision making entities and technological artefacts. They are governed through public policy that unravels in a multi-scale institutional context, ranging from norms and values to technical standards. For example, to integrate biogas infrastructure in a region, various forms of governance, laws and regulations need to be implemented. To effectively design these requirements, insights into socio-technical systems can be gained through agent based modelling and simulation. To implement such social concepts in agent-based models of infrastructure systems, we designed a modelling framework called MAIA, based on the Institutional Analysis and Development framework of Elinor Ostrom. This paper will explain how MAIA can be used to model a biogas energy infrastructure in the Netherlands.

1. ENDORSING PARTNERS
Structuring Sociotechnical Complexity in
Infrastructure Systems:
The Biogas system
The following are confirmed contributors to the business and policy dialogue in Sydney:
•
Rick Sawers (National Australia Bank)
•
Nick Greiner (Chairman (Infrastructure NSW)
Monday, 30th September 2013: Business & policy Dialogue
Tuesday 1 October to Thursday,
Dialogue
3rd
October: Academic and Policy
Presented by: Dr Amineh Ghorbani, Faculty of Technology, Policy and
Management, Delft University of Technology
www.isngi.org
www.isngi.org

2. Structuring
Socio-technical
Complexity in
Infrastructure
Systems
The Biogas System
prof. Dr. Ir. Margot Weijnen
Delft
University of
Technology
Challenge the future
ISNGI 2013

3. Outline
•Introduction
•Biogas Energy System
•Conceptual Modelling with MAIA
•Modelling the Biogas System
•Simulation Results
•Conclusion
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4. Introduction
•Infrastructures not static: constant evolution
as perceptions and goals of the stakeholders
change.
•With technological advancement
• Infrastructures grow in size, become
interconnected.
• The robustness of these systems increases but
also their complexity.
•Biogas system, an example of complex sociotechnical infrastructure.
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5. The Biogas Infrastructure
•European 2020 targets (compared to 1990):
• 20% CO2 emission reduction,
• 20% energy from renewables
• 20% increase in energy efficiency
•Biogas production stimulated by Renewable
Energy Directive as a mean for renewable
energy production and CO2 reduction.
•Potential for biogas production in the
Netherlands: estimated 60PJ in 2030
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6. Biogas Potential in the
Netherlands
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7. Barriers for Biogas Production
in the Netherlands
• Investor uncertainty, a large barrier in the Netherlands.
• Low investment costs and high variable costs.
• Biogas governance not clearly defined.
• Current institutions, complex and time consuming.
• Economies of scale important, but hard to realize in the
Netherlands.
• A gap between European energy and environmental policy.
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8. Research question
What are the conditions for, and characteristics
of a robust biogas infrastructure system?
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9. ABM for Infrastructure
•Suitable for modelling the biogas system and
other infrastructure systems with:
• Distributed character
• Highly dynamic environment
• Interaction flexibility
•However, agent-based models are:
• Difficult to build
• high programming skills
• Concepts such as policy, rules, norms intangible in simulations
• Difficult to involve stakeholders and domain
experts
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10. Conceptual Modelling
To identify and define concepts and relations in a
simulation before programming.
• Bridges the gap between real world system &
simulation.
• Share ideas and perspectives with experts in
advance, involve stakeholders in the simulation
process.
• Significant time savings later on in the process.
• Possibility to reuse and rebuild a simulation due to
readable documentation (conceptual model).
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11. MAIA
Modelling Agent system using
Institutional Analysis
• Framework to conceptualize agent-based models of sociotechnical system.
• Based on the IAD framework (Ostrom) and other social theories
(e.g., structuration, social mechanism).
• Can be considered as a template (or abstract ontology) of
socio-technical concepts and relations.
• Source for qualitative data collection.
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12. MAIA Structures
Five interrelated structures to conceptualize
different aspects of an infrastructure system:
1.
Collective structure: agents, their characteristics and decision making.
2.
Constitutional structure: the rules, norms, regulations and cultures in
the system.
3.
Physical Structure: The physical artefacts and physical environment,
their composition and connections.
4.
Operational Structure: The activities that take place in the system.
5.
Evaluative Structure: The connection between the outcomes of the
system and other four aspects.
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13. The MAIA Meta-model
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14. MAIA user interface
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15. Modelling the Biogas Infrastructure
– Collective Structure
Factory
household
-location [x,y]
-energyNeed [type][GJ/yr]
-willingness [boolean]
-willingnessToPay [€/GJ]
-environmentalGoals [% renewable]
-energyConsumerID [float]
CO2 market
-CO2Price [€/ton]
Energy Consumer
Gas Market
-gasPrice [€/Nm3]
+Pre-negotiate energy need()
0..*
Electricity Market
-electricityPrice [€/kWh]
-pre-negotiate
0..1
Potential Producer
Energy company
Industrial consumer
-location [x,y]
-energyNeed [type][GJ/yr]
-willingness [boolean]
-willingnessToPay [€/GJ]
-environmentalGoals [% renewable]
-energyConsumerID [float]
+Invite energy consumers()
+Pre-negotiate biogas demand()
+Inventorize biogas need()
Landfill
... D a
Water Treatment
-location [x,y]
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Agricultural Firm
-location [x,y]
af
-location [x,y]
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16. Modelling the Biogas Infrastructure
– Physical Structure
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17. Modelling the Biogas Infrastructure
– Operational Structure
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18. Modelling the Biogas Infrastructure
– Constitutional Structure
•Formal Institutions
• Environmental permits
• SDE+ subsidy
•Norms
• Collaborative behaviour
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19. Simulation outcomes
Agricultural firm performance.
The size of the bubble indicates
the risk of an individual
agricultural firms, which ranges
from 0% to 3.15%, with an
average of 0.92% for all 240
scenarios.
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20. Simulation results
• A robust biogas infrastructure system can develop without any
subsidies
• 54% of cases: no biogas production, not economically viable.
• 46% of cases: positive cash flow.
• Around 69% of the projects have a negative Net Present Value.
• Prices for co-substrates, natural gas and CO2 largely determine
the feasibility and profitability of biogas production.
• Longer depreciation periods & increasing natural gas prices
increase the initial profitability of biogas, but also expose
agricultural firms to the risks and uncertainty of external price
developments.
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21. Conclusion
Conceptualization using MAIA
• Sufficient completeness & sufficient level of
detail to allow modellers without any domain
specific knowledge to implement a simulation.
• Conceptual model requires scientific rigor, for
which the five MAIA structures can be used to
efficiently gather the required knowledge and
data.
• The online MAIA tool used to structure the data in cards and
exchange with other modellers or programmers to extend or
implement the model.
• Special attention to internal decision models &
agent actions at the smallest scale.
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22. JASSS:MAIA: A Framework For Developing
Agent-based Social Simulations
Amineh Ghorbani
a.ghorbani@tudelft.nl
THANK YOU!
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