Monday, 30th September 2013: Business & policy Dialogue

Tuesday 1 October to Thursday, 3rd October: Academic and Policy Dialogue

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ENDORSING PARTNERS

The following are confirmed contributors to the business and policy dialogue in Sydney:

• Rick Sawers (National Australia Bank)

• Nick Greiner (Chairman (Infrastructure NSW)

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Structuring Socio-technical Complexity in Infrastructure Systems:

The Biogas system Presented by: Dr Amineh Ghorbani, Faculty of Technology, Policy and Management, Delft University of Technology

Challenge the future

Delft University of Technology

The Biogas System prof. Dr. Ir. Margot Weijnen

ISNGI 2013

Structuring Socio-technical Complexity in Infrastructure

Systems

2 Titel van de presentatie ISNGI 2013 Next Generation Infrastructures

Outline

•Introduction

•Biogas Energy System

•Conceptual Modelling with MAIA

•Modelling the Biogas System

•Simulation Results

•Conclusion

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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 socio-technical infrastructure.

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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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Biogas Potential in the Netherlands

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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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Research question

What are the conditions for, and characteristics

of a robust biogas infrastructure system?

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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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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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MAIA Modelling Agent system using Institutional Analysis

• Framework to conceptualize agent-based models of socio-

technical 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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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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The MAIA Meta-model

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MAIA user interface

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Modelling the Biogas Infrastructure – Collective Structure

... D a af

+Pre-negotiate energy need()

Energy Consumer

-location [x,y]-energyNeed [type][GJ/yr]-willingness [boolean]-willingnessToPay [€/GJ]-environmentalGoals [% renewable]-energyConsumerID [float]

Factory

-location [x,y]-energyNeed [type][GJ/yr]-willingness [boolean]-willingnessToPay [€/GJ]-environmentalGoals [% renewable]-energyConsumerID [float]

Energy company-gasPrice [€/Nm3]

Gas Market

-electricityPrice [€/kWh]Electricity Market

-CO2Price [€/ton]CO2 market

-location [x,y]Landfill

-pre-negotiate 0..1

0..*

+Invite energy consumers()+Pre-negotiate biogas demand()+Inventorize biogas need()

Potential Producer

-location [x,y]Water Treatment

-location [x,y]Agricultural Firm

household

Industrial consumer

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Modelling the Biogas Infrastructure – Physical Structure

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Modelling the Biogas Infrastructure – Operational Structure

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Modelling the Biogas Infrastructure – Constitutional Structure

•Formal Institutions • Environmental permits • SDE+ subsidy

•Norms • Collaborative behaviour

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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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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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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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THANK YOU!

JASSS:MAIA: A Framework For Developing

Agent-based Social Simulations

Amineh Ghorbani

a.ghorbani@tudelft.nl