SE4SG 2013 Presentation by Fanny Boulaire at 2nd International Workshop on Software Engineering Challenges for the Smart Grid. Please cite our workshop at Ian Gorton, Yan Liu, Heiko Koziolek, Anne Koziolek, and Mazeiar Salehie. 2013. 2nd international workshop on software engineering challenges for the smart grid (SE4SG 2013). In Proceedings of the 2013 International Conference on Software Engineering (ICSE '13). IEEE Press, Piscataway, NJ, USA, 1553-1554.

1. Queensland University of Technology
MODAM: A MODular Agent-Based
Modelling Framework
Fanny Boulaire, Mark Utting, Robin Drogemuller

2. a university for the worldreal
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Background
• Developing a planning tool to help decision-makers plan
for the future grid
– Optimal investment strategies for distribution networks over large areas
and long planning horizons
– Consider the role of renewables, storage, and demand management, as
well as network upgrades
• Development of a framework that models both
1. technical network constraints
2. economic and sustainability challenges of minimising network cost
and carbon intensity

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Overall Electricity consumption
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Electricityconsumption(kWh)
Residential Consumption for small feeder
How to capture all
this information?

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Software Architecture
(detailed simulation of a given scenario) (find the lowest cost
scenario out of 1000s)
network
Weather
Battery
Metered
consumption
Billing

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Agent-Based Modelling
• ABM is used to model complex systems comprised of autonomous
and interacting agents
• When to use an ABM?
– “When it is important that agents have a spatial component to their behaviours
and interactions
– When scaling-up to arbitrary levels is important in terms of the number of
agents, agent interactions and agent states
– When the past is no predictor of the future because the processes of growth and
change are dynamic“ [1]
• Agents can be specified at various scales, defining the granularity of
the model
-> ABM is used to represent the different system units accurately and
dynamically, following the changes over time and at different levels of
detail in the distribution network
5
1 C. M. Macal and M. J. North, "Agent-Based Modeling And
Simulation," in 2005 Winter Simulation Conference, 2005.

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Examples of Outputs
1. Simulate customer demand over the next 20 years
2. Simulate PV output from temperature and cloud data
3. Find optimal battery placement in a network, and determine the
best battery control algorithms to shave peak load
4. Model transformer load, temperature, and loss of life
Zone 1: Effect of PV on
peak demand
Zone 2: Effect of PV on peak demand
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0.98
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1.04
Voltage(p.u.)
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Voltage(p.u.)
Battery Locations
Optimal battery placement:
bus33=38.5kVA, bus52=15kVA
Voltage profile at peak load

7. a university for the worldreal
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Modular Agent-Based Model
• Built for extensibility
– New elements
– New behaviours of existing
assets
• and flexibility
– Different data inputs
• Extension of model to other
domains
MODAM Framework
Network Assets
PV Assets
Known
PVs
PV AgentsWeather
Batteries
Vehicles
Battery
Control
Historical
PV output
PV
Penetration
Rates
3 phase
network
Reader
SWER
Reader
Demand
Reader
DSM
calculations

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The modular architecture
• Eclipse Plugins – OSGi bundles
• Breakdown of the software into reusable modules
– Module = Name + Assets + Agents + Data
• Extension points for MODAM
<?xml version="1.0" encoding="UTF-8"?>
<?eclipse version="3.4"?>
<plugin>
<extension-point id="dataprovider" name="Data Provider"
schema="schema/datareader.exsd"/>
<extension-point id="agentfactory" name="Agent Factory"
schema="schema/agentfactory.exsd"/>
<extension-point id="assetfactory" name="Asset Factory"
schema="schema/assetfactory.exsd"/>
</plugin>

9. a university for the worldreal
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The modular architecture
• Modularity within the agent-based model
– Separation into assets and agents
• Ease when trying new behaviour (e.g. new policy, or battery
control algorithm)
• Maintains fixed parameters (e.g. premise consumption due to
building characteristics vs. tenants behaviour)
• Modularity when populating the model
– Use of data providers to switch between databases

10. a university for the worldreal
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Lifecycle of the ABM simulation tool

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Setting up a simulation
+M= assetreader
+C=assetreader.NetworkReader
+C=assetreader.LocationReader
+M=demandreader
+C=demandreader.historical.HistoricalDemandReader
+C=demandreader.billing.BillingDataReader
+M=assetnetwork
+C=assetnetwork.ergon.NetworkAssetFactory
+C=assetnetwork.agent.NetworkAgentFactory
-from=2010-01-01 -to=2010-01-08
-output=tempOutDir
Scenario
Configuration
Files (XML)

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Conclusion and Future Work
• Implementation of a modular agent-based model
as a planning tool for the future grid
– Use of existing technology (OSGi) and
– Adaptation of agent-based model for modularity
• Future work
– Increase the number of plugins and test the use of the
modular agent-based model

13. a university for the worldreal
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Fanny.Boulaire@qut.edu.au
Thank you for your attention