L. Andrew Bollinger

PhD studentSection Energy & IndustryFaculty of Technology, Policy & ManagementTU Delft

26 March 2012SPM 4530

ABM in practice – 2 models

Contents

1. A finished model – Metals and mobile phones1. Problem formulation & actor identification

2. System identification & decomposition

…

10. Model use

2. A model in progress – Adaptation of electricity infrastructures to climate change

• What is the problem?• How am I using ABM to address it?

A finished model – Metals and mobile phones

Gold: 0.026-0.033gSilver: 0.11-0.90gPalladium: 0.00-0.09gCopper: 9.30-20.68g

In, Ta, Li, Ni, Zn, Sn, Al…

44% hibernating41% reused4% disposed3% recycled

Sources: R. Geyer, V.D. Blass, 2009; Nokia, 2008

How can we recover a larger proportion of metals in mobile phones?

1. Problem formulation and actor identification

1. Problem formulation and actor identification

OEMs and suppliers

Retailers

Consumers

3rd party collectors

Refurbishers

Metal recoverers

Service providers

2. System identification and decomposition

System scope: global flows of metals in mobile phones

What are the relevant elements and relationships?

2. System identification and decomposition

2. System identification and decomposition

phoneManufactureAphoneManufactureBphoneManufactureCphoneManufactureDphoneRetailNSPCollectionphoneRetailNSPCollectionPayusedPhoneRetailnewPhoneConsumptionnewHighEndPhoneConsumptioncheapPhoneConsumptioncheapHighEndPhoneConsumptionusedPhoneConsumptionphoneCollectionphoneTestingRefurbishingDisassemblyindustrialMetalRecoverybackyardMetalRecovery

Complete agent set

Decision-making rules• Purchasing• Processing• Investment

for…if... then...else...while...

Goods• Mobile phones• Components (batteries, circuit boards, other components)• Metals (gold, copper, silver, palladium)

3 & 4. Concept formalization and model formalization

3 & 4. Concept formalization and model formalization

private static void pickContractsForThisGoodName(OWLModel owlModel, OWLIndividual buyingAgent, OWLIndividual techInstance, String goodLabelNeeded, double totalNeeded) throws Exception {

ArrayList<OWLIndividual> contracts = new ArrayList<OWLIndividual>();

//System.out.println("calculating expected profits");calculateExpectedProfits(owlModel, buyingAgent, techInstance,

goodLabelNeeded);

String agentName = AgentWrapper.getLabel(owlModel, buyingAgent);//System.out.println("buyingAgent is " + buyingAgent);

String query;query = "SELECT distinct ?physicalFlowContract " + "WHERE { " +

//get where this comes from"?physicalFlowContract :from ?from ."

+//"?from :label ?sellerLabel ." +

//get the physical flow"?physicalFlowContract :physicalFlow ?

physicalFlow ." +

//get the price"?

physicalFlowContract :economicProperties ?econProp ." +"?econProp rdf:type :Price ." +"?econProp :value ?priceValue ." +

//get the mass//"?physicalFlow :physicalProperties ?

physProp ." +//"?physProp rdf:type :Mass ." +//"?physProp :value ?massValue ." +

//don't pick contracts that have been signed already!

"?physicalFlowContract :signedByBuyer false ." +

"?physicalFlowContract :signedBySeller false ." +

5. Software implementation

public void MassBalanceCalculations(OWLModel owlModel){

double worldMarketStockOfGold = worldMarketStockOfGoldForMassBalanceCalculation(owlModel);

double goldInInputStocksOfManufacturers = goldInInputStockOfManufacturersForMassBalanceCalculation(owlModel);

double stockOfGoldInPhones = stockOfGoldInPhonesForMassBalanceCalculation(owlModel);

double environmentStockOfGoldWaste = environmentStockOfGoldWasteForMassBalanceCalculation(owlModel);

double industrialMetalRecoveryAgentsStockOfGold = industrialMetalRecoveryAgentsStockOfGoldForMassBalanceCalculation(owlModel);

double backyardMetalRecoveryAgentsStockOfGold = backyardMetalRecoveryAgentsStockOfGoldForMassBalanceCalculation(owlModel);

double industrialMetalRecoveryAgentsStockOfGoldWaste = industrialMetalRecoveryAgentsStockOfGoldWasteForMassBalanceCalculation(owlModel);

double backyardMetalRecoveryAgentsStockOfGoldWaste = backyardMetalRecoveryAgentsStockOfGoldWasteForMassBalanceCalculation(owlModel);

double massBalance = initialWorldMarketStockOfGold -

worldMarketStockOfGold -

goldInInputStocksOfManufacturers-

stockOfGoldInPhones -

environmentStockOfGoldWaste -

industrialMetalRecoveryAgentsStockOfGold-

backyardMetalRecoveryAgentsStockOfGold-

industrialMetalRecoveryAgentsStockOfGoldWaste-

backyardMetalRecoveryAgentsStockOfGoldWaste;

System.out.println("Mass balance for gold is " + massBalance);

6. Model verification

Video 1

Video 2

7. Experimentation

phoneManufactureAphoneManufactureBphoneManufactureCphoneManufactureDphoneRetailNSPCollectionphoneRetailNSPCollectionPayusedPhoneRetailnewPhoneConsumptionnewHighEndPhoneConsumptioncheapPhoneConsumptioncheapHighEndPhoneConsumptionusedPhoneConsumptionphoneCollectionphoneTestingRefurbishingDisassemblyindustrialMetalRecoverybackyardMetalRecovery

Complete agent set

• 33,516 simulation runs

• 15 parameters varied

• 100 timesteps (quarters) each

• 400 GB of data collected

How can we recover a larger proportion of metals in mobile phones?

Technical lifetime of a mobile phoneMobile phone use time Metal content of a mobile phone Price of goldPrice of copperPrice of silverPrice of palladiumPrice of componentsPrice of batteriesAccessibility of collection pathwaysMotivation of consumersIncentive costDisposal tendency of consumersCost of mobile phone manufactureGold recovery rate of recyclersPreferences of consumers

7. Experimentation

8. Data analysis

% query for the necessary datacursorA=exec(connA, [strcat('SELECT tick, age, price, remainingLifeTime, heldByName, goodname FROM andy_5dec2009_phonehistory WHERE runNumber = 60 order by goodname, tick;')]);

%Get the data out cursorA=fetch(cursorA); DataMat = cursorA.Data; a = DataMat;

tick = cell2mat(a(:,1)); age = cell2mat(a(:,2)); price = cell2mat(a(:,3)); remaininglifetime = cell2mat(a(:,4)); location = a(:,5);

…

quarterofmanufacture = tick - age;

scatter(age,price); newtick = sprintf('%03.0f', tick(1)); saveas(gcf, strcat(num2str(newtick),'.png'));

8. Data analysis

9. Model validation

10. Model use

Bollinger, L.A., C. Davis, I. Nikolic and G.P.J. Dijkema. Modeling metal flow systems – Agents vs. equations. Journal of Industrial Ecology, In press.

Nikolic, I.; L.A. Bollinger and C.B. Davis: Agent Based Modeling of large-scale socio-technical metal networks, pp. 1-10. In: Proceedings of the TMS Annual Meeting & Exhibition 2010, 14-18 Feb. (2010). At: Seattle, USA.

A model in progress

Climate change adaptation of electricity infrastructures

Source: Renewables International

Minutes

Average interruption time per customer per year (2007)

Reliability of the Dutch electricity infrastructure

man

ufac

ture

r

grid

des

ign

insta

llatio

n

oper

atio

n

exca

vatio

n

subs

iden

ce

moi

sture

obse

lesc

ence

/ wea

r

weath

er

secu

rity

othe

r0

10

20

30

40

50

60

70

2007

2006

2005

2004

2003

Causes of power failures in the Dutch high-voltage grid

Source: EnergieNed

Impact of weather on the Dutch electricity infrastructure

(1) De Groot, 2006(2) Wilbanks, et al, 2008(3) Rothstein and Halbig, 2010(4) Bresser, et al, 2005

The (anticipated) impacts of climate change

Climate change and energy infrastructures

(1) De Groot, 2006(2) Wilbanks, et al, 2008(3) Rothstein and Halbig, 2010(4) Bresser, et al, 2005

The (anticipated) impacts of climate change on energy systems

2010 Pakistan floods: • Damage to 10,000 transmission lines, Power

shortfall > 3 GW, Shut down largest refinery

2005 Hurricane Katrina (Louisiana, USA): • Power cut to 2.3 million homes, Destroyed

significant oil and gas infrastructure

2006 drought / heat wave (Netherlands): • Imposition of cooling water restrictions, reduced

reserve capacity

Extreme weather events and energy infrastructure

Netherlands

Europe

3. Altered investment strategies in the power sector

1. Cooling water restrictions and a reduced reserve capacity in the electricity grid

2. Increased market penetration of air conditioners

TenneT declares “code red” – Aug. 10, 2003

“Europe Decides Air-Conditioning Is Not So Evil” – NYT, Aug. 13, 2003

EU study: “Investment needs for future adaptation measures in EU power plants due to effects of climate change” – March, 2011

Effects of the 2003 drought / heat wave

Key characteristics of climate change and energy infrastructures:

1. The impacts of climate change occur on multiple timescales

2. Actors will adapt independently to perceived climatic changes

3. There exist significant uncertainties associated with the nature and severity of climate change

Long-term evolution model

Operational performance model

Policy scenarios

Climate scenarios

(agent-based model)

Modeling framework:

Modeling framework

Research question:

How can we develop effective strategies to support the resilience of the Dutch electricity infrastructure to climate change?

Source: TenneT Quality and Capacity Plan 2010-2016

The high-voltage electricity grid in the

Netherlands

Proof-of-concept model

Consumers

Power companies

Grid operator

Neighboring countries

Agents

Load

Generators

Transmission grid

Loads & generators

Technologies

How much electricity to use

How much electricity to produce

When/where to repair/add capacity

How much electricity to produce/use

Decisions

Proof-of-concept model - setup

Consumers

Power companies

Grid operator

Neighboring countries

Agents

Load

Generators

Transmission grid

Loads & generators

Technologies

How much electricity to use

How much electricity to produce

When/where to repair/add capacity

How much electricity to produce/use

Decisions

Demand distribution

Production distribution

Network topology

Voltages at nodes

Power flows of lines

Proof-of-concept model - setup

5 climate change scenarios:

1. No climate change2. Mild3. Moderate4. Severe5. Very severe

4 investment strategies:

1. No investment2. Build new links3. Increase capacity4. Both

Consumers

Power companies

Grid operator

Neighboring countries

Agents

Load

Generators

Transmission grid

Loads & generators

Technologies

How much electricity to use

How much electricity to produce

When/where to repair/add capacity

How much electricity to produce/use

Decisions

Demand distribution

Production distribution

Network topology

Voltages at nodes

Power flows of lines

Proof-of-concept model - setup

Climate change severity Climate change severity

Investment strategy

Investment strategy

Mean consumer satisfaction Average line load

Proof-of-concept model - results

Case study 2

Multiple spatial scales

Case study 1Case study 3

1. How might different grid planning procedures affect resilience to climate change?

2. How might long-term changes in wind patterns and solar radiation across Europe affect the ability to meet demand with renewables?

3. How might evolving infrastructure interconnections at the local level affect resilience?

Case studies - Implementation

AgentSpring platform

Base model

Case study 1

Case study 2

Case study 3