1

Decision Modeling and Game Theory Development

Colloquium at DTS-SBUNasir J. Sheikh B.Sc., M.Sc., Ph.D.Department of Technology and SocietyCollege of Engineering and Applied SciencesState University of New York, Koreanasir.sheikh@sunykorea.ac.krnasir.sheikh@stonybrook.eduJuly 19, 2016

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The World of Decision Making Hierarchical Decision Modeling Assessment of Solar PV Technologies Game Theory Development Related Research at SUNY Korea

Outline

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The World of Decision Making

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Decision Making

Ref: www.boku.ac.at/mi

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Decision by Loudest Voice• The highest authority

• The risk taker• The recognized expert

Recommendations

*BOGSAT: Bunch of Guys/Gals Sitting Around A Table

Data Science, Dashboards, Analytics

B O G S A T*

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Decisions and Effect on Change

Sound Decision

Effective Execution

Successful Change

Decision is not Outcome

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Decision LevelsLevel Duration ProcessingStrategic Days/Weeks/Months Rigorous Process with

Check on Decision Quality

Operational/Significant/Material

Hours/Days Deliberate Process

Consumer/Personal Milliseconds Automatic/ Subconsciously

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Evolution of Decision Research

Decision

Making

Decision

Analysis

Decision

Quality

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Challenges to Quality Decisions

Organizational Complexity Parties in conflict Individual &

organizational differences

Values, desires, and motivations

Initial convictions Fundamentally

different frames Personalities Competencies Degrees of Power Resource Availability

Group dynamics

Ref: Carl Spetzler

Analytic Complexity Uncertainty Dynamic situations Options Inter-related important variables Multiple alternatives Inter-related decision criteria Multiple players with

competing perspectives

Facilitative Leadership

Heuristics

Rigorous Decision

Processes/Modeling

Decision Analysis

Low High

Low

High

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Hierarchical Decision Modeling

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Appropriate frame Creative doable alternatives Meaningful, reliable information Clear values and trade-offs Logically correct reasoning Commitment to action

Decision Elements of Strategic Decision Quality

Ref: Carl Spetzler

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Use a Hierarchical Decision Model

HDM Problem/Overall

Objective

Perspective1 Perspective 2 Perspective N

Criteria A

Criteria B

Alternative 1 Alternative 3

Alternative 2 Alternative 4 …….

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Strategic Planning Resource Allocation Source Selection Business Policy Public Policy Program Selection Technology Selection etc.

HDM Applications

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Can be used to address complex problems Steps

◦ State the objective◦ Define/select the criteria◦ Select the alternatives◦ Obtain the relative importance of the criteria◦ Determine the relative contribution of each

criterion top the objective◦ Compare the alternatives with respect to lowest

level criteria

HDM Modeling

Alternatives•Fund ASCR (Adult Stem Cell Research)•Fund ESCR (Embryonic Stem Cell Research)•No Funding

Medical Treatment

(0.8)

Economic Profits

(0.2)

Medical Advancement

(0.6)

Social(0.4)

(Oversight: 1.00)

Opportunities(0.3)

Funding(0.6)

Commercialization(0.4)

Costs(0.3)

Medical Development

(0.4)(Losing

Competition: 1.000) Moral

Issues

(0.7)

Religious Issues

(0.3)

Social Risks(0.6)

Risks(0.4)

Stem Cell Research

Ref: Example by Thomas L. Saaty

HDM Example

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Fund ASCR Fund ESCR No Funding

  Opportunities(0.3) Costs(0.3) Risks (0.4) Final Outcome

Fund ASCR 0.4 0.4 0.3 0.36Fund ESCR 0.5 0.3 0.3 0.36No fund 0.1 0.3 0.4 0.28

Assessment of Solar Photovoltaic Technologies Using Multiple Perspectives and Hierarchical Decision Modeling

Social

Technical

EconomicEnvironmental

Political

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Outline1. Background/Motivation for Research2. Research Questions & Objectives3. Research Process and Methodology4. Research Results5. Research Assumptions, Limitations,

and Future Work6. Conclusion

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Background / Motivation for Research Develop a framework for assessment

of renewable technologies having broad societal implications using decision modeling.

Literature review of energy-related multiple perspectives and decision modeling revealed gaps.

Focus on PV technologies assessment.

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Solar PV: Growth in Global Deployment

Global Solar PV Electricity Production by End-Use Sector 2010-2050 (2009) [1]

Worldwide Distribution of PV Electricity [2]

Multiple Deployment Types

Global Expansion

20[1] “Energy Technology Roadmaps: Charting a low-carbon energy revolution,” 2009.[2] EPIA, “Global market outlook for photovoltaics until 2014,” European Photovoltaic Industry Association, 2010.

- PV is globally deployed- PV supports multiple types of deployment- Pricing at power parity

Solar PV: Technologies

21[Ref: L. Kazmerski, “Best Research Cell Efficiencies Chart,” National Renewable Energy Laboratory (NREL), 2010. ]

-Mixed landscape of technologies- Five generations- Efficiency improving- Not easy to select best technology

Multiple Perspectives: S-T-E-E-P Social

◦ Impact on society Technical

◦ Technical performance Economic

◦ Cost of technology diffusion, market adoption, and life-cycle costs (push-pull-sustenance)

Environmental◦ Impact on the environment and ecosystem

Political◦ Political motivation, policies and regulations,

market special interest, compliance, and security

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Value of Using Multiple Perspectives Evaluate which technology is the

“winning technology” under STEEP perspectives

Identify and prioritize areas of improvements for alternative technologies being considered

Applicable to decision makers with different worldviews – e.g. electric utility, policy maker, manufacturer, etc.

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Literature Review: Research Gaps Wide variety of multicriteria decision

making methods used in renewable energy comparisons but not all 5 STEEP perspectives considered

STEEP assessments at conceptual level not translating to specific action*

Specific and actionable criteria needed

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* Example: In economic perspective—as part of STEEP decision models—product cost is not decomposed into a bill of materials: PV module, Balance-of-System (BoS), etc. This is typically performed as a separate economic analysis, but not as part of decision modeling.

Research Objectives Develop a comprehensive method to

evaluate solar PV technologies under the STEEP perspectives

Enable decisions in a complex environment with competing perspectives and criteria

Perspectives

Mission Tech. Value

Social Technical Economic Environmental Political

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Research Questions How can a robust, operationalizable STEEP

model be developed? How can this model be standardized for a

broad variety of PV/renewable energy technologies?

How can decision makers with different worldviews use this model? - Policy makers - Energy utilities - Technology suppliers - Universities/National

labs

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Decision makers’ worldview may be defined as the overall perspective from which the decision making body sets priorities.

Research Process

Stage 1: Hierarchical Decision Modeling

Stage 2: Expert Panel Selection Stage 3: Data Collection Stage 4: Analysis of Results Stage 5: Sensitivity Analysis Stage 6: Validation

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Research Process Flow

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Stage 1: Hierarchical

Decision Modeling

Stage 2: Expert Panel Selection

Stage 3: Data Collection

Stage 4: Analysis of Results

Stage 5: Sensitivity Analysis

Stage 6: Validation

Hierarchical Decision Modeling: Justification

Includes multiple perspectives Includes built-in biases of experts Frequently used multi-criteria decision model for

complex problems Diverse strategies can be developed based on

decision making worldview Enables new alternatives to be added - e.g.

candidate technologies to be assessed

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Mulitcriteria Decision Models*

Description

Hierarchical Decision Model (HDM)

Value measurement model with multiple levels. Also refer to bullet points above.

Multi-Attribute Utility Theory (MAUT)

Value measurement models which include probabilities.

Outranking (French School) ELECTRE, PROMETHEE. Useful for initial screening, but not assessment of alternatives.*Reference: E. Loken, “Use of multicriteria decision analysis methods for energy planning problems,”

Renewable and Sustainable Energy Reviews, vol. 11, no. 7, pp. 1584–1595, Sep. 2007

Research Process – Stage 1Hierarchical Decision Modeling

Explicit articulation of decision elements

Comparisons of quantitative and qualitative competing decision elements

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Hierarchical Decision Model Framework for Assessment of Technologies*

31*Research based on N. Gerdsri and D. F. Kocaoglu, “A Quantitative Model for the Strategic Evaluation of Emerging Technologies,” Proceedings of the Portland International Conference on Management of Engineering and Technology, Portland (PICMET), Seoul, Korea, 2004.

Renewable Energy Technology Assessment

P1

P2

P3

P4

PK=5

C21

CJk1

C11

C1K

C2K

CJkK

... ...

F1,11

F2,11

FXj,11

...F1,JkK

F2,JkK

FXj,JkK

Mission (M)

Perspectives (Pk)

Criteria (Cjkk)

Factors (Fxj,jkk)

Desirability Functions (DFxj,jkk)

Technology Characteristics (tn,xj,jkk)

Candidate Technologies (Tn) T1

T2

TN

…….T3

...

…….

…….

DF1,JkK

DF2,JkK

DFXj,JkK

...DF1,11

DF2,11

DFXj,11

...

t1,1,11

t2,1,11

tN,1,11

t1,Xj,JkK

t2,Xj,JkK

tN,Xj,JkK

......

…….

…….

Technology Value Formula Descriptions of Variables

TVn = pk ∙cjk,k ∙fxjk ,jk,k ∙V(tn,xjk ,jk,k)Xjkxjk=1

Jkjk=1

Kk=1

Where

𝑇𝑉𝑛 ∶ Technology value of technology (n) as a candidate for fulfilling the mission of determining the best technology. Values range from 0 to 100. 𝑝𝑘 ∶ Relative priority of perspective (𝑘) with respect to the mission 𝑐𝑗𝑘,𝑘 ∶ Relative importance of criterion (𝑗𝑘) with respect to perspective (𝑘) 𝑓𝑥𝑗𝑘,𝑗𝑘,𝑘 ∶ Relative importance of factor (𝑥𝑗𝑘)with respect to criterion (𝑗𝑘)

𝑉ቀ𝑡𝑛,𝑥𝑗𝑘,𝑗𝑘,𝑘ቁ ∶ Desirability value of the performance and physical characteristics of technology (𝑛) for factor (𝑥𝑗𝑘), criterion (𝑗𝑘), and perspective (𝑘). The desirability values are along the desirability function for that specific technology characteristic and values range from 0 to 100. 𝑡𝑛,𝑥𝑗𝑘,𝑗𝑘,𝑘 : Performance and physical characteristics (metrics) of technology (𝑛)

Technology Value

32*Research based on N. Gerdsri and D. F. Kocaoglu, “A Quantitative Model for the Strategic Evaluation of Emerging Technologies,” Proceedings of the Portland International Conference on Management of Engineering and Technology, Portland (PICMET), Seoul, Korea, 2004.

Hierarchical Decision Model for PV Technologies

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Technical

Module Energy Efficiency

Power Density

Module Durability

Module Reliability Potential Induced Degradation (PID) Performance

PV Module Design Flexibility

Life of PV Module

State of Power Plant Installations Worldwide State of Field Performance

Maintenance

Social

Job Creation

Health Effects – During Production Phase

Health Effects – During Operations Phase

Economic

Total Purchase Cost of PV Modules to Utility Warranty/Maintenance Cost Total Associated Inverter and Balance-of-System Purchase Cost

Disposal Cost Levelized Cost of Electricity (LCOE) Return on Investment

Cost of Risk

Supply Chain Maturity

Environmental

Emission of Greenhouse Gases and Pollutants During Production Negative Ecological Footprint

Use of Available Land

Recyclability at End-of-Life

Waste Chemicals at End-of-Life

Waste Gases at End-of-Life

Water Consumption During Operations Consumption of Other Materials During Operations

Political

Government Incentives

Regulatory Risk

National Priority

Assessment of Photovoltaic Technologies Using Multiple

Perspectives

Negative Publicity

Global Production/ Supply Volume Use of Rare Elements (e.g. Indium, Tellurium)

Use of Hazardous Materials (e.g. Cadmium)

Relations with Local Politicians

Conformance to Existing Political, Legal, Management Constructs by Utilities

Local Sourcing

Candidate PV Technologies

c-Si – most common a-Si – common and less expensive

than c-Si CIGS – most common thin film CdTe – common thin film OPV – emerging plastic thin film (not

deployed in large scale)

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c-Si: mono/poly crystalline silicona-Si: amorphous siliconCIGS: copper indium gallium (di)selenideCdTe: cadmium telluride OPV: organic PV (polymer/plastic)

Research Process: Stage 2Expert Panels Selection Six Expert Panels

◦ Decision makers◦ Social scientists ◦ Technologists and engineers◦ Economists◦ Environmental scientists◦ Political scientists

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Perspectives

Mission Technology Assessment

Social Technical Economic

Environmental Political

Expert Panels Selection

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Expert Panel Type Experts

Decision makers to rank the perspectives for the northwest United States electric utility worldview 3Social scientists to rank the contribution of each criterion to the social perspective 10Technologists and engineers to rank the contribution of each criterion to the technical perspective 12Economists to rank the contribution of each criterion to the economic perspective 11Environmental scientists to rank the contribution of each criterion to the environmental perspective 10Political scientists to rank the contribution of each criterion to the political perspective 9

Expert Panel for Criteria Desirability Functions

Experts

Social perspective 11

Technical perspective 13

Economic perspective 8

Environmental perspective 8

Political perspective 8

Experts balanced from: academia, industry, and national labs

Average expert experience is 20 years

Research Process: Stage 3Data Collection Data collection using experts

◦ PSU-HSRRC Approval for Human Subjects Research (IRB)

◦ Use combination of emails and web instruments (Qualtrics and HDM Software)

◦ Finalization of HDM◦ Judgment quantification with pair-wise

comparisons (for relative importance of elements using constant sum)

◦ Desirability function for each criterion

37PSU-HSRRC: Portland State University, Human Subjects Research Review Committee

HDM Element Contributions to the MissionSocial0.15

S1: Job Creation0.050

S2: Health Effects - During Production

Phase0.039

S3: Health Effects - During Operations

Phase0.033

S4: Negative Publicity

0.027

Technical 0.22

T1: Module Energy Efficiency

0.029

T2: Power Density0.015

T3: Module Durability

0.024

T4: Module Reliability

0.029

T5: Potential Induced

Degradation (PID) Performance

0.040

T6: PV Module Design Flexibility

0.018

T7: State of Power Plant Installation

Worldwide0.013

T8: State of Field Performance

0.013

T9: Maintenance Required

0.011

T10: Life of PV Panel0.013

Economic0.30

E1: Total Purchase Cost of PV Panels to Utility

0.033

E2: Warranty/ Maintenance Cost

0.024

E3: Total Associated Inverter and Balance-of-System Purchase Cost

0.033

E4: Disposal Cost0.015

E5: Levelized Cost of Electricity (LCOE)

0.090

E6: Return on Investment

0.054

E7: Cost of Risk0.024

E8: Supply Chain Maturity

0.012

E9: Global Production/Supply

Volume0.009

E10: Use of Rare Elements

0.006

Environmental0.20

N1: Emission of Greenhouse and Pollutants During

Production0.028

N2: Negative Ecological Footprint

0.026

N3: Use of Available Land0.024

N4: Use of Hazardous Materials

0.044

N5: Water Consumption During

Operations0.024

N6: Consumption of Other Materials

During Operations0.016

N7: Recyclability at End-of-Life

0.016

N8: Waste Chemicals at End-of-Life

0.014

N9: Waste Gases at End-of-Life

0.012

Political0.12

P1: National Priority0.011

P2: GovernmentIncentives

0.034

P3: Regulatory Risk0.028

P4: Relations with Local Politicians

0.014

P5: Local Sourcing0.016

P6: Conformance to Existing Political,

Legal, Management Constructs by

Utilities0.018

38Top-ranked

HDM Element Contributions to the MissionSocial0.15

S1: Job Creation0.050

S2: Health Effects - During Production

Phase0.039

S3: Health Effects - During Operations

Phase0.033

S4: Negative Publicity

0.027

Technical 0.22

T1: Module Energy Efficiency

0.029

T2: Power Density0.015

T3: Module Durability

0.024

T4: Module Reliability

0.029

T5: Potential Induced

Degradation (PID) Performance

0.040

T6: PV Module Design Flexibility

0.018

T7: State of Power Plant Installation

Worldwide0.013

T8: State of Field Performance

0.013

T9: Maintenance Required

0.011

T10: Life of PV Panel0.013

Economic0.30

E1: Total Purchase Cost of PV Panels to Utility

0.033

E2: Warranty/ Maintenance Cost

0.024

E3: Total Associated Inverter and Balance-of-System Purchase Cost

0.033

E4: Disposal Cost0.015

E5: Levelized Cost of Electricity (LCOE)

0.090

E6: Return on Investment

0.054

E7: Cost of Risk0.024

E8: Supply Chain Maturity

0.012

E9: Global Production/Supply

Volume0.009

E10: Use of Rare Elements

0.006

Environmental0.20

N1: Emission of Greenhouse and Pollutants During

Production0.028

N2: Negative Ecological Footprint

0.026

N3: Use of Available Land0.024

N4: Use of Hazardous Materials

0.044

N5: Water Consumption During

Operations0.024

N6: Consumption of Other Materials

During Operations0.016

N7: Recyclability at End-of-Life

0.016

N8: Waste Chemicals at End-of-Life

0.014

N9: Waste Gases at End-of-Life

0.012

Political0.12

P1: National Priority0.011

P2: Government Incentives

0.034

P3: Regulatory Risk0.028

P4: Relations with Local Politicians

0.014

P5: Local Sourcing0.016

P6: Conformance to Existing Political,

Legal, Management Constructs by

Utilities0.018

39Top-ranked

Criterion added by experts

Perspective priorities represent Worldview of Electric Utility. These maybe different for other worldviews.

All STEEP perspective have significance.

Desirability Functions: Examples

40

0

20

40

60

80

100

0 10 20 30 40 50

Desir

abilit

y

Worst Technological Metric Best

Desirability Function:Simple Case-Linearly Proportional

0

20

40

60

80

100

0 20 40 60 80 100

Desi

rabi

lity

Worst Technological Metric Best

Desirability Function(Using Mean of Expert Panel Values)

Based on Research by D. Kocaoglu

Desirability Function (DF) maps technology metric to desirability value (DV).

Maps quantitative and qualitative metrics to a DV between 0 – 100.

0

20

40

60

80

100

100% 80% 60% 40% 20% 0%

Desir

abili

ty V

alue

Module Energy Efficiency

Desirability Function

0

20

40

60

80

100

>200 W/m2

151-200 W/m2

101-150 W/m2

51-100 W/m2

1-50 W/m2

0 W/m2

Desir

abili

ty V

alue

Power Density

Desirability Function

0

20

40

60

80

100

Desir

abili

ty V

alue

Module Durability: Performance After 25 Years

Desirability Function

0

20

40

60

80

100

Desir

abili

ty V

alue

Module Reliability: Percent of Failed Modules

Desirability Function

Desirability Functions From Research

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0

20

40

60

80

100

>300 101-300 25-100 1-24 0

Desir

abili

ty V

alue

Number of Jobs Created

Desirability Function

0

20

40

60

80

100

None Very Low

Low Medium High Very High

Desir

abili

ty V

alue

Negative Health Effects - During Production Phase

Desirability Function

0

20

40

60

80

100

None Very Low

Low Medium High Very High

Desir

abili

ty V

alue

Negative Health Effects - During Operations Phase

Desirability Function

0

20

40

60

80

100

None Very Low

Low Medium High Very High

Desir

abili

ty V

alue

Negative Publicity

Desirability Function

PV Technologies: Desirability Values

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Social Perspective

Job

Crea

tion

Heal

th E

ffect

s -

Durin

g Pr

oduc

tion

Phas

e He

alth

Effe

cts -

Du

ring

Ope

ratio

ns

Phas

e

Neg

ative

Pub

licity

c-Si 75 95 100 90

a-Si 75 95 100 90

CIGS 43 95 98 90

CdTe 43 95 98 90

OPV 43 95 98 90

Technical Perspective

Mod

ule

Ener

gy

Effici

ency

Pow

er D

ensit

y

Mod

ule

Dura

bilit

y

Mod

ule

Relia

bilit

y

Pote

ntial

Indu

ced

Degr

adati

on (P

ID)

Perf

orm

ance

PV M

odul

e De

sign

Flex

ibili

ty

Stat

e of

Pow

er

Plan

t Ins

talla

tion

Wor

ldw

ide

Stat

e of

Fie

ld

Perf

orm

ance

Mai

nten

ance

Life

of P

V Pa

nel

c-Si 78 95 90 100 100 91 100 100 78 72

a-Si 66 95 90 100 100 91 100 100 78 72

CIGS 43 81 25 100 100 100 83 100 78 42

CdTe 43 81 25 100 100 100 83 100 78 42

OPV 10 39 17 17 51 100 0 12 78 12

Research Process: Stages 4Analysis of Results Ranking the technology alternatives Determining “technology values” to

compare different technologies Identifying gaps-from-the best-level Managing inconsistencies and

disagreements in expert judgments

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Analysis of Results Electric Utility Worldview – US

Northwest

44

c-Si a-Si CIGS CdTe OPVTV 81.97 80.92 75.03 74.34 59.69

0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00

100.00

Tech

nolo

gy V

alue

PV Technology Value (TV)

OPV: Improvements Needed to be Top Ranked

45

Criterion OPV Performance Metric Value*

Current ImprovedS1 Job Creation 1-24 25-100T1 Module Energy Efficiency 3% 20%T2 Power Density 51-100 W/m2 151-200 W/m2

T3 Module Durability 10-20% 81-90%T4 Module Reliability (Failure Rate) 11-15% <1%T5 Potential Induced Degradation (PID)

Performance 11-15% <5%

T7 State of Power Plant Installation Worldwide Not Deployed Heavily Deployed

T8 State of Field Performance Testing Initiated Tested > 10 YearsT10 Life of PV Panel 1-9 Years 16-25 YearsE1 Total Purchase Cost of PV Panels to Utility 76-100% 26-50%E3 Total Associated Inverter and Balance-of-

System Purchase Cost 101-200% 76-100%E6 Return on Investment <5% 11-15%E7 Cost of Risk 21-30% <10%E8 Supply Chain Maturity Ad Hoc ExtendedE9 Global Production/Supply Volume No Supply Supply Exceeds

DemandP5 Local Sourcing Very Low Complete

*This scenario applies to a US NW Electric Utility worldview. 16 Criteria need to be improved.

Dominant Perspective (0.96/1.00)

Best Medium Worst

Social c-Si, a-Si CIGS, CdTe OPVTechnical c-Si, a-Si CIGS, CdTe OPVEconomic c-Si, a-Si CIGS, CdTe OPVEnvironmental OPV c-Si, a-Si CIGS, CdTePolitical c-Si, a-Si CIGS, CdTe OPV

46

Research Process: Stage 5Sensitivity Analysis Calculating the impact of the variation of an

element (or combination of elements) on the decision

PV Technology rank order changes with dominant environmental perspective

47

Game Theory Development

Strategic Decision Making Strategic decision making has two main

branches:◦ Decision modeling (consensus)◦ Game theory (dissent and conflict).

Research in decision modeling has mainly focused on the ranking of alternative choices based on a consensus or combined judgments of experts and decision makers.

However, a decision model can also be used to form the basis of rational conflict and dissent.

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Consensus Approach: STEEP Values

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STEEP Perspective Relative Value

Social 0.12

Technical 0.23

Economic 0.27

Environmental 0.22

Political 0.15

50

Technology Values under Consensus for Five PV Candidate Technologies

50

Ranking: 1 2 3 4 5

Dominant Strategy: Case of Conflict

Example: Social Perspective is dominant

51

STEEP Perspective Relative Value

Social 0.96

Technical 0.01

Economic 0.01

Environmental 0.01

Political 0.01

Technology Rankings Under Different Dominant Strategies

Dominant

Strategy

Social Technical

Economic

Environmental

Political

c-Si 1 1 1 2 1a-Si 2 2 2 3 2CIGS 3 3 3 4 3CdTe 4 4 4 5 4OPV 5 5 5 1 5

52

Only this ranking changed

Even under dissent decisions can be the same.

Relationship to Game Theory Concepts

Game – HDM model (representing players) Players – perspectives (rational) Payoffs – ranking of alternatives (ordinal

values) Incomplete Information Variable Sum Game Non-Cooperative Game Normal (not extensive) Game

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Current Game Theory Limitations Small number of players Small number of decision elements Large number of assumptions Otherwise the calculus becomes too

complex

54

Hierarchical Decision Modeling may provide an alternate approach.

Conclusion and Future Work Applications of HDM can be for cooperation

and conflict by leveraging different aspects of the model.

Future research will focus on developing game theory for HDM and also applying this approach to applications in energy, finance, and healthcare.

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Related Research at SUNY Korea - DTS

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Multi-dimensional assessment of tinnitus treatments

Strategic roadmapping for policy and technology foresight: deployment of solar technology

Assessment of arms suppliers to establish an import policy

Determinants of policy change in technology standardization

Determining success factors for small to medium businesses (SMEs)

SUNY Korea Ph.D. Research Related to Decision Modeling

Thank you.

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Backup Slides

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• Founding faculty and Assistant Professor in the Department of Technology and Society, College of Engineering and Applied Sciences at State University of New York, Korea.

• Research interests include technological innovation and commercialization, technology assessment, decision modeling, and analytics

• 30 years of industry experience in building global information and communications technologies (ICT) businesses as an executive in technology management, sales, and marketing.

• Ph.D. degree in Technology Management from Portland State University, M.S. degree in Electrical Engineering from King Fahd University of Petroleum and Minerals, Saudi Arabia, and B.S. degree in Electrical Engineering from Middle East Technical University, Turkey.

Bio

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Strategic decision making theory involving multiple players has two branches. One is based on decision modeling and the other on game theory. Decision modeling is the research area of the author and relies on a consensus approach to rank decision alternatives. This decision analysis methodology is useful in addressing complex scenarios involving multiple decision makers and criteria. The criteria may be qualitative or quantitative and typically, compete for priority. It may be also the case of comparing “apples to oranges.” The main focus of this lecture is hierarchical decision modeling using multiple perspectives. The ranking of alternative choices is based on a consensus or combined judgments of experts and decision makers. This lecture examines the case of a hierarchical decision model that is used to assess alternative solar photovoltaic (PV) technologies under multiple perspectives including: social, technical, economic, environmental, and political (STEEP). However, this decision model can also be used to form the basis of rational conflict and dissent which is the domain of game theory and is more oriented towards conflict and cooperation to decide the winning strategy. Dissent is evident if only one dominant perspective is considered to evaluate the alternate PV technologies. By using such a decision modeling approach outcomes for both consensus and dissent scenarios can be observed. This research could be developed further to form a basis of a type of game theory with large numbers of players and preferential criteria. This area may be important since traditional game theory has limitations here.

Abstract

Renewable Energy Technology Assessment

P1

P2

P3

P4

PK=5

C21

CJk1

C11

C1K

C2K

CJkK

... ...

Mission (M)

Perspectives (Pk)

Criteria (Cjkk)

Desirability Functions (DFjkk)

Technology Characteristics (tn,jkk)

Candidate Technologies (Tn) T1

T2

TN

…….T3

…….

DF1,K

DF2,K

DFJk,K

...DF11

DF21

DFJk1

...

t1,11

t2,11

tN,11

t1,JkK

t2,JkK

tN,JkK

......

…….

…….

HDM with No Factors

62

𝑇𝑉𝑛 = 𝑝𝑘 ∙𝑐𝑗𝑘,𝑘 ∙𝑉൫𝑡𝑛,𝑗𝑘,𝑘൯𝐽𝑘

𝑗𝑘=1𝐾

𝑘=1

Research Process: Stage 6Validation

63

Research Validation Test Methods When Applied

Content ValidityPilot testing, evaluation by experts, and literature review

During research instrument and model preparation

Construct ValidityPilot testing, evaluation by experts, and literature review

After the development of the model

Criterion-Related

Validity

Pilot testing, use of expert judgment, and literature review

After the results are compiled

ReliabilityStatistical and built-in consistency and disagreement analysis

After the results are compiled

PracticabilityPilot testing and checking for inherent practicability

During pilot testing