dals up 09 cruz

+Systems Thinking in Dynamic Planning of

Energy Systems

Jose B. Cruz, Jr. The Ohio State University

Columbus, Ohio USA

University of the

Philippines

College of Engineering

Diliman, Quezon City Engineering

Theatre 17 July 2009

Distinguished Alumni Lecture Series

+Why So Much Focus on Energy?

  Rapid depletion of fossil fuels during the past 100 years, from accumulations of fossils during the past hundreds of millions of years.

  Negative impact of burning fossil fuels on the environment – global warming.

2

+How Did This Arise?

  Engineering inventions, and advances in science and technology during the 20th century have transformed society into one with intense and pervasive use of electrical energy.

3

+ NAE List of 20th Century Greatest Engineering Achievements

In rank order

1  Electrification

2  Automobile

3  Airplane

4  Water Supply and Distribution

5  Electronics 4


In rank order

6  Radio and Television

7  Agricultural Mechanization

8  Computers

9  Telephone

10  Air Conditioning and Refrigeration

5


In rank order

11  Highways

12  Spacecraft

13  Internet

14  Imaging

15  Household Appliances

6


In rank order

16  Health Technologies

17  Petroleum and Petrochemical Technologies

18  Laser and Fiber Optics

19  Nuclear Technologies

20  High-performance Materials 7

+Consequences of Our Modern Way of Living

  Electrification led to other great engineering achievements.

 Each of the 19 other achievements is closely coupled to the availability of electricity. Each implies greater use of energy.

8


  The total energy demanded by the technologically transformed world can not be met without the availability of high energy density fossil fuels (coal and oil).

9


 Continued high rate of burning of fossil fuels using current technologies releases carbon dioxide and other gases, contributing to global warming, and placing earth and humanity at great risk.

10

+Energy Expenditures (USA)

(From EIA AEO 2008)

11

+Energy Production and Consumption

(From EIA AEO 2008)

12

+Energy Production by Fuel

(From EIA AEO 2008)

13

+Energy Consumption by Fuel

(From EIA AEO 2008)

14

+ Characteristics of Large-Scale Systems

  Presence of more than one stakeholder and decision-maker

  Presence of dynamics (next state depends on current state and current action)

15

+What Is a Dynamic System?

  Simplest Class: Modeled by an ordinary differential equation, where the independent variable is time.   Example: Vertical motion of an automobile tire moving on a rough road

md2y

dt2+ d

dy

dt+ ky = f. This is usually written

in vector-matrix form as

dx

dt= 0 1

−k / m −d / m

⎡

⎣⎢⎢

⎤

⎦⎥⎥x + 0

f / m

⎡

⎣⎢⎢

⎤

⎦⎥⎥

where x =x

1

x2

⎡

⎣

⎢⎢

⎤

⎦

⎥⎥,x

1= y, x

2=

dy

dt

when simulating in MATLAB 16

+Discrete-time Dynamics

  Difference equations rather than differential equations are used.

  Example: Autoregressive Moving Average (ARMA)

yk+ a

n−1y

k−1+ ... + a

0y

k−n=

bmu

k+ b

m−1u

k−1... + b

0u

k−m

Vector-Matrix Representation:x

k+1= Ax

k+ Bu

17

+What is Game Theory?

 Game Theory is a body of knowledge concerning decision-making in a system with two or more Decision Makers.

 A player is a decision maker or a controller.

 A player chooses a decision, strategy, or control.

 A decision choice is based on available information.

 Associated with each player is a cost or pay-off function.

 A cost depends on one or more decisions.

 Much of game theory deals with how a player selects a decision.

18

+Very Brief History of Game Theory   Mathematical foundation of game theory:

 John von Neumann and O. Morgenstern, Theory of Games and Economic Behavior, Princeton University Press, 1944 [1].

 Game theory cuts across multiple disciplines of mathematics, operations research, economics, political science, control theory, and engineering.

 For a recent brief history see

  Jose B. Cruz, Jr. and Xiaohuan Tan, Dynamic Noncooperative Game Models for Deregulated Electricity Markets, Nova Publishers, 2009 [2,Section 2.1].

19

+Different Solution Concepts in Game Theory  Players are assumed to be rational.

 Zero-sum, min-max, max-min:  Pure strategies (deterministic choice)  Mixed strategies (choice of random distribution)

 Nash equilibrium for nonzero sum games.

 Pareto optimality.

 Stackelberg equilibrium.

20

+Dynamic Game Theory

For DM1:Find {u

01,u

11,...,u

N−11 }

to "optimize" J1

For DM2:Find {u

02,u

12,...,u

N−12 }

to "optimize" J2

Ji= L

Ni (x

N) + L

ki

k=0

N−1

∑ (xk,u

k1 ,u

k2)

21

+ For Energy Systems, What Game Concept Is Appropriate?

• The government is one of the DMs and it will be a dominant player.• The dynamic Stackelberg strategy (Leader-Follower) is appropriate.• The dominant player is the Leader and announces its sequence of strategies first.

22

+Static Stackelberg Game

• Let there be two players, Player 1 and Player 2. • ui is the decision variable of Player i, ui ∈Ui , i = 1, 2. • Ji (u1,u2 ) is the scalar cost function of Player i, i = 1, 2. • One player, called the Leader, declares its decision strategy first. • The other player is called the Follower. • H. von Stackelberg, The Theory of the Market Economy,Oxford University Press, English translated ed., 1952 [3].

23

+Static Stackelberg Game -2

• Reaction Set of Player 1: D1 = {(u1,u2 ) ∈U1 ×U2 : T: U2 →U1, u1 = Tu2, J1(Tu2,u2 ) ≤ J1(u1,u2 ) for all u1 ∈U1, for each u2 ∈U2 }. • Stackelberg strategy pair with Player 2 as Leader, Player 1 as Follower: (u1S2,u2S2 ) ∈{(u1S2,u2S2 ) ∈D1 : J2(u1S2,u2S2 ) ≤ J2(u1,u2 ) for all (u1,u2 ) ∈D1}. • Similarly Player 1 may be the Leader and Player 2 the Follower.

24

+Historical Roots of Dynamic Games

4. R. P. Isaacs, Differential Games: a Mathematical Theory with Applications to Warfare and Pursuit, Control and Optimization. New York: John Wiley and Sons, 1955. First book on dynamic games.

5. Y.C. Ho, “Differential Games, Dynamic Optimization, and Generalized Control Theory,” Journal of Optimization Theory and Applications, Vol. 6, No. 3, 1970. Clarified connections of control theory to dynamic game theory.

6. T. Basar and G.J. Olsder, Dynamic Noncooperative Game Theory, 2nd Edition (revised), the Society for Industrial and Applied Mathematics, 1998. Comprehensive and extensive treatment of dynamic games.

25

+Dynamic Games

The evolution of a discrete-time dynamic system is modeled by difference equations x(k +1) = f (x(k),u1(k),u2(k),k), where x(k) is the state vector, ui (k) is the control or decision vector of Player i, and k is discrete time or time stage, k = 0,1, 2,...,N, and f is a mapping from x, u1, u2, and t to the space of x. The sequence {x(k)} describes the evolution of the state as a consequence of the application of decisions u1, u2 applied at preceding time stages. A continuous time model is described by a set of ordinary differential equations x = f (x(t),u1(t),u2(t)), t ∈[t0,tf ], x(t0 ) = x0 and the symbols are defined similarly. An open loop control is a time sequenceui = {ui (0),ui (1),...,ui (N −1)}, starting at a given state x(0) = x0.A closed loop control is a sequence{ui (k,x(k))} = {ui (0,x(0)),ui (1,x(1)),...,ui ((N −1),x(N −1))}.

26

+Stackelberg Strategies for Dynamic Games*

  First considered by Chen and Cruz, and Simaan and Cruz:

7. C.I. Chen and J.B. Cruz, Jr., “Stackelberg Solution for Two-Person Games with Biased Information Patterns,” IEEE Trans. on Automatic Control, Vol. AC-17, No. 6, December 1972, pp. 791-798.

8. M. Simaan and J.B. Cruz, Jr., “On the Stackelberg Strategy in Nonzero-Sum Games,” Journal of Optimization Theory and Applications, Vol. 11, No. 5, May 1973, pp. 533-555.

9. M. Simaan and J.B. Cruz, Jr., “Additional Aspects of the Stackelberg Strategy in Nonzero-Sum Games,” Journal of Optimization Theory and Applications, Vol. 11, No. 6, June 1973, pp. 613-626.

27

+Dynamic Stackelberg Games

• A player, say Player 2, called the Leader, commits to a strategyfor the entire horizon of the game and announces it before the start of the game.• The other player, Player 1, called the Follower is aware of the Leader's commitment and as a rationale decision maker proceeds to optimize its cost function J1(u

1,u2 ) with respect to its choice for a

control sequence u1, taking into account the Leader's commitmnt to a specific control sequence u2.• The Leader notes how the Follower will react, and chooses u2 to optimize J2(u1,u2 ) under the condition that u1 is a reaction to u2.

28

+Dynamic Stackelberg Games - 2

• Define the reaction set of the Follower as the set of of u1 sequence for each possible announced commitment of u2 sequence by the Leader:R1 = {(u1,u2 ) : J1(u

1,u2 ) ≤ J1(v1,u2 ) for all v1 ∈U1 and

for each u2 ∈U2 }• If Player 1 is the Leader a similar reaction set for the Follower is defined:R2 = {(u1,u2 ) : J2(u1,u2 ) ≤ J2(u1,v 2 ) for all v 2 ∈U2 and

for each u1 ∈U1}• The Leader selects its decision from the reaction set of the Follower that results in the minimum of its cost function. 29

+2-stage 3-state dynamic game example

2,5

7,7

5,1

6,3

8,3

X=2

X=1

X=0

X=2

X=1

X=0

X=1 5,5

9,0

3,1

2,0

12,2

1,7

16,10

5,-3

3,3

0,6

4,5

0,1

0,0

1,1

1,0

0,0

0,1 1,0

1,1

0,1 0,0

1,1 1,0

0,0

0,1 1,0

1,1

30

+Determining a Stackelberg Closed Loop Strategy  At the initial state x = 1, each Player chooses a decision of 0 or 1.

 At time 1, state x = 2, each player chooses a decision of 0 or 1.



 Each decision maker or Player has 16 choices

31

+16 Choices for Players

ci1 ci2 ci3 ci4 ci5 ci6 ci7 ci8 ci9 Ci10

Ci11

Ci12

Ci13

Ci14

Ci15

Ci16

ui(0,1) 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1ui(1,2) 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1ui(1,1) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1ui(1,0) 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1

32

+Reaction Sets

R1c = {(c115,c21), (c18, c22 ), (c113, c23 ), (c16, c24 ), (c111, c25 ), (c14, c26 ), (c19, c27 ), (c12, c28 ), (c115, c29 ), (c116, c210 ), (c15, c211), (c111, c212 ), (c16, c213 ), (c112, c214 ), (c11, c215 ), (c12, c216 )}

R2c = {(c11, c211), (c12, c211), (c13, c2 ), (c14, c211), (c15, c211), (c16, c211), (c17, c211), (c18, c211), (c19, c211), (c110, c23 ), (c111, c211), (c112, c23 ), (c113, c211), (c114, c23 ), (c115, c211), (c116, c23 )}

For example, for u2 = c26 (0,1,0,1) Player 1 minimizes J1 and

gets u1 = c14,(0, 0, 1 ,1). This is repeated for each u2 = c2 j ,

thus obtaining R1c . 33

+Choices for Player 1 when u2=(0,1,0,1)

2,5

7,7

5,1

6,3

8,3

X=2

X=1

X=0

X=2

X=1

X=0

X=1 5,5

9,0

3,1

2,0

12,2

1,7

16,10

5,-3

3,3

0,6

4,5

0,0

1,0

0,1

1,1

0,0

1,0

0,1

1,1

u1 = (0,0,1,1)

34

+Stackelberg Example

There are two closed loop Stackelberg controls with Player 2 as Leader, (c15, c211) and (c16, c212 ), both leading to J1S2

c = 7 and

J2S2c = 2 and to the same trajectory x(1) = 2 and x(2) = 1. At time

t = 1, the remaining controls are (u1,u2 ) = (1,0) and the remaining costs are J1 = 2 and J2 = 5. Supose that Player 2 considers violating its commitment made at time t = 0 regarding its control at time t = 1. Its closed loop Stackelberg strategy for a game starting at t = 1 and x(1) = 2 is (u1,u2 ) = (0,1) leading to J1 = 6 andJ2 = 3. It will be tempted to violate its commitment to reduce its cost.

35

+Stackelberg Example -2

 This example shows that the closed loop Stackelberg strategy violates Bellman’s principle of optimality. That is, the continuation of a previously announced closed loop strategy, starting at a later time, is not necessarily a closed loop Stackelberg strategy for a new game starting at the later time.

 This example is in Simaan and Cruz [Ref 9].

36

+ Stackelberg Example -3

 For the same example in [9] it was shown that the open loop strategy for a game starting at t=0, x=1 violates the principle of optimality.

 If a Leader violates its commitment made at an earlier time and changes its strategy at a later time with a new commitment, there will be a credibility problem. The Follower may not believe a subsequent commitment by the Leader.

37

+Stackelberg Example - 4  This violation of the principle of optimality is known as time-inconsistency in economics.

 The credibility problem and the time-inconsistency problem suggest that a Stackelberg-like closed loop strategy that satisfies the principle of optimality would be an acceptable suboptimal alternative.

 Such a strategy, called Feedback Stackelberg was introduced in [7], precisely defined and fully described in [9]. It is a suboptimal closed loop Stackelberg-like strategy. But it is time-consistent.

38

+Feedback Stackelberg Strategies  Principal property: The principle of optimality holds. (Time-consistency holds).

 Dynamic programming can be applied.

 Optimal Cost-to-go at stage k is the sum of the incremental cost at stage k plus the optimal cost-to-go at the next stage k+1, where the optimization is performed stage by stage starting with the last stage, in the sense of Stackelberg.

39

+ Dynamic Stackelberg Strategies

  Feedback Stackelberg strategies proposed for the first time in Simaan and Cruz 1973 are suboptimal but time-consistent and widely used in macroeconomics.   The same methodology can be applied to energy systems.

40

+Dynamic Stackelberg Strategies Are Pervasive in Macroeconomics

  Kydland and Prescott published a paper in 1977 showing that the government strategy is time-inconsistent (violates the principle of optimality of Bellman’s Dynamic Programming). This paper revolutionized the entire field of macroeconomics.

  Kydland published a more theoretical paper in 1975, used as a reference in Kydland and Prescott, 1977).

41

+Dynamic Stackelberg Strategies Are Pervasive in Macroeconomics

  Kydland and Prescott won the Nobel prize in economics in 2004.

  Kydland, 1975 referred to Simaan and Cruz, 1973, where time-inconsistency is proved. Simaan and Cruz, 1973 is a reference in Kydland’s Ph.D. dissertation supervised by Prescott at Carnegie Mellon University in 1974.

42

+Related Developments in Economics 12. Finn Kydland, “Equilibrium Solutions in Dynamic

Dominant Player Models,” Journal of Economic Theory, 15, 307-325, 1977. Kydland states that the dominant solution, open loop or closed loop are time-inconsistent. Suggests that a feedback solution is self-enforcing. Cites Simaan and Cruz [8,9].

13. Guido Taballini, “Finn Kydland and Edward Prescott’s Contribution to the Theory of Macroeconomic Policy,” Scand. J. of Economics, 107(20), 203-216, 2005. Taballini notes that Kydland [12] cites Simaan and Cruz [8,9].

43

+Related Developments in Economics

10. Finn Kydland, “Noncooperative and Dominant Player Solution in Discrete Dynamic Games, “International Economic Review, Vol. 16, No. 2, June 1975, pp. 321-335. Cites Simaan and Cruz: “The dominant player problem, on the other hand, has only recently received a little attention in the game literature, and the two interesting papers by Simaan and Cruz [24,25] should be mentioned.”

11. Finn E. Kydland and Edward C. Prescott, “Rules Rather Than Discretion: The Inconsistency of Optimal Plans,” The Journal of Political Economy, Vol. 85 No. 3 (June 1977), pp. 473-492. This paper is one of the bases for Kydland and Prescott to be selected for the 2004 Nobel Prize in Economics.

44

+

Presented at the 29th APAMS, July 13 - 15, 2009

Introduction to Current Joint Work with R R Tan and A B Culaba, DLSU

 Energy consumption is closely coupled with both economic growth and greenhouse gas emissions.

 Despite the increasing popularity of renewables, the world remains highly dependent on fossil fuels for transportation, power generation and industrial use.

 Various novel solutions are at inherent disadvantage compared to entrenched technologies due to network externalities.

+


Some Examples of Nascent Energy Supply Chains

 Biofuel production systems from dedicated energy crops

 Fossil-based electricity production with carbon capture and storage

 The “hydrogen economy”

+


Motivating Case  In the Philippines, Jatropha curcas has been touted as a promising dedicated energy crop for biodiesel production

 However, investments in upstream (farm-level) production capacity has not been matched by corresponding growth in downstream (oilseed pressing and conversion) capacity

 This imbalance in the J. curcas supply chain is typical of nascent energy systems.

+


The Basic Model (Cruz et al., 2009)

Axt = yt

xt+1 = B(zt – yt) + xt

where:

A = technical coefficient matrix

xt = sectoral total output vector at t

yt = sectoral net output vector at t

B = influence matrix

Material and energy balances of physical streams

Response of production capacity to deficits or surpluses

+


Key Assumptions  Matrix A reflects scale-invariant physical relationships such as process yields

 Matrix B reflects econometrically determined collective behavioral responses of supply chain agents

 Vector x reflects total system outputs, including intermediates

 Vector actual y reflects net system outputs, while z gives the desired output level.

 Production capacities are assumed to respond to surpluses or deficits incurred in the previous time interval.

+


The Basic Model (Cruz et al., 2009)

xt+1 = (I – BA)xt + Bzt

zt = Kxt + zo

where:

K = control matrix

zo = baseline target output

xt+1 = (I – BA + BK)xt + Bzo

(I – BA) defines the dynamic characteristics of the system.

Adaptive target output level is introduced

(I – BA + BK) now defines the dynamic characteristics of the controlled system.


The Extended Model

Material and energy balances of physical streams

Response of production capacity to deficits or surpluses Lagged influences

may be interpreted probabilistically

+


The Extended Model

Denoting

The extended model is thus reduced to the same form as the previous one.


Case Study 1 (Scenario 1, Cruz et al., 2009)

Energy crop

Biofuel

Land

Farming Biofuel production

+


Case Study 1 (Cruz et al., 2009)



Farm capacity increases with oilseed surplus

Farm capacity responds to biodiesel surplus or deficit

Biodiesel production capacity exhibits sluggish response


Case Study 4

We revisit Case 3, but introduce time lags with:

(a1, a2, a3)T = (0.3, 0.5, 0.2)T

+


Key Implications

 Undesirable dynamic characteristics in nascent energy supply chains may arise due to feedback loops in physical linkages or information flows.

 Control theory can be used to systematically design interventions to suppress undesirable system behavior.

 Such interventions can come in the form of policy instruments or economic incentives/disincentives.

+


Conclusions

 We have extended our dynamic input-output model for nascent energy supply chains to incorporate weighted time lags in the capacity response.

 This extension allows for added flexibility in modeling real systems wherein changes in production capacity may be subject to time delays.

+Virgilio T. Villancio Program Leader

Integrated R&D on Jatropha curcas for Biodiesel UP Los Banos

Pending Collaboration with UPLB

+

 Growing demand for biofuels

 Unstable prices of crude oil

  Rising prices of vegetable oils

 Need for non-food sources of oil

 Higher value of by-products as additional source of revenue

OPPORTUNITIES

+It is locally known as Tubang bakod, Tuba-tuba, Kasla, Tubang aso, Tubang silangan, tawa-tawa

Planted in fences for hedges, thus the term Tubang bakod

Leaves are used as herbal medicine for fractures

Seeds are grounded and used to poison fish thus the term Tuba

+

•  2,000‐5,000kg/hectare/year(dependingonthequalityofJatrophaseedandsoil)

•  0.3‐9kg/treeseedproducBon

•  Canbearfruitthroughouttheyear

•  Oilyield30–40%crudenon‐edibleoil

•  0.75–2tonsbiodiesel/hectare

+

FEEDSTOCKPRODUCTION

GermplasmManagement,Varietalimprovement,seedtechnology,provenancetesNng

Nurserydevelopment

DevelopmentofproducNonsystems,prototypeplantaNon

SoilFerNlitymanagement

Pestanddiseasesmanagement

FloweringandfruiNngphysiology

PostProducNonmanagement

TechnologypromoNon

PROCESSINGANDUTILIZATION

MechanicalprocessingEnzymaNcprocessingProcessingofby‐productsWastemanagement

MARKETDEVELOPMENT

ProductdevelopmentandpromoNon

Establishmentofthevaluechain

GOALS

RuralemploymentIncomegeneraNonEnergyindependenceCleanerenvironment

SOCIAL ECONOMICS POLICY ENVIRONMENTAL

Capacitydevelopment

BUSINESSANDENTERPRISE

DEVELOPMENT

PNOC FUNDED DOST-PCARRD FUNDED CHED FUNDED

+ FLOWERINGANDFRUITINGPHYSIOLOGY

+

Maturedpods

•  3fruitclustersperbranchperfruiBngseason

•  12fruitsperbunch•  2.66seedsperfruit•  48branchespertree•  1,600treesperhectare•  1,400seedsperkg•  5,250kgperhectare

+

Map for Jatropha suitability

+Godilano, 2008

+

JATROPHA PLANTATION AT ZAMBOANGUITA, DUMAGUETE

+ R & D Plan for OSU, DLSU, UPLB

  Investigate Total Dynamic Supply Chain.

  Investigate genetic reengineering of Jatropha curcas for improved total supply of biodiesel (oil content, continuous harvesting, less water needs).

  Develop strategies for various stakeholders

  Investigate dynamic policy interventions. 75

+

Opo! Game Theory pa!

+ The Engineer of 2020

A Study by the National Academy of Engineering

77

The premise

� Past: Engineering and engineering education

were reactive, responding to change.

� Today: Rapid change signals that it is time to

reverse the paradigm.

� Premise: If we anticipate the future and are

proactive about changing engineering and

engineering education, we can shape a

significant, dynamic role for our profession.

The process

� Phase I: Imagining the future and the challenges it will present to engineering: Woods Hole Workshop.

� Phase II: Considering how engineering education should prepare for that future: Washington DC Summit.

National Academy of Engineering

Steering Committees

� Wayne Clough, Chair, Ga Tech� Alice Agogino, UC Berkeley� George Campbell, Cooper Union� James Chavez, Sandia Labs� David Craig, Reliant Energy� Jose Cruz, Ohio State� Peggy Girshman, NPR� Daniel Hastings, MIT� Michael Heller, UC San Diego� Deborah Johnson, U Virginia� Alan Kay, H-P� Tarek Khalil, U Miami� Robert Lucky, Telcordia Technologies� John Mulvey, Princeton� Sharon Nunes, IBM� Sue Rosser, Georgia Tech� Ernest Smerdon, U Arizona

� Wayne Clough, Chair, Ga Tech� Alice Agogino, UC Berkeley� Mark Dean, IBM� Deborah Grubbe, DuPont� Randy Hinrichs, Microsoft� Sherra Kerns, Olin College� Alfred Moye, H-P� Diana Natalicio, UT at El Paso� Siman Ostrach, Case West Res� Ernest Smerdon, U Arizona� Karan Watson, Texas A&M� David Wisler, GE Aircraft Engines

Phase I Phase II

Context for engineering

� Breakthroughs in technology

� Demographics

� Challenges

� Economic/societal forces

Sustainable Technology

Photonics/optics Manufacturing

Logistics

Microelectronics/ telecommunications

Biotechnology/ nanomedicine

Nanotechnology

Breakthroughs

Demographics

� 8 billion people; a 25% increase since 2000.

� Balance tipped toward urbanization.

� Youth “bulge” in underdeveloped nations while

developed nations age.

� If the world condensed to 100 people:

�56 in Asia

�16 in Africa

� 7 in Eastern Europe/Russia

� 4 in the United States

Challenges

� Fresh water shortages

� Aging infrastructure

� Energy demands

� Global warming

� New diseases

� Security

Economic/societal forces� High speed communications / Internet

� Removal of trade barriers

� Terrorist attacks; wars in Iraq, Afghanistan

� Emergence of technology-based economies in other nations

� Sustained investment in higher education in countries like China, India

Social, global and professional

context of engineering practice

� Population is more diverse.

� Social, cultural, political forces will shape and affect

the success of technological innovation.

� Consumers will demand higher quality,

customization.

� Growing imperative for environmental sustainability.

� Increasing focus on managing risk and assessment

with view to security, privacy, and safety.

Engineering’s image

� Public that understands and appreciates the

impact of engineering on socio-cultural systems.

� Public that recognizes engineering’s ability to

address the world’s complex and changing

challenges.

� Engineers will be well grounded in the

humanities, social sciences, and economics as

well as science and mathematics.

Aspirations for the Engineer of 2020

Engineering without boundaries

� Embrace potentialities offered by creativity,

innovation, and cross-disciplinary fertilization.

� Broaden influence on public policy and the

administration of government, nonprofits, and

industry.

� Recruit, nurture and welcome underrepresented

groups to engineering.


Engineering a sustainable society

� Lead the way toward wise, informed,

economical, and sustainable development.

� Assist in the creating of an ethical balance

in standard of living for developing and

developed countries alike.


Educating the engineer of 2020

� Reconstitute engineering curricula and related

educational programs to prepare today’s

engineering students for the careers of the future.

� Create a well-rounded education that prepares

students for positions of leadership and a

creative and productive life.


Attributes of the engineer of 2020� Strong analytical skills

� Practical ingenuity, creativity; innovator

� Good communication skills

� Business, management skills

� High ethical standards, professionalism

� Dynamic/agile/resilient/flexible

� Lifelong learner

� Able to put problems in their socio-technical and operational context

� Adaptive leader

To succeed� Attract best and brightest with

a forward-looking educational

experience – Phase II.

� Educate them to be ready:

�To implement new technology.

�To focus on innovation.

�To understand global trends.

Thoughts from the Phase II summit

� Some needs have not changed:

� A sound grounding in science

� The learning experience of great lectures

� Studio experiences with open-ended problem solving

� Other things have really changed:

� Access to IT creates challenge of coupling deep learning

with instant gratification

� Means and ends of using computers to bring the world to

campus and enrich learning

� Design tools and sophisticated instruments that enable

students to experience the excitement of engineering

Charles Vest

Thoughts from the Phase II summit

� Research/co-op experience with real problems

� Experience with real-world tools and teams

� Encourage and recognize diversity

� Social, ethical aspects of engineering

�What students need to learn instead of what

we want to teach

� Creative and practical thinking

Arden Bement

Highlights from Phase II summit

� Break out of the present mold

� Education, not just curriculum

� Career, not just jobs

�Multiple models, not just one

� Leadership, not just teamwork

�More coordination with industry

� Cross-disciplinary emphasis

More highlights from Phase II summit

� Emphasis on innovation

� Systems approach

� Larger context for engineering

and technology

� Non-engineering career tracks

� Global perspective

�Market forces, macroeconomics

� Sense of urgency

+References   The National Academies Summit on America’s

Energy Future: Summary of a Meeting, National Research Council, 2008

http://www.nap.edu/catalog/12450.html

  Electricity from Renewable Resources: Status, Prospects, and Impediments, National Research Council, 2009

http://www.nap.edu/catalog/12619.html

  J. B. Cruz, Jr., R. R. Tan, A. B. Culaba, J-A. Ballacillo, “A Dynamic Input-Output Model foe Nascent Bioenergy Supply Chains,” Applied Energy, 2009.

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+References

  The Engineer of 2020: Visions of Engineering in the New Century, National Academy of Engineering, 2004.

  Educating the Engineer of 2020: Adapting Engineering Education to the New Century, National Academy of Engineering, 2005.

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