Retrospective: Three Years of Grid ProgramsJosh GouldTechnology to Market

October 6, 2015

Agenda

‣Facts

‣Analysis

‣Conclusions

‣Special thanks

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Emerging Grid Challenges

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– Increasing wind and solar generation

– Decentralization of generation– Aging infrastructure– Changing demand profiles– Increasing natural gas generation– Cybersecurity threats

‣All of these challenges require greater power system flexibility

Evolution of Grid Requirements

GRIDModernization

Affordable

Safe

Accessible

Reliable

Clean

Secure

Resilient

Flexible

Federal Power Act 1930s

Blackouts1960s

Oil Embargo, Environmental concerns

1970s

9/11Stuxnet2000s

HurricanesKatrina, Sandy

Polar Vortex2010s

Increasing Dynamicsand Uncertainty

2020s

Source: Adapted from DOE Grid Tech Team

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Responsive Demands– Scheduling large loads (e.g. industrial)– Mobilize large numbers of small assets

Power Flow Controllers– AC Power Flow Controllers– High Voltage DC Systems

Energy Storage Optimization– Scheduling energy flows– Coordination of diverse storage assets– Stochastic optimization of generation

Transmission Topology Optimization– Optimal line switching– Corrective switching actions

‣ Advances in power electronics, computational technologies, and mathematics offer new opportunities for optimizing grid power flows

New Potential Sources of Network Flexibility

Project Categories• Power Flow Controllers

– Power flow controllers for meshed AC grids– Multi-terminal HVDC network technologies

• Grid Control Architectures– Optimization of power grid operation; incorporation of uncertainty into operations;

distributed control and increasing customer optimization

GoalsKickoff Year 2011

Projects 15Total Investment $39 Million

Program Director

Tim Heidel(Rajeev Ram)

GENI ProgramGreen Electricity Network Integration

• Enable 40% variable generation penetration

• > 10x reduction in power flow control hardware (target < $0.04/W)

• > 4x reduction in HVDC terminal/line cost relative to state-of-the-art

GENI Portfolio

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HVDCEPR Cable

HVDC Multi-terminal Network Converters

DC Breaker

HVDC Hardware

Power Flow Control Hardware

Cloud Computing & Big Data

Topology Control (Line Switching)

Distributed Series Reactor

Magnetic Amplifier

Economic Optimization Contingency Management

Demand Response

Resilient Cloud / Data Replication

Compact Dynamic Phase Angle Regulator

Transformerless UPFC

Power System Optimization

Stochastic Unit Commitment

Distributed Grid Optimization (Prosumers)

Energy Storage OptimizationAC-OPF

GENI Related Projects• University of Illinois Urbana-Champaign

• “Cyber-Physical Modeling and Analysis for a Smart and Resilient Grid”• Pacific Northwest National Laboratory

• “Non-Wire Methods for Transmission Congestion Management through Predictive Simulation and Optimization”

• University of California Berkeley• “Micro-Synchrophasors for Distribution Systems”

OPEN 2012 OPEN FUNDING SOLICITATIONS FOR ALL ENERGY TECHNOLOGY AREAS

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Summary

*Source: Crunchbase

• 19 total grid projects (many more non-grid projects, not discussed here)

• >$50M in total ARPA-E funding, including plus-ups

• 11 projects complete, 1 eliminated, 7 active

• $98M in venture capital raised* – • Smart Wires: $46M• Varentec: $29M• AutoGrid: $22M

• Three new companies formed:• Boston University NewGrid, Inc.• Caltech Energy Adaptive Networks (EAN)• Georgia Tech ProsumerGrid, Inc.

• Dozens of partnerships, many pilots, some commercial deployments (AutoGrid, Smart Wires, UC Berkeley Open 2012)

Agenda

‣Facts

‣Analysis

‣Conclusions

‣Special thanks

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Analysis sought to address: What are common characteristics of success?• Ran multiple linear regression analysis with project impact as dependent

variable, 20 other factors as independent variables

• Values on a 1 – 10 scale: For instance, 1=no impact, 10=industry-changing impact

• Take analysis with the appropriate “grain of salt”: • Small sample size (n=19)• Some subjectivity in criteria (i.e., not using an “objective” or quantitative

score for a independent variable such as motivation)• Haven’t thought of everything – likely important variables we’re missing

• Raw data on the P drive so welcome to double-check or undertake further analyses (note: need “stat plus” Excel add-on for those with Macs)

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#1: Team Motivation

0 1 2 3 4 5 6 7 8 9 1001

2

34

56

7

89

10

Motivation

Impa

ctTeam motivation by far the most significant predictor of impact (R^2=.97)

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#2: Team Communication

0 1 2 3 4 5 6 7 8 9 1001

2

34

56

7

89

10

Communication

Impa

ct

Ability of team to communicate internally, and with external stakeholders (R^2=.71)

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#3: Active Listening

0 1 2 3 4 5 6 7 8 9 1001

2

34

56

7

89

10

Listening

Impa

ctWillingness and ability to listening critically to external stakeholders (e.g.,

customers, partners, ARPA-E, etc.) (R^2=.55)

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#4: Organizational Buy-In

0 1 2 3 4 5 6 7 8 9 100123456789

10

Org Buy In (10=totally convinced, 1=completely skeptical)

Impa

ct

To what extent are the organization(s) behind project teams bought in to the ARPA-E project and its goals? (R^2=.53)

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What were NOT predictors of success?

1 2 3 4 5 6 7 8 9 1001

2

34

56

7

89

10

Tech Risk

Impa

ct

Surprisingly, the extent of technical risk was not a significant predictor of impact (R^2=.1)

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What else were not predictors of success?Entering a market with a few dominant competitors was not a major impediment

(R^2=-.22)

2 3 4 5 6 7 8 9 100123456789

10

Dominant Competitors (10=few dominant, 0=highly diffuse)

Impa

ct

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Interesting questions the analysis doesn’t answer

1. How different would these results look on a larger sample size?

2. To what extent are the results characteristic of grid projects only? (strongly suspect the answer is “no” but cannot prove it)

3. Would the results look different if measured at different project stages?

4. “Policy” question: To what extent – if at all – should measuring predictors of success affect the selection and screening process?

Agenda

‣Facts

‣Analysis

‣Conclusions

‣Special thanks

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Conclusion #1: Performer commitment key ingredient for impact• General rule: If ARPA-E cares more about the fate of your project than you, you

will fail

0 1 2 3 4 5 6 7 8 9 1001

2

34

56

7

89

10

Motivation

Impa

ct

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Conclusion #2: Commitment could be a function of incentives• Where project participants stand to reap a clear and direct benefit from making

an impact, they tend to be more motivated

• Conversely, institutions that dis-incent activities conducive to impact also tend to get what they incent

• Important note: Even within institution types, incentives can be different (e.g., all universities do not have the same incentives, support structure, tech transfer staff, etc.)

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Conclusion #3: Values matter and are related to results• Incentives are – to some extent – a measure of values

• Values guide:• How people spend their time• What is rewarded and respected at a given institution• What (and who) is accorded status and respect

• Since communication and active listening are strong predictors of organizational success, hypothesis of good organizational values would include (but not be limited to):

• Active listening: We communicate better when all voices are heard. But active listening is not a synonym for “hearing.”’ This involves the willingness and courage to seek out differing opinions, and ensure those opinions are understood

• Respect for diversity: Active listening becomes more valuable when it’s informed by different types of people. Not just the obvious “identity” categories, but also diversity of experience, skills, academic backgrounds and institutions, network, knowledge, etc.

• Constructive confrontation: Active listening is not a synonym for “consensus.” Effective communication should highlight (and encourage) differences of opinion

Conclusion #4: Building relationships with the right people, with the right values…takes time and effort

‣ Unusual relationship breadth and depth necessary for new grid technology adoption‣ ARPA-E filled gaps for those teams who lacked these relationships‣ If effectively transitioned to new staff, relationships will continue to yield benefits to ARPA-E and its

performers

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Conclusion #5: We can’t do it all ourselves

Program Description‣ Month-long commercialization bootcamp for NSF-funded

academics‣ Highly rigorous, demanding, intensive, completed on-site‣ Taught by seasoned entrepreneurs and business peopleGENI Relationship‣ Supported ARPA-E signing Memorandum of Understanding

(MOU) with NSF to allow for ARPA-E I-Corps participation‣ Influenced curriculum development ‣ One GENI team (Georgia Tech) completed program, another

considering applyingOutcome‣ Team once disinterested in commercialization formed a

company, won $100K ACC business plan competition‣ Built customer relationships, business skills, and confidence

in team with virtually no prior commercial experience

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Takeaway: Third party resources and relationships were a critical input for commercial outcomes. They acted as a “force multiplier”, imparting skills and expertise in a highly intensive, hands-on way

Program Description‣ Run by Berkeley’s Haas business school, promising early

technologies are matched to teams of cross-disciplinary grad students who assess its technical and commercial viability

‣ Accompanied by formal coursework taught by industry experts and seasoned entrepreneurs

‣ Result is 1,000 hours of technology evaluation and market assessment for each project with final report and presentation

GENI Relationship‣ Pre-existing relationship between Josh Gould and C2M

principals‣ One grid-related Open FOA project participated (2013), as did

Caltech project (2014)Outcome‣ Team once disinterested in commercialization formed a

company, hired world class CEO, raised funding‣ Built customer relationships, market understanding, and

extremely influential group of mentors some of whom now serve on company board

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Conclusion #6: ARPA-E is a service organization

• Retrospective analysis begs the question: Why do we work at ARPA-E? What purpose do we hope to serve – individually and collectively – beyond what was enunciated by Congress?

• While personal motivations will always be present (e.g., what ARPA-E does for you), we’re at our best as a service organization

• That is, we provide a service to multiple constituents (customers):• The American taxpayer• Congress• But most importantly, the performer

• Where we serve performers by doing all we can to maximize their chance of impact (or eliminating those with no such chance), we do well by all the constituents above, and ourselves

• Service mindset/outlook helps reinforce values and culture

Agenda

‣Facts

‣Analysis

‣Conclusions

‣Special thanks

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Special Thanks

ARPA-E GENI Team

Rajeev RamProgram Director

Tim HeidelProgram Director

Paul BasolaProgram Support

Colin SchauderTechnical Support

Sameh ElsharkawyTechnical Support

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Special Thanks

T2M Pioneers

Cheryl Martin Ilan Gur

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Please stay in touch

“You don’t have to say yes, but you always take the call” Peder Maarberg on what it means to be an ARPA-E Alum

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www.arpa-e.energy.gov

December 2013

June2014

Program Timeline & Selected T2M Highlights

December 2011

July 2012

October 2012

November 2012

December 2012

June 2012

Josh Gould hired: Full-time

tech-to-market support

January2015

April 2012

Rajeev Ram leaves, transitions

to Tim Heidel

SWG milestones re-negotiated

SWG deploys at TVA

Autogrid raises $9M

SWG raises $10M

Program Launch

February 2013

1st GENI annual meeting

March 2013

Autogrid E.ON intro @ Summit

2nd annual GENI meeting

April2014

Autogrid raises $13M from e.on,

“graduates” ARPA-E, Georgia

Tech spin-out raises $100K

Bill Gates invests in Varentec

November2014

SWG raises $18M, “graduates”

Final GENI Annual Meeting

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Techno-Economic Analysis Unique for Grid

fImportance• Industry adoption requires demonstrating results on real, sizable power system (10K

bus)• Open, free, publicly available power systems (IEEE 118 bus) not credible to industry

fChallenges• Real power systems extremely difficult to obtain due to legal, security & privacy

concerns• Utility-provided models flawed (substantial bad data) • Therefore, significant time (1 year) & money (up to $1M) required to produce techno-

economic models with commercial relevance

fOur Strategy• Scale: Address industry relevance in program metrics by requiring results on 10K

nodes• External expertise: Commission EPRI to do a comparative study of power flow

control devices on real power systems• New program: Grid Data will develop real, publicly available power systems

FOA Targets (Power Flow Controllers)

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Primary Technical Targets (from Original Funding Opportunity Announcement)TEST BED: Minimum of 3 controllers/terminals connected on a small-scale mesh with a minimum of 5 nodes. Terminals configured for operation at > 10kV. (Individual elements tested at relevant transmission level voltages (>100kV).)

RESILIENCY: Protocol for testing the resiliency and stability of the interconnected controllersBI-DIRECTIONAL FLOW CONTROL: Software controls with simulated latency used to demonstrate full bi-directional control of real and reactive power flowsHIGH EFFICIENCY: Conversion efficiency of controllers/terminals must be > 99%

COMMERCIAL FEASIBILITY: A cost-benefit analysis for a single controlled link using the proposed technology on the transmission grid is requiredAC MESH CONTROLLERS: >10x reductions in cost (target cost < $0.04/W)

MULTI-TERMINAL HVDC CONTROLLERS: >4x reductions in terminal and line cost

FOA Targets (Grid Control Architectures)

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Primary Technical Targets (from Original Funding Opportunity Announcement)

SCALABILITY: Capable of managing large dynamical systems (>10,000 nodes)

VALIDATION: Real-world datasets supplied by transmission operators or utilities

FEASIBILITY: Consideration of sensing, communications, computational, and actuation (ramp and dispatch) challenges for implementation in “real-time” markets

FAILSAFE: Designs where a safe, “dumb” operation occurs in the event of local or wide- area failure or attack

Power Flow Control Power flow control: the ability to change

the way that power flows through the grid by actuating line switching hardware or by controlling high voltage devices connected in series or in shunt with transmission lines

‣ Power flow control includes the ability to:– Control the impedance on a major transmission line– Inject a controlled voltage in series with a line– Provide reactive voltage support for long lines so that they can

be loaded to their thermal limits– Switch line circuit breakers to redirect power to other lines

35Map: U.S. Department of Energy Office of Science and Technology osti.gov

Significant Effort to Build 3rd Party relationships Necessary for Success on Grid

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Every GENI performer needs buy-off from multiple constituents before commercial deployment

Regulators Utilities & RTO/ISO

Financiers Big Companies

Others

Description • Public Utility Commissions (PUCs)

• NERC• FERC• Local regulatory

groups

• Utilities include co-ops, munis, IOUs

• RTO/ISOs coordinate between utilities and Co-ops in a given region

• Project finance• Venture capital (VC)• State agencies

(NYSERDA, CEC)• Other financing

mechanisms (debt, angels, government, crowd-funding)

• GE, IBM, S&C, SPX• European &

Japanese: ABB, Alstom, Mitsubishi, Mitsui, Sony, Schneider

• Consultants• EPRI• EEI• Thought leaders• Government

(EERE, DOE, DOD, IEEE)

• Law firms• Management talent

Why they matter

Influence utility & RTO purchasing decisions, set priorities and timing for deployment of grid technologies

Most utilities & RTOs have monopoly on technology deployment in certain regions of the grid. Bestow legitimacy on vendors, low cost of capital, big balance sheet

Finance young companies and/or finance deployment of technology (e.g., project finance)

Ownership of channels, customer relationships, big balance sheets, looking for growth opportunities, innovation challenged

Can assist ARPA-E teams during cooperative agreement, provide network for handoffs, fill in key management team gaps

What they need

• Data on safety, effectiveness and cost culminating in rate-basing

• Safety and reliability data

• Clear path for PUC approval

• Buy off from engineers and businesspeople

• Confidence that a team, business and technology can meet their goals for financial returns

• Integration into or extension of current products

• Buy off from technical and business team

• IP

• Compelling reason to be involved

• The right fit with performer

• Security, reliability, economic development, industry growth

Time required

1 – 5 years 1 – 5 years Weeks – Years Months – Years Varies