Applying ISI Capabilities to Energy Challenges

Gordon RoeslerISI Directors’ Offsite12-13 January 2010

2

The Century of Scarcity

Food

Water

Environment Energy

UN World Population Projection2008 Revision

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

1980 2000 2020 2040 2060

Median

High

Low

constant fertility

3

ISI’s Strengths Fit a Gap in Energy Research

“Supersystems”, system-of-systems— Physical models of very complex distribution system, i.e.

“the grid” (smart or not)— Machine learning/artificial intelligence for improved real-

time control— Communications network(s) interwoven with physical

network— Fault-tolerant real-time computing

Decision support and system engineering— Data mining— Pre-deployment: complex decision space— Post-deployment: operator support, visualization,

pattern recognition

4

Grid reliability: a deep intellectual challenge

2003: Local conditions cascaded to blackout— 50 million customers dropped— 61.8 GW power tripped off— Economic cost $4-10B

General causes— Cascading line tripping by dynamic line loading

often leads to unsuccessful line restoration attempts

— Cascading equipment tripping by overexcitation— Loss of synchronism due to angle instability— Oscillatory instability causing self-exciting inter-

area oscillations— Exceeding allowed frequency range (over and

under frequency) due to imbalance in active power between generation and load

— Voltage instability / collapse

5

ISI Opportunities to Enhance Grid Reliability

2003 Outage Task Force selected recommendations— “prudent expenditures for…reliability (including investments in

new technologies)”— “DoE should expand…research programs on reliability-related

tools and technologies”— “Improve…training and certification”— “Better real-time tools for operators and reliability

coordinators”— “Improve quality of system modeling data”— “Require use of time-synchronized data recorders”— “Clarify criteria for identification of operationally critical

facilities”— “Develop corporate-level IT security governance and

strategies”— “Improve IT forensic and diagnostic capabilities”

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Phasor measurement units:

the holy grail?40 PMUs installed as of

2005SCE installing 70 more over

next 18 months (goal: one at each substation)

Objective: perfect knowledge of phases linear transform to determine loads and sources

Too few PMUs under-determined problem

Algorithms for real-time control not developed

GPSline

As of 2005

ISI’s capabilities in data mining, modeling, and artificial intelligence could advance PMU use for real-time control

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Human issues related to grid reliability

Operators were ineffective at all stages of 2003 blackout— Inadequate visibility into long-range state— Displays and indications confusing— Inadequate training and simulation

Increase in automation to date at grid control centers has increased operator overload— Need to distil information— Build machine learning into warning systems

50% of transmission & distribution workforce is eligible for retirement NOW— Knowledge capture— Training of new work force— Automated backups

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Opportunities in discussion with potential

sponsorsGrid reliability

— Utility discussions, may grow to regional alliance

Disaster planning and recovery— Possible partnership with CalTech (earthquake warning)— Possible role in contingency planning for wave demo site

Renewables— Congressional (wave energy)— Buoyant energy storage (with U. Hawaii, conceptual)— Analyze impact on grid stability

Workforce training, operator assistance— Utility discussions

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Formal proposals

Agent-based control of next-generation Naval ship electric plants— ONR Energy BAA— Teamed with West Virginia U.

Energy distribution for autonomous maritime surveillance— DARPA STO— Develops SPIDR in new domain

Existing Optimizati

onEngineRules and

constraints

UUVs

harvesting

Storage, structure,

etc.N best

feasible designs

PM, SMEs

Missions

Optimizationpreferences

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USC University Park Campus Smart Grid

Project

$120M project led by LADWP, with participation of:— USC Energy Institute: overall

university lead; demand response; behavioral studies

— ISI: cyber security; demand response; modeling; project coordination

— UCLA: electric vehicle aspects

90 day preliminary planning phase commenced Jan. 1

PHASE – I[ 1Q/2010 –3Q/2010 ]

PHASE – III[ 2011 –2014 ]

PHASE – IV[ 2015 &

BEYOND ]

1. DEMAND RESPONSE (DR)

2. BEHAVIORAL STUDIES

3. CYBER-SECURITY (CS)

4. ELECTRICAL VEHICLES (EV)

FOUR INITIATIVES FOR THE LADWP SMART GRID PROJECT DEMONSTRATION

LONG-TERM DEVELOPMENT & SUPPORT (TBD)

1. SHORT-RANGE DR

1. LONG-RANGE DRLADWP PLANNING

3. SHORT-RANGE CS

3. LONG-RANGE CS

2. SHORT-RANGE BEHAVIORAL STUDIES

2. LONG-RANGE BEHAVIORAL STUDIES

4. ELECTRICAL VEHICLES

START

START & COMPLETE

END START END END

LONG-TERM SUPPORT

TASKS SUPPORTING FOUR INITIATIVES

ISI CANDIDATE TASKS PERFORMED COOPERATIVELY WITH USC RESEARCHERS

NON-ISI TASKSSYSTEM INTEGRATION OF USC & ISI ACTIVITIES

PHASE – II[ 3Q/2010 –1Q/2011 ]

DECISION POINTGO/NO-GO MILESTONE 1

DECISION POINTGO/NO-GO MILESTONE 2

ISI MAIN CANDIDATE TASKS

PHASE – I[ 1Q/2010 –3Q/2010 ]

PHASE – III[ 2011 –2014 ]

PHASE – IV[ 2015 &

BEYOND ]

1. DEMAND RESPONSE (DR)

2. BEHAVIORAL STUDIES

3. CYBER-SECURITY (CS)

4. ELECTRICAL VEHICLES (EV)

FOUR INITIATIVES FOR THE LADWP SMART GRID PROJECT DEMONSTRATION

LONG-TERM DEVELOPMENT & SUPPORT (TBD)

1. SHORT-RANGE DR

1. LONG-RANGE DRLADWP PLANNING

3. SHORT-RANGE CS

3. LONG-RANGE CS

2. SHORT-RANGE BEHAVIORAL STUDIES

2. LONG-RANGE BEHAVIORAL STUDIES

4. ELECTRICAL VEHICLES

START

START & COMPLETE

END START END END

LONG-TERM SUPPORT

TASKS SUPPORTING FOUR INITIATIVES

ISI CANDIDATE TASKS PERFORMED COOPERATIVELY WITH USC RESEARCHERS

NON-ISI TASKSSYSTEM INTEGRATION OF USC & ISI ACTIVITIES

PHASE – II[ 3Q/2010 –1Q/2011 ]

DECISION POINTGO/NO-GO MILESTONE 1

DECISION POINTGO/NO-GO MILESTONE 2

ISI MAIN CANDIDATE TASKS