Summary Presentation: Systems Engineering …smartgridcenter.tamu.edu/sgc/doc/SGW_slides/16_25-16_50_Systems...Summary Presentation: Systems Engineering Research in Energy. Ding, Gautam,

Summary Presentation: Systems Engineering Research in Energy

Ding, Gautam, Gibson, Huang, Johnson, Moreno-Centeno

April 17, 2013

About usYu Ding,Professor of Industrial & Systems Eng. and of Electrical & Computer Engineering

Natarajan Gautam,Associate Professor of Industrial & Systems Eng. and of Electrical & Computer Engineering

Rick Gibson,Associate Professor of Geology & Geophysics

Jianhua Huang,Professor of Statistics

Andy Johnson,Associate Professor of Industrial & Systems Engineering

Erick Moreno-Centeno,Assistant Professor of Industrial & Systems Engineering

Systems Engineering Focus

Statistics &Data Analytics

Optimi-zationMethodologies

& Tool Sets

Stochasticprocesses

Economics & Game Theory

Geophysics Modeling

ProblemsAddressed

• System-level models and performance metrics;• Predictive models and uncertainty quantification;• Production, economics, social assessment;• Large-scale optimization for decision making.

Systems Engineering Impact

Wind turbine reliability& maintenance

Robust adaptivetopology control

Data center information & resource management

Data acquisition design for hydrocarbon exploration

Regulation for distribution costs and SO2 and NOx emissions

Yu Ding & Jianhua Huang• Expertise

– Machine learning and data analytics;– Big Data applications to energy systems;– Quality, reliability, and maintenance engineering.

[email protected] [email protected]

Wind speed (m/s)

Low production efficiency

High production efficiency

Pow

er o

utpu

t (K

W)

• Estimate the endogenous power curve:– A system-level performance metric for turbine performance assessment;– Enhance wind power prediction.

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Yu Ding & Jianhua Huang

• Multi-fidelity modeling of localized wind field:– Turbine anemometers: in-situ but not calibrated; more than 20% error.– Mast anemometers: calibrated but lack spatial resolution.– Impact: characterization of the wake effect, modeling of turbine

interactions, and enhancement in wind power prediction.

Met mastTurbine

16.5 miles

5.7

mile

s

Natarajan Gautam• Expertise

– Stochastic systems: analysis and control;– Queuing models;– Networks and optimization.

[email protected]

Cryogenic tunnel freezerDynamically adjust cryogen level based on sensed loadEnergy savings: 20-60%

• Applications

Data CentersWith L. NtaimoVirtualization, cluster sizing, voltage/freq. scalingIT energy savings: 25-50%

Underwater SensorsWith R. GibsonRouting and battery changeCost savings: 60%

Natarajan Gautam• Future work

sensors

actuators

Building

Building’s state

Control SystemOptimal

lighting level

Historical weather data + prediction

Controlling energy consumption in buildings

Stochastic models for hour-ahead solar power predictions

Tracking customer behavior in smart-metered systems

Ener

gy U

sage

Low

High

Normalized Time

SmartMeterinstalled

Applied for rebate

Ideal Customer

Oblivious Customer

Inconsistent Customer

Energy-conscious Customer

Richard Gibson• Expertise

– Modeling of seismic wave propagation;– Seismic reservoir characterization;– Seismic data acquisition design and optimization.

[email protected]

Seismic survey designQuantitative, model-basedmeasures of image quality;assess effects of knownsubsurface structure

• Applications

Numerical modelingWith Y. EfendievMultiscale finite elementmethods; simulations ofhighly heterogeneous media

VOI-Seismic AcquisitionWith E. Bickel

Quantify value of competingseismic acquisition tech-nologies

Richard Gibson• Cableless acquisition of seismic data is growing because of recent

technological advances:− Facilitates acquisition in areas with environmental concerns, difficult

terrain, or human populations − Challenges exist in developing systems with real-time access to data

Impact: high quality data in difficult environments

Real-time data access• drive-by or fly-by• rib and backbone architecture

(radio/fiber optic)• potential optimizations of

retrievalGeophone, power, storage

May bury instrumentsin case of wildlife!

Andrew Johnson• Expertise

– Regulation of • distribution prices;• SO2 and NOx generation in coal power plants

– Productivity and efficiency analysis.

[email protected]

Distribution Cost RegulationQuantitative, model-based measures of efficient cost; Control for operating environment

• Applications

Estimating marginal abatement costsConsider multiple pollutant and abatement processes to quantify the cost associated with lower pollution levels

Andrew JohnsonOur research group developed the model currently being used to regulate 86 regional distributors in Finland for the 2012-2015 regulation period

An efficient isocost surface for electricity distribution in Finland based on 2010 data

Map of distribution regions in Finland

Erick Moreno-Centeno• Expertise

– Computational optimization and combinatorial optimization;– Network flows;– Integer programming.

[email protected]

• Applications:− Optimization of the smart grid;− Efficient algorithms for (Big) Data mining.

• Corrective Topology controlNew

Paradigms

• Practical• Effective• Efficient

OptimizationAlgorithms

Load Shed

Demandfully met

Erick Moreno-Centeno• Practical, effective and efficient algorithm for corrective topology

control:− Given an N-2 contingency prevents significantly more load shed than

traditional control,− Finds near-optimal topology two orders of magnitude faster than best

known tool.− Impact: FASTER load recovery and MORE load shed prevented.

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MIP_HMFDP (k)MDFP (Best)

% o

f Loa

d Sh

ed P

reve

nted

10 20 30 40 50 60Minutes from contingency

with Topology control (1 switch / 10 min)no Topology control (pure re-dispatch)Absolute best with no Topology Control

SE in Energy: Summary

− Modeling strengths: • System level; • Stochasticity and uncertainty; • Physical-natural-societal interactions;

− Solution techniques: • Large-scale optimization; • Machine learning and data mining;• Queuing networks; • Computational methods;

− Impacts: • Reliability of energy systems;• Efficiency and cost of energy production;• Timeliness and quality of decision making.