s. keshav technology...

LECTURE 2/3: SMART GRIDS TECHNOLOGY OVERVIEW

S. KeshavUniversity of Waterloo

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TOPICS

Problems with today’s electrical grid

Smart grid technologies Solar energy Storage

Some smart grid research areas

Internet vs. grid

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TODAY’S ELECTRICAL GRID

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MOSTLY DIRTY…

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capacity

OVERPROVISIONED BY DESIGN

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INEFFICIENT

5% better efficiency of US grid

= zero emission from 53 million cars

6http://www.oe.energy.gov/

OSSIFIED

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Post-war distribution infrastructure is reaching EOL

UNEVENLY DISTRIBUTED

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https://yearbook.enerdata.net/#world-electricity-production-map-graph-and-data.html

China’s population > 4 X USA’s population

POORLY MEASURED

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POORLY CONTROLLED

Electrons are not addressible

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WITH LITTLE STORAGE

11http://ieso-public.sharepoint.com/

SMART GRID VISION

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Source: European Technology Platform Vision Document

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Renewable generation to reduce carbon footprint


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Efficient management to reduce peak/average ratio


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Storage to decouple supply and demand


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Pervasive sensing, communication, control


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Self-contained ‘microgrids’ with energy transactions


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Heavy investment for grid deployment/renewal

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“The future is already here – it's just not evenly distributed.

The Economist, December 4, 2003”

― William Gibson

THE FUTURE IS HERE!

Portugal was 100% powered by renewables from May 7 to May 11, 2016

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100% RENEWABLE-POWERED CITIES

Burlington, USA

Vermont’s largest city

Wind, solar, hydro, and biomass

Reykjavik, Iceland

Hydropower and geothermal

All cars and public transit fossil-free by 2040

Basel, Switzerland

Own energy supply company

90% hydropower and 10% wind

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GETTING THERE

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BUILDING BLOCKS OF THE SMART GRIDNew energy technologyWind Solar Storage Electric vehicles

Digitalization Communication, computation, sensing, control

Transactive energy Based on blockchain

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WIND

25“Revolution Now,” US DOE Sept. 17. 2013

WIND POWER CENTS/KILOWATT-HOUR

INSTALLED CAPACITY (GW)

SOLAR

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Source: EvSales.blogspot.com

Cumulative EV sales

*Includes Battery as well as Hybrid Electric Vehicles

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PERVASIVE CONTROL IS A REALITY

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PERVASIVE COMPUTATION

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BUT MANY ISSUES REMAIN UNSOLVED

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1. STORAGE IS EXPENSIVE

Buying 1 KWh = 10c Storing 1 KWh = ~$250!

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2. TOO MANY DISTRIBUTED GENERATORS?

34US EIA

3. CONTROL OVER MANY TIME SCALES

35Jeff Taft, Cisco

4. COMPLEX CONTROL ARCHITECTURE

36Cisco

5. CONSUMERS HAVE NO INCENTIVE TO SAVE

Energy savings of 10% $10/month

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6. UTILITIES HAVE LITTLE INCENTIVE TO BE EFFICIENT!

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7. ENERGY DATA IS PERSONAL

8. SENSORS ARE ENERGY-LIMITED

9. EV SALES ARE TINY

EV fraction of vehicle fleet is less than 1% in 2019

A DEEPER DIVE INTO THREE BUILDING BLOCKS

Solar energy

Storage

Blockchain

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SOLAR ENERGY

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INSOLATION

1367.7 W/m2 in space

1000 W/m2 at sea level on a clear day

Typical level is 800-850 W/m2

Typical panel is 2m x 1m produces 275-310Wabout 20% efficient

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PRACTICAL CONSIDERATIONS

ShadowingTemperature ~0.5% decline per degree over 25C

Age ~0.5% decline per year

Tilt angle ~10% reduction if flat

Orientation fixed vs. tracking

Wind load

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GRID INTERTIE

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INVERTER

DC -> AC

A “smart inverter” will cut off the panel if voltage exceeds a limit

Can generate AC leading or laggingVAR support

A per-panel microinverteradds costbut prevents shading lossand provides per panel MPPT

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IV CURVES

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SOLAR CELL IV CURVE

51A PV cell acts as a current source, with voltage across load increasing linearly with the load resistance.

Increasing load resistance

MPPT

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An MPPT trackermaintains the effectiveload resistance at thevalue that maximizesthe power generated by the PV.

STORAGE

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AN OVERVIEW OF STORAGE

Basics

Applications

Storage system design

Modeling

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BASICS

Storage stores energy (like bits in a hard drive) Measured in Joules or Watt-hours

Rate at which energy is drawn or stored is power

Power is measured in Watts (like bits/sec)

Energy = power * time 1 Joule = 1 Watt * 1 second 1 kWh = 1000 W * 3600 s = 3.6 million Joules

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“Bytes”

“Bits/s”

TYPES

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POWER VS. ENERGY (RAGONE CHART)

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3

7

STORAGE: APPLICATIONS

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INSIGHT

Storage decouples supply and demand

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ABSTRACT MODELStorage reshapes

S(t): Input variable power

to

D(t): Desirable output power

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11

S(t)

D(t)

603

APPLICATIONS

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REDUCING CURTAILMENT FROM A SOLAR FARM

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Pmax

C

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SELF CONSUMPTION

Germany Trade and Invest 2014

HOW DO WE SIZE?

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https://www.tesla.com/en_CA/powerwall

What is the cheapest combination of solar PV and battery sizes that will achieve a target loss-of-load probability (LOLP)?

STORAGE SYSTEM DESIGN

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Example objectives

• Target loss of power probability

• Target waste of power probability

• Maximizing overall revenue

• Minimizing carbon footprint

STORAGE SYSTEM DESIGN• Offline Design

• Choice of elements• elements of the matching systems

• Sizing of each element

• Operation

• control rules

Energy matching system

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STORAGE OPERATION

When to charge?

What source to charge from?

How much to charge?

When to discharge?

How much to discharge?

What load to discharge to?

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THE TROUBLESOME COUPLING

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Sizing Operation

Choice of technologies

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CostBatteryParams

Perf.tartget

STORAGE MODELING

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‘IMPERFECTIONS’

Size-dependent• Maximum charge/discharge rates• Power capacity• Voltage limits (translates to energy limits)

Size-independent• Round-trip efficiency • Leakage

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MODELING STORAGE

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MODEL

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CYCLE LIFE PERFORMANCE(US18650VC3)

Charge: 23deg.C, 4.2V, 1.9A(CC/CV), 100mA cut Discharge: 23deg.C, 10A, 2.5V cut off rest 0.5h

SMART GRID RESEARCH

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OVERVIEW

Methodology

Areas

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METHODOLOGY

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1. DECIDE SYSTEM GOALS

Scalability

Reliability

Stability

Robustness

Backward-compatibility

OptimalityChoice of objective function is critical

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2. OPTIMIZATION OBJECTIVES

Minimize costCapital expenditure (capex)Operational expenditure (opex)

Energy useMinimize energy useMinimize energy > threshold

Power useMimimize peakMinimize averageMaximize ‘flatness’

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time

power

threshold

OPTIMIZATION OBJECTIVES II

Maximize comfortThermal comfortLighting

Multiple objectivesNeed to trade off one for the other“Free” gains from elasticity and efficiency

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3. EXPLOITING LOAD FLEXIBILITY

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Time shift with same energy

time

power

3. EXPLOITING ELASTICITY

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Peak reduction with same energy

time

power

3. EXPLOITING REDUCTION

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Peak reduction with reduction in energy

time

power

3. EXPLOITING DISCOMFORT

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cost

comfort

minimum comfort

efficiencygains

optimalcost

“S” or logisticscurve

4. CONTROL CHOICES

One-time (provisioning) Choice of elements Sizing

Ongoing (operational) Direct Setpoint

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4. CONTROL CHOICES II

Locus of control Centralized Distributed Market-based (incentive-compatible)

Frequency of control One-time Repeated (dynamic)

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5. THEORETICAL BASES

Optimization Linear Non-linear/heuristic

Control theory

Game theory

Queueing theory

Soft computing (neural networks)

Statistical machine learning

Network calculus/Stochastic network calculus

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Obtain dataset

Problem formulation Analysis Insights

Data miningMachine learningBig data analyticsWhat-if analysisSimulation

Effect of new technologies

6. DATA-DRIVEN APPROACH

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SOME PROBLEM AREAS

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THE AREAS

A. Demand response

B. Storage

C. Distributed generation

D. Microgrids

E. Electric vehicles

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A. DEMAND RESPONSE

Classically, generators follow load

DR: incentive structure to persuade grid users to reduce peak demandsflatter peak-to-average ratio reduces capexnot using peaking generators reduces both opex and carbonfootprint

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LOAD SHAPING

93Exploits elasticity

APPROACHES

Congestion pricing time-of-usereal-time

Condition for connectionEVsDeveloping countries

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CHALLENGES

How to pass on savings to users?

Can’t have human in loop too annoying too slow

Complex interactions with user comfort

Potentially perverse effectswith elastic loads, peak follows lower prices

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B. STORAGE SYSTEM DESIGN AND ANALYSIS

Potentially changes character of gridbecomes like natural gas or water

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WHERE TO PLACE STORAGE?

Generationevens out renewables

Transmissionreduces line capacities

Distributionreduces sizing

Home/business tariff reduction

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CHALLENGES

Cost$400/KWh vs. $0.1/KW

Power vs. energy

Coupling of technology, sizing and operation

Exploiting new forms of storageProcess storageThermal storage

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THE TROUBLESOME COUPLING

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6

Sizing Operation

Choice of technologies

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CostBatteryParams

Perf.tartget

C. DISTRIBUTED GENERATION INTEGRATION

Integration of renewable energy sources into the gridWindSolarMicro-hydroWave

For both large and small installations

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CHALLENGES

Many small-scale sources instead of a few large-scale sources

Sources are inherently stochasticAggregate behaviour is complex

Sources are intermittentNeed to be ‘firmed up’What is their correlation structure?

Sources are at edges rather than at coreViolates assumption of one-way flow

Access capacity constraints101

D. MICROGRIDS

Contextsremote rural areasminingdeveloping countries

Reduces transmission losses and carbon footprint

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PROBLEM OVERVIEW

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Wind

Solar

Diesel

Grid

Storage

Load

Storage

PROBLEM FORMULATION

Use local generation and storage to be self-sufficientgeneration can be renewable or a diesel genset

Goal is to minimize capex and opexsizingoperation rules

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APPROACHES

Classic stochastic optimization problemgeneration and loads are unpredictable

Numerical analysis

Mathematical modeling

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CHALLENGES

Complexity arises due to interaction betweenuser comfortdemand responsecorrelated loadshidden costs fuel shipmentfuture diesel costcarbon footprintCharacterizing tail of outage probability distribution

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E ELECTRIC VEHICLES

Significant load1 EV = 3 peaking homes

Significant storage (V2G)1 EV battery = 1 US home for 1 day

Significant barriers to adoptionrange anxietyhigh capex outweighs low opex

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KEY PROBLEMS

How to control charging? to avoid overload

Integration into microgriddealing with mobility

Where to place chargers? to extend range

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CHALLENGES

V2G is currently inefficient

Charge/discharge cycle reduces battery life

Charging and charger placement depends on mobility and ‘stationarity’ patterns unpredictable?

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INSPIRATION FROM THE INTERNET

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S. Keshav and C. Rosenberg, How Internet Concepts and Technologies Can Help Green and Smarten the Electrical Grid, CCR, January 2011.

GRID AND INTERNET

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Grid

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Electrons

Load

Transmission line

Battery/energy store

Demand response

Transmission network

Distribution network

Stochastic generator

Internet

= Bits

= Source

= Communication link

= Buffer

= Congestion control

= Tier 1 ISP

= Tier 2/3 ISP

= Variable bit rate source

DIFFERENCES

One-way vs. two-way

Grid has almost no storage

Can’t copy electrons!

Electrons not addressible

Generators have ramp rates

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Every trajectory on the LHS has an equivalent on the RHS

• can use teletraffic theory to study transformer sizing

O. Ardakanian, S. Keshav, and C. Rosenberg. On the Use of Teletraffic Theory in Power Distribution Systems, e-Energy ’12.

EQUIVALENCE THEOREM

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USING THE THEOREM

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“RAINBARREL” MODEL

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uncontrolledstochastic input

uncontrolledstochastic output

rangeWhat barrel size to avoidoverflow and underflow“with high probability”?

ENVELOPE IDEA

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lower envelope ≤ Σinput ≤ upper envelope

lower envelope ≤ Σ output ≤ upper envelope

Envelopes determine an equivalent constant rate and are computed from a dataset of trajectories

STOCHASTIC ENVELOPES

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P((Σinput - lower envelope) > x) = ae-x

P((upper envelope –Σinput) > x) = be-x

STOCHASTIC NETWORK CALCULUS

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Equivalence

Wang, Kai, et al. "A stochastic power network calculus for integrating renewable energy sources into the power grid.”Selected Areas in Communications, IEEE Journal on 30.6 (2012): 1037-1048.

EXAMPLE

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ELECTRICITY STORAGE

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HOW MUCH TO BUY?

STATE OF THE ART

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https://www.tesla.com/en_CA/powerwall

OUR APPROACH

Data-drivenFinds most economical combination to achieve a quality

of service target:

loss-of-load probability (LOLP)

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PracticalUses limited historical load and solar irradiance data

RobustConfidence in meeting

the LOLP target despite future being unknown

F. Kazhamiaka, C. Rosenberg and S. Keshav, "Practical Strategies for Storage Operation in Energy Systems: Design and Evaluation," IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1602-1610, Oct. 2016.

MATHEMATICAL PROGRAMMING

Inputs: Set of yearly solar traces (energy vs. time) Set of yearly load traces (energy vs. time)

Outputs: Number of panels: C (W) Size of storage: B (kWh)

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Power balance

Battery constraints

Loss-of-load constraint

Minimize system cost

PROBLEMS

Requires knowledge of the future

Sizing is not robust to slight changes in inputs

Need accurate yet simple models for storage

Problem is both non-linear and mixed-integer

126

F. Kazhamiaka, S. Keshav, C. Rosenberg, and K.-H. Pettinger, Simple Spec-Based Modelling of Lithium-Ion Batteries, IEEE Transactions on Energy Conversion, Vol 33, No. 4, December 2018.

AN ISOMORPHISM

O. Ardakanian, S. Keshav, and C. Rosenberg,On the Use of Teletraffic Theory in Power Distribution Systems, Proc. e-Energy, May 2012.

RESTATED PROBLEM

Given a load trace (departure process)Choose a buffer size

and a scaling factor

and a solar trace (unit arrival process)

to meet a target underflow probability

CUMULATIVE NET ARRIVAL PROCESS

_ =Cumulative net arrival process

Time

Wh

Depends on number of panels C and storage size B

Solar trace (arrival process)

C x

Load trace (departure process)

∫( )

STOCHASTIC NETWORK CALCULUS

1. For a given battery and panel size:a) Upper envelope on the cumulative net arrival process

stochastically bounds the battery fill processb) Lower envelope bounds the drain processc) A standard theorem lets us compute the LOLP

2. Grid search to compute the best value

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Upper Envelope

Time

Lower Envelope

F. Kazhamiaka, S. Keshav, and C. Rosenberg, Robust and Practical Approaches for Solar PV and Storage Sizing, Proc. ACM eEnergy 2018, June 2018.

STOCHASTIC NETWORK CALCULUSInput: S, D, target LOLP, operating policy

Method: 1. For a given B and C, characterize upper and lower

bounds on net power arrival to battery with a set of envelopes

2. Search the set to find the best matching envelope (lowest bound on LOLP)

3. Repeat 1. and 2. to find the cheapest <B, C>

Output: <B, C> pair whose LOLP is upper-boundedby the target LOLP

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Envelope

Cumulative net power

Time

ANALYTIC RESULTS

Minimizing storage size to smooth solar/wind sources

Optimal participation of a solar or wind farm in day-ahead energy markets*

Modeling of imperfect storage devices*

Optimal operation of diesel generators to deal with power cuts in developing countries*

132Joint work with Y. Ghiassi-Farrokhfal, S. Singla

CONCLUSIONS

We’re well on our way to the Smart Grid

But many challenges remain

An exciting and complex research area

133

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