Opportunities and Challenges of Smart Grids · 15.06.2010 Eseia International Summer School 2014 2...

15.06.2010

Eseia International Summer School 2014

• Opportunities and Challenges of Smart Grids

• Univ.-Prof. Lothar Fickert


15.06.2010

Eseia International Summer School 2014 2

• Basic Power and Energy Grid-related Considerations

• Smart Grids

• Research Topics (TU Graz)

– Smart Meters

– Smart Efficiency(Losses)

– Smart Fault Location (Grid Faults)

– Smart Safety (Grid Faults)

– Smart Emergency (ICT)

– Electric Vehicle (EV)

– Autonomy (Storage)

– Smart Voltage control („bits vs. excavator“)

– Static System Stability (P/f control)

– Dynamic System Stability (Low Voltage Ride Through)

• –Austrian and International Developments

Overview

15.06.2010


Basics

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• Smart Grids


– Smart Meters











Overview

15.06.2010


Basic Power and Energy Grid-related Considerations

15.06.2010


Basic Power and Energy Grid-related Considerations

Electric stove

Time of the day

Detached house

Time of the day

Time of the day Time of the day

500 detached houses Larger area

Development of a Daily Load Diagram

In practice the recording is given as energy reading every 15 minutes

Approximation: P = W / t (Approximation)

15.06.2010


0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Gesamter Lastgang

Gesamter Lastgang

System elasticity through the adaptive (P/f-control) production of

• fossil power plants or

• nuclear power plants

Daily Load Curve

Energy Considerations

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Load curve – Duration curve – Energetic summation curve

Load curve Duration curve

Daily load curve Daily duration curve Energetic summation

curve

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Levels of Transportation of Electrical Energy

High/

extra high voltage

400 / 220 / 110 kV

Medium voltage

30 / 25 / 20 /

10 / 6 kV

Low voltage

0,4 kV

Transportation

Consumers

Voltage levels Types of grids

Infeed

Transportation grids

Infeed: large power

stations

Distribution grids

Infeed: decentralized

power stations

Rules of Thumb:

Nominal Voltage of a grid: kV = km

Nominal Voltage for transportation of energy: kV = MVA

Doubling of the nominal Voltage cuts the losses to a quarter

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Case 1:

The outer poles rotate:

Case 2:

The inner coil rotates in

the fixed magnetic field

Case 3:

The inner poles rotate =

AC (Alternating Current)

generator

Induction law:

Magnetic flux is cosine-shaped

Induces voltage is sine-shaped

Rotating machines are still the

most used energy converters

and the backboen of the

stability of electrical grids

induziert

C

dU E dr

dt

AC (Alternating Current) Generators

15.06.2010


jL t1 û (t) U cos e Û·et R·

2j t

3L2

·T 2ˆ ˆ û (t) U cos t U cos t R· e·e U3

·3

4j t

3·

L3

2T 4ˆ ˆ û (t) U cos t U cos t Re Ue3 3

· · ·

AC (Alternating Current) Generator – Three phase

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Salient pole generator

- Low speed (water power stations,

- wind converters

Turbogenerator

- High speed

- Steam power plants

AC (Alternating Current) Generators – Types

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AC (Alternating Current) Generators – Types

Salient pole generator

- Low speed (water power stations,

- wind converters

Turbogenerator

- High speed

- Steam power plants

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:

Three Phase Voltages

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Generator Load Behaviour

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Generator Synchronous Coupling

more input of converted power

= increase in speed of generator 1

Increase in speed of generator 1

= synchronous operation

generator 1 generator 2

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Active and Reactive Power

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P UIcos

Q UIsin

Active Power

Re-active Power

resistance

reactance

P(t)

Q(t

)

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Active Power Re-active Power

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Transformer: Voltage and Current Conversion

Passive element – no storage

Pin = Pout

Uin x Iin = Uout x Iout

Iout : Iin = Uin : Uout

Example:

Uin = 10 kV Uout = 110 kV

Iin = 1000 A Iout = 91 A

Application: long distance power transfer

Example:

RLine = 1 Ω 10 kV: PLoss = 3 x I2 X R = 3000 kW

110 kV: PLoss = 3 x I2 X R = 25 kW = 0,8% of 3000 kW

η = 99,2%

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Rule of thumb:

transport distance in km = voltage level in kV

„km = kV“

extra high voltage: 400 / 220 kV up to 400 / 220 km

high voltage: 132 / 110 kV up to 132 / 110 km

medium voltage 20 / 10 kV up to 20 / 10 km

low voltage 0,4 kV up to 400 meters

Voltage Levels acc. to Austrian ELWOG

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Total and Specific Production Cost (1)

Units produced Units produced Units produced

Proportional Costs Prop. + invariable Costs Cost per Unit

P T T/u

P = p x u T = F + p x u T/u = F/u + p

hyperbola with offset

F

P

p

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Total and Specific Production Cost (2)

P1 … e.g. classic thermal power plant (P1 = gas pp, K = carbon fired pp)

P2 … e.g. water / wind / P.V. power plant

P3 … e.g. emergency Diesel generator set

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STYRIAN ACADEMY for Sustainable Energies

Smart Grids

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• Smart Grids


– Smart Meters











Overview

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Overview


• Smart Grids

• Smart Meters

• TU Graz Research – Smart Efficiency

– Smart Fault Location

– Smart Safety

– Smart Emergency

• Austrian and International Developments

15.06.2010


Levels of Transportation of Electrical Energy

High/

extra high voltage

400 / 220 / 110 kV

Medium voltage

30 / 25 / 20 /

10 / 6 kV

Low voltage

0,4 kV

Transportation

Consumers


Infeed


Infeed: large power

stations

Distribution grids


power stations

Rules of Thumb:

Nominal Voltage of a grid: kV = km

Nominal Voltage for transportation of energy: kV = MVA

Doubling of the nominal Voltage cuts the losses to a quarter

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Time-wise and Local Dislocation

Netz 1

Verbrauch P1

Netz 2

Verbrauch P2

MW MW

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Netz 1

Verbrauch P1

Netz 2

Verbrauch P2

MW MW

P2

P1

Time-wise and Local Dislocation

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Distance of Electricity Transport: „ MVA = kV“

„MVA = kV“ Full load operation

1 MVA = 1 kV

General Typical Values

„MVA = kV“

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Distance of Electricity Transport: „kV = km“

„kV = km“ Full load operation

„kV = km“ 1 km = 1 kV

General Typical Values

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Rule:

kV = MVA

„The nominal power of the connected device in MVA equals the voltage level in kV

Maxwel‘ls equations & σ 1,5A/mm2

Maxwel‘ls equations & Voltage drop < 5%

Rule:

kV = MVA

„Electrical power cannot be transported over a longer distance in km

than the voltage level is in kV“

“

Distance of Electricity Transport : Rules of Thumb

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Smart Grids are power grids,

with a coordinated management,

based on bi-directional communication,

between

grid components

generators

energy storages and

consumers

to enable an energy-efficient and

cost-efficient system operation

that is ready for future challenges of the energy system.

Source: National Technology Platform Smart Grids Austria

34

Smart Grids: Definition (1)

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Source: Federal Ministry for Transport, Innovation and Technology, Austria

35

Smart Grids in Austria – Artist‘s Vision

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• Easy connection and operation of

an increasing number of

decentralised generators (DG)

• Consumer:

Participant of the system

Information and options regarding

the offered services (tariffs)

• Reduction of the negative

environmental impact

• Increase of reliability and secure

supply with electrical energy

36

Smart Grids: Definition (2)

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Developing the Active Distribution Grid

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• Electricity Infrastructure as a basis for the achievement

of policy goals towards sustainability

• Integration of renewables and dispersed generation

• Increase of efficiency in the energy system

• Resource optimization in the power system

• Robust and secure power supply

• New services and technologies like electromobility

• Self‐sufficient energy regions with a high degree of responsibility for their sustainable energy supply

38

Why Smart Grids? (1) Electrical Energy

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39

Why Smart Grids? (2)

Increase of

efficiency

Extension of renewables

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Source: vgl. dazu Djapic et al. (2007): Taking an Active Approach. IEEE power & energy magazine July/August 2007,

1540-7977/07/$25.00©2007 IEEE. S. 70.

40

Smart Grids - Benefits for Society

15.06.2010


• Power supply concepts based on Smart Grid Technologies provide

the possibility of Autonomous supply (off-grid-mode)

Parallel operation

• New challenges for

Grid information systems

Protection equipment

Process control

Information and communication technologies (ICT)

Tariffing

• Advantages

Efficient use of available energy

Coordination of micro-generation units (photovoltaics, CHP, wind, solar …)

Integration of the communication infrastructure

Interoperability of measuring devices / services

41

Smart Grids: Challenges

15.06.2010


Source: National Technology Platform Smart Grids Austria

Technical aspects:

intelligent management

systems with

communication from

producer to consumer

Legal aspects:

adjusting of

framework conditions

Economical aspects:

new market models

& reward systems

Intelligent

components

System operation and

management

Customer and market

Communication and

information infrastructure

42

Smart Grids: Aspects and Thematic Areas

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Smart Meter

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• Smart Grids


– Smart Meters











Overview

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• Smart-Metering-Definition as per SNT-VO 2009 §10 Z10 (current):

“Smart metering” is … distinct measurement of electrical energy and

their time of use by use of electronic, digital, remotely read meters without the

acquisition of (detailes) power values.”

•Directive 2009/72/EC – annex I; Measures on consumer protection

• Roll out is subject to an economic validation

• Long-term costs, benefit for the market, individual consumers

• Economic validation (deadline 3th of September 2012)

If positive validation: 80% of the consumers equipped with

intelligent measurement systems till 2020

45

Smart-Metering-Definition

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46

Schematic structure of a current Smart Meter System

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• Analysis of energy self consumption

3-phase Smart Meter

load current is linearly increased

obtained values correspond to the mentioned values in the data

sheets

minimize self energy consumption

losses are higher than losses of ferraris meter

• Data for the consumer & grid operator

separate data data transfer rates can be increased

47

Investigated features (2)

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Source: Statistik Austria, 2008

Total electrical energy

consumption 57.000 GWh

Major appliances:

oven, stove, washing mashine,

laundry dryer, dishwasher, freezer,

fridge, water boiler

48

Electrical Energy Share in Austria

15.06.2010


• Demand side management

Reducing peak load of grids

Operation of energy efficient powerplants for base load

• Tariff selection

Signal lights (low cost / high cost)

Automatic selection by Smart Meter

Intelligent electrical devices

• Remote control

Heating system

Water boiler

49

Using Smart Meters to enhance the Energy Efficiency

15.06.2010


2006: DIRECTIVE 2006/32/EC OF THE EUROPEAN PARLIAMENT AND

OF THE COUNCIL

• Recommendation of the use of smart meters

2009: DIRECTIVE 2009/72/EC OF THE EUROPEAN PARLIAMENT AND

OF THE COUNCIL

• Compulsory introduction of smart metering in all Member States

• Optional economic assessment of the Member State

• Smart metering roll-out (current): 80% of customers by 2020

• Strengthening the active participation of customers

W. Boltz,,Informationsveranstaltung Smart Metering und Sicherheit, überarbeitet

Smart Metering in the EU

15.06.2010


Austrian Law (ElWOG Novelle 2010)

• Obligation for network operators to daily recording of "specific

consumption meter readings" at 15-minutes intervals and to save them.

• Obligation for energy suppliers to submit upon customer's request free

monthly consumption information

• Bidirectional communication interface with four external anyway quantity

gauges

• Any other use of this interface for the control of the counter is not

foreseen

• Requirement to shut off the customer's system from the distance ... as

well as to limit the maximum respect of electrical power

Smart Metering in Autria

15.06.2010


Encryption and protection against access by third parties as per

"acknowledged state of the art"

In Europe, the communication of the smart meters is run

• 70-80% by PLC

• 20 - 30% on wireless communication

In the U.S., the ratio is reversed.

Use of Frequency Bands

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Smart

Efficiency

(Losses)

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• Smart Grids


– Smart Meters

– Smart Efficiency (Losses)










Overview

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Overview


• Smart Grids

• Smart Meters

• TU Graz Research – Smart Efficiency

– Smart Fault Location

– Smart Safety

– Smart Emergency

• Austrian and International Developments

55

Use of Frequency Bands

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Smart Meter

+ Data analyzer (optional)

56

Monitoring of Energy Consumption (1)

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• Monitoring of Energy Consumption

Electric devices equipped with chip

Smart Plug (IC & M-Bus)

Nonintrusive load monitoring

• Analysis of energy consumption of devices with large share on

total load

Energy efficient use

Deviation of energy consumption

Warnings due to remarkable changes in energy consumption

Share of total energy consumption

57

Monitoring of Energy Consumption (2)

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Electrical energy saving potential in Austrian “α-households“

Assumption: -10% per α-household

Global annual increase of

electrical energy:

2,4 % (0,024 p.u.)

Total electrical

energy consumption100% 1 p.u.

Households 33% 0,33 p.u.

"α-households" 5% 0,05 p.u.

Saving potential in

these "α-households" 7% 0,07 p.u.

Total savings 0,35% 0,0035 p.u.

0,12% 0,0012p.u.

58

Critical Aspects of Smart Grids (1)

15.06.2010


Visualisation of the saving potential

1.) No saving

2.) One-time effect & continuous improvement

2,4% … 365 days

0,12% … 18 days

59

Critical Aspects of Smart Grids (2)

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Motivation

General loss calculation methods

Parameters of measurement based loss calculation

Loss calculation based on standard load profiles

Loss correction function

Application to an existing low voltage network

Conclusion and Outlook

www.eco-eco.de

www.bauthermographie-infarot.de

Gmeserv.de

Losses

15.06.2010


Austria (9 Mio inhabitants, 2010) 3,35 TWh [4,1%]

www.e-control.at

Average cost ~ 55 €/MWh

www.apg.at

~180 Mio €/a

Dimension of network losses – selected European countries

Motivation (1)

ncy

http://www.e-control.at/http://www.e-control.at/http://www.e-control.at/http://www.apg.at/

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Actual calculations are based on:

Financial differentiation method

Information about the magnitude

No allocation to single assets

HOW MUCH but not WHERE they occur

Load and loss factors

Maximal losses have to be known

Not valid for entire low voltage networks

Statistical Methods

Detailed user information often not available

High calculation effort

Actual calculation methods

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Effects using 15-minutes-load data

Averaging time

Unbalanced loads

Reactive power

In future 15-minutes-load data will be available

(Austrian regulation - 95% Smart Meter density - until 2020)

New opportunity Asset based loss calculation

Asset optimisation

Motivation (2)

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0 2 4 6 8 10 12 14 16 18 20 22 240

25

50

75

100

125Resolution of measured values - 15 min

I [ A

]

time [h]

I_1

0 1 2 3 4 5 6 7 8 9 101

1.05

1.10

1.15

1.20

1.25

1.30

consumed energy in the analysed grid area in MWh/week

CF

A

calculated CFA

approximated CfA

1-second 15-minutes

0 2 4 6 8 10 12 14 16 18 20 22 240

25

50

75

100

125

I [A

]

time [h]

I1

Influence of averaging time

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0 5 10 15 201

1.5

2.0

2.5

3

3.5

4

consumed energy in analyzed grid area in MWh/week

CF

U

calculated CFU

approximated CfU

Correction function unbalanced loading

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

25

50

75

100

125

curr

ent in

A

time in h

I1

I2

I3

IN

High impact on losses

Single phase loads CFU=6

Influence of unbalanced loading

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Correction factor reactive power

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 151

1.1

1.2

1.3

1.4

1.5

1.6

consumed energy in the analysed grid area in MWh/week

CF

R

calculated CF_R

0.75 0.8 0.85 0.9 0.95 10

5

10

15

20

25

30

An

za

hl d

er

Me

ssu

nge

ncos phi

0 0.1 0.2 0.3 0.4 0.50

5

10

15

20

25

An

za

hl d

er

Me

ssu

nge

n

tan²

measure

ments

measure

ments

Mainly +10 - +20%

Influence of reactive power

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Measured load profiles were hardly available

Available:

Allocation to “Standard Load Profiles” (SLP)

Annual energy consumption

Asset data

• Loss calculation based on SLP’s High resolution measurements

Network outgoing feeder

High share of household loads

Determination of the “Loss Correction function” (LCf) Based on high resolution measurement values

Combined with the allocated Standard load profiles

Loss calculation based on Standard load profiles

15.06.2010


Available:

Allocation to “Standard Load Profiles” (SLP)

Annual energy consumption

Asset data

Not available

Measured load profiles

High resolution measurements Network outgoing feeder

High share of household loads

Determination of the “Loss Correction function” (LCf) Based on high resolution measurement values

Combined with the allocated Standard load profiles

Asset based loss calculation

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• Measurements on

• low voltage lines to

analyse

• - Unbalance coeffizient

• - Load variation

• - Granularity

Measurements - Low Voltage Grid

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0 100 200 300 400 500 600 700 800 900 10000

0.2

0.4

0.6

0.8

1.0

1.2

consumed energy in the analysed grid area in MWh/a

LC

F

Loss Correction Faktor (LCF) calculated using standard load profiles

Loss Correction Function (LCf) approximated

- Calculation for each asset

and each conductor section

Loss Correction Function (LCf)

15.06.2010


Technical losses

Joule effected losses in cables

No fuses

No meters

No transformers

Load flow calculations

Low voltage network

15-minutes-values

from Standard load profiles

Application of the „Loss Corection function“

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Methodology – Calculation

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Rural Village Suburban Urban

46 97 108 115

Investigated low voltage networks – settlement areas

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0 10 20 30 40 50 60 70 80 90 1000

1

2

3

losses in %

0 10 20 30 40 50 60 70 80 90 1000

1

2

3lo

sses in %

0 10 20 30 40 50 60 70 80 90 1000

500

1.000

branch nr.

consum

ed e

nerg

yin

MW

h/a

Village

97

Settlement area

calculated with LCf

Results – example village

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Results of loss calculations

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Metering data loss calculation

Ideally based on 15-minutes-load-profile-data

Assed data is mainly in a good condition and theoretically available

Unbalanced loading

Areas with low load densities

Short term load peaks (Averaging time)

Necessary during detailed analyses at single branches with low

load density

Standard load profiles can be regarded as alternative method

Using the developed Loss Correction Function

High deviation in areas with low load densities

Good matches in the analysed provincial urban and suburban areas

Results / Summary

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Efficient low voltage network

A lot of optimisations can just be economically realised during revision and

maintenance work

Generally the analysed low voltage network is in an good condition

Excavation work especially in urban areas expensive

Outlook – Next steps

Integration of additional losses

Metering

Fuses

Transformers

Integration of Smart Metering Data

Losses are everywhere, but the losses are not as high as thought.

Conclusion and Outlook

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Electric

Vehicle (EV)

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• Smart Grids


– Smart Meters











Overview

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• Motor rating 25 kW

• Range 70 km

• Power (charging) 3 kW (13 Amp/230 Volt)

• Efficieny 0,21 kWh/km

• Full charge (0 100%)

• Charging time 4 h

• Energy consumed = Battery capacity 12 kWh

• Typical Operation (6000 km / year)

• Refreshing charge every 35 km,

• 170x per year

• Charging time 3,5 h

• Energy consumed 10 kWh

• Prices (new)

• updated from 1994 26 000,-- €

• battery (included) 16 000,-- €

•Annual costs • consumption 1500 kWh

• Energy costs 120 €

• Grid costs 120 €

• Taxes etc. 20 €

• Total 260 €

Basic Features of an electric vehicle (no prototype)

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Basic Features of an electric vehicle (no prototype)

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0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Gesamter Lastgang

Gesamter Lastgang




Daily Load Curve


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Daily Load Curve - Breakdown of Energy Sources

System elasticity stressed by additional fluctuating P.V. power infeed

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Rest=Gas

Solar

Wasser

Gas

P.V.

Hydro P


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additional EV charging

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E-Auto-Tanken

Rest=Gas

Solar

Wasser

Gas

P.V.

Hydro P

Charging EV


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0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E-Auto-Tanken

Rest=Gas

Solar

Wasser

Gas P.V. Hydro P

Charging EV



increased hydropower generation


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System elasticity stressed by increased fluctuating P.V. power infeed


increased Hydropower generation

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E-Auto-Tanken

Rest=Gas

Solar

Wasser

Gas P.V. Hydro P

Charging EV


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System elasticity stressed by additional EV charging


sunless day

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E-Auto-Tanken

Rest=Gas

Solar

Wasser

Gas P.V. Hydro P

Charging EV

Challenge: optimization of the power plant park


15.06.2010


Gas

P.V.

Hydro

Charging

• Max 30 km distance to next workshop

» 1 hour,

» 20,-- € Taxi

• i.e. about one political district in Austria

• Cost Aspect for the retailers:

» Training on electric vehicles

» Spare parts

» For each brand

Challenge: establishment of a dense service network

Maintenance and Repair

15.06.2010


Gas

P.V.

Hydro

Charging

• Socket single phase 13 Amp / 3 kW 4 Std

three phase 9 kW 1,5 Std

• Future: 1.5 hours charging time takes ... 5 minutes: 150 kW per loading point

• For e.g. 4 loading points: 1 power transformer

• (cost?)

• Turnover?

Challenge: creating and funding the infrastructure

Network Load and Adaption Costs

15.06.2010


Gas

P.V.

Hydro

Charging

Challenge: Source of energy / power supply for Air-Condition

• Energy intensive, auxiliary power unit

• Heating cost: Price of stand-by heater at present 800, - €

• Cooling problem

Heating / Air Condioning

15.06.2010


Gas

P.V.

Hydro

Charging

• Nominally 70 km

• Minus 14 km reserve

• Available 56 km

• One way 28 km

• Consumption of totally available number of cycles due to

• short ranges and

• frequent refueling

•

Challenge: Increase of battery capacity and

closer “refuelling” network

Operating Range

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Gas

P.V.

Hydro

Charging

• Consumption incl. batt. losses 21 kWh / 100 km

• Grid efficiency 95% 22 kWh

• Power station efficiency 55% 40 kWh ~ 4 ltr gasoline

• Rebound effect due to gasoline tax elimination

• Mileage cost-effective, but unfavorable ratio of purchase cost

vs. mileage

• Tax issues

Challenge: Increase of battery efficiency

electric power conversion efficiency (CHP)

mass production of EV

Cost Efficiency for the Owner

15.06.2010


• Consideration of

charging costs (with / without grid fee)

sale profit

nominal cycle duration

battery costs

Challenge: Regulation in the field of electricity economics

V2G “Vehicle to Grid"

15.06.2010


Battery capacity: 12 kWh

Optimistic calculation

Duty cycles: 1500

Energy turnover (ηBattery = 100% ) 18 MWh

Buying price of energy 40 €/MWh

Net costs + taxes 0

Selling price of energy 180 €/MWh

Spread 140 €/MWh

Gross profit 18 MWh x 140 €/MWh

= 2’500 €

vs.

cost of battery 16’000 €

Loss = - 13’500 € … in 5 years

V2G “Vehicle to Grid“ – Revenue vs Costue

15.06.2010



Smart Fault

Location

15.06.2010



• Smart Grids


– Smart Meters











Overview

15.06.2010


Visualisation of the voltage of

a faulty branch line

97

Innovative Application: Smart Fault Location (1)

15.06.2010


With decentralised generator (DG) Without decentralised generator (DG)

Control Room: Percentage visualisation of the voltage at several

metering points of the analyzed branch

98

Innovative Application: Smart Fault Location (2)

15.06.2010



Smart

Safety

15.06.2010



• Smart Grids


– Smart Meters











Overview

15.06.2010


• Bidirectional short circuit currents

changing of the direction and the magnitude

incompatibility of existing protection systems

• Neutral point treatment of the decentralized generator (DG)

missing defined neutral point to earth connection (grounding)

defined neutral point to earth connection

• Goal: providing of personal safety in each operation mode

101

Objectives

15.06.2010


T-N-System

102

Innovative Application: Smart Safety (1)

15.06.2010


T-N-System Transformer disconnected

103


15.06.2010


T-N-System I-T-System Transformer disconnected

104


15.06.2010


T-N-System

105


15.06.2010


T-N-System Transformer disconnected

106


15.06.2010


?

T-N-System T-?-System Transformer disconnected

107


15.06.2010


108


15.06.2010


Demo-Case IFEA AMIS Analogous 3-phase Grid-Model for

teaching and research

109


15.06.2010



Smart

Emergency

15.06.2010



• Smart Grids


– Smart Meters











Overview

15.06.2010


+ 2,4 % p.a

5 %

85 %

10 %

appr.50 %

appr.40 %

appr.10 %

10 %

0 % ICT

losses

annual increase

Data Processing

Communication

Transmission of Inform.

Energy-Consumption

without ICT

Use of Electrical Energy

15.06.2010


• How far an innovative public (not public), cost-effective

emergency supply for special needs can be provided with Smart

Meters?

intact internal power supply for switching operations is

necessary

cooperation of Smart Meters, decentralized generators and

diesel generator sets

supply of critical infrastructure in case of a fault for

ICT-systems

emergency call facilities

emergency light

Innovative Application Emergency power supply for

sensitive consumers using Smart Meters!

•

113

Investigated features

15.06.2010


• Wide area blackouts

Electronic equipment of the everday commodities can only be

operated limited

• Uninterrupted energy supply

Maintenance of supply of critical infrastructure (e.g.

emergency supply, emergency light, cash dispenser)

• Fully operative information- and communication

technology (ICT)

Requires 10% of the total electrical grid power

• Use of smart grid technologies

Smart Meter

Increased integration of decentralised generation units

Disconnection of uncritical loads in case of a blackout

Selective connection of loads after clearing the wide area blackout

114

Innovative Application: Smart Emergency

15.06.2010


Physical combination of AC- and ICT- supplycells

15.06.2010


Dependency of the

general ICT- Infrastructure

from

the public power supply

Distribution of medias for

emergency calls

0 % 0 %

9 %

27 %

64 %

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

not very low low heavily very heavily

fracti

on

in

%

44 % 49 %

0 %

6 % 1 %

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

fixed network

mobile telephony

e-mail own line others

fracti

on

in

%

Dependency - Opinion polls

15.06.2010


Load limitation by Smart Meters (200 W)

approx. 2500 sensitive consumers

Investment for each consumer and year: 7 €

117

Innovative Application: Smart Emergency

15.06.2010



Smart Voltage

control („bits

vs. excavator“)

15.06.2010



• Smart Grids


– Smart Meters











Overview

15.06.2010


Voltage Profile along a Line (1)

Incandescent lamps:

Reduction of lifetime

15.06.2010


~

~

U

-DU2=

+d2

+DU1= -d1

110-kV- Netz

~

Voltage Profile along a Line (2)

15.06.2010


Demo-Net „Biosphärenpark Großes Walsertal“

15.06.2010


Voltage Rise by Decentralized Infeed vo

ltag

e i

n p

u

voltage rise

busbar ss busbar infeed

15.06.2010


Voltage Control with Current Compounding

Set value of voltage regulator

Transformer current

15.06.2010


Possible Positions of Voltage Regulators

HV/MV-Transfo MV/LV-Transfo Customer

Intermediate MV-Transfo Intermediate LV-Transfo

15.06.2010


• UW 1 2 3 4 5G DEA

DUT

DUL

T1

Ltg1

NS1

T2

Ltg2

NS2

T3

Ltg3

NS3

T4

Ltg4

NS4

T5

Ltg5

NS5

MS

110 %

100 %

90 %

UMS, Schwachlast

UMS, Starklast

UW 1 2 3 4 5110 %

100 %

90 %

UMS, Schwachlast

UW 1 2 3 4 5

UMS, Starklast

without generation

Voltage Profile without Compound Control

With generation

15.06.2010


•

DUMaxMin

UNSmax

105

100

95

90

DUT

DUL

UMS

UNSmin

UW 1 2 3 4 5

UNS 3 + 2,5% (Starklast)

MS-Schwachlast

MS-Starklast

UNS 1UNS 2

UNS 3 (Starklast)

UNS 4

UNS 5

UNS 3 - 2,5% (Starklast)

UNS 3 - 2,5% (Schwachlast)

UNS 3 + 2,5% (Schwachlast)

UNS 3 + 5% (Schwachlast)

UNS 3 (Schwachlast)

UW 1 2 3 4 5 G DEA

DUT

DUL

T1

Ltg1

NS1

T2

Ltg2

NS2

T3

Ltg3

NS3

T4

Ltg4

NS4

T5

Ltg5

NS5

MS

Voltage Profile with Compound Control

15.06.2010


• UW 1 2 3 4 5 G DEA

DUT

DUL

T1

Ltg1

NS1

T2

Ltg2

NS2

T3

Ltg3

NS3

T4

Ltg4

NS4

T5

Ltg5

NS5

MS

DUMaxMin

UNSmax

105

100

95

90

DUT

DUL

UMS

DUopt

Umin opt

Umax opt

UNSmin

UW 1 2 3 4 5UNS 3 - 2,5% (Starklast)

MS-Schwachlast

MS-Starklast

UNS 1UNS 2

UNS 3

UNS 4

UNS 5UNS 3 + 2,5% (Starklast)

UNS 3 - 2,5% (Schwachlast)

UNS 3 + 2,5% (Schwachlast)

UNS 3 + 5% (Schwachlast)

!

!

!

!

! !

Voltage Profile with Compound Control and MV/LV Regulation

15.06.2010


UW

Netz

VP

UN

U P, Q,

Gen Load Gen Load

Dynamic Behavoiur of On Load Tap Changers (OLTC)

15.06.2010



System

Stability (P/f

control)

15.06.2010



• Smart Grids


– Smart Meters











Overview

15.06.2010


Rules of Thumb:

df/dt = - 5 x ΔP / PN (Hz / sec)

Limits: Δf ≤1 Hz AND tcontrol ≈ 1s

High/

extra high voltage

400 / 220 / 110 kV

Medium voltage

30 / 25 / 20 /

10 / 6 kV

Low voltage

0,4 kV

Transportation

Consumers


Infeed


Infeed: large power

stations

Distribution grids


power stations

System Stability– P(f) Closed Loop Control (1)

15.06.2010



Less speed = less frequency More power

Centrifugal Pendulum

15.06.2010


Principle of the variable-speed turbine in

island operation mode with an electrical load

15.06.2010


Behavior of the variable-speed turbine in

island operation with complete de-loading

15.06.2010



island operation mode under increasing electrical load

15.06.2010



island operation mode under variable electrical load

15.06.2010



parallel island operation mode with an electrical load

15.06.2010


Behavior of two variable-speed turbines in

island operation mode under variable electrical load

15.06.2010


W = W … Law of conservation of energy

Win = Wout

ʃPTurb dt = ʃPelectr dt + Θω2/2 | d/ dt PTurb = Pelectr + Θω*dω/dt | ω = 2πfelectr ~ωmech

ΘωN* dω/dt = PTurb - Pelectr

introducing H … inertia constant | :

ΘωN2/2 = PTurb*H

d(f/fN) / d(t/H) = Δp /2

with Δp = (PTurb - Pelectr) / PGenset

Example:

f = 50 Hz H = 3 (gas tubine) …5s … 8 (steam turbine) s

df/dt = 5*Δp [Hz/s]

Psystem = 10’000 MW 9’700 MW (= - 300 MW)

Δp = (PTurb - Pelectr ) /PGenset = - 300 / 10'000 = - 0,03

df/dt = - 0,15 Hz/s


15.06.2010


appr.50 %

appr.4 %

50 Hz

100 %

f [Hz]

P [MW] in % 80 %

90 %

overload

Loss of Generation – P(f) Closed Loop Control

Maxim

um

Pow

er

Outp

ut

of

Genset

No-load frequency (speed)

Load a

t t-

0

Load a

t t+

0

n

n

P 1 P

f f

D

D

ΔP

Δf

15.06.2010


PP0 Pn

D D, f

DP

n

f

fn

PP0 Pn

f

fn

Loss of Generation – P(f) Closed Loop Control

„Classic“ droop control

Droop control with dead band

Dead band

15.06.2010

Eseia International Summer School 2014 NRST 143

Example: Loss of Generation – P(f) Primary Regulation

Dynamic Frequency Drift after Loss of Load

df/dt = - 5 x ΔP / PN

Δf

Dynamic

Static

Europe:

100 mHz 3000 MW

3000 MW

15.06.2010


Development of the Capacity Index

λUCTE = ΔP/Δf

Connection of CENTREL

Disconnection of

Yugoslavia

15.06.2010


Secondary Regulation

Taks of the Secondary Regulation:

• Keeping the power and energy exchange in line of the exchange

contracts between different grid areas

• To bring back the operating points of the power plants involved in the

primary control to their pre-fault status (widening the elasticity)

• To bring back the system frequency to its pre-fault status (nominal frequency)

15.06.2010


Frequency after a loss of generation of 1300 MW (Loss of a power station)

Secondary Regulation Example

Primary control

Secondary control

15.06.2010


0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Gesamter Lastgang

Gesamter Lastgang




Daily Load Curve


15.06.2010




0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Rest=Gas

Solar

Wasser

Gas

P.V.

Hydro P


15.06.2010





0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E-Auto-Tanken

Rest=Gas

Solar

Wasser

Gas

P.V.

Hydro P

Charging EV


15.06.2010



0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E-Auto-Tanken

Rest=Gas

Solar

Wasser

Gas P.V. Hydro P

Charging EV



increased hydropower generation


15.06.2010



System elasticity stressed by increased fluctuating P.V. power infeed



0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E-Auto-Tanken

Rest=Gas

Solar

Wasser

Gas P.V. Hydro P

Charging EV


15.06.2010



System elasticity stressed by additional EV charging


sunless day

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

E-Auto-Tanken

Rest=Gas

Solar

Wasser

Gas P.V. Hydro P

Charging EV

Challenge: optimization of the power plant park


15.06.2010



Dynamic

System

Stability

15.06.2010



• Smart Grids


– Smart Meters









– Dynamic System Stability

(Low Voltage Ride Through)


Overview

15.06.2010


Low Voltage Ride Through (LVRT)

15.06.2010


Moment of

short circuit

Lower voltage band Nnominal voltage


15.06.2010


Trägheitszeitkonstante H = 0.32 s

Fehlerklärungsdauer tcl = 200ms

Spannung

Wirkleistung Drehzahl

Fall 1a


15.06.2010


Spannung

Wirkleistung Drehzahl

Fall 1b


15.06.2010



Storage

15.06.2010


Public distribution network

Photovoltaic power plant (PV)

Accumulator

Household load

Legend:

Generation

Consumption

Load management

system (LM)

∑ETotal = min (Aim)

Concept

15.06.2010


• Accumulator capacity (kWh) daily storage vs. long-

term storage

• Stored energy (kWh) under consideration of

charge/discharge cycles

• Inverter active power (kW) to charge and discharge

the accumulator

• Fulfil grid service e.g. voltage stability or power-

frequency control

Requirements Accumulator

15.06.2010


• Active power photovoltaic power plant (IPV)

• State-of-charge accumulator (IAccu)

• Inverter control (OInverter) to charge/discharge the

accumulator

• Process control by conductors (OLoad)

• Energy consumption for the selected process (IProcess)

Parameter load management system

15.06.2010


Inverter

Public

distribution

system

PV module

OLoad

Accu

IProcces

OInverter

Household load (1-phase)

Load

(1-phase)

Load

(1-phase)

Load

(1-phase)

Load

(1-phase)

Conductor

Conductor

Conductor

Conductor

Accumulator

Inverter

3-stage model

active power PV

Load

management

system

Detailed illustration of the concept

15.06.2010


Type Power

consumption Switch-on time Switch-off time Colour

[-] [W] [hh:mm]; [hh:mm] [hh:mm]; [hh:mm] [-]

television 350 08:00 14:21 brown

Washing machine 2000 14:30 15:26 black

Coffee maker 1200 07:00 07:02 violet

Waterboiler 2000 07:10; 07:30 07:11; 07:32 rose

Electric stove 1 (hob) 2500 12:15 12:29 gray

Electric stove 1 (hob) 2500 11:38 12:15 orange

Electric stove 1 (oven) 2500 10:00 11:37 red

Fridge 70 00:00 23:59 blue

Small water

heater/storage 2000 13:00; 13:20 13:07; 13:27

pink

Standby-Consumption 100 00:00 23:59 cyan

PV-plant 5000 00:00 23:59 green

Determination of Generation and Consumption (1)

15.06.2010


generation

individual

loads

Determination of Generation and Consumption (2)

15.06.2010


Residual

load

generation

individual

loads

Storage Layout – No Connection to Public Grid (1)

15.06.2010


Integrated Power Determination of Storage Capacity

Egeneration = 22,8 kWh

Econsumption = 9,2 kWh

EStorage = 13,6 kWh

Assumptions:

Storage capacity: 10 kWh

↓

Depth of discharge: 40%

↓

Usable energy: 6 kWh

Residual

load

Storage full

Storage empty

generation

Storage Layout – No Connection to Public Grid (2)

15.06.2010


Akkumulator:

Starting value: 3,0 kWh (00:00)

Usable storage : 6,0 kWh

Final value: 5,1 kWh (23:59)

generation

Residual

load

Storage full

Storage empty

sunny sunny

With Connection to Public Grid - Maximum Solar Conditions

15.06.2010


Akkumulator:

Starting value: 3,0 kWh (00:00)

Usable storage : 6,0 kWh

Final value : 5,1 kWh (23:59)

generation

Residual

load

Storage full

Storage empty

overcast overcast

With Connection to Public Grid - Reduced Solar Conditions

15.06.2010



Austrian and

International

Developments

15.06.2010



• Smart Grids


– Smart Meters











Overview

15.06.2010


Source: Projekt DG Demonetz

100%

15%

30%

70% 30% Saving

70% Saving

85% Saving

Smart Grid Case Study

Energie AG Netz


Salzburg Netz GmbH


VKW Netz AG

Business as Usual (BAU)

Grid Reinforcement

Cost shares and savings of a selected Austrian Smart Grid solution compared to BAU

173

Austrian Examples - Benefits for the Integration of DG

15.06.2010



174

Austria: Renewable power scheduled until 2020*

15.06.2010


To bundle the strength of different stakeholders

To efficiently use synergies of the different Stakeholders

To show competence through international visible light-house projects

To indicate, how to overcome existing barriers

175

Objectives NTP Smart Grids Austria

15.06.2010


•

SG platform El.

Companies Coordinator:

DI Strebl

(Salzburg Netz)

in VEÖ

DI Tauschek

SG industry

platform Coordinator:

DI Lugmaier

(Siemens)

in FEEI

Dr. Bernhardt

SG research

actors in AT Coordinator:

DI Brunner

(AIT)

Smart Grids Austria (SGA) Coordination:

DI Lugmaier, DI Strebl, DI Brunner

Dr. Bernhardt, Dr. Tauschek

Advisory

Council

Relevant external

Stakeholder

Ministries Regulator

…

Smart Grid Austria working Groups

use cases – business models, standardisation, framework conditions,

data aspects, SG demonstration and implementation

International SG actors

176

Structure NTP Smart Grids Austria

15.06.2010


Download:

www.smartgrids.at

177

Roadmap Smart Grids Austria

http://www.smartgrids.at/

15.06.2010


• Total Market potential until 2030 (based on ETP Smart Grid figures)

• Assumption that until 2030 energy supply investments of approximately 16.000 Billion US $ worldwide and 500 Billion Euro in Europe will be necessary. Focus on Transmission and Distribution!

• Example for grid control market potential:

• The german market for control systems (SCADA) will rise from 20 Mio. Euro to 250 Mio. Euro per year in 2020 (Source: trend research, 2008)

• Example for generation side market potential:

• Potential cost reduction per additional installed distributed kW -compared to conventional grid extension (Source: Projekt DG Demonetz, 2008)

• Example for consumer side market potential:

• Within the management of consumers (Smart Home, Smart Industry, Smart Metering) until 2012 a turnover of 7.800 Millionen Euro (worldwide) is expected. (Source: Siemens AG, 2008)

178

Examples for International Smart Grid Market Potentials

15.06.2010


• Smart Grids

Part of an existing distribution grid

Offer the possibility to supply small isolated systems (micro grids)

Information- and communications technolgies (ICT)

• Involvement of distributed (small, renewable) power sources

Reduction of CO2 emissions

Reduction of transmission losses

Increased supply security

• Smart Grids enable a more

Efficient

Secure and

Ecological power supply

179

Conclusion

15.06.2010


• Opportunities and Challenges of Smart Grids

• Univ.-Prof. Lothar Fickert


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