the application of boundary layer climatology and urban wind …€¦ · green solutions must...

Dr. Keith Sunderland1, Dr. Gerald Mills2, Prof. Michael Conlon1

1 School of Electrical & Electronic Engineering, Dublin Institute of Technology, Ireland 2 School of Geography, Planning and Environmental Policy, University College Dublin, Ireland

12th June, 2014

‘The application of boundary layer

climatology and urban wind power

potential in smarter electricity networks’

AMERICAN METEOROLOGICAL SOCIETY CONFERENCE

Overview

Aims and Objectives

Research Context/ Motivation

Methodology

Findings

Future Work

Conclusions

AMERICAN METEOROLOGICAL SOCIETY2014

Aims & Objectives

Research Aim:

To develop novel modelling capability that is inclusive of the power engineering complexities associated with urban (electricity) network integration of small/micro wind generation, and informed by urban climate research

Background Reference

Urban Classification

Backgrounduu

STDuUrban

DistributionNetwork

Cable Parameters/Configurations

Network Structure

Consumer configuration/loading

Earthing Configurations

surface roughness parameterisation

z ,0 zd

T.I.

Wind turbine 'pure' (zero-turbulence)

power characteristic

AMC Power Flow

Algorithm

1.

Radial

Mesh

Turbulence normalised network node voltage/voltage unbalance profile

Network node voltage/voltage unbalance profile based on the 'mean' urban wind resource

LCOE evaluation

Pu(turbulent)

u(mean)P

2. Observations

Slide 1 K. Sunderland (z0, zd)

Aims & Objectives

Research Aim:




Backgrounduu

STDuUrban

DistributionNetwork


Network Structure




z ,0 zd

T.I.



AMC Power Flow

Algorithm

1.

Radial

Mesh



LCOE evaluation

Pu(turbulent)

u(mean)P

2. Observations

Synergetic application of urban climatology (BLT) and electrical power engineering

Empirical wind mapping/modelling cognisant of urban heterogeneity – in conjunction with urban wind observations


Aims & Objectives

Research Aim:




Backgrounduu

STDuUrban

DistributionNetwork


Network Structure




z ,0 zd

T.I.



AMC Power Flow

Algorithm

1.

Radial

Mesh



LCOE evaluation

Pu(turbulent)

u(mean)P

2. Observations


Overview

Aims and Objectives


Methodology

Findings

Future Work

Conclusions


Research Context

CEN

TRA

LISE

DD

ISTR

IBU

TED

RENEWABLE ENERGYFOSSIL FUEL

PAST FUTURE

Increasing need for Localised Supply within urban load centres

Time

Ener

gy F

low

Bi-

Dir

ecti

on

al E

ner

gy F

low

DISTRIBUTED GENERATION

GENERATION

TRANSMISSION

DISTRIBUTION

CONSUMPTIONCONSUMPTION

DISTRIBUTION

TRANSMISSION

GENERATION

25kV

110/220/400 kV

38/20/10 kV

400/230 V

38/20/10kV &

400/230V

Slide 2 K. Sunderland

Research Context

CEN

TRA

LISE

DD

ISTR

IBU

TED

RENEWABLE ENERGYFOSSIL FUEL

PAST FUTURE

Increasing need for Localised Supply within urban load centres

Time

Ener

gy F

low

Bi-

Dir

ecti

on

al E

ner

gy F

low

DISTRIBUTED GENERATION

GENERATION

TRANSMISSION

DISTRIBUTION

CONSUMPTIONCONSUMPTION

DISTRIBUTION

TRANSMISSION

GENERATION

25kV

110/220/400 kV

38/20/10 kV

400/230 V

38/20/10kV &

400/230V

Voltage/VAR control testing

Micro Gen programme

Grid behaviour at high wind

Smart

Generation

Research

De

plo

ym

en

t

Demonstration

De

ve

lo

pm

en

t

Smart

Pricing

Smart meter pricing trial

Future smart pricing options

Smart

Networks

Grid 25 Strategy

Voltage conversion of MV Networks

Self healing networks pilot Smarter

Wind Farm operations

Smart

Operations

Smart Grid Ireland

Smart meter technology trialElectric

Vehicle Incentives

Smart Meter user trial

Smart

Users

DSM programmes

Smart commercial applications


Research Motivation

Micro/Small Wind Electricity Generation

Urban Locations

Heterogeneity, Complex

Building Morphology

Chaotic, Turbulent Airflows

Energy Capture Capability?

Rural Locations

Uninhibited Air Flows

Statistically Predictable

Airflows

Maximum Energy

Optimisation


Research Motivation

Micro/Small Wind Electricity Generation

Urban Locations

Heterogeneity, Complex

Building Morphology

Chaotic, Turbulent Airflows

Energy Capture Capability?

Rural Locations

Uninhibited Air Flows

Statistically Predictable

Airflows

Maximum Energy

Optimisation


WHY URBAN WIND?

Population Centres

Transmission/Distribution losses

Green solutions must include wind

Smarter energy diversification must be

inclusive of wind within urban centres BUT

solutions predicated on the resource and not

specifically the technology are needed

Research Motivation


Smart Cities…. Smart Grids

o An amalgamation of communication and electrical capabilities that allow utilities to understand, optimize, and regulate demand, supply, costs and reliability.

Facilitating electrical providers to interact with the power delivery system and determine whether electricity is being used and from where it can be drawn during the time of crisis and peak demand. On the demand side – the smart grid empowers the consumer to become a ‘prosumer’…

Research Motivation


Why is a Smart Grid needed?

o Future grid networks must be competitive and supportive of environmental objectives and sustainability

o Reliability, flexibility, accessibility and cost-effectiveness are the primary objectives

o Should accommodate both central and dispersed generation

o Options for end-users to be more interactive with both market and grid; promoting the concept of a ‘prosumer’

Research Motivation


Why is a Smart Grid needed?

o Future grid networks must be competitive and supportive of environmental objectives and sustainability

o Reliability, flexibility, accessibility and cost-effectiveness are the primary objectives

o Should accommodate both central and dispersed generation

o Options for end-users to be more interactive with both market and grid; promoting the concept of a ‘prosumer’

Therefore the means of applying the primary energy resource (Wind) in this regard within urban centres must be achieved

Overview

Aims and Objectives


Methodology

Findings

Future Work

Conclusions


Urban Effects & Wind Modelling Research Methodology

(Electrical) Distribution Network considered in terms of both local climate zones

Two zones categorised in terms of local heterogeneity: (Urban and Suburban)

Urban Environmnet

Suburban

Urban





Urban Environmnet

Suburban

Urban

[SL]

CITY

[C]

[CH]

[SH]




Urban Environmnet

Suburban

Urban




AIRPORT

– Rural reference



AIRPORT

– Rural reference

Boundary Layer Climatology in terms

of surface roughness classification



AIRPORT

– Rural reference



zdz1

z0201z

Roughness Sub-Layer

Canopy Sub-Layer

Urban Boundary Sub-Layer

Inertial Sub-LayerLogarithmic Extrapolation

(Rural) Airport Background

Internal Boundary Layer

(Statistically) Laminar Airflow based on a 'constant' frictional velocity

H(z )

(z*)

1u

Logarithmic Extrapolation

uRural

u(z*)

u(z )UBL

Wieranga, Bottema approximation and a Logarithmic extrapolation based on fitted surface roughness parameterisation



AIRPORT

– Rural reference



zdz1

z0201z

Roughness Sub-Layer

Canopy Sub-Layer

Urban Boundary Sub-Layer

Inertial Sub-LayerLogarithmic Extrapolation

(Rural) Airport Background

Internal Boundary Layer

(Statistically) Laminar Airflow based on a 'constant' frictional velocity

H(z )

(z*)

1u

Logarithmic Extrapolation

uRural

u(z*)

u(z )UBL

z

ROUGHNESS Sub-Layer

INERTIAL Sub-Layer

d

Hmz

z0

CANOPY

z*

Logarithmic

Profile

u(z*)

Transitionary

Profile

Mac

Dona

ld

COST

Exponential

Profile

Wieranga, Bottema approximation and a Logarithmic extrapolation based on fitted surface roughness parameterisation

K. Sunderland

(Standardised) Distribution

Network analysis

o Single-phase 4-Wire

(and Ground)

o Complex/unbalanced

(consumer) load

configurations

DwG & DN Implications Research Methodology

K. Sunderland


Network analysis


(and Ground)


(consumer) load

configurations

Energy flow - Mono-

directional Power Flow


K. Sunderland


Network analysis


(and Ground)


(consumer) load

configurations

Energy flow - Mono-



25/16sq, (Concentric Neutral) L3



4xcore 185sq, XLPE

4xcore 70sq, XLPE

38

SUBSTATION

B

CDEFH

J

I

G

1234

6

7

5

9 81011

121314

15161718

2927 28

26252423

37363534

333231305453

51 525049

47 48

73

72

71

70

74

64

63

61

62

60

69

68

67

66

65

59

58

57

56

55

21 22

46

45

44

43

42

41

40

39

2019

A

4xcore 70sq, Al

K. Sunderland


Network analysis


(and Ground)


(consumer) load

configurations

Energy flow - Mono-






4xcore 185sq, XLPE

4xcore 70sq, XLPE

38

SUBSTATION

B

CDEFH

J

I

G

1234

6

7

5

9 81011

121314

15161718

2927 28

26252423

37363534

333231305453

51 525049

47 48

73

72

71

70

74

64

63

61

62

60

69

68

67

66

65

59

58

57

56

55

21 22

46

45

44

43

42

41

40

39

2019

A

4xcore 70sq, Al

Consumer

Earthing Connection

Pillar Earthing

Connection

Substation

Transformer

(Mini) Pillar

Consumer

Connection/Load

V4

V3

V2

V1

Pillar (i) Pillar (i+1)

Transformer

Earthing

Connection

K. Sunderland


Network analysis


(and Ground)


(consumer) load

configurations

Energy flow - Mono-






4xcore 185sq, XLPE

4xcore 70sq, XLPE

38

SUBSTATION

B

CDEFH

J

I

G

1234

6

7

5

9 81011

121314

15161718

2927 28

26252423

37363534

333231305453

51 525049

47 48

73

72

71

70

74

64

63

61

62

60

69

68

67

66

65

59

58

57

56

55

21 22

46

45

44

43

42

41

40

39

2019

A

4xcore 70sq, Al

Consumer

Earthing Connection

Pillar Earthing

Connection

Substation

Transformer

(Mini) Pillar

Consumer

Connection/Load

Ig(i)

Icons(i)[g]

Icons(i)[n]

cons(i)[c]

In(i+1)

Ic(i+1)

Ib(i+1)

Ia(i+1)

V4

V3

V2

V1

Ic(i)

Ib(i)

Ia(i)

In(i)

Pillar (i) Pillar (i+1)

Transformer

Earthing

Connection

7 8

13

…. Enhanced modelling cognisant of the inherent complexities associated with connectivity and unbalanced load/generation integration at final consumer level

K. Sunderland

Embedded Generation Issues

o Bi-directional power flow

o Network Power Quality

management

o Safety implications


K. Sunderland


o Bi-directional power

flow


management



K. Sunderland


o Bi-directional power flow


management



3

2

1.u.A.ρ.cP rotorairpMech

Overview

Aims and Objectives


Methodology

Findings

Ongoing Work

Conclusions


Surface Roughness Parameterisation

Results & Findings

Urban Observations & Modelling


Surface Roughness Parameterisation

Results & Findings



For each 300 sector, surface roughness was estimated by varying iteratively until the best fit was achieved so as to minimise the error between the predicted wind speed, based on the background climate, and the observed wind speed

CH SH

Observed Wieranga

Model Bottema

Model Log-

Model Observed Wieranga

Model Bottema

Model Log-

Model

Roughness length (z0)

-- 1.15 1.15 0.8713 -- 0.55 0.55 0.5171

Friction velocity

ratio -- 1.0 1.3312 1.7022 -- 1.0 1.2636 1.5512

uM [m/s] 4.5992 4.9728 3.2281 4.6165 4.4401 4.9804 3.5795 4.3940

uS [m/s] 2.1288 2.2497 1.4604 2.0885 2.1712 2.2269 1.6005 1.9647

MAE [m/s] -- 0.7113 1.4248 0.6133 -- 0.9392 1.0635 0.7594

RMSE[m/s] -- 0.9790 1.6878 0.8651 -- 1.2202 1.3873 1.0479

Observation/Modelling: high-platform observations

Results & Findings



0 2 4 6 8 10 12 14 16 18 200

5

10

15

20

[B]

u(modelled) [m/ s]

u(o

bs

erv

ed

) [m

/s

0 2 4 6 8 10 12 14 16 18 200

5

10

15

20

[A]

u(modelled) [m/ s]

u(o

bs

erv

ed

) [m

/s]

Raw Data

y = 0.8987x + 0.4831

r2 = 0.916

Raw Data

y = 0.7931x + 0.8724

r2 = 0.8765

0

5

10

15

20

25

C(C

ap

ac

ity

Fa

cto

r) [

%]

7 8 9 10 11 12 13 14 15 16 170

500

1000

1500

2000

2500

C(E

ne

rgy

) [k

Wh

]

z [m]

0

5

10

15

20

S(C

ap

ac

ity

Fa

cto

r) [

%]

5 6 7 8 9 10 11 120

500

1000

1500

2000

2500

S(E

ne

rgy

) [k

Wh

]

z [m]

89.6

29.524.4

20.4

17.9

13.1

38.5

[A]

[B]

95.9

50.9

34.1

25.2

20.2

54.2

14.3

16.9

14.3

15.7

Urban Comparison

Suburban Comparison

Observation vs. Modelling

Urban Comparison

Suburban Comparison

Scattergram comparison of high-platform

observed and modelled wind speeds

(Nov. 2010 –to– Jan 2011)

Energy implications with respect to height

variation for a wind generator at both sites

(Nov. 2010 –to– Jan 2011)

Results & Findings



Results & Findings

Distribution Network Reaction


SUBSTATION

B

CDEFH

J

73

72

71

70

7469

68

67

66

65A

1234

79 8

10111213

141516

171829

27 282625

24233736

35343332

3130545351 52

504947 48

G

6 5

21 22

46

45

44

43

42

41

40

39

2019

38

I

64

63

61

62

60

59

58

57

56

55

Typical Mean Year of Wind Speed (Markov Chain)

Results & Findings

Distribution Network Reaction

Slide11 K. Sunderland

SUBSTATION

B

CDEFH

J

73

72

71

70

7469

68

67

66

65A

1234

79 8

10111213

141516

171829

27 282625

24233736

35343332

3130545351 52

504947 48

G

6 5

21 22

46

45

44

43

42

41

40

39

2019

38

I

64

63

61

62

60

59

58

57

56

55

B 1 2 3 4 5 6 C D E F GH I J 1 2 3 4 5 6 7 8 910

1.03

1.04

1.05

1.06

1.07

1.08

1.09

Pillar/Customer

Vo

lta

ge

[p

u])

Line-1

B 1 2 3 4 5 6 C D E FGH I J 1 2 3 4 5 6 7 8 910

1.02

1.04

1.06

1.08

1.1

Pillar/Customer

Vo

lta

ge

[p

u])

Line-2

B 1 2 3 4 5 6 C D E F GH I J 1 2 3 4 5 6 7 8 910

1.04

1.06

1.08

1.1

Pillar/Customer

Vo

lta

ge

[p

u])

Line-3

B 1 2 3 4 5 6 C D E F GH I J 1 2 3 4 5 6 7 8 910

0

1

2

3

4

x 10-3

Pillar/Customer

Vo

lta

ge

[p

u])

Neutral

Overview

Aims and Objectives


Methodology

Findings

Future Work

Conclusions


Future Work


J. T. Millward-Hopkins, et al., "Estimating Aerodynamic Parameters of Urban-Like Surfaces with Heterogeneous Building Heights," Boundary-Layer Meteorology, vol. 141, pp. 443-465, 2011/12/01 2011.

J. T. Millward-Hopkins, et al., "Aerodynamic Parameters of a UK City Derived from Morphological Data," Boundary-Layer Meteorology, vol. 146, pp. 447-468, 2013/03/01 2013

To be applicable from the ISL into the RSL, neighbourhoods of homogeneity need to be identified – distinctly different surfaces can be considered separately

Future Work


Rastered Digital Elevation Model (DEM) - - building footprints (Dublin)

Divide the city into distinct neighbourhood regions – Adaptive Grid

Geometric Parameterisation: Employing an adaptive grid to calculate the geometric parameters

Future Work


Rastered Digital Elevation Model (DEM) - - building footprints (Dublin)

Divide the city into distinct neighbourhood regions – Adaptive Grid

Geometric Parameterisation

Morphemetric Model

z0

Overview

Aims and Objectives


Methodology

Findings

Future Work

Conclusions


Conclusions

In the context of smart cities and smarter (electricity) grids, this type of research is essential if renewable energy is to facilitate a cultural shift towards an era of prosumers.

In terms of the limits available to wind energy extraction in an urban context., the analyses illustrated limited opportunities below a height 2 → 4 x zHm

By linking urban wind observations to a background reference, an empirical logarithmically matched profile was possible. (Analytical linkages to observations within the canopy suggested that knowledge of the background resource in this regard is of limited value)

Analyses of a fully described 4-wire unbalanced section of Dublin city network, in respect of increasing levels of prosumer (with a grid-tied commercially available DwG), illustrated that for exemplar consumer load and a typical mean year of wind speed, voltage tolerance breaches are unlikely and of marginal concern (

Thank you

e: [email protected]

the application of boundary layer climatology and urban wind …€¦ · green solutions must...

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