Metaheuristics-based Optimal Reactive Power Management in Offshore Wind Farms-Master Thesis-TU Delft-Theologi Aimilia-Myrsini

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Metaheuristics-based Optimal Managementof Reactive Power Sources in Offshore Wind Farms

Aimilia-Myrsini Theologi 05-10-2016

Supervisors: Dr. Jose Luis Rueda Torres, TU Delft

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Outline

• Motivation

• Research approach

• Wind Speed Forecasting

• MVMO

• Optimization

• Grid Code Requirements

• Results

• Conclusions & Future WorkMVMO=Mean Variance Mapping Optimization

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Motivation

• Grid Code Requirements

Safe, secure and economic operation

• Uncoordinated management of Var sources in real-time operations:

increase of losses

decrease of efficiency

reduced life-time of switchable devices

Motivation & Research Tasks

Researchapproach

Wind Speed Forecasting

MVMO

Optimization

Grid CodeRequirements

Results

Conclusions & Future Work

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Research tasks

Development of wind speed forecasting method, which will be incorporated in optimal reactive power management


Researchapproach


MVMO

Optimization


Results


Formulation and solution of reactive power sources operation in a coordinated manner

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Definition of Objective Function (1)

Approach 2: optimization is performed for 24-hours time horizon

where,

real power losses of the system for each hour t the operational cost of the number of tap changes

for each hour t

, cost coefficients

Approach 1: 24-hours, every hour is optimized individuallyMotivation & Research Tasks

Researchapproach


MVMO

Optimization


Results


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Predictive Optimization (HVDC link)

PCC=Point of Common CouplingMVMO=Mean Variance Mapping OptimizationHVDC=High Voltage Direct Current


Researchapproach


MVMO

Optimization


Results


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Predictive Optimization (AC cable)Motivation & Research Tasks

Researchapproach


MVMO

Optimization


Results


PCC=Point of Common CouplingMVMO=Mean Variance Mapping OptimizationAC=Alternating Current

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Definition of Objective Function (2)

Vector of parameters to be optimized:

]

Discrete variablesContinuous variables => Mixed-integer non-linear problem => Necessity for metaheuristic methods

𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒𝑶𝑭

𝒊≤ 𝒊𝒍𝒊𝒎𝒔≤ 𝒔𝒍𝒊𝒎

𝒒𝑾𝑻𝑮𝒎𝒊𝒏 ≤𝒒𝑾𝑻𝑮≤𝒒𝑾𝑻𝑮

𝒎𝒂𝒙

𝒕𝒂𝒑𝑻𝒓 ,𝒎𝒊𝒏≤ 𝒕𝒂𝒑𝑻𝒓 ≤ 𝒕𝒂𝒑𝑻𝒓 ,𝒎𝒂𝒙


Researchapproach


MVMO

Optimization


Results


OF=Objective Function

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Accurate wind speed forecasting

• Reduces the risk of uncertainty

• Contributes to better grid planning

• Reserves power for integrating wind power

=> Beneficial for optimal operation of a power system


Researchapproach


MVMO

Optimization


Results


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Time-scales

Very short-term (few minutes to 1 hour)

Short-term (1 hour to several hours)

Medium-term (several hours to 1 week)

Long-term (1 week to one year)


Researchapproach


MVMO

Optimization


Results


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NN-based forecasting method (1)

+ Data-driven approach

+ Remarkable effectiveness in short-term forecasting

Over a year of historical data is necessary

Implementation in MATLAB => “Neural Network Toolbox”

NN=Neural Network


Researchapproach


MVMO

Optimization


Results


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Old approach: increase amount of input data

New approach: divide the one year data into days

NN-based forecasting method (2)Motivation & Research Tasks

Reactive Power in Power Systems

Researchapproach


MVMO

Optimization


Results


NN=Neural Network

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• Feed forward neural network

NN-based forecasting method (3)

• Back propagation learning algorithm

• One Hidden layer (5 neurons)


Researchapproach


MVMO

Optimization


Results


Unidirectional flow of dataNN=Neural Network

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Day-ahead prediction

• The evaluation criteria is:

AMAPE=Average Mean Absolute Percentage Error

𝑨𝑴𝑨𝑷𝑬=𝟏𝟎𝟎𝟐𝟒 ∑

𝒉=𝟏

𝟐𝟒¿𝒑𝒓𝒆𝒅𝒊𝒄𝒕𝒆𝒅 𝒔𝒑𝒆𝒆𝒅𝒉−𝒂𝒄𝒕𝒖𝒂𝒍 𝒔𝒑𝒆𝒆𝒅𝒉∨

¿𝒂𝒗𝒆𝒓𝒂𝒈𝒆𝒘𝒊𝒏𝒅 𝒔𝒑𝒆𝒆𝒅 ¿

Season AMAPE (%)Winter 21,05Spring 14,82

Summer 16,37Fall 18,26


Researchapproach


MVMO

Optimization


Results


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Metaheuristic Techniques in Power Systems – MVMO

MVMO

Single-agent search algorithm

Internal search range is restricted to [0,1]

Special mapping function

Enhanced performance in terms of convergence speed

MVMO=Mean Variance Mapping Optimization


Researchapproach


MVMO

Optimization


Results


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START

Termination criteria satisfied ?

STOP

Generate random solutions in range [0,1]

Denormalize parameters & feed to model in PowerFactory

Run load flow calculations and obtain P,Q values

Fitness evaluation by using de-normalized variables

Yes

No

MVMO flowchart


Researchapproach


MVMO

Optimization


Results



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Evolutionary mechanism Storing solutions

𝒉 (𝒙 , 𝒔𝟏 , 𝒔𝟐 , 𝒙 )=𝒙 ∙ (𝟏−𝒆−𝒙 ∙𝒔𝟏 )+(𝟏− 𝒙𝒊 ) ∙𝒆−(𝟏−𝒙 ) ∙𝒔𝟐

where,

The variable is always between the range [0,1] for every

Mapping function


Researchapproach


MVMO

Optimization


Results


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ImplementationMotivation & Research Tasks

Researchapproach


MVMO

Optimization


Results


]

Provides parameters

Performsload flow

calculations

Provides Q values

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Parameters to be optimized

3-winding Transformer with OLTC

WTG

s

WTG=Wind Turbine GeneratorOLTC=On Load Tap Changer


Researchapproach


MVMO

Optimization


Results


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Far-offshore wind farm (288 MW) with HVDC interconnection link

OLTC=On Load Tap ChangerHVDC=High Voltage Direct CurrentAC=Alternating Current

System InformationNumber of wind turbines: 48Number of 3-winding transformer with OLTC: 2


Researchapproach


MVMO

Optimization


Results


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Borssele Near-shore wind farm (600 MW) with AC cables

System InformationNumber of wind turbines: 100Number of 3-winding transformer with OLTC: 4


Researchapproach


MVMO

Optimization


Results


OLTC=On Load Tap ChangerAC=Alternating Current

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Case studies

• Far-offshore wind farm – DC connected (280 MW)

CASE 1 Offshore Transformers (x2)

Min. (Losses) for 24-hours, for the current operating point


Min. (Losses + Tap Changes) for 24-hours, for a predicted time horizon

CASE 3 Onshore Transformers (x2)


CASE 4 Onshore Transformers (x2)






CASE 7 On- & Off- shore Transformers (x2)


CASE 8 On- & Off- shore Transformers (x2)



Researchapproach


MVMO

Optimization


Results


• Near-shore Borssele wind farm – AC connected (600 MW)

DC=Direct CurrentAC=Alternating Current

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Grid Code Requirements (1)-Far-offshore DC connected wind farm


Researchapproach


MVMO

Optimization


Results


PCC=Point of Common CouplingWTG=Wind Turbine GeneratorHVDC=High Voltage Direct CurrentDC=Direct CurrentAC=Alternating Current

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Researchapproach


MVMO

Optimization


Results


Grid Code Requirements (2)-Near-shore AC connected Borssele wind farm

PCC=Point of Common CouplingWTG=Wind Turbine GeneratorDC=Direct CurrentAC=Alternating Current

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Case 1 – Far-offshore: Min.(Losses)


Researchapproach


MVMO

Optimization


Results


2 4 6 8 10 12 14 16 18 20 22 24

6

8

10

12

14

16

18

20

22

Red

uctio

n of

loss

es (

% )

Time ( h )

INPUT: predicted

wind speed

2 4 6 8 10 12 14 16 18 20 22 244

5

6

7

8

9

10

11

12

Pred

icte

d W

ind

Spee

d ( m

/ s

)

Time ( h )

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-2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5 2,0 2,5

P ( % PN )

Q ( MVar )

80

20

100

0 2 4 6 8 10 12 14 16 18 20 22 240,80

0,85

0,90

0,95

1,00

1,05

1,10

1,15

1,20

BUS A BUS B BUS C BUS D

Volta

ges

of 3

3 kV

Bus

es

Time ( h )

Grid Code Requirementssatisfied

Case 1 – Far-offshore: Min.(Losses)


Researchapproach


MVMO

Optimization


Results


-0,45 -0,30 -0,15 0,00 0,15 0,30 0,45 0,60

0,8

0,9

1,0

1,1

1,2U ( p.u. )Q/Pmax at PCC

Q/Pmax

5,02 m/s

11,51 m/s

8,33 m/s

.

.

.

.

.

.

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Case 7 – Near-shore: Min.(Losses)


Researchapproach


MVMO

Optimization


Results


INPUT: predicted

wind speed

2 4 6 8 10 12 14 16 18 20 22 2410

15

20

25

30

35

40

45

50

55

60

Red

uctio

n of

the

OF

( % )

Time ( h )

2 4 6 8 10 12 14 16 18 20 22 246

7

8

9

10

11

12

Pred

icte

d W

ind

Spee

d ( m

/ s

)

Time ( h )

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Grid Code Requirementssatisfied

Case 7 – Near-shore: Min.(Losses)


Researchapproach


MVMO

Optimization


Results


-2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5 2,0 2,5

0

20

40

60

80

100

P ( % PN )

Q ( MVar )

-60 -45 -30 -15 0 15 30 45 60

0

20

40

60

80

100

P ( % PN ) LV_A 66 kV Bus MV_A 66 kV Bus LV_B 66 kV Bus MV_B 66 kV Bus

Q at Offshore PCC ( MVar )

-150 -120 -90 -60 -30 0 30 60 90 120

0

20

40

60

80

100

P ( % PN ) Onshore PCC A Onshore PCC B

Q at Onshore PCC ( MVar )

initial curveacceptable deviation

7,4 m/s

11,51 m/s

9,15 m/s

.

.

.

.

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Case 8 – Near-shore: Min.(Losses+Tap Changes)


Researchapproach


MVMO

Optimization


Results


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

5000

10000

15000

20000

25000

30000

35000

Cumulative Initial Cost Cumulative Optimum Cost

Cos

t ( E

uro

)

Time ( h )

41,12 %reduction

𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒𝑶𝑭=∑𝒕=𝟏

𝟐𝟒(𝒘𝟏 ∙𝑷 𝑳 ,𝒕+𝒘 𝟐 ∙𝑶𝑳𝑻𝑪𝒄𝒐𝒔𝒕 ,𝒕)

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2 4 6 8 10 12 14 16 18 20 22 24

-10

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

Tap

posi

tions

Time ( h )

Onshore Transformer A Onshore Transformer B

2 4 6 8 10 12 14 16 18 20 22 24

-6

-4

-2

0

2

4

6

8

Tap

posi

tions

Time ( h )

Offshore Transformer A Offshore Transformer B

2 4 6 8 10 12 14 16 18 20 22 24

-9

-8

-7

-6

-5

-4

-3

-2

-1

0


Tap

posi

tions

Time ( h )

2 4 6 8 10 12 14 16 18 20 22 24

-2

-1

0

1

2

Tap

posi

tions

Time ( h )

Offshore Transformer A Offshore Transformer B

Current operating

point

Predicted time

horizon

Comparison Case 7 & 8Transformers Tap Positions


Researchapproach


MVMO

Optimization


Results


Min. (Losses)

Min. (Losses+Tap changes)

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Robustness of MVMO


Researchapproach


MVMO

Optimization


Results



Case 8 – Near-shore: Min.(Losses+Tap Changes)

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Researchapproach


MVMO

Optimization


Results


Conclusions

1. Predictive optimization leads to better results

2. High efficiency of MVMO in optimal reactive power management

3. Topology of the wind turbines affects MVMO convergence

0 100 200 300 400 500 600 700 800 900 10003,8

4,0

4,2

4,4

4,6

4,8

5,0

Act

ive

pow

er lo

sses

( M

W )

Number of iterations0 100 200 300 400 500 600 700 800 900 1000

0

5

10

15

20

25

30

Act

ive

pow

er lo

sses

( M

W )

Number of iterations

Case 7 Case 1


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Researchapproach


MVMO

Optimization


Results


Future Work

Optimal design of NN structure

Include forecasting error in the optimization

NN=Neural Network

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PublicationsBook Chapters

A.M.Theologi, J.L.Rueda, and M.Ndreko, “Optimal Compliance of Reactive Power Requirements in Near-shore Wind Power Plants”, Application of Modern Heuristic Optimization Techniques in Power and Energy Systems, Wiley Publishing

J.L.Rueda, A.M.Theologi, “Optimal Power Flow Test Bed and Performance Evaluation of Modern Heuristc Optimization Algorithms”, Application of Modern Heuristic Optimization Techniques in Power and Energy Systems, Wiley Publishing

Journal Paper

A.M.Theologi, and J.L.Rueda, “MVMO-based Approach for Coordinated Operation of Reactive Power Sources in Offshore Wind Farms”, Swarm and Evolutionary Computation

Conference Papers

A.M.Theologi, and J.L.Rueda, “Optimal Management of Reactive Power Sources in Far-offshore Wind Power Plants”, Proceedings of the IEEE Manchester PowerTech 2017, Manchester, UK, June 2017

A.M.Theologi, and J.L.Rueda, “Short-term Wind Speed Forecasting for Optimization in Offshore Wind Farms”, Proceedings of the IEEE ISGT Latin America 2017, Quito, Equador, September 2017

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Thank you for your attention!

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Line LoadingsMotivation & Research Tasks

Researchapproach


MVMO

Optimization


Results


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Transformer Loadings


Researchapproach


MVMO

Optimization


Results


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Researchapproach


MVMO

Optimization


Results


Forecasting Error

Actual vs. Predicted Wind Speed

Hour

Win

d Sp

eed

(m/s

)

Predicted speedActual speed

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Comparison Case 1 & 2Transformers Tap Positions

Current operating

point

Predicted time

horizon2 4 6 8 10 12 14 16 18 20 22 24

-2

-1

0

1

2

Tap

posi

tions


Time ( h )

2 4 6 8 10 12 14 16 18 20 22 24

-5

-4

-3

-2

-1

0

Time ( h )

Tap

Posi

tions

Offshore Transformer T1 Offshore Transformer T2

Min. (Losses +Tap

changes)

Min. (Losses)


Researchapproach


MVMO

Optimization


Results


Engineering

Metaheuristics-based Optimal Reactive Power Management in Offshore Wind Farms-Master Thesis-TU Delft-Theologi Aimilia-Myrsini