IGREENGrid 2 Public Workshop -...

IGREENGrid 2nd Public Workshop

Javier Contreras

University of Castilla – La Mancha

[email protected]

04/12/2014

Madrid, Spain

Outline

Introduction

RES (Renewable Energy Sources)

Forecasting Tools

EES (Electrical Energy Storage) for Insular

Networks

Power Analysis Tools

Scheduling Tools

Planning Tools

Overview and Final Notes

2

IGREENGrid 2nd Public Workshop, Madrid, Spain 04/12/2014

Introduction (1/14)

3


Sao Miguel - Azores

(EDA)

La Graciosa - Canary Islands (ITC) Pantelleria - Italy (W4E) Crete - Greece (HEDNO)

Great Island of

Braila - Romania

(Electrica)

Introduction (2/14)

4


Introduction (3/14)

5


SiNGULAR Consortium SiNGULAR Consortium

5 Universities 5 Universities 3 Distribution 3 Distribution

System Operators

8 Energy

Utilities/SMEs

8 Energy Companies/

Utilities/SMEs

• UBI (Portugal) (Coordinator)

• AUTH (Greece)

• UCLM (Spain)

• POLITO (Italy)

• UPB (Romania)

• EDA (Portugal)

• HEDNO (Greece)

• Electrica (Romania)

• SmartWatt (Portugal)

• ITC (Spain)

• Concepto Sociológico (Spain)

• W4E (Italy)

• Commune di Pantelleria (Italy)

7 European countries: Portugal, Spain, Italy, Switzerland, Greece, Romania & Cyprus

Different types/sizes of islands will be considered

Introduction (4/14)

6


Investigate Investigate

Effects of large-scale

integration of RES and

DSM on the planning

and operation of

insular (non-

interconnected)

electricity grids

Recommend

insular electricity grids

Recommend

Scalable and Replicable Solutions for regulatory, technical and economic challenges of integrating very large shares of RES in insular electricity grids

Develop &

Develop &

Validate

Operation and

planning procedures

and tools in five pilot

sites

Introduction (5/14)

7


Pilot Sites Peak

Load [MW]

Voltage

Levels [kV]

Generation Mix

Crete

Greece (HEDNO) 650 150/20/15/.4

Thermal (Steam, ICE, CCGT)

/Wind/PV

Sao Miguel

Azores (EDA) 75 60/30/10/.4

Thermal (ICE)

/Geothermal/Hydro/Wind

Great Island of

Brailla Romania

(Electrica)

15 110/20/.4 Mainland Romania + Wind

Pantelleria

Italy (W4E) 7 10.5/.4 Thermal (ICE)/PV/(Wave)

La Graciosa

Canary Islands (ITC) 1.2 20/.4 Thermal (ICE)/(Wind)/(PV)

Pilot Sites

Introduction (6/14)

8


Introduction (7/14)

9


Introduction (8/14)

10


Inertial Sea Wave Energy Converter -

ISWEC 1:8 scaled model test

http://www.pantelleriaisland.it/

Introduction (9/14)

11


Batteries

Control and power

conditioning unit

Loads

Microgrid for La Graciosa: combine PV,

wind and diesel systems to supply the

electrical needs

Introduction (10/14)

12


Area 278 km2

Height 1.501 m.

Population 10.890 inhab.

Electricity Plant (Diesel)

nominal power 13,3 MW

Peak demand 7 MW

Wind Farm 11,5 MW

Hydroelectric Substation 11,3 MW

Pumping Station 6 MW

Upper Reservoir 380.000 m3

Lower Reservoir 150.000 m3

New Diesel systems 0

RES penetration 80%

Total energy:

44,87 GWh

Wind Energy: 27 GWh

Hydro: 6,87 GWh

Diesel: 11 GWh

Wind-Pumped-Hydro System


13



14


High High electricity

costs

2 - 5 times the values of

continental grids

High potential for energy efficiency measures

RES competitive to conventional

generation

High level of High level of RES

High variability of hourly

generation mix & costs

Need for RES forecasting tools

High potential for active demand

High variability in consumption

High variability in consumption

Need for demand

forecasting

High potential for active demand

Too small to

market

Too small to have

electricity market

Difficult to institute short-

term offer-based auctions

Dynamic price signal based on

actual generation costs

A/S market (provided by small-scale resources)

Increased

Services

Increased need for

Balancing Services

Demand Response programs

Storage / Hybrid Plants

Electric Vehicles

Why Insular/Singular?


15


Active DSOs

Distribution level RES

Residential level DR

Large-Scale Wind

Network Infrastructure

Electric Vehicles

Energy Efficiency Storage

Smart &

Sustainable Grid


16


Faster exploitation of the rich RES potential of the

islands

Reduction in fuel imports and the associated costs for

electricity production

Reduction in CO2 emissions, Cleaner environment,

Stimulation of tourism in the islands

Enhancement of system security, reliability, and

quality of supply through the provision of fast and low-

cost reserves from flexible loads, EES and PEVs, and

the active participation of small electricity consumers

Strong synergies between energy-oriented academic

and industry partners with complementary skills and

expertise

We are working

towards…

RES Forecasting Tools (1/5)

17


Meteorological forecast service Meteorological forecast service

Regional level

forecast

MV substation MV substation

level forecast

Individual RES Individual RES

facilities

Generation Generation

Scheduling

Price signals

Predictive DSM

RES generation RES generation

management

Predictive Predictive

Power flow

Wind farms

PV plants

Small hidro

Consumption

Predictive Predictive

reliability

Assets Forecast

Application


0

1

2

3

4

5

6

7

8

9

10

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168

Win

d P

ow

er

(MW

)

Time horizon (hours)

Averaged Measured Wind Power (nospillage)

Deterministic wind power forecast

Probabilistic Forecast ( Q10-Q90 )

0.00

0.01

0.02

0.03

0.04

0.05

0.1

9

0.9

3

1.6

7

2.4

2

3.1

6

3.9

1

4.6

5

5.3

9

6.1

4

6.8

8

7.6

3

8.3

7

9.1

1

Fre

qu

en

cy

Bins

KDE

Beta Distribution 0.00

0.01

0.02

0.03

0.04

0.05

0.1

9

0.9

3

1.6

7

2.4

2

3.1

6

3.9

1

4.6

5

5.3

9

6.1

4

6.8

8

7.6

3

8.3

7

9.1

1

Fre

qu

en

cy

Bins

KDE

Beta Distribution

0.00

0.05

0.10

0.15

0.20

0.25

0.1

9

0.9

3

1.6

7

2.4

2

3.1

6

3.9

1

4.6

5

5.3

9

6.1

4

6.8

8

7.6

3

8.3

7

9.1

1

Fre

qu

en

cy

Bins

KDE

Beta Distribution

0.00

0.02

0.04

0.06

0.08

0.10

0.1

9

0.9

3

1.6

7

2.4

2

3.1

6

3.9

1

4.6

5

5.3

9

6.1

4

6.8

8

7.6

3

8.3

7

9.1

1

Fre

qu

en

cy

Bins

KDE

Beta Distribution

KDE - Kernel Density Estimation forecast technique

18

AUPEC 2014 – Smart Power for Everyone


The model provides a point forecast and a probabilistic forecast

(between percentile of 5% and 95%). The forecast is refreshed every

24 hours. NWP data (air density, wind speed, wind direction, etc.) is

used to feed the model.

19



Forecasting Day

Forecasting Hour 0h, 6h, 12h, 18h

If there´s more than one variable of the same type

Upper quantile Beta distribution

Lower quantile Beta Distribution

Refresh Buttom

Image Download

Forecasting Variable

Point Forecast

Uncertainty Forecast

Download Options

Recent past forecast for 24 hours-ahead

Forecast Up to 7 days

Forecast platform

20



Wind forecast (Azores)

and more...

Website

of the platform

Load forecast (Crete)

PV forecast (Crete)

21

AUPEC 2014 – Smart Power for Everyone

http://smartwatt.net/singular/



EES for Insular Networks (1/5)

EES

PUMPED

HYDRO-

POWER

ENERGY

STORAGE

(PHES)

COMPRES

SED AIR

ENERGY

STORAGE

(CAES)

FLYWHEELS

ENERGY

STORAGE

SYSTEM

(FESS)

FLOW

BATTERIES

ENERGY

STORAGE

(FBES)

CHEMICAL STORAGE /

BATTERIES ENERGY STORAGE

(BESS)

Lead-Acid Li-Ion NaS

APPLICABLE

GRID SYSTEM

SIZE

Mostly > 200

MW > 500 MW

100 kW - 200

MW 25 kW - 10 MW < 10 MW < 10 MW > 100 MW

22


Different technologies for different system

sizes and application

Different objective functions:

Maximum integration of RES in stand-alone

applications

Reliability of the autonomous system


EES will support

the operation of

an LV network

EES will support

the operation of

an LV network

Power system of La Graciosa Island

Load PV

EES associated to micro-

generation

LV

23



Desalination plant in Lanzarote

Po

we

r (k

W)

Power input to desalination Plant

Time

WITH storage

NO storage

EES associated to large wind

farm and desalination plant

EES will provide

continuous power to

the plant

EES will provide

continuous power to

the plant

Power output of wind Farm

Wind Power Production

Wind Power Prediction Time

Po

we

r (k

W)

24



Load

Thermal Power

Wind Power

Curtailments for 2011~12% Wind production

Data obtained by HEDNO

for 2011

For each wind farm

calculated (Matlab

model):

• Theoretical wind power

production

• Wind power

curtailments

25



Grid Code Requirements

Insular

Networks Rated Voltage

(kV) Voltage range

Rated

Frequency

(Hz)

Frequency range

(Hz) Power factor

Harmonics/

Flicker

Portugal 6, 6.9, 10, 5,

30, 60 - 50 ±2% (95%)

±15% (100%) Not specified EN Standards

Spain 220, 132, 66 105% (0.3s) 49.85-0.15 49.85-0.25 [5min]

47.5-5 [5min] 0.95 inductive

to 0.95

capacitive

EN Standards

Italy 10.5 85-110% 47.5-51.5 0.8 to 1

inductive IEC Standards

Greece 6.6, 15, 20,

150 LV and MV:

±10%

HV: -5% to 8%

50 49-51Hz [95%]

42.5-57.5 [100%] IEC 61000-3-7 IEC Standards

Denmark 10, 15, 20, 30,

33, 50, 60,

132, 150

±10% 49.5-50.2 50.2-52 [15min]

47-47.5 [20sec] 0.975 inductive

to 0.975

capacitive

EN and IEC

Standards

Germany 110, 220, 380 49-50.5 47.5-51.5 0.95 inductive

to 0.95

capacitive

EN and IEC

Standards

26


Power Analysis Tools (1/10)

Photovoltaic systems

Loss factor components

Energy from sea waves (based on ISWEC prototype)

ISWEC model

RES Characterization

27




4 6 8 10 12 14 16 18 200

200

400

600

800

1000

1200

140007/18/2012

hours

Irra

dia

nce[W

/m2]

data

1 day before

data

2 days before

data

3 days before

Gpyr

clear sky

4 6 8 10 12 14 16 18 200

200

400

600

800

1000

1200

140007/23/2012

hours

Irra

dia

nce[W

/m2]

data

1 day before

data

2 days before

data

3 days before

Gpyr

variable sky

• Examples of days with their categorization

• 3-hour predicted values interpolated in polynomial

form

4 6 8 10 12 14 16 18 200

200

400

600

800

1000

1200

140007/12/2012

hours

Irra

dia

nce[W

/m2]

data

1 day before

data

2 days before

data

3 days before

Gpyr

“broken” clouds

The “broken clouds” phenomenon appears when sky is mainly clear, but the passage of clouds affects

irradiance evolution; the phenomenon is evident starting from 1-minute measurements; if the 15-min

average of active power is calculated, the presence of broken clouds is smoothed

28




• Comparison between PV power measurements and

simulations

• Cases with variable sky and clear sky

0 10 20 30 40 50 600

100

200

300

400

500

600

700

800

90007/12/2012

quarter of hours

Avera

ge A

C p

ow

er

[kW

]

Pfore

Pmeas

0 10 20 30 40 50 600

100

200

300

400

500

600

700

800

90007/18/2012

quarter of hours

Avera

ge A

C p

ow

er

[kW

]

Pfore

Pmeas

• Bad results? • NO: failure of a portion in the PV arrays (confirmed)

• The model is useful also for diagnosis purposes

29




• Comparison between PV power measurements and

simulations

• Case with variable sky

0 10 20 30 40 50 600

100

200

300

400

500

600

700

800

90007/23/2012

quarter of hours

Avera

ge A

C p

ow

er

[kW

]

Pfore

Pmeas

No failure in the PV arrays

The model is effective

30



Electricity from sea waves

ISWEC 1:8 scaled model test

(Insean, Rome)

• Sealed hull

• Gyroscope

• Power generator (PTO)

PTO

hull

gyro

ISWEC (Inertial Sea Wave

Energy Converter)

31




Estimation of the

ISWEC

performance wave

height

Hs(m)

wave period Te (s)

Scatter diagram:

occurrences (h)

Spectrum

Power Matrix

power output (kW)

wave

height

Hs(m)

Wave

Height

Σ Productivi

ty

wave period Te (s)

32



33

η(t)

Pi (kW)

t (s)

Pg (kW)

t (s)

• Energy storage

(Batteries)

• Power storage

(Ultra Capacitors)

• Flywheel (possible storage) • Sea waves

• Electricity grid




34

Energy storage allows to:

• decouple the generated power from the power delivered to the grid

• smooth the power output

• give constant power to the grid in some time intervals

• delay and predict the delivered power level variation



Probabilistic load-flow results

Test system: voltage profiles at the 16 nodes during the day

Time step for the analysis: 10 min, single Monte Carlo repetition

Voltage

profiles

supply nodes

1, 2 and 3

35



Reliability Model with Network

Reconfiguration

36


Scheduling Tools (1/8)

Scenario Generation for Dependent Stochastic

Processes

37

space

Historical Data

( ) ( ) ( ) ( )d Dp P s t q Q t

SARIMAModel

B B y B B

Dependent

Sto

chastic P

rocesses

Forecast Model Error Time Series Scenario Generation



38


Scenario Generation for Wind production

Example – 2 Wind Farms (Cross-Correlated)

Anemos Aiolikis – Kasteli, 6.3 MW Ydroaioliki – Kasteli, 9.35 MW

0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

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

Win

d P

rod

uct

ion

[MW

]

Hours

REAL FORECAST

0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

8,00

9,00

10,00

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

Win

d P

rod

uct

ion

[MW

]

Hours

REAL FORECAST

Initial Set: 50

Scenarios Reduced Set: 20

Scenarios


39


Scenario Generation for PV production

Example – 2 PV Plants (Cross-Correlated)

Attiki (0.15 MW) Viotia (1.0 MW)

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

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

PV

Pro

du

ctio

n [M

W]

Hours

REAL FORECAST

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

1,00

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

PV

Pro

du

ctio

n [M

W]

Hours

REAL FORECAST

Initial Set: 50

Scenarios Reduced Set: 20

Scenarios


40


Subject to:

Network Constraints

Subject to:

Thermal and RES Units

Operating Constraints

Power Balance and Reserve

Req.

Network Constraints

Minimize [Total Production

Cost]

Forecasts/Scenarios for

(Load, RES Production,

etc.)

Forecasts/Scenarios for

System Parameters

(Load, RES Production,

etc.)

Unit Data Unit Data

(Technical, Economic,

Availabilities, etc.) Network Data

MILP models

Unit Commitment &

Unit Commitment &

Dispatch Schedule

(Energy, Reserves)

Shadow Prices of

Constraints (SMP,

LMP, …)

Shadow Prices of

System

Constraints (SMP,

LMP, …)

Power Flows in

Network Lines


Scheduling Models – Improvements in the

Production Mix

41



Scheduling Models – Improvements in the

Production Mix

42



Integrated Software / General Features

43


Stand-alone application

Connection with MySQL Database

Multi-lingual (English, Greek, …)

Easy-to-use by dispatchers

Interactive Map


Scheduling with probabilistic forecast

44


Planning Tools (1/10)

45


Development of optimization models for generation and network expansion

planning

Minimize the investment and operating cost of candidate RES generator, cost of

system losses and cost of existing power plants


46


RES Generation Expansion Model


47


RES Generation Expansion Model


48


Case Study: La Graciosa (Canary Islands)


49


Joint RES and Distribution Expansion Model

Node without demand

Existing substation

Candidate substation

Existing fixed feeder

Existing replaceable feeder

Candidate branch to install new feeder

1

6

4

5

7

17

8

9

10

122

11

18

16

21

151922

25

24

26

14

20

13

23

3


50


Joint RES and Distribution Expansion Model:

Solution 1

1

6

4

5

7

17

8

9

10

122

11

18

16

21

151922

25

24

26

14

20

13

23

3

TR1A1

A1

A1

A1A1

R1

P1

W1

W2

W2 P2

W1 P1

P1

Node without demand

Node with demand

Existing substation

Uninstalled substation

Alternative 1 in branch subject to replacement


Alternative 1 in prospective branch


R1

R2



Alternative 1 for candidate transformer


Alternative 1 for conventional generator


Alternative 1 for wind generator


C1

C2

W1

W2

A1

A2

TR1

TR2

A1

A2


51



Solution 2

Node without demand

Node with demand

Existing substation






R1

R2









C1

C2

W1

W2

A1

A2

TR1

TR2

A1

A2

1

6

4

5

7

17

8

9

10

122

11

18

16

21

151922

25

24

26

14

20

13

23

3

TR1A1

A1

A1

A1

A1

R1

P1

W1

W1

W2 P2

W1 P1

P1

W1

R1


52



Solution 3

Node without demand

Node with demand

Existing substation






R1

R2









C1

C2

W1

W2

A1

A2

TR1

TR2

A1

A2

1

6

4

5

7

17

8

9

10

122

11

18

16

21

151922

25

24

26

14

20

13

23

3

TR1

A1A1

A1

A1P1

W1

W1

P2W1 P1

P1

W1

R1

TR1

W2

A1


53


Costs [k$] Solution 1 Solution 2 Solution 3

Investment 0247.459 0256.974 0280.850

Maintenance 0205.154 0203.972 0204.581

Production 2576.963 2581.107 2585.652

Losses 0000.328 0000.328 0000.302

Unserved energy 0000.000 0000.121 0000.000

Total 3029.904 3042.502 3071.385

Results


54


Product Service

SoS and QoS MW

Energy MWh

Demand Side Participation

DR DSM

Market delivers price signals

Market V-Q Restrictions

Tariff

Hedging Risk Adversion

Demand

Response)

Demand (including Demand

Response)

Adequacy of

Supply

Adequacy of

Supply

Marginal

Cost (LMP)

Marginal

Cost (LMP)

Generation

planning

Generation

expansion

planning Network

planning

Network

expansion

planning

Through demand-side

programs, customers will be

encouraged to be more

flexible in consumption

Need for integrating demand

response programs into long-

term investment planning

Create an adequate

regulatory framework that

allows network solutions

beyond the “investing in

copper” approach

Consumers’ willingness to adjust demand

profile.

• Own-price elasticity

• Cross-price elasticity


(1/2)

55


Our research is oriented towards the development of

methodologies, mathematical models and software

tools for the efficient, reliable and secure operation of

insular electricity networks under large-scale integration

of various RES technologies (e.g. wind, PV, small hydro

and wave energy).

The aim is to develop integrated sophisticated tools for

RES forecasting, as well as for short-term scheduling

and

long-term planning of insular electricity grids, involving

risk and uncertainty in a smart grid environment, and

also EES, electric vehicles, VPPs and DSM.


(2/2)

56


The ultimate goal is the generation of effective

solutions and information so that the integration of

insular and highly variable RES is maximized, while the

associated negative effects from a technical and

economic perspective are minimized, towards the

enhancement of the system security, reliability, and

quality of supply.

To sum up: advanced mathematical optimization models

are being developed, tested and validated in real-world

cases, handling forecasting, operations and planning

of power systems in an integrated, novel and improved

manner.

IGREENGrid 2nd Public Workshop

Javier Contreras

University of Castilla – La Mancha

[email protected]

04/12/2014

Madrid, Spain

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