Lehrstuhl für Energiewirtschaft und Anwendungstechnik Prof. Dr.-Ing. U. Wagner, Prof. Dr. rer. nat. Th. Hamacher Integration of renewable energies: competition

Lehrstuhl für Energiewirtschaft und AnwendungstechnikProf. Dr.-Ing. U. Wagner, Prof. Dr. rer. nat. Th. Hamacher

Integration of renewable energies: competition between storage, the power grid and flexible demand

Thomas Hamacher


Introduction


Introduction

New power system

Cross sectorcoupling

New controls

New marketmechanism

New hierarchy ofsystemMicro-grid


Energy models

Energy Models

Eco

nom

y

Tech

nolo

gica

l cha

nge

Tech

nolo

gy

Hum

an b

ehav

iour

Env

ironm

ent


5

Database of renewable energies generation time series

• Wind speed in 50 m

• Radiation• Temperature in 2

m• Air pressure• Others

Available variables

• 1979 – “now”

Available timeframe

• NASA: MERRA-Reanalysis dataset

Data source

2/3 °resolution 540 data points

1/2

°re

solu

tion

3

61 d

ata

poin

ts

1 ho

ur

reso

lutio

n

8760

/878

4

data

poi

nts

~1,7 bn. data points per variable per year

Time series data

Static Data

• NASA• Other US/EU

Agencies• Universities

Data source

• Earth surface properties (land/sea, elevation, roughness of surface, …)

• Country/region boundaries• Others

Available data

Illustrations (pictures and videos) for reports and lectures

Renewable energies generation time series for modeling and statistical analysis

0 50 100 150 2000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Leistung gleichverteiltSchlechtester StandortBester StandortBevorzugung guter StandorteStarke Bevorzugung guter Standorte

Data processing based on different input data End products

Source Janker


Warming up with wind-statistics

Source Janker



Source Janker



Source Janker


A model to describe future power markets (URBS)

The year 2050 is modelled

Each country is a node in the model

New investments and power plant scheduling are the result of cost

minimisation Wind and PV are described by hourly resolved time series


The model: assumptions

Technology

Investment Cost

Fix Cost Lifetime

[€/kW] [€/kW] [a]

CCGT 750 11 30

PV- rooftop

1080 29 25

PV-utility

801 22 25

Wind-on 932 31 25

Wind-off 1495 60 20

Biomass 2450 80 25

Region Electricity demand [TWh]

Europe 3000

Trukey 509

MENA 970

In the year 2050 CO2-emissions are reduced by 95 % compared to the year 1990.


Wind as low cost option

0

25

50

75

100

125

150

175

200LCOE and install ratios of processes

DK

DK

NO

GB IE DK

NO

CZ

EG

EG

DZ

LY SE

SA EE

MA

DE

NL

SA TN

IT-S LV GR

DZ

JO LU LT LY CZ

MA

EG

SY

RO

AT

SY PL SI

NO TR

BE

LY DZ

FR

IT-N TN

DE

NL FI

BG JO SA

IT-S

GR

ES

-SE

S-E SK

LU HU

DK

GB

RO IE AT

CZ SI

BE

ES

-NW PL

IT-N PT

BG SE

CH

DE

ES

-SE

S-E DE

SK

HU

IT-S

GR

PT

LV LT EE

ES

-NW

ES

-S FR FI

ES

-NW SI

IT-S

ES

-SE

S-E

RO NL

BG

ES

-EG

RG

RB

EE

S-N

WR

OB

G FR

IT-N

IT-S

IT-N CH

HU

AT SI

LU IEG

BD

KS

KB

EN

LD

ES

EC

ZN

O FI

LV PL

EE

LT

© TUM IfE 69-028-L11

0

0.5

1

LCOE [€/MWh]

install ratio


Results



Results


Storage Option

100 €

1,000 €

10,000 €

1 10 100 1,000 10,000 100,000 1,000,000

inve

stm

ent

[€/k

W]

power [kW]

pumpedstorage

flywheel

lead-acidbattery

flow battery

sodium-sulforbattery

li-ion battery

hydrogen storage

© TUM IfE 81-001-L-13


Storage Option

10 €

100 €

1,000 €

10,000 €

1 10 100 1,000 10,000 100,000 1,000,000 10,000,000

inve

stm

ent

[€/k

Wh]

capacity [kWh]

pumped storage

flywheel

lead-acidbattery

flowbattery

sodium-sulforbattery

li-ionbattery

© TUM IfE 81-002-L-13


Model IMAKUS – structure

Model IMAKUS

© IfE, 61-074-F-11

Model MOWIKA

Power Plant Investment Planning

(Intertemporal)

Model MESTAS

Storage Investment Planning

(Green Field)

Unit Commitment and Economic Dispatch

Model MOGLIE

Determination ofSystem Firm Capacitiy

Determination of Required Minimum Installed Capacity

Input Data

Portfolio Data

Economic and Technical Parameters

Consumer Load

Must-run Feed-in(Renewable and CHP)

Constraint Parameters

Source: Kuhn, P.: Iteratives Modell zur Optimierung von Speicherausbau und –betrieb in einem Stromsystem mit zunehmend fluktuierender Erzeugung


Electricity Generation in Scenario with 15 % Lower Demand and 80 % Share of RES in 2050

-100

0

100

200

300

400

500

600

2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Ele

ctr

icit

y G

en

era

tio

n i

n T

Wh

RES & CHP Curtailment

Storages

Solar

Wind - Offshore

Wind - Onshore

Hydro

CHP

Geothermal

Biomass

Oil

Gas Turbine

CCGT

Hard Coal

Hard Coal 700°C

Lignite

Lignite 700°C

Nuclear

61405B13

Source: Kuhn, P; Kühne, M.; Heilek, C.: Integration und Bewertung erzeuger- und verbraucherseitiger Energiespeicher, KW21-Bericht, 2012


Storage expansion in Scenario with 15 % Lower Demand and 80 % Share of RES in 2050

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

2010 2015 2020 2025 2030 2035 2040 2045 2050

H2-CCGT

AA-CAES

PSP

PSP(existing)

61412B13

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Ins

tall

ed

Sto

rag

e C

apa

city

in

GW

h

H2-CCGT

AA-CAES

PSP

PSP(existing)

61412D13

0

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Ins

tall

ed

Ch

arg

ing

Po

wer

in

MW

H2-CCGT

AA-CAES

PSP

PSP(existing)

61413D13

0

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Ins

tall

ed

Dis

char

gin

g P

ow

er

in M

W

61414D13

Source:Kuhn, P; Kühne, M.; Heilek, C.:Integration und Bewertung erzeuger- und verbraucherseitiger Energiespeicher, KW21-Bericht, 2012

Charging Discharging

Capacity


Storage capacity expansion – comparison of different scenarios

20

Source: Kuhn, P; Kühne, M.; Heilek, C.: Integration und Bewertung erzeuger- und verbraucherseitiger Energiespeicher, KW21-Bericht, 2012

4,0

4,9

0,2

3,1

1,4

2,0

0 1 2 3 4 5

15% lower demand / 80% RES

25% lower demand / 80% RES

3% higher demand / 60% RES

with V2G

storage heating

NTC unbounded - 25 € per MWh

storage capacity expansion in the year 2050 in TWh

EV

DS

ME

xpo

rt

© If

E,

4836

4H13

Bas

e


Model predictive control of building automation

MPCelectrical

HVAC system buildingelectricityprice,weatherdata

self-adaptingmodel

€ °C


Conclusion

Large networks favour the integration of renewables, especially wind orlarge networks would favour the penetration of wind.

A better understanding of storage requires a better understanding of cross sector couplings and depends on the final mix.

Flexible demand is already possible in current systems (for examplebuilding controls) but requires quite sophisticated prediction systems.

Documents

Lehrstuhl für Energiewirtschaft und Anwendungstechnik Prof. Dr.-Ing. U. Wagner, Prof. Dr. rer. nat. Th. Hamacher Integration of renewable energies: competition