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Faculty of Business and Economics , Chair of Energy Economics, Prof. Dr. Mst Interaction of sector coupling technologies with further flexibility options in energy systems with www.ee2.biz different PV-Wind shares Christoph Zphel ENERDAY,

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SLIDE 1

Faculty of Business and Economics, Chair of Energy Economics, Prof. Dr. Möst

www.ee2.biz

Interaction of sector coupling technologies with further flexibility

  • ptions in energy systems with

different PV-Wind shares

Christoph Zöphel

ENERDAY, 12th of April 2019

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SLIDE 2

TU Dresden, Chair of Energy Economics 2 12th of April 2019

Agenda

  • 1. Motivation
  • 2. Development of high RES scenarios with different wind-PV share in

central western Europe

  • 3. Model approach including different flexibility options and sector

coupling technologies

  • 4. Results without further restrictions for sector coupling technologies
  • 5. Influence of enforced sector coupling with varying flexibility
  • 6. Summary
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SLIDE 3

TU Dresden, Chair of Energy Economics 3 12th of April 2019

Future European renewable energy expansion mainly based on fluctuating renewable energy

Differences in the electricity generation characteristics for wind and PV

  • Availability
  • Temporal
  • PV is correlating daily with demand
  • Wind is correlating seasonally with

demand

  • Spatial
  • Day-night dependency of PV generation

results in high spatial correlation

  • Stronger local variability of wind

generation leads to spatial balancing effects Additionally, future RES expansion not only driven by techno-economical factors, but also by challenges regarding land use and acceptance

(seasonal) mo monthly me means 2014

Me Mean an hour urly correl elation

  • n

Me Mean an seas ason

  • nal

al correl elation

  • n

PV PV 0,78 0,98 Wind Ons nsho hore 0,25 0,72 Wind nd

  • ff

ffsho hore 0,29 0,75 0,0 0,5 1,0 1,5 2,0 2,5 normalized power Month of a year

PV Wind

  • nshore

Wind

  • ffshore

Demand

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SLIDE 4

TU Dresden, Chair of Energy Economics 4 12th of April 2019

Studies included with

  • Europe as observed region
  • Scenarios for the years beyond 2030
  • Data for installed capacities or generation

Varying future PV shares in Literature as basis for scenario development

n = 26

PV share PV V shar share in in RE RES S gen eneration mix ix

EU Reference Scenario 2050 Europe 2017 (EEA (2017)) TYNDP Scenario DG 2040 Roadmap 2050 BMWi long-term scenarios, Base scenario 2050 Eurelectric Power Choices Scenario 2050 Greenpeace Revolution Scenario (100 % RES)

Sce Scenario PV PV-Wind shar share Hig igh PV 50:50

40:60

REF REF 30:70

20:80

Hig igh Win ind 10:90

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SLIDE 5

TU Dresden, Chair of Energy Economics 5 12th of April 2019

GIS based restriction of raster areas

  • Nature reserve
  • Height and slope

+ urban areas and meadows MERRA 2 weather data for

  • Solar radiation & temperature
  • Wind speed and roughness

Weather- and GIS-Data based optimal wind and PV expansion up to share of 80% of today’s electricity demand in CWE

Transformation in PV and wind electricity generation time series

Optimal wind and PV expansion for each of the scenarios and countries Objective: Minimizing specific investment costs Restriction:

  • Wind and PV share of 80% of today’s CWE electricity demand

(~ 2,700 TWh)

  • Restriction of land use
  • Country specific minimal and maximal RES expansion

Land use potentials for wind and PV

RES potentials for 17 countries (1481 raster à 0,5 ° x 0,625 ° / 68 x 68 km)

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SLIDE 6

TU Dresden, Chair of Energy Economics 6 12th of April 2019 Inst stalled d cap apac acities

Re Resi sidual load d dur duration

  • n cur

curves ves at 80 80 % % RE RES sha hare acr cross all model elled ed co count untries es

Resulting flexibility need in the scenarios

  • More than 1000 GW fluctuating

RES in each scenario

  • Lower availability of PV results in

higher capacity requirements

  • Small differences in positive

residual load peak

  • Increasing amount surplus

energy and negative peak with increasing PV share due to feed in characteristics

1010 584 203 386 546 723 65 92 121 200 400 600 800 1000 1200 1400 1600 High PV REF High Wind

Capacity [GW]

wind offshore wind onshore PV

  • 800
  • 600
  • 400
  • 200

200 400

[GW] [Geordnete Stunden]

High PV REF High Wind

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SLIDE 7

TU Dresden, Chair of Energy Economics 7 12th of April 2019

Model based analysis of optimal combinations of flexibility

  • ptions

Endogenous Investment (Greenfield) Objective: Cost minimization of investments Temporal resolution: Cluster based selection of 10 representative weeks Dispatch Objective: Cost minimal dispatch of fixed flexible capacities Hourly resolution for an entire year Input Infrastructure Supply Side Demand Side

  • 17 countries
  • Technology specific power plant

characteristic

  • 3 storages, 6 DSM processes
  • Heat pumps, E-vehicles,

electrolyser

  • European transmission capacity

(NTC)

  • RES capacity and generation

profiles per country

  • Prices for fuel und emission
  • Costs of flexibility options
  • Hourly electricity demand per

country (ENTSO-E 2018)

  • Hourly district heat demand

profiles per country

  • Hourly charging and parking

profile for e-vehicles per country

Modelling Electricity market model (ELTRAMOD) Output

  • Installed conventional and

renewable capacities

  • Installed storage and DSM

capacities

  • NTC-expansion
  • Investment in Power-to-X

technologies

  • Investment costs

Optimal flexibility mix in an electricity market perspective

  • Technology specific dispatch

energy

  • Export-Import flows
  • Integrated/curtailed RES
  • CO2-emissions
  • Dispatch costs

p q t=n-1

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SLIDE 8

TU Dresden, Chair of Energy Economics 8 12th of April 2019

System boundaries and selected sectors coupling technologies

Electricity sector District heating Passenger Transport Hydrogen demand

  • f industry

Heat pump CHP Vehicle kilometres Heat storages Electrolyser BEV Fluctuating RES Dispatchable power plants Storages / DSM Import/Export Electricity demand Gas boiler (natural gas) Steam reforming (natural gas) ICE (gasoline)

Benchmark- process

Heating demand

Bot

  • ttom-up elec

ectric icit ity y mar market mod model el Simplif lifie ied representatio tion of f se sele lected ene nergy y de demand se sectors

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SLIDE 9

TU Dresden, Chair of Energy Economics 9 12th of April 2019

Installed capacities

Trade-off between storages and NTC in case of unrestricted endogenous investment in flexibility options

  • High correlation of RES

generation results in lowest NTC expansion and highest installed storage capacity in High PV scenarios

  • Lowest installed conventional

capacities in REF scenario due to storage and NTC mix

  • Investment in full DSM potential

to balance shorter term fluctuations

  • Only endogenous investments

in heat pumps due to high

  • pportunity costs of benchmark

process

Installed sector coupling technologies

Base scenarios

74 12 54 148 149 152 184 152 96 57 57 57 195 315 253

100 200 300 400 500 600 700 800 High PV Ref High Wind [GW] NTC DSM Storages Renew. Power plants

  • Conv. Power

plants

61 63 64

10 20 30 40 50 60 70 High PV Ref High Wind [GW] BEV (charging power) Electrolyser Heat pump

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SLIDE 10

TU Dresden, Chair of Energy Economics 10 12th of April 2019

Goal to decarbonise energy system might enforce electrification of parts of other sector

Basic scenarios No restrictions for energy supply by PtX

Besides heat pumps no further endogenous investments in Power-to-X (PtX) technologies  No electricity market based incentives for sector coupling within scenario framework

Enforced sector coupling Exogenous restrictions for energy supply by PtX with low and high flexibility Power-to-Heat Power-to-Vehicle Power-to-Gas

  • Heat supply by heat

pumps to cover 50% of district heat demand

  • Additional electricity

demand: 242 TWh

  • 50 % BEV for passenger

transport

  • Charging power: 11 kW
  • Additional electricity

demand: 260 TWh

  • 50 % of industries

hydrogen demand by electrolysers

  • Additional electricity

demand: 195 TWh Low flexibility (LF)

  • Without thermal energy

storages

  • Uncontrolled charging
  • Maximal full load hours of

electrolysers High flexibility (HF)

  • With thermal energy

storages

  • Bi-directional charging
  • With hydrogen storages
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SLIDE 11

TU Dresden, Chair of Energy Economics 11 12th of April 2019

Enforced sector coupling influences investment in flexibility

  • ptions differently
  • Increase in electricity demand

requires additional power plant capacities

  • Higher flexibility of PtX decreases

need for additional power plants

50 100 150 200 High PV Ref High Wind

Installed capacity [GW]

Con

  • nventional powe

power pl plants ts

base LF HF 50 100 150 200 High PV Ref High Wind

Installed capacity [GW]

Storages

base LF HF 100 200 300 400 High PV Ref High Wind

Installed capacity [GW]

NT NTC

base LF HF

  • Low flexibility of PtX technologies

slightly increases storage capacities in most of the scenarios

  • Significant reduction of storage

requirements with highly flexible sector coupling

  • Small impact of sector coupling

technologies on NTC expansion

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SLIDE 12

TU Dresden, Chair of Energy Economics 12 12th of April 2019

Wind-PV share and level of flexibility influences the achievable emission reduction by sector coupling

  • Electrification of other sectors

decreases emissions in these sectors and compensates increase in emissions in electricity sector in most scenarios

  • In base case, highest CO2 emissions in

the High PV scenario, due to highest amount of conv. electricity generation

  • For inflexible sector coupling, CO2

emissions increase with higher PV shares due to discontinuous PV generation and higher conv. electricity generation

  • Flexible sector coupling allows for better

use of RES surplus phases, resulting in emission reductions by 11 % (High Wind) and 20 % (High PV) compared to base case

100 200 300 400 500 600 REF base REF LF REF HF [Mio. t/a] Hydrogen Passenger transport Heating Electricity

Composition of emissions with different sector coupling approaches (REF as example) Total emissions* within system boundaries

*CO2 Emissions include benchmark processes as well as hourly emission factors for direct electricity demand and electricity for sector coupling

400 450 500 550 600 650 700 High PV REF High Wind

[Mio. t/a]

base LF HF

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SLIDE 13

TU Dresden, Chair of Energy Economics 13 12th of April 2019

Summary

  • Lower availability and higher correlation of PV generation lead to

higher flexibility requirements in energy systems with higher PV shares

  • Wind-PV share in total RES generation influences strongly

composition of optimal flexibility provision

  • An enforced sector coupling requires additional conventional

capacities (if there is no further RES expansion) and less storages capacities

  • Nevertheless, further emission reductions can be achieved when

electrification substitutes carbon intensive benchmark processes in respective sectors

  • Only with flexible sector coupling RES surplus phases can be used
  • ptimally and CO2 Emissions can be reduced significantly
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SLIDE 14

Fakultät für Wirtschaftswissenschaften, Lehrstuhl für Energiewirtschaft, Prof. Dr. Möst