Model coupling across scales - 03 Heinrichs (Jülich... · Wind years/ climate change Changes in user behaviour Capture structural changes 17 Challenges & limits of model coupling

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Model coupling across scales

04. December 2014 | Heidi U. Heinrichs

An introduction to methodological aspects related to model coupling

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Flexibility Definition Needs Approaches

Model coupling Energy system models Types Dimensions Challenges & limits

Conclusions

1

Agenda

Institute for Energy and Climate ResearchSystems Analysis and Technology Evaluation (IEK-STE)

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„… ability to respond to – and to balance – supply and demand under rapid and large imbalances...“ [Gracceva & Zeniewski, 2014]

“…expresses the extent to which a power system can modify electricity production or consumption in response to variability, expected or otherwise.” [IEA, 2011]

2

Flexibility…


[IEA, 2012]

Different definitions of flexibility of energy systems exist.

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3

The need to address flexibility


Past High share of thermal power plants = sufficient flexibility

Trend Increasing share of volatile renewable energy sources (RES) = (short- and long-term) uncertainties

Source Mainly forecast deviations (i.e. wind feed-in, electricity exchange, end-use demand, fuel prices)

Options Demand response, grid and storage expansion, excess capacity, curtailment of RES

Previous sources of flexibility decrease. New sources and options need to be taken into account in analysis

approaches.

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here: focus on model coupling including the systems perspective (= energy system model)

4

Approaches to address flexibility


(Basic) heuristics Model coupling Specific indicators

Loss of Load Probability (LOLP)

Loss of Load Expectation (LOLE)

Magnetic & kinetic reserves (Hmag, Hkin)

Energy system model

Unit commitment /dispatch model

Macroeconomic models

Availability factors Reserve factors Function of RES

penetration level Operating reserve

requirements

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Full energy system Technology-rich (bottom-up) Medium- to long-term, multiple period time horizon Aggregation

Temporal = time slices (i.e. from 6 to 144) Spatial = limited number of regions

5

Typical characteristics of energy system models


No direct account of short-term uncertainties possible.

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Unidirectional

6

Types of model coupling


Iterative

Semi (derive heuristics) More than 2

model A model B model A model B

model A model B model C model A model B

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Unidirectional

Case study: Ireland, 2020 Motivation of coupling: accepting that one specific modelling

tool cannot model everything Results:

Crosschecking the technical appropriateness Most important technical constraint = start costs

7

Types of model coupling – example I


TIMES PLEXOS

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Types of model coupling – example I


[Deane et al., 2012]

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Iterative + more than 2

Case study: Germany, until 2030 Motivation of coupling: to cover divergent trends and their

interdependencies Results:

Equilibria between electricity costs & EV market share and between national & European power plant expansions

9

Types of model coupling – example II


EV-PEN/LVP PERSEUS-EU PERSEUS-DE

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Types of model coupling – example II


.

electricity imports and exports of DECO2 certificate prices

power plantexpansion in DE

Electric mobility model Energy system model PERSEUS-EMO*

DE + T-gridEU1 incl. ETS2

EV electricity demandEV load shifting potential

electricity and CO2 certificate prices

*PERSEUS-EMO: Program Packages for Emission Reduction Strategies for Energy Use and Supply – Electric Mobility, 1EU: only those countries who mainly influences the German energy system, 2ETS: Emission Trading System

passenger road transport

technicalEV potential

economicEV potential

EV marketpenetration

mobility surveys

EU EV market penetration

[Heinrichs, 2013]

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Temporal

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Dimensions of differences


Spatial

Method Optimization Simulation Heuristic …

System boundary

… …

macroeconomicsenergy system

supplysector

demandsector

distributionsector

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Case study: Germany, until 2030 Motivation: to analyse the impacts of EV on the German grid Results:

simple heuristic for spatial distribution of new power plants no need for new power plant sites

12

Dimensions of differences – example I


IKARUS grid model

Spatial System boundary Temporal Method

Germany grid nodes

energy system electricity grid

time slices hours

LP simulation

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13

Dimensions of differences – example I

legendnew power plant

GasLigniteCoal

power plants 2010NuclearLigniteGasCoal

legendpower plants 2030

LigniteGasCoal

[Linssen et al., 2012]Institute for Energy and Climate ResearchSystems Analysis and Technology Evaluation (IEK-STE)

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t Case study: Germany, until 2030 Motivation: to analyse sectoral interdependencies of including

road transport in the EU ETS Results: cross sectoral efficient CO2 abatement strategies

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Dimensions of differences – example II


COMIT PERSEUS

Spatial System boundary Temporal Method

Germany Europe

energy system road transport

time slices years

LP simulation

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Dimensions of differences – example II


Passenger road transport

CO2 emission tradingFreight road transport

fuel price

certificatedemand

CO2 market

fuel demand/ CO2 demand

train and IWW

company cars

fuel price

fuel demand/ CO2 demand

car demand

Agent

oil companies

Agents

shipper, haulieror carrier

Agents

car companies(Agents)

private cars

Class

Class

With:information flows

ZEW shipment databaseIWW inland waterwaysMOP German Mobility Panel database

car demand

ZEW

logit choice

households

Agents

MOP

transport demand

vehicles

cc‐indigenous resources

cc‐regional fuelmarket

uraniumlignite

localgaslocalcoal

ligniteheavy‐/fueloiluraniumhydro‐river/reservoir

hydro‐river/reservoir/smallwind‐on/offbiomass/‐gas/‐wastegeothermal

worldgasworldcoalworldoil

cc‐regional fuelnode

cc‐industrialheatgrid

cc‐ind.powergrid

‐imp

cc‐industrialsupply

from‐storage

cc‐green

generators

hydro‐smallwind‐on/offbiomass/‐gas/‐wastegeothermal

cc‐district‐heat

cc‐districtheat consumers

cc‐electricity consumers

cc‐heat

consumers

districtheat

cc‐electr‐district‐demand

cc‐heat‐demand

heat_use electricity_use

cc‐pumpedstorage

cc‐externalgridnode

to_storage

heat/heat‐smallchp

cc‐internalgridnodecc‐utilitysupply

cc‐dc_cable‐node

gascoaloil

neigh‐bouringcountry

electricity demandheat demand

regional energy carrierworld market fuels

district‐heat

cc‐renew‐ables

cc‐industrial producers

cc‐ind.powergrid

‐exp

cc‐district heating

cc‐utilityproducersfossil

cc‐utilityproducershydro

cc‐utilityproducersnuclear

electricityheat

CO2 marginal cost

allowance demand of road transport, EV market penetrationand electricity demand

[Heinrichs et al., 2014]

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General: Global optimum Convergence in iterative model coupling (bang-bang) Computational capacity requirements (hard- & software, time) Expertise in each modelling tool Data basis

Obligation of confidentiality (in collaborations) Different base years/ calibration (possibly high effort) Different methods (i.e. costs & end user prices)

16

Challenges & limits of model coupling


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Temporal Characteristic years single year Wind years/ climate change Changes in user behaviour Capture structural changes

17

Challenges & limits of model coupling


Spatial RES (repowering, potential,

investment decisions/ capability) Demand (demographic/ migration

movement, economic growth, behaviour, public perception)

Method Objective function Discount rate Basic assumptions of approaches …

System boundary Packages of measures/

technology types Technical assumptions (CHP

heat led? ) Impact of different sectoral detail

level on model results

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Flexibility need to be addressed in energy system models Several approaches exist to address flexibility in energy

system models Model coupling is one approach to address flexibility Different types and dimensions of model coupling exist One of the biggest challenges: enough capacity (time,

human resources, hard-/software, data) to cover the scales of model coupling

Possible correlations with uncertainties of other time horizons (compared to flexibility) should be additionally taken into account

18

Conclusions


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Thank you very much for your attention!

04. December 2014 | Heidi U. Heinrichscontact: [email protected]

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[Gracceva & Zeniewski,2014][Enzensberger,2003]

[Rosen,2007]

[Deane et al.,2012]

[Drouineau et al.,2014]

[Welsch et al.,2014]

[Pesch et al.,2014]

[Heinrichs,2013]

[Heinrichs et al.,2014]

20

References I


Gracceva F, Zeniewski P. A systematic approach to assessing energy security in a low-carbon EU energy system. Applied 123 (2014) 335-348.Enzensberger N. Entwicklung und Anwendung eines Strom- und Zertifikatemarktmodells für den europäischen Energiesektor. Fortschr.-Ber. VDI Reihe 16 Nr. 159. Düsseldorf: VDI Verlag 2003.Rosen J. The future role of renewable energy sources in European electricity supply – A model-based analysis for the EU-15. Universitätsverlag Karlsruhe 2007.Deane J P, Chiodi A, Gargiulo M, Ó Gallachóir B P. Soft-linking of a power systems model to an energy systems model. Energy 42/1 (2012) 303-312.Drouineau M, Maizi N, Mazauric V. Impacts of intermittent sources on the quality of power supply: The key role of reliability indicators. Applied Energy 116/1 (2014) 333-343.Welsch M, Mentis D, Howells M. Long-term energy systems planning: accounting for short-term variability and flexibility. in Jones L E (editor). Renewable Energy Integration. Academic Press 2014.Pesch T, Allelein H-J, Hake J-F. Impacts of the transformation of the German energy system on the transmission grid. Eur. Phys. J. Special Topics 223 (2014) 2561-2575.Heinrichs H. Analyse der langfristigen Auswirkungen von Elektromobilität auf das deutsche Energiesystem im europäischen Energieverbund, KIT Scientific Publishing, Karlsruhe 2013.Heinrichs H, Jochem P, Fichtner W. Including road transport into the EU-ETS: a model based analysis of the German electricity and transport sector. Energy 69 (2014) 708-720.

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[IEA, 2011]

[IEA, 2012]

[Linssen et al., 2012]

21

References II


IEA. Harnessing Variable Renewables - A Guide to the Balancing Challenge, OECD/IEA, Paris, 2011.IEA. Energy Technology Perspectives 2012: Pathways to a Clean Energy System, International Energy Agency, Paris, 2012.J. Linssen, A. Schulz, S. Mischinger, H. Maas, C. Günther, O. Weinmann, E. Abbasi, S. Bickert, M. Danzer, W. Hennings, E. Lindwedel, S. Marker, V. Schindler, A. Schmidt, P. Schmitz, B. Schott, K. Strunz, P. Waldowski. Netzintegration von Fahrzeugen mit elektrifizierten Antriebssystemen in bestehende und zukünftige Energieversorgungs-strukturen, Advances in Systems Analyses 1, Schriften des Forschungszentrums Jülich Reihe Energie & Umwelt / Energy & Environment Band / Volume 150, 2012.

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Model coupling across scales - 03 Heinrichs (Jülich... · Wind years/ climate change Changes in user behaviour Capture structural changes 17 Challenges & limits of model coupling