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Model coupling across scales An introduction to methodological aspects related to model coupling Mitglied der Helmholtz-Gemeinschaft 04. December 2014 | Heidi U. Heinrichs Agenda Flexibility Definition Needs Approaches

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Mitglied der Helmholtz-Gemeinschaft

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 Research Systems 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…

Institute for Energy and Climate Research Systems Analysis and Technology Evaluation (IEK-STE) [IEA, 2012]

Different definitions of flexibility of energy systems exist.

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3

The need to address flexibility

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

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

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

(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

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

No direct account of short-term uncertainties possible.

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  • Unidirectional

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Types of model coupling

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

  • 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

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

TIMES PLEXOS

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Types of model coupling – example I

Institute for Energy and Climate Research Systems Analysis and Technology Evaluation (IEK-STE) [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

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

EV-PEN/LVP PERSEUS-EU PERSEUS-DE

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Types of model coupling – example II

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

  • .

electricity imports and exports of DE CO2 certificate prices power plant expansion in DE

Electric mobility model Energy system model PERSEUS-EMO*

DE + T-grid EU1 incl. ETS2 EV electricity demand EV 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 technical EV potential economic EV potential EV market penetration mobility surveys

EU EV market penetration

[Heinrichs, 2013]

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  • Temporal

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Dimensions of differences

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

  • Spatial
  • Method
  • Optimization
  • Simulation
  • Heuristic
  • …
  • System boundary

… … macroeconomics energy system supply sector demand sector distribution sector

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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

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

IKARUS grid model Spatial System boundary Temporal Method Germany  grid nodes energy system  electricity grid time slices  hours LP  simulation

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Dimensions of differences – example I

legend new power plant

Gas Lignite Coal

power plants 2010

Nuclear Lignite Gas Coal

legend power plants 2030

Lignite Gas Coal

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

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  • 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

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

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

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

Passenger road transport CO2 emission trading Freight road transport

fuel price certificate demand

CO2 market

fuel demand/ CO2 demand

train and IWW company cars

fuel price fuel demand/ CO2 demand car demand Agent

  • il companies

Agents

shipper, haulier

  • r carrier

Agents

car companies

(Agents)

private cars

Class Class

With: information flows ZEW shipment database IWW inland waterways MOP German Mobility Panel database

car demand

ZEW

logit choice households

Agents

MOP

transport demand vehicles

cc‐ indigenous resources

cc‐regional fuelmarket

uranium lignite localgas localcoal lignite heavy‐/ fueloil uranium hydro‐river/ reservoir hydro‐river/reservoir/small wind‐on/off biomass/‐gas/‐waste geothermal worldgas worldcoal worldoil

cc‐ regional fuelnode cc‐ industrial heatgrid cc‐ind. powergrid ‐imp cc‐ industrial supply

from‐storage

cc‐ green generators

hydro‐small wind‐on/off biomass/ ‐gas/‐waste geothermal

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‐ pumped storage cc‐ external gridnode

to_storage heat/ heat‐ smallchp

cc‐internalgridnode cc‐utilitysupply cc‐ dc_cable‐ node

gas coal

  • il

neigh‐ bouring country electricity demand heat demand regional energy carrier world market fuels

district‐ heat

cc‐ renew‐ ables cc‐ industrial producers cc‐ind. powergrid ‐exp cc‐ district heating cc‐utility producers fossil cc‐utility producers hydro cc‐utility producers nuclear electricity heat

CO2 marginal cost allowance demand of road transport, EV market penetration and 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)

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Challenges & limits of model coupling

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

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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

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

  • 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

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

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Thank you very much for your attention!

  • 04. December 2014 | Heidi U. Heinrichs

contact: h.heinrichs@fz-juelich.de

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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

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

  • n 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

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

  • 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.