Model coupling across scales - 03 Heinrichs (Jülich... · Wind years/ climate change Changes in...
Transcript of Model coupling across scales - 03 Heinrichs (Jülich... · Wind years/ climate change Changes in...
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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
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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]
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Flexibility…
Institute for Energy and Climate ResearchSystems Analysis and Technology Evaluation (IEK-STE)
[IEA, 2012]
Different definitions of flexibility of energy systems exist.
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The need to address flexibility
Institute for Energy and Climate ResearchSystems 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)
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Approaches to address flexibility
Institute for Energy and Climate ResearchSystems 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
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Typical characteristics of energy system models
Institute for Energy and Climate ResearchSystems 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 ResearchSystems 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
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Types of model coupling – example I
Institute for Energy and Climate ResearchSystems Analysis and Technology Evaluation (IEK-STE)
TIMES PLEXOS
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Types of model coupling – example I
Institute for Energy and Climate ResearchSystems 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
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Types of model coupling – example II
Institute for Energy and Climate ResearchSystems 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 ResearchSystems Analysis and Technology Evaluation (IEK-STE)
.
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
Institute for Energy and Climate ResearchSystems Analysis and Technology Evaluation (IEK-STE)
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
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Dimensions of differences – example I
Institute for Energy and Climate ResearchSystems 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
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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road transport in the EU ETS Results: cross sectoral efficient CO2 abatement strategies
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Dimensions of differences – example II
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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 ResearchSystems Analysis and Technology Evaluation (IEK-STE)
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)
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Challenges & limits of model coupling
Institute for Energy and Climate ResearchSystems 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
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Challenges & limits of model coupling
Institute for Energy and Climate ResearchSystems 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
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Conclusions
Institute for Energy and Climate ResearchSystems Analysis and Technology Evaluation (IEK-STE)
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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]
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References I
Institute for Energy and Climate ResearchSystems 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 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]
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References II
Institute for Energy and Climate ResearchSystems 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.