Evaluating the capacity of Integrated Assessment Models (IAMs)
to represent system integration challenges of wind and solar power Robert Pietzcker*, Falko Ueckerdt, Gunnar Luderer
PIK – Potsdam Institute for Climate Impact Research
Yvonne Scholz, Hans‐Christian Gils
DLR ‐ Deutsches Zentrum für Luft‐ und Raumfahrt
Samuel Carrara, Harmen Sytze de Boer, Jacques Després, Shinichiro Fujimori, Nils Johnson, Alban Kitous, Patrick Sullivan
Supported by EU FP7 Project ADVANCE (FP7/2012 n°308329)
IEW 2016, Cork, June 2nd
*pietzcker@pik‐potsdam.de
MOTIVATION
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Large‐scale IAMs are important
IAM characteristics:
• Global coverage (all emitting & resource providing countries)
• Coverage of all (energy) sectors + agriculture
• Coverage of full 21st century
IAMs are needed for policy advice
Create self‐consistent long‐term global mitigation scenarios that • allow to put NDCs into context of global climate targets
• help identify crucial bottleneck technologies
• derive consistent mid‐term energy and capacity needs
3
3
What is the challenge?
IAMs are aggregated, abstract representations of complex
dynamics
4

How do we know if an IAM represents the complex
dynamics in an adequate way?

Evaluation (Validation?) 4
Power sector: how to model wind & solar
Why focus on power sector and wind / solar? • Power sector decarbonizes earliest and deepest
• Temporal variability and spatial heterogeneity
 Modeling challenge: 5 year model time step vs. hourly fluctuations
• Substantial development of bottom‐up knowledge over last five years
5
5
VRE integration in IAMs – state in 2012
Net share of wind and solar [%]
Many different modeling approaches*, e.g.
• No integration challenges
• Hard upper bounds (e.g., max 30% VRE)
• Small number of time slices
Wide range of results –
due to real uncertainty, or
due to modeling issues,
outdated cost data, etc?
Year
6
* Luderer et al, (2014) „The role of renewable energy in climate stabilization: results from the EMF27 scenarios“. Climatic Change
6
ADVANCE project
Aim: • Improve the representation of VRE integration challenges
• Six IAMS: AIM/CGE, IMAGE, MESSAGE, POLES, REMIND, WITCH
Approach:
• Develop insights with detailed hourly power sector model
• Develop data set on wind and solar resource, and correlation with load
• Develop/improve different IAM power sector modeling approaches
• Evaluate these modeling approaches
7
How to evaluate different approaches?
Qualitative
• Identify crucial features of power systems and the effect that wind and
solar have on it
• Evaluate if a certain approach can represent these features
Quantitative:
• Run diagnostic IAM scenarios, compare IAM results to results from
detailed hourly power sector model
8
QUALITATIVE EVALUATION
9
Qualitative evaluation framework
• 17 power sector features that are well‐aligned with modeling topics
Investment dynamics
Power system operation
Temporal matching of VRE and demand
Storage
Grid
Investment into dispatchable technologies
Investment into VRE
Expansion dynamics
Capital stock inertia and vintaging
Structural shift
Love of variety
Dispatch
Flexibility and ramping
Capacity adequacy
Curtailment
Wind/solar complementarity
Demand profile evolution
Short‐term storage
Seasonal storage
Demand response (incl. electric vehicles & V2G)
Grid expansion linked to VRE
Pooling effect from grid
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Stylized features – example Investment Dynamics
Investment dynamics
Investment into dispatchable techn.
Model
Investment into VRE
Structural shift
homogeneous good; Int.Opt. provides feedback share dependent on effects of VRE on VRE‐
++
MESSAGE ++ flex&cap constraint ++
share‐dependent (+) flex. & partially reproduce RLDC cap. equation (+)
shape (++)
possible
Int.Opt. provides full region‐specific RLDCs +++ feedback on effects of VRE ++
REMIND +++
with 4 load bands
investment on RLDC
possible
WITCH
+
homogeneous good; flex&cap constraint creates demand for +
peak‐load technologies (+)
Int.Opt. provides feedback on effects of VRE on +
flexibility constraint and capacity equation (+)
possible, but limited by CES4 with elasticity 5
* Pietzcker et al: „Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power” (under review at Energy Economics).
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Stylized features ‐ example 2
Power system operation
Model
Flexibility and ramping
POLES
++
EU: explicit ramping on hourly representative days (+++); Non‐EU: only ex‐post check of ramping/flexibility (‐)
REMIND
+
indirectly through RLDC‐driven switch to low‐capital flexible technologies
* Pietzcker et al: „Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power” (under review at Energy Economics).
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Stylized features ‐ example 3
Model
MESSAGE
POLES
REMIND
Seasonal storage
+
+
++
Storage
Demand response (incl. electric vehicles & V2G)
endogenous investment into hydrogen electrolysis to use curtailed electricity
0
EU: Endogenous H2 electrolysis (+) reacts to curtailment (+); Non‐EU: +
no seasonal storage (‐)
Endogenous H2 electrolysis uses curtailed electricity (+); CF depends on curtailment (+)
0
na
EU: explicit V2G & DR modeling (++); Non‐EU: heuristic modeling on combinatorial RLDC1 with region‐mixed data2 (‐)
na
* Pietzcker et al: „Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power” (under review at Energy Economics).
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Qualitative evaluation framework
Investment dynamics
Investment into dispatchable techn.
Model
AIM/CGE 0
IMAGE +++
MESSAGE ++
Capital stock inertia & vintaging
homogeneous good
+
na
+
region‐specific RLDCs with 20 load bands
non‐exponential ++ (+) vintaging (+) 0
of capacities
na
++
homogeneous good; share dependent flex&cap constraint partially reproduce RLDC shape (++)
non‐exponential ++ (+) vintaging (+) ++
of capacities constraints on expansion rate that can be weakened at additional cost
++
but combinatorial RLDC
+
(‐) with region‐mixed 2
data (‐); REMIND +++
WITCH
+
region‐specific RLDCs with 4 load bands
1 non‐exponential ++ (+) vintaging (+) 0
of capacities
na
Storage
Investment into VRE
0
RLDC load bands (+++); POLES
exponential vintaging
Expansion dynamics
Structural shift
Curtailment and storage increase LCOE
Curtailment and storage increase LCOE (+); backup cost markups partially emulate additional VRE interaction (+)
++
possible
++
logit
++
possible
++
logit
Int.Opt. provides feedback on effects of VRE on VRE‐
++
share‐dependent (+) flex. & cap. equation (+)
possible
+
Curtailment increases investment LCOE
+
+
possible, but limited by slow convergence of non‐cost logit parameters adjustment costs Int.Opt. provides full non‐exponential that increase non‐
+++ feedback on effects of VRE ++
++ (+) vintaging (+) ++
linearly with fast investment on RLDC
of capacities
expansion
homogeneous good; flex&cap constraint with fixed parameters +
creates demand for peak‐
load technologies (+)
exponential vintaging
+
hard constraints +
on expansion rate
Love of variety
Int.Opt. provides feedback on effects of VRE on flexibility constraint and capacity equation (+)
AIM/CGE +
IMAGE
++
intertemporal optimization & expansion MESSAGE +
constraints ensure variety
++
logit
POLES
+
3
intertemporal opt. & adj. +
costs ensure REMIND
variety
possible
WITCH
possible, but +
Short‐term storage
Model
4 limited by CES
with elasticity 5
+
CES
4
AIM/CGE 0
IMAGE
+++
MESSAGE +
na
Flexibility and ramping
0
na
na
na
0
na
0
na
+
Region‐wide pooling contained ex ante in the RLDC
Exogenous storage investments based on VRE‐shares(+), effect 0
on curtailment & capacity based on DIMES
na
0
na
+
aggregated grid cost markups based on distance of VRE to load centers
+
Region‐wide pooling contained ex ante in the RLDC
endogenous investment into hydrogen electrolysis 0
to use curtailed electricity
na
0
na
+
Region‐wide pooling contained ex ante in the RLDC
EU: explicit V2G & DR modeling (++); Non‐EU: heuristic modeling on +
Endogenous storage driven by share‐dependent flex equation and curt. (+++), but relies only on US data to model the effect of storage (‐‐)
++
dispatch on RLDC +
with 156 time slices
technologies can be used in flexible or ++
baseload mode
flexibility constraint in combination with two modes of operation for ++ RLDC‐derived CV for VRE ++
dispatchable technologies
++
EU: hourly dispatch EU: explicit ramping on on 12 representative hourly representative days (+++); ++ days (+++); Non‐EU: only +
Non‐EU: dispatch on ex‐post check of 2 days (‐)
ramping/flexibility (‐)
REMIND
++
dispatch according to RLDC with 4 +
loadbands
WITCH
0
na
+
+
EU: Endogenous storage on EU: Endogenous H2 representative days (+++), but electrolysis (+) reacts to only within‐day storage (‐); Non‐ +
+
curtailment (+); Non‐EU: no EU: exogenous within‐day seasonal storage (‐)
storage on RLDC basis (‐)
1
combinatorial RLDC with 2
region‐mixed data (‐)
Region‐wide pooling EU: endogenous grid modeling for dispatch explicitly (+++); investment heuristic modeled in EU (++); based on use (not value), +
Investment RLDC only benefit for peak reduction not contains country‐level modeled (‐); Non‐EU: no grid (‐)
pooling (‐) ++
Exogenous inv. into storage Endogenous H2 based on VRE‐shares (+); effect electrolysis uses curtailed ++
0
on curtailment, capacity and electricity (+); CF depends RLDC shape from DIMES (+)
on curtailment (+)
na
+
aggregated grid costs depending on VRE share
+
Region‐wide pooling contained ex ante in the RLDC
+
Endogenous storage modeling driven by capacity & flexibility 0
equation with fixed coeffcients
na
+
aggregated grid costs depending on VRE share
+
Region‐wide pooling contained ex ante in the RLDC
Curtailment
indirectly through RLDC‐
driven switch to low‐
++ RLDC‐derived CV for VRE ++
capital technologies
POLES
Pooling effect from grid
na
0
Temporal matching of VRE and demand
Capacity adequacy
0
Grid expansion linked to VRE
Exogenous storage investments based on VRE‐shares; effect on 0
curtailment based on DIMES
Power system operation
Dispatch
Model
Grid
Demand response (incl. electric vehicles & V2G)
Seasonal storage
Wind/solar complementarity
Demand profile evolution
wind‐solarRLDC (+++); based on region‐
specific RLDC
+
0
na
based on region‐
specific RLDC
wind‐solar RLDC (+++); ++ parameterized backup req. ignore 0
wind/solar correlation (‐) na
based on region‐
specific RLDC
basic uses wind‐solar RLDC (+++); relies representation of on single wind‐solar mix per ++
+
changing region to parameterize flex. & cap. importance of equations (‐)
different sectors
1
no cross‐product interation (‐); no effect on capacity/dispatch (‐)
EU: based on dispatch EU: explicit W&S interaction in basic 2
model (+++); Non‐EU: representative days for dispatch representation of RLDC can lead to (+++); Non‐EU: combinatorial +
changing overcapacity in regions + based on combinatorial +
2
2
3
importance of RLDC (‐) with region‐
RLDC (‐) with region‐mixed data (‐
where VRE match peak 3
different sectors
)
demand (‐)
mixed data (‐)
RLDC(++); combinatorial indirectly through RLDC‐
driven switch to low‐
++ RLDC‐derived CV for VRE ++
capital technologies
flexibility constraint with CV for each VRE type +
+
fixed parameters
decreases with VRE share
based on region‐
specific RLDC
+++
implicitly contained in +
the CES function
explicit wind‐solar interaction from RLDC
0
na
non‐linear CES function favours mix of wind and solar
0
na
Qualitative evaluation framework can
• provide in‐depth insights into strenghts/limitations of different approaches
• help IAM teams to prioritize model improvements
* Pietzcker et al: „Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power” (under review at Energy Economics).
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QUANTITATIVE EVALUATION
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REMIX model results for validation
• Hourly dispatch and investement model REMIX
• Covers all of Europe
• Optimizes investment into and dispatch of
– Fossil technologies
– Transmission grid
– Storage
• Scenarios cover a wide range of VRE shares
Scholz et al: “Application of a high‐detail energy system model to derive power sector characteristics at high wind and solar
shares” (under review at Energy Economics). 16
Compare IAMs to REMIX: Capacity Factor
Decrease of capacity factor of residual system (dispatchable technologies + storage) with increasing VRE share
 Decent agreement
* Pietzcker et al: „Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power” (under review at Energy Economics).
17
Compare IAMs to REMIX: Storage & Curtailment
Convex increase of storage and curtailment with increasing VRE share
Storage
Curtailment & H2 production
Similar shape, rough agreement
IAMs favour storage over curtailment
* Pietzcker et al: „Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power” (under review at Energy Economics).
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AGGREGATED IAM RESULTS
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Wind and Solar – before & after ADVANCE
Net share of wind and solar
[%]
• Update representation of wind and solar: integration challenges, technology costs, resource potentials
• With new models, W&S results under climate policy are more robust accross models
• Contribution of wind and solar increased in all models
* Pietzcker et al: „Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power” (under review at Energy Economics).
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Summary
• We developed a qualitative framework to evaluate power sector
modeling approaches and applied it to six IAMs
– Insights for users of model results and modelers themselves
• We quantitatively evaluated power sector modeling results from
IAMS by comparing to hourly dispatch/ investment model
• We found that improving the representation of W&S integration
challenges and updating resource & cost data
– yields more robust results
– increases cost‐optimal deployment of wind and solar
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Thank you!
References:
Main content: Pietzcker, R.C., Ueckerdt, F., Carrara, S., De Boer, H.‐S., Després, J., Fujimori, S., Johnson, N., Kitous, A., Scholz, Y., Sullivan, P., Luderer, G.: “Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power” (under review at Energy Economics).
Luderer et al, (2014) „The role of renewable energy in climate stabilization: results from the EMF27 scenarios“. Climatic Change 123, 427–441. doi:10.1007/s10584‐013‐0924‐z.
Ueckerdt, F., Brecha, R., Luderer, G., Sullivan, P., Schmid, E., Bauer, N., Böttger, D., Pietzcker, R., 2015. Representing power sector variability and the integration of variable renewables in long‐term energy‐economy models using residual load duration curves. Energy 90, Part 2, 1799–1814. doi:10.1016/j.energy.2015.07.006
Ueckerdt, F., Pietzcker, R.C., Luderer, G., Giannousakis, A., Scholz, Y., Stetter, D.: “Decarbonizing global power supply under region‐specific consideration of challenges and options of integrating variable renewables in the REMIND model” (under review at Energy Economics).
Scholz, Y., Gils, H.C., Pietzcker, R.C.: “Application of a high‐detail energy system model to derive power sector characteristics at high wind and solar shares” (under review at Energy Economics). Comments very welcome: pietzcker@pik‐potsdam.de
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