energy scenarios and methodological...
TRANSCRIPT
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Energy Scenarios and Methodological Developments
Prof. Ruud Egging, NTNU
12 June 2018
41st IAEE International ConferenceGroningen, Netherlands
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Lies, Damned Lies, and … Scenarios?
Free after Mark Twain
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According to an analysis by XYZ…• Forbidding CCS may double the cost of the energy transition• Shale gas has larger global warming impact than coal• Domestic electricity bills in Norway will increase by 25% if it would
become the green battery for Europe• An ETS floor price of € 20 per ton will hamper recovery of the EU
economy • To meet climate mitigation targets we must all become
vegetarians• A sudden influx of one million refugees in Germany will damage
economic growth and cause a major budget deficit• Great Britain can negotiate better bilateral trade deals than as
part of the EU• It is very unlikely for Ajax to not qualify against Rosenborg over
two matches in EL qualifications• If I had massaged my scalp with magic oil every day over the last
two decades I would still have had a full head of hair
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Before reading an article, see who has written it,
and what is their interest
Mrs. Inge van Beek, my English teacher in high school, about 30 years ago
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Outline
• Energy scenarios– Scenarios– What is a scenario?– What is a good scenario?– How to develop a good scenario?
• Methodological developments - Linking models– Model types and characteristics– Levels of model linkage and data exchange– Benefits and challenges of model linking
• Linking in practice– LCE21 SET-Nav Navigating the Roadmap for Clean, Secure and
Efficient Energy Innovation
– Model linking in SET-Nav
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ENERGY SCENARIOS
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Stanford Energy Modeling Forum
• Comparing scenario results with (partially) harmonized input data:
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Russian Share of Western European supply in 2020 – eight different models
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52%
13%
EMF Report 23 Sept 2007
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Western European Prices relative to 2005
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170%
78%
EMF Report 23 Sept 2007
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US supply sources 2020, relative to 2006
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EMF Report 23 Sept 2007
Actual Cons will likely be around high end ☺
But net trade only few %, possibly exports , not 15%-30%
2006
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SHELL – Temperature increase for three scenarios
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Mountains and Oceans explore alternative socio-political pathways (…) with emissions as an open-ended outcome. Sky (…) progressively driven (…) to achieve net-zero emissions by 2070
shell.com/energy-and-innovation/the-energy-future/scenarios.html
Shell asked MIT to check scenario Sky on temp increase, it levels out below 2C after 2060 ☺
Sky developed TO MEET 450ppm…!
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World Energy Outlook (WEO) 2017 p.145
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New Policies Sustainable Development
To meet climate targets, consumption of the most polluting fossil fuel has to decrease most…
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WHAT DOES ALL THIS MEAN?
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IEA World Energy Outlook 2017 p. 40
• IEA scenario results (…) sometimes mis-characterised as forecasts. They are not.
• Each scenario depicts an alternative future, a pathway along which the world could travel (…)
• (IEA does provide short to medium-term forecasts (…) but) there are no longterm IEA forecasts; (…) simply too many variables in play (…)
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WHAT IS A SCENARIO?
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What is a scenario?
• A postulated* sequence or development of events. (Oxford dictionary)
• A modelling scenario:
– A choice of exogenous input values consistent with someone's perspective on a possible future development + ???
+ the endogenous model outcomes
+ interpretations of model results…?
+ all model modifications in development, testing …?
+ all data modifications in compilation, validation, calibration, exchange, …?
• case, case study, experiment, simulation, what-if analysis, pathway, storyline, …?
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What is a good scenario?
• Provides relevant insight for good decision making• Backed up by a coherent qualitative storyline• Consistent assumptions and boundary conditions• Logical and transparent parameterization• Developed with model strengths and weaknesses in mind• High quality model data sets and implementation• Try not answer questions model is not suitable for• Does not repackage assumptions as new results• Is compared against a good and clear benchmark• Is one of several scenarios exploring a wide enough range of
futures• Model, inputs and results documented transparently• Results verifiable
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STORY LINES
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Story line
• Qualitative setting and underpinning for quantitative scenario.
• geopolitical, cultural, societal context, main trends/developments, major events
• Auer & Zach 2009.
– story lines: clusters of uncertainties
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Story lines
• Reference
– base case, benchmark, business as usual, continuing trends, current policies, …
• Contrasts:
– Scope out a wide enough range of possible futures based on dimensions spanning extreme outcomes of fundamental drivers
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European Energy Futures
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Credit: Bruggink May 2005, The Next 50 years – Four
European Energy Futures, ECN
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Penetration of renewables…
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Credit: Auer and Zach, 2009, Definition of a Consistent Scenario Framework for ….
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SET-Nav Energy Transition Pathways
National champions
Directed vision
Diversifi-cation
Local-ization
CooperationD
isru
pti
ve
Inn
ova
tio
n
Path
dep
en
den
cy
Isolation
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Some terms adjusted for clarity
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How to develop a good set of scenarios
• What is scope of desired insights and contributions?• What models would be suitable to provide such insight?• Brainstorm on events and drivers affecting relevant
outcomes• Open discussion on
– logical grouping of events and drivers: contrasting dimensions & coherent storylines
– Parameterizations (probably need some iterations)• Numerical and qualitative analysis and interpretation• Present insights in a way suitable for intended audience
– Give a fair account, no cherry-picking– Explain main drivers, incl. assumptions and boundary
conditions.
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Pix credit: https://pixabay.com
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METHODOLOGICAL DEVELOPMENT - MODEL LINKING
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Three of the main model types
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I.A.M.
C.G.E.
P.E.
CAP endow
Income from CAP
Income (Y)
Expenditure good 2
LAB endow
Income from LAB
Expenditure good 1
Production good 2
Production good 1
Integrated Assessment ModelComputational General EquilibriumPartial Equilibrium Pix credit: Dreamstime.com
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Characteristics of some model types used for policy support and analysis
IAM CGE P.E.
Perspective Holistic Economy (Energy) sector
Metrics and relations
Aggregate economic, technical & physical
Aggregate economic
Detailed economic & physical
Climate + - -
Environment + - -
Carbon cycle + - -
Land use & forestry + - -
Oceans & water + - -
Economy & trade + + -
Spatial detail - + +
Technical detail - - +
Temporal detail - - +
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For illustration only
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Let each model type do what it’s good at
• Isolated– Define consistent parameterizations for all models– Run models – Explain to audience why results are inconsistent
• Integrated– Develop one model combining all strengths of all models– See it fail miserably trying to find a solution
• (Soft / Hard) Linked– Decide relative strengths of each model and which
(exogenous) inputs are based on (endogenous) outputs of others
– Define consistent parameterizations– Iteratively run models and exchange data until results are
consistent
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Linking models - why
• No single model can covers all aspects in all desired detail.• Need consistent & coherent quantitative representations across
models to analyze scenarios• Models have different
– Approaches– Foci: strengths & weaknesses– Scopes, scales / granularities & information structure– Exogenous (parameters) vs endogenous variables / boundary
conditions– Physical vs monetary representation; units of measurement→Features give complementary strengths but pose challenges for exchanging data
• One model’s results are another’s boundary conditions
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Using relative strengths:
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I.A.M. C.G.E. P.E.
▪ Climate▪ Land use▪ Global trade▪ Emission ceilings / costs
▪ Energy demand
▪ Energy prices▪ Outputs▪ Prices
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Challenge: Spatial granularity
• Aggregating geographical regions:
– Extremes become less extreme
• Uncorrelated high and low values cancel out each other
– Transportation losses and bottlenecks disappear
• Less or no intra-regional transport
– More aggregate model will show lower need for flexibility & storage.
source: www.vecteezy.com
source: www.worldatlas.com
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Challenge: Temporal Granularity
• Aggregating time periods:– Extremes become less extreme
• High & low values cancel out each other over longer periods
• More aggregate model will show lower need for flexibility & storage.
• “Supply side”– Reduces demand flexibility due to shiftable loads– Reduces the number of “cycles” affecting life
expectancy of batteries
• Related: reconciling stochastic model outputs with inputs for a deterministic model
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Challenge: Technical granularity
• Having only one type of coal-fired power plant, one type of gas-fired, etc.. gives a much harsher merit order.
• Grid / Entry-exit capacities NTC vs loop flows
• …
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Challenge: units of measurement
• P.E. Energy
– TJ, MWh, QBtu, bcm, Mtoe
– Bcm: • Density? Calorific value Groningen gas - scm
• Heating value? Lower HV-Higher HV
• Liquefied?
• CGE: Monetary
– USD, Euro, Yen,
– of what year?
– …
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Challenge: version control
• Models & data sets
• Requires professionalization and discipline
• More complex for shared, open-access models & data
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Challenge: convergence
• Model(s) dependent– Some theoretical results may apply, but..– Not all models may be optimization or equilibrium
models– Exchange potential with models not overly sensitive for
input changes and no erratic result behavior– Test this early– Cycling? Intervene …
• NTNU PhD candidate P.I. Helgesen extensive research on linking & integration regional energy systems & regional CGE– TIMES Norway – REMES CGE– It always converges
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Linking models – how?
• Middleware
– all models exchange with one central DB:
– Reduce number of links from M(M-1) to 2(M+2)
• Upfront agreement on
– Technical, Spatial, Temporal resolutions
– Standardized data table formats
– Units of measurement
– Unique identifiers for data sets and entries
• Automated scripts for hard-linking
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SET-NAV
Navigating the Roadmap for Clean, Secure and Efficient Energy Innovation
SET-Nav EC H2020 grant 691843
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SET-Nav Key pillars
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• Combining theory of
technology innovation,
diffusion & spill-overs with
large-scale energy-
economy-engineering
models.
• Developing
methodological framework
& infrastructure for
effective model
integration to adequately
capture interdependencies
across levels, energy
carriers, and sectors.
• Twelve case studies
• Four pathways
Enhancing
modelling
capacities
Stakeholder
dialogue &
dissemination
Strategic
policy analysis
enhancing innovation
towards a clean, secure and efficient
energy system
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Case: centralised vs. decentralised electricity supply
TEPES
Enertile
Case Study
Analysis
Electricity generation, Capacities,
Region definition
Capacities, Investments,
Region definition, NTCs,
Investment Options
for the gridElectricity
generation, Investments,
Costs, Emissions,
Fuel consumption
Detailed grid costs
Challenges: next slide
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Two power system models:
• Enertile: power system optimization• Tepes: power transmission expansion planning• Enertile→ Tepes:
– electricity capacities – electricity generation
• Tepes→ Enertile:– detailed transmission grid expansion results– unit expansion costs
• Challenge:– Tepes considers individual lines– Enertile works with regions– need sophisticated approach to aggregate /
disaggregate exchanged data
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TEPES
EMPIRE
Analysis
Electricity generation,
Electricity capacities
RAMONA
Grid Investments
EGMM
Gas production cost,
Gas infrastructure
Gas demand, Gas prices
NEXUS security
Gas operation,Gas infrastructure
Electricity generation, Electricity capacities
Gas operation, infrastructure,
investments, costs, emissions
Electricity generation, capacities, investments,
fuel mix, costs, emissions
System security/reliability indices electricity/gas
Detailed grid costsGrid investments Gas demand,
Gas prices
Case: unlocking flexibility in power & gas supply systems
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Cases▪ All models calibrated to PRIMES decarboniz scenario (E3MLab & IIASA 2016)
▪ Nuclear restricted [current cap levels; no replacement]
▪ No CCS
▪ Flexi-Grid - Transmission
▪Transmission expansion allowed (Electricity)
▪EMPIRE: intra-day demand response + curtailment
→ Grid expansion & hydro main sources flexibility
▪ Flexi-1 – electricity storage
▪Transmission is restricted [current levels + 10Y ENTSO-E NW plan]
→ More electricity storage and gas thermal plants.
▪ Flexi-2 – gas infrastructure
▪Transmission is restricted [idem]
▪Electricity storage expensive
▪Demand response (current levels)
→ Gas thermal plants main flexibility provider.
E3MLab and IIASA (2016). Technical report on Member State results of the EUCO policy scenarios. EUCO 27scenario. Energy Modelling, European Commission. https://ec.europa.eu/energy/en/data-analysis/energy-modelling
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EU power generation mix
gas
solar pv
wind onshore
nuclear
coallignite
hydro
w-offs
Gas primarily base-load in 2025; in 2050 balancing.
Utilization 64 (+/-1)% in 2025; 15% in 2050.
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Can we learn from others?
• We need openness and transparency to create credibility and accountability
• Climate modeling community is much further on this than energy modeling community
• Social sciences it has become normal to make data sets accessible to reviewers
45Ruud Egging 2018 IAEE Groningen
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Discussion points
• Projections more than two decades into the future are meaningless & create a false sense of understanding and control.
• There are too many scenarios. Every five years the modeling community should establish a reference scenario that everyone will use as benchmark.
• All our models are black boxes. We customize assumptions, inputs and scenarios to create the message that we want to create.
• Every published paper & report should have accessible online– All raw & processed & calibrated inputs & raw output data – An extensive sensitivity analysis, and an “executable” so that
experiments can be replicated by anyone.– And after ten years: a retrospective analysis explaining why
results were wrong & needed improvements.• Funding agencies should only fund projects which adhere to this
Ruud Egging 2018 IAEE Groningen 46
Pix credit: www.advocatesouth.co.nz
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THANK YOU
Acknowledgement: SET-Nav EC H2020 grant 691843& Special thanks to post.doc dr. Pedro Crespo del Granado