Scenarios for Decarbonizing the European Electricity Sector

Präsentation zum Thema: "Scenarios for Decarbonizing the European Electricity Sector"—  Präsentation transkript:

1 Scenarios for Decarbonizing the European Electricity Sector
40th IAEE International Conference Meeting the Energy Demand of Emerging Economies 18-21 June 2017, Singapore Scenarios for Decarbonizing the European Electricity Sector 1 Clemens Gerbaulet, Christian von Hirschhausen, Claudia Kemfert, Casimir Lorenz, Pao-Yu Oei

2 Motivation How to achieve decarbonization in European Electricity?
Technology portfolio options Nuclear power has a difficult time to survive in electricity markets: U.S., Europe, Japan, etc. European Emission reduction targets European Union energy and climate package: Dismantling and Waste disposal unclear and expected to be costly 80-95% reduction of greenhouse gases by 2050 (base: 1990) Falling costs of alternative sources Ratification of the Paris Agreement to limit the rise in global temperature to below 2°C Renewable energies storage technologies No Large scale CCTS plants The EU “Reference Scenario” is based on Fossil fuels (with carbon capture, transport, and storage – CCTS) Nuclear energy Renewables Adequate planning and long term goals are required for successful energy system transformation towards decarbonization Decarbonization of the electricity sector is a must to reach the climate target The EU Reference Scenario 2016 bases this decarbonization on CCTS, Nuclear and RES Development of Nuclear and CCTS seem very unfavorable, RES cost decline way more optimistic Hypothesis: Nuclear power and CCTS are unlikely to be a significant part of a cost minimizing electricity mix in the future Taking into account the long-term decarbonization target in today’s investment decision can avoid stranded assets

3 Determining cost-effective pathways in the electricity sector
dynELMOD: Linear program to determine cost-effective development pathways in the European electricity sector Model: 33 European countries 31 conventional or renewable generation and storage technologies 9 investment periods, five-year steps 2020 – 2050 Good storage representation (including reservoirs, DSM) Approximation of loop-flows in the HVAC electricity grid CCTS and CO2 storage constraints Investment Investment into Conventional and renewable generation, cross-border capacities Reduced time series used Dispatch Investment result from step 1 fixed Time series with 8760 hours (validate result adequacy) Assumptions Time series calculation PTDF calculation Boundary conditions Investment Outputs Investment into generation capacities, storage, transmission capacities Generation and storage dispatch Emissions by fuel Flows, imports, exports dynELMOD ist ein lineares Stromsektormodell. Es ermittelt länderscharf die Entwicklung von Erzeugungskapazitäten, Speichern und Übertragungskapazitäten in 33 Europäischen Ländern, über neun Investitionszeitpunkte bis 2050. Dafür benötigt man Informationen zu den heute herrschenden Rahmenbedingungen wie dem bestehende Kraftwerksportfolio, dem Netz, und zeitreihen, andererseits prognosen wie sich z.b. Kosten oder erneuerbarenpotenziale entwickeln können. Diese werden zuerst zusammengefasst, z.b. wird aus dem vorhin gezeigten leitungsscharfen netz, eine power transfer distribution matrix abgeleitet, und aus den bestehenden Zeitreihen eine verkürzte Zeitreihe erstellt, da das Modell sonst nicht zu berechnen wäre. Zwei Berechnungsschritte: ermittelt besagte Investitionen, hier wird eine verkürzte zeitreihe verwendet Dann werden Investitionen fixiert und mit einer ganzjährigen Zeitreihe die genauen Erzeugungsmengen; Emissionen und grenzüberschreitenden Flüsse, Importe, Exporte ermittelt. Full Dispatch

4 Application Scenario Scenario description Default scenario
European electricity sector development 2015 – 2050 Default assumptions from dynELMOD Serves as baseline for comparison Reduced foresight Characteristics: decisions makers only aware of the CO2 target of the upcoming five-year period Used to identify stranded investments resulting from such a myopic vision Budget approach Characteristics: aggregate emission budget for the entire period from 2015 to 2050 Emission allocation over time endogenous, allows for a higher degree of decision Assumption: abatement takes place earlier Decarbonization by 2040 Characteristics: Full decarbonization by 2040 Question: Can renewables alone enable fast decarbonization?

5 Renewables become dominant electricity source in Europe
Electricity Generation in Europe 2015 – 2050 No new nuclear, hard coal, or lignite power plants emerge Natural gas usage reduces after 2030 to become backup technology Renewables become dominant electricity source Storage capacities (>400GW installed in Europe) balance fluctuations

6 Myopic investments leads to stranded fossil investments
Investments in gas power plants in reduced foresight scenario vs. default scenario Reduced Foresight leads to additional 70 GW of investments These invesments become stranded, as the CO2 emission target limits their production

7 Myopic behavior slightly increases CO2 emissions from gas
CO2 emissions by fuel and scenario In 2020 and 2025, slightly more emissions from hard coal and lignite From 2030 onwards, emissions form gas are increased, as more gas fueled capacities are available

8 Budgetary emission approach accelerates decarbonization
CO2 emissions between the default and the “emission budget” scenarios (2020 – 2050) When a CO2 emission target spanning the whole model horizon is in place, the CO2 emission pathway leads to an earlier decarbonization starting in 2030

9 Faster decarbonization
Electricity Generation in the default and faster decarbonization scenarios Total Generation Difference Faster decarbonization leads to fewer investments into gas capacities No new nuclear power plants are built Share of additional renewables similar to default scenario, after 2045 more solar

10 Electricity system cost decrease over time
Overall electricity system costs (2020 – 2050), by segment The system cost over time increases until 2030, driven by an increase in overall investment cost, which is offset by a constant decrease in electricity generation cost The average system cost per MWh decreases substantially until 2050.

11 Conclusion: Risk of stranded fossil investments calls for stringent climate policies
No Nuclear power plants are built by the model in any scenario Reduced foresights leads to stranded investments Reduced Foresight leads to additional 70 GW of investments Better overall planning in default scenario, with lower overall system costs Risk of stranded fossil investments calls for long term climate policies Budgetary approach early decarbonization until 2030, then “plateuing” of emissions, further reduction directly before 2050, as induced by model constraints Faster decarbonization leads to fewer investments into gas capacities, and no new nuclear power plants are built Increase in speed of decarbonization can be accomodated by renewables

12 Thank You for Your Attention!
Casimir Lorenz Workgroup for Economic and Infrastructure Policy (TU Berlin) German Institute for Economic Research (DIW Berlin) / Tel:

13 AC line aggregation to PTDF:
No model covers it all… How to determine decarbonization pathway? Goal: Electricity sector Decarbonization Question: How to reach sector decarbonization in a cost efficient way? Method: Cost-minimizing sector model with investment Development of dynELMOD European scale Investment into conventional & renewable generation, storage, grid Dynamic investment model over multiple time-steps ( ) Dispatch results for a full year (validate result adequacy) A good storage representation (including reservoirs, DSM) Approximation of loop-flows in the HVAC electricity grid CCTS and CO2 storage constraints Open source Other examples for partial equilibrium electricity sector models with investment Net transfer capacities to connect zones: Ludig et al., (2011); Richter, (2011) country resolution; 18 Zones: Pleßmann and Blechinger (2017) Separate transmission model iteratively connected: Fürsch et al. (2013) Stochastic renewable infeed, NTCs: Spiecker and Weber (2014) Loop-flows with PTDF, limited storage implementation: Hagspiel et al. (2014) High temporal resolution, low spatial resolution: Zerrahn and Schill, (2015) 𝑃𝑇𝐷 𝐹 𝑙,𝑛𝑛 = 𝑛 𝐻 𝑙,𝑛 ∗ 𝐵 𝑛,𝑛𝑛 −1 𝑃𝑇𝐷 𝐹 𝑙,𝑧 𝑙𝑖𝑛𝑒𝑧𝑜𝑛𝑎𝑙 = 𝑛∈𝑧 𝑃𝑇𝐷 𝐹 𝑙,𝑛 𝑐𝑜𝑢𝑛𝑡 𝑛∈𝑧 ∀𝑖𝑐 𝑃𝑇𝐷 𝐹 𝑧,𝑧𝑧,𝑧𝑧𝑧 𝑧𝑜𝑛𝑎𝑙 = 𝑘 𝑃𝑇𝐷 𝐹 𝑙𝑙,𝑧𝑧𝑧 𝑙𝑖𝑛𝑒𝑧𝑜𝑛𝑎𝑙 − 𝑗 𝑃𝑇𝐷 𝐹 𝑙𝑙𝑙,𝑧𝑧𝑧 𝑙𝑖𝑛𝑒𝑧𝑜𝑛𝑎𝑙 AC line aggregation to PTDF: 18:00 2:30 In den vorhergegangenen Forschungsfragen habe ich die Effekte der Veränderungen unterschiedlicher Aspekte auf das Stromsystem ermittelt. Interessant ist aber auch der Einfluss von bestimmten Faktoren auf die Sektortransformation selbst untersuchen zu können. Daher benötigt man ein Modell, das diesen endogen bestimmt. Daher habe ich mit casimir Lorenz zusammen das modell dynELMOD entwickelt. Um die Fragen zu beantworten: Was sind kosteneffiziente Entwicklungspfade, und was sind relevante Treiber für die Technologiewahl? Das modell deckt den europäischen Stromsektor ab, und ermittelt Investitionen in Erzeugung, Speicher, und Netze in einem dynamischen Setting 2015 bis Es hat eine gute Speicherabbildung inklusive Reserviours und abbildung von loop-flows im Wechselstromnetz. Gleichzeitig verfolgt es einen Open source ansatz. Es existieren eine Reihe von Modellen, die sich der Entwicklung der Erzeugungsportfolios in Europa oder einzelnen Ländern befassen. Jedes dieser Modelle fokussiert sich auf einen besonderen Aspekt, der natürlich mit einem Detailverlust an anderer Stelle einhergeht. Oft Länderkopplung Transportmodell (NTCs), separates Modell für Netzausbau mit iterativer Kopplung. Am nächsten Hagspiel et al 2014, aber nicht so gute Speicherabbildung (weniger Detail und keine Reservoirs). DIETER hohe Zeitliche Auflösung mit Verkehrssektor, aber nur ein Land, und nur ein Investitionszeitpunkt.

14 Dispatch 2050 Germany in February shows substantial imports
Hour-to-hour operation of the German electricity system in 2050 (first two weeks of February) German electricity imports in February 2050 come in decreasing order from Denmark, Switzerland, Netherland, France and Austria. The imports and exports with Sweden and Poland are even in total Germany exports 960MW on average to the Czech Republic.

15 Hour-to-hour operation of the Italian electricity system in 2050 (first two weeks of February)
In February 2050 Italy is also dependent on Storage and Imports Solar infeed is higher than in Germany

16 dynELMOD objective function
dynELMOD Objective function: Cost minimization Objective function Generation costs Investment costs 22:00 0:30 Die Zielfunktion von dynELMOD ist Minimierung der Gesamsystemkosten: Erzeugung, Investitionskosten, Kapazitätskosten (O&M), Netzausbaukosten Capacity costs (e.g. O&M) Line expansion costs

17 Side note on time-series reduction
Time-series reduction is not a new topic: Approaches: k-means cluster (Green et al. 2014, Munoz et al. 2016), Hierarchical cluster (Nahmmacher et al. 2016), MILP Optimization (van der Weijde and Hobbs 2012, de Sisternes and Webster 2013, Poncelet et al. 2015), Load curve approximation (Ueckerdt et al. 2015) Requirements for dynELMOD: Continuous curve with daily variation structure (RES, load); seasonal structure (RoR, reservoirs); minimum and maximum values and average, for renewables the estimated full load hours; “smoothness” or hourly rate of change characteristic (otherwise need for flexibility could be under- or overestimated) Step 1: Hour selection use every 25th hour of the full time series, starting at 7th hour  351 hours, additionally include 24 consecutive hours with lowest renewable infeed Step 2: Time series smoothing moving average window, size determined by hand Step 3: Time Series scaling DNLP: Full-load hours, minimum, maximum value 22:30 1:30 Ein weiterer wichtiger Punkt in der Anwendung von Investitionsmodellen ist die Herausforderung, dass komplette Zeitreihen eines ganzen Jahres nicht verwendet werden können, da Modell zu groß würde. Daher zeitreihen reduction nötig, die Zeitreihencharacteristika möglichst akkurat widerspiegeln Hierfür existieren bereits Ansätze [VORSTELLEN] Die Anforderung von dynELMOD insbes. Im Hinblick auf Reservoureinsatz etc war es einerseits saisonale Reihenfolgen abbilden zu können, andererseits auch tägliche Variation gut darzustellen. Minimal, und maximalausschlag, sowie der mittelwert oder z.b. die Entwicklung der Volllaststunden von Erneuerbaren sollten auch berücksichtigt warden können. Gleichzeitig sollte die Änderungsrate oder “Rauhigkeit” der Zeitreihen nicht verfälscht werden. The goal in trying to determine the window size is to keep the time-dependent characteristic in place and meeting the time series’ variation target In calculation with 8,760 hours no smoothing, except for data with a monthly resolution  no more “jumps” Hierarchical clustering anwendung basierend auf nachmmacher: Després et al. 2017