354 research outputs found
Wie alt werden fossil gefeuerte Kraftwerke
No full textWie alt werden fossil gefeuerte Kraftwerke?Themenbereich: Szenarien zur Reduktion von TreibhausgasenoderThemenbereich: Strom- u. Wärmeerzeugung sowie SpeicherPETER MARKEWITZ(1) , MARTIN ROBINIUS(1), DETLEF STOLTEN(1)Forschungszentrum Jülich GmbHInstitut für Energie- und Klimaforschung (IEK-3)Motivation und zentrale FragestellungDie Fortschreibung des Bestandes fossil gefeuerter Kraftwerke spielt in Energie- sowie Strommarkt-projektionen eine wichtige Rolle. Hierfür werden in vielen Untersuchungen sogenannte technische Lebensdauern angenommen. Mit Hilfe solcher Annahmen sowie der Kenntnis der Inbetriebnahmejahre einzelner Kraftwerkblöcke werden anlagenscharf Außerbetriebnahmejahre abgeleitet. Für einen Kraftwerksbestand lässt sich somit die rückläufige Kapazitätsentwicklung über einen Zukunftszeitraum angeben. Solche Projektionen werden wiederum dazu benutzt, einen zukünftigen Kapazitätsneubaubedarf abzuschätzen. Die Angabe der technischen Lebensdauer ist somit eine wichtige Stellgröße. Viele Studien berufen sich bei der Annahme von Lebensdauern auf Erfahrungswerte, ohne diese jedoch näher zu spezifizieren. Im Rahmen einer Ex Post Analyse wird am Beispiel der in Deutschland in den letzten 35 Jahren stillgelegten fossil gefeuerten Kraftwerke gezeigt, wie sich die Lebensdauer von Kraftwerken bis heute verändert hat. Die Analysen tragen zur Fundierung und Einordnung von Lebensdauerannahmen bestehender und künftiger Energieprojektionen bei.Methodische VorgangsweiseDie Untersuchungen werden auf der Basis einer Kraftwerksdatenbank durchgeführt, die seit 1980 besteht und die neben den heute aktiven Kraftwerken auch alle stillgelegten Kraftwerksblöcke enthält. Darüber hinaus werden in die Analysen auch die Kraftwerksblöcke einbezogen, die der Bundesnetzagentur von den Kraftwerksbetreibern zur Stilllegung angezeigt wurden.In vielen Strommarktmodellen wird eine Korrelation zwischen Wirkungsgradentwicklung und Kraftwerksalter angenommen und für die Fortschreibung von Kraftwerkswirkungsgraden genutzt. Die mit Hilfe von Regressionsanalysen ermittelten Wirkungsgrade werden in vielen Strommarktmodellen wiederum auf reale Kraftwerksblöcke übertragen. Anhand einiger bestehender Kraftwerksblöcke, für die detaillierte Angaben vorliegen, wird gezeigt, ob und inwieweit eine solche Übertragung zulässig ist.Ergebnisse und SchlussfolgerungenDie Lebensdaueranalysen verdeutlichen, dass die Lebensdauer fossil gefeuerter Kraftwerke im Laufe der letzten 3 Dekaden deutlich zugenommen hat. Lag sie für Steinkohle zwischen 1990 und 1999 noch bei einem leistungsgewichteten Durchschnittwert von 33 Jahren, betrug dieser in den letzten anderthalb Dekaden ca. 41 Jahre. Die für eine Stilllegung angezeigten Steinkohlekraftwerke weisen einen vergleichbaren Wert von mehr als 42 Jahren auf. Eine ähnliche Entwicklung ist auch bei Kraftwerken zu beobachten, die mit Braunkohle, Öl oder Gas gefeuert werden.Die Wirkungsgradanalysen deuten darauf hin, dass bei blockscharfen Strommarktanalysen eine Übertragbarkeit von Wirkungsgraden, die aus Regressionsanalysen abgeleitet werden, sehr kritisch zu hinterfragen ist. Eine besondere Schwierigkeit bei der Abschätzung von Wirkungsgraden besteht darin, den Einfluss von Retrofitmaßnahmen auf die Effizienz eines Kraftwerks abzuschätzen. Die Analysen zeigen, dass der Unterschied zwischen errechneten und tatsächlichen Wirkungsgraden mehrere Prozentpunkte betragen kann. Es ist anzunehmen, dass eine derartige Differenz einen erheblichen Einfluss auf Merit Order Analysen haben dürfte
An Energy Concept to Supply the Transport Sector with Hydrogen from Renewable Energy Sources
No full text21st World Hydrogen Energy Conference 2016. Zaragoza, Spain. 13-16th June, 2016An Energy Concept to Supply the Transport Sectorwith Hydrogen from Renewable Energy SourcesM. Robinius1*, S. Schiebahn oder T. Grube2 and D. Stolten31Forschungszentrum Jülich GmbH, Electrochemical Process Engineering (IEK-3), D-52425 Jülich, Germany2 Forschungszentrum Jülich GmbH, Electrochemical Process Engineering (IEK-3), D-52425 Jülich, Germany3 Forschungszentrum Jülich GmbH, Electrochemical Process Engineering (IEK-3), D-52425 Jülich, Germany(*) [email protected] minimize the anthropogenic impact of the climate system the increase of the global temperature should be below2 degrees Celsius. Therefor all sectors have to be decarbonated to a specific value. In Germany for example thegreenhouse gases have to be decreased overall sectors by 80 to 95 % by 2050 compared to 1990. Hence there is a needfor an energy concept in which not only one sector for example the electrical energy sector is considered. For thisreason the IEK-3 developed an energy concept as well as a model in which two of the biggest sectors in terms ofgreenhouse gases can be implemented: the electrical energy and the transport sector. This paper gives an overviewabout this concept and shows selected results of the model. The concept is driven by the increasing amount of variablerenewable energy sources (VRES). The VRES feed-in from wind turbines and photovoltaic systems depends onweather and is only partially predictable. As a result, to decarbonize this sector the installed capacity of VRES has to beabout the factor 3 over the peak load. This is leading to a changing regime in which the VRES producing temporarilymore energy than is needed from the conventional load as well as can be transported from the electrical grid. Thissurplus energy can be used to produce hydrogen via electrolysis and can then supply the transport sector.Introduction and OverviewThe energy concept relies on a high share of VRES and the so called “power-to-gas” approach. This approach usesthe surplus of the Renewable Energy Sources (RES) to produce hydrogen and oxygen via electrolysis. The hydrogencan then for example been used in the transport sector by fuel cell cars. Schiebahn et al. (2015) [1] and Robinius (2015)[2] showing the potential as well as a technological overview for this approach. While Baufume et al. (2013) [3]describing the calculation for a nationwide German hydrogen pipeline infrastructure Robinius et al. (2014) [4] analyzingthe optimal placement of electrolysis. Both papers and models have no interconnection at all and therefor the goal ofthis paper is to combine both models and show by analyzation of the energy concept the capability of this combination.The Electrical Energy SectorThe model calculates the hourly residual load for 11,268 municipalties in Germany. Therfore the VRES as well as theload has been deatailed analyzed and integrated. Futhermore the high voltage grid (380 and 220 kV) can be considered.Figure 1 shows an example of the installed capacity, theproduced electricity and the full load hours in Germany bythe year 2050 in the model. For this the user has to setinitially the installed capacity and the choosen weather year.Afterwards the model calculates the produced electricity aswell as the residual load for each hour under considerationof the high voltage grid. The residual load will be thencalculated by:87601P P P PDemand ,t RES ,t Im port ,t Export ,tt Where Demand ,t P is the hourly demand of each municipality,RES ,t P is the hourly production of electricity from all RES,Im port ,t P is the imported power to Germany and Export ,t P isthe exported power from Germany. This means if theresidual load is negative there is suplus energy and if theresidual load is positive there is a need for conventional power plants.Table 1. Sections of the abstract to be changed01000200030004000500060007000050100150200250300350400Full load hours [h]Produced electricity [TWh]Installed capacity [GW]Installed capacity [GW] Produced electricity [TWh]Full load hours [h]Figure 1. Example of the installed capacity, producedelectricity and full load hours by the year 2050in Germany21st World Hydrogen Energy Conference 2016. Zaragoza, Spain. 13-16th June, 2016Figure 2 shows the accumulated residual energy for one year in 11,268 municipalties in Germany with the data from figure 1. The negative residual energy which can be used to produce hydrogen is especially located in the north of Germany. This is due to the high amount of on- and offshore wind which are also located mainly in the north in Germany.The Transport SectorFigure 3 (left) shows the possible amount of fuel cell vehicles in Germany and the summarized hydrogen demand in the model as well as the demand from an agent based model after Keles et al. (2008) [5]. For the model of the IEK-3 among others a geospatial model has been developed which distributes the summarized hydrogend demand to 413 counties in Germany. Therfore different indicators for each county like the GDP was taken into considertation. The peak demand is in the year 2052 with 2.93 million tons. Afterwards the demand decreases because of the more efficient fuel cell cars. Figure 3 (right) shows the location of the electroysis (red stars), the transmission grid (red lines), the distribution grid (black lines) as well as all 9,968 hydrogen fuel stations to supply Germany with hydrogen from RES. Futhermore a detailed econmic analysis waReferences[1] Schiebahn, S., Grube, T., Robinius, M., Tietze, V., Kumar, B., und Stolten, D.; Power to gas: Technological overview, systems analysis and economic assessment for a case study in Germany. International Journal of Hydrogen Energy, 2015. 40(12): p. 4285-4294.[2] Robinius, M.; The German Energiewende and the Potential for Power to Gas. 2015. DOI: 10.13140/RG.2.1.4569.2641.[3] Baufumé, S., Grüger, F., Grube, T., Krieg, D., Linssen, J., Weber, M., Hake, J.-F., und Stolten, D.; GIS-based scenario calculations for a nationwide German hydrogen pipeline infrastructure. International Journal of Hydrogen Energy, 2013. 38(10): p. 3813-3829.[4] Robinius, M., Rodriguez, R.A., Kumar, B., Andresen, B., Stein, F.T., Schiebahn, S., und Stolten, D.; Optimal placement of electrolysers in a German power-to-gas infrastructure, in World Hydrogen Energy Conference,2014: Gwangju, Korea.[5] Keles, D., Wietschel, M., Möst, D., und Rentz, O.; Market penetration of fuel cell vehicles – Analysis based on agent behaviour. International Journal of Hydrogen Energy, 2008. 33(16): p. 4444-4455.01230102030402015202020252030203520402045205020552060Hydrogen demand [Mil. t]Fuel Cell Vehicles [Mil. units]BrennstoffzellenfahrzeugeWasserstoffbedarfResidual energy[MWh/km²]-3.000.000 - 2.500-2.500 - -1.700-1.700 - -1200-1.200 - -830-830 - -460-460 - -120-120 - 175175 - 545545 - 1.5351.535 - 50.600Peak H2-demand 2,93 Mil. tFigure 2. Residual energy in 11,268 municipalities in GermanyFigure 3. Left: Quantity of fuel cell cars and hydrogen demand in Germany related to Keles et al. (2008) and IEK3. Right: Dedicated hydrogen pipeline grid to supply the German transport sectorFuel Cell Vehicles Lead Scenario[5] Scenario 3[5] Hydrogend demand Sources Transmission HUBs Distribution Hydrogen fuel stations Countie
Effect of cascade storage system topology on the cooling energy consumption in fueling stations for hydrogen vehicles
No full textOne of the main obstacles of the diffusion of fuel cell electric vehicles (FCEV) is the refu-
eling system. The new stations follow the refueling protocol from the Society of Auto-
motive Engineers where the way to reach the target pressure is not explained. This work
analyzes the thermodynamics of a hydrogen fueling station in order to study the effects of
the cascade storage system topology on the energy consumption for the cooling facility. It
is found that the energy consumption for cooling increases, expanding the total volume of
the cascade storage system. Comparing the optimal and the worst volume configurations
of the cascade storage tanks at different ambient temperatures, the energy saving is
approximately 12% when the average ambient temperature is 20 C and around 20% when
the average ambient temperature is 30 C. The energy consumption for cooling is signifi-
cantly influenced by the topology of the cascade storage system and it is particularly
relevant in the case of low daily-dispensed amount of hydrogen
Economics of Hydrogen
No full textRobinius et al. provide valuable information needed for a discussion of the potential role of hydrogen for decarbonizing energy systems. They first discuss major technical and economic characteristics of hydrogen supply systems, followed by potential end-use applications of hydrogen fuels of different origin (“color coding”), fuel supply cost estimates, and an overview of the various hydrogen production, supply, and storage options. Hydrogen-related policy and regulatory aspects are discussed as well as safety and public acceptance issues. Finally, it deals with the willingness to pay of consumers for different alternative fuel vehicle characteristics. The review concludes by arguing that “green hydrogen” is widely accepted among consumers, that costs are expected to decline rapidly with the market diffusion of hydrogen technologies, and that policymakers’ and business interest is presently on the rise.publishedVersio
Economically-Limited Onshore Wind Capacity and Production in European Nations
No full textICREN Conference:Title: Economically-Limited Onshore Wind Capacity and Production in European NationsAbstract:In the context of meeting CO2 emission reduction targets to fulfill climate action plans, expansion of renewable energy sources into future energy systems represents a common research theme. Onshore wind turbines currently hold one of the highest shares in renewable electricity generation worldwide, with the second highest growth over the last 25 years [1], which will surely continue in the future. It is well known that electricity generation from wind turbines is characterized by spatially-sensitive intermittency, and furthermore that the placement of turbines is strongly influenced by the sociotechnical criteria; such as proximity to settlements, terrain suitability, and conservation efforts. Nevertheless, wind scenario studies commonly do not account for all of these concerns in detail; typically over simplifying the impact of sociotechnical criteria on the final distribution of a desired capacity across a study region. To improve upon this deficiency, the work described here incorporates contemporary techniques for the simulation of hourly wind turbine performance at large spatial scales in coordination with land eligibility concerns. Using the European continent as the study region, the applied method proceeds as follows. First, every 1 km2 location in Europe is simulated at the hourly level considering multiple turbine models and 36 weather years (Figure 1). A previous land eligibility result [2] is used along with a turbine placement algorithm to identify the maximal number of turbines which can be placed in the available areas with a minimal distance of 850 meters enforced between turbines. The placements are then matched to their expected FLH, and a cost model [3] is used to determine the best turbine model and the associated levelized cost of electricity (LCOE) for each placement following the approach of Robinius et. al[4]. Ordering by cheapest LCOE, the average FLH and LCOE of each country is determined as a function of installed capacity (sample trends displayed in Figure 2). By choosing an economically-constrained average LCOE of 11, 9, 7, and 5 Euro-ct/kWh, it finally becomes possible to determine the economically-limited capacity and production within each of the evaluated countries, shown in Table 1. References:[1] IEA. “Renewables Information 2017”. 2018. ISBN: 978-92-64-27811-0. url:https://www.iea.org/publications/freepublications/publication/renewables-information---2017-edition---overview.html[2] Ryberg, D. S., Robinius, M., & Stolten, D. (2017). Methodological Framework for Determining the Land Eligibility of Renewable Energy Sources. arXiv preprint arXiv:1712.07840.[3] Fingersh, L., Hand, M., & Laxson, A. (2006). Wind turbine design cost and scaling model (No. NREL/TP-500-40566). National Renewable Energy Lab.(NREL), Golden, CO (United States).[4] Robinius, M., Otto, A., Syranidis, K., Ryberg, D. S., Heuser, P., Welder, L., ... & Stolten, D. (2017). Linking the power and transport sectors—Part 2: Modelling a sector coupling scenario for Germany. Energies, 10(7), 957
Kostenoptimale Versorgungssysteme für ein vollautarkes Einfamilienhaus
No full textKostenoptimale Versorgungssysteme für ein vollautarkes EinfamilienhausThema: Strom- und Wärmeerzeugung sowie SpeicherLeander Kotzur (1), Peter Markewitz(1), Martin Robinius (1), Detlef Stolten(1,2)(1) Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany(2) Chair for Fuel Cells, RWTH Aachen University, c/o Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, GermanyMotivation und zentrale FragestellungIm Wohngebäudesektor ist derzeit ein Trend zu teilautarken Systemen basierend auf Photovoltaik (PV) und Batteriespeichern zu beobachten. Längerfristig ist eine vollautarke Versorgung zur Deckung des Strom- und Wärmebedarfs vorstellbar. Eines der ersten vollautarken Gebäude wurde in der Schweiz im Juni 2016 eingeweiht [1]. Vor diesem Hintergrund stellt sich die Frage, wie das Potential dieser Systeme ist, welche sich zu 100% selbst versorgen und somit nicht mehr auf die allgemeine Stromversorgung sowie Energieträgerlieferungen (z.B. Erdgas, Erdöl oder Biomasse) angewiesen sind. Dazu werden in diesem Beitrag Kosten und Auslegung autarker Versorgungssysteme für ein Einfamilienhaus ermittelt und geeignete Technologien identifiziert.Methodische VorgangsweiseEs wurde ein Mixed-Integer Linear Program (MILP) erstellt, welches das Energieversorgungs-system eines Wohngebäudes abbildet. Ziel ist es, eine kostenoptimale Technologiekonfiguration für die Wärme- und Stromversorgung zu 8760 Stunden im Jahr zu ermitteln. Als Technologien werden Photovoltaikmodule, Batterien, Wärmpumpe, Tauchsieder, sowie ein Wärmespeicher für das Referenzsystem berücksichtigt. Potentielle Systemerweiterungen sind ein Elektrolyseur, ein Wasserstoffdruckspeicher und eine Brennstoffzelle. Zusätzlich wird zur alternativen Wasserstoffumwandlung das mögliche Technikportfolio noch um eine reversible Fest-Oxid Brennstoffzelle (rSOC) erweitert, welche als Elektrolyseur und Brennstoffzelle betrieben werden kann. Zur alternativen Wasserstoffspeicherung besitzt das Modell auch die Option, ein Liquid Organic Hydrogen Carrier (LOHC) System zu integrieren, bestehend aus Hydrier- und Dehydriereinheit, sowie einem LOHC-Tank. Abbildung 1: Betrachtetes VersorgungssystemDas betrachte System ist in Abbildung 1 dargestellt.Basierend auf den Wetterdaten eines Testreferenzjahrs des DWD [2] wurden die spezifischen Einspeisezeitreihen für die Photovoltaikanlagen simuliert [3]. Die Stromlast basiert auf gemessenen Daten [4], während die Wärmelast mit einem 1R1C-Model berechnet wurde.Als Referenzgebäude wurde der Neubau eines Einfamilienhauses gewählt [5].Ergebnisse und SchlussfolgerungenWie in Abbildung 2 dargestellt, wurde das Versorgungssystem des Wohngebäudes für verschiedene Systemkonfigurationen optimiert. Der alleinige Einsatz von Batterien als Speichertechnologie resultiert für eine 100%ige Selbstversorgung im teuersten System und stellt den Referenzfall dar. Mit einem Einsatz von Wasserstoff als Speichermedium lassen sich im Vergleich dazu die Systemkosten um 20% senken, wobei der Einsatz einer rSOC die Kosten um 24% reduziert. Der Wärmebedarf des Gebäudes kann dabei vollständig durch die Abwärme der Brennstoffzelle gedeckt werden. Der Einsatz von LOHC als Speichermedium reduziert die Systemkosten weiter auf 44% der Referenzkosten. Die Wärme wird hierbei durch eine Wärmepumpe bereitgestellt, da die Abwärme der rSOC zur Dehydrierung des LOHC’s genutzt wird. Wenn eine Batterie diesem System zugeschaltet wird, kann weiterhin eine signifikant kleinere Auslegung des Wasserstoffsystems erzielt werden. Dieses wird durch die Zwischenspeicherung der Batterie konstanter über den Jahresverlauf betrieben und setzt somit spezifisch mehr Wasserstoff um. Folglich kann das günstigste vollautarke System mit einem kombinierten Einsatz von rSOC, LOHC und Batterie realisiert werden. Abbildung 2: Resultierende Zusammensetzung der Systemkosten, wobei die Kosten des Batterie-Systems als Referenz zu 100% gesetzt wurdenLiteratur1. Diermann, R., Ohne Netz - Erstes völlig energieautarkes Mehrfamilienhaus der Welt fertiggestellt, in Wirtschaftswoche. 2016.2. DWD, Testreferenzjahre (TRY). 2012.3. Andrews, R.W., et al., Introduction to the Open Source PV_LIB for Python Photovoltaic System Modeling Package. 2014.4. Tjaden, T., et al., Repraesentative elektrische Lastprofile fuer Wohngebaeude in Deutschland auf 1-sekuendiger Datenbasis. 2015.5. Bracke, J., et al., Techno-ökonomische Bewertung von Energie-Autarkie für die Energieversorgung von Einfamilienhäusern. Zeitung für Energiewirtschaft, 2016. 40: p. 127-137
Impact of Wind Year Selection on the Design of Optimized Energy Systems with Variable Renewable Energy Sources
No full textImpact of Wind Year Selection on the Design of Optimized Energy Systems with Variable Renewable Energy SourcesDilara Gulcin Caglayan 1, Jochen Linssen 1, Martin Robinius 1, Detlef Stolten 1,2 1 Institute of Energy and Climate Research - Electrochemical Process Engineering (IEK-3)Forschungszentrum Juelich GmbH, 52425 Juelich, Germany2 Chair for Fuel Cells, RWTH Aachen University, c/o Institute of Energy and Climate Research - Electrochemical Process Engineering (IEK-3)Forschungszentrum Juelich GmbH, 52425 Juelich, [email protected], [email protected], [email protected], [email protected] Renewable energy sources (RES) will play a crucial role in future sustainable energy systems. For analyzing scenarios concerning the future energy systems design, spatio-temporal modeling of the electricity generation of renewable energies is essential. Most analyses performed on renewable energy sources have been conducted using historical data of either one specific reference year or an average of some years. In this study, the impact of using wind speeds of different years ranging between 1980 and 2015 on the future designs of a multi-national, wind-based energy system is analyzed. In this system, renewable electricity generation is used for the production of hydrogen for mobility demand via electrolysis in several European countries: Germany, Netherlands, Belgium, Luxemburg, Switzerland, France and Italy. The applied optimization model takes into account onshore and offshore wind electricity generation, while the hydrogen demand is assumed for the case where market penetration is 75% fuel cell electric vehicles. The installed capacities of technologies to meet the demand are optimized for each year, thereby revealing the total annual cost (TAC) of the system in each case. Additionally, installed capacities of onshore and offshore wind energies for each year and region are presented. It is seen that the TAC of the system is strongly sensitive to the choice of reference year; for example changing by approximately 20% between 1997 and 1998. Furthermore, significant variation of optimization results in regards to the capacities installed in each region with respect to reference year can be observed.Keywords: Variable renewable energy, wind energy, effect of wind year, energy systems optimization, power-to-hydrogen
Strom- und Gasmarktdesign zur Versorgung des deutschen Straßenverkehrs mit Wasserstoff
No full textThe German government has set targets to reduce greenhouse gas emissions by 40% by 2020, 55% by 2030, 70% by 2040 and 80-95% by 2050 compared to 1990 as reference year. As well as meeting other requirements, these targets can be achieved by raising the contribution of renewably- generated power to Germany’s gross electricity consumption to 80% by 2050. Based on Germany’s potential, intermittent energy sources (IES) such as on- and off-shore wind, as well as photovoltaics, are necessary sources that must be utilized in order to achieve these ambitious targets. Because of the intermittency of these sources, there will be times in which surplus power generated could be used for example for the transport sector. During these periods of surplus power, the storage capacity of hydrogen allows for a so-called “power-to-gas” concept whereby the surplus power can be used to produce hydrogen and oxygen by means of electrolyzers. The aim of this thesis is to identify and develop a market design that is characterized by high penetration levels of IES, supplemented by the use of hydrogen in the transport sector. Furthermore, the aim was to develop a model in which the electricity and gas sector, including a hydrogen pipeline grid, is represented so as to analyze and validate selected market designs. Therefore, potential electricity and gas markets, as well as the most important potential share and stake holders of a hydrogen infrastructure, are analyzed. With the model developed in this thesis, an existing energy concept has been developed, analyzed and evaluated. In addition, the distribution of the hydrogen production costs was calculated by employing a Monte Carlo Simulation analysis. The developed energy concept relies on 170 GW onshore and 60 GW offshore wind capacity and these dominate the model. This leads to surplus power, especially in the federal states of Lower Saxony, Schleswig-Holstein and Mecklenburg-Hither Pomerania. To supply the estimated peak hydrogen demand in 2052 with 2.93 Million tons, a total capacity of 20 GW of electrolyzes in 15 counties must be installed. The necessary hydrogen pipelines from IES sources to 9,968 hydrogen fuel stations will require a 12,104 km transmission pipeline which will cost an estimated €6.68 billion and for distribution, a total length of 29,671 km will be required, with an estimated cost of €12 billion.Furthermore, for three separate cases that can be distinguished by their respective input parameters, the profitability of an electricity and gas market design to supply the German transport sector with hydrogen is demonstrated. This analysis was also performed by means of a Monte Carlo Simulation. It shows that, with a target cost of 22.9 ct/kWh, the probability of pretax hydrogen production cost, including the infrastructure, laying under the target costs, are 81%
A modeler's guide to handle complexity in energy systems optimization
No full textDetermining environmentally- and economically-optimal energy systems designs and operations is complex. In particular, the integration of weather-dependent renewable energy technologies into energy system optimization models presents new challenges to computational tractability that cannot only be solved by advancements in computational resources. In consequence, energy system modelers must tackle the complexity of their models by applying various methods to manipulate the underlying data and model structure, with the ultimate goal of finding optimal solutions. As which complexity reduction method is suitable for which research question is often unclear, herein we review different approaches for handling complexity. We first analyze the determinants of complexity and note that many drivers of complexity could be avoided a priori with a tailored model design. Second, we conduct a review of systematic complexity reduction methods for energy system optimization models, which can range from simple linearization performed by modelers to sophisticated multi-level approaches combining aggregation and decomposition methods. Based on this overview, we develop a guide for energy system modelers who encounter computational limitations
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