Trade-Offs einer Eigenversorgung Deutschlands in einem 100% erneuerbaren europäischen Stromsystem

Energiesystemoptimierungsmodelle (ESOMs) werden häufig für die Politikberatung eingesetzt. Sie können beispielsweise aufzeigen, wie ein zukünftiges kostenoptimiertes europäisches Energiesystem aussehen könnte, welches das Dekarbonisierungsziel des European Green Deal erfüllt. Zielen ESOM aber ausschließlich auf die Minimierung von Kosten ab, können im Ergebnis durch die (Aus-)Nutzung von Anlagenstandorten mit sehr hohen erneuerbaren Energiepotentialen (EE-Potentiale) politisch unerwünscht hohe Importabhängigkeiten entstehen. Studien zeigen, dass eine vollständige Netto-Stromeigenversorgung auf nationaler Ebene möglich wäre, im Gegenzug die Gesamtkosten aber um 7% steigen würden. Allerdings wird dabei die Rolle von Wasserstoffimporten vernachlässigt. Die Auswirkungen auf Infrastruktur und Energiesystemkosten können sich regional stark unterscheiden, je nach erneuerbaren Energiepotentialen und der Abhängigkeit von Wasserstoff. Die vorliegende Studie untersucht die Trade-Offs zwischen der Vermeidung von Importabhängigkeiten und den damit einhergehenden Kosten mit einem besonderen Fokus auf Deutschland

Trade-Offs einer Eigenversorgung Deutschlands in einem 100% erneuerbaren europäischen Stromsystem

Energiesystemoptimierungsmodelle (ESOMs) werden häufig für die Politikberatung eingesetzt. Sie können beispielsweise aufzeigen, wie ein zukünftiges kostenoptimiertes europäisches Energiesystem aussehen könnte, welches das Dekarbonisierungsziel des European Green Deal erfüllt. Zielen ESOM aber ausschließlich auf die Minimierung von Kosten ab, können im Ergebnis durch die (Aus-)Nutzung von Anlagenstandorten mit sehr hohen erneuerbaren Energiepotentialen (EE-Potentiale) politisch unerwünscht hohe Importabhängigkeiten entstehen. Studien zeigen, dass eine vollständige Netto-Stromeigenversorgung auf nationaler Ebene möglich wäre, im Gegenzug die Gesamtkosten aber um 7% steigen würden. Allerdings wird dabei die Rolle von Wasserstoffimporten vernachlässigt. Die Auswirkungen auf Infrastruktur und Energiesystemkosten können sich regional stark unterscheiden, je nach erneuerbaren Energiepotentialen und der Abhängigkeit von Wasserstoff. Die vorliegende Studie untersucht die Trade-Offs zwischen der Vermeidung von Importabhängigkeiten und den damit einhergehenden Kosten mit einem besonderen Fokus auf Deutschland

Improving energy system design with optimization models by quantifying the economic granularity gap: The case of prosumer self-consumption in Germany

Energy system models are widely used to inform the political decisions required to successfully mitigate climate change in the energy sector. The energy system optimization models (ESOMs) used to identify cost-minimal transformation pathways assume the perfect behavior of market participants from a central planner's perspective. Neglecting the decision-making under uncertainties or biased perceptions and attitudes leads to inaccurate assumptions regarding the requirements of a successful energy transition. In particular, ESOMs underestimate the required capacities for power generation, storage, and transmission compared with real-world energy systems, a phenomenon known as the "economic granularity gap". Agent-based models (ABMs) are helpful tools for capturing the behavior of market actors. Hence, attempts have been made to identify and alleviate this phenomenon through the coupling of ESOMs and ABMs. In this paper, we propose an automated workflow for such model coupling and quantify the economic granularity gap for the case of photovoltaic-prosumer self-consumption. Our results show that the current business models and regulatory frameworks affecting prosumer self-consumption patterns require the adaptation of cost-minimal energy system designs. However, if correctly implemented, instruments such as dynamic tariffs could narrow the economic granularity gap, shifting real-world energy systems closer to their ideal counterparts

Strategic policy targets and the contribution of hydrogen in a 100% renewable European power system

The goal of the European energy policy is to achieve climate neutrality. The long-term energy strategies of various European countries include additional targets such as the diversification of energy sources, maintenance of security of supply, and reduction of import dependency. When optimizing energy systems, these strategic policy targets are often only considered in a rudimentary manner and thus, the understanding of the corresponding interdependencies is lacking. Moreover, hydrogen is considered as a key component of a fully decarbonized energy system, but its role in the power sector remains unclear due to the low round-trip efficiencies. This study reveals how fully decarbonized European power systems can benefit from hydrogen in terms of overall system costs and the achievement of strategic policy targets. We analyzed a broad spectrum of scenarios using an energy system optimization model and varied model constraints that reflect strategic policy targets. Our results are threefold. First, compared to power systems without hydrogen, systems using hydrogen realize savings of 14-16% in terms of the total system costs. Second, the implementation of a hydrogen infrastructure reduces the number of infeasible scenarios when structural policy targets are considered within the power system. Third, the role of hydrogen is highly diverse at a national level. Particularly, in countries with low renewable energy potential, hydrogen plays a crucial role. Here, high levels of self-sufficiency and security of supply are achieved by deploying hydrogen-based power generation of up to 46% of their annual electricity demand, realized via imports of green hydrogen

A Pathway for the German Energy Sector Compatible with a 1.5°C Carbon Budget

We present an energy transition pathway constrained by a total CO2 budget of 7 Gt allocated to the German energy system after 2020, the Budget Scenario (BS). We apply a normative backcasting approach for scenario building based on historical data and assumptions from existing scenario studies. The modeling approach combines a comprehensive energy system model (ESM) with REMix—a cost optimization model for power and heat that explicitly incorporates sector coupling. To achieve the necessary CO2 reduction, the scenario focuses on electrifying all end use sectors until 2030, adding 1.5–2 million electric vehicles to the road per year. In buildings, 400,000–500,000 heat pumps would be installed annually by 2030, and the share of district heating would double until 2050. In the scenario, coal needs to be phased out by 2030. Wind and Photovoltaic (PV) capacities would need to more than double to 290 GW by 2030 and reach 500 GW by 2050. The BS results indicate that a significant acceleration of the energy transition is necessary before 2030 and that this higher pace must be maintained thereafter until 2050

Enabling energy systems research on HPC

Energy systems research strongly relies on large modeling frameworks. Many of them use linear optimization approaches to calculate blueprints for ideal future energy systems, which become increasingly complex and so do the models. The state-of-the-art to compute them is the application of shared-memory computers combined with approaches to reduce their size. We overcome this and implement a full automatized workflow on HPC using a newly developed solver for distributed memory architectures. Moreover, we address the challenge of uncertainty in scenario analysis by performing sophisticated parameter variations for large-scale power system models, which cannot be solved in the conventional way. Preliminary results show that we are able to identify clusters of future energy system designs, which perform well from different perspectives of energy system research and also consider disruptive events. However, we also see that our approach provides most insights when being applied to rather complex than simple models

A multi-perspective approach for exploring the scenario space of future power systems

There are many possible future energy systems – many of them unforeseen. We explore the range of parameter uncertainty and quantify parameter interrelations to generate multiple scenarios. Only sensible parameter combinations remain as in-puts to an energy system optimization and coupled models. In the past, computa-tional limitations have been a major obstacle to calculate such an enormous space of scenarios. Opposed to that, we use high-performance computing. To utilize the HPC-system efficiently the parallel solver for linear programs PIPS-IPM++ is applied. We integrate it into a tool chain of different components including sce-nario generation, energy system optimization and results evaluation and tackle the challenge of coupling a large diversity of software packages in a fully automated HPC workflow. This enables the calculation of all scenarios in a matter of days. Furthermore, we use a set of 37 indicators to provide a comprehensive assess-ment of the simulated energy systems. In this way, we cover multiple perspec-tives, such as system adequacy, security of supply or behavior of market actors. Whereas scenarios with low spatial resolution do not lead to clear results, higher resolutions do. Yet, we identified three clusters of scenarios, among which a group with high natural gas dependency is found. This allows to study disruptive events like price shocks in a vast parameter space and to identify countermeasures for the long-term

Szenarien mit Energieinfrastrukturausfällen unter Einbezug multipler Parameterunsicherheiten

Der Einsatz von Modellen zur Erstellung und Untersuchung von Szenarien ist ein wesentliches Instrument der Energiesystemanalyse. Für die Politikberatung ist die Frage nach der Verlässlichkeit von solchen Szenarien von großer Wichtigkeit, da diese mit großen Unsicherheiten behaftet sein können. Diesem Problem wird in im Forschungsprojekt UNSEEN begegnet: durch das Abfahren eines sehr großen Parameterraums konnten bereits mehr als 1000 Energieszenarien automatisch generiert, berechnet und ausgewertet werden, darunter auch 100 räumlich hoch-aufgelöste Stromsystemmodelle Deutschlands. Letztere Modelle eignen sich auch zur Untersuchung der Auswirkungen von Ausfällen der darin explizit modellierten Energieinfrastrukturen, also von Kraftwerksstandorten, Übertragungsnetzleitungen und Umspannwerken. Der wesentlichen Herausforderung, dafür eine Vielzahl aufwendiger Modellrechnungen performant durchzuführen, begegnen wir mittles eines auf High-Performance-Computing angepassten Modellierungs-Workflows, welcher den entstehenden Szenarioraum auf Basis multi-kriterieller Indikatoren (u. a. zu Angemessenheit, Betriebssicherheit und Wirtschaftlichkeit) bewertbar macht. Die ersten Analysen dieses Szenarioraums zeigen, dass >Best-Perfoming< Szenarien verhältnismäßige geringe Zubauraten für Windkraft aufweisen, bei einer Reduktion der CO2-Emissionen im Stromsektor um 85%-89% gegenüber 1990

Bridging granularity gaps to decarbonize large-scale energy systems - The case of power system planning

The comprehensive evaluation of strategies for decarbonizing large- scale energy systems requires insights from many different perspectives. In energy systems analysis, optimization models are widely used for this purpose. However, they are limited in incorporating all crucial aspects of such a complex system to be sustainably transformed. Hence, they differ in terms of their spatial, temporal, technological, and economic perspective and either have a narrow focus with high resolution or a broad scope with little detail. Against this background, we introduce the so- called granularity gaps and discuss two possibilities to address them: increasing the resolutions of the established optimization models, and the different kinds of model coupling. After laying out open challenges, we propose a novel framework to design power systems in particular. Our exemplary concept exploits the capabilities of power system optimization, transmission network simulation, distribution grid planning, and agent- based simulation. This integrated framework can serve to study the energy transition with greater comprehensibility and may be a blueprint for similar multi-model analyses