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

Survival outcomes in a prospective randomized multicenter Phase III trial comparing patients undergoing anatomical segmentectomy versus standard lobectomy for non-small cell lung cancer up to 2 cm

OBJECTIVES The oncological equivalence of anatomical segmentectomy for early stage non-small cell lung cancer (NSCLC) is still controversial. Primary aim of this study was survival outcomes in combination with improved quality of life after segmentectomy compared with lobectomy in patients with pathological stage Ia NSCLC (up to 2 cm, 7th edition) MATERIALS AND METHODS: We conducted a prospective, randomized, multicenter phase III trial to confirm the non-inferiority of segmentectomy to lobectomy in regard to prognosis (trial No. DRKS00004897). Patients were randomized to undergo either segmentectomy or lobectomy and followed up for 5-years survival and tumor recurrence. The 5-year hazard ratio comparing lobectomy with segmentectomy was required to remain above 0.5. RESULTS Between October 2013 and June 2016, 108 patients with verified or suspected NSCLC up to 2 cm diameter were enrolled; 54 were assigned to lobectomy and 54 (1 drop-out) to segmentectomy. In-hospital and 90 days mortality was 0% in both groups. Overall survival at 5 years was 86.52% in the lobectomy compared to 78.21% in the segmentectomy group (HR = 0.61, (95% CI 0.23-1.66), p-value of non-inferiority test, p-ni = 0.687). Disease free survival was 77.29% for the lobectomy and 77.96% for the segmentectomy patients (HR = 1.50, (95% CI 0.60-3.76), p-ni = 0.019). At a median follow-up of 5 years, no differences were noted in either the locoregional or distant recurrent disease in both groups (9.4% vs 7.4%, p-ni = 0.506). CONCLUSION Overall survival, locoregional and distant recurrences was not significantly difference for patients undergoing either segmentectomy or lobectomy for stage Ia NSCLC. The targeted non-inferiority of segmentectomy to lobectomy could not be proven for primary endpoint overall survival, but was significant for the secondary endpoint of disease free survival

Closed-loop separation control over a sharp edge ramp using Genetic Programming

We experimentally perform open and closed-loop control of a separating turbulent boundary layer downstream from a sharp edge ramp. The turbulent boundary layer just above the separation point has a Reynolds number $Re_{\theta}\approx 3\,500$ based on momentum thickness. The goal of the control is to mitigate separation and early re-attachment. The forcing employs a spanwise array of active vortex generators. The flow state is monitored with skin-friction sensors downstream of the actuators. The feedback control law is obtained using model-free genetic programming control (GPC) (Gautier et al. 2015). The resulting flow is assessed using the momentum coefficient, pressure distribution and skin friction over the ramp and stereo PIV. The PIV yields vector field statistics, e.g. shear layer growth, the backflow area and vortex region. GPC is benchmarked against the best periodic forcing. While open-loop control achieves separation reduction by locking-on the shedding mode, GPC gives rise to similar benefits by accelerating the shear layer growth. Moreover, GPC uses less actuation energy.Comment: 24 pages, 24 figures, submitted to Experiments in Fluid

Speeding up Energy System Models - a Best Practice Guide

Background Energy system models (ESM) are widely used in research and industry to analyze todays and future energy systems and potential pathways for the European energy transition. Current studies address future policy design, analysis of technology pathways and of future energy systems. To address these questions and support the transformation of today’s energy systems, ESM have to increase in complexity to provide valuable quantitative insights for policy makers and industry. Especially when dealing with uncertainty and in integrating large shares of renewable energies, ESM require a detailed implementation of the underlying electricity system. The increased complexity of the models makes the application of ESM more and more difficult, as the models are limited by the available computational power of today’s decentralized workstations. Severe simplifications of the models are common strategies to solve problems in a reasonable amount of time – naturally significantly influencing the validity of results and reliability of the models in general. Solutions for Energy-System Modelling Within BEAM-ME a consortium of researchers from different research fields (system analysis, mathematics, operations research and informatics) develop new strategies to increase the computational performance of energy system models and to transform energy system models for usage on high performance computing clusters. Within the project, an ESM will be applied on two of Germany’s fastest supercomputers. To further demonstrate the general application of named techniques on ESM, a model experiment is implemented as part of the project. Within this experiment up to six energy system models will jointly develop, implement and benchmark speed-up methods. Finally, continually collecting all experiences from the project and the experiment, identified efficient strategies will be documented and general standards for increasing computational performance and for applying ESM to high performance computing will be documented in a best-practice guide

How to explore a large scenario space of future power systems? - A multi-perspective analysis for Germany

Motivation and research question: There are four challenges in energy systems analysis. The first is that future pathways are highly contingent on assumptions [1]. The second is computational limitations. The third is that, given a certain methodology, only few aspects of scenarios are usually analyzed [2]. The fourth is the identification of desirable futures within the target triangle of energy supply (affordability, security, sustainability). Addressing these problems means to create many scenarios using powerful hardware and software (problem 1), to sample from a huge parameter space (problem 2), coupling different tools, (problem 3), and to evaluate scenarios from multi-dimensional perspectives (problem 4). Methods We implemented a high-performance computing (HPC) workflow on the supercomputer JUWELS [3]. To utilize this HPC system efficiently, the parallel solver for linear programs, PIPS-IPM++ [4], has been applied. This solver is part of a tool chain including scenario generation, energy system optimization (REMix [5]), agent-based market simulation (AMIRIS [6]) and results evaluation based on a set of multi-dimensional indicators. In particular, we coupled a large diversity of software packages in a fully automated workflow (JUBE [7]) enabling the calculation of a multitude of large-scale scenario analyses in a matter of days. The real-world problem investigated is future power supply in Germany. It is either modeled as market where the interactions of decentral actors are simulated, or operation and investment planning are optimized from a central planner’s perspective. For the latter, the model comprises 479 network nodes that represent unique locations of transformer substations in the transmission grid. Neighboring countries, different weather profiles and techno-economic parameters are also part of the parametrization. To sample the huge parameter space [8], a literature research considering about 50 sources derives statistical descriptors of the most important parameter values to be varied. Results and conclusions First results with 1000 simplified optimization models proof the plausibility of our approach. As next step, we investigated 120 spatially fully resolved power systems. We compiled a set of more than 40 indicators [9] to provide comprehensive assessments of the simulated power systems cover quantities, such as electricity prices, energy self-sufficiency rate, ecosystem quality or grid congestion. Our results show correlations between indicators as expected, e.g., a high renewable energy penetration corresponds to low CO2-emissions, etc. Points of interest are all scenarios where a majority of indicators show values one standard deviation above or below the mean of all scenarios. Overall, there are few points of interest, i.e. systems where many indicators would point to a system that is satisfactory concerning system affordability, security and sustainability. Yet, differences between scenarios are small, i.e. t-tests between desirable and undesirable systems are not significant. With our final results, we expect to pave the way to more robust energy system modeling, e.g. evaluating systems in terms of resilience by extending our methodology for both unit commitment modeling and scenarios of infrastructure outages. This enables the derivation of measures for preparing for disruptive events like price shocks in the vast parameter space

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 solcher Szenarien von großer Wichtigkeit, da diese mit großen Unsicherheiten behaftet sind. Diesem Problem wird 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. Um eine Vielzahl aufwendiger Modellrechnungen performant durchzuführen, wurde ein auf High-Performance-Computing angepasster Modellierungs-Workflows entwickelt. Er macht den entstehenden Szenarioraum auf Basis multi-kriterieller Indikatoren (u. a. zu Angemessenheit, Betriebssicherheit und Wirtschaftlichkeit) bewertbar. Die ersten Analysen dieses Szenarioraumes zeigen, dass „Best-Perfoming“ Szenarien verhältnismäßige geringe Zubauraten für Windkraft aufweisen, bei einer Reduktion der CO2-Emissionen im Stromsektor um 85%-89% gegenüber 1990

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

Tackling challenges in energy system research with HPC

Energy system optimization models are one of the central instruments for the successful realization of the energy transition towards renewable sources. We have identified three major challenges to overcome the current limitations in energy system research. First, studying the future is subject to large uncertainties and these uncertainties are usually tackled with modeling of just a small subset of all possible scenarios. This has proven to be inadequate since most models are highly sensitive to input data. Second, the widely-used commercial solvers show poor scalability and are limited to single shared-memory compute nodes. Thus, models are defined with a lower resolution and technological diversity than necessary. The third challenge is that single models usually tend to investigate only certain aspects of an energy system, which do not cover all parts of future pathways. To overcome those limitations, we inspect the conceivable parameter space by using a hitherto unattained number of model-based scenarios. Therefore, we have implemented an automated parameter sampling based on a broad literature review, and a self-developed distributed-memory solver that outperforms commercial solvers. In addition, we have coupled different types of models in an automated, parallelized workflow. We use this workflow for a case study of the German power system. By evaluating more than 3600 scenarios, we observe a clear dominance of photovoltaics in future system designs. Efficiently leveraging the capability of HPC by combining those approaches could be a game changer for the energy-system analysis community and could ensure a better applicability for real world policy support