Can resource classes substitute spatial resolution in energy system models? A spatial scaling analysis.

While regional aggregation of areas can increase the computation speed of energy system models (ESM), this can also lead to an underestimation of the localised high quality of variable renewable energy (VRE) sources, which vary strongly depending on the site. Partly, the power transmission grid is able to level out the locality of production and consumption with energy systems spanning across regions as far as the European continent with large amount of power traded, which leads to a spatial averaging of the feed in profiles. Resource classes can subdivide VRE potentials into different quality classes of distinct feed-in profiles and potentials to compensate for the coarser resolution of the aggregated regions. This will lead to higher full load hours for the better located VRE power plants and thus lower levelised costs of electricity for those plants. This talk will examine the trade-offs between a high spatial resolution on the one hand and the accuracy of the feed in of VRE sources on the other hand. Therein the focus lies on the dispatch and expansion of the different technologies. The investigation focusses on wind resource classes, since wind speeds vary much more spatially than solar irradiance. Germany has been chosen for this investigation as its wind resource is diverse: the flatt northern parts offer high wind potentials whereas the resource quality in the south is much more dependent on the local topology

Speeding up energy system optimization models - lessons learned from heuristic approaches, parallel solvers and large scale models

Most state-of-the art optimizing energy system models are characterised by a high temporal and spatial resolution to include detailed information of local weather conditions. This became necessary through the integration of renewable energy sources such as photovoltaics and wind energy. Similarly the integration of cross-sectoral technologies for the decarbonisation of the energy, heating and transportation sector makes energy system models more complex and as a consequence the time required for solving the problem. To address this increasing computational demand the BEAM-ME project brought together experts from the fields of energy systems analysis, mathematics, operations research, and informatics to establish interdisciplinary solutions. The talk provides an overview of the final project results and more in-depth highlights from two stage heuristic approaches and the parallel interior point solver PIPS-IPM++. Depending on the problem at hand and available computation resources a speed-up factor of up to 26 was achieved. Taking up the results from the BEAM-ME project an outlook on the follow-up project UNSEEN shows how the significant reduction in time required for solving the problems can be used to generate a more holistic view on the near-optimal solution space. This allows providing decision makers with a wide range of alternatives showing the trade-offs between several decision criteria

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

Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System

Improvements in modeling energy systems of populous emerging economies are highly decisive for a successful global energy transition. The models used – increasingly published open source – still suffer from the lack of appropriate open data. As an illustrative example, we take the Brazilian energy system, which has great potential for renewable energy resources but still relies heavily on fossil fuels. We provide a comprehensive open dataset for scenario analyses, which can be directly used with the popular open energy system model PyPSA and other modeling frameworks. It includes three categories: 1) time series data of variable renewable potentials, electricity load profiles, inflows for the hydropower plants, and cross-border electricity exchanges, 2) geo-referenced data for 27 defined regions, and 3) tabular data, which contains power plant data with installed and planned generation capacities, aggregated grid network topology, biomass thermal plant potential, as well as scenarios of energy demand. This data fosters global or country-specific energy system studies based on open data relevant to decarbonizing Brazil’s energy system

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

Methods to reduce computation times of linear optimising Energy System Models

This talk concentrates on the scaling of approximative solution due to aggregation of linear energy system models. The scaling is performed both on the temporal scale as well as the regional scale