Balancing and Intraday Market Design: Options for Wind Integration

EU Member States increase deployment of intermittent renewable energy sources to deliver the 20% renewable target formulated in the European Renewables Directive of 2008. To incorporate these intermittent sources, a power market needs to be flexible enough to accommodate short-term forecasts and quick turn transactions. This flexibility is particularly valuable with respect to wind energy, where wind forecast uncertainty decreases significantly in the final 24 hours before actual generation. Therefore, current designs of intraday and balancing markets need to be altered to make full use of the flexibility of the transmission system and the different generation technologies to effectively respond to increased uncertainty. This paper explores the current power market designs in European countries and North America and assesses these designs against criteria that evaluate whether they are able to adequately handle wind intermittency.Power market design, integrating renewables, wind energy, balancing, intraday

Impact of market design and social acceptance on the regional distribution of wind energy - simulation results for Germany

In Germany, in 2017 the support scheme for renewables changed to capacity auctions for wind power. While in theory auctions have a high degree of overall target fulfilment, the regional distribution of investments varies based on the implemented market designs. Results from 2017 show that changes in market design lead to a significant shift of investments in the south of Germany: While previously support schemes incentivised investments, in 2017 almost no wind projects were accepted. This paper investigates the regional distribution of investments into wind energy, when different designs of capacity auctions are implemented. The modelling approach draws on a parallel analysis of technical potential, expected revenues and social acceptance to determine economic wind power investments. The European electricity model REMix is then used to simulate investments in wind energy in order to analyze: What measures and incentives on a German/European level could be beneficial to meet regional targets? Overview: In recent years social acceptance of investment projects for renewables, power plants and power lines have gained significant attention by sociologists and policy makers. Results of previous studies show (Sonnberger 2017), that with regard to wind energy people tend to have a considerably high level of trust and acceptance of wind power projects in general. However, when wind power stations are planed close to settlements, the level of acceptance decreases sharply. Policy makers in Germany (in the state of North Rhine-Westphalia and Bavaria) therefore proposed to "avoid acceptance problems" by introducing a minimum distance of wind farms to settlements. The paper therefore aims to answer the following questions: - How can public acceptance be integrated into the analysis of wind investments? Which measures and market incentives can increase public acceptance for additional investments into wind power? - What incentives provide different remuneration schemes and auction design on the regional distribution of wind energy? - What regulatory measures and incentives on a German and European level could be beneficial to reach the regional targets (e.g. the states targets)? (here: targets of the state of Baden-Wuerttemberg) The analysis was conducted for Germany and the state of Baden-Wuerttemberg. Methods: The selected modelling approach consists of three parts: 1. Technical potential: A geodata (GIS) based analysis has been used to investigate the technical potential of wind power in different regions in Germany. The model REMix-Endat uses a high resolution spatial analysis and determines potential areas within Germany, where investments into additional wind farms could talke place. The calculation is based on distance from settlements, infrastructure, type of landscape (landcover data), slope and altitude . In a second step the model determines, based on historic windspeeds, the estimated hourly feed-in of renewables in future years for these different locations. 2. Market design and incentives: In a second step, a modell analyzing the long term investment incentives from different political frameworks and market designs will be investigated for different regions and types of locations. The model determines the expected net present value for each location. 3. Public acceptance: Aspects of public acceptance will be integrated into the model. While general acceptance for wind is high, local acceptance must be accomplished to enable investments. Two path for increasing local acceptance are investigated: Avoiding acceptance problems in the first place and creating distributional fairness and trust. The effect of both measures on the energy system and long term investments are investigated. Technical potential, market incentives and results from the acceptance analysis are integrated into the European energy system model REMix. Based on the estimated hourly dispatch and price estimates for representative years up to 2050, an overall potential can be determined for each location. The modell depicts investments - it especially adds wind (an PV) capacity to meet the renewable targets for Germany. A first analysis of market design and incentives for investments and detailed description can be found in Borggrefe (2018). This paper extends the model and focuses on the integration of aspects of "public acceptance" Results: Creating acceptance by "avoiding acceptance problems" as it is proposed/implemented by some states in Germany will not provide sufficient areas for wind power onshore. Investments into other more expensive renewable technologies will increase costs of electricity procurement. A second set of scenarios analyses the impact of a "fair distribution" of wind capacity across all regions in Germany on market results, system stability and target fulfillment. In the coming decades this will lead to increasing costs for investment and remuneration. In the long run the resulting energy system also provides benefits, because cost extensive investments into grid can be saved

Teaching Power-Sector Models Social and Political Awareness

Energy-system scenarios are widely used to relate the developments of the energy supply and the resulting carbon-emission pathways to political measures. To enable scenario analyses that adequately capture the variability of renewable-energy resources, a specialised type of power-sector model (PSM) has been developed since the beginning of this century, which uses input data with hourly resolution at the national or subnational levels. These models focus on techno-economic-system optimisation, which needs to be complemented with expert socioeconomic knowledge in order to prevent solutions that may be socially inacceptable or that oppose political goals. A way to integrate such knowledge into energy-system analysis is to use information from framework scenarios with a suitable geographical and technological focus. We propose a novel methodology to link framework scenarios to a PSM by applying complexity-management methods that enable a flexible choice of base scenarios that are tailored to suit different research questions. We explain the methodology, and we illustrate it in a case study that analyses the influence of the socioeconomic development on the European power-system transition until 2050 by linking the power-sector model, REMix (renewable-energy mix), to regional framework scenarios. The suggested approach proves suitable for this purpose, and it enables a clearer link between the impact of political measures and the power-system development

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

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

Next generation energy modelling - benefits of applying parallel optimization and high performance computing

Quantitative energy system modelling based on primarily linear mathematical optimization provide a basis to answer political, technical, and economic questions regarding the future development of electricity systems. In order to capture the complexity of future decentralized electricity system, models are required to become increasingly complex. This presentation outlines how high performance computing (HPC) can be applied to energy system modelling and describes challenges and benefit that arise when complex energy models based on linear and non-linear algorithms are applied to parallel computing. The presentation draws on results from the project BEAM-ME funded by the German Federal Ministry for Economic Affairs and Energy. The research project aims at improving computational performance of energy system models. Within this project the consortium of researchers from different research fields (system analysis, mathematics, operations research and informatics) develop new strategies to increase computational performance of energy system models and to apply energy system models to high performance computing. This presentation presents intermediate results from a benchmark analysis when applying the energy system model REMix to HPC. The presentation concludes with a discussion on future challenges for energy system modelling and how HPC can enable energy modelers to improve scenarios, market design and policy advice

BEAM-ME Introducing the model experiment (MEXT)

On May 22, 2019, the final workshop of the BEAM-ME project took place in Aachen, Germany. In the BEAM-ME project years (realization of acceleration strategies of application-oriented mathematics and computer science for optimizing energy system models)we have been working on the acceleration of energy system for the last three. The aim was to reduce existing computing times and to enable even more detailed energy system models. The consortium consists of an interdisciplinary team of modellers, computer scientists, mathematicians and experts from the field of high performance computing. Within the framework of the project we have developed new algorithms and methods to calculate distributed linear optimization systems and to solve them on supercomputers. In an accompanying model experiment, seven research institutions in the field of energy system modelling compared and further developed conceptual approaches for accelerating models. This presentation provides an overview of the model experiment within the BEAM-ME project

Marktdesign und Verteilungseffekte: Auswirkungen der Rahmensetzungen für Erneuerbare Energien auf die regionale Verteilung und langfristige Entwicklung des Energiesystems

Marktdesign und Ordnungsrahmen haben einen hohen Einfluss sowohl auf den Zubau als auch auf die regionale Verteilung von Technologien. Der zukünftige Energiemix der einzelnen deutschen Bundesländer hängt in hohem Maße von den nationalen und europäischen Rahmenbedingungen ab. Vor dem Hintergrund der fortlaufenden Anpassungen der Förderung stellt sich die Frage, wie sich der gesetzte Rahmen auf den Zubau von erneuerbaren Energien in einzelnen Regionen auswirkt und welche langfristigen Entwicklungen dadurch im Energiesystem zu erwarten sind. Im Rahmen dieses Beitrags wird untersucht, welche Anreize unterschiedliche Ausschreibungs- und Förderungsdesigns auf die Investitionsanreize und Verteilungswirkung beim Zubau von Windanlagen haben. Darüber hinaus wird bewertet, welche regulatorischen Rahmenbedingungen günstig für die mittel- und langfristige EE-Zielerreichung der betrachteten Regionen sind. Methodik: In einem ersten Schritt werden, aufbauend auf GIS-basierten Analysen, die zukünftigen technischen Potentiale für Wind- und PV-Anlagen in Baden-Württemberg und Deutschland ermittelt. Unter Verwendung des DLR-Modells EnDAT wird anhand von Geo-Daten Kosten- und Volllaststunden-Potenzialkurven erneuerbarer Energien für Europa mit hoher regionaler Auflösung bestimmt. Durch die Kopplung mit dem am DLR entwickelten Energiesystemmodells REMix wird anhand ausgewählter Stichjahre untersucht, welche Anreize von unterschiedlichen Ausschreibungsverfahren und Preisbildungsmechanismen entfacht werden. Der Fokus liegt auf dem Zubau von Windanlagen in den unterschiedlichen Bundesländern. Ein Vergleich der Wirtschaftlichkeit spezifischer EE-Technologien an beispielhaften Standorten in Nord- und Süddeutschland bildet die Grundlage für die Bewertung der Fördermechanismen und der resultierenden Verteilungswirkungen. Es werden unterschiedliche Ausschreibungsmechanismen, sowie eine detaillierte Differenzierung der Technologien (Starkwind- und Schwachwind-Anlagen, Ausrichtung der Solaranlagen und Potentiale/Bedarf für den Lastfolgebetrieb) untersucht. Ergebnisse: Ausgehend von der Wirtschaftlichkeit der Einzelanlagen und der vorhandenen Potentiale, erfolgt eine Abschätzung der zukünftigen technischen und wirtschaftlichen EE-Ausbaupotentiale für verschiedene Regionen in Deutschland. Anhand von drei Szenarien wird gezeigt wie sich langfristig unterschiedliche Ausschreibungs- und Fördermechanismen auf den Energiemix, die Netze und den Speicherbedarf in Deutschland und in Baden-Württemberg auswirken. Die Ergebnisse zeigen, dass das bestehende Ausschreibungsdesign nur geringe Anreize für systemfreundliche Schwachwindanlagen sowie für Winderzeugung in Süddeutschland bieten. Abschließend wird diskutiert, wie das Marktdesign und die Preisbildungsmechanismen für Systeme mit hohen Anteilen Erneuerbarer generell ausgestaltet werden sollten und wie im Speziellen ein günstiges Marktdesign für Wind mit Bezug auf die Klimaziele in Baden-Württemberg aussehen könnte. Die vorgestellten Szenarien-Ergebnisse wurden im Rahmen des Projekts "Energiesystemanalyse Baden-Württemberg" (EnSys-BaWü) entwickelt. Das Projekt wurde im Jahr 2017 im Rahmen des Förderprogramms "Baden-Württemberg Programm Lebensgrundlage und ihre Sicherung" (BWPLUS) durch das Ministerium für Umwelt und Energie Baden-Württemberg gefördert

Speed-up methods for energy system models - a model experiment: flashlights and lessons learned in the BEAM-ME project

On May 22, 2019, the final workshop of the BEAM-ME project took place in Aachen, Germany. In the BEAM-ME project years (realization of acceleration strategies of application-oriented mathematics and computer science for optimizing energy system models)we have been working on the acceleration of energy system for the last three. The aim was to reduce existing computing times and to enable even more detailed energy system models. The consortium consists of an interdisciplinary team of modellers, computer scientists, mathematicians and experts from the field of high performance computing. Within the framework of the project we have developed new algorithms and methods to calculate distributed linear optimization systems and to solve them on supercomputers. In an accompanying model experiment, seven research institutions in the field of energy system modelling compared and further developed conceptual approaches for accelerating models. This presentation provides an overview of results and the ten lessons learned from the model experiment