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Infrastructure for Future Electricity Markets (AFEM-INFRA)

English title Infrastructure for Future Electricity Markets (AFEM-INFRA)
Applicant Demiray Turhan Hilmi
Number 153774
Funding scheme NRP 70 Energy Turnaround
Research institution Forschungsstelle Energienetze ETH Zürich
Institution of higher education ETH Zurich - ETHZ
Main discipline Electrical Engineering
Start/End 01.01.2015 - 31.12.2018
Approved amount 499'900.00
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All Disciplines (3)

Discipline
Electrical Engineering
Civil Engineering
Mechanical Engineering

Keywords (4)

Electricity markets; Electricity Grids; Renewable energy potential; "Secure" RES Integration

Lay Summary (German)

Lead
Das Projekt AFEM untersucht die Struktur und Gestaltung der Energiemärkte der Zukunft um die Ziele der Energiestrategie 2050 zu erreichen. Von zentraler Bedeutung ist dabei die Bewertung des Potentials der Schweiz in Bezug auf variable erneuerbare Energiequellen wie Windkraft oder Solarenergie und deren entsprechende Integration in das Schweizer Stromnetz.
Lay summary

Stromnetze in ihrer heutigen Form können erneuerbare Energiequellen nur zu einem gewissen Grad aufnehmen. Bei bestehenden herkömmlichen Kraftwerken kann die gelieferte Energie einfach und direkt durch Regulierung der Primärenergiequellen angepasst werden, oder die Kraftwerke liefern eine konstante Bandlast. Dies steht im starken Gegensatz zu einem künftigen Versorgungsszenario, bei dem die massgebliche Menge der eingespeisten Energie davon abhängen wird, wieviel Sonnenstrahlung oder Wind zu einem bestimmten Zeitpunkt zur Verfügung stehen.

Bei einem hohen Erzeugungsanteil an erneuerbaren Energien können Situationen entstehen, bei denen der Strombedarf nicht voll gedeckt werden kann. 
Um kritische Situationen durch diese erhöhte Variabilität zu vermeiden, müssen die Netzbetreiber ausreichend Reserveenergie zur Verfügung stellen und zu den Nachfragepunkten übertragen. Die Menge der verfügbaren Reserveenergie ist dabei eine wichtige Kenngrösse für den sicheren Netzbetrieb. 

Ziel des Projektes ist es, zu bestimmen, wie gross in der Schweiz das Potential für erneuerbare Energiequellen ist, welche in das Stromnetz integriert werden können, ohne die Versorgungssicherheit zu beeinträchtigen. 
Damit einhergehend erfolgt eine detaillierte Analyse der Netzstruktur, des Energieportfolios sowie der der geographischen und klimatischen Bedingungen. 
Es werden geeignete Optimierungsalgorithmen entwickelt, um die besten Standorte für Windkraft- und Solaranlagen für verschiedene Zukunftsszenarien zu bestimmen, und um eine sichere Energieversorgung mit einem möglich hohen Anteil an erneuerbaren Energie zu gewährleisten.

Direct link to Lay Summary Last update: 21.10.2014

Responsible applicant and co-applicants

Employees

Publications

Publication
Spatio-temporal potential of a biogenic micro CHP swarm in Switzerland
Buffat René, Raubal Martin (2019), Spatio-temporal potential of a biogenic micro CHP swarm in Switzerland, in Renewable and Sustainable Energy Reviews, 103, 443-454.
Combining Investment, Dispatch, and Security Models - An Assessment of Future Electricity Market Options for Switzerland
Garrison Jared B., Demiray Turhan, Abrell Jan, Savelsberg Jonas, Weigt Hannes, Schaffner Christian (2018), Combining Investment, Dispatch, and Security Models - An Assessment of Future Electricity Market Options for Switzerland, in 2018 15th International Conference on the European Energy Market (EEM), LodzIEEE, USA.
A scalable method for estimating rooftop solar irradiation potential over large regions
Buffat René, Grassi Stefano, Raubal Martin (2018), A scalable method for estimating rooftop solar irradiation potential over large regions, in Applied Energy, 216, 389-401.
Automatic selection of weights for GIS-based multicriteria decision analysis: site selection of transmission towers as a case study
Veronesi Fabio, Schito Joram, Grassi Stefano, Raubal Martin (2017), Automatic selection of weights for GIS-based multicriteria decision analysis: site selection of transmission towers as a case study, in Applied Geography, 83, 78-85.
Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning
Veronesi F, Grassi S (2016), Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning, in Journal of Physics: Conference Series, 749, 012001-012001.
Feature-Aware Surface Interpolation of Rooftops Using Low-Density Lidar Data for Photovoltaic Applications
Buffat René (2016), Feature-Aware Surface Interpolation of Rooftops Using Low-Density Lidar Data for Photovoltaic Applications, Springer International Publishing, Cham, 337-350.
Statistical learning approach for wind resource assessment
Veronesi F., Grassi S., Raubal M. (2016), Statistical learning approach for wind resource assessment, in Renewable and Sustainable Energy Reviews, 56, 836-850.
Effect of increased renewables generation on operation of thermal power plants
Eser Patrick, Singh Antriksh, Chokani Ndaona, Abhari Reza S. (2016), Effect of increased renewables generation on operation of thermal power plants, in Applied Energy, 164, 723-732.
Estimation of the Global Solar Energy Potential and Photovoltaic Cost with the use of Open Data
KorfiatiAthina, GkonosCharalampos, VeronesiFabio, GakiAriadni, GrassiStefano, SchenkelRoland, VolkweinStephan, RaubalMartin, HurniLorenz (2016), Estimation of the Global Solar Energy Potential and Photovoltaic Cost with the use of Open Data, in International Journal of Sustainable Energy Planning and Management, 9, 17-30.
Comparison of hourly and daily wind speed observations for the computation of Weibull parameters and power output
Veronesi Fabio, Grassi Stefano (2015), Comparison of hourly and daily wind speed observations for the computation of Weibull parameters and power output, in 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC), Marrakech, MoroccoIEEE, USA.
Validation of CM SAF SARAH solar radiation datasets for Switzerland
Buffat Rene, Grassi Stefano (2015), Validation of CM SAF SARAH solar radiation datasets for Switzerland, in 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC), MarrakechIEEE, USA.
Optimal placement of wind turbines on a continuous domain: An MILP-based approach
Marseglia G.R., Arbasini A., Grassi S., Raubal M., Raimondo D.M. (2015), Optimal placement of wind turbines on a continuous domain: An MILP-based approach, in 2015 American Control Conference (ACC), Chicago, IL, USAIEEE, USA.

Collaboration

Group / person Country
Types of collaboration
High Voltage Laboratory - ETH Zürich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
Prof. Dr. Felix Kienast - Department of Environmental System Science ETH Zürich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
Forschungsstelle Nachhaltige Energie- und Wasserversorgung (FoNEW) - Uni Basel Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Centre for Energy Policy Economics - ETH Zurich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Planning of Landscape and Urban Systems - ETH Zürich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results

Knowledge transfer events

Active participation

Title Type of contribution Date Place Persons involved
AFEM Advisory Board and Partners meeting Workshop 19.11.2015 ETH Zürich, Switzerland Eser Patrick; Garrison Jared; Veronesi Fabio; Demiray Turhan Hilmi;


Self-organised

Title Date Place
SNF Site Visit #2 30.08.2017 ETH Zürich, Energy Science Center, Switzerland
AFEM Stakeholders Workshop on Scenarios 28.11.2016 ETH Zurich, Energy Science Center, Switzerland
SNF Site Visit #1 23.11.2015 ETH Zürich, Switzerland

Communication with the public

Communication Title Media Place Year
Media relations: print media, online media Bewertung zunkünftiger Strommärkte bulletin.ch German-speaking Switzerland 2017

Associated projects

Number Title Start Funding scheme
179053 Spatially and temporal explicit forecast model of broad front bird migration using radar surveillance data 01.05.2018 Project funding (Div. I-III)

Abstract

The key issues addressed in the umbrella project "Assessing Future Electricity Markets" (AFEM) require a preliminary quantification of the exploitable potential of stochastic renewable energy (RE) sources and a feasibility analysis of their grid integration. Indeed, existing power systems are designed to accommodate significant variability in the form of changes in load or the loss of generation (either planned or unplanned). However, as stochastically varying renewable power generation increases to higher levels, the additional variability can jeopardize system operation. To account for the added variability, the utility operators must have a increased amount of generation available to cover either a surplus- or a lack of RE production. This flexible generation is referred to as reserve energy. In order to balance generation with load on a minute-by-minute, hourly, or daily basis, the variability of both the generation and the load must be examined, and different types of reserve energy are required, depending on the considered timescale and required amount. While traditional measures such as loss-of-load probability (LOLP) and loss-of-load expectation (LOLE) are used to identify system reliability concerns, reserve requirements represent a key signal concerning power system operating measures and requirements, and more in general the overall system security (e.g. voltage violations and overloads) is also affected by the increased presence of volatile energy sources. As a first step then it is required to assess how much potential there is for renewable energy sources in the Swiss electricity system and where they would be precisely located, which requires a detailed geophysical and geographic modelling and analysis of Switzerland inclusive of climatic and seasonal conditions. Furthermore, possible network expansions will also affect power flow in the grid, but their planning, approval and construction process is considerably onerous, time-consuming and often subject to a certain degree of decisional uncertainty, so that the likelihood that individually planned expansions may be effectively integrated into the grid (and within which timeframe) will have to be assessed. This information can then be employed in a final stage where the impact of renewable energy (RE) sources on system operation (featuring the updated topology) in terms of load supply, reserve requirements and network security will be investigated and numerically established.
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