Nick Harder
Contact
nick.harder@inatech.uni-freiburg.de
+49 761 203-97774
Office: Solar Info Center,
4th floor (stairs "West"),
room 04.005
Curriculum Vitae
Academic education
2019 - now | PhD Student at INATECH at the University of Freiburg
2017 - 2019 | Master study " Sustainable Systems Engineering " at the University of Freiburg, Germany. Degree: M.Sc.
2013 - 2017 | Bachelor study "Petroleum Engineering" at branch of Russian State University for Oil and Gas named after I.M.Gubkin in Tashkent, Uzbekistan. Degree: B. Sc.
Work experience
2019 - now | Research associate at INATECH at the University of Freiburg
2018 - 2019 | Research assistant at Fraunhofer ISE Freiburg
Awards
Publications
filter list : Years: 2022 |
2020 | show all Stoy S, Harder N, Heinemann C, Kaiser M, Sandhaas A, Senkpiel C, Weidlich ADekarbonisierung der deutschen Industrie − Potentiale zur Elektrifizierung und Flexibilisierung der Prozesswärme 2022 Energiewirtschaftliche Tagesfragen , volume : 72, issue : 11, pages : 13 - 16» show abstract « hide abstract Abstract In der deutschen Industrie muss ein Großteil des Anlagenparks mit Lebensdauern von bis zu 70 Jahren bis 2030 erneuert werden. Hierbei ist es für die Erreichung von Klimaneutralität zwingend erforderlich, dass neue CO2-arme Technologien zum Einsatz kommen. Insbesondere die Elektrifizierung der Prozesswärmeerzeugung als Haupttreiber der Emissionen in der deutschen Industrie bietet ein hohes Potential für die Dekarbonisierung. Zudem können elektrifizierte Prozesse zum Teil auch Flexibilität bereitstellen, um auf das schwankende Angebot von Wind- und Solarstrom reagieren zu können. Qussous R, Harder N, Schäfer M, Weidlich AIncreasing the realism of electricity market modeling through market interrelations 2022 Proceedings of the 1st International Workshop on Open Source Modelling and Simulation of Energy Systems , pages : 1 - 6
Download file Qussous R, Harder N, Weidlich AUnderstanding Power Market Dynamics by Reflecting Market Interrelations and Flexibility-Oriented Bidding Strategies 2022 Energies , volume : 15, issue : 2, supplement : 494, pages : 1 - 24» show abstract « hide abstract Abstract Power markets are becoming increasingly complex as they move towards (i) integrating higher amounts of variable renewable energy, (ii) shorter trading intervals and lead times, (iii) stronger interdependencies between related markets, and (iv) increasing energy system integration. For designing them appropriately, an enhanced understanding of the dynamics in interrelated short-term physical power and energy markets is required, which can be supported by market simulations. In this paper, we present an agent-based power market simulation model with rule-based bidding strategies that addresses the above-mentioned challenges, and represents market participants individually with a high level of technical detail. By allowing agents to participate in several interrelated markets, such as the energy-only market, a procurement platform for control reserve and a local heat market representing district heating systems, cross-market opportunity costs are well reflected. With this approach, we were able to reproduce EPEX SPOT market outcomes for the German bidding zone with a high level of accuracy (mean absolute percentage error of 8 /MWh for the years 2016–2019). We were also able to model negative market prices at the energy-only market realistically, and observed that the occurrence of negative prices differs among data inputs used. The simulation model provides a useful tool for investigating different short-term physical power/energy market structures and designs in the future. The modular structure also enables extension to further related markets, such as fuel, CO2, or derivative markets.
Download file Harder N, Qussous R, Weidlich AThe cost of providing operational flexibility from distributed energy resources 2020 Appl Energ , volume : 279, pages : 1 - 16» show abstract « hide abstract Abstract Flexible household devices, such as heat pumps combined with thermal energy storage or battery energy storage units, can provide flexibility to the electricity sector. However, to make flexibility available to the market, it has to be correctly quantified, and its cost has to be estimated. In this work, a methodology for generic flexibility quantification is proposed and developed in a Python environment using model predictive control. The chosen methodology allows to quantify the adjustable power, and also to determine the corresponding cost of the flexibility provision. It was observed that the available flexibility and its cost is influenced by many factors such as system components, human behavior, building thermal parameters, and price signals. Also, the inclusion of even a low share of households with batteries or electric vehicles smoothens the aggregated flexibility profile, and a considerable amount of flexibility is available at almost any point in time.
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