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Gnam, Lukas
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Official Name
Gnam, Lukas
Main Affiliation
Akademische Titel
Dipl.-Ing. Dr.
Email
lukas.gnam@forschung-burgenland.at
ORCID
Scopus Author ID
57202650961
Status
staff
Research Outputs
Now showing 1 - 10 of 14
- PublicationOptimization-Based Operation of District Heating Networks: A Case Study for Two Real Sites(2023)
; ; ; - PublicationLangfristige Prognose für den Wärmebedarf eines Nahwärmenetzes unter Berücksichtigung demografischer Entwicklungen(2020-02)
; ; In dieser Arbeit wird ein Modell zur Prognose des Fernwärmebedarfs von Nahwärmenetzen für die Jahre 2030 und 2050 unter Berücksichtigung von Klima- und demografischen Entwicklungen vorgestellt. Dabei wird mit einer leicht rückläufigen Entwicklung der Bevölkerungszahlen, einer steigenden Anzahl an Wohnungen und Gebäuden insgesamt einem Anstieg der Beschäftigten in der Industrie gerechnet. Die Ergebnisse zeigen eine generelle Zunahme der notwendigen Wärmeenergie, eine erhöhte Spitzenleistung im Szenario 2050 sowie einen deutlich erhöhten Wärmebedarf für Kühlung im Sommer unter der Annahme der Nutzung sorptionsgestützter Kühlung. Im Falle der Modellregion ist die Wärmeversorgung mit der bestehenden Infrastruktur bis ins Jahr 2030 gesichert, darüberhinausgehend sind jedoch Erweiterungen erforderlich.560 190 - Publication
69 146 - PublicationA Case Study of Socially-Accepted Potentials for the Use of End User Flexibility by Home Energy Management Systems(MDPI, 2020-12-25)
; ; ; ; ; Due to the increase of volatile renewable energy resources, additional flexibility will be necessary in the electricity system in the future to ensure a technically and economically efficient network operation. Although home energy management systems hold potential for a supply of flexibility to the grid, private end users often neglect or even ignore recommendations regarding beneficial behavior. In this work, the social acceptance and requirements of a participatively developed home energy management system with focus on (i) system support optimization, (ii) self-consumption and self-sufficiency optimization, and (iii) additional comfort functions are determined. Subsequently, the socially-accepted flexibility potential of the home energy management system is estimated. Using methods of online household survey, cluster analysis, and energy-economic optimization, the socially-accepted techno-economic potential of households in a three-community cluster sample area is computed. Results show about a third of the participants accept the developed system. This yields a shiftable load of nearly 1.8 MW within the small sample area. Furthermore, the system yields the considerably larger monetary surplus on the supplier-side due to its focus on system support optimization. New electricity market opportunities are necessary to adequately reward a systemically useful load behavior of households.174Scopus© Citations 5 - PublicationPreferences on Home Energy Management Systems - A Study of Potentials for the Use of End User Flexibilities(2020-09)
; ; ; ; Due to the increase of volatile renewable energy resources, additional flexibility in the electricity system is necessary to ensure a technically and economically efficient network operation. To tap flexibilities like private storages, participatory developed and user-friendly technical solutions are essential. In this work, the acceptance and the market potential of a home energy management system with focus on i) grid optimization, ii) self-consumption optimization, and iii) additional functions for increased comfort is presented. This is determined by means of a representative survey. The main results show that the perceived usefulness and accessibility are the most important influence factors affecting the end user’s intention to use. Based on the given acceptance, the solution provides a market potential of one third of the users for an increased use of flexibilities in the investigated area.717 456 - PublicationForecasting and Optimization Approaches Utilized for Simulating a Hybrid District Heating System(Hochschule Luzern, 2020-10)
; ; ; The historically grown centralized energy system is undergoing massive changes due to the transformation from centralized energy production with large assets (e.g. fossil-thermal power plants) towards a sustainable, clean and decentralized energy system. This transformation is based on the inclusion of renewable energy sources (RESs) (e.g., wind and solar) into the classical systems. However, as the energy production stemming from RESs is extremely volatile and thus challenging to predict, new approaches have to be found in order to guarantee a successful integration of RESs into the existing infrastructure. In the Austrian state of Burgenland approximately 1,000 MW of wind capacity is available. As already mentioned above, the high volatility of wind energy together with forecast uncertainties hinders the optimal integration of this RES into the existing energy system. Furthermore, the successful deployment of wind turbines was based on an attractive but timely limited subsidy scheme with a fixed feed-in tariff. As these subsidies now come to an end for more and more wind turbines and future support systems will rely on market premiums and tendering models, new approaches and business models have to be devised in order to sustain the rapid transformation of the classical energy systems. In the research project HDH Demo in close cooperation with the city of Neusiedl am See, Burgenland, Austria, the aim is to integrate wind energy into the existing district heating grid of the city. This is realized by utilizing power-to-heat technologies, e.g., heat pumps. However, an economically feasible and successful integration is based on accurate forecasts for both, wind production and district heating demand as well as the actual energy prices. Therefore, this work evaluates the applied data-driven forecasting methods. In particular, ensemble approaches that combine autoregressive models with artificial intelligent techniques are used to exploit the strengths of different methods (e.g. stability, flexibility). To compare the model performance, an overview on the accuracy and efficiency of the ensembles by using appropriate score metrics (e.g. RMSE, MAPE, R²) is given. Furthermore, a mixed integer linear optimization model is presented for computing optimized schedules for the different components (e.g., heat pumps, energy storage units, biomass boiler) of the district heating grid. Together, these two approaches, forecasting and optimization, are used to investigate and evaluate different business models, which help to ensure the future market integration of wind production.199 158 - Publication
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