Gnam, Lukas
Official Name
Gnam, Lukas
Main Affiliation
Akademische Titel
Dipl.-Ing. Dr.
Email
lukas.gnam@forschung-burgenland.at
ORCID
Scopus Author ID
57202650961
Status
staff
17 results
Now showing 1 - 10 of 17
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Item type:Publication, Interdisciplinary aspects of increasing the hosting capacity for photovoltaics in an Austrian context(Institution of Engineering and Technology (IET), 2025-01)Scopus© Citations 1 57 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A simulation study on the integration of wind in a district heating system(Leykam, 2020-11-26); ; Nacht, Thomas134 359 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Langfristige 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.603 494 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 172 287 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modellierungstool für den Einsatz von Warmwasserspeichern als Flexibilitäten(Holzhausen, 2022); ; ; ; 106 377 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 174 485 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A 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.1Scopus© Citations 14 223 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 35 34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Selbstlernendes Empfehlungssystem zur Steigerung der Behaglichkeit(Leykam, 2019-11); ; Within the project „Empower Citizens” technical as well as social and health aspects are combined in order to increase the subjective comfort inside a flat with respect to room temperature, CO2 concentration, humidity, and so forth. Therefore, a low-cost system advisory system is developed which reads the aforementioned data from sensors situated in the different rooms of a flat. Using these measurements advices are given in order to increase the resident’s comfort. The advices are based on the computation of the so-called predicted mean value (PMV) which aims at capturing the comfort with respect to different parameters, e.g., age, body weight, and height. To achieve this goal, first, a co-simulation using Matlab and IDA-ICE was conducted utilizing a detailed model of the investigated flat. The model is split into two different zones, living room and bedroom, where a zone is defined through the presence of dedicated sensors inside the zone. The sensor data are transmitted to a central station, which reads also outside temperature and humidity, and form the basis for the PMV computation. If the PMV exceeds pre-defined limits, an advice is given, either to open a window or the shadow a window. Additionally, an advice is output if the CO2 concentration is too high. Furthermore, the advisory system is capable of learning how well the given advices are followed by the resident. Thus, the total number and the times at which advices are output can be flexibly adjusted by the system itself to optimally suit the resident’s fondness for following the advices. To evaluate the developed advisory system a parameter study was conducted evaluating different reference cases with respect to the parameters affecting the PMV calculation, e.g., age and bodyweight. Additionally, different motivations to follow the given advices were modeled within these reference cases. The simulation results prove that following the advices given by the advisory system leads to increased comfort for the residents while keeping the increase in energy demand for heating occurring from more ventilation to a minimum. Additionally, the study shows that ventilation only in the morning results in high CO2-concentrations heavily influencing the resident’s comfort. urthermore, this study presents a first easy-to-install hardware prototype comprised of a RaspberryPi 3B+ and an ARDUINO MKR1000 where the developed algorithm for the advisory system can be deployed. This prototype includes also the required sensors to monitor room temperature and CO2 concentration. In order to output the computed advices, it is equipped with an LCD display acting as human machine interface.693 939 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forecasting 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.224 512