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Schindler, Markus
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Official Name
Schindler, Markus
Main Affiliation
Akademische Titel
BSc MSc
Email
markus.schindler@forschung-burgenland.at
Status
staff
Research Outputs
Now showing 1 - 5 of 5
- PublicationAn interdisciplinary approach of a local peer-to-peer energy trading model for a more sustainable power grid(SDEWES Centre, 2021-10)
; ; ; ; ; This work investigates a novel distance-based electricity tariff applicable to energy communities. Based on a mixed integer linear optimization model computational studies are conducted to investigate the effects of this tariff on a small-scale community consisting of 22 consumers and 5 prosumers. Additionally, the potential of electric storage systems supporting the use of locally produced energy is investigated. The results of the computational model prove that an energy community generates incentives photovoltaic plants and the installation of energy storage systems. Complementary to the simulations, the flexibility options are supported by a user survey focusing on technology attributes for local energy market models, proving that there is a significant awareness for the use of energy storage systems.562 33082 - Publication
73 1125 - 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.202 6110 - PublicationOptimization-Based Operation of District Heating Networks: A Case Study for Two Real Sites(2023)
; ; ; 7 1Scopus© Citations 1