Prof. Dr.-Ing. Marco Pruckner

Telefon | (0931) 31-89054 |
Telefax | (0931) 31-86632 |
marco.pruckner@uni-wuerzburg.de | |
Raum | |
Anschrift | Lehrstuhl für Kommunikationsnetze (Informatik III) Am HublandD-97074 Würzburg |
2022[ to top ]
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Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data in Journal of Energy Storage (2022). 48 103856.
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Joint analysis of regional and national power system impacts of electric vehicles - A case study for Germany on the county level in 2030 in Applied Energy (2022). 315 118945.
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Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems in 2022 IEEE Conference on Technologies for Sustainability (SusTech) (2022). 187–194.
2021[ to top ]
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Unsupervised data-preprocessing for Long Short-Term Memory based battery model under electric vehicle operation in Journal of Energy Storage (2021). 38 102598.
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Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning in Applied Energy (2021). 285 116382.
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Dynamic modeling and sensitivity analysis of a stratified heat storage coupled with a heat pump and an organic rankine cycle in 2021 Winter Simulation Conference (WSC) (2021). 1–12.
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Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data in Energies (2021). 14(16)
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Frequency Control Reserve Provision from a Fleet of Shared Autonomous Electric Vehicles in 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2021). 1–6.
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Benchmarking a Decentralized Reinforcement Learning Control Strategy for an Energy Community in 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (2021). 385–390.
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Smart Charging and Renewable Grid Integration - A Case Study Based on Real-Data of the Island of Porto Santo in Sustainable Energy for Smart Cities, J. L. Afonso, V. Monteiro, J. G. Pinto (eds.) (2021). 200–215.
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A comprehensive study on battery electric modeling approaches based on machine learning in DACH Conference on Energy Informatics (2021). 4(3) 1–17.
2020[ to top ]
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Life Cycle Assessment of a Reversible Heat Pump�Organic Rankine Cycle�Heat Storage System with Geothermal Heat Supply in Energies (2020). 13(12)
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Electric Vehicle Charge Management for Lowering Costs and Environmental Impact in 2020 IEEE Conference on Technologies for Sustainability (SusTech) (2020). 1–7.
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Shared Autonomous Electric Vehicles and the Power Grid: Applications and Research Challenges. in ISGT-Europe (2020). 1151–1155.
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FlexAbility - Modeling and Maximizing the Bidirectional Flexibility Availability of Unidirectional Charging of Large Pools of Electric Vehicles in Proceedings of the Eleventh ACM International Conference on Future Energy Systems, e-Energy ’20 (2020). 121�132.
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A scenario-based study on the impacts of electric vehicles on energy consumption and sustainability in Alberta in Applied Energy (2020). 268 114961.
2019[ to top ]
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Sharing of Energy Among Cooperative Households Using Distributed Multi-Agent Reinforcement Learning. in ISGT Europe (2019). 1–5.
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Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids in Energies (2019). 12(21)
2018[ to top ]
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Coordinated Multi-Agent Reinforcement Learning for Swarm Battery Control. in CCECE (2018). 1–4.
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A comprehensive electricity Market Model using simulation and Optimization Techniques. in WSC, B. Johansson, S. Jain (eds.) (2018). 2095–2106.
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Rebalancing and fleet sizing of Mobility-on-demand Networks with combined simulation, Optimization and Queueing Network Analysis. in WSC, B. Johansson, S. Jain (eds.) (2018). 1527–1538.
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Coordinating E-Mobility Charging for Frequency Containment Reserve Power Provision. in e-Energy, H. Schmeck, V. Hagenmeyer (eds.) (2018). 556–563.
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Including a virtual battery storage into thermal unit commitment. in Comput. Sci. Res. Dev. (2018). 33(1-2) 223–229.
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Reinforcement Learning Control Algorithm for a PV-Battery-System Providing Frequency Containment Reserve Power. in SmartGridComm (2018). 1–6.
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Reversible Heat Pump Organic Rankine Cycle Systems for the Storage of Renewable Electricity in Energies (2018). 11(6)
2017[ to top ]
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Electrification of public bus transport under the usage of electricity generated by renewables in 2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE) (2017). 314–319.
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SWARM � Providing 1 MW FCR power with residential PV-battery energy storage � Simulation and empiric validation in 2017 IEEE Manchester PowerTech (2017). 1–6.
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Towards an impact study of electric vehicles on the Italian electric power system using simulation techniques. in RTSI (2017). 1–5.
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Spatial and Temporal Charging Infrastructure Planning Using Discrete Event Simulation. in SIGSIM-PADS, W. Cai, Y. M. Teo, P. Wilsey, K. Jin (eds.) (2017). 249–257.
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Analysis of Various Charging Strategies for Electrified Public Bus Transport Utilizing a Lightweight Simulation Model in Proceedings of the 1st E-Mobility Power System Integration Symposium (2017).
2016[ to top ]
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The impact of electric vehicles on the german energy system. in SpringSim (ANSS), J. J. Padilla, A. Tolk, S. Jafer (eds.) (2016). 23.
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Modeling the impact of electrical energy storage systems on future power systems in 2016 IEEE Electrical Power and Energy Conference (EPEC) (2016). 1–7.
2015[ to top ]
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Hierarchical Simulation of the German Energy System and Houses with PV and Storage Systems. in D-A-CH EI, Lecture Notes in Computer Science, S. Gottwalt, L. König, H. Schmeck (eds.) (2015). (Vol. 9424) 12–23.
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Ein Simulationsmodell f{\"u}r den Energieumstieg in Bayern (2015). Cuvillier Verlag.
2014[ to top ]
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Modeling country-scale electricity demand profiles. in Winter Simulation Conference, S. J. Buckley, J. A. Miller (eds.) (2014). 1084–1095.
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On the profit enhancement and state estimation services in the smart grid. in ISGT (2014). 1–5.
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Modeling and simulation of electricity generated by renewable energy sources for complex energy systems. in SpringSim (ANSS) (2014). 4.
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Gekoppeltes Energiesystemmodell f{\"u}r den Energieumstieg in Bayern in Energiesymposium 2014 (2014).
2013[ to top ]
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A hybrid simulation model for large-scaled electricity generation systems. in Winter Simulation Conference (2013). 1881–1892.
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A simulation model to analyze the residual load during the extension of highly fluctuating renewables in Bavaria, Germany in 4th International Conference on Power Engineering, Energy and Electrical Drives (2013). 540–545.
2012[ to top ]
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Towards a simulation model of the Bavarian electrical energy system. in GI-Jahrestagung, LNI, U. Goltz, M. A. Magnor, H.-J. Appelrath, H. K. Matthies, W.-T. Balke, L. C. Wolf (eds.) (2012). (Vol. P-208) 597–612.
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A study on the impact of packet loss and latency on real-time demand response in smart grid. in GLOBECOM Workshops (2012). 1486–1490.
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An approach of a simulation model to analyze the future energy balance of Bavaria in 2012 International Conference on Smart Grid Technology, Economics and Policies (SG-TEP) (2012). 1–4.