Prof. Dr.-Ing. Marco Pruckner

• Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study. Bayer, Daniel R.; Haag, Felix; Pruckner, Marco; Hopf, Konstantin. In 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 1–6. IEEE, 2024.
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  Short-term forecasting of residential electricity demand is an important task for utilities. Yet, many small and medium-sized utilities still use simple forecasting approaches such as Synthesized Load Profiles (SLPs), which treat residential households similarly and neither account for renewable energy installations nor novel large consumers (e.g., heat pumps, electric vehicles). The effectiveness of such “one-fits-all” approaches in future grid states—where decentral generation and sector coupling increases—are questionable. Our study challenges these forecasting practices and investigates whether Machine Learning (ML) approaches are suited to predict electricity demand in today's and in future grid states. We use real smart meter data from 3,511 households in Germany over 34 months. We extrapolate this data with future grid states (i.e., increased decentral generation and storage) based on a digital twin of a local energy system. Our results show that Long Short-Term Memory approaches outperform SLPs as well as simple benchmark estimators with up to 68.5% lower Root Mean Square Error for a day-ahead forecast, especially in future grid states. Nevertheless, all prediction approaches perform worse in future grid states. Our findings therefore reinforce the need (a) for utilities and grid operators to employ ML approaches instead of traditional demand prediction methods in future grid states and (b) to prepare current ML methods for future grid states.

  @inproceedings{10612563, abstract = {Short-term forecasting of residential electricity demand is an important task for utilities. Yet, many small and medium-sized utilities still use simple forecasting approaches such as Synthesized Load Profiles (SLPs), which treat residential households similarly and neither account for renewable energy installations nor novel large consumers (e.g., heat pumps, electric vehicles). The effectiveness of such “one-fits-all” approaches in future grid states—where decentral generation and sector coupling increases—are questionable. Our study challenges these forecasting practices and investigates whether Machine Learning (ML) approaches are suited to predict electricity demand in today's and in future grid states. We use real smart meter data from 3,511 households in Germany over 34 months. We extrapolate this data with future grid states (i.e., increased decentral generation and storage) based on a digital twin of a local energy system. Our results show that Long Short-Term Memory approaches outperform SLPs as well as simple benchmark estimators with up to 68.5% lower Root Mean Square Error for a day-ahead forecast, especially in future grid states. Nevertheless, all prediction approaches perform worse in future grid states. Our findings therefore reinforce the need (a) for utilities and grid operators to employ ML approaches instead of traditional demand prediction methods in future grid states and (b) to prepare current ML methods for future grid states.}, author = {Bayer, Daniel R. and Haag, Felix and Pruckner, Marco and Hopf, Konstantin}, booktitle = {2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)}, keywords = {pruckner}, month = {06}, pages = {1-6}, publisher = {IEEE}, title = {Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study}, year = 2024 }
• Lightweight Smart Charging vs. Immediate Charging with Buffer Storage: Towards a Simulation Study for Electric Vehicle Grid Integration at Workplaces. Benz, Paul; Pruckner, Marco. In Proceedings of the Winter Simulation Conference, of WSC ’23, pp. 922–933. IEEE Press, 2024.
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  The present study investigates the extension of an existing simulation model combining system dynamics and discrete event simulation by linear optimization for an electric vehicle charging system. The existing simulation framework is extended by a smart charging strategy based on linear programming in order to exploit the flexibility of real charging processes at a workplace parking lot for a better integration of solar photovoltaic electricity generation. Therefore, different smart charging strategies are evaluated. In multiple simulation runs, the strategies are compared with immediate charging using a stationary battery energy storage system for intermediate storage of electricity generated by solar photovoltaic. Results show that smart charging strategies can achieve similarly good results with respect to the self-sufficiency rate and self-consumption rate. In the context of a 100kWp PV system the combination of optimizing charging rates and stationary battery energy storage resulted in self-sufficiency rates of more than 90\% in the simulation.

  @inproceedings{10.5555/3643142.3643218, abstract = {The present study investigates the extension of an existing simulation model combining system dynamics and discrete event simulation by linear optimization for an electric vehicle charging system. The existing simulation framework is extended by a smart charging strategy based on linear programming in order to exploit the flexibility of real charging processes at a workplace parking lot for a better integration of solar photovoltaic electricity generation. Therefore, different smart charging strategies are evaluated. In multiple simulation runs, the strategies are compared with immediate charging using a stationary battery energy storage system for intermediate storage of electricity generated by solar photovoltaic. Results show that smart charging strategies can achieve similarly good results with respect to the self-sufficiency rate and self-consumption rate. In the context of a 100kWp PV system the combination of optimizing charging rates and stationary battery energy storage resulted in self-sufficiency rates of more than 90\% in the simulation.}, author = {Benz, Paul and Pruckner, Marco}, booktitle = {Proceedings of the Winter Simulation Conference}, keywords = {pruckner}, pages = {922–933}, publisher = {IEEE Press}, series = {WSC '23}, title = {Lightweight Smart Charging vs. Immediate Charging with Buffer Storage: Towards a Simulation Study for Electric Vehicle Grid Integration at Workplaces}, year = 2024 }
• Data-driven heat pump retrofit analysis in residential buildings: Carbon emission reductions and economic viability. Bayer, Daniel R.; Pruckner, Marco. In Applied Energy, 373, p. 123823. Elsevier BV, 2024.
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  Heat pumps replacing existing gas furnaces, the predominant heating system in Europe, are crucial for achieving global CO2 emission reduction targets. Previous studies have simulated this reduction potential for a few buildings, revealing substantial variations in results. Moreover, additional CO2 emission reductions are proven to be possible with photovoltaic installations and battery energy storage systems. However, as these come with high investment costs, the question remains whether these are economically viable. Moreover, as the building stock shows a high degree of diversity and the heat demand is dependent on the annual weather conditions, the CO2 emission reduction and the cost-effectiveness of incorporating photovoltaics or battery systems should be analyzed for all buildings in a city over multiple years. In this paper, we address these questions by utilizing a digital twin of a German city encompassing all residential buildings, capturing the diversity of the building stock. Our findings indicate that retrofitting heat pumps, only considering the heating system, reduces average annual CO2 emissions by 18.6% to 38.9% across different years. The variance in emission reduction, up to 1.6 percentage points, is not year-dependent. The maximum CO2 reduction on the building level is achieved by combining heat pumps with photovoltaic and battery systems for most buildings, averaging a 31.2% to 43.6% reduction even in a Central European climate with low winter photovoltaic generation. Economically, a heat pump retrofit with photovoltaic installation emerges as the most beneficial setting for most buildings in the studied city, effectively balancing cost and emission reduction.

  @article{2024_Bayer_AppliedEnergy, abstract = {Heat pumps replacing existing gas furnaces, the predominant heating system in Europe, are crucial for achieving global CO2 emission reduction targets. Previous studies have simulated this reduction potential for a few buildings, revealing substantial variations in results. Moreover, additional CO2 emission reductions are proven to be possible with photovoltaic installations and battery energy storage systems. However, as these come with high investment costs, the question remains whether these are economically viable. Moreover, as the building stock shows a high degree of diversity and the heat demand is dependent on the annual weather conditions, the CO2 emission reduction and the cost-effectiveness of incorporating photovoltaics or battery systems should be analyzed for all buildings in a city over multiple years. In this paper, we address these questions by utilizing a digital twin of a German city encompassing all residential buildings, capturing the diversity of the building stock. Our findings indicate that retrofitting heat pumps, only considering the heating system, reduces average annual CO2 emissions by 18.6% to 38.9% across different years. The variance in emission reduction, up to 1.6 percentage points, is not year-dependent. The maximum CO2 reduction on the building level is achieved by combining heat pumps with photovoltaic and battery systems for most buildings, averaging a 31.2% to 43.6% reduction even in a Central European climate with low winter photovoltaic generation. Economically, a heat pump retrofit with photovoltaic installation emerges as the most beneficial setting for most buildings in the studied city, effectively balancing cost and emission reduction.}, author = {Bayer, Daniel R. and Pruckner, Marco}, journal = {Applied Energy}, keywords = {pruckner}, month = 11, pages = 123823, publisher = {Elsevier BV}, title = {Data-driven heat pump retrofit analysis in residential buildings: Carbon emission reductions and economic viability}, volume = 373, year = 2024 }
• Benchmarking Aggregation-Disaggregation Pipelines for Smart Charging of Electric Vehicles. Strobel, Leo; Pruckner, Marco. In Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems, of e-Energy ’24, pp. 84–96. Association for Computing Machinery, Singapore, 2024.
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  As the global energy landscape shifts towards renewable energy and the electrification of the transport and heating sectors, national energy systems will include more controllable prosumers. Many future scenarios contain millions of such prosumers with individualistic behavior. This poses a problem for energy system modelers. Memory and runtime limitations often make it impossible to model each prosumer individually. In these cases, it is necessary to model the prosumers with representatives or in aggregated form. Existing literature offers various aggregation methods, each with strengths, drawbacks, and an inherent modeling error. It is difficult to evaluate which of these methods perform best. Each paper presenting a new aggregation method usually includes a performance evaluation. However, what is missing is a direct comparison on the same benchmark, preferably by a neutral third party that is not associated with any of the compared methods. This paper addresses this gap by introducing a benchmark to evaluate the end-to-end performance of multiple aggregation-disaggregation pipelines, specifically focusing on electric vehicles (EVs). Our study assesses the performance of the common representative profile (REP) approach, four different versions of the virtual battery (VB) approach, the Flex Object (FO) method, and the Dependency-based FlexOffer (DFO) method. The results show that each method has a clear use case. Depending on the price signal, additional median charging costs of 2\%-50\% are incurred using an aggregation method, compared to the optimal charging costs (i.e., the charging costs resulting from optimizing the EVs directly, without aggregation). The representative profile approach results in the lowest additional costs (2\%-20\%), while the Flex Object (FO) and Dependency-based FlexOffer (DFO) methods allow for error-free disaggregation, which is advantageous in real-world use cases.

  @inproceedings{10.1145/3632775.3661946, abstract = {As the global energy landscape shifts towards renewable energy and the electrification of the transport and heating sectors, national energy systems will include more controllable prosumers. Many future scenarios contain millions of such prosumers with individualistic behavior. This poses a problem for energy system modelers. Memory and runtime limitations often make it impossible to model each prosumer individually. In these cases, it is necessary to model the prosumers with representatives or in aggregated form. Existing literature offers various aggregation methods, each with strengths, drawbacks, and an inherent modeling error. It is difficult to evaluate which of these methods perform best. Each paper presenting a new aggregation method usually includes a performance evaluation. However, what is missing is a direct comparison on the same benchmark, preferably by a neutral third party that is not associated with any of the compared methods. This paper addresses this gap by introducing a benchmark to evaluate the end-to-end performance of multiple aggregation-disaggregation pipelines, specifically focusing on electric vehicles (EVs). Our study assesses the performance of the common representative profile (REP) approach, four different versions of the virtual battery (VB) approach, the Flex Object (FO) method, and the Dependency-based FlexOffer (DFO) method. The results show that each method has a clear use case. Depending on the price signal, additional median charging costs of 2\%-50\% are incurred using an aggregation method, compared to the optimal charging costs (i.e., the charging costs resulting from optimizing the EVs directly, without aggregation). The representative profile approach results in the lowest additional costs (2\%-20\%), while the Flex Object (FO) and Dependency-based FlexOffer (DFO) methods allow for error-free disaggregation, which is advantageous in real-world use cases.}, address = {New York, NY, USA}, author = {Strobel, Leo and Pruckner, Marco}, booktitle = {Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems}, keywords = {pruckner}, pages = {84–96}, publisher = {Association for Computing Machinery}, series = {e-Energy '24}, title = {Benchmarking Aggregation-Disaggregation Pipelines for Smart Charging of Electric Vehicles}, year = 2024 }

• OMOD: An open-source tool for creating disaggregated mobility demand based on OpenStreetMap. Strobel, Leo; Pruckner, Marco. In Computers, Environment and Urban Systems, 106, p. 102029. Elsevier BV, 2023.
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  @article{Strobel_2023, author = {Strobel, Leo and Pruckner, Marco}, journal = {Computers, Environment and Urban Systems}, keywords = {pruckner}, month = 12, pages = 102029, publisher = {Elsevier BV}, title = {OMOD: An open-source tool for creating disaggregated mobility demand based on OpenStreetMap}, volume = 106, year = 2023 }
• A Large Scale Simulation of the Electrification Effects of SAVs. Iacobucci, Riccardo; Pruckner, Marco; Schm{\"o}cker, Jan-Dirk. In Smart Energy for Smart Transport, E. G. Nathanail, N. Gavanas, G. Adamos (eds.), pp. 115–124. Springer Nature Switzerland, Cham, 2023.
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  Shared autonomous electric vehicles (SAEVs) are expected to soon revolutionize the transport system, with cheap and convenient taxi-like services. SAEVs can induce a significant modal shift from other modes of travel, with effects ranging from beneficial, like reducing private car use, to negative, like making public transport uneconomical and increasing congestion. This modal shift will also be influenced by electrification, as charging time and range limitations will influence availability of these vehicles. In this work, we expand a SAEV simulationIacobucci, R.Pruckner, M.Schm{\"o}ecker, J.-D. model with mode choice to incorporate electric vehicles. In our model travelers choose between their current mode and SAEVs, which in addition to the fare, has a waiting time which depends on the real-time demand and fleet availability. We use a trip generation model based on a large person trip survey from Germany as our input data and scale-up the survey to obtain an OD matrix for the whole wider Munich area on a 1 km{\$}{\$}^2{\$}{\$}2mesh area and representing instances of all trips in the city. As the basis for our extension, we build on a previously published efficient and scalable simulation model. Thanks to its scalability, we are able to perform our simulation with the resulting 4.6 million trips per day over a whole week. We study the effect of electrification on modal shift considering different battery capacities and charging station numbers. We will show at what level of these three decision variables the impact of electrification is negligible compared to our previous results with (non-electric) SAVs.

  @inproceedings{10.1007/978-3-031-23721-8_9, abstract = {Shared autonomous electric vehicles (SAEVs) are expected to soon revolutionize the transport system, with cheap and convenient taxi-like services. SAEVs can induce a significant modal shift from other modes of travel, with effects ranging from beneficial, like reducing private car use, to negative, like making public transport uneconomical and increasing congestion. This modal shift will also be influenced by electrification, as charging time and range limitations will influence availability of these vehicles. In this work, we expand a SAEV simulationIacobucci, R.Pruckner, M.Schm{\"o}ecker, J.-D. model with mode choice to incorporate electric vehicles. In our model travelers choose between their current mode and SAEVs, which in addition to the fare, has a waiting time which depends on the real-time demand and fleet availability. We use a trip generation model based on a large person trip survey from Germany as our input data and scale-up the survey to obtain an OD matrix for the whole wider Munich area on a 1 km{\$}{\$}^2{\$}{\$}2mesh area and representing instances of all trips in the city. As the basis for our extension, we build on a previously published efficient and scalable simulation model. Thanks to its scalability, we are able to perform our simulation with the resulting 4.6 million trips per day over a whole week. We study the effect of electrification on modal shift considering different battery capacities and charging station numbers. We will show at what level of these three decision variables the impact of electrification is negligible compared to our previous results with (non-electric) SAVs.}, address = {Cham}, author = {Iacobucci, Riccardo and Pruckner, Marco and Schm{\"o}cker, Jan-Dirk}, booktitle = {Smart Energy for Smart Transport}, editor = {Nathanail, Eftihia G. and Gavanas, Nikolaos and Adamos, Giannis}, keywords = {pruckner}, pages = {115–124}, publisher = {Springer Nature Switzerland}, title = {A Large Scale Simulation of the Electrification Effects of SAVs}, year = 2023 }
• Modeling of Annual and Daily Electricity Demand of Retrofitted Heat Pumps based on Gas Smart Meter Data. Bayer, Daniel R.; Pruckner, Marco. In Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, of BuildSys ’23. ACM, 2023.
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  @inproceedings{bayer2023modeling, author = {Bayer, Daniel R. and Pruckner, Marco}, booktitle = {Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation}, keywords = {pruckner}, month = 11, publisher = {ACM}, series = {BuildSys ’23}, title = {Modeling of Annual and Daily Electricity Demand of Retrofitted Heat Pumps based on Gas Smart Meter Data}, year = 2023 }
• Generalized State of Health Estimation Approach based on Neural Networks for Various Lithium-Ion Battery Chemistries. Bockrath, Steffen; Pruckner, Marco. In Proceedings of the 14th ACM International Conference on Future Energy Systems, of e-Energy ’23. ACM, 2023.
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  @inproceedings{Bockrath_2023, author = {Bockrath, Steffen and Pruckner, Marco}, booktitle = {Proceedings of the 14th ACM International Conference on Future Energy Systems}, keywords = {pruckner}, month = {06}, publisher = {ACM}, series = {e-Energy ’23}, title = {Generalized State of Health Estimation Approach based on Neural Networks for Various Lithium-Ion Battery Chemistries}, year = 2023 }
• Residential photovoltaic and energy storage systems for sustainable development: An economic analysis applied to incentive mechanisms. D’Adamo, Idiano; Dell’Aguzzo, Alessandro; Pruckner, Marco. In Sustainable Development. Wiley, 2023.
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  @article{D_Adamo_2023, author = {D’Adamo, Idiano and Dell’Aguzzo, Alessandro and Pruckner, Marco}, journal = {Sustainable Development}, keywords = {pruckner}, month = {06}, publisher = {Wiley}, title = {Residential photovoltaic and energy storage systems for sustainable development: An economic analysis applied to incentive mechanisms}, year = 2023 }
• Battery Management System for On-Board Data-Driven State of Health Estimation for Aviation and Space Applications. Bockrath, Steffen; Wachtler, Johannes; Wenger, Martin; Schwarz, Radu; Pruckner, Marco; Lorentz, Vincent RH. In 2023 IEEE Space Computing Conference (SCC), pp. 92–100. IEEE, 2023.
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  @inproceedings{bockrath2023battery, author = {Bockrath, Steffen and Wachtler, Johannes and Wenger, Martin and Schwarz, Radu and Pruckner, Marco and Lorentz, Vincent RH}, booktitle = {2023 IEEE Space Computing Conference (SCC)}, keywords = {pruckner}, organization = {IEEE}, pages = {92–100}, title = {Battery Management System for On-Board Data-Driven State of Health Estimation for Aviation and Space Applications}, year = 2023 }
• The demand potential of shared autonomous vehicles: a large-scale simulation using mobility survey data. Iacobucci, Riccardo; Donhauser, Jonas; Schmöcker, Jan-Dirk; Pruckner, Marco. In Journal of Intelligent Transportation Systems, pp. 1–22. Informa UK Limited, 2023.
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  @article{Iacobucci_2023, author = {Iacobucci, Riccardo and Donhauser, Jonas and Schmöcker, Jan-Dirk and Pruckner, Marco}, journal = {Journal of Intelligent Transportation Systems}, keywords = {pruckner}, month = {05}, pages = {1–22}, publisher = {Informa UK Limited}, title = {The demand potential of shared autonomous vehicles: a large-scale simulation using mobility survey data}, year = 2023 }
• A digital twin of a local energy system based on real smart meter data. Bayer, Daniel; Pruckner, Marco. In Energy Informatics, 6(1). Springer Science and Business Media {LLC}, 2023.
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  The steadily increasing usage of smart meters generates a valuable amount of high-resolution data about the individual energy consumption and production of local energy systems. Private households install more and more photovoltaic systems, battery storage and big consumers like heat pumps. Thus, our vision is to augment these collected smart meter time series of a complete system (e.g., a city, town or complex institutions like airports) with simulatively added previously named components. We, therefore, propose a novel digital twin of such an energy system based solely on a complete set of smart meter data including additional building data. Based on the additional geospatial data, the twin is intended to represent the addition of the abovementioned components as realistically as possible. Outputs of the twin can be used as a decision support for either system operators where to strengthen the system or for individual households where and how to install photovoltaic systems and batteries. Meanwhile, the first local energy system operators had such smart meter data of almost all residential consumers for several years. We acquire those of an exemplary operator and discuss a case study presenting some features of our digital twin and highlighting the value of the combination of smart meter and geospatial data.

  @article{2023_Bayer_DT_LocalEnergySystem, abstract = {The steadily increasing usage of smart meters generates a valuable amount of high-resolution data about the individual energy consumption and production of local energy systems. Private households install more and more photovoltaic systems, battery storage and big consumers like heat pumps. Thus, our vision is to augment these collected smart meter time series of a complete system (e.g., a city, town or complex institutions like airports) with simulatively added previously named components. We, therefore, propose a novel digital twin of such an energy system based solely on a complete set of smart meter data including additional building data. Based on the additional geospatial data, the twin is intended to represent the addition of the abovementioned components as realistically as possible. Outputs of the twin can be used as a decision support for either system operators where to strengthen the system or for individual households where and how to install photovoltaic systems and batteries. Meanwhile, the first local energy system operators had such smart meter data of almost all residential consumers for several years. We acquire those of an exemplary operator and discuss a case study presenting some features of our digital twin and highlighting the value of the combination of smart meter and geospatial data.}, author = {Bayer, Daniel and Pruckner, Marco}, journal = {Energy Informatics}, keywords = {pruckner}, month = {03}, number = 1, publisher = {Springer Science and Business Media {LLC}}, title = {A digital twin of a local energy system based on real smart meter data}, volume = 6, year = 2023 }
• State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles. Bockrath, Steffen; Lorentz, Vincent; Pruckner, Marco. In Applied Energy, 329, p. 120307. 2023.
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  An accurate aging forecasting and state of health estimation is essential for a safe and economically valuable usage of lithium-ion batteries. However, the non-linear aging of lithium-ion batteries is dependent on various operating and environmental conditions wherefore the degradation estimation is a complex challenge. Moreover, for on-board estimations where only limited memory and computing power are available, a state of health estimation algorithm is needed that is able to process raw sensor data without complex preprocessing. This paper presents a data-driven state of health estimation algorithm for lithium-ion batteries using different segments of partial discharge profiles. Raw sensor data is directly input to a temporal convolutional neural network without the need of executing feature engineering steps. The neural network is able to process raw sensor data and estimate the state of health of battery cells for different aging and degradation scenarios. After executing Bayesian hyperparameter tuning together with a stratified cross validation approach for splitting the training and test data, the achieved generalized aging model estimates the state of health with an overall root mean squared error of 1.0%.

  @article{BOCKRATH2023120307, abstract = {An accurate aging forecasting and state of health estimation is essential for a safe and economically valuable usage of lithium-ion batteries. However, the non-linear aging of lithium-ion batteries is dependent on various operating and environmental conditions wherefore the degradation estimation is a complex challenge. Moreover, for on-board estimations where only limited memory and computing power are available, a state of health estimation algorithm is needed that is able to process raw sensor data without complex preprocessing. This paper presents a data-driven state of health estimation algorithm for lithium-ion batteries using different segments of partial discharge profiles. Raw sensor data is directly input to a temporal convolutional neural network without the need of executing feature engineering steps. The neural network is able to process raw sensor data and estimate the state of health of battery cells for different aging and degradation scenarios. After executing Bayesian hyperparameter tuning together with a stratified cross validation approach for splitting the training and test data, the achieved generalized aging model estimates the state of health with an overall root mean squared error of 1.0%.}, author = {Bockrath, Steffen and Lorentz, Vincent and Pruckner, Marco}, journal = {Applied Energy}, keywords = {pruckner}, pages = 120307, title = {State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles}, volume = 329, year = 2023 }

• Simulation and analysis of a Carnot Battery consisting of a reversible heat pump/organic Rankine cycle for a domestic application in a community with varying number of houses. Scharrer, Daniel; Bazan, Peter; Pruckner, Marco; German, Reinhard. In Energy, 261, p. 125166. 2022.
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  The storage of electric energy is considered to be a crucial element for grids with a high share of renewable energy generation. Carnot Batteries are a promising technology regarding sector coupling or peak shaving applications. In the domestic sector, current research focuses on an application of Carnot Battery systems in single houses with solar power to improve their self-sufficiency and reduce expenditures. However, due to the non-cellular structure (in contrast to li-ion) economies of scale contribute significantly to the viability of the technology. This is why a joint analysis of entire housing communities is necessary, as focusing only on one house neglects possibilities that arise with larger Carnot Battery systems. The Carnot Battery system is modelled after a pilot plant as a reversible heat pump (20 kW)/organic Rankine cycle (7–13 kW) system coupled with a sensible hot water storage. Operation data from a local energy supplier is used to validate the heat storage component of the simulation model, while the thermal machines are based on simulated and experimental data of the pilot plant. An extensive and comprehensive parameter variation is done to find possible application scenarios of the pilot plant and evaluate its financial viability in a community of varying number of houses. Major findings are, that the possible savings the pilot plant Carnot Battery can achieve in such a community depends on the market conditions considered and that it is not financial under current German market conditions. Varying the feed-in tariff and heat costs showcases the limits of a financial application. Based on current projections for the feed-in tariff, an application for a community of 10–30 houses becomes financial in the near future and saves more than 100 € on electricity costs per house per year, with an amortization period of 14 years depending on interest rates.

  @article{SCHARRER2022125166, abstract = {The storage of electric energy is considered to be a crucial element for grids with a high share of renewable energy generation. Carnot Batteries are a promising technology regarding sector coupling or peak shaving applications. In the domestic sector, current research focuses on an application of Carnot Battery systems in single houses with solar power to improve their self-sufficiency and reduce expenditures. However, due to the non-cellular structure (in contrast to li-ion) economies of scale contribute significantly to the viability of the technology. This is why a joint analysis of entire housing communities is necessary, as focusing only on one house neglects possibilities that arise with larger Carnot Battery systems. The Carnot Battery system is modelled after a pilot plant as a reversible heat pump (20 kW)/organic Rankine cycle (7–13 kW) system coupled with a sensible hot water storage. Operation data from a local energy supplier is used to validate the heat storage component of the simulation model, while the thermal machines are based on simulated and experimental data of the pilot plant. An extensive and comprehensive parameter variation is done to find possible application scenarios of the pilot plant and evaluate its financial viability in a community of varying number of houses. Major findings are, that the possible savings the pilot plant Carnot Battery can achieve in such a community depends on the market conditions considered and that it is not financial under current German market conditions. Varying the feed-in tariff and heat costs showcases the limits of a financial application. Based on current projections for the feed-in tariff, an application for a community of 10–30 houses becomes financial in the near future and saves more than 100 € on electricity costs per house per year, with an amortization period of 14 years depending on interest rates.}, author = {Scharrer, Daniel and Bazan, Peter and Pruckner, Marco and German, Reinhard}, journal = {Energy}, keywords = {pruckner}, pages = 125166, title = {Simulation and analysis of a Carnot Battery consisting of a reversible heat pump/organic Rankine cycle for a domestic application in a community with varying number of houses}, volume = 261, year = 2022 }
• Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data. Heinrich, Felix; Pruckner, Marco. In Journal of Energy Storage, 48, p. 103856. Elsevier, 2022.
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  @article{heinrich2022virtual, author = {Heinrich, Felix and Pruckner, Marco}, journal = {Journal of Energy Storage}, keywords = {pruckner}, pages = 103856, publisher = {Elsevier}, title = {Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data}, volume = 48, year = 2022 }
• Synergy of Unidirectional and Bidirectional Smart Charging of Electric Vehicles for Frequency Containment Reserve Power Provision. Schlund, Jonas; German, Reinhard; Pruckner, Marco. In World Electric Vehicle Journal, 13(9). 2022.
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  Besides the integration of renewable energies, electric vehicles pose an additional challenge to modern power grids. However, electric vehicles can also be a flexibility source and contribute to the power system stability. Today, the power system still heavily relies on conventional technologies to stay stable. In order to operate a future power system based on renewable energies only, we need to understand the flexibility potential of assets such as electric vehicles and become able to use their flexibility. In this paper, we analyzed how vast amounts of coordinated charging processes can be used to provide frequency containment reserve power, one of the most important ancillary services for system stability. Therefore, we used an extensive simulation model of a virtual power plant of millions of electric vehicles. The model considers not only technical components but also the stochastic behavior of electric vehicle drivers based on real data. Our results show that, in 2030, electric vehicles have the potential to serve the whole frequency containment reserve power market in Germany. We differentiate between using unidirectional and bidirectional chargers. Bidirectional chargers have a larger potential but also result in unwanted battery degradation. Unidirectional chargers are more constrained in terms of flexibility, but do not lead to additional battery degradation. We conclude that using a mix of both can combine the advantages of both worlds. Thereby, average private cars can provide the service without any notable additional battery degradation and achieve yearly earnings between EUR 200 and EUR 500, depending on the volatile market prices. Commercial vehicles have an even higher potential, as the results increase with vehicle utilization and consumption.

  @article{wevj13090168, abstract = {Besides the integration of renewable energies, electric vehicles pose an additional challenge to modern power grids. However, electric vehicles can also be a flexibility source and contribute to the power system stability. Today, the power system still heavily relies on conventional technologies to stay stable. In order to operate a future power system based on renewable energies only, we need to understand the flexibility potential of assets such as electric vehicles and become able to use their flexibility. In this paper, we analyzed how vast amounts of coordinated charging processes can be used to provide frequency containment reserve power, one of the most important ancillary services for system stability. Therefore, we used an extensive simulation model of a virtual power plant of millions of electric vehicles. The model considers not only technical components but also the stochastic behavior of electric vehicle drivers based on real data. Our results show that, in 2030, electric vehicles have the potential to serve the whole frequency containment reserve power market in Germany. We differentiate between using unidirectional and bidirectional chargers. Bidirectional chargers have a larger potential but also result in unwanted battery degradation. Unidirectional chargers are more constrained in terms of flexibility, but do not lead to additional battery degradation. We conclude that using a mix of both can combine the advantages of both worlds. Thereby, average private cars can provide the service without any notable additional battery degradation and achieve yearly earnings between EUR 200 and EUR 500, depending on the volatile market prices. Commercial vehicles have an even higher potential, as the results increase with vehicle utilization and consumption.}, author = {Schlund, Jonas and German, Reinhard and Pruckner, Marco}, journal = {World Electric Vehicle Journal}, keywords = {pruckner}, number = 9, title = {Synergy of Unidirectional and Bidirectional Smart Charging of Electric Vehicles for Frequency Containment Reserve Power Provision}, volume = 13, year = 2022 }
• Modeling and Simulation to Improve Real Electric Vehicles Charging Processes by Integration of Renewable Energies and Buffer Storage. Sing, Konstantin; Mertiny, Pierre; Pruckner, Marco. In 2022 Winter Simulation Conference (WSC), pp. 867–878. 2022.
  • [ BibTeX ]
  • [ DOI ]
  @inproceedings{10015432, author = {Sing, Konstantin and Mertiny, Pierre and Pruckner, Marco}, booktitle = {2022 Winter Simulation Conference (WSC)}, keywords = {pruckner}, pages = {867-878}, title = {Modeling and Simulation to Improve Real Electric Vehicles Charging Processes by Integration of Renewable Energies and Buffer Storage}, year = 2022 }
• Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems. Bayer, Daniel; Pruckner, Marco. In 2022 IEEE Conference on Technologies for Sustainability (SusTech), pp. 187–194. 2022.
  • [ Abstract ]
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  Systems for heating, ventilation and air-conditioning (HVAC) of buildings are traditionally controlled by a rule-based approach. In order to reduce the energy consumption and the environmental impact of HVAC systems more advanced control methods such as reinforcement learning are promising. Reinforcement learning (RL) strategies offer a good alternative, as user feedback can be integrated more easily and presence can also be incorporated. Moreover, multi-agent RL approaches scale well and can be generalized. In this paper, we propose a multi-agent RL framework based on existing work that learns reducing on one hand energy consumption by optimizing HVAC control and on the other hand user feedback by occupants about uncomfortable room temperatures. Second, we show how to reduce training time required for proper RL-agent-training by using parameter sharing between the multiple agents and apply different pretraining techniques. Results show that our framework is capable of reducing the energy by around 6% when controlling a complete building or 8% for a single room zone. The occupants complaints are acceptable or even better compared to a rule-based baseline. Additionally, our performance analysis show that the training time can be drastically reduced by using parameter sharing.

  @inproceedings{9794179, abstract = {Systems for heating, ventilation and air-conditioning (HVAC) of buildings are traditionally controlled by a rule-based approach. In order to reduce the energy consumption and the environmental impact of HVAC systems more advanced control methods such as reinforcement learning are promising. Reinforcement learning (RL) strategies offer a good alternative, as user feedback can be integrated more easily and presence can also be incorporated. Moreover, multi-agent RL approaches scale well and can be generalized. In this paper, we propose a multi-agent RL framework based on existing work that learns reducing on one hand energy consumption by optimizing HVAC control and on the other hand user feedback by occupants about uncomfortable room temperatures. Second, we show how to reduce training time required for proper RL-agent-training by using parameter sharing between the multiple agents and apply different pretraining techniques. Results show that our framework is capable of reducing the energy by around 6% when controlling a complete building or 8% for a single room zone. The occupants complaints are acceptable or even better compared to a rule-based baseline. Additionally, our performance analysis show that the training time can be drastically reduced by using parameter sharing.}, author = {Bayer, Daniel and Pruckner, Marco}, booktitle = {2022 IEEE Conference on Technologies for Sustainability (SusTech)}, keywords = {pruckner}, month = {04}, pages = {187-194}, title = {Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems}, year = 2022 }
• Feasibility of completely electrified two-way car sharing. Strobel, Leo; Pruckner, Marco. In 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 206–211. 2022.
  • [ Abstract ]
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  Car sharing is a more sustainable approach to personal mobility than vehicle ownership, especially if car sharing services electrify their fleets. However, due to the limited range and slow charging process of electric vehicles, car sharing providers might face problems keeping the vehicles adequately charged. This paper analyzes 4.5 years of real booking data from a German two-way car sharing provider. The dataset includes information on the booking time window, driven distance, and location. We use this data to study the customer behavior and simulate the past operation with a completely electrified fleet. Based on the simulation, we determine whether charging problems exist and how they can be solved by adapting the charging rate, battery capacity, and number of charging points. Our results indicate that the operation of the service with modern electric vehicles is entirely feasible. Per car sharing station, two charging points are sufficient if vehicles do not have to be connected immediately upon arrival (by the customer), but can connect later once another vehicle has finished charging. Somewhat problematic is that 4% of the bookings have a longer driven distance than the vehicle's range. In these cases, the disutility to customers is unclear but, in all likelihood, manageable. Furthermore, we find that 50% of the charging events can be shifted by more than 10h, indicating significant flexibility that could be utilized for smart charging and the provision of ancillary services in the future.

  @inproceedings{9961032, abstract = {Car sharing is a more sustainable approach to personal mobility than vehicle ownership, especially if car sharing services electrify their fleets. However, due to the limited range and slow charging process of electric vehicles, car sharing providers might face problems keeping the vehicles adequately charged. This paper analyzes 4.5 years of real booking data from a German two-way car sharing provider. The dataset includes information on the booking time window, driven distance, and location. We use this data to study the customer behavior and simulate the past operation with a completely electrified fleet. Based on the simulation, we determine whether charging problems exist and how they can be solved by adapting the charging rate, battery capacity, and number of charging points. Our results indicate that the operation of the service with modern electric vehicles is entirely feasible. Per car sharing station, two charging points are sufficient if vehicles do not have to be connected immediately upon arrival (by the customer), but can connect later once another vehicle has finished charging. Somewhat problematic is that 4% of the bookings have a longer driven distance than the vehicle's range. In these cases, the disutility to customers is unclear but, in all likelihood, manageable. Furthermore, we find that 50% of the charging events can be shifted by more than 10h, indicating significant flexibility that could be utilized for smart charging and the provision of ancillary services in the future.}, author = {Strobel, Leo and Pruckner, Marco}, booktitle = {2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)}, keywords = {pruckner}, month = 10, pages = {206-211}, title = {Feasibility of completely electrified two-way car sharing}, year = 2022 }
• Joint analysis of regional and national power system impacts of electric vehicles - A case study for Germany on the county level in 2030. Strobel, Leo; Schlund, Jonas; Pruckner, Marco. In Applied Energy, 315, p. 118945. Elsevier {BV}, 2022.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @article{Strobel_2022, author = {Strobel, Leo and Schlund, Jonas and Pruckner, Marco}, journal = {Applied Energy}, keywords = {pruckner}, month = {06}, pages = 118945, publisher = {Elsevier {BV}}, title = {Joint analysis of regional and national power system impacts of electric vehicles - A case study for Germany on the county level in 2030}, volume = 315, year = 2022 }

• A comprehensive study on battery electric modeling approaches based on machine learning. Heinrich, Felix; Klapper, Patrick; Pruckner, Marco. In DACH Conference on Energy Informatics, 4(3), pp. 1–17. Springer, 2021.
  • [ BibTeX ]
  @article{heinrich2021comprehensive, author = {Heinrich, Felix and Klapper, Patrick and Pruckner, Marco}, journal = {DACH Conference on Energy Informatics}, keywords = {pruckner}, number = 3, pages = {1–17}, publisher = {Springer}, title = {A comprehensive study on battery electric modeling approaches based on machine learning}, volume = 4, year = 2021 }
• Dynamic modeling and sensitivity analysis of a stratified heat storage coupled with a heat pump and an organic rankine cycle. Scharrer, Daniel; Pruckner, Marco; Bazan, Peter; German, Reinhard. In 2021 Winter Simulation Conference (WSC), pp. 1–12. IEEE, 2021.
  • [ BibTeX ]
  @inproceedings{scharrer2021dynamic, author = {Scharrer, Daniel and Pruckner, Marco and Bazan, Peter and German, Reinhard}, booktitle = {2021 Winter Simulation Conference (WSC)}, keywords = {pruckner}, organization = {IEEE}, pages = {1–12}, title = {Dynamic modeling and sensitivity analysis of a stratified heat storage coupled with a heat pump and an organic rankine cycle}, year = 2021 }
• Benchmarking a Decentralized Reinforcement Learning Control Strategy for an Energy Community. Ebell, Niklas; Pruckner, Marco. In 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 385–390. IEEE, 2021.
  • [ BibTeX ]
  @inproceedings{ebell2021benchmarking, author = {Ebell, Niklas and Pruckner, Marco}, booktitle = {2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)}, keywords = {pruckner}, organization = {IEEE}, pages = {385–390}, title = {Benchmarking a Decentralized Reinforcement Learning Control Strategy for an Energy Community}, year = 2021 }
• Frequency Control Reserve Provision from a Fleet of Shared Autonomous Electric Vehicles. Iacobucci, Riccardo; Donhauser, Jonas; Schm{\"o}cker, Jan-Dirk; Pruckner, Marco. In 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 1–6. IEEE, 2021.
  • [ BibTeX ]
  @inproceedings{iacobucci2021frequency, author = {Iacobucci, Riccardo and Donhauser, Jonas and Schm{\"o}cker, Jan-Dirk and Pruckner, Marco}, booktitle = {2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)}, keywords = {pruckner}, organization = {IEEE}, pages = {1–6}, title = {Frequency Control Reserve Provision from a Fleet of Shared Autonomous Electric Vehicles}, year = 2021 }
• Unsupervised data-preprocessing for Long Short-Term Memory based battery model under electric vehicle operation. Heinrich, F.; Noering, F.K.-D.; Pruckner, M.; Jonas, K. In Journal of Energy Storage, 38, p. 102598. 2021.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @article{HEINRICH2021102598, author = {Heinrich, F. and Noering, F.K.-D. and Pruckner, M. and Jonas, K.}, journal = {Journal of Energy Storage}, keywords = {pruckner}, pages = 102598, title = {Unsupervised data-preprocessing for Long Short-Term Memory based battery model under electric vehicle operation}, volume = 38, year = 2021 }
• Smart Charging and Renewable Grid Integration - A Case Study Based on Real-Data of the Island of Porto Santo. Strobel, Leo; Schlund, Jonas; Brandmeier, Veronika; Schreiber, Michael; Pruckner, Marco. In Sustainable Energy for Smart Cities, J. L. Afonso, V. Monteiro, J. G. Pinto (eds.), pp. 200–215. Springer International Publishing, Cham, 2021.
  • [ Abstract ]
  • [ BibTeX ]
  The penetration of battery electric vehicles is increasing. Due to their ability to store electrical power and to shift charging events, they offer a wide range of opportunities with regard to renewable grid integration and lowering the overall CO2 emissions. This is particularly evident for isolated microgrids, such as the Portuguese island of Porto Santo. In this paper, we conduct a data analysis of real-world charging data of 20 electric vehicles operated on the island of Porto Santo. We provide insights into the charging behavior of different users and analyze the opportunity of smart charging for better renewable grid integration using linear optimization models. The data analysis shows that drivers prefer home rather than publicly available charging stations, flexible charging events occur mostly overnight and the charging flexibility of the fleet decreases over the project duration. With regard to make charging more flexible, we can see that smart charging can help to raise the share of electricity generated by renewable energy sources for charging the electric vehicles to up to 33{\%}.

  @inproceedings{10.1007/978-3-030-73585-2_13, abstract = {The penetration of battery electric vehicles is increasing. Due to their ability to store electrical power and to shift charging events, they offer a wide range of opportunities with regard to renewable grid integration and lowering the overall CO2 emissions. This is particularly evident for isolated microgrids, such as the Portuguese island of Porto Santo. In this paper, we conduct a data analysis of real-world charging data of 20 electric vehicles operated on the island of Porto Santo. We provide insights into the charging behavior of different users and analyze the opportunity of smart charging for better renewable grid integration using linear optimization models. The data analysis shows that drivers prefer home rather than publicly available charging stations, flexible charging events occur mostly overnight and the charging flexibility of the fleet decreases over the project duration. With regard to make charging more flexible, we can see that smart charging can help to raise the share of electricity generated by renewable energy sources for charging the electric vehicles to up to 33{\%}.}, address = {Cham}, author = {Strobel, Leo and Schlund, Jonas and Brandmeier, Veronika and Schreiber, Michael and Pruckner, Marco}, booktitle = {Sustainable Energy for Smart Cities}, editor = {Afonso, Jo{\ a}o L. and Monteiro, Vitor and Pinto, Jos{\'e} Gabriel}, keywords = {pruckner}, pages = {200–215}, publisher = {Springer International Publishing}, title = {Smart Charging and Renewable Grid Integration - A Case Study Based on Real-Data of the Island of Porto Santo}, year = 2021 }
• Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning. Tuchnitz, Felix; Ebell, Niklas; Schlund, Jonas; Pruckner, Marco. In Applied Energy, 285, p. 116382. 2021.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @article{TUCHNITZ2021116382, author = {Tuchnitz, Felix and Ebell, Niklas and Schlund, Jonas and Pruckner, Marco}, journal = {Applied Energy}, keywords = {pruckner}, pages = 116382, title = {Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning}, volume = 285, year = 2021 }
• Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data. Barthel, Vincent; Schlund, Jonas; Landes, Philipp; Brandmeier, Veronika; Pruckner, Marco. In Energies, 14(16). 2021.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @article{en14164961, author = {Barthel, Vincent and Schlund, Jonas and Landes, Philipp and Brandmeier, Veronika and Pruckner, Marco}, journal = {Energies}, keywords = {pruckner}, number = 16, title = {Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data}, volume = 14, year = 2021 }

• Life Cycle Assessment of a Reversible Heat Pump�Organic Rankine Cycle�Heat Storage System with Geothermal Heat Supply. Scharrer, Daniel; Eppinger, Bernd; Schmitt, Pascal; Zenk, Johan; Bazan, Peter; Karl, J�rgen; Will, Stefan; Pruckner, Marco; German, Reinhard. In Energies, 13(12). 2020.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @article{en13123253, author = {Scharrer, Daniel and Eppinger, Bernd and Schmitt, Pascal and Zenk, Johan and Bazan, Peter and Karl, J�rgen and Will, Stefan and Pruckner, Marco and German, Reinhard}, journal = {Energies}, keywords = {pruckner}, number = 12, title = {Life Cycle Assessment of a Reversible Heat Pump�Organic Rankine Cycle�Heat Storage System with Geothermal Heat Supply}, volume = 13, year = 2020 }
• FlexAbility - Modeling and Maximizing the Bidirectional Flexibility Availability of Unidirectional Charging of Large Pools of Electric Vehicles. Schlund, Jonas; Pruckner, Marco; German, Reinhard. In Proceedings of the Eleventh ACM International Conference on Future Energy Systems, of e-Energy ’20, p. 121�132. Association for Computing Machinery, Virtual Event, Australia, 2020.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @inproceedings{10.1145/3396851.3397697, address = {New York, NY, USA}, author = {Schlund, Jonas and Pruckner, Marco and German, Reinhard}, booktitle = {Proceedings of the Eleventh ACM International Conference on Future Energy Systems}, keywords = {pruckner}, pages = {121�132}, publisher = {Association for Computing Machinery}, series = {e-Energy '20}, title = {FlexAbility - Modeling and Maximizing the Bidirectional Flexibility Availability of Unidirectional Charging of Large Pools of Electric Vehicles}, year = 2020 }
• A scenario-based study on the impacts of electric vehicles on energy consumption and sustainability in Alberta. Doluweera, Ganesh; Hahn, Fabian; Bergerson, Joule; Pruckner, Marco. In Applied Energy, 268, p. 114961. 2020.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @article{DOLUWEERA2020114961, author = {Doluweera, Ganesh and Hahn, Fabian and Bergerson, Joule and Pruckner, Marco}, journal = {Applied Energy}, keywords = {pruckner}, pages = 114961, title = {A scenario-based study on the impacts of electric vehicles on energy consumption and sustainability in Alberta}, volume = 268, year = 2020 }
• Shared Autonomous Electric Vehicles and the Power Grid: Applications and Research Challenges. Pruckner, Marco; Eckhoff, David. In ISGT-Europe, pp. 1151–1155. IEEE, 2020.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/isgteurope/PrucknerE20, author = {Pruckner, Marco and Eckhoff, David}, booktitle = {ISGT-Europe}, crossref = {conf/isgteurope/2020}, keywords = {pruckner}, pages = {1151-1155}, publisher = {IEEE}, title = {Shared Autonomous Electric Vehicles and the Power Grid: Applications and Research Challenges.}, year = 2020 }
• Electric Vehicle Charge Management for Lowering Costs and Environmental Impact. Apostolaki-Iosifidou, Elpiniki; Pruckner, Marco; Woo, Soomin; Lipman, Timothy. In 2020 IEEE Conference on Technologies for Sustainability (SusTech), pp. 1–7. 2020.
  • [ BibTeX ]
  • [ DOI ]
  @inproceedings{9150524, author = {Apostolaki-Iosifidou, Elpiniki and Pruckner, Marco and Woo, Soomin and Lipman, Timothy}, booktitle = {2020 IEEE Conference on Technologies for Sustainability (SusTech)}, keywords = {pruckner}, pages = {1-7}, title = {Electric Vehicle Charge Management for Lowering Costs and Environmental Impact}, year = 2020 }

• Sharing of Energy Among Cooperative Households Using Distributed Multi-Agent Reinforcement Learning. Ebell, Niklas; Gütlein, Moritz; Pruckner, Marco. In ISGT Europe, pp. 1–5. IEEE, 2019.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/isgteurope/EbellGP19, author = {Ebell, Niklas and Gütlein, Moritz and Pruckner, Marco}, booktitle = {ISGT Europe}, crossref = {conf/isgteurope/2019}, keywords = {pruckner}, pages = {1-5}, publisher = {IEEE}, title = {Sharing of Energy Among Cooperative Households Using Distributed Multi-Agent Reinforcement Learning.}, year = 2019 }
• Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids. Spitzer, Martin; Schlund, Jonas; Apostolaki-Iosifidou, Elpiniki; Pruckner, Marco. In Energies, 12(21). 2019.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @article{en12214059, author = {Spitzer, Martin and Schlund, Jonas and Apostolaki-Iosifidou, Elpiniki and Pruckner, Marco}, journal = {Energies}, keywords = {pruckner}, number = 21, title = {Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids}, volume = 12, year = 2019 }

• A comprehensive electricity Market Model using simulation and Optimization Techniques. Steber, David; Hubler, Jakob; Pruckner, Marco. In WSC, B. Johansson, S. Jain (eds.), pp. 2095–2106. IEEE, 2018.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/wsc/SteberHP18, author = {Steber, David and Hubler, Jakob and Pruckner, Marco}, booktitle = {WSC}, crossref = {conf/wsc/2018}, editor = {Johansson, Björn and Jain, Sanjay}, keywords = {pruckner}, pages = {2095-2106}, publisher = {IEEE}, title = {A comprehensive electricity Market Model using simulation and Optimization Techniques.}, year = 2018 }
• Reinforcement Learning Control Algorithm for a PV-Battery-System Providing Frequency Containment Reserve Power. Ebell, Niklas; Heinrich, Felix; Schlund, Jonas; Pruckner, Marco. In SmartGridComm, pp. 1–6. IEEE, 2018.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/smartgridcomm/EbellHSP18, author = {Ebell, Niklas and Heinrich, Felix and Schlund, Jonas and Pruckner, Marco}, booktitle = {SmartGridComm}, crossref = {conf/smartgridcomm/2018}, keywords = {pruckner}, pages = {1-6}, publisher = {IEEE}, title = {Reinforcement Learning Control Algorithm for a PV-Battery-System Providing Frequency Containment Reserve Power.}, year = 2018 }
• Coordinating E-Mobility Charging for Frequency Containment Reserve Power Provision. Schlund, Jonas; Steinert, Ronny; Pruckner, Marco. In e-Energy, H. Schmeck, V. Hagenmeyer (eds.), pp. 556–563. ACM, 2018.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/eenergy/SchlundSP18, author = {Schlund, Jonas and Steinert, Ronny and Pruckner, Marco}, booktitle = {e-Energy}, crossref = {conf/eenergy/2018}, editor = {Schmeck, Hartmut and Hagenmeyer, Veit}, keywords = {pruckner}, pages = {556-563}, publisher = {ACM}, title = {Coordinating E-Mobility Charging for Frequency Containment Reserve Power Provision.}, year = 2018 }
• Including a virtual battery storage into thermal unit commitment. Steber, David; Pruckner, Marco; Schlund, Jonas; Bazan, Peter; German, Reinhard. In Comput. Sci. Res. Dev., 33(1-2), pp. 223–229. 2018.
  • [ BibTeX ]
  • [ URL ]
  @article{journals/ife/SteberPSBG18, author = {Steber, David and Pruckner, Marco and Schlund, Jonas and Bazan, Peter and German, Reinhard}, journal = {Comput. Sci. Res. Dev.}, keywords = {pruckner}, number = {1-2}, pages = {223-229}, title = {Including a virtual battery storage into thermal unit commitment.}, volume = 33, year = 2018 }
• Coordinated Multi-Agent Reinforcement Learning for Swarm Battery Control. Ebell, Niklas; Pruckner, Marco. In CCECE, pp. 1–4. IEEE, 2018.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/ccece/EbellP18, author = {Ebell, Niklas and Pruckner, Marco}, booktitle = {CCECE}, crossref = {conf/ccece/2018}, keywords = {pruckner}, pages = {1-4}, publisher = {IEEE}, title = {Coordinated Multi-Agent Reinforcement Learning for Swarm Battery Control.}, year = 2018 }
• Rebalancing and fleet sizing of Mobility-on-demand Networks with combined simulation, Optimization and Queueing Network Analysis. Bazan, Peter; Djanatliev, Anatoli; Pruckner, Marco; German, Reinhard; Lauer, Christoph. In WSC, B. Johansson, S. Jain (eds.), pp. 1527–1538. IEEE, 2018.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/wsc/BazanDPGL18, author = {Bazan, Peter and Djanatliev, Anatoli and Pruckner, Marco and German, Reinhard and Lauer, Christoph}, booktitle = {WSC}, crossref = {conf/wsc/2018}, editor = {Johansson, Björn and Jain, Sanjay}, keywords = {pruckner}, pages = {1527-1538}, publisher = {IEEE}, title = {Rebalancing and fleet sizing of Mobility-on-demand Networks with combined simulation, Optimization and Queueing Network Analysis.}, year = 2018 }
• Reversible Heat Pump Organic Rankine Cycle Systems for the Storage of Renewable Electricity. Staub, Sebastian; Bazan, Peter; Braimakis, Konstantinos; Müller, Dominik; Regensburger, Christoph; Scharrer, Daniel; Schmitt, Bernd; Steger, Daniel; German, Reinhard; Karellas, Sotirios; Pruckner, Marco; Schl�cker, Eberhard; Will, Stefan; Karl, J�rgen. In Energies, 11(6). 2018.
  • [ BibTeX ]
  • [ URL ]
  • [ DOI ]
  @article{en11061352, author = {Staub, Sebastian and Bazan, Peter and Braimakis, Konstantinos and Müller, Dominik and Regensburger, Christoph and Scharrer, Daniel and Schmitt, Bernd and Steger, Daniel and German, Reinhard and Karellas, Sotirios and Pruckner, Marco and Schl�cker, Eberhard and Will, Stefan and Karl, J�rgen}, journal = {Energies}, keywords = {pruckner}, number = 6, title = {Reversible Heat Pump Organic Rankine Cycle Systems for the Storage of Renewable Electricity}, volume = 11, year = 2018 }

• Spatial and Temporal Charging Infrastructure Planning Using Discrete Event Simulation. Pruckner, Marco; German, Reinhard; Eckhoff, David. In SIGSIM-PADS, W. Cai, Y. M. Teo, P. Wilsey, K. Jin (eds.), pp. 249–257. ACM, 2017.
  • [ BibTeX ]
  • [ URL ]
  @inproceedings{conf/pads/PrucknerGE17, author = {Pruckner, Marco and German, Reinhard and Eckhoff, David}, booktitle = {SIGSIM-PADS}, crossref = {conf/pads/2017}, editor = {Cai, Wentong and Teo, Yong Meng and Wilsey, Philip and Jin, Kevin}, keywords = {pruckner}, pages = {249-257}, publisher = {ACM}, title = {Spatial and Temporal Charging Infrastructure Planning Using Discrete Event Simulation.}, year = 2017 }
• Analysis of Various Charging Strategies for Electrified Public Bus Transport Utilizing a Lightweight Simulation Model. Dicke, Paul; Meyer, Benedikt; Pruckner, Marco. In Proceedings of the 1st E-Mobility Power System Integration Symposium. Berlin, Germany, 2017.
  • [ BibTeX ]
  @inproceedings{dickeanalysis, address = {Berlin, Germany}, author = {Dicke, Paul and Meyer, Benedikt and Pruckner, Marco}, booktitle = {Proceedings of the 1st E-Mobility Power System Integration Symposium}, keywords = {pruckner}, title = {Analysis of Various Charging Strategies for Electrified Public Bus Transport Utilizing a Lightweight Simulation Model}, year = 2017 }
• SWARM � Providing 1 MW FCR power with residential PV-battery energy storage � Simulation and empiric validation. Steber, David; Pruckner, Marco; Bazan, Peter; German, Reinhard. In 2017 IEEE Manchester PowerTech, pp. 1–6. 2017.
  • [ BibTeX ]
  • [ DOI ]
  @inproceedings{7981091, author = {Steber, David and Pruckner, Marco and Bazan, Peter and German, Reinhard}, booktitle = {2017 IEEE Manchester PowerTech}, keywords = {pruckner}, pages = {1-6}, title = {SWARM � Providing 1 MW FCR power with residential PV-battery energy storage � Simulation and empiric validation}, year = 2017 }
• Electrification of public bus transport under the usage of electricity generated by renewables. Dicke, Paul; Meyer, Benedikt; Pruckner, Marco. In 2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE), pp. 314–319. 2017.
  • [ BibTeX ]
  • [ DOI ]
  @inproceedings{8056931, author = {Dicke, Paul and Meyer, Benedikt and Pruckner, Marco}, booktitle = {2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE)}, keywords = {pruckner}, pages = {314-319}, title = {Electrification of public bus transport under the usage of electricity generated by renewables}, year = 2017 }
• Towards an impact study of electric vehicles on the Italian electric power system using simulation techniques. Longo, Michela; Lutz, Nina M.; Daniel, Luca; Zaninelli, Dario; Pruckner, Marco. In RTSI, pp. 1–5. IEEE, 2017.
  • [ BibTeX ]
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  @inproceedings{conf/rtsi/LongoLDZP17, author = {Longo, Michela and Lutz, Nina M. and Daniel, Luca and Zaninelli, Dario and Pruckner, Marco}, booktitle = {RTSI}, crossref = {conf/rtsi/2017}, keywords = {pruckner}, pages = {1-5}, publisher = {IEEE}, title = {Towards an impact study of electric vehicles on the Italian electric power system using simulation techniques.}, year = 2017 }

• The impact of electric vehicles on the german energy system. Pruckner, Marco; German, Reinhard. In SpringSim (ANSS), J. J. Padilla, A. Tolk, S. Jafer (eds.), p. 23. ACM, 2016.
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  @inproceedings{conf/springsim/PrucknerG16, author = {Pruckner, Marco and German, Reinhard}, booktitle = {SpringSim (ANSS)}, crossref = {conf/springsim/2016-2}, editor = {Padilla, Jose J. and Tolk, Andreas and Jafer, Shafagh}, keywords = {pruckner}, pages = 23, publisher = {ACM}, title = {The impact of electric vehicles on the german energy system.}, year = 2016 }
• Modeling the impact of electrical energy storage systems on future power systems. Pruckner, Marco. In 2016 IEEE Electrical Power and Energy Conference (EPEC), pp. 1–7. 2016.
  • [ BibTeX ]
  • [ DOI ]
  @inproceedings{7771724, author = {Pruckner, Marco}, booktitle = {2016 IEEE Electrical Power and Energy Conference (EPEC)}, keywords = {pruckner}, pages = {1-7}, title = {Modeling the impact of electrical energy storage systems on future power systems}, year = 2016 }

• Hierarchical Simulation of the German Energy System and Houses with PV and Storage Systems. Bazan, Peter; Pruckner, Marco; Steber, David; German, Reinhard. In D-A-CH EI, Vol. 9424 of Lecture Notes in Computer Science, S. Gottwalt, L. König, H. Schmeck (eds.), pp. 12–23. Springer, 2015.
  • [ BibTeX ]
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  @inproceedings{conf/energieinformatik/BazanPSG15, author = {Bazan, Peter and Pruckner, Marco and Steber, David and German, Reinhard}, booktitle = {D-A-CH EI}, crossref = {conf/energieinformatik/2015}, editor = {Gottwalt, Sebastian and König, Lukas and Schmeck, Hartmut}, keywords = {pruckner}, pages = {12-23}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Hierarchical Simulation of the German Energy System and Houses with PV and Storage Systems.}, volume = 9424, year = 2015 }
• Ein Simulationsmodell f{\"u}r den Energieumstieg in Bayern. Pruckner, Marco. Cuvillier Verlag, 2015.
  • [ BibTeX ]
  @book{pruckner2015simulationsmodell, author = {Pruckner, Marco}, keywords = {pruckner}, publisher = {Cuvillier Verlag}, title = {Ein Simulationsmodell f{\"u}r den Energieumstieg in Bayern}, year = 2015 }

• Modeling country-scale electricity demand profiles. Pruckner, Marco; Eckhoff, David; German, Reinhard. In Winter Simulation Conference, S. J. Buckley, J. A. Miller (eds.), pp. 1084–1095. IEEE/ACM, 2014.
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  @inproceedings{conf/wsc/PrucknerEG14, author = {Pruckner, Marco and Eckhoff, David and German, Reinhard}, booktitle = {Winter Simulation Conference}, crossref = {conf/wsc/2014}, editor = {Buckley, Stephen J. and Miller, John A.}, keywords = {pruckner}, pages = {1084-1095}, publisher = {IEEE/ACM}, title = {Modeling country-scale electricity demand profiles.}, year = 2014 }
• Modeling and simulation of electricity generated by renewable energy sources for complex energy systems. Pruckner, Marco; German, Reinhard. In SpringSim (ANSS), p. 4. SCS/ACM, 2014.
  • [ BibTeX ]
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  @inproceedings{conf/springsim/PrucknerG14, author = {Pruckner, Marco and German, Reinhard}, booktitle = {SpringSim (ANSS)}, crossref = {conf/springsim/2014-2}, keywords = {pruckner}, pages = 4, publisher = {SCS/ACM}, title = {Modeling and simulation of electricity generated by renewable energy sources for complex energy systems.}, year = 2014 }
• Gekoppeltes Energiesystemmodell f{\"u}r den Energieumstieg in Bayern. Pruckner, Marco; Seifert, Gaby; Luther, Matthias; German, Reinhard. In Energiesymposium 2014. Graz, Austria, 2014.
  • [ BibTeX ]
  @inproceedings{pruckner2014gekoppeltes, address = {Graz, Austria}, author = {Pruckner, Marco and Seifert, Gaby and Luther, Matthias and German, Reinhard}, booktitle = {Energiesymposium 2014}, keywords = {pruckner}, title = {Gekoppeltes Energiesystemmodell f{\"u}r den Energieumstieg in Bayern}, year = 2014 }
• On the profit enhancement and state estimation services in the smart grid. Awad, Abdalkarim; Pruckner, Marco; Bazan, Peter; German, Reinhard. In ISGT, pp. 1–5. IEEE, 2014.
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  @inproceedings{conf/isgt/AwadPBG14, author = {Awad, Abdalkarim and Pruckner, Marco and Bazan, Peter and German, Reinhard}, booktitle = {ISGT}, crossref = {conf/isgt/2014}, keywords = {pruckner}, pages = {1-5}, publisher = {IEEE}, title = {On the profit enhancement and state estimation services in the smart grid.}, year = 2014 }

• A simulation model to analyze the residual load during the extension of highly fluctuating renewables in Bavaria, Germany. Pruckner, Marco; German, Reinhard. In 4th International Conference on Power Engineering, Energy and Electrical Drives, pp. 540–545. 2013.
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  @inproceedings{6635666, author = {Pruckner, Marco and German, Reinhard}, booktitle = {4th International Conference on Power Engineering, Energy and Electrical Drives}, keywords = {pruckner}, pages = {540-545}, title = {A simulation model to analyze the residual load during the extension of highly fluctuating renewables in Bavaria, Germany}, year = 2013 }
• A hybrid simulation model for large-scaled electricity generation systems. Pruckner, Marco; German, Reinhard. In Winter Simulation Conference, pp. 1881–1892. IEEE, 2013.
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  @inproceedings{conf/wsc/PrucknerG13, author = {Pruckner, Marco and German, Reinhard}, booktitle = {Winter Simulation Conference}, crossref = {conf/wsc/2013}, keywords = {pruckner}, pages = {1881-1892}, publisher = {IEEE}, title = {A hybrid simulation model for large-scaled electricity generation systems.}, year = 2013 }

• Towards a simulation model of the Bavarian electrical energy system. Pruckner, Marco; Bazan, Peter; German, Reinhard. In GI-Jahrestagung, Vol. P-208 of LNI, U. Goltz, M. A. Magnor, H.-J. Appelrath, H. K. Matthies, W.-T. Balke, L. C. Wolf (eds.), pp. 597–612. GI, 2012.
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  @inproceedings{conf/gi/PrucknerBG12, author = {Pruckner, Marco and Bazan, Peter and German, Reinhard}, booktitle = {GI-Jahrestagung}, crossref = {conf/gi/2012}, editor = {Goltz, Ursula and Magnor, Marcus A. and Appelrath, Hans-Jürgen and Matthies, Herbert K. and Balke, Wolf-Tilo and Wolf, Lars C.}, keywords = {pruckner}, pages = {597-612}, publisher = {GI}, series = {LNI}, title = {Towards a simulation model of the Bavarian electrical energy system.}, volume = {P-208}, year = 2012 }
• A study on the impact of packet loss and latency on real-time demand response in smart grid. Pruckner, Marco; Awad, Abdalkarim; German, Reinhard. In GLOBECOM Workshops, pp. 1486–1490. IEEE, 2012.
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  @inproceedings{conf/globecom/PrucknerAG12, author = {Pruckner, Marco and Awad, Abdalkarim and German, Reinhard}, booktitle = {GLOBECOM Workshops}, crossref = {conf/globecom/2012w}, keywords = {pruckner}, pages = {1486-1490}, publisher = {IEEE}, title = {A study on the impact of packet loss and latency on real-time demand response in smart grid.}, year = 2012 }
• An approach of a simulation model to analyze the future energy balance of Bavaria. Pruckner, Marco; German, Reinhard. In 2012 International Conference on Smart Grid Technology, Economics and Policies (SG-TEP), pp. 1–4. 2012.
  • [ BibTeX ]
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  @inproceedings{6642368, author = {Pruckner, Marco and German, Reinhard}, booktitle = {2012 International Conference on Smart Grid Technology, Economics and Policies (SG-TEP)}, keywords = {pruckner}, pages = {1-4}, title = {An approach of a simulation model to analyze the future energy balance of Bavaria}, year = 2012 }