Digitalization and System Technology

Topic 2 “Digitalization and System Technology” addresses R&D on technical system design and resilient (real-time) operation of decentralized integrated energy systems. For this purpose, Topic 2 will develop models, methods and tools for optimization-based design, scheduling and control of energy systems and demonstrate the technical feasibility of different hardware and software solutions in smart energy system labs, e.g. Energy Lab 2.0 at the KIT, Living Lab Energy Campus at the FZJ and the DLR Grid-Lab. The temporal resolution and timeframe of the quantitative technical simulation and optimization models ranges from μs to years, whereby model structures and parameters will be iteratively refined, e.g. based on insights gained in the technical real-world laboratories.

Subtopic 2.1: Digitalization and Systems Technology for Flexibility Solutions

  1. D. Anagnostos, T. Schmidt, S. Cavadias, D. Soudris, J. Poortmans, F. Catthoor: A Method for Detailed, Short-Term Energy Yield Forecasting of Photovoltaic Installations, Renewable Energy 130, 122 (2019) doi:10.1016/j.renene.2018.06.058.
  2. P. Kuhn, B. Nouri, S. Wilbert, C. Prahl, N. Kozonek, T. Schmidt, Z. Yasser, L. Ramirez, L. Zarzalejo, A. Meyer, L. Vuilleumier, D. Heinemann, P. Blanc, R. Pitz-Paal, Validation of an All-Sky Imager–Based Nowcasting System for Industrial PV Plants. Progress in Photovoltaics: Research and Applications 26, 608, (2018) doi:10.1002/pip.2968.
  3. S. Arens, K. Derendorf, F. Schuldt, K. von Maydell, C. Agert, Effect of EV Movement Schedule and Machine Learning-Based Load Forecasting on Electricity Cost of a Single Household, Energies 11, 2913 (2018) doi:10.3390/en11112913.
  4. M. Kühnel, B. Hanke, S. Geißendörfer, K. von  Maydell, C. Agert, Energy forecast for mobile Photovoltaic systems with focus on trucks for cooling applications, Progress in Photovoltaics 25, 525, (2017) doi:10.1002/pip.2886.
  5. D. Peters, R. Völker, T. Kilper, M. Calais, T. Schmidt, C. Carter, K. von Maydell, C. Agert, Model-Based Design and Simulation of Control Strategies to Maximize the PV Hosting Capacity in Isolated Diesel Networks - Using Solar Short-Term Forecasts for Predictive Control of Diesel Generation  Proc. of 32nd European Photovoltaic Solar Energy Conference and Exhibition (2016) doi: 10.4229/EUPVSEC20162016-6EO.1.4.
  6. P. Schäfer, H.G. Westerholt, A.M. Schweidtmann, S. Ilieva, A. Mitsos, Model-based bidding strategies on the primary balancing market for energy-intense processes, Comput. Chem. Eng. 120, 4 (2019) doi:10.1016/j.compchemeng.2018.09.026
  7. P. Kohlhepp, H. Harb, et int., D. Müller, V. Hagenmeyer, Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies, Renew. Sust. Energ. Rev. 101, 527 (2019) doi:10.1016/j.rser.2018.09.045
  8. S. Deutz, et int., W. Leitner, A. Mitsos, S. Pischinger, A. Bardow, Cleaner production of cleaner fuels: wind-to-wheel–environmental assessment of CO2-based oxymethylene ether as a drop-in fuel, Energ. Environ. Sci. 11, 331 (2018) doi:10.1039/C7EE01657C
  9. A. Mitsos, N. Asprion, C. A. Floudas, et al., Challenges in process optimization for new feedstocks and energy sources, Comput. Chem. Eng. 113, 209 (2018) doi:10.1016/j.compchemeng.2018.03.013
  10. T. Schütz, X. Hu, M. Fuchs, D. Müller, Optimal design of decentralized energy conversion systems for smart microgrids using decomposition methods, Energy 156, 250 (2018) doi:10.1016/j.energy.2018.05.050
  11. B. Bahl, M. Lampe, P. Voll, A. Bardow, Optimization-based identification and quantification of demand-side management potential for distributed energy supply systems, Energy 135, 889 (2017) doi:10.1016/j.energy.2017.06.083
  12. L. Barth, V. Hagenmeyer, N. Ludwig, D. Wagner,How much demand side flexibility do we need?: Analyzing where to exploit flexibility in industrial processes, e-Energy`18 Proceedings 43 (2018) doi: 10.1145/3208903.3208909
  13. S. Waczowicz, I. Konotop, D. Westermann, V. Hagenmeyer, R. Mikut, et al., Virtual Storages as Theoretically Motivated Demand Response Models for Enhanced Smart Grid Operations, Energy Technol., 4, 163 (2016),  doi:10.1002/ente.20150031
  14. H. Schwarz, V. Bertsch, W. Fichtner,Two-stage stochastic, large-scale optimization of a decentralized energy system: a case study focusing on solar PV, heat pumps and storage in a residential quarter, OR Spectrum 40, 265 (2017) doi: 10.1007/s00291-017-0500-4
  15. P. Jochem, M. Schönfelder, W. Fichtner,An Efficient Two-stage Algorithm for Decentralized Scheduling of Micro-CHP Units, Eur. J. Oper. Res. 245, 862 (2015) doi:10.1016/j.ejor.2015.04.016
  16. G. Elbez, H. Keller, V. Hagenmeyer, A New Classification of Attacks against the Cyber-Physical Security of Smart Grids. ACM 13th International Conference on Availability,Reliability and Security, (2018) doi:10.1145/3230833.3234689

 

Subtopic 2.2: Design, Operation and Digitalization of the Future Energy Grids

  1. S. P. Melo, U. Brand, T. Vogt, J-.S. Telle, F. Schuldt, K. von Maydell, Primary frequency control provided by hybrid battery storage and power-to-heat system, Applied Energy 233, 220 (2019) doi:10.1016/j.apenergy.2018.09.177
  2. M. Kühnel, B. Hanke, Y. Baranova, O. Weigel, I.W. Stuermer, A. McMaster, S. Maebe, K. von Maydell, Design of Hybrid-Minigrids in South African Rural Areas under Consideration of Social and Cultural Aspects,  Proc. of 32nd European Photovoltaic Solar Energy Conference and Exhibition (2018) doi: 10.4229/35thEUPVSEC20182018-6CO.4.4
  3. S. Sass, A. Mitsos, Optimal operation of dynamic (energy) systems: When are quasi-steady models adequate? Comput. Chem. Eng. 124, 133 (2019) doi:10.1016/j.compchemeng.2019.02.011
  4. A, M. Schweidtmann, A. Mitsos,Deterministic global optimization with artificial neural networks embedded, J. Optimiz. Theory App. 180, 925 (2019) doi:10.1007/s10957-018-1396-0
  5. H. Harb, J. N. Paprott, et int., R. Streblow, D. Müller, Decentralized scheduling strategy of heating systems for balancing the residual load, Build. Environ. 86, 132 (2015) doi:10.1016/j.buildenv.2014.12.015
  6. D. Müller, A. Monti, S. Stinner, T. Schlösser, T. Schütz, et. al., Demand side management for city districts, Build. Environ. 91, 283 (2015) doi:10.1016/j.buildenv.2015.03.026
  7. M. Lauster, J. Teichmann, M. Fuchs, R. Streblow, D. Müller, Low order thermal network models for dynamic simulations of buildings on city district scale, Build. Environ. 73, 223 (2014) doi:10.1016/j.buildenv.2013.12.016
  8. D.E. Hollermann, D.F. Hoffrogge, F. Mayer, M. Hennen, A. Bardow, Optimal (n− 1)-reliable design of distributed energy supply systems, Comput. Chem. Eng. 121, 317(2019) doi:10.1016/j.compchemeng.2018.09.029
  9. N. Baumgärtner, B. Bahl, M. Hennen, A. Bardow, RiSES3: Rigorous Synthesis of Energy Supply and Storage Systems via time-series relaxation and aggregation, Comput. Chem. Eng. 127, 127 (2019) doi:10.1016/j.compchemeng.2019.02.006
  10. D.E. Majewski, M. Wirtz, M. Lampe, A. Bardow, Robust multi-objective optimization for sustainable design of distributed energy supply systems, Comput. Chem. Eng. 102, 26 (2017) doi:10.1016/j.compchemeng.2016.11.038
  11. T. Leibfried, T. Mchedlidze, N. Meyer-Hübner, D. Wagner, F. Wegner et al., Operating Power Grids with Few Flow Control Buses, e-Energy`15 Proceedings, 289 (2015) doi: 10.1145/2768510.2768521
  12. R. Sander, M. Suriyah, T. Leibfried, Characterization of a Countercurrent Injection-Based HVDC Circuit Breaker, IEEE T. Power Electr. 33, 2948 (2018) doi:10.1109/TPEL.2017.2709785
  13. N. Meyer-Huebner, M. Suriyah, T. Leibfried, Distributed Optimal Power Flow in Hybrid AC-DC Grids, IEEE T Power Syst, (2019) doi:10.1109/TPWRS.2019.2892240
  14. V. Bertsch, W. Fichtner, A participatory multi-criteria approach for power generation and transmission planning, Ann. Oper. Res. 245, 177 (2016) doi:10.1007/s10479-015-1791-y
  15. T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power System Analysis, J. Open Res. Softw., 6, (2018) doi:10.5334/jors.188
  16.  D. Schlachtberger, T. Brown, S. Schramm, M. Greiner, The Benefits of Cooperation in a Highly Renewable European Electricity System, Energy 134, 469 (2017) doi:10.1016/j.energy.2017.06.004
  17. T. Mühlpfordt, T. Faulwasser, L. Roald, V. Hagenmeyer, Solving optimal power flow with non-gaussian uncertainties via polynomial chaos expansion. IEEE 56th Annual Conference on Decision and Control (CDC) p. 4490 (2017) doi:10.1109/CDC.2017.8264321

 

Subtopic 2.3: Smart Areas and Research Platforms

  1. E.E. Ferg, F. Schuldt, J. Schmidt, The challenges of a Li-ion starter lighting and ignition battery: A review from cradle to grave; J. Power Sources 423, 380 (2019) doi:/10.1016/j.jpowsour.2019.03.063.
  2. B. Hanke, D. Peters, M. Kühnel, et int., K. von Maydell, C. Agert, Reducing the Grid Load of South African Office Building by Implementation of Energy Efficiency Measures and Installation of Demand Optimized PV;  Proc. of 32nd European Photovoltaic Solar Energy Conference and Exhibition (2017) doi:10.4229/EUPVSEC20172017-6EO.2.6
  3. P. Remmen, M.  Lauster, et int., T. Osterhage, D. Müller, TEASER: an open tool for urban energy modelling of building stocks, J. Build. Perform. Simu. 11, 84(2018) doi:10.1080/19401493.2017.1283539
  4. F. Bünning, R. Sangi, D. Müller, A Modelica library for the agent-based control of building energy systems, Appl. Energ. 193, 52(2017) doi:10.1016/j.apenergy.2017.01.053          
  5. D. Calì, P Matthes, K. Huchtemann, R. Streblow, D. Müller, CO2 based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings, Build. Environ. 86, 39(2015) doi:10.1016/j.buildenv.2014.12.011
  6. R. Sangi, A. Kümpel, D. Müller, Real-life implementation of a linear model predictive control in a building energy system, J. Build. Eng. 22, 451 (2019) doi:10.1016/j.jobe.2019.01.002
  7. Y. Liu, F. Schreiner, M. Lao, M. Noe, M. Doppelbauer, Design of a superconducting DC demonstrator for wind generators, IEEE T. Energy Conver. 33, 1955 (2018) doi:10.1109/TEC.2018.2846721
  8. D. Kottonau, E. Shabagin, M. Noe, S. Grohmann, Opportunities for High-Voltage AC Superconducting Cables as Part of New Long-Distance Transmission Lines, IEEE Trans. Applied Superconductivity 27, (2017) doi:10.1109/TASC.2017.2652856
  9. N. Ludwig, S. Waczowicz, C. Düpmeier, R. Mikut, V. Hagenmeyer, et al., Concept and benchmark results for Big Data energy forecasting based on Apache Spark, J. Big Data 5, e11 (2018) doi:10.1186/s40537-018-0119-6
  10. H. Maaß, H. Cakmak, R. Mikut,W. Süß, K. Stucky, U. Kühnapfel, V. Hagenmeyer, et al., Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis, EURASIP J. Adv. Sig. Pr. 14, (2015) doi:10.1186/s13634-015-0203-4
  11. D. Bernet, M. Hiller: Grid-Connected Voltage Source Converters with integrated Multilevel-Based Active Filters, IEEE Ener. Conv., (2018) doi:10.1109/ECCE.2018.8557648
  12. V. Hagenmeyeret al.: Information and communication technology in Energy Lab 2.0: Smart energies system simulation and control center with an open-street-map-based power flow simulation example, Energy Technology 4, 145 (2016) doi:10.1002/ente.201500304
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