Dr. Chathura Wanigasekara

E-Mail: o2c-energy-and-environment@uni-bremen.de

Energy and Environment

Projects

Ongoing

SmartFarm2
The overarching goal of SmartFarm2 is to demonstrate the potential of self-consumption optimisation using real objects in order to create incentives to install or continue to operate RE systems even after the guaranteed EEG compensation expires. Since highly automated systems from the field of AI are to be developed for self-consumption optimisation, the aim is to equip a test field with 101 real demonstrators with hardware so that, on the one hand, data-driven models can be created using the recorded information and, on the other hand, the degree of automation can be directly validated using real applications.

Time period: 01.02.2021 - 31.08.2024
Leadership: Prof. Dr. Christof Büskens, Dr.-Ing. Francesca Jung, Lars Kappertz

Int2Grids - Integration of intelligent neighbourhood networks into integrated networks
The overall objective of the joint project is the integration of "neighbourhood networks" into the higher-level network management and their potential contribution to the provision of network and system services. First, a static analysis of the possibilities of neighbourhood networks for measures to provide network and system services is carried out. Based on this, the network is then interpreted as a dynamic bi-level optimization problem, whereby the interconnected network generates setpoint specifications that are to be implemented by the neighborhood networks.

Time period: 01.05.2020 - 31.12.2023
Leadership: Prof. Dr. Christof Büskens

DiSCO₂-Bremen: Data-based and intelligent simulation of traffic for CO₂ reduction in Bremen
The aim of the DiSCO₂ project is to develop a digital twin of Bremen's road traffic, which can predict and improve traffic flow by intelligently switching traffic signals to reduce CO₂ emissions. Methods from the fields of Big Data and machine learning will be applied.

Time period: 01.07.2020 - 31.12.2022
Leadership: Prof. Dr. Christof Büskens

HVDC-MMC_with_MPC: A new method for reducing the power dissipation of MMC high-voltage converters using MPC
The aim of the project is to improve the efficiency of high-voltage DC transmission by using optimal control algorithms in high voltage DC inverters. The research project focuses on the development, implementation and testing of a power and real-time capable model predictive control (MPC) for the control of a modular high voltage inverter (HVDC-MMC).

Time period: 01.07.2020 - 30.06.2023
Leadership: Prof. Dr. Christof Büskens

Completed

Model predictive control of the live steam quantity in power plants
The project aims to improve the efficiency of a lignite-fired power plant by using adaptive modeling and model-predictive control.

Time period: 01.07.2018 - 30.04.2021
Leadership: Prof. Dr. Christof Büskens, Dr. Matthias Knauer

SmartFarm
The overall objective of the project is the development of tools that allow to recommend a cost-optimized design of plant components for a farm and to optimize the self-consumption, energy export and import under economic aspects for the next hours and days. A further goal is the development of methods for the economic modelling and evaluation of general constraints regarding a sustainable business model.

Time period: 01.01.2016 - 31.03.2019
Leadership: Prof. Dr. Christof Büskens, Dr.-Ing. Mitja Echim

Blindleistungsregelung in Smart-Grids
The aim of this project is to be able to prevent overload situations in distribution grids in the future by automated control of the load flow. For this purpose, a physical model for the description of the power grid is required. Based on this model, an optimization of the load flow under physical constraints is carried out with the help of the optimization software WORHP.

Time period: since 01.07.2014
Leadership: Prof. Dr. Christof Büskens, Dr.-Ing. Mitja Echim

Online Optimisation of Cogeneration Plants
Industrial cogeneration plants serve to reduce operating costs because the plants can generally be operated flexibly with fewer staff and a higher availability. Cogeneration has a long tradition in Germany and has been used for a great number of years in a variety of power plant configurations. The term cogeneration stands for all processes whereby a power plant simultaneously generates several target energies, i.e. mechanical energy, electrical energy, heat or cooling, from the energies fed in, said target energies then being supplied for the heating and lighting of rooms, for example.

Time period: since 01.06.2004
Leadership: Prof. Dr. Christof Büskens

Publications

1. M. Lachmann, J. Maldonado, W. Bergmann, F. Jung, M. Weber, C. Büskens.
   Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System.
   Energies, 13(8), 2084, 2020.

   DOI: 10.3390/en13082084

2. M. Lachmann, F. Jung, C. Büskens.
   Computationally efficient identification of databased models applied to a milk cooling system.
   Conference of Computational Interdisciplinary Science, CCIS, 19.03.-22.03.2019, Atlanta, USA.
   Campinas: Galoa, 2020.

   online at: https://proceedings.science/ccis-2019/papers/computationally-efficient-identification-of-databased-models-applied-to-a-milk-cooling-system

3. F. Jung, C. Büskens.
   Probabilistic Data-Based Models for a Reliable Energy Management.
   2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 12.06-15.06.2018, , Italy.
   Conference Proceedings, pp. 1-6, 2018.

   DOI: 10.1109/EEEIC.2018.8494364

4. W. Heins, C. Büskens.
   Two-Level Forecast-Based Energy and Load Management for Grid-Connected Local Systems Using General Load and Storage Models.
   18th International Conference on Environment and Electrical Engineering (EEEIC), 12.06-15.06.2018, , Italy.
   
5. F. Jung, M. Lachmann, C. Büskens.
   SmartFarm - Data based optimization for optimal energy management.
   88th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM).
   Proc. Appl. Math. Mech., 17(1):741-742, 2017.

   DOI: 10.1002/pamm.201710338

6. T. Dewenter, W. Heins, B. Werther, A. Hartmann, C. Bohn, H. Beck.
   Parameter Optimization of a Virtual Synchronous Machine in a Microgrid.
   International Journal of Power and Energy Systems, 36(4):129-137, 2016.

   online at: http://www.actapress.com/PDFViewer.aspx?paperId=45446

7. F. Jung.
   Entwicklung robuster Prognosen für ein Energiemanagementsystem anhand datenbasierter Modellierungsverfahren unter Berücksichtigung von Unsicherheiten.
   Dissertationsschrift, Universität Bremen, 2018.

   online at: Elektronische Bibliothek der Universität Bremen

8. S. Chen.
   Datenbasierte Modellierung und Optimierung von Kraft-Wärme-Kopplungsanlagen.
   Dissertationsschrift, Universität Bremen, 2017.

   online at: Elektronische Bibliothek der Universität Bremen

9. A. Berger, F. Jung, M. Echim, C. Büskens, M. Schollmeyer.
   Optimal Expansion Planning for an Electric Distribution Network with the NLP Solver WORHP.
   88th GAMM Annual Meeting of the international Association of Applied Mathematics and Mechanics (GAMM).
   Proc. Appl. Math. Mech., pp. 735-736, 2017.

   DOI: 10.1002/pamm.201710335

10. S. Chen, D. Wassel, C. Büskens.
    High-Precision Modeling, Simulation and Optimization of Cogeneration Plants.
    Energy Technology, Special Issue: Energy, Science & Technology Conference, 4: 177-186, WILEY-VCH, 2016.

    DOI: 10.1002/ente.201500244

11. F. Jung, C. Büskens, M. Siefert.
    Physical and Data-Based Model to Improve the Power Forecast of a Wind Power Plant .
    13th Wind Integration Workshop, 11.11.-13.11.2014, Berlin, Germany.
    Proceedings of the 13th Wind Integration Workshop, pp. 483-488, Energynautics GmbH, 2014.
12. B. Blume.
    Effiziente Modellgenerierung und optimale Steuerung technischer Systeme am Beispiel von Komponenten von Biogasanlagen.
    Dissertationsschrift, Universität Bremen, mbv, Mensch und Buch Verlag, 2012.
13. B. Blume, C. Büskens.
    Datenbasierte Modellierung und Simulation von Biogasanlagen mit statischen und dynamischen Modellen.
    ASIM 2009 - 20th Symposium Simulation Techniques, September 2009, Cottbus, Germany.
    ASIM-Mitteilung AMB 124, ASIM 2009 - Extended Abstracts, A. Gnauck, B. Luther (Eds.), pp. 110-113, Shaker Verlag, 2009.
14. B. Blume, C. Büskens, D. Wassel.
    Messdatengestützte Modellierung und Simulation einer Gasturbine.
    ASIM 2009 - 20th Symposium Simulation Techniques, September 2009, Cottbus, Germany.
    ASIM-Mitteilung AMB 124, ASIM 2009 - Extended Abstracts, A. Gnauck, B. Luther (Eds.), pp. 231-234, Shaker Verlag, 2009.