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Computer Science 7

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Control of energy systems based on self-learning algorithms

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Control of energy systems based on self-learning algorithms


Reinforcement Learning control algorithm for a PV-Battery system
Reinforcement Learning control algorithm for a PV-Battery system

Project Description

The worldwide expansion of decentralized generation units, such as photovoltaic systems and wind power plants, means that the generation of electrical energy depends on the fluctuating supply of renewable energy sources. In the project "Control of energy systems based on self-learning algorithms", new self-learning control algorithms for energy systems are investigated and compared with classical control algorithms. Models written in Python will make it possible to create several self-learning agents and simulate them in a common environment.

It is also planned to model further components such as electric charging stations in order to take into account the influence of electromobility on the power system.

Project Period

    2017-05-16 –

Project Leaders

    Marco Pruckner

Project Members

    Niklas Ebell

Related Publications

    • Ebell N., Pruckner M.:
      Coordinated Multi-Agent Reinforcement Learning for Swarm Battery Control
      2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) (Quebec Stadt, Quebec, 2018-05-21 - 2018-05-23)
      In: 2018 IEEE Canadian Conference on Electrical Computer Engineering (CCECE) 2018
      DOI: 10.1109/CCECE.2018.8447851
      BibTeX: Download
    • Ebell N., Heinrich F., Schlund J., Pruckner M.:
      Reinforcement Learning Control Algorithm for a PV-Battery-System Providing Frequency Containment Reserve Power
      2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (Aalborg, 2018-10-29 - 2018-10-31)
      In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) 2018
      DOI: 10.1109/SmartGridComm.2018.8587480
      BibTeX: Download
    • Ebell N., Gütlein M., Pruckner M.:
      Sharing of Energy Among Cooperative Households Using Distributed Multi-Agent Reinforcement Learning
      2019 IEEE Innovative Smart Grid Technologies Europe (Bucharest, 2019-09-29 - 2019-10-02)
      DOI: 10.1109/ISGTEurope.2019.8905520
      URL: https://ieeexplore.ieee.org/document/8905520
      BibTeX: Download
    • Tuchnitz F., Ebell N., Schlund J., Pruckner M.:
      Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning
      In: Applied Energy 285 (2021)
      ISSN: 0306-2619
      DOI: 10.1016/j.apenergy.2020.116382
      BibTeX: Download
    • Ebell N., Pruckner M.:
      Benchmarking a Decentralized Reinforcement Learning Control Strategy for an Energy Community
      2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (Aachen, 2021-10-25 - 2021-10-28)
      DOI: 10.1109/smartgridcomm51999.2021.9632323
      BibTeX: Download
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