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Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

Author

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  • Ksenija Stepanovic

    (Faculty of Electrical Engineering, Mathematics and Computer Sciences, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands)

  • Jichen Wu

    (Faculty of Electrical Engineering, Mathematics and Computer Sciences, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
    Flex Technologies, Atoomweg 7, 3542 AA Utrecht, The Netherlands)

  • Rob Everhardt

    (Flex Technologies, Atoomweg 7, 3542 AA Utrecht, The Netherlands)

  • Mathijs de Weerdt

    (Faculty of Electrical Engineering, Mathematics and Computer Sciences, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands)

Abstract

The integration of pipeline energy storage in the control of a district heating system can lead to profit gain, for example by adjusting the electricity production of a combined heat and power (CHP) unit to the fluctuating electricity price. The uncertainty from the environment, the computational complexity of an accurate model, and the scarcity of placed sensors in a district heating system make the operational use of pipeline energy storage challenging. A vast majority of previous works determined a control strategy by a decomposition of a mixed-integer nonlinear model and significant simplifications. To mitigate consequential stability, feasibility, and computational complexity challenges, we model CHP economic dispatch as a Markov decision process. We use a reinforcement learning (RL) algorithm to estimate the system’s dynamics through interactions with the simulation environment. The RL approach is compared with a detailed nonlinear mathematical optimizer on day-ahead and real-time electricity markets and two district heating grid models. The proposed method achieves moderate profit impacted by environment stochasticity. The advantages of the RL approach are reflected in three aspects: stability, feasibility, and time scale flexibility. From this, it can be concluded that RL is a promising alternative for real-time control of complex, nonlinear industrial systems.

Suggested Citation

  • Ksenija Stepanovic & Jichen Wu & Rob Everhardt & Mathijs de Weerdt, 2022. "Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning," Energies, MDPI, vol. 15(9), pages 1-25, April.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3290-:d:806551
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    References listed on IDEAS

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