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AQ-Learning-Based Demand Response Algorithm for Industrial Processes with Operational Flexibility

In: Handbook of Smart Energy Systems

Author

Listed:
  • Farzaneh Karami

    (Ghent University
    KU Leuven)

  • Manu Lahariya

    (Ghent University)

  • Guillaume Crevecoeur

    (Ghent University)

Abstract

This chapter defines a Q-learning reinforcement learning policy to develop demand response (DR) for the management of the energy consumption of energy-intensive industrial customers (EICUs). The main idea is to exploit the flexibility offered in the control system equipped with a buffer (storage) system and thus consume and store energy when beneficial. This stabilizes the power balance in the grid by managing efficient energy flow and thus decreasing the dependency on energy generated from fossil fuels and reducing carbon emissions. Results confirmed that the presented dynamic pricing DR algorithm can boost service provider efficiency, lower energy costs for EICUs, and balance energy supply and demand in the electricity market.

Suggested Citation

  • Farzaneh Karami & Manu Lahariya & Guillaume Crevecoeur, 2023. "AQ-Learning-Based Demand Response Algorithm for Industrial Processes with Operational Flexibility," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 3009-3025, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_172
    DOI: 10.1007/978-3-030-97940-9_172
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