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

Farzaneh Karami (), Manu Lahariya () and Guillaume Crevecoeur ()
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Farzaneh Karami: Ghent University
Manu Lahariya: Ghent University
Guillaume Crevecoeur: Ghent University

A chapter in Handbook of Smart Energy Systems, 2023, pp 3009-3025 from Springer

Abstract: 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.

Keywords: Buffer/ storage; Energy flexibility; Q-learning reinforcement learning; Industrial process (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_172

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DOI: 10.1007/978-3-030-97940-9_172

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