Reinforcement learning-based composite differential evolution for integrated demand response scheme in industrial microgrids

Mughees, Neelam; Jaffery, Mujtaba Hussain; Mughees, Anam; Ansari, Ejaz A.; Mughees, Abdullah

doi:10.1016/j.apenergy.2023.121150

Cited by 15 publications

(1 citation statement)

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“…The objective was to reduce total energy costs and battery EES degradation costs, along with maximising renewable energy resources usage. The authors highlight combining this approach with transfer learning or deep learning to speed up the policy identification [33]. This has also been applied elsewhere [34] [35].…”

Section: State Of the Art Of Rl In Industrial Demand Responsementioning

confidence: 99%

A review of reinforcement learning based approaches for industrial demand response

Shah,

Zhou,

et al. 2024

Preprint

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Industrial demand response plays a key role in mitigating the operational challenges of smart grid brought by massive proliferation of distributed energy resources. However, industrial plants have complex and intertwined processes, which provides barriers for their participation in industrial demand response programs. This is in part due to the complexity and uncertainties of approximating systems models. More recently, reinforcement learning has emerged as a data-driven control technique for sequential decision-making under uncertainty. This emergence is strongly coupled with the abundance of data offered by advanced information technologies. The potential of applying reinforcement learning in industrial demand response is identified in this work by comparing pivotal aspects of reinforcement learning with the requirements of industrial demand response schemes.

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Section: State Of the Art Of Rl In Industrial Demand Responsementioning

confidence: 99%