2023
DOI: 10.1109/jsyst.2023.3248320
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Reinforcement Learning-Based Demand Response Management in Smart Grid Systems With Prosumers

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Cited by 14 publications
(1 citation statement)
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“…A deep reinforcement learning model is designed in [23] to optimize the energy consumption of a household equipped with several DERs towards reducing prosumers' energy cost while accounting for their comfort-level characteristics. The retail and wholesale energy markets are analyzed in [24] by introducing a reinforcementlearning-based, price-based DRM mechanism that enables the energy management system to determine its optimal retail market energy price and prosumers' energy consumption to jointly maximize profit and prosumer utility. A reinforcement-learning-based DRM model is introduced in [25] to perform energy scheduling of smart homes' energy storage systems in order to minimize energy cost given announced energy prices.…”
Section: Related Workmentioning
confidence: 99%
“…A deep reinforcement learning model is designed in [23] to optimize the energy consumption of a household equipped with several DERs towards reducing prosumers' energy cost while accounting for their comfort-level characteristics. The retail and wholesale energy markets are analyzed in [24] by introducing a reinforcementlearning-based, price-based DRM mechanism that enables the energy management system to determine its optimal retail market energy price and prosumers' energy consumption to jointly maximize profit and prosumer utility. A reinforcement-learning-based DRM model is introduced in [25] to perform energy scheduling of smart homes' energy storage systems in order to minimize energy cost given announced energy prices.…”
Section: Related Workmentioning
confidence: 99%