Reinforcement Learning-Based Demand Response Management in Smart Grid Systems With Prosumers

Sangoleye, Fisayo; Jao, Jenilee; Faris, Kimberly; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon

doi:10.1109/jsyst.2023.3248320

Cited by 8 publications

(1 citation statement)

References 28 publications

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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%

Coalitional Demand Response Management in Community Energy Management Systems

Kemp,

Siraj,

Tsiropoulou

2023

Energies

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With the advent of the Distributed Energy Resources within smart grid systems, traditional demand response management (DRM) models need to be redesigned to capture prosumers’ energy consumption requests and dynamic behavior within the energy market. In this paper, a coalitional DRM model is introduced based on the principles of Game Theory and reinforcement learning to dynamically determine prosumers’ formation in local energy trading communities and their optimal energy consumption. A hedonic game-theoretic model is introduced to enable prosumers to autonomously and dynamically select an energy trading community considering the partially available information regarding prosumers’ energy generation and consumption characteristics and utility companies’ provided rewards per community. Then, a log-linear reinforcement learning model is proposed to enable each prosumer to distributedly determine their optimal energy consumption. A detailed evaluation of the proposed coalitional DRM model is performed based on real data collected from the southwest area of the USA.

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Section: Related Workmentioning

confidence: 99%