Markov chain based stochastic model of electric vehicle parking lot occupancy in vehicle-to-grid

Kumar, Santosh; Udaykumar, R. Y.

doi:10.1109/iccic.2015.7435806

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Estimation of the aggregated storage capacity of EVs using a stochastic modelling approach was developed in [15, 16]. In [17], a stochastic model was developed to estimate the occupancy behaviour and arrival/departure pattern of EVs in WPLs using Markov chain analysis.…”

Section: Introductionmentioning

confidence: 99%

Stochastic modelling of electric vehicle behaviour to estimate available energy storage in parking lots

2020

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Section: Introductionmentioning

confidence: 99%

Stochastic modelling of electric vehicle behaviour to estimate available energy storage in parking lots

2020

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Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

Arwa

Folly

2020

IEEE Access

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The utility grids are undergoing several upgrades. Distributed generators that are supplied by intermittent renewable energy sources (RES) are being connected to the grids. As RES generations get cheaper, more customers are opting for peer-to-peer energy interchanges through the smart metering infrastructure. Consequently, power management in grid-tied RES-based microgrids has become a challenging task. This paper reviews the applications of reinforcement learning (RL) algorithms in managing power in grid-tide microgrids. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. In this paper, various challenges associated with the control of power in grid-tied microgrids are described. The application of RL techniques in addressing those challenges is reviewed critically. This review identifies the need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids. Finally, the paper gives directions for future research, e.g., the hybridization of intrinsic and extrinsic reward schemes, the use of transfer learning to improve the learning outcomes of RL in complex power systems environments and the deployment of priority-based experience replay in post-disaster microgrid power flow control. INDEX TERMS Electric vehicle charging station, energy management, Markov Decision Process, microgrid, reinforcement learning.

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GEV Aggregator Placement in Distribution Network Using 802.16 Protocol

Kumar

M.S

Afeta

et al. 2017

2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

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Markov chain based stochastic model of electric vehicle parking lot occupancy in vehicle-to-grid

Cited by 4 publications

References 11 publications

Stochastic modelling of electric vehicle behaviour to estimate available energy storage in parking lots

Stochastic modelling of electric vehicle behaviour to estimate available energy storage in parking lots

Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

GEV Aggregator Placement in Distribution Network Using 802.16 Protocol

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