Deep Reinforcement Learning for Continuous Electric Vehicles Charging Control With Dynamic User Behaviors

Yan, Linfang; Chen, Xia; Zhou, Jianyu; Chen, Yin; Wen, Jinyu

doi:10.1109/tsg.2021.3098298

Cited by 96 publications

(39 citation statements)

References 32 publications

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“…where t a and t d are the EV arriving time and departing time, respectively; k 1 and k 2 are the shape parameters which can be established based on insights of the occupant's driving behavior, the occupant's sensitivity to electricity price and the tolerance to SOC concern [33]. Figure 3 provides a visible demonstration of how the SOC concern involves overtime in a charging duration.…”

Section: Ev Model With the Occupant's Soc Concernmentioning

confidence: 99%

“…As the EV may become the major (only) transportation for a household and even for a sustained community, the expected EV state of charge would be another serious concern for residential occupants [31][32][33]. In [31], the authors incorporated the dynamic of driver's behaviors into the EV charging model and proposed a stochastic game approach to address the renewable energy uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [32] introduces a time anxiety concept to address the uncertain events in the charging duration and a game theory-based approach to solve the optimal EV charging problem. Yan et al [33] proposed a new index to measure driving anxiety that to characterize the driver's discomfort on the driving range and uncertain events which are changed by the driving experience quantitatively. However, all the related work above ignored the complex interaction between EV and other major appliances such as PV and HVAC, resulting in piecemeal studies in the context of the HEMS.…”

Section: Introductionmentioning

confidence: 99%

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PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Liu

2021

Sustainability

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Rooftop photovoltaics (PV) and electrical vehicles (EV) have become more economically viable to residential customers. Most existing home energy management systems (HEMS) only focus on the residential occupants’ thermal comfort in terms of indoor temperature and humidity while neglecting their other behaviors or concerns. This paper aims to integrate residential PV and EVs into the HEMS in an occupant-centric manner while taking into account the occupants’ thermal comfort, clothing behaviors, and concerns on the state-of-charge (SOC) of EVs. A stochastic adaptive dynamic programming (ADP) model was proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant’s clothing decisions, and the EV’s charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV’s arrival SOC. The nonlinear and nonconvex thermal comfort model, EV SOC concern model, and clothing behavior model were holistically embedded in the ADP-HEMS model. A model predictive control framework was further proposed to simulate a residential house under the time of use tariff, such that it continually updates with optimal appliance schedules decisions passed to the house model. Cosimulations were carried out to compare the proposed HEMS with a baseline model that represents the current operational practice. The result shows that the proposed HEMS can reduce the energy cost by 68.5% while retaining the most comfortable thermal level and negligible EV SOC concerns considering the occupant’s behaviors.

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Section: Ev Model With the Occupant's Soc Concernmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Liu

2021

Sustainability

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“…Zhou et al [21] combined Nash equilibrium and Lyapunov optimization and developed an incentive-based distributed scheduling of EV charging. Yan et al [22] have designed a model-free deep-reinforcement learning-based approach for an optimal charging control strategy. Khaki et al [23] have designed a hierarchical distributed framework for EV charging, based on ADMM.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of ADMM in transferring a centralized scheduling framework to a decentralized or distributed framework for the distribution network with community energy systems is presented in [24]. The algorithms in [20][21][22][23] show the effectiveness of distributed EV charging scheduling and control, but the operation stability of the distribution network is not considered. OPF is widely adapted in power system operations to deal with practical issues.…”

Section: Introductionmentioning

confidence: 99%

Distributed Electric Vehicle Charging Scheduling with Transactive Energy Management

Chen

2021

Energies

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A distributed electric vehicle (EV) charging scheduling strategy with transactive energy (TE) management is presented in this paper to deal with technical issues in distribution network operation and discuss the economic benefits of EV charging. At an individual EV level, EV owners propose bids to actively participate in the distribution system operation. At the node level, an electric vehicle aggregator (EVA) optimally allocates the available charging power to meet EV charging requirements and cost benefits. At the distribution network level, a distribution system operator (DSO) integrates an electricity price market clearing mechanism with the optimal power flow (OPF) technique to ensure the reliability of the distribution network. Moreover, a distributed algorithm is discussed for solving the EV charging problem with transactive energy management (TEM). The clearing electricity price is achieved through a negotiation process between the DSO and EVAs using the alternating direction method of multipliers (ADMM). The presented EV charging scheduling with TEM is tested on a modified IEEE 33-bus distribution network scenario with 230 EV charging loads. The simulation results demonstrate the effectiveness of the TE-based EV charging scheduling system.

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Review for Smart Grid Dispatch

Zhao

et al. 2023

Engineering Applications of Computational Methods

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Deep Reinforcement Learning for Continuous Electric Vehicles Charging Control With Dynamic User Behaviors

Cited by 96 publications

References 32 publications

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

Distributed Electric Vehicle Charging Scheduling with Transactive Energy Management

Review for Smart Grid Dispatch

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