Distributed Electric Vehicles Charging Management Considering Time Anxiety and Customer Behaviors

Alsabbagh, Amro; Wu, Brian; Ma, Chengbin

doi:10.1109/tii.2020.3003669

Cited by 53 publications

(15 citation statements)

References 24 publications

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“…Paying attention to EV owner customer satisfaction, in [9], [10] battery swapping strategies are derived to optimize the charging station cost while ensuring a fast fueling service. In [11], a charging strategy is developed to reduce user electricity cost by employing incentive programs, whereas in [12] the same goal is achieved by considering user time anxiety.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Energy Pricing of an Electric Vehicle Parking Lot

Zanvettor

Casini

Smith

et al. 2022

IEEE Trans. Smart Grid

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Section: Introductionmentioning

confidence: 99%

Stochastic Energy Pricing of an Electric Vehicle Parking Lot

Zanvettor

Casini

Smith

et al. 2022

IEEE Trans. Smart Grid

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“…Centralized dispatches are adopted in [22][23][24]30], in which EVs have to provides lots of private information I such as bidding and capacity to a central agent. Although [25][26][27][28][29] use decentralized EVs scheduling, it is essential to provide their own historical or rival information for decision making. Besides, [29,30] need to calculate Nash equilibrium point, which is time consuming.…”

Section: Introductionmentioning

confidence: 99%

“…Although [25][26][27][28][29] use decentralized EVs scheduling, it is essential to provide their own historical or rival information for decision making. Besides, [29,30] need to calculate Nash equilibrium point, which is time consuming. Based on the above discussions and comparisons of the decision making of the EV parking lots in Table I, decentralized decision making of aggregated EVs relies on neither historical and rival information nor Nash equilibrium point is worth to study.…”

Section: Introductionmentioning

confidence: 99%

A Multiagent Competitive Bidding Strategy in a Pool-Based Electricity Market With Price-Maker Participants of WPPs and EV Aggregators

Gao

Chan

Xia

et al. 2021

IEEE Trans. Ind. Inf.

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Large-scale renewable energy suppliers and electric vehicles (EVs) are expected to become dominated participants in future electricity market. In this paper, a competitive bidding strategy is formulated for wind power plants (WPPs) and EV aggregators in a pool-based day-ahead electricity market. A bilevel multi-agent based model is proposed to study their bidding behaviors, with market clearing completion in the lower level and revenue maximization in the upper level. A stochastic framework is developed to incorporate the uncertainties in maximal power production of WPPs and EV aggregators and bid prices of other participants. The process of bidding decision is formulated as a stochastic game with incomplete information, in which electricity suppliers including WPPs and EV aggregators are considered as players of the game, their lack of information in this stochastic market environment is counterbalanced by a multi-agent reinforcement learning (MARL) algorithm named win or learn fast policy hill climbing (WoLF-PHC) with maximizing their own profits by self-game. The feasibility and effectiveness of the proposed model and the WoLF-PHC solution approach are successfully illustrated using a modified IEEE 6-bus system and a modified 118-bus system with different numbers of market players.

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“…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%

“…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. 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.…”

Section: Introductionmentioning

confidence: 99%

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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Distributed Electric Vehicles Charging Management Considering Time Anxiety and Customer Behaviors

Cited by 53 publications

References 24 publications

Stochastic Energy Pricing of an Electric Vehicle Parking Lot

Stochastic Energy Pricing of an Electric Vehicle Parking Lot

A Multiagent Competitive Bidding Strategy in a Pool-Based Electricity Market With Price-Maker Participants of WPPs and EV Aggregators

PV-EV Integrated Home Energy Management Considering Residential Occupant Behaviors

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