Bi-level programming model approach for electric vehicle charging stations considering user charging costs

Li, Jiyong; Liu, Chengye; Wang, Yasai; Chen, Ran; Xu, Xiaoshuai

doi:10.1016/j.epsr.2022.108889

Cited by 15 publications

(3 citation statements)

References 17 publications

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“…Equation ( 16) [57] considers the charging and discharging efficiency ( f EV ) of the EV. Where, (C EV,Pr ) is the cost of power loss, (R EV ) is the EV range, (D Home EV ) is the distance between the EV and home, (CAP total EV ) is the total EV battery capacity, and (C pr (t)) is the power price at time t.…”

Section: Modelling Of Ev Soc and Availabilitymentioning

confidence: 99%

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

2023

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These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology.

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Section: Modelling Of Ev Soc and Availabilitymentioning

confidence: 99%

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

2023

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“…This increased focus on energy conservation and environmental impact has driven the development of global EV markets (Zhang et al, 2018). Additionally, the integration of renewable energy sources and energy storage systems, bolstered by the joint optimization of routing and charging scheduling for multiple EVs using IoT technology, has demonstrated significant economic and environmental benefits (Li et al, 2023;Tang et al, 2021;Ravichandran et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Bi-level Programming Model for Optimal EV Charging Scheduling and Operation in Residential Communities

Zhu,

Gan,

Dai

et al. 2024

Preprint

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t: This study addresses the challenge of disorderly large-scale electric vehicle (EV) charging 1 and its impact on distribution networks by focusing on residential communities as key EV gathering 2 places. We propose a dynamic pricing environment and develop a bi-level programming model 3 capturing the relationship between retailers and EV owners (EVOs). By transforming the bi-level 4 programming into a mixed-integer linear programming problem using the Karush-Kuhn-Tucker 5 conditions and the duality theorem, we obtain an optimal pricing strategy that benefits both parties. 6 We conduct a sensitivity analysis to examine the impact of various factors on the pricing strategy, 7 offering insights for retailers’ scheduling and operational decisions. The efficacy of the proposed 8 algorithm is demonstrated through a case study, highlighting its potential to guide decisions for both 9 retailers and EVOs in residential community EV charging. This work contributes to managing the 10 increasing prevalence of electric vehicles and their impact on power distribution networks, fostering 11 sustainable and efficient energy management strategies in residential communities.

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“…They thought about how the climate, the traffic of EVs, and tariffs are only a few of the factors that affect the strain on EV charging stations. In [30], it is suggested that a bi-level planning approach be used to find a middle ground between the clashing demands of EV consumers and investors. They increased the overall enjoyment of people receiving services while at the same time optimizing the expenses incurred by economies all over the world.…”

Section: Introductionmentioning

confidence: 99%

A Recommendation System for Electric Vehicles Users Based on Restricted Boltzmann Machine and WaterWheel Plant Algorithms

Ibrahim,

El-Kenawy,

Eid

et al. 2023

IEEE Access

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Ensuring reliable and easily accessible charging infrastructure becomes crucial as more people adopt electric vehicles. This study introduces a recommendation system designed to assist electric vehicle users in finding convenient charging stations, enhancing the charging experience, and reducing range anxiety. The system employs advanced data analysis techniques to offer personalized suggestions based on users' preferences. Real-time data on factors like charging station availability, individual preferences, and past usage patterns are collected and processed using a restricted Boltzmann machine-learning algorithm. The waterwheel plant algorithm, known for its effectiveness in solving complex optimization problems, is utilized to optimize the parameters of the restricted Boltzmann machine. The recommendation system considers various user preferences, including charging speed, cost, network compatibility, amenities, and proximity to the user's current location. The system aims to minimize user frustration, improve charging performance, and enhance customer satisfaction by addressing these aspects. Results indicate the system's efficiency in suggesting convenient charging locations. The study explores the statistical significance of the optimized waterwheel plant algorithm and restricted Boltzmann machine model through Wilcoxon rank-sum and Analysis of Variance tests.

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Bi-level programming model approach for electric vehicle charging stations considering user charging costs

Cited by 15 publications

References 17 publications

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit

A Bi-level Programming Model for Optimal EV Charging Scheduling and Operation in Residential Communities

A Recommendation System for Electric Vehicles Users Based on Restricted Boltzmann Machine and WaterWheel Plant Algorithms

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