Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism

Yin, WanJun; Wen, Tao; Zhang, Chao

doi:10.1016/j.energy.2022.125627

Cited by 26 publications

(6 citation statements)

References 10 publications

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“…This is consistent with the findings of Zhang et al (Zhang et al, 2019). Notably, the spatial factors not just refer to the cost of land usage for building ECFs, but also include others, for example, the retrofit costs for the power systems for avoiding to impact of EVs' peak-charging-load period (Wu & Pang, 2023;Yin et al, 2023). From a holistic perspective, therefore, at this stage, it is important to plan the layout of charging facilities in order to take into account the charging convenience of EVs and the minimization of the overall investment cost.…”

Section: Discussionsupporting

confidence: 87%

Dynamic analysis of the investment decision of electric vehicle charging facilities and the promotion effect measurement for electric vehicles

Shi,

Chen,

Wang

2023

EJTIR

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This paper aims to analyze the deep reason why there exists hesitation when investors decide whether invest in EV charging facilities (ECFs). To this end, a series of theoretic models are built and derived, and some enlightening results are got. The main results confirm that charging facility investors are insufficiently motivated to follow a moderately aggressive investment strategy in the early stages of EV development. For stimulating ECFs’ investment, the marginal conditions in which the investors choose active or conservative investment strategies to lay out charging facilities are analyzed, and the effects under different ECFs investment strategies are quantized in terms of driving the market development of EVs. Based on the findings, relevant policy suggestions are proposed. Finally, to verify the gained results, a case study in the context of China is given.

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Section: Discussionsupporting

confidence: 87%

Dynamic analysis of the investment decision of electric vehicle charging facilities and the promotion effect measurement for electric vehicles

Shi,

Chen,

Wang

2023

EJTIR

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“…According to the principle of the lowest electricity cost, the strategy of preheating the battery pack by the charging pile can be adjusted according to the time-of-use (TOU) electricity price. However, at present, the TOU electricity price is mainly applied to optimizing pure EV charging loads and guiding users' charging and discharging behaviors [21][22][23]. OuYang et al [24] proposed a multi-objective optimization charging strategy covering user travel demand, charging cost, energy loss, and other aspects using the time-of-use electricity price.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Programming of Electric Vehicle Reservation Charging and Battery Preheating Strategies Considering Time-of-Use Electricity Price

Zhu,

Bao,

Yao

et al. 2024

WEVJ

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Electric vehicles can effectively make use of the time-of-use electricity price to reduce the charging cost. Additionally, using grid power to preheat the battery before departure is particularly important for improving the vehicle mileage and reducing the use cost. In this paper, a dynamic programming algorithm is used to optimize the battery AC (Alternating Current) charging–preheating strategy to minimize the total cost of battery charging and preheating, with the charging current and battery preheating power consumption as the control variables. The cost difference between the optimized control strategy and the conventional preheating strategy was analyzed under different ambient temperatures (−20~0 °C) and different target travel times (7:00~12:00). The simulation results show that the optimized control strategy makes the state of charge (SOC) and temperature of the battery reach the set value at the user’s target departure time, and the total cost of the grid is the lowest. Compared with the conventional preheating strategy, the optimized control strategy can utilize the power grid energy in the valley price area and reduce the opening time of the positive temperature coefficient (PTC) heater in the flat and the peak price zones. Furthermore, the cost utilization rate can reach 18.41~73.96%, and the cost-saving effect is significant.

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“…References [17,18] established a dynamic tariff adjustment model that divided the tariff into a fixed component and a variable component; Reference [19] divided dynamic pricing into punishment pricing and incentive pricing; Reference [20] established a quarter-hourly vehicle-to-grid dynamic timesharing pricing model based on deep deterministic policy gradient reinforcement learning algorithm; Reference [21] provided a detailed introduction to a dynamic electricity pricing mechanism, but the calculation of the elasticity coefficient matrix is complex and difficult to implement in practical application. The above literature studied the dynamic tariff adjustment mechanism, but ignored the guiding effect of the peak and valley hours characteristics of the time-of-use tariff on the charging behavior of EVs, and lacked the theoretical derivation of the tariff adjustment mechanism and the basis for key parameter selection.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Zhong,

Che,

Zhang

2024

ENERGY

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Electric vehicle (EV) is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future. However, a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff. Therefore, this paper proposes a dynamic timeof-use tariff mechanism, which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean (FCM) clustering algorithm, and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period. Based on the proposed tariff mechanism, an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established. Then, a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem. Finally, the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI. The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid, and can effectively reduce the grid load variance and improve the grid load curve.

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Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism

Cited by 26 publications

References 10 publications

Dynamic analysis of the investment decision of electric vehicle charging facilities and the promotion effect measurement for electric vehicles

Dynamic analysis of the investment decision of electric vehicle charging facilities and the promotion effect measurement for electric vehicles

Dynamic Programming of Electric Vehicle Reservation Charging and Battery Preheating Strategies Considering Time-of-Use Electricity Price

Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

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