An electric vehicle charging load prediction model for different functional areas based on multithreaded acceleration

Guo, Zhengyang; Bian, Haihong; Zhou, Chengang; Ren, Quance; Gao, Yudi

doi:10.1016/j.est.2023.108921

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…The day is segmented into six time periods, and the likelihood of users selecting functional areas varied across these periods. Consequently, the transition probabilities are derived from the data in reference [32], as illustrated in Fig. 5.…”

Section: Prediction Of Ev Charging Demand and Hv Hydrogen Charging De...mentioning

confidence: 99%

Coordinated Planning of Electricity-Hydrogen Integrated Energy System Considering Lifecycle Carbon Emissions

Bian,

Zhou,

Guo

et al. 2024

IEEE Access

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Hydrogen energy is a promising solution to reaching carbon emissions peak and carbon neutrality. With the development of hydrogen energy devices, coupling hydrogen energy with renewable energy for power generation can compensate for the intermittency and instability of renewable energy. Furthermore, the two-stage coordinated planning of integrated energy system (IES) and electricity-hydrogen integrated energy system (EHIES), compared to independent planning, can help generate additional benefits and reduce carbon emissions. Therefore, this paper proposes a coordinated planning of EHIES considering lifecycle carbon emissions. In the first stage, aiming to minimize investment costs, network losses, voltage deviations, and lifecycle carbon emissions, the equipment capacity and location of EHIES is determined. The life cycle assessment (LCA) method is used to quantify the lifecycle carbon emissions of the EHIES, preparing for the coordinated operation in the second stage. Subsequently, in the second stage, using carbon emissions flow to allocate users' responsibility for emissions, the carbon emissions per unit electricity of the IES system are calculated. With the optimal cost of independent operation for IES and EHIES as the coordinated operation objective, the alternating direction method of multipliers (ADMM) is employed for sequential solution, obtaining the carbon emissions reduction amount, and iteratively updating until convergence. Finally, the effectiveness of the proposed method is verified by using the IEEE 33-bus power distribution network (PDN) and natural gas 6 node systems. The results show that compared to independent planning, the two-stage coordinated planning method reduces the overall operating cost of IES by 3.81% and decreases carbon emissions by 15.89%. This method is effective in reducing the carbon emissions of IES. INDEX TERMSADMM, Two-stage coordinated planning model, Electricity-Hydrogen Integrated Energy System, Lifecycle carbon emissions NOMENCLATURE ACRONYMS ADMM Alternating direction method of multipliers DES Distributed energy system EHIES Electricity-hydrogen integrated energy system ESS Electric storage systems EV Electric vehicles FC Fuel cells FCS Fast-charging station

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Section: Prediction Of Ev Charging Demand and Hv Hydrogen Charging De...mentioning

confidence: 99%

Coordinated Planning of Electricity-Hydrogen Integrated Energy System Considering Lifecycle Carbon Emissions

Bian,

Zhou,

Guo

et al. 2024

IEEE Access

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“…They did this by integrating influences from weather conditions and traffic congestion and introducing an energy consumption model. In Reference [28], additional factors such as road grade, saturation level, traffic conditions, classification of streets, and delay at signalized intersections were taken into account. Moreover, a novel algorithm for time-optimal path decision was proposed to optimize the selection of routes.…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Bian,

Ren,

Guo

et al. 2024

Energies

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A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

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“…EVs are widely embraced by the market for their advantages of low carbon emissions, high efficiency, and low maintenance costs [5,6]. However, constraints still exist regarding the widespread adoption of large-scale EV travel and charging infrastructure, such as World Electr.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Battery Swapping Station and Multiple Uncertainties

Bian,

Ren,

Guo

et al. 2024

WEVJ

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In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users.

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An electric vehicle charging load prediction model for different functional areas based on multithreaded acceleration

Cited by 6 publications

References 37 publications

Coordinated Planning of Electricity-Hydrogen Integrated Energy System Considering Lifecycle Carbon Emissions

Coordinated Planning of Electricity-Hydrogen Integrated Energy System Considering Lifecycle Carbon Emissions

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Battery Swapping Station and Multiple Uncertainties

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