Configuration optimization and benefit allocation model of multi-park integrated energy systems considering electric vehicle charging station to assist services of shared energy storage power station

Gao, Jianwei; Gao, Fangjie; Yu, Yang; Wu, Haoyu; Zhang, Yi; Peng, Liang

doi:10.1016/j.jclepro.2022.130381

Cited by 24 publications

(10 citation statements)

References 20 publications

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“…Existing research has mainly focused on two aspects: energy storage scheduling at charging stations [1][2][3][4] and recommendation of charging strategies for electric vehicles [5][6][7][8]. Energy storage scheduling involves storing electrical energy generated by photovoltaic power generation [9][10][11] and managing cross-temporal energy dispatch to allocate electricity across different charging scenarios, mitigating sustained load pressure on the power grid [12].…”

Section: Introductionmentioning

confidence: 99%

Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

Shen,

Wu,

Zhang

et al. 2024

IJACSA

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With the rapid development of electric vehicles, their charging strategies significantly impact the overall power grid. Solving the spatiotemporal scheduling problem of vehicle charging has become a hot research topic. This paper focuses on recommending suitable charging stations for electric vehicles and proposes a scalable accelerated intelligent charging strategy recommendation algorithm based on Deep Q-Networks (DQN).The strategy recommendation problem is formulated as a Markov decision process, where the continuous sequence of regional charging requests within a time slice is fed into the DQN network as the input state, enabling optimal charging strategy recommendations for each electric vehicle. The algorithm aims to maintain regional load balance while minimizing user waiting time. To enhance the algorithm's applicability, a scalable, accelerated charging strategy framework is further proposed, which incorporates information filtering and shared experience pool mechanisms to adapt to different expansion scenarios and expedite strategy iterations in new scenarios. Simulation results demonstrate that the proposed DQN-based strategy recommendation algorithm outperforms the shortest path-first strategy, and the scalable, accelerated charging strategy framework achieves a 64.3% improvement in iteration speed in new scenarios, which helps to reduce the cloud server load and saves overheads.

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Section: Introductionmentioning

confidence: 99%

Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

Shen,

Wu,

Zhang

et al. 2024

IJACSA

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“…With increasing carbon emissions and a push to achieve carbon neutrality, a new power system based on clean energy is crucial for societal and economic development. − Because wind and photovoltaic power generation are unpredictable and variable, , the use of energy storage systems (ESSs) has been steadily increasing in recent years . Lithium-ion batteries (LIBs) are an example of such ESSs that are widely used because of their low cost, high efficiency, and long cycle life. − However, under long-term operation, various forms of mechanical, electrical, and thermal abuse stimulate the occurrence of thermal runaway (TR) in LIBs. − Some gases (CO 2 , CO, C 2 H 2 , C 2 H 4 , etc.…”

Section: Introductionmentioning

confidence: 99%

Adsorption and Sensing Performance of Vented Gases from Lithium-Ion Batteries on Transition-Metal-Doped GeTe Nanosheets: A DFT Investigation

Zhang

Tang

Yuan

et al. 2023

ACS Appl. Nano Mater.

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Online monitoring of vented gases released by lithium-ion batteries (LIBs) during the early stages of thermal runaway is a potential method for ensuring the operational state of LIBs. Using the density functional theory method, the adsorption and sensing performances of transition-metal (TM) (Rh, Au, Pd, and Ag)-doped GeTe monolayers toward vented gases from LIBs were analyzed by calculating the adsorption energy, charge transfer, deformation charge density, density of states, work function, sensitivity, and recovery time. Results demonstrate that TM-doped GeTe monolayers have superior adsorption properties and sensitivity for CO 2 , CO, C 2 H 2 , and C 2 H 4 compared with pure GeTe. This study provides theoretical guidance for designing a new gas nanosensor to detect the vented gases emitted from LIBs during thermal runaway.

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“…However, RE volatility and the high cost of ES investment are significant variables influencing their development [2]. The emergence of shared energy storage power stations (SESPS) and new energy vehicles, particularly electric vehicles (EV), allows for the suppression of RE instability and the reduction of ES investment costs [3]. However, in cold regions, the usage of EVs is uneconomical [4], and hydrogen fuel vehicles (HFVs) have replaced EVs as the primary equipment.…”

Section: Introductionmentioning

confidence: 99%

“…However, the ES investment costs in these studies remain significant. To address this issue, Gao et al [3] used EV charging stations to help SESPS in serving multiple parks, significantly reducing SESPS capacity. The Gao et al study, however, does not apply to all scenarios, particularly EVs in cold locations.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Configuration and Scheduling Model of a Multi-Park Integrated Energy System Based on Sustainable Development

Gao

Huang

et al. 2023

Electronics

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To maximize the utilization of renewable energy (RE) as much as possible in cold areas while reducing traditional energy use and carbon dioxide emissions, a three-layer configuration optimization and scheduling model considering a multi-park integrated energy system (MPIES), a shared energy storage power station (SESPS), and a hydrogen refueling station (HRS) cooperation based on the Wasserstein generative adversarial networks, the simultaneous backward reduction technique, and the Quantity-Contour (WGAN-SBR_QC) method is proposed. Firstly, the WGAN-SBR_QC method is used to generate typical scenarios of RE output. Secondly, a three-layer configuration and schedule optimization model is constructed using MPIES, SESPS, and HRS. Finally, the model’s validity is investigated by selecting a multi-park in Eastern Mongolia. The results show that: (1) the typical scenario of RE output improved the overall robustness of the system. (2) The profits of the MPIES and HRS increased by 1.84% and 52.68%, respectively, and the SESPS profit increased considerably. (3) The proposed approach increased RE utilization to 99.47% while reducing carbon emissions by 32.67%. Thus, this model is a reference for complex energy system configuration and scheduling, as well as a means of encouraging RE use.

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Configuration optimization and benefit allocation model of multi-park integrated energy systems considering electric vehicle charging station to assist services of shared energy storage power station

Cited by 24 publications

References 20 publications

Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

Adsorption and Sensing Performance of Vented Gases from Lithium-Ion Batteries on Transition-Metal-Doped GeTe Nanosheets: A DFT Investigation

Optimal Configuration and Scheduling Model of a Multi-Park Integrated Energy System Based on Sustainable Development

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