Article
Energy & Fuels
Ruiyang Jin, Yuke Zhou, Chao Lu, Jie Song
Summary: This study considers the coordination of charging power in electric vehicles by charging stations to participate in demand response and alleviate grid overload. By innovatively applying a decentralized decision-making framework and deep reinforcement learning algorithms, the proposed method can trade off the revenue from demand response and user satisfaction, as well as reduce the peak load of the charging station.
Article
Thermodynamics
Yanbin Li, Jiani Wang, Weiye Wang, Chang Liu, Yun Li
Summary: This paper examines the location strategies of electric vehicle charging stations (EVCS) in China from the perspective of private investors, considering the competitive environment. A three-level location model with dynamic pricing is developed, and the soft actor-critic reinforcement learning algorithm is used to train the optimal pricing strategy for EVCS. Case studies based on an industrial park in China verify the proposed methodology, showing that it can provide more economical and scientific location decisions than traditional methods. The dynamic pricing method based on reinforcement learning can also serve as a reference for the location and operation of more EVCSs.
Article
Thermodynamics
Zhonghao Zhao, Carman K. M. Lee, Jiage Huo
Summary: This study addresses the optimal deployment of electric vehicle charging stations in the transportation and power distribution networks, which is a critical issue for the mass adoption of EVs. A finite-discrete Markov decision process formulation is proposed in a reinforcement learning framework to solve the curse of dimensionality problem. The proposed approach, which utilizes a LSTM-based recurrent neural network with an attention mechanism, outperforms other baseline approaches in terms of solution quality and computational time.
Article
Automation & Control Systems
Liangliang Hao, Jiangliang Jin, Yunjian Xu
Summary: This article studies the problem of online pricing and charging scheduling for a public electric vehicle (EV) charging station under stochastic electricity prices and renewable generation. A novel scheme called laxity differentiated pricing (LDP) is proposed to balance electricity cost and opportunity cost, and a model-free soft actor critic (SAC) algorithm is used to reduce the action dimensionality. Numerical results show that the proposed approach outperforms alternative methods with various pricing and charging schemes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Tao Qian, Chengcheng Shao, Xuliang Li, Xiuli Wang, Zhiping Chen, Mohammad Shahidehpour
Summary: In this paper, a multi-agent deep reinforcement learning (MA-DRL) method is proposed to model the pricing game and determine the optimal charging prices for electric vehicle charging stations (EVCSs) in urban transportation networks (UTNs). By analyzing the charging demand and formulating the price competition problem as a game with incomplete information, the MA-DRL approach is used to learn the charging pricing strategies and approximate the Nash Equilibrium (NE) of the pricing game.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Civil
Linfeng Liu, Zhiyuan Xi, Kun Zhu, Ran Wang, Ekram Hossain
Summary: This paper investigates the placement problem of idle mobile charging stations (MCSs) in an Internet of Electric Vehicles (IoEV) to enhance the proportion of charged electric vehicles (EVs) and reduce charging expenses. A Federated Learning based Placement Decision Method of Idle MCSs (FL-PDMIM) is proposed to predict future charging positions by exploiting the historical routes of MCSs, thereby reducing waiting time and improving performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zuzhao Ye, Yuanqi Gao, Nanpeng Yu
Summary: This paper proposes a novel centralized allocation and decentralized execution reinforcement learning framework to maximize the profit of electric vehicle charging stations. By allocating electric vehicles to waiting or charging spots in a centralized process, and allowing each charger to make its own charging/discharging decision in a decentralized process, this framework significantly improves the scalability and sample efficiency of the reinforcement learning algorithm.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Civil
Cheng Fang, Haibing Lu, Yuan Hong, Shan Liu, Jasmine Chang
Summary: Significant developments in electric vehicle technologies, such as extreme fast charging, have been witnessed in the past decade. However, the lack of fast charging stations remains a major barrier to wider EV deployment. To address this issue, establishing a fast charging sharing system and implementing a smart dynamic pricing scheme are crucial steps forward.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Civil
Sangjun Bae, Sebastien Gros, Balazs Kulcsar
Summary: This research focuses on maximizing revenue for fast-EVCSs using AI algorithms, and finds that RL approaches may misuse personal information of EVUs. Intuitive guidelines are suggested to prevent such abuse.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhonghao Zhao, Carman K. M. Lee, Jingzheng Ren, Yung Po Tsang
Summary: This study aims to determine the best deployment plan for EV fast charging stations in a transportation network with limited budget. The objective is to maximize the quality of service with respect to waiting time and range anxiety from the perspective of EV customers. The study proposes a novel reinforcement learning framework using a finite discrete Markov decision process to address the curse of dimensionality problem and a recurrent neural network with an attention mechanism for unsupervised learning.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Mao Tan, Zhuocen Dai, Yongxin Su, Caixue Chen, Ling Wang, Jie Chen
Summary: With the increase in the number of electric vehicles, battery swapping is seen as promising due to its short waiting time. However, it is challenging to achieve efficient scheduling in a large scale battery swap station due to the uncertainty of the power grid and EV behavior. To address this, a new bi-level scheduling model is proposed, combining deep reinforcement learning for optimal power allocation and MILP subproblems for battery dispatching. Experimental results show excellent performance and cost reduction, benefiting both the battery swap station and the power grid in peak shaving and valley filling.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Energy & Fuels
Sangyoon Lee, Dae-Hyun Choi
Summary: Smart electric vehicle charging stations with distributed energy resources are essential for increasing profit and maintaining grid stability. However, prediction errors of PV generation outputs and EV loads may decrease profit and destabilize the grid. To address this, a two-stage framework utilizing deep reinforcement learning is proposed for scheduling and voltage control.
Article
Green & Sustainable Science & Technology
Ruisheng Wang, Zhong Chen, Qiang Xing, Ziqi Zhang, Tian Zhang
Summary: This article proposes a modified rainbow-based deep reinforcement learning strategy to optimize the scheduling of charging stations, aiming to improve operating efficiency and economic benefits. By considering the interaction among electric vehicles, charging stations, and distribution networks, a comprehensive information perception model is constructed to extract the required environmental state. The results show that the proposed method effectively reduces operating costs and improves new energy consumption.
Article
Energy & Fuels
Li Cui, Qingyuan Wang, Hongquan Qu, Mingshen Wang, Yile Wu, Le Ge
Summary: With the rapid development of electric vehicles (EVs) and charging infrastructures, the unbalanced utilization rate of fast charging stations (FCSTs) and the long waiting time for charging have become major issues. This paper uses deep reinforcement learning (DRL) to solve these problems by proposing a dynamic pricing strategy for FCSTs. The strategy includes a traffic flow prediction model, an estimation of EV charging requirements, and a dynamic pricing strategy based on deep deterministic policy gradient (DDPG) learning. Simulation results show that the proposed strategy effectively improves the profit of FCSTs, alleviates road congestion, and enhances user satisfaction.
Article
Energy & Fuels
Kang Wang, Haixin Wang, Zihao Yang, Jiawei Feng, Yanzhen Li, Junyou Yang, Zhe Chen
Summary: This paper proposes a deep transfer reinforcement learning (DTRL)-based charging method for electric vehicles (EVs) to transfer the trained RL-based charging strategy to a new environment. The uncertainty problem of EV charging behaviors is formulated as a Markov Decision Process (MDP) with an unknown state transfer function. An RL-based charging strategy using deep deterministic policy gradient (DDPG) is trained with extensive driving and environmental data samples. A charging method based on transfer learning (TL) and DDPG is proposed to transfer the trained RL-based charging strategy to the new environment. Simulations demonstrate that the proposed approach reduces outliers and shortens the development time of the EV charging strategy in the new environment.
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.