Article
Computer Science, Information Systems
Ilkka Jokinen, Matti Lehtonen
Summary: This study analyzed the coincidence factors of charging loads in different locations for a large-scale BEV fleet, taking into account available charging power and ambient temperature. The results showed that the coincidence factors of charging were negatively correlated with available charging power and ambient temperature. Workplaces, hotels, and homes were the main charging locations, but the overall coincidence of charging remained low. The relative standard deviation of composite load was small for a large number of BEVs, while the opposite was found for a small number of BEVs.
Article
Thermodynamics
WanJun Yin, Xuan Qin
Summary: This paper proposes a method to solve the optimal scheduling problem of large-scale electric vehicles connected to the grid. By considering multiple factors and using a high-confidence wind power scenario, the collaborative optimization of coal-fired power generation, wind power generation, and electric vehicles is achieved.
Article
Economics
Mehrsa Khaleghikarahrodi, Gretchen A. Macht
Summary: Policymakers have set goals to promote electric vehicle adoption and charging station deployment, and computational models are being used to determine the optimal placement of charging stations. This study analyzes data from a state-wide network of public charging stations to identify different patterns of user behavior. The results highlight four distinct user types and provide valuable insights for designing user-centric charging infrastructure.
Article
Energy & Fuels
Dingsong Cui, Zhenpo Wang, Peng Liu, Shuo Wang, Yiwen Zhao, Weipeng Zhan
Summary: The large-scale deployment of electric vehicles presents challenges to the distribution network, but also provides opportunities for power operation. Previous research on EV charging scheduling focused on slow-charging behavior, neglecting fast-charging behavior. This study provides an in-depth understanding of EV user fast-charging behavior in public stations using a Variational-Bayesian Gaussian mixture model. Charging energy, duration, and dwelling duration after charging are considered in the cluster model to support charging recommendation strategy and power allocation decision. Inspired by previous studies, a charging behavior prediction framework is proposed that improves prediction accuracy and priority evaluation.
Article
Energy & Fuels
Yang Zhao, Zhenpo Wang, Zuo-Jun Max Shen, Fengchun Sun
Summary: A novel data-driven framework for large-scale charging energy predictions has been developed in this study, accurately taking into account various factors and demonstrating superior accuracy and stability compared to existing prediction models. This framework can further be utilized for cloud-based battery diagnoses and large-scale forecasting of EV energy demands.
Article
Thermodynamics
Siobhan Powell, Gustavo Vianna Cezar, Elpiniki Apostolaki-Iosifidou, Ram Rajagopal
Summary: This paper presents a novel modeling approach to generate rapid demand estimates for large-scale scenarios of controlled charging in transportation electrification. The approach utilizes machine learning to model the effect of load modulation control on aggregate charging profiles, replacing traditional optimization approaches. Additionally, statistical representations of real charging session data are used to generate uncontrolled charging demand for various scenarios.
Article
Thermodynamics
Dingding Hu, Kaile Zhou, Fangyi Li, Dawei Ma
Summary: With the rapid development of electric vehicles, understanding different types of EV users is crucial for business innovation in the EV sector. This study proposes an integrated approach using data mining and clustering analysis to classify EV users into six groups and provides marketing strategies to improve user loyalty and profitability for charging service enterprises.
Article
Environmental Studies
Haiming Cai, Fan Wu, Zhanhong Cheng, Binliang Li, Jian Wang
Summary: Charging station planning is critical for public transport electrification. However, there is insufficient experience and knowledge regarding the factors that influence charging station utilization, especially for electric taxis. This paper uses data from Shenzhen to analyze the relationship between charging station utilization and urban form and demand for taxi services. The findings suggest that demand for taxi services has a non-linear relationship with utilization, and metro station density positively correlates with utilization.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
Article
Thermodynamics
Elias Hartvigsson, Maria Taljegard, Mikael Odenberger, Peiyuan Chen
Summary: This study analyzes the geographical distribution impact of electric vehicle charging on power system violations in residential areas. The results show that the risk of violations is highest in cities, lower in urban areas, and significantly fewer in rural areas. The study also finds that an electricity price optimized charging strategy increases the risk of violations in certain city areas. Additionally, there are variations in violations in different price areas in Sweden.
Article
Energy & Fuels
Ping Xue, Yue Xiang, Jing Gou, Weiting Xu, Wei Sun, Zhuozhen Jiang, Shafqat Jawad, Huangjiang Zhao, Junyong Liu
Summary: This study aims to assess the comprehensive impacts of spatial-temporal EV charging on the system from the perspectives of electricity system reliability and EV charging service reliability. It proposes a comprehensive reliability index system and introduces a charging load model that considers traffic constraints and users' charging willingness. The study analyzes reliability impacts from various factors and discusses the maximum system reliability when the EV capacity ratio to DG capacity is 3:1.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Energy & Fuels
Jin Zhang, Zhenpo Wang, Eric J. Miller, Dingsong Cui, Peng Liu, Zhaosheng Zhang
Summary: This paper utilizes three-month real-world travel and charging records of 25,489 electric passenger vehicles in Beijing to quantitatively study the travel patterns and charging behaviors of electric vehicles and support charging demand prediction. A multi-level and multi-dimensional framework is proposed to extract electric vehicle behavior characteristic parameters and a clustering model is used to classify the users. A trip-chain-simulation-based charging demand prediction model is established to accurately predict real-world charging demands. The results provide a foundation for charging infrastructure planning and the impact of charging behaviors on grid load.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Hyeon Woo, Yongju Son, Jintae Cho, Sung-Yul Kim, Sungyun Choi
Summary: With the exponential increase in the penetration rate of EVs, the charging load is causing new issues in the power system such as voltage drop. Improperly located EVCSs exacerbate these issues, leading to concentrated charging demand. This paper proposes a method for optimal EVCS placement considering installation cost, drivers' preferences, and existing charging stations. By estimating charging demands using kernel density estimation and modeling driver preferences, an integer nonlinear programming problem is formulated and solved using a minimax genetic algorithm. Simulations based on real data demonstrate the effectiveness of the proposed method in achieving dispersed charging demand.
Article
Thermodynamics
Rishabh Ghotge, Ad van Wijk, Zofia Lukszo
Summary: This study analyzes the effectiveness of an off-grid solar photovoltaic system for charging electric vehicles in a long-term parking lot. The research found that a relatively high proportion of vehicles leave with inadequate charge during low irradiance winter months. Strategies were formulated to allocate energy in the system in order to reduce the number of vehicles leaving with low state of charge, with prioritizing vehicles with low state of charge being the most effective strategy identified.
Article
Engineering, Electrical & Electronic
Francesco Zinnari, S. Strada, Mara Tanelli, Simone Formentin, Sergio M. Savaresi
Summary: With increasing concerns about global warming and urban pollution, battery electric vehicles (BEVs) are gaining popularity worldwide. However, high purchase prices, limited battery range, and insufficient public charging infrastructure hinder their market uptake. This study uses a massive real-world dataset to evaluate the rationality of range anxiety and assess the electrification potential of a fleet of more than 50000 vehicles. The results show that BEVs have the potential to meet the range needs of the existing fuel-powered vehicle fleet without altering owners' routines, and identify potential charging station sites based on real charging demand.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Environmental Studies
Constance Crozier, Thomas Morstyn, Malcolm McCulloch
Summary: This paper proposes a stochastic data-driven model that accurately captures diversity in individual consumer behavior. Through a case study of UK residential charging, it demonstrates that existing approaches may overestimate the increase in peak distribution network demand by 50%, highlighting the importance of using locally representative vehicle usage data.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2021)
Article
Energy & Fuels
Siobhan Powell, Emre Can Kara, Raffi Sevlian, Gustavo Vianna Cezar, Sila Kiliccote, Ram Rajagopal
Article
Multidisciplinary Sciences
Elizabeth Buechler, Siobhan Powell, Tao Sun, Nicolas Astier, Chad Zanocco, Jose Bolorinos, June Flora, Hilary Boudet, Ram Rajagopal
Summary: Understanding the changes in electricity consumption during the COVID-19 pandemic provides insights into society's response to shocks and extreme events. This study quantifies the changes in electricity consumption in 58 different countries/regions and examines their relationship with government restrictions, health outcomes, GDP, mobility metrics, and electricity sector characteristics. The results show that stricter government restrictions and larger decreases in mobility, particularly in the retail and recreation sectors, are closely related to decreases in electricity consumption, but these relationships are strongest during the initial phase of the pandemic. There are also indications that decreases in electricity consumption are related to pre-pandemic sensitivity to holidays, suggesting further avenues for research.
Article
Thermodynamics
Siobhan Powell, Gustavo Vianna Cezar, Elpiniki Apostolaki-Iosifidou, Ram Rajagopal
Summary: This paper presents a novel modeling approach to generate rapid demand estimates for large-scale scenarios of controlled charging in transportation electrification. The approach utilizes machine learning to model the effect of load modulation control on aggregate charging profiles, replacing traditional optimization approaches. Additionally, statistical representations of real charging session data are used to generate uncontrolled charging demand for various scenarios.
Article
Energy & Fuels
Siobhan Powell, Gustavo Vianna Cezar, Liang Min, Ines M. L. Azevedo, Ram Rajagopal
Summary: The electrification of transport can pose challenges to power grid operations if charging is not properly managed. Different types of charging control and infrastructure scenarios have different impacts on the grid. Controlling the charging load and promoting daytime charging can reduce storage requirements and emissions.
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.
