Article
Energy & Fuels
Maximilian Schucking, Patrick Jochem
Summary: By considering uncertainties in energy consumption and charging times, an optimization framework is proposed to minimize the total cost of ownership for commercial electric vehicles. The study reveals a significant impact of uncertain mobility patterns on the optimal solution, while the trade-off between battery and charging capacity can greatly reduce the total cost.
Article
Engineering, Civil
Seyed Sajjad Fazeli, Saravanan Venkatachalam, Ratna Babu Chinnam, Alper Murat
Summary: This study proposes a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers' behaviors. The results demonstrate the effectiveness of the proposed approach, along with a case study based on publicly available data sources and sensitivity analysis. The research also introduces an outer approximation decomposition algorithm to address computational challenges for large-scale instances.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Teng Long, Qing-Shan Jia, Gongming Wang, Yu Yang
Summary: This paper presents an efficient and scalable real-time scheduling method for handling the charging demands of plug-in electric vehicles (PEV), demonstrating through simulations that the proposed method provides high computation efficiency and scalability while reducing operating costs for charging stations. Compared to existing methods, it outperforms in terms of charging policy search capabilities and performance guarantee.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Engineering, Civil
Selin Hulagu, Hilmi Berk Celikoglu
Summary: In this article, a variant of the electric vehicle routing problem is proposed, which explicitly considers the intermediate nodes. The study aims to provide an optimal routing plan for shuttle vehicles in a university settlement, taking into account the real road network, passenger demand, vehicle dynamics, and battery recharging. A mathematical program is formulated to minimize multiple objectives in terms of cost. The findings suggest that considering the actual road network is significant in exact routing solutions, despite network complexity being a challenge.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Chemistry, Physical
Alireza Akbari-Dibavar, Vahid Sohrabi Tabar, Saeid Ghassem Zadeh, Ramin Nourollahi
Summary: The paper discusses the optimal management of charging stations by analyzing the energy management of a hybrid charging station integrated with a photovoltaic system. Uncertainties in parameters like photovoltaic generation, market prices, and load demands are modeled using two-stage stochastic programming, with a proposed robust optimization approach for day-ahead market price uncertainty.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Transportation Science & Technology
Matthew Zalesak, Samitha Samaranayake
Summary: This study investigates the feasibility of using electric vehicles in online, high-capacity ridepooling systems. It proposes a mixed integer linear program to expand past algorithms on ridepooling to electric vehicle fleets, and demonstrates a faster, scalable algorithm with similar performance, emphasizing the importance of knowledge and estimates of future demand in the online setting.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Energy & Fuels
Xie Zeng, Muhammad Shahzad Nazir, Mehrdad Khaksar, Kentaro Nishihara, Hai Tao
Summary: With the increasing global energy demand and environmental concerns, renewable energy sources are being utilized as alternatives to fossil fuels. The transportation sector is shifting towards electrified vehicles, such as PEVs and PHEVs, which can connect to the grid for energy exchange. The concept of microgrids is introduced to integrate RESs and optimize the capabilities of electric vehicles through smart infrastructure.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Reza Bayani, Saeed D. Manshadi, Guangyi Liu, Yawei Wang, Renchang Dai
Summary: This research presents a novel approach to reducing renewable generation curtailment and increasing system flexibility by means of electric vehicles' charging coordination. The proposed method is validated through three case studies.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2022)
Article
Operations Research & Management Science
Yuxuan Dong, Rene De Koster, Debjit Roy, Yugang Yu
Summary: In this study, a dynamic vehicle allocation policy is designed by modeling the system as a semi-open queuing network and using a Markov decision process model. Experiments show that the policy is near optimal in small-scale networks and outperforms benchmark policies in large-scale realistic scenarios. An interesting finding is that reserving idle vehicles to wait for future short-distance customer arrivals can be beneficial even when long-distance customers are waiting.
TRANSPORTATION SCIENCE
(2022)
Article
Energy & Fuels
Gurkan Soykan, Gulfem Er, Ethem Canakoglu
Summary: This study determines the optimal configuration of an isolated microgrid system with renewable sources and energy storage systems using a two-stage stochastic programming-based multi-objective optimization model. The effects of different electric vehicle roles and configurations, as well as charger size, on the sizing of the microgrid are simulated and analyzed.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Energy & Fuels
Khalil Gholami, Ali Azizivahed, Ali Arefi
Summary: Electric Vehicle Fleets (EVFs) will play a significant role in future transportation systems due to their environmental friendliness. This study proposes a new management strategy for scheduling the charging/discharging of EVFs in hybrid AC-DC microgrids under high penetration of renewable energy sources (RESs), aiming to minimize operational costs and address technical constraints. Information-gap decision theory (IGDT) is utilized to handle uncertainties, and the proposed strategy is evaluated using modified 69-bus and 94-bus systems. The results demonstrate the effectiveness and applicability of the strategy in reducing operational costs and enhancing technical aspects.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Social Issues
Ellen Roemer, Jorg Henseler
Summary: This paper investigates the drivers and barriers of acceptance of electric vehicles among employees in corporate fleets. The findings reveal that employees' environmental concern is the key factor triggering initial usage, but later, product-related factors such as enjoyment, ease of use, perceived risks, and relative advantage become more influential. The study provides important implications for decision-makers in management and policy to promote the adoption of electric vehicles in corporate fleets.
TECHNOLOGY IN SOCIETY
(2022)
Article
Engineering, Electrical & Electronic
Tao Qian, Chengcheng Shao, Xiuli Wang, Qian Zhou, Mohammad Shahidehpour
Summary: This paper focuses on the online scheduling problem of shared autonomous electric vehicle (SAEV) fleets, including charging management, routing, and rebalancing strategies. A novel framework called shadow-price deep reinforcement learning (shadow-price DRL) is proposed to address the challenge of time-varying trip demands. The framework combines a rigorous PTN operation model and a data-driven model-free DRL-based algorithm. Case studies validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Construction & Building Technology
Huaidong Li, Alireza Rezvani, Jiankun Hu, Kentaro Ohshima
Summary: This paper studies the optimal day-ahead scheduling of microgrids considering renewable power generation, electric vehicles, and storage systems. The problem is modeled as a scenario-based stochastic optimization problem using the Monte-Carlo simulation method and solved using the modified shuffled frog leaping algorithm. The framework considers various charging/discharging patterns of EVs and is verified against other algorithms on a test microgrid.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Computer Science, Interdisciplinary Applications
Welverton R. Silva, Fabio L. Usberti, Rafael C. S. Schouery
Summary: We introduce the electric vehicle sharing problem (EVSP), which is about the planning and operation of electric car-sharing systems with one-way rental. The aim is to find the maximum total daily rental time for fulfilling customer demands using the existing fleet. We prove that the EVSP is NP-hard and provide four formulations based on space-time network flow models, along with computational results.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Energy & Fuels
Cedric Clastres, Olivier Rebenaque, Patrick Jochem
Summary: This paper investigates the potential benefits for French prosumers from providing demand response (DR) in different electricity markets. The study finds that although demand response volumes can represent around 20% of self-consumption, due to uncertainty and compensations to the supplier, the actual percentage drops to 6%. Therefore, the savings from self-consumption and demand response in France are not enough to cover the investment cost in a battery storage system (BSS), and a demand response premium is needed in order to reach the break-even point.
ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS
(2023)
Article
Green & Sustainable Science & Technology
Viktor Slednev, Patrick Jochem, Wolf Fichtner
Summary: The impact of electric vehicles on the electricity grid varies depending on the specific environment, with minor effects on the highest grid level but more severe effects on nodal prices and greenhouse gas emissions. Comprehensive analysis considering multiple grid levels and their interactions is more suitable for understanding the implications of electric vehicles on the electricity grid.
JOURNAL OF INDUSTRIAL ECOLOGY
(2022)
Article
Green & Sustainable Science & Technology
S. Misconel, R. Leisen, J. Mikurda, F. Zimmermann, C. Fraunholz, W. Fichtner, D. Moest, C. Weber
Summary: This paper discusses the importance of transparency in energy system models and the challenges in comparing results from diverse model approaches due to different data structures and mathematical methods. By focusing on specific output parameters, the study identifies that differences in results are largely attributed to conceptual and methodological variations among the models. The range of obtained results is influenced not only by mathematical approaches, but also by the foresight perspective adopted by the models.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Energy & Fuels
Christian Will, Nico Lehmann, Nora Baumgartner, Sven Feurer, Patrick Jochem, Wolf Fichtner
Summary: Electricity from renewable and/or nuclear sources can significantly reduce the carbon emissions of electric vehicles. However, there is limited knowledge on how carbon-neutral charging services (CNCS) should be designed to attract consumers. A survey of private consumers in Germany found that awareness and importance are highest for the energy source and regionality attributes. Price, energy source, and regionality are the most important factors when choosing a CNCS.
Article
Environmental Sciences
Lukas Hermwille, Stefan Lechtenbohmer, Max Ahman, Harro van Asselt, Chris Bataille, Stefan Kronshage, Annika Tonjes, Manfred Fischedick, Sebastian Oberthur, Amit Garg, Catherine Hall, Patrick Jochem, Clemens Schneider, Ryna Cui, Wolfgang Obergassel, Panagiotis Fragkos, Saritha Sudharmma Vishwanathan, Hitlon Trollip
Summary: Decarbonizing global steel production requires a fundamental transformation, which can be facilitated by a sectoral climate club that addresses technical, economic, and political uncertainties.
NATURE CLIMATE CHANGE
(2022)
Article
Economics
Nico Lehmann, Daniel Sloot, Armin Ardone, Wolf Fichtner
Summary: German households are willing to pay a small premium of less than 2% for regional electricity generation, and there are differences in willingness to pay among respondents. Households with stronger regional product beliefs and green values have a higher willingness to pay. This information can be used by practitioners to market regional electricity more effectively.
Article
Economics
Nico Lehmann, Daniel Sloot, Armin Ardone, Wolf Fichtner
Summary: This study examined the preferences of German consumers regarding demand response (DR) programs, specifically quota schemes. The results showed that consumers' choices for quota scheme designs are mainly influenced by the time period of consumption restriction and financial compensation. Furthermore, the preferences shifted when consumers had the freedom to choose whether to participate or not, indicating that certain preferences for DR programs may not translate into willingness to participate. Additionally, sociodemographic characteristics had a limited influence on these preferences, with females, older individuals, and those purchasing green electricity showing slightly higher willingness to participate.
Article
Engineering, Electrical & Electronic
Jann M. Weinand, Sabrina Ried, Max Kleinebrahm, Russell McKenna, Wolf Fichtner
Summary: This study examines the feasibility of energy autonomy in German municipalities and concludes that while it is technically feasible in many cases, it is not economically viable under current conditions. The findings contribute to future national energy system planning.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Correction
Environmental Sciences
Lukas Hermwille, Stefan Lechtenbohmer, Max Ahman, Harro van Asselt, Chris Bataille, Stefan Kronshage, Annika Tonjes, Manfred Fischedick, Sebastian Oberthur, Amit Garg, Catherine Hall, Patrick Jochem, Clemens Schneider, Ryna Cui, Wolfgang Obergassel, Panagiotis Fragkos, Saritha Sudharmma Vishwanathan, Hilton Trollip
NATURE CLIMATE CHANGE
(2022)
Editorial Material
Economics
Patrick Jochem, Xiao Luo, Marlene O'Sullivan, Stephan Mueller
Article
Energy & Fuels
Alexandra Martz, Uwe Langenmayr, Sabrina Ried, Katrin Seddig, Patrick Jochem
Summary: The increasing adoption of battery electric vehicles (BEVs) has resulted in greater demand for electricity and has posed new challenges for the energy system and the electricity grid. The critical factor lies in the occurrence of load peaks during simultaneous charging processes, rather than the additional energy demand. This study analyzes a comprehensive dataset of 2.6 million empirical charging processes and explores the identification of different charging process groups, which is essential for the successful integration of BEVs into the energy system.
Article
Energy & Fuels
Philipp Hesel, Sebastian Braun, Florian Zimmermann, Wolf Fichtner
Summary: In 2020, the European Commission announced a hydrogen strategy to support the construction of a European hydrogen infrastructure, setting a target of 40 GW installed electrolyser capacity within the EU by 2030. This work proposes a novel approach to integrate hydrogen into existing long-term optimization electricity market dispatch models and conducts a detailed analysis of the German hydrogen and electricity markets.
Article
Environmental Studies
Nora Baumgartner, Franziska Kellerer, Manuel Ruppert, Sebastian Hirsch, Stefan Mang, Wolf Fichtner
Summary: Vehicle-to-grid (V2G) is crucial for the integration of a large number of EVs into the energy system. A survey was conducted to explore user preferences and requirements in the context of a V2G charging tariff. The results showed that EV owners have a preference for a climate-neutral charging strategy.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2022)
Article
Multidisciplinary Sciences
Ying Deng, Karl-Kien Cao, Wenxuan Hu, Ronald Stegen, Kai von Krbek, Rafael Soria, Pedro Rua Rodriguez Rochedo, Patrick Jochem
Summary: Improvements in modelling energy systems of populous emerging economies are crucial for successful global energy transition. The currently used open source models still lack appropriate open data. With Brazil's energy system as an example, we provide a comprehensive open dataset that includes time series data, geospatial data, and tabular data, enabling further energy system studies based on open data relevant to decarbonization.
Article
Economics
Nico Lehmann, Daniel Sloot, Christopher Schuele, Armin Ardone, Wolf Fichtner
Summary: Germany has been expanding its renewable energy generation in response to climate change. The preference for regional electricity tariffs among household consumers remains uncertain, with little knowledge about the factors underlying these preferences. Using a choice experiment and data from customers of a municipal energy supplier in Southwest Germany, this study examines consumers' preferences for regional electricity and the role of environmental and regional motivations. The results show that, on average, regional generation is the second most important attribute after price, and consumer preferences are driven by regional rather than environmental motivations. Furthermore, preferences become more nuanced when respondents are assigned to distinct clusters, with above-average regional motivations supporting these preferences. The policy implications of these findings are discussed.
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.
