Article
Energy & Fuels
S. J. Sultanuddin, R. Vibin, A. Rajesh Kumar, Nihar Ranjan Behera, M. Jahir Pasha, K. K. Baseer
Summary: Electric vehicles (EV) have become the preferred option in transportation due to their environmental and energy sustainability. However, uncontrolled EV charging can increase consumer costs and overload the grid. This research proposes an improved reinforcement learning charging management system to prevent grid overload. Under realistic operating conditions, the proposed approach provides an adjustable, scalable, and flexible strategy for an electric car fleet. Compared to an uncontrolled charging strategy, the proposed reinforcement learning technique reduces the variance of the overall load by 68%.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Civil
Shuai Mao, Yan Wang, Quanxue Guan, Yunjian Xu
Summary: This study addresses the joint battery charging and replenishment scheduling problem in a battery swapping charging system, taking into account random electric vehicle arrivals, renewable generation, and electricity prices. The proposed approach integrates structural properties, such as threshold-charging and least demand first structures, to reduce the dimensionality of the action space. Experimental results demonstrate that the proposed approach outperforms various structural charging and replenishment policies as well as a vanilla soft actor-critic algorithm, achieving significant cost savings of 7.16%-78.61% and 6.53%-93.73%.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xi Chen, Haihui Wang, Fan Wu, Yujie Wu, Marta C. Gonzalez, Junshan Zhang
Summary: This article presents a model of the electric vehicle (EV) charging network as a cyber-physical system that is coupled with transportation networks and smart grids. An EV charging station recommendation algorithm is proposed to create synergy between transportation networks and smart grids and utilize EV charging activities as a load-balancing tool to transfer energy between unbalanced distribution grids.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Energy & Fuels
Jean-Michel Clairand, Mario Gonzalez-Rodriguez, Irvin Cedeno, Guillermo Escriva-Escriva
Summary: Many governments are encouraging cleaner transportation, and the introduction of electric buses is seen as a way to mitigate the pollution caused by public transportation. However, there are technological challenges in terms of charging and scheduling for electric buses, which are different from internal combustion ones.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Management
Pengyu Yan, Kaize Yu, Xiuli Chao, Zhibin Chen
Summary: This study proposes a Markov decision process to optimize the charging and order-dispatching schemes for an e-hailing EV fleet. An online approximation algorithm is developed using the model-based reinforcement learning framework and a novel SARSA(A)-sample average approximation architecture. The proposed approach increases the daily revenue by an average of 31.76% and 14.22%, respectively, compared with existing methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Energy & Fuels
Rudraksh S. Gupta, Arjun Tyagi, S. Anand
Summary: This study focuses on the impact of electric vehicles on the electric grid and the optimal allocation of rapid charging stations, as well as presenting future trends and current challenges. A comprehensive review provides insights into the electrification trend of electric vehicles in the transportation sector.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Thermodynamics
Jean-Michel Clairand, Mario Gonzalez-Rodriguez, Rajesh Kumar, Shashank Vyas, Guillermo Escriva-Escriva
Summary: This study proposes an optimal siting and sizing approach for an electric taxi charging station, considering transportation and power system needs, with particular attention to taxi drivers' requirements. Network modeling and sensitivity analysis are used to explore interest points and uncertainties in traffic flow in Quito, the capital of Ecuador.
Article
Environmental Studies
Sierra Spencer, Zhe Fu, Elpiniki Apostolaki-Iosifidou, Timothy E. Lipman
Summary: Effective management of electric vehicle charging is crucial for reducing peak electricity demand, increasing utilization of renewable energy resources, and lowering charging costs. Studies have shown that optimization measures can successfully shift charging load from high grid cost periods to low grid cost periods, and effectively relocate charging events across different time periods and locations.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2021)
Article
Thermodynamics
Ziyue Jiang, Jingzuo Han, Yetong Li, Xinyu Chen, Tianduo Peng, Jianliang Xiong, Zhan Shu
Summary: This study proposes an optimization methodology for EV charging station layout that considers EV charging behavior, sequential charging demand, and its impact on the power system. Applying this methodology to Jiangxi, the recommended charging station capacities in workplaces, residences, and shopping centers achieve increased renewable energy integration and reduced carbon emissions.
Article
Computer Science, Information Systems
Bei Li, Jiangchen Li, Mei Han
Summary: In this article, deep reinforcement learning is used to determine the refuelling price for HFCEVs in a real-world transportation network. The simulation results show that this method can reduce the total travel time of the network and the total operation costs of the power network.
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)
Review
Environmental Studies
S. Satheesh Kumar, B. Ashok Kumar, S. Senthilrani
Summary: This review article provides a comprehensive overview of research on renewable solar photovoltaic (PV) nanogrids for electric vehicle (EV) charging. The article describes the characteristics of small-scale power systems that incorporate reliability, control, and power quality (PQ) and includes PV as the power source, battery energy storage systems (BESS), smart inverters, and EV charging stations. The article evaluates control algorithms based on metrics such as voltage profiles, renewable penetration, PV curtailment, and net power flows. It also discusses the dynamic operation of four different BESS control algorithms for solar EV charging nanogrids and highlights the importance of addressing research gaps and future trends in this field.
ENERGY & ENVIRONMENT
(2023)
Article
Energy & Fuels
Sangyoon Lee, Dae-Hyun Choi
Summary: A privacy-preserving distributed deep reinforcement learning (DRL) framework is proposed to maximize the profits of smart EVCSs without sharing EVCS data. Numerical examples demonstrate the effectiveness of the proposed approach under varying conditions.
Review
Transportation
Gregory Carlton, Selima Sultana
Summary: Research on equity in EV charging is still in its early stages, with a lack of clear normative evaluations compared to the wider transportation equity literature. Perspectives on charging equity are mainly dominated by North American and European views, with limited perspectives from other regions. There are concerns that charging incentivisation schemes and planning efforts may favor wealthier individuals, and there are differences in charging needs and desires between high and low adoption groups. However, these findings are limited in geographical and philosophical contexts, and there are gaps in the literature for new methodological and topical contributions to this area.
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
Can Dang, Xifan Wang, Chengcheng Shao, Xiuli Wang
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
(2019)
Article
Engineering, Electrical & Electronic
Shijun Tian, Xifan Wang, Xiuli Wang, Chengcheng Shao, Rong Ye
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
(2019)
Article
Engineering, Electrical & Electronic
Tao Qian, Chengcheng Shao, Xiuli Wang, Mohammad Shahidehpour
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Engineering, Electrical & Electronic
Tao Qian, Chengcheng Shao, Xuliang Li, Xiuli Wang, Mohammad Shahidehpour
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Engineering, Electrical & Electronic
Chengcheng Shao, Chenjia Feng, Mohammad Shahidehpour, Quan Zhou, Xiuli Wang, Xifan Wang
Summary: This paper proposes an optimal operation strategy for the integrated electric power and hydrogen system (IPHS) using hydrogen tube trailers for transportation. The solution method based on the alternating direction method of multipliers (ADMM) coordinates HES and EPS constraints. Case studies verify the validity of the model and confirm the necessity of considering HES in enhancing EPS operation.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Chenjia Feng, Chengcheng Shao, Xifan Wang
Summary: This paper proposes a fast UC method based on LSTC, which reduces the scale of UC and solution time by load clustering and constraint adjustment, considering the stochastic nature of wind power. The validity and feasibility of this method are verified in practical case studies, providing an efficient tool for the planning and operation of large-scale power systems.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2021)
Article
Engineering, Multidisciplinary
Wenjing Dong, Chengcheng Shao, Chenjia Feng, Qian Zhou, Zhaohong Bie, Xifan Wang
Summary: This article studies the operation of the integrated electric power and hydrogen system (IPHS) with a focus on the demand response of hydrogen fuel cell vehicles (HFCVs). Through the formulation of a refueling load model, the development of an optimal IPHS operation model, and the use of a Lagrangian relaxation-based method, the researchers found that guiding HFCV refueling properly can lead to decreased overall operational costs.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(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, Electrical & Electronic
Tao Qian, Chengcheng Shao, Di Shi, Xiuli Wang, Xifan Wang
Summary: This paper proposes a deep learning-based automatic mechanism design method to improve budget balance in the local energy market. By constructing a convolutional neural network and utilizing gated recurrent units to handle non-stationary bidding environments, this method is efficient and incentive compatible.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Electrical & Electronic
Chenjia Feng, Chengcheng Shao, Yunpeng Xiao, Zhaoyang Dong, Xifan Wang
Summary: This paper proposes a bi-level strategic operation model for hydrogen energy service providers (HESPs), which coordinates bidding, hydrogen production, and hydrogen transportation to minimize overall costs. The model also simulates market clearing to estimate the impact of HESP behavior on electricity prices. A model reformulation technique is developed to connect discrete-time hydrogen production and continuous-time hydrogen transportation. The proposed model is validated and the necessity of coordinating hydrogen production and transportation in the strategic operation of HESPs is confirmed through case studies.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Electrical & Electronic
Chengcheng Shao, Ke Li, Zechun Hu, Mohammad Shahidehpour
Summary: This paper proposes a coordinated planning method for investment and operation of emerging urban energy infrastructures. By optimizing the location, size, and development strategies of refueling stations, as well as the coordination among electric power, natural gas, and transportation networks, the investment and operation costs of urban energy infrastructures can be reduced.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Electrical & Electronic
Chengcheng Shao, Ke Li, Tao Qian, Mohammad Shahidehpour, Xifan Wang
Summary: This paper proposes the generalized user equilibrium (GUE) method for the coupled power-transportation network (CPTN) operation, which tightens the interaction between the urban power distribution network (PDN) and transportation network (TN). The GUE concept is introduced to describe the steady traffic flow distribution under the constraints placed by PDN operation on TN. A GUE-based coordination model is established for CPTN with PDN generation scheduled and TN traffic assigned simultaneously. The effectiveness of the proposed model and method is verified through case studies on a real-world network, demonstrating the necessity of GUE and its potential in improving the CPTN operation.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
Ke Li, Chengcheng Shao, Zechun Hu, Mohammad Shahidehpour
Summary: This paper proposes a method for coordinated planning of PDN-TN, which determines the optimal deployment of new roads, placement of EVCSs, and expansion of PDNs. The method considers drivers' behaviors, traffic flow assignment, and rationality of users. Numerical studies validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Civil
Chengcheng Shao, Tao Qian, Yanan Wang, Xifan Wang
Summary: The paper proposes a coordinated planning method for power distribution networks and XFC EV charging stations, considering on-site batteries. It studies the operation of XFC stations, the influence on distribution networks, and introduces on-site batteries to optimize XFC energy usage.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)