Article
Energy & Fuels
Ahmet Mandev, Patrick Plotz, Frances Sprei, Gil Tal
Summary: This study investigated the charging behavior of plug-in hybrid electric vehicles (PHEV) and found that most users do not charge their vehicles overnight and engage in additional charging on 20-26% of driving days. The study also indicated that the utility factor should not be the sole measure of PHEV environmental performance.
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
Computer Science, Information Systems
Yuxuan Jiang, Qiang Ye, Bo Sun, Yuan Wu, Danny H. K. Tsang
Summary: This article discusses the management of charging processes for electric vehicles in a parking lot, emphasizing the importance of coordinating charging rates to smooth out the load profile. Statistical patterns in EV dynamics, such as peak arrival times, are incorporated into the charging rate coordination. A deep reinforcement learning approach is adopted to address the challenges posed by unknown state transition probabilities and inconsistent action spaces in the Markov decision process model. The customized model developed in this study, along with a deep policy gradient algorithm, outperforms benchmarks according to numerical results.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Theron Smith, Joseph Garcia, Gregory Washington
Summary: The ARVF algorithm is an efficient real-time PEV charging control method that utilizes fuzzy logic to adjust charging rates. Research demonstrates that when there is a significant deviation between forecasted and actual baseloads, the real-time capability of ARVF is more advantageous.
APPLIED SCIENCES-BASEL
(2021)
Article
Energy & Fuels
Ahmet Mandev, Patrick Ploetz, Frances Sprei, Gil Tal
Summary: The study investigated the daily charging behavior of over 10,000 Chevrolet Volt PHEVs and found that a percentage of users do not charge overnight, while additional charging occurs on 20-26% of driving days. It also concluded that the utility factor should not be the sole measure of environmental performance for PHEVs.
Article
Engineering, Civil
Amir Mirheli, Leila Hajibabai
Summary: This study presents a bi-level optimization program for designing and managing electric vehicle charging infrastructure, which effectively determines the optimal location, capacity, and pricing scheme.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Satadru Dey, Munmun Khanra
Summary: This article addresses the cybersecurity challenges associated with the large scale deployment of plug-in electric vehicles (PEVs) by exploring control-oriented approaches. Two algorithms for detecting cyberattacks on PEV battery packs during charging are discussed, including a static detector and a dynamic detector. A filter-based design approach for the dynamic detector is proposed to consider stability, robustness, and attack sensitivity as multiobjective criteria. Theoretical analysis and simulation studies show the effectiveness of the algorithms in detecting denial-of-charging (DoC) and overcharging attacks, indicating the superiority of the dynamic detector.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Automation & Control Systems
Jangkyum Kim, Hyeontaek Oh
Summary: This article proposes an efficient power operation scheme for EV charging facility by jointly analyzing various power rate policies, battery wear-out costs, and uncertainty issues caused by EV users' behavior. The effectiveness of the proposed scheme is analyzed using real-world EV operation datasets, showing a reduction of 20.5% in overall cost and 17.3% in peak power compared to other benchmark models when applied in an actual power system.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Review
Energy & Fuels
Emilia M. Szumska
Summary: This paper analyzes the availability of existing charging infrastructure equipped with fast charging points for electric vehicles in European Union countries and discusses EU policy on zero-emission vehicles and technical issues related to charging infrastructure. Based on a review of the current state of charging infrastructure and development plans in light of the EU Green Deal, it is concluded that the fast charging infrastructure for electric cars is still insufficiently developed in many regions. Due to the economic diversity of EU countries, the development of charging infrastructure varies, highlighting the importance of locating fast charging points primarily along the TEN-T network and highways.
Article
Energy & Fuels
Hua Wang, De Zhao, Yutong Cai, Qiang Meng, Ghim Ping Ong
Summary: This study developed a fast-charging facility planning model based on taxi trajectory data, which can help in planning electric vehicle charging facilities and save investment costs. The findings show that considering battery degradation and vehicle heterogeneity in charging demand can lead to more accurate planning solutions.
Article
Thermodynamics
Jonatas Augusto Manzolli, Joao Pedro F. Trovao, Carlos Henggeler Antunes
Summary: This study developed an optimization model to handle the charging schedule of electric bus fleets, aiming to minimize charging costs and consider battery degradation and energy trading. Through a real-world case study and sensitivity analysis, it was found that selling energy back to the grid may be economically attractive under certain charging cost conditions, and operation costs could be reduced by 38%.
Article
Energy & Fuels
Soomin Woo, Sangjae Bae, Scott J. Moura
Summary: This paper investigates the challenge of planning an Electric Vehicle (EV) charging facility that offers high quality service while keeping costs low. By proposing an optimization model with demand management strategies, the study aims to achieve a facility that guarantees high quality service at a minimal cost.
Article
Engineering, Manufacturing
Marianne Guillet, Maximilian Schiffer
Summary: Range and charge anxiety are significant barriers to the wider adoption of electric vehicles (EVs). A solution to this problem is to quickly and reliably locate suitable charging stations, which can alleviate drivers' anxieties and promote EV uptake. Existing commercial services for finding charging stations struggle with data inaccuracy due to conventional vehicles blocking access. Recent studies have explored stochastic search methods to account for availability uncertainty and minimize drivers' detour to available stations. However, both practical and theoretical approaches have not considered driver coordination through centralized data sharing. This research focuses on coordinated stochastic search algorithms to reduce visit conflicts and enhance the drivers' charging experience.
PRODUCTION AND OPERATIONS MANAGEMENT
(2023)
Article
Economics
Hyunhong Choi, Jeongeun Lee, Yoonmo Koo
Summary: The availability of charging facilities plays a critical role in electric vehicle (EV) adoption. Governments need to consider additional charging facility rollout policies even as the initial lack of charging availability improves. The opinions of conventional internal combustion engine vehicle (ICEV) owners are important in making policy decisions for EV charging facilities, as they may also value their rollout and some may become future EV owners. This study used the contingent valuation method to estimate how ICEV owners' willingness to pay changes under different facility rollout stages and types.
Article
Transportation Science & Technology
Jinhua Ji, Yiming Bie, Linhong Wang
Summary: To address the shortage of charging facilities for electric cars (ECs) and maximize the utilization of charging facilities for electric buses (EBs), a charging facility sharing strategy is proposed. This strategy allows ECs to use EBs' charging piles for a fee during specific time windows. A nonlinear integer programming model is developed to determine the vehicle types allowed to be charged in each time window, allocate daily service trips and charging trips to each EB, and optimize the overall costs and revenues. An algorithm combining enumeration method and branch and price is used to solve the optimization model. The proposed method is verified using a real EB route, demonstrating its effectiveness in increasing revenue for public transit companies and facilitating EC charging without disrupting EB schedules.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Computer Science, Information Systems
Jiatu Hong, Hongxun Hui, Hongcai Zhang, Ningyi Dai, Yonghua Song
Summary: The rapid growth of renewable energies poses higher requirements on the power system. Inverter air conditioners, accounting for a significant portion of city power consumption, have great regulation potential but challenging to control. This article proposes a distributed control scheme for large-scale dispersed IACs to provide operating reserve, featuring a normalization approach and a distributed consensus algorithm with a nonlinear protocol. Numerical studies confirm the feasibility and performance of this method.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Dongdong Zhang, Hongyu Zhu, Hongcai Zhang, Hui Hwang Goh, Hui Liu, Thomas Wu
Summary: This paper proposes a multi-objective optimization model for smart integrated energy system considering demand responses and dynamic prices, aiming to achieve optimal operation between different entities. The model includes a flexible two-dimensional demand response model and optimized dynamic energy prices, which help improve the economy and reliability of the system and promote interaction among multiple entities.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Electrical & Electronic
Yilin Wen, Zechun Hu, Shi You, Xiaoyu Duan
Summary: The mathematical formulation of the exact aggregate feasible region (AFR) for multiple distributed energy resources (DERs) is derived, and approximate models are proposed. Numerical simulations demonstrate the accuracy of the exact model, and the second-order approximate model performs best in terms of balancing accuracy, economics, and computational complexity.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Engineering, Electrical & Electronic
Xiaohe Yan, Chenghong Gu, Hongcai Zhang, Nian Liu, Furong Li, Yonghua Song
Summary: Existing capacity-based network pricing calculates network costs using discounted cash flows, but fails to account for uncertainties and flexibilities of network users. This paper proposes Incremental Cost Network Pricing based on Real Options (ICOC) as a new pricing method that incorporates real options theory to reflect network user uncertainties on network investment. The ICOC scheme allows network operators to delay investment and pay waiting costs based on options' value, thereby avoiding irreversible investment due to uncertainties. The cost is allocated to network users based on their nodal incremental costs. The proposed method is demonstrated on a practical network with different user types (uncertain, flexible, certain and nonflexible), and shows the ability to set cost-reflective price signals based on network user behavior.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Hongyi Li, Hongxun Hui, Hongcai Zhang
Summary: This paper focuses on tackling random packet drops in decentralized energy management. A novel consensus algorithm is proposed to recover the lost information, and its convergence and optimality are theoretically proven. Case studies confirm the effectiveness of the algorithm.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Green & Sustainable Science & Technology
Ge Chen, Hongcai Zhang, Hongxun Hui, Yonghua Song
Summary: This paper proposes a learning-based surrogate model to manage distributed renewable generation uncertainties by converting joint chance constraints into quantile-based forms. Two multi-layer perceptron models are trained to predict constraint violations and power loss, and data augmentation and calibration steps are developed to enhance model performance.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Green & Sustainable Science & Technology
Xinwei Shen, Qiuwei Wu, Hongcai Zhang, Liming Wang
Summary: We propose a planning method that optimizes the submarine cable layout of the offshore wind farm electrical collector system (OWF-ECS) with a double-sided ring topology, meeting the N-1 criterion on cable faults, and considering cable length and power losses. The proposed method uses mixed-integer quadratic programming (MIQP) based on the Capacitated Vehicle Routing Problem (CVRP) and power network expansion planning to approximate power losses. It also introduces cross-avoidance constraints and a minimum k-degree center tree model to improve convergence. Case studies show the effectiveness of the proposed method in reducing total cost and cable investment compared to conventional heuristic methods and Google OR-tools.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Engineering, Electrical & Electronic
Ke Li, Chengcheng Shao, Hongcai Zhang, Xifan Wang
Summary: Electric vehicles (EVs) are rapidly developing and EV charging stations (EVCS) are essential for meeting charging demand and promoting transportation electrification. This study presents a pricing method to maximize profits for EVCS owners, the charging service providers. A three-level pricing framework is established, where EVCSs set charging prices, EVs make route and charging choices, and electricity prices are determined in the power distribution network. An optimal pricing model is developed, considering traffic flow, power generation, and electricity prices, and a decomposition algorithm is proposed to address computational burdens and feasibility issues. Real-world case studies validate the effectiveness and benefits of the proposed method.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
Lingming Kong, Hongcai Zhang, Wei Li, Hao Bai, Ningyi Dai
Summary: This article proposes a spatial-temporal scheduling framework for optimizing the operation of electric bus fleets in power-transportation coupled networks. By scheduling the plug-in locations and charging/discharging profiles, the framework aims to minimize operational costs and maximize revenue by providing flexibility to the power network. The model explicitly describes the constraints of both power and transportation networks and adopts chance-constrained programming to address uncertainty in fleet energy consumption.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Energy & Fuels
Peipei Yu, Hongcai Zhang, Yonghua Song
Summary: In this study, a novel safe deep reinforcement learning (DRL) control method is proposed for a district cooling system (DCS) to provide regulation services. The method is model-free and adaptive to uncertainties from regulation signals and cooling demands. By combining the barrier function with traditional DRL, a safe DRL controller is constructed, which can avoid unsafe explorations during training and improve training efficiency. Case studies demonstrate the increased effectiveness and superiority of the proposed control method compared to traditional methods.
Article
Engineering, Electrical & Electronic
Zhenyi Wang, Peipei Yu, Hongcai Zhang
Summary: This paper proposes a privacy-preserving framework that combines federated learning and transfer learning to evaluate the regulation capacity of HVAC systems in different building types. The framework achieves privacy protection, addresses data insufficiency, and ensures high evaluation accuracy through classified federated learning and cross-type transfer learning algorithms.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
Peipei Yu, Hongcai Zhang, Yonghua Song, Hongxun Hui, Chao Huang
Summary: This paper proposes a DCS regulation capacity offering strategy based on deep reinforcement learning, which can effectively tackle various uncertainties. It also introduces a novel intrinsic-motivated method based on pseudo-count to improve the efficiency of the training. Numerical studies based on a realistic DCS system illustrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Green & Sustainable Science & Technology
Jinshuo Su, Hongcai Zhang, Hui Liu, Lei Yu, Zhukui Tan
Summary: This study proposes a method to improve the dynamic performance of secondary frequency control in islanded microgrids. By using membership function values for trade-off, transient frequency regulation and frequency error elimination can be achieved. Additionally, an adaptive delay compensator is introduced to effectively mitigate the adverse effects of communication delays on secondary frequency control.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Green & Sustainable Science & Technology
Hongyi Li, Hongxun Hui, Hongcai Zhang
Summary: This paper proposes a blockchain-empowered microgrid energy management framework that utilizes a consensus-based algorithm and a collusion prevention mechanism to address the challenges of decentralized energy management. The effectiveness and feasibility of the proposed method are demonstrated through theoretical proofs and experimental validations.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Engineering, Electrical & Electronic
Xiaohe Yan, Hongcai Zhang, Chenghong Gu, Nian Liu, Furong Li, Yonghua Song
Summary: This paper proposes a dynamic pricing signal for energy storage (ES) operation based on the truncated strategy, aiming to reduce the network investment cost. The design includes an operation strategy for ES to optimize network investment considering uncertainties, as well as a time of use (ToU) pricing scheme to reflect the impact of ES operation on network investment. The proposed method demonstrates a significant reduction in network charges with ES operation, ensuring fairness and efficiency of the pricing signal.
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
(2023)
