Article
Energy & Fuels
Yang Lei, Dan Wang, Hongjie Jia, Jiaxi Li, Jingcheng Chen, Jingru Li, Zhihong Yang
Summary: Planning a regional integrated energy system (RIES) based on different energy types and addressing clean energy and carbon neutrality globally is a hot topic in energy planning. Due to the growth of energy system construction cycle and the use of multiple energy sources, multi-stage planning considering uncertainty and construction time sequence has become the primary research direction. This study proposes a multi-stage scenario tree generation method and energy price determination method to minimize overall costs and optimize benefits like energy hub income and carbon emission reduction. Results show that advanced energy hub construction can benefit income and emission reduction, but requires more initial investment impacting renewable energy planning and construction.
Article
Computer Science, Interdisciplinary Applications
Yu Yang
Summary: This study proposes an optimization algorithm for blend planning under parameter uncertainties. It formulates the problem as a chance-constrained program and uses various relaxation and approximation techniques to find the global optimum of a deterministic approximation. The proposed method is evaluated through numerical cases in steel and gasoline productions, showing its solving time, probabilistic feasibility, and solution quality.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Fahad Saleh Alismail
Summary: This paper proposes a robust optimal planning strategy for addressing the challenges of optimal site selection and network expansion in modern power systems, utilizing a mixed-integer linear programming approach. The simulation results demonstrate the effectiveness of the strategy in finding the optimal size and location of ESS, as well as determining the optimal candidates and transfer capacity for tie-lines expansion planning.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Bining Zhao, Jesse Bukenberger, Mort Webster
Summary: A multi-stage and multi-scale stochastic generation expansion planning (GEP) model is proposed to represent uncertainties in load and renewable generation. The study finds that scenario partitioning methods are more effective in determining appropriate investment levels, while covariance-based approximations perform the best overall.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Joseph C. Lemaitre, Kyra H. Grantz, Joshua Kaminsky, Hannah R. Meredith, Shaun A. Truelove, Stephen A. Lauer, Lindsay T. Keegan, Sam Shah, Josh Wills, Kathryn Kaminsky, Javier Perez-Saez, Justin Lessler, Elizabeth C. Lee
Summary: The article introduces a flexible scenario modeling pipeline that provides support for public health decision makers to compare projections of epidemic trajectories and healthcare impacts from COVID-19 under different intervention scenarios in different locations. The article also points out the limitations of the model and future development directions.
SCIENTIFIC REPORTS
(2021)
Article
Thermodynamics
Ian J. Scott, Pedro M. S. Carvalho, Audun Botterud, Carlos A. Silva
Summary: For investment decision making and market modeling in the energy sector, it is important to consider a wide range of uncertainties. The difference between deterministic and stochastic solutions increases non-linearly when uncertainties across multiple inputs are combined. Combining uncertainty sources by adding a limited number of scenarios to multiple sources of uncertainty outperforms adding additional scenarios to any individual source of uncertainty.
Article
Thermodynamics
Hui Zhang, Jiye Wang, Xiongwen Zhao, Jingqi Yang, Zainab Ali Bu Sinnah
Summary: This study proposes a hydrogen-based multi-carrier energy system that improves energy efficiency through thermal water storage and hydrogen tank storage. It utilizes renewable energy sources like wind and solar power to simultaneously increase energy utilization and reduce carbon emissions. The environmental and economic goals are satisfied through a weighted-sum multi-objective method, and the trade-off solution between operation and emission costs is obtained through a max-min fuzzy method. Chance constraint programming is used to manage the risk associated with stochastic optimization, leading to reduced operation costs.
Article
Operations Research & Management Science
Patrizia Beraldi, Maria Elena Bruni
Summary: This paper analyzes the enhanced index tracking (EIT) investment strategy and proposes two different models to optimize portfolio performance by controlling returns and negative deviation. The experimental results show that these models can closely track the benchmark and achieve better performance out-of-sample.
OPERATIONAL RESEARCH
(2022)
Article
Robotics
Jiawei Fu, Xiaotong Zhang, Zhiqiang Jian, Shitao Chen, Jingmin Xin, Nanning Zheng
Summary: Velocity planning is crucial for autonomous driving as it generates the velocity profile based on a reference path. However, existing algorithms often neglect uncertainties in driving contexts, leading to potential risks. To address this, we propose an efficient safety-enhanced velocity planning algorithm (ESEVP) that considers uncertainties from trajectory prediction and velocity tracking using chance constraints. ESEVP formulates velocity planning as quadratic programming and explores candidate solutions through a fast planning space construction method, ensuring efficiency and covering all interaction possibilities. Experimental results prove ESEVP's superiority in terms of safety, comfort, and driving efficiency, and its successful deployment in real traffic demonstrates its practical competitiveness.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Thermodynamics
Elena Raycheva, Blazhe Gjorgiev, Gabriela Hug, Giovanni Sansavini, Christian Schaffner
Summary: Power systems globally are undergoing changes due to the rapid transformation of the generation mix. These changes in the power system configuration may increase the risk of failures and lower security of supply. To ensure secure development while considering costs, it is necessary to have adequate tools for assessing and preparing for future energy transition scenarios. This paper presents a risk-informed approach for generation and transmission expansion planning that integrates cost-based planning with risk-based transmission expansion planning, resulting in cost-effective solutions that guarantee system security and account for the risk implications of changes.
Article
Robotics
Gokhan Alcan, Ville Kyrki
Summary: This letter proposes a safe trajectory optimization and control approach based on constrained differential dynamic programming (DDP) for systems with uncertainties and nonlinear safety constraints. The approach ensures that the constraints are not violated by using constraint tightening and linear control gains. The empirical evaluation in simulation and physical hardware implementation demonstrates the computational feasibility and applicability of the approach.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Green & Sustainable Science & Technology
Fardin Niazvand, Saeed Kharrati, Farshad Khosravi, Abdollah Rastgou
Summary: This paper proposes a scenario-based assessment strategy for optimal hub scheduling by considering uncertain parameters and CCUS technology, aiming to minimize cost and pollution. Three planning horizons are investigated, and the problem is modeled as RMIP and solved in GAMS software. Results show that utilizing CCUS technology effectively reduces costs and pollution.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Management
Rick Jeuken, Michael Forbes, Michael Kearney
Summary: Coking coal is crucial for steel production, with its quality affecting the quality of the produced steel. Blending and processing the coal in a certain order can improve the product quality.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Automation & Control Systems
Paolo Bevilacqua, Marco Frego, Luigi Palopoli, Daniele Fontanelli
Summary: This article presents a framework for planning activities with a robotic navigation assistant, focusing on activity and motion planners. The activity planner composes abstract activities and probabilistic parameters to synthesize a plan, while the motion planner ensures physical feasibility and compatibility with user and environment constraints. The final plan aims to respect user constraints and optimize satisfaction.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Electrical & Electronic
Weikun Liang, Shunjiang Lin, Mingbo Liu, Qiong Wang, Yuquan Xie, Xuan Sheng
Summary: This study proposes a method to solve the economic dispatch problem in regional integrated energy systems (RIES), taking into account the pipeline dynamics and using the method of characteristics and data-driven techniques to achieve high computational accuracy. In addition, a stochastic multiple energy storage model considering the pipeline dynamics is established, and an improved approximate dynamic programming algorithm is proposed to efficiently solve this model. Test results on an actual RIES demonstrate the correctness and efficiency of the proposed methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Chenghan Zhou, Hongjie Jia, Xiaolong Jin, Yunfei Mu, Xiaodan Yu, Xiandong Xu, Binghui Li, Weichen Sun
Summary: A two-stage robust optimization method is proposed for buildings' space heating loads in an integrated community energy system. The hierarchical relationship between the ICES operator and consumers is formulated using bi-level optimization to achieve optimal heating pricing strategy. The thermal inertia of SHLs is modeled to provide heating demand response based on optimal heating sale prices. The ICES operator decides optimal energy purchase schedules from upper energy systems using a robust optimization method. The whole optimization model is transformed into a MILP based on various mathematical techniques. Numerical results show the effectiveness of the proposed model in balancing the interests of the ICES operator and consumers, and the improved profit using the robust optimization method.
Article
Automation & Control Systems
Yu Jin, Qian Xiao, Hongjie Jia, Yanchao Ji, Tomislav Dragicevic, Remus Teodorescu, Frede Blaabjerg
Summary: This article proposes a simplified and fast software-based fault detection and localization approach for the grid-connected modular multilevel converter. By calculating and comparing the errors between measured and estimated state variables, switch faults can be detected and localized. A modified Pauta criterion is used to confirm the faults. Simulation and experimental results demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Humanities, Multidisciplinary
Xingwei Li, Jiachi Dai, Xiaowen Zhu, Jingru Li, Jinrong He, Yicheng Huang, Xiang Liu, Qiong Shen
Summary: The green development behavior of construction enterprises contributes to resource recycling and environmental protection. However, there is no consensus on the mechanism behind this behavior. This study uses the Theory of Planned Behavior to examine the factors that influence the green development behavior of construction enterprises.
HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS
(2023)
Article
Automation & Control Systems
Qian Xiao, Hongjie Jia, Yi Tang, Yu Jin, Yunfei Mu, Remus Teodorescu, Frede Blaabjerg
Summary: A dual-layer modulated model predictive control scheme is proposed to achieve fast dynamics and fixed switching frequency for the cascaded H-bridge converter. The scheme reduces the number of evaluated control options for output current prediction by considering each phase cluster as a whole and selecting optimal vectors. Adaptive zero-sequence voltage is injected based on predicted cluster voltage differences to improve cluster voltage balancing speed. The proposed scheme avoids complicated weighting factor design and achieves fixed switching frequency through pulsewidth modulation.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Qian Xiao, Yu Jin, Josep Pou, Hongjie Jia, Yunfei Mu, Remus Teodorescu, Frede Blaabjerg
Summary: This article proposes a space-vector-equalized predictive current control scheme for the three-phase modular multilevel converter (MMC) to address the challenges of control options and steady-state tracking errors. By considering each arm of the MMC as a whole, the evaluated control options can be significantly reduced. Optimal vectors are selected and their dwell times are calculated based on the predicted output currents. Compensation terms are added to improve the steady-state performance. Experimental results demonstrate fast dynamic response and excellent steady-state performance for the three-phase MMC.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Civil
Kecheng He, Hongjie Jia, Yunfei Mu, Xiaodan Yu, Xiaohong Dong, Youjun Deng
Summary: This paper proposes a bilevel planning framework for coordinating truck mobile chargers (TMCs) and fixed chargers (FCs) on highways to enhance charging flexibility for electric vehicle (EV) users. A collaborative location optimization (CLO) approach is developed to determine optimal charging station locations, while a collaborative capacity optimization (CCO) approach optimizes the capacity of TMCs and FCs. The framework employs various techniques, such as origin-destination analysis, Floyd algorithm, and Monte Carlo simulation, to generate charging demand distribution, and utilizes the improved income approach (IIA) to capture heterogeneity in EV users' charging behavior. The proposed framework and method are demonstrated to be effective through numerical study.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Review
Engineering, Electrical & Electronic
Qian Xiao, Yu Jin, Hongjie Jia, Yi Tang, Allan Fagner Cupertino, Yunfei Mu, Remus Teodorescu, Frede Blaabjerg, Josep Pou
Summary: This article provides a comprehensive review of fault diagnosis and fault-tolerant control methods for MMC under submodule failures. A comparison of different fault diagnosis methods is conducted and verification results are provided to analyze the advantages and disadvantages of popular fault-tolerant control methods. The review concludes with a discussion of future trends and research opportunities.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Xiangke Li, Minghao Wang, Chaoyu Dong, Wentao Jiang, Zhao Xu, Xiaohua Wu, Hongjie Jia
Summary: The paper introduces paralleled BILCs for HMG, which provide a flexible and reliable power interaction between ac and dc subgrids with high power density. A DUC is proposed to achieve resilience reinforcement and global economic operation. The economic droop controls f(ac) - lambda(ac) and v(dc) - lambda(dc) are employed for ac DGs and dc DGs to decrease generation expenses, while coordinating the normalized ac subgrid's frequency and dc subgrid's voltage for economic power interaction.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Jiebei Zhu, Zhipeng Shen, Lujie Yu, Siqi Bu, Xialin Li, Chi Yung Chung, Campbell D. Booth, Hongjie Jia, Chengshan Wang
Summary: This paper proposes a novel bilateral inertia and damping emulation (BIDE) control scheme for VSC-HVDC transmission systems, which can provide autonomous inertial and damping responses to interconnected asynchronous AC grids. The proposed approach is communication-free and utilizes locally measured variables to obtain essential information for inertia and damping emulation. Modal analysis is conducted to investigate the impacts of BIDE-emulated inertia and damping on system stability, and controller hardware-in-the-loop experiments are used to verify the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Guihong Zhang, Junzhi Ren, Yuan Zeng, Fei Liu, Shibin Wang, Hongjie Jia
Summary: With the increase in renewable energy and DC transmission, synchronous generators in power grids are being replaced, causing a reduction in inertia, which affects the frequency stability of the grid. This study introduces the concept of virtual inertia and proposes an evaluation method for power grids with high-penetration electronic devices. A minimum inertia estimation method considering fast frequency response is also proposed, and the effectiveness of the model is demonstrated through a case study based on the South-East Australian system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jingru Li, Xiaofeng Chen, Peiyu Zheng, Qiang Wang, Zhi Yu
Summary: Knowledge Distillation (KD) is used to train smaller student models using a larger pretrained teacher model. Data-Free KD (DFKD) methods have been proposed to address privacy concerns in decentralized data systems like blockchain by extracting prior knowledge from teacher networks and synthesizing data for KD. This paper introduces Generative Knowledge Distillation (GenKD), a new DFKD framework that uses deep generative models (DGMs) to reduce the search space of data generation and achieve high-quality pseudo samples.
Article
Energy & Fuels
He Meng, Hongjie Jia, Tao Xu, Wei Wei, Xiaoyu Wang
Summary: The urgency of energy transition requires the rapid development of renewable energy and improvement of system efficiencies. However, the unpredictable nature of excessive renewable energy poses challenges to stable and efficient power system operation. Battery energy storage systems (BESSs) are crucial in mitigating random fluctuations and optimizing green energy usage. Additionally, an AC/DC hybrid distribution system can combine the benefits of both AC and DC subsystems without incurring additional losses during power conversion. This paper presents a bi-level optimization model for allocating BESS capacity in AC/DC hybrid distribution systems, considering the flexibility of voltage source converters and power conversion systems. Case studies and simulation results demonstrate the effectiveness of this model in suppressing voltage fluctuations and improving the cost-benefit analysis of BESSs from a life cycle perspective.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2023)
Article
Energy & Fuels
Tao Zhang, Yunfei Mu, Hongjie Jia, Xinying Wang, Tianjiao Pu
Summary: An optimal power flow (OPF) model for islanded distribution networks equipped with soft open points (SOPs) is proposed in this paper, which is of great significance in restoring critical loads. Unlike in the grid-connected mode, the adequacy of local power generation in distribution networks is critical for islanded systems. To exploit the available resources, an optimal secondary droop control strategy is introduced for the islanded distribution networks, thereby minimizing load shedding. The efficiency and accuracy of the formulated OPF problem are guaranteed by proposing a successive mixed-integer second-order cone programming (S-MISOCP) algorithm for handling the nonlinear islanded power flow formulations. The effectiveness of the proposed approach is tested through two case studies incorporating a modified IEEE 33-bus system and IEEE 123-bus system.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Xiaoyu Wang, Hongjie Jia, Zibo Wang, Xiaolong Jin, Youjun Deng, Yunfei Mu, Xiaodan Yu
Summary: This paper investigates the application of building thermal energy storage in Peer-to-Peer (P2P) energy trading and proposes a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. A real-time P2P energy trading method based on model predictive control is proposed, along with a distributed trading actions implementation method based on continuous double auction. Numerical results show that the proposed method can reduce the operational cost of prosumers by 3.7% and promote the local integration of renewable energy by 3.1%.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
Article
Energy & Fuels
Jiarui Zhang, Yunfei Mu, Zeqing Wu, Zhe Liu, Yi Gao, Hongjie Jia, Hairun Li
Summary: An affine arithmetic-based model predictive control approach is proposed to balance the heating supply reliability and operation economy during power outages. Numerical studies show that the method can maintain minimum thermal demand and reduce operation cost under uncertainties.
IET ENERGY SYSTEMS INTEGRATION
(2023)
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.