Article
Engineering, Electrical & Electronic
Hui Xiao, Feiyu Long, Linjun Zeng, Wenqin Zhao, Jun Wang, Yihang Li
Summary: This paper proposes an optimization model for the regional integrated energy system (RIES) that considers multiple uncertainties, multi-energy coupling, and integrated demand response (IDR). The model includes renewable energy, upper grid, coupling devices, energy storage equipment, electric vehicles (EVs), and electrical, heating, and cooling loads. By comparing and analyzing the mathematical characteristics of the uncertainty of sources and loads, the robust optimization (RO) is used to model the uncertainty of sources, and the stochastic optimization (SO) is used to describe the uncertainty of loads. The proposed model is validated with an actual RIES, and the results demonstrate that the RIES model considering multiple uncertainties and multi-energy coupling can effectively ensure the cooperative operation of the RIES and improve its reliability and economy.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Energy & Fuels
Keyong Hu, Ben Wang, Yang Feng, Shihua Cao, Lidong Wang, Wenjuan Li
Summary: This study focuses on the safe and economic operation of integrated energy system (IES) in the presence of multiple uncertainties. A optimization model considering uncertainties in renewable energy output, energy purchase prices and integrated demand responses is proposed, using combined cooling heating and power as an example. Mathematical models and constraints are provided, and different methods are used to model uncertainty sources. A day-ahead optimal scheduling model for IES with multiple uncertainties is established using the stochastic scenario method and robust optimization method, and the whale optimization algorithm is improved to obtain the optimal solution.
ENERGY SCIENCE & ENGINEERING
(2023)
Article
Energy & Fuels
Shengnan Xiao, Qingzheng Guan, Weitao Zhang, Lianying Wu
Summary: This paper proposes a scheduling model for combined power and desalination systems to maximize total economic benefits while considering time-dependent electricity prices. Results show that the multistage flash system operates continuously throughout the scheduling cycle, while reverse osmosis systems only run in the Spring and may be shut down in other seasons based on electricity demand. Optimal scheduling of CPD systems improves operational stability and efficiency.
Article
Engineering, Marine
Zhibo Zhang, Bowen Zhou, Guangdi Li, Peng Gu, Jing Huang, Boyu Liu
Summary: This paper proposes a novel dual-layer distributed optimal operation methodology for managing multiple controllable distributed fuel-based microturbines in island microgrids. The proposed method effectively reduces the operating costs of island microgrids, unifies the operational status of microturbines, and achieves plug-and-play capability.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Jueming Hu, Yuhao Wang, Yutian Pang, Yongming Liu
Summary: Maintenance is crucial for the safety and integrity of infrastructures. This study aims to find the optimal maintenance policy that minimizes the maintenance cost while meeting system reliability requirements. By formulating the maintenance optimization as a Markov Decision Process and using a modified Reinforcement Learning method, the proposed approach, LPRT, considers both deterministic and stochastic maintenance scheduling with an infinite horizon. Numerical examples and comparisons with existing methods demonstrate the effectiveness and accuracy of LPRT. Parametric studies provide insights into the impact of uncertainty, subproblem size, and the number of stochastic stages on the maintenance cost.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Engineering, Electrical & Electronic
Hamid Karimi, Mahdieh Monemi Bidgoli, Shahram Jadid
Summary: This paper proposes an economic-environmental scheme for integrating the electrical, water, thermal, and cooling sections of a multi-energy system, aiming to increase efficiency and synergy in modern distribution grids. The use of industrial desalination units to provide potable water is considered, with a preference for groundwater resources due to high energy consumption. However, overuse of groundwater resources leads to various problems, such as climate changes and a lack of adequate water and food supply. Therefore, a multi-objective decision-making scheme is proposed to minimize operating costs and groundwater usage. Furthermore, thermal and electrical demand-side management, along with dispatchable and energy storage systems, are employed to address the intermittent behavior of renewable generation. The optimization results demonstrate that the proposed model reduces groundwater extraction by 26.8% with only a 1.12% increase in operating cost.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Thermodynamics
Chenyu Guo, Xin Wang, Yihui Zheng, Feng Zhang
Summary: The paper introduces a real-time dynamic optimal energy management method based on deep reinforcement learning algorithm to address energy management issues in microgrids effectively. Compared to traditional methods, the proposed approach offers higher computational accuracy and efficiency. Through offline training and online operation, the algorithm can learn from historical data to capture the uncertainty characteristics of renewable energy and load consumption.
Article
Thermodynamics
Peng Li, Zixuan Wang, Jiahao Wang, Weihong Yang, Tianyu Guo, Yunxing Yin
Summary: Integrated energy system (IES) is considered an effective way to alleviate energy supply pressure and improve energy efficiency. However, existing studies have not fully considered the coordination of uncertainty addressing and demand responses over different scheduling stages in the two-stage optimal operation method.
Article
Engineering, Electrical & Electronic
Yao Cai, Zhigang Lu, Yao Pan, Liangce He, Xiaoqiang Guo, Jiangfeng Zhang
Summary: This paper proposes a novel hybrid AC/DC MEMG structure based on a three-stage SST, and a two-level optimal scheduling model considering generation and load uncertainties, as well as integrated demand response based on Stackelberg game. A case study is conducted to validate the effectiveness of the proposed model, showing improved total benefit of EHO and DRA by 5.75% due to the demand response strategy.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Kshitij Bhatta, Qing Chang
Summary: There has been significant progress in smart manufacturing through advancements in automation systems, robotic technology, big data analytics, and Artificial Intelligence and Machine Learning algorithms. The paper addresses the importance of system productivity, product quality, and machine maintenance in smart manufacturing systems. A model is developed using a Heterogeneous Graph Structure that integrates robots, workstation processes, and product quality, and a control problem is formulated in the Decentralized Partially Observable Markov Decision Process framework. Multi-Agent Reinforcement Learning is employed to solve the problem and a case study demonstrates the effectiveness of the proposed control strategy.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Energy & Fuels
Zitong Zhang, Jing Shi, Wangwang Yang, Zhaofang Song, Zexu Chen, Dengquan Lin
Summary: In this paper, a bi-layer scheduling method for microgrids based on deep reinforcement learning is proposed to achieve economic and environmentally friendly operations. The framework of day-ahead and intra-day scheduling is established, and the implementation scheme for price-based and incentive-based demand response for the flexible load is determined. The bi-layer scheduling model of the microgrid is established, and the particle swarm optimization algorithm is used for day-ahead scheduling while the deep reinforcement learning algorithm is adopted for intra-day online scheduling. The proposed method is verified using actual microgrid data and shown to achieve optimization of scheduling cost and calculation speed, suitable for microgrid online scheduling.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2023)
Article
Energy & Fuels
Gang Wu, Ting Li, Min Li, Peng Lan, Ruiguang Ma, Tiannan Ma, Deng Jingwei
Summary: This paper proposes a cooperative optimization scheduling model to enhance the operation flexibility and economy of transmission-distribution integrated electricity-gas systems (TD-IEGS). By considering the coordinated operation of multi-energy coupling devices and utilizing the second order cone relaxation method, the model can reduce total operation cost and promote wind power utilization.
Article
Energy & Fuels
Jiandong Duan, Fan Liu, Yao Yang
Summary: An optimal operation model for integrated electricity and natural gas systems considering demand response uncertainty is proposed in this study. The uncertainty of wind power is dealt with using chance-constrained, and the models of three demand response loads are established. A distributed optimization model is used, and the effectiveness and convergence of the method are verified through a case study.
Article
Energy & Fuels
Tao Hai, Jincheng Zhou, Ammar K. Alazzawi, Tetsuya Muranaka
Summary: One of the best solutions to overcome environmental, technical and economic problems in the power system is the use of PHEVs. This article focuses on scheduling a microgrid with PHEVs and RESs to achieve economic, technical and environmental benefits. The intermittent behavior of renewable resources, PHEVs and loads is modeled using the MCS. The MSS algorithm is applied to resolve the optimization problem and it outperforms conventional algorithms.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Green & Sustainable Science & Technology
Ali Mobasseri, Marcos Tostado-Veliz, Ali Asghar Ghadimi, Mohammad Reza Miveh, Francisco Jurado
Summary: In recent years, multi-energy microgrids have become an important framework for utilizing clean and efficient electro-thermal resources and integrating multi-energy storage facilities. This paper proposes a hybrid robust energy management tool for such microgrids, effectively addressing uncertainties brought by unpredictable demand and renewable generation. The proposed methodology demonstrates high accuracy and computational efficiency.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Physics, Multidisciplinary
Fangfang Zhang, Zhe Huang, Lei Kou, Yang Li, Maoyong Cao, Fengying Ma
Summary: In this paper, a new 9-dimensional complex chaotic system with quaternion is proposed for the encryption of smart grid data. Pseudo-random sequences generated by the new chaotic system are used to encrypt power data. The verification results show that the proposed encryption scheme is technically feasible and available for power data encryption in smart grid.
Article
Energy & Fuels
Yang Li, Ruinong Wang, Yuanzheng Li, Meng Zhang, Chao Long
Summary: In a modern power system with a growing proportion of renewable energy, accurate wind power prediction is important for power grid dispatching plans. Traditional methods have concerns regarding data privacy and data islands. To address these issues, a scheme called federated deep reinforcement learning (FedDRL) is proposed, which combines federated learning and deep reinforcement learning (DRL). By sharing model parameters instead of private data, FedDRL can obtain an accurate prediction model while protecting data privacy and relieving communication pressure. Simulation results demonstrate the superiority of FedDRL in terms of forecasting accuracy compared to traditional methods.
Article
Computer Science, Artificial Intelligence
Yuanzheng Li, Shangyang He, Yang Li, Yang Shi, Zhigang Zeng
Summary: This article proposes a federated multiagent deep reinforcement learning algorithm for energy management in multimicrogrids. The federated learning mechanism is introduced to ensure data privacy and security. Experimental results demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Energy & Fuels
Yang Li, Meng Han, Mohammad Shahidehpour, Jiazheng Li, Chao Long
Summary: A community integrated energy system (CIES) is a significant component of the energy internet and smart city, providing a new solution to energy utilization and environmental pollution. To address uncertainties in renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A generative adversarial network based on the Wasserstein distance is proposed to generate scenarios for RGs, and an integrated demand response mechanism is developed to promote renewable energy consumption.
Article
Energy & Fuels
Jiankai Gao, Yang Li, Bin Wang, Haibo Wu
Summary: This paper proposes an MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework for the energy management problem of an MMG system. An improved MASAC algorithm is also introduced to deal with uncertainties and enhance the generalization ability of DRL. Experimental results demonstrate the effectiveness and superiority of the proposed method in power complementarity and operating cost reduction of the MMG system.
Article
Automation & Control Systems
Jia Cui, Mingze Gao, Xiaoming Zhou, Yang Li, Wei Liu, Jiazheng Tian, Ximing Zhang
Summary: This paper proposes a demand response method considering multiple flexible loads to characterize the integrated demand response resources. A physical process analytical deduction model is proposed to improve the classification of flexible loads. An improved WGAN-gradient penalty model is developed to enhance the modeling effect and convergence speed. The joint implementation of the PPAD and IWGAN-GP models reveals the correlation between flexible loads and an intelligent offline database is built to handle nonlinear factors in different response scenarios.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Chao Yang, Gaoshen Liang, Tianle Cheng, Yang Li, Shaoyan Li
Summary: This paper proposes an optimization method of restoration path to reconfigure the skeleton network for the blackout alternating current-direct current hybrid power grid. In this method, the influence of the restoration path on the system strength is derived in detail, the relationship between restoration path and line-commutated converter high-voltage direct current restoration characteristic is quantified, and non-tree paths are considered to establish an optimization model of restoration path.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2023)
Editorial Material
Energy & Fuels
Yang Li, Shunbo Lei, Xia Chen, Chao Long, Yifan Zhou, Young-Jin Kim
Article
Green & Sustainable Science & Technology
Yang Li, Jiting Cao, Yan Xu, Lipeng Zhu, Zhao Yang Dong
Summary: This study proposes a Transformer-based method for short-term voltage stability assessment in power systems, which addresses the issue of data imbalance by utilizing a conditional Wasserstein generative adversarial network with gradient penalty. Extensive numerical tests on the IEEE 39-bus test system demonstrate the robust performance of the proposed method under class imbalances and noisy environments.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2024)
Article
Engineering, Electrical & Electronic
Yang Li, Shitu Zhang, Yuanzheng Li, Jiting Cao, Shuyue Jia
Summary: This article proposes a new method for short-term voltage stability assessment using phasor measurement unit measurements, which overcomes limitations of existing methods in adapting to topological changes, sample labeling, and handling small datasets by employing deep transfer learning. Experimental results demonstrate the method's strong adaptability to topological changes and significant improvement in model evaluation accuracy on small-scale datasets.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Lei Kou, Jinbo Wu, Fangfang Zhang, Peng Ji, Wende Ke, Junhe Wan, Hailin Liu, Yang Li, Quande Yuan
Summary: An encryption algorithm for offshore wind power based on two-dimensional lagged complex logistic mapping (2D-LCLM) and Zhou Yi eight trigrams is proposed. The algorithm demonstrates resistance to common attacks and has excellent encryption performance.
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
(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.
