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Highway charging scheduling for battery electric vehicles is a complex research issue depending on the fast charging capacity provided and the information available in coordinating drivers' multiple charges at charging stations. Moreover, user's partially-known preferences and potential dynamic events remain extra challenges in maximizing user's satisfaction, improving the revenue of highway charging stations and utilizing the limited charging capacities. In such separate and simultaneous markets, users are reasonably modelled as the self-interested agents who aim to advance their own benefits but negotiable on their charging schedules. In this paper, we propose a simultaneous multi-round auction to address the highway charging scheduling problem, where users are allowed to bid and compromise on their preferred stops, charging time and energy simultaneously at separate charging stations. The objective is to maximize the total revenue of these stations. In the course of auction, users can gradually figure out how can their charges fit together by adaptively adjusting their bids placed at different stations. As a result, high-quality solutions are obtained and user's privacy can be preserved by progressively eliciting their private preferences as necessary. In addition, we also develop a dynamic scheduling algorithm to address the changes of user's reserved charges and unexpected arrivals of other vehicles. We conduct extensive experiments to validate our approach, the results demonstrate that it can achieve high efficiency with partial private information, as well as a higher revenue with dynamic scheduling algorithm. It can also greatly reduce the total waiting time of users against the first-come-first-serve policy.
Luyang Hou; Jun Yan; Chun Wang; Leijiao Ge. A Simultaneous Multi-Round Auction Design for Scheduling Multiple Charges of Battery Electric Vehicles on Highways. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -13.
AMA StyleLuyang Hou, Jun Yan, Chun Wang, Leijiao Ge. A Simultaneous Multi-Round Auction Design for Scheduling Multiple Charges of Battery Electric Vehicles on Highways. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-13.
Chicago/Turabian StyleLuyang Hou; Jun Yan; Chun Wang; Leijiao Ge. 2021. "A Simultaneous Multi-Round Auction Design for Scheduling Multiple Charges of Battery Electric Vehicles on Highways." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-13.
High voltage direct current (HVDC) is expected to bring forth large capacity, long transmission distance, and asynchronous grid interconnection. To quantitatively analyze the protection systems of HVDC, an evaluation system is proposed with a novel indicator framework and an innovative weighting method for the assessment of HVDC operating status. The novel indicator framework includes 31 indicators from the perspectives of reliability, fault monitoring, operational maintenance, control efficiency, and system redundancy. A self-learning interval analytic hierarchical process is used to decide the weights of the indicators based on the maximum entropy method. The optimal subjective weights of the indicators can be obtained by the self-learning process, considering not only the fuzziness of single expert scoring but also the difference between experts’ weights. A real HVDC project in Hubei province, China, was studied to verify the effectiveness of the proposed evaluation system.
Leijiao Ge; Yuanliang Li; Xinshan Zhu; Yue Zhou; Ting Wang; Jun Yan. An Evaluation System for HVDC Protection Systems by a Novel Indicator Framework and a Self-Learning Combination Method. IEEE Access 2020, 8, 152053 -152070.
AMA StyleLeijiao Ge, Yuanliang Li, Xinshan Zhu, Yue Zhou, Ting Wang, Jun Yan. An Evaluation System for HVDC Protection Systems by a Novel Indicator Framework and a Self-Learning Combination Method. IEEE Access. 2020; 8 (99):152053-152070.
Chicago/Turabian StyleLeijiao Ge; Yuanliang Li; Xinshan Zhu; Yue Zhou; Ting Wang; Jun Yan. 2020. "An Evaluation System for HVDC Protection Systems by a Novel Indicator Framework and a Self-Learning Combination Method." IEEE Access 8, no. 99: 152053-152070.
This paper investigates the possible cyber-physical attacks on voltage stability monitoring of a power transmission system. By considering a smart adversary that can launch a vector attack based on power flow equations, conventional voltage stability monitoring systems based on voltage stability indexes will fail to detect the instability issue. This can mislead the Power System Operator (PSO) to take inappropriate corrective action. In order to prevent and to combat such a malicious attack, a new indicator for efficient intrusion detection and a novel mitigation algorithm is proposed. This proposed detection algorithm employs the multi-port equivalent circuit of the power system to calculate the Thevenin Equivalent (TE) parameters. These TE parameters are then used to calculate an indicator, which reveals the attack on Phasor Measurement Unit (PMU) data. The proposed mitigation scheme then helps PSO to detect which PMU is under-attack. Furthermore, the amount of injected attack vectors is computed or estimated depending on the number of compromised PMUs. The effectiveness of the proposed techniques is demonstrated via illustrative case studies.
Mohsen Ghafouri; Minh Au; Marthe Kassouf; Mourad Debbabi; Chadi Assi; Jun Yan. Detection and Mitigation of Cyber Attacks on Voltage Stability Monitoring of Smart Grids. IEEE Transactions on Smart Grid 2020, 11, 5227 -5238.
AMA StyleMohsen Ghafouri, Minh Au, Marthe Kassouf, Mourad Debbabi, Chadi Assi, Jun Yan. Detection and Mitigation of Cyber Attacks on Voltage Stability Monitoring of Smart Grids. IEEE Transactions on Smart Grid. 2020; 11 (6):5227-5238.
Chicago/Turabian StyleMohsen Ghafouri; Minh Au; Marthe Kassouf; Mourad Debbabi; Chadi Assi; Jun Yan. 2020. "Detection and Mitigation of Cyber Attacks on Voltage Stability Monitoring of Smart Grids." IEEE Transactions on Smart Grid 11, no. 6: 5227-5238.
The concept of smart city urges for green technology to reduce carbon emission to ameliorate the global warming. Following this footprint, transportation sector is experiencing a paradigm shift and the transition to electric vehicles (EVs) has prodigious plausibility in reducing carbon emission. However, the anticipated EV penetration is hindered by several challenges, among them are their shorter driving range, slower charging rate and the lack of ubiquitous availability of charging locations, which collectively contribute to range anxieties for EVs drivers. Meanwhile, the expected immense EV load onto the power distribution network may compromise the power quality. To reduce the range anxiety, we present a two-stage solution to provision and dimension a DC fast charging station (CS) network that minimizes the deployment cost while ensuring a certain quality of experience for charging e.g., acceptable waiting times and shorter travel distances to charge. This solution also maintains the power quality by considering the distribution grid capacity, determining transformers' rating to support peak demand of EV charging and adding a minimum number of voltage regulators based on the impact over the power distribution network. We propose, evaluate and compare two CS network expansion models to determine a cost-effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future EV charging and conventional load demands. We also propose two heuristic methods and compare our solution with them. Finally, a custom built Python-based discrete event simulator is developed to test our outcomes.
Mohammad Ekramul Kabir; Chadi Assi; Hyame Alameddine; Maurice Antoun; Jun Yan. Demand-Aware Provisioning of Electric Vehicles Fast Charging Infrastructure. IEEE Transactions on Vehicular Technology 2020, 69, 6952 -6963.
AMA StyleMohammad Ekramul Kabir, Chadi Assi, Hyame Alameddine, Maurice Antoun, Jun Yan. Demand-Aware Provisioning of Electric Vehicles Fast Charging Infrastructure. IEEE Transactions on Vehicular Technology. 2020; 69 (7):6952-6963.
Chicago/Turabian StyleMohammad Ekramul Kabir; Chadi Assi; Hyame Alameddine; Maurice Antoun; Jun Yan. 2020. "Demand-Aware Provisioning of Electric Vehicles Fast Charging Infrastructure." IEEE Transactions on Vehicular Technology 69, no. 7: 6952-6963.
The transition to electric vehicles (EVs) has prodigious plausibility in reducing green house gas (GHG). But EVs acceptance is, however, hindered by several challenges; among them is their avidity for quicker charging at lower price. This article considers a photovoltaic (PV)-powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate-dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainty. An integer linear programming (ILP)-based centralized system is first proposed to minimize the charging price per EV. Due to the larger computational time, we subsequently present two game theoretic algorithms, i.e., game 1 and game 2. In game 1, players are oblivious of upcoming charging requests, whereas in game 2, players consider the future anticipated load to select their charging strategies. The games are shown to converge to a Nash equilibrium. The average unit price of game 2 is shown to be the same as the one of the optimal solution and takes considerably less computation time than the centralized method.
Mohammad Ekramul Kabir; Chadi Assi; Mosaddek Hossain Kamal Tushar; Jun Yan. Optimal Scheduling of EV Charging at a Solar Power-Based Charging Station. IEEE Systems Journal 2020, 14, 4221 -4231.
AMA StyleMohammad Ekramul Kabir, Chadi Assi, Mosaddek Hossain Kamal Tushar, Jun Yan. Optimal Scheduling of EV Charging at a Solar Power-Based Charging Station. IEEE Systems Journal. 2020; 14 (3):4221-4231.
Chicago/Turabian StyleMohammad Ekramul Kabir; Chadi Assi; Mosaddek Hossain Kamal Tushar; Jun Yan. 2020. "Optimal Scheduling of EV Charging at a Solar Power-Based Charging Station." IEEE Systems Journal 14, no. 3: 4221-4231.
This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader-follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers.
Zhenxing Li; Jun Yan; Wenwu Yu; Jianlong Qiu. Adaptive Event-Triggered Control for Unknown Second-Order Nonlinear Multiagent Systems. IEEE Transactions on Cybernetics 2020, 1 -10.
AMA StyleZhenxing Li, Jun Yan, Wenwu Yu, Jianlong Qiu. Adaptive Event-Triggered Control for Unknown Second-Order Nonlinear Multiagent Systems. IEEE Transactions on Cybernetics. 2020; ():1-10.
Chicago/Turabian StyleZhenxing Li; Jun Yan; Wenwu Yu; Jianlong Qiu. 2020. "Adaptive Event-Triggered Control for Unknown Second-Order Nonlinear Multiagent Systems." IEEE Transactions on Cybernetics , no. : 1-10.
Zhenxing Li; Jun Yan; Wenwu Yu; Jianlong Qiu. Event-Triggered Control for a Class of Nonlinear Multiagent Systems With Directed Graph. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2020, 1 -8.
AMA StyleZhenxing Li, Jun Yan, Wenwu Yu, Jianlong Qiu. Event-Triggered Control for a Class of Nonlinear Multiagent Systems With Directed Graph. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2020; ():1-8.
Chicago/Turabian StyleZhenxing Li; Jun Yan; Wenwu Yu; Jianlong Qiu. 2020. "Event-Triggered Control for a Class of Nonlinear Multiagent Systems With Directed Graph." IEEE Transactions on Systems, Man, and Cybernetics: Systems , no. : 1-8.
This paper proposes a new robust localization of mobile robot (MR) in the complex environment with non-line-of-sight (NLOS) situation. Two novel measurement processing strategies are proposed to achieve accurate recognition of NLOS measurements. In addition, an improved particle filter (PF) based on genetic algorithm (GA) is presented, where GA is introduced to improve the resampling process so PF can effectively overcome sample degradation while reducing computational complexity. The effectiveness of the algorithm is evaluated through a series of experiments and simulations. The proposed method demonstrates better accuracy than traditional methods, and can realize real-time, accurate and stable positioning of MRs in different types of NLOS environments.
Xingzhen Bai; Liting Dong; Leijiao Ge; Hongxiang Xu; Jinchang Zhang; Jun Yan. Robust Localization of Mobile Robot in Industrial Environments With Non-Line-of-Sight Situation. IEEE Access 2020, 8, 22537 -22545.
AMA StyleXingzhen Bai, Liting Dong, Leijiao Ge, Hongxiang Xu, Jinchang Zhang, Jun Yan. Robust Localization of Mobile Robot in Industrial Environments With Non-Line-of-Sight Situation. IEEE Access. 2020; 8 (99):22537-22545.
Chicago/Turabian StyleXingzhen Bai; Liting Dong; Leijiao Ge; Hongxiang Xu; Jinchang Zhang; Jun Yan. 2020. "Robust Localization of Mobile Robot in Industrial Environments With Non-Line-of-Sight Situation." IEEE Access 8, no. 99: 22537-22545.
Interval type-2 fuzzy sets (IT2FSs) can provide more flexibility than type-1 fuzzy sets (T1FSs) for depicting uncertain information, and multi-attribute decision making (MADM) problems with interval type-2 fuzzy information have received increasing attention. A new projection-based regret theory method is proposed to solve MADM problems under IT2FSs environments. First, a projection model of IT2FSs is defined that takes both the distance and angle information into consideration. Second, integrating the proposed projection model with regret theory, new utility and regret-rejoice functions are developed, respectively. Finally, a case study is provided to demonstrate the effectiveness of the proposed method. Sensitivity analysis shows the stability of the proposed method, and the ranking order does not change with different parameters. Comparisons are made with existing approaches to illustrate the advantage of the proposed method in reflecting decision makers’ psychological factors.
Huidong Wang; Xiaohong Pan; Jun Yan; Jinli Yao; Shifan He. A projection-based regret theory method for multi-attribute decision making under interval type-2 fuzzy sets environment. Information Sciences 2019, 512, 108 -122.
AMA StyleHuidong Wang, Xiaohong Pan, Jun Yan, Jinli Yao, Shifan He. A projection-based regret theory method for multi-attribute decision making under interval type-2 fuzzy sets environment. Information Sciences. 2019; 512 ():108-122.
Chicago/Turabian StyleHuidong Wang; Xiaohong Pan; Jun Yan; Jinli Yao; Shifan He. 2019. "A projection-based regret theory method for multi-attribute decision making under interval type-2 fuzzy sets environment." Information Sciences 512, no. : 108-122.
This paper considers an electric vehicle charging scheduling setting where vehicle users can reserve charging time in advance at a charging station. In this setting, users are allowed to explicitly express their preferences over different start times and the length of charging periods for charging their vehicles. The goal is to compute optimal charging schedules that maximize the social welfare of all users given their time preferences and the state of charge of their vehicles. Assuming that users are self-interested agents who may behave strategically to advance their own benefits rather than the social welfare of all agents, we propose an iterative auction, which computes high-quality schedules and, at the same time, preserves users' privacy by progressively eliciting their preferences as necessary. We conduct a game theoretical analysis on the proposed iterative auction to prove its individual rationality and the best response for agents. Through extensive experiments, we demonstrate that the iterative auction can achieve high-efficiency solutions with a partial value information. Additionally, we explore the relationship between scheduling efficiency and information revelation in the auction.
Luyang Hou; Chun Wang; Jun Yan. Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling. IEEE Transactions on Intelligent Transportation Systems 2019, 21, 3332 -3343.
AMA StyleLuyang Hou, Chun Wang, Jun Yan. Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling. IEEE Transactions on Intelligent Transportation Systems. 2019; 21 (8):3332-3343.
Chicago/Turabian StyleLuyang Hou; Chun Wang; Jun Yan. 2019. "Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling." IEEE Transactions on Intelligent Transportation Systems 21, no. 8: 3332-3343.
This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (NMPC) structure, a neural-network-based identification is established first to reconstruct the unknown system dynamics. Then, the actor–critic scheme is adopted with a critic network to estimate the index performance function and an action network to approximate the optimal control input. Meanwhile, as the MPC strategy can effectively determine the current control by solving a finite horizon open-loop optimal control problem, in the proposed algorithm, the infinite horizon is decomposed into a series of finite horizons to obtain the optimal control. In each finite horizon, the finite ADP algorithm solves the optimal control problem subject to the terminal constraint, the control constraint, and the disturbance. The uniform ultimate boundedness of the closed-loop system is verified by the Lyapunov approach. Finally, the ADP-based NMPC is conducted on two different cases and the simulation results demonstrate the quick response and strong robustness of the proposed method.
Lu Dong; Jun Yan; Xin Yuan; Haibo He; Changyin Sun. Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming. IEEE Transactions on Cybernetics 2018, 49, 4206 -4218.
AMA StyleLu Dong, Jun Yan, Xin Yuan, Haibo He, Changyin Sun. Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming. IEEE Transactions on Cybernetics. 2018; 49 (12):4206-4218.
Chicago/Turabian StyleLu Dong; Jun Yan; Xin Yuan; Haibo He; Changyin Sun. 2018. "Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming." IEEE Transactions on Cybernetics 49, no. 12: 4206-4218.
This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine gearbox. Different from traditional approaches where feature extraction and classification are separately designed and performed, this study aims to automatically learns effective fault features directly from raw vibration signals and meanwhile classifies the type of faults in a single learning framework, thus leading to an end-to-end learning based fault diagnosis system for wind turbine gearbox without additional signal processing techniques and diagnostic expertise. Considering multi-scale characteristics inherent in vibration signals of a gearbox, in this paper, a new multiscale convolutional neural network (MSCNN) framework is proposed to perform multi-scale feature extraction and classification simultaneously. A key advantage of the proposed MSCNN is the ability to learn complementary and rich fault pattern features at different time scales by incorporating multiscale learning into the CNN architecture, which greatly improves feature learning to enable better diagnosis performance. The proposed MSCNN framework is evaluated through experiments on a wind turbine test rig. The results demonstrate that the proposed MSCNN framework outperforms state-of-the-art methods and exhibits superior robustness against noise.
Guoqian Jiang; Haibo He; Jun Yan; Ping Xie. Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox. IEEE Transactions on Industrial Electronics 2018, 66, 3196 -3207.
AMA StyleGuoqian Jiang, Haibo He, Jun Yan, Ping Xie. Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox. IEEE Transactions on Industrial Electronics. 2018; 66 (4):3196-3207.
Chicago/Turabian StyleGuoqian Jiang; Haibo He; Jun Yan; Ping Xie. 2018. "Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox." IEEE Transactions on Industrial Electronics 66, no. 4: 3196-3207.
Clustering of data with high dimension and variable densities poses a remarkable challenge to the traditional density-based clustering methods. Recently, entropy, a numerical measure of the uncertainty of information, can be used to measure the border degree of samples in data space and also select significant features in feature set. It was used in our new framework based on the sparsity-density entropy (SDE) method to cluster the data with high dimension and variable densities. First, SDE conducts high-quality sampling for multidimensional data and selects the representative features using a method called sparsity score entropy (SSE). Secondly, the clustering results and noises are obtained adopting a new density-variable clustering method called density entropy (DE). DE automatically determines the border set based on the global minimum of border degrees and then adaptively performs cluster analysis for each local cluster based on the local minimum of border degrees. The effectiveness and efficiency of the proposed SDE framework are validated on synthetic and real data sets in comparison with several clustering algorithms. The results showed that the proposed SDE framework concurrently detected the noises and processed the data with high dimension and various densities.
Sheng Li; Lusi Li; Jun Yan; Haibo He. SDE: A Novel Clustering Framework Based on Sparsity-Density Entropy. IEEE Transactions on Knowledge and Data Engineering 2018, 30, 1575 -1587.
AMA StyleSheng Li, Lusi Li, Jun Yan, Haibo He. SDE: A Novel Clustering Framework Based on Sparsity-Density Entropy. IEEE Transactions on Knowledge and Data Engineering. 2018; 30 (8):1575-1587.
Chicago/Turabian StyleSheng Li; Lusi Li; Jun Yan; Haibo He. 2018. "SDE: A Novel Clustering Framework Based on Sparsity-Density Entropy." IEEE Transactions on Knowledge and Data Engineering 30, no. 8: 1575-1587.
Data-driven approaches have gained increasing interests in the fault detection of wind turbines (WTs) due to the difficulty in system modeling and the availability of sensor data. However, the nonlinearity of WTs, uncertainty of disturbances and measurement noise, and temporal dependence in time-series data still pose grand challenges to effective fault detection. To this end, this paper proposes a new fault detector based on a recently developed unsupervised learning method, denoising autoencoder (DAE), which offers the learning of robust nonlinear representations from data against noise and input fluctuation. A DAE is used to build a robust multivariate reconstruction model on raw time-series data from multiple sensors, and then, the reconstruction error of the DAE trained with normal data is analyzed for fault detection. In addition, we apply the sliding-window technique to consider temporal information inherent in time-series data by including the current and past information within a small time window. A key advantage of the proposed approach is the ability to capture the nonlinear correlations among multiple sensor variables and the temporal dependence of each sensor variable simultaneously, which significantly enhanced the fault detection performance. Simulated data from a generic WT benchmark and field supervisory control and data acquisition data from a real wind farm are used to evaluate the proposed approach. The results of two case studies demonstrate the effectiveness and advantages of our proposed approach.
Guoqian Jiang; Ping Xie; Haibo He; Jun Yan. Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information. IEEE/ASME Transactions on Mechatronics 2017, 23, 89 -100.
AMA StyleGuoqian Jiang, Ping Xie, Haibo He, Jun Yan. Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information. IEEE/ASME Transactions on Mechatronics. 2017; 23 (1):89-100.
Chicago/Turabian StyleGuoqian Jiang; Ping Xie; Haibo He; Jun Yan. 2017. "Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information." IEEE/ASME Transactions on Mechatronics 23, no. 1: 89-100.
This paper proposed a fault-tolerant approach based on information fusion (IF) to automatically locate the transient voltage disturbance source (TVDS) in smart distribution grids. We first defined three credibility factors that will influence the reliability of the direction-judgments at each power quality monitor (PQM). Then we proposed two rules of influence and a verification factor for the distributed generation (DG) integration. Based on the two sets of direction-judgment criteria, a novel decision-making method with fault tolerance based on the IF theory is proposed for automatic location of the TVDS. Three critical schemes, including credibility fusion, conflict weakening, and correction for DG integration, have been integrated in the proposed fusion method, followed by a reliability evaluation of the location results. The proposed approach was validated on the IEEE 13-node test feeder, and the TVDS location results demonstrated the effectiveness and fault tolerance of the IF based approach.
Guoqing Weng; Feiteng Huang; Jun Yan; Xiaodong Yang; Youbing Zhang; Haibo He. A Fault-Tolerant Location Approach for Transient Voltage Disturbance Source Based on Information Fusion. Energies 2016, 9, 1092 .
AMA StyleGuoqing Weng, Feiteng Huang, Jun Yan, Xiaodong Yang, Youbing Zhang, Haibo He. A Fault-Tolerant Location Approach for Transient Voltage Disturbance Source Based on Information Fusion. Energies. 2016; 9 (12):1092.
Chicago/Turabian StyleGuoqing Weng; Feiteng Huang; Jun Yan; Xiaodong Yang; Youbing Zhang; Haibo He. 2016. "A Fault-Tolerant Location Approach for Transient Voltage Disturbance Source Based on Information Fusion." Energies 9, no. 12: 1092.
The carbon emissions trading market and direct power purchases by large consumers are two promising directions of power system development. To trace the carbon emission flow in the power grid, the theory of carbon emission flow is improved by allocating power loss to the load side. Based on the improved carbon emission flow theory, an optimal dispatch model is proposed to optimize the cost of both large consumers and the power grid, which will benefit from the carbon emissions trading market. Moreover, to better simulate reality, the direct purchase of power by large consumers is also considered in this paper. The OPF (optimal power flow) method is applied to solve the problem. To evaluate our proposed optimal dispatch strategy, an IEEE 30-bus system is used to test the performance. The effects of the price of carbon emissions and the price of electricity from normal generators and low-carbon generators with regards to the optimal dispatch are analyzed. The simulation results indicate that the proposed strategy can significantly reduce both the operation cost of the power grid and the power utilization cost of large consumers.
Jun Yang; Xin Feng; Yufei Tang; Haibo He; Chao Luo; Jun Yan. A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers. Energies 2015, 8, 9087 -9106.
AMA StyleJun Yang, Xin Feng, Yufei Tang, Haibo He, Chao Luo, Jun Yan. A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers. Energies. 2015; 8 (9):9087-9106.
Chicago/Turabian StyleJun Yang; Xin Feng; Yufei Tang; Haibo He; Chao Luo; Jun Yan. 2015. "A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers." Energies 8, no. 9: 9087-9106.