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Prof. Jie Xing
DONGHUA UNIVERSITY

Basic Info


Research Keywords & Expertise

0 New Energy
0 Power System Planning
0 Power System optimization
0 Power System and Automation
0 Stability & control

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Short Biography

Jie Xing received the Ph.D. degree from Shanghai Jiao Tong University, China, in 2010. She is current an associate professor with the College of Information Science and Technology, Dong Hua University, Shanghai, China. Her research interests are planning, stability and control of power system.

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Journal article
Published: 09 June 2021 in Energies
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Bidirectional coupling systems for electricity and natural gas composed of gas units and power-to-gas (P2G) facilities improve the interactions between different energy systems. In this paper, a combined optimization planning method for an electricity-natural gas coupling system with P2G was studied. Firstly, the characteristics of the component model of the electricity-natural gas coupling system were analyzed. The optimization planning model for the electricity-natural gas coupling system was established with the goal of minimizing the sum of the annual investment costs and the annual operation costs. Based on the established model, the construction statuses for different types of units, power lines, and pipelines and the output distribution values for gas units and P2G stations were optimized. Then, the immune algorithm was proposed to solve the optimization planning model. Finally, an electricity-natural gas coupling system composed of a seven-node natural gas system and a nine-node power system was taken as an example to verify the rationality and effectiveness of the model under different scenarios.

ACS Style

Jie Xing; Peng Wu. Optimal Planning of Electricity-Natural Gas Coupling System Considering Power to Gas Facilities. Energies 2021, 14, 3400 .

AMA Style

Jie Xing, Peng Wu. Optimal Planning of Electricity-Natural Gas Coupling System Considering Power to Gas Facilities. Energies. 2021; 14 (12):3400.

Chicago/Turabian Style

Jie Xing; Peng Wu. 2021. "Optimal Planning of Electricity-Natural Gas Coupling System Considering Power to Gas Facilities." Energies 14, no. 12: 3400.

Journal article
Published: 30 April 2021 in Sustainability
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State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method.

ACS Style

Jie Xing; Peng Wu. State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter. Sustainability 2021, 13, 5046 .

AMA Style

Jie Xing, Peng Wu. State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter. Sustainability. 2021; 13 (9):5046.

Chicago/Turabian Style

Jie Xing; Peng Wu. 2021. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter." Sustainability 13, no. 9: 5046.

Journal article
Published: 23 April 2021 in Fractal and Fractional
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The contribution of this article is mainly to develop a new stochastic sequence forecasting model, which is also called the difference iterative forecasting model based on the Generalized Cauchy (GC) process. The GC process is a Long-Range Dependent (LRD) process described by two independent parameters: Hurst parameter H and fractal dimension D. Compared with the fractional Brownian motion (fBm) with a linear relationship between H and D, the GC process can more flexibly describe various LRD processes. Before building the forecasting model, this article demonstrates the GC process using H and D to describe the LRD and fractal properties of stochastic sequences, respectively. The GC process is taken as the diffusion term to establish a differential iterative forecasting model, where the incremental distribution of the GC process is obtained by statistics. The parameters of the forecasting model are estimated by the box dimension, the rescaled range, and the maximum likelihood methods. Finally, a real wind speed data set is used to verify the performance of the GC difference iterative forecasting model.

ACS Style

Jie Xing; Wanqing Song; Francesco Villecco. Generalized Cauchy Process: Difference Iterative Forecasting Model. Fractal and Fractional 2021, 5, 38 .

AMA Style

Jie Xing, Wanqing Song, Francesco Villecco. Generalized Cauchy Process: Difference Iterative Forecasting Model. Fractal and Fractional. 2021; 5 (2):38.

Chicago/Turabian Style

Jie Xing; Wanqing Song; Francesco Villecco. 2021. "Generalized Cauchy Process: Difference Iterative Forecasting Model." Fractal and Fractional 5, no. 2: 38.

Journal article
Published: 01 March 2012 in Bulletin of the Polish Academy of Sciences: Technical Sciences
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Calculation of interval damping ratio under uncertain load in power system The problem of small-signal stability considering load uncertainty in power system is investigated. Firstly, this paper shows attempts to create a nonlinear optimization model for solving the upper and lower limits of the oscillation mode's damping ratio under an interval load. Then, the effective successive linear programming (SLP) method is proposed to solve this problem. By using this method, the interval damping ratio and corresponding load states at its interval limits are obtained. Calculation results can be used to evaluate the influence of load variation on a certain mode and give useful information for improvement. Finally, the proposed method is validated on two test systems.

ACS Style

Jie Xing; C. Chen; P. Wu. Calculation of interval damping ratio under uncertain load in power system. Bulletin of the Polish Academy of Sciences: Technical Sciences 2012, 60, 151 -158.

AMA Style

Jie Xing, C. Chen, P. Wu. Calculation of interval damping ratio under uncertain load in power system. Bulletin of the Polish Academy of Sciences: Technical Sciences. 2012; 60 (1):151-158.

Chicago/Turabian Style

Jie Xing; C. Chen; P. Wu. 2012. "Calculation of interval damping ratio under uncertain load in power system." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 1: 151-158.

Original articles
Published: 28 June 2010 in Electric Power Components and Systems
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Conventional optimal active power dispatch without considering small-signal stability constraints may violate the small-signal stability requirement. This article investigates the optimal active power dispatch problem with small-signal stability constraints. A new optimal active power dispatch model with small-signal stability constraints is built in order to ensure the generation optimization result that can satisfy the given small-signal stability requirement. The successive linear programming method is used to solve the new model. The proposed methodology is validated by two test systems.

ACS Style

Jie Xing; Chen Chen; Peng Wu. Optimal Active Power Dispatch with Small-signal Stability Constraints. Electric Power Components and Systems 2010, 38, 1097 -1110.

AMA Style

Jie Xing, Chen Chen, Peng Wu. Optimal Active Power Dispatch with Small-signal Stability Constraints. Electric Power Components and Systems. 2010; 38 (9):1097-1110.

Chicago/Turabian Style

Jie Xing; Chen Chen; Peng Wu. 2010. "Optimal Active Power Dispatch with Small-signal Stability Constraints." Electric Power Components and Systems 38, no. 9: 1097-1110.

Journal article
Published: 07 May 2008 in IEEE Transactions on Power Systems
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This paper studies the minimum load cutting problem existing in the process of transmission network expansion planning when load is uncertain and expressed in interval number. The interval most minimum load cutting model is established and can be solved to get the maximum value of the minimum load cutting number when load is interval uncertain. The solution can be used to evaluate the safety of the planning schemes under interval uncertainty and guide the new plans' making under interval load. The load values in the optimal solution of this problem have been proved to be at their lower or upper limits. Two different algorithms are proposed to solve this model. One is better in being capable of getting global optimal solution, while the other is better in calculation speed. Both of them are compared with two traditional methods that are generally used to evaluate the system's safety under uncertainty in aspects of precision and speed. This evaluation method is applied to the greedy randomized adaptive search procedure algorithm to solve the transmission expansion planning problem under interval load. The case results show the rightness and validity of the model and algorithms proposed in this paper.

ACS Style

Peng Wu; Haozhong Cheng; Jie Xing. The Interval Minimum Load Cutting Problem in the Process of Transmission Network Expansion Planning Considering Uncertainty in Demand. IEEE Transactions on Power Systems 2008, 23, 1497 -1506.

AMA Style

Peng Wu, Haozhong Cheng, Jie Xing. The Interval Minimum Load Cutting Problem in the Process of Transmission Network Expansion Planning Considering Uncertainty in Demand. IEEE Transactions on Power Systems. 2008; 23 (3):1497-1506.

Chicago/Turabian Style

Peng Wu; Haozhong Cheng; Jie Xing. 2008. "The Interval Minimum Load Cutting Problem in the Process of Transmission Network Expansion Planning Considering Uncertainty in Demand." IEEE Transactions on Power Systems 23, no. 3: 1497-1506.