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Your search keyword '"Wei, Zhinong"' showing total 412 results
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412 results on '"Wei, Zhinong"'

1. Urban Energy System Expansion Planning Considering Short-Term Operational Flexibility

Author

WEI Zhinong, YANG Li, CHEN Sheng, MA Junchao, PENG Yan, FEI Youdie

Subjects
urban energy system, carbon emission quota, short-term operational flexibility, expansion planning, energy hub (eh), Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Naval architecture. Shipbuilding. Marine engineering, VM1-989
Abstract

Urban cities are the main force of energy consumption and carbon emission. In the context of “dual carbon”, promoting low-carbon transformation of urban energy systems has become the top priority of urban planning. However, while the share of renewable energy output increases, the requirement for system flexibility also increases. To this end, an urban energy system expansion planning model that accounts for both long-term and short-term uncertainties is proposed. Multiple forms of energy, including electricity, gas, and heat are encompassed in this model. At the planning level, uncertainty and operational flexibility during the real-time operation stage are estimated, and a stochastic optimization approach is employed for solving. The capacity expansion of renewable energy generators and energy hubs (EHs) is considered by the model, with the imposition of carbon emission quota constraints to ensure the attainment of carbon emission reduction targets. The results show that the model can effectively improve the economy of urban energy system and the rate of consumption of renewable energy, and can meet different carbon-emission reduction requirements.

Published
2024
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13. Distributed Robust State Estimation for Hybrid AC/DC Distribution Systems using Multi-Source Data

Author

Huang, Manyun, Zhao, Junbo, Wei, Zhinong, Pau, Marco, and Sun, Guoqiang

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Hybrid AC/DC distribution systems are becoming a popular means to accommodate the increasing penetration of distributed energy resources and flexible loads. This paper proposes a distributed and robust state estimation (DRSE) method for hybrid AC/DC distribution systems using multiple sources of data. In the proposed distributed implementation framework, a unified robust linear state estimation model is derived for each AC and DC regions, where the regions are connected via AC/DC converters and only limited information exchange is needed. To enhance the estimation accuracy of the areas with low measurement coverage, a deep neural network (DNN) is used to extract hidden system statistical information and allow deriving nodal power injections that keep up with the real-time measurement update rate. This provides the way of integrating smart meter data, SCADA measurements and zero injections together for state estimation. Simulations on two hybrid AC/DC distribution systems show that the proposed DRSE has only slight accuracy loss by the linearization formulation but offers robustness of suppressing bad data automatically, as well as benefits of improving computational efficiency., Comment: 8 pages, 12 figures

Published
2020

14. Parameter identification of lithium-ion battery based on least squares algorithm with variable forgetting factor

Author

ZHU Weiping, CHEN Guowang, WEI Zhinong, and SONG Xingtao

Subjects
lithium-ion battery, model parameters, online identification, variable forgetting factor, second-order rc, least squares algorithm, Applications of electric power, TK4001-4102
Abstract

Power battery performance plays a pivotal role in the comprehensive performance of electric vehicles, and thus accurate identification of the parameters of the lithium-ion battery model is crucial for subsequent state-of-charge estimation and state-of-health prediction of the battery system. In order to improve the accuracy of parameter identification algorithm of lithium-ion battery model, a second-order RC equivalent circuit model of the battery is established, and a least-squares algorithm based on variable forgetting factor is used to identify the parameters of the lithium-ion battery model online. By building a test platform for charge and discharge experiments, based on the experimental data of two different operating conditions, the proposed algorithm, recursive least squares algorithm and traditional forgetting factor least squares algorithm are used to identify the parameters, and the accuracy of the proposed algorithm is described based on the comparison of the error between the estimated port voltage and the actual value obtained from the experimental test. The experimental results show that the recursive least squares algorithm based on the variable forgetting factor shows fast convergence and high estimation accuracy in the identification of lithium-ion battery parameters.

Published
2023
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