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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Zenghui Zhang Xinhui Lu Key Laboratory of Process Optimization and Intelligent Decision-making Schoolof Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

An industrial park containing distributed generations (DGs) can be seen as a microgrid. Due to the uncertainty and intermittency of the output of DGs, it is necessary to add battery energy storage system (BESS) in industrial parks. The battery state of health (SOH) is an important indicator of battery life. It is necessary to fully consider the battery SOH during the energy optimization of industrial parks. In this work, a two-stage model suitable for charge and discharge optimization of BESSs in industrial park microgrids is proposed. The first stage of the model is a charge and discharge scheduling of a BESS considering the SOH. This stage mainly schedules the charge and discharge time and the total amount of time. The second stage of the model is employed to further determine the charge and discharge allocation of all battery packs to achieve BESS life prolongation. The experimental results show that the introduction of SOH makes the scheduling results more realistic. Also, it balances the changes in the SOHs of all battery packs.

关键词： Schedules Job shop scheduling Uncertainty Rain Microgrids System integration Discharges (electric)

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A Credit Management Mechanism for Peer-to-Peer Electricity Trading in Blockchain Environment

A Credit Management Mechanism for Peer-to-Peer Electricity T...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Jinhuan Guo Kaile Zhou Jie Chong Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

With the rapid development of distributed generation, peer-to-peer (P2P) electricity trading between microgrids in blockchain environment has become one of the indispensable ways to consume the local renewable energy. The existing credit management for P2P electricity trading generally realized through the smart contract of penalty, which cannot show the risk identification and risk aversion behaviors of each microgrid. Therefore, this study proposes a newly credit management mechanism to reduce the defaults, which achieves credit-based risk control optimization and credit-based continuous double auctions. In the first stage, microgrids make robust control optimize to submit a trading strategy that follows contracts as far as possible. In the second stage, which realizes rewards and penalties through credit-based equivalent price conversion. The experimental results show that the proposed credit management mechanism can improve microgrids' interests and ensure fair environment.

关键词： Robust control Renewable energy sources Smart contracts Microgrids System integration Power markets Real-time systems

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Media Attention and ESG Greenwashing: Evidence from Chinese Listed Companies

SSRN

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SSRN 2023年

作者： Jiang, Cuixia Li, Xiuxiu Xu, Qifa Liu, Junhang School of Management Hefei University of Technology Anhui Hefei230009 China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Ministry of Education Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Anhui Hefei230009 China

We investigate the relationship between media attention and environmental, social, and governance (ESG) greenwashing using the data of Chinese A-share listed companies from 2011 to 2020. Our results show that media attention can inhibit corporate ESG greenwashing behavior. Moreover, this inhibiting effect is mainly caused by paper media and becomes more prominent in non-state-owned enterprises (non-SOEs). The mediation analysis proves that media attention plays an important role in improving investor attention, thereby inhibiting corporate ESG greenwashing. The moderation analysis indicates that the effect of media attention on inhibiting ESG greenwashing is weakened in companies with more analyst following and higher quality of internal control. Our study provides a new view on the governance of ESG greenwashing in China. © 2023, The Authors. All rights reserved.

关键词： Quality control

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Distributionally Robust optimization for Internet Data Center with Renewable Energy

Distributionally Robust Optimization for Internet Data Cente...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Zhineng Fei Xinhui Lu Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

More and more internet data centers (IDCs) are trying to use renewable energy sources (RESs). However, powering IDCs with intermittent RESs presents a significant challenge. In addition, power and workload management in IDCs has great potential to reduce energy consumption, carbon footprints and energy cost. This study proposes an optimal load dispatch model for an IDC with battery energy storage system (BESS), which aims to lower the total costs. To accommodate the uncertainties of wind power, a data-driven distributionally robust optimization (DDRO) method is adopted. Then the column-and-constraint generation method (C&CG) is used to solve the corresponding optimization problem. In the experiment with the real-world traces of workload arrival and wholesale electricity price, three scheduling scenarios are investigated. Moreover, robust optimization and stochastic programming are implemented for comparison. Experimental results reveal that total costs of the IDC can be effectively reduced by adopting BESS and implementing workload dispatch. Meanwhile, the results also demonstrate the effectiveness of DDRO approach.

关键词： Renewable energy sources Data centers Costs Uncertainty Stochastic processes System integration Wind power generation

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A MILP Model and Two-Stage Heuristic Algorithm for Vehicle-Assisted Multi-Drone Inspection Routing Problem

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IET intelligent Transport Systems 2025年第1期19卷

作者： Xia, Yuanqing Luo, He Dong, Rui Yin, Youlong Lin, Shizhong Wang, Guoqiang School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization & Intelligent Decision-Making Ministry of Education Hefei China Engineering Research Center for Intelligent Management of Aerospace System Anhui Province Hefei China Management Center of UAV Inspection Operation State Grid Anhui Electric Power Co. Ltd Hefei China Anhui Power Transmission & Transformation Engineering Co. Ltd Hefei China

This paper introduces a vehicle-assisted multi-drone inspection routing problem (VAMDIRP), which enables the vehicle to repeatedly traverse roads, thereby reducing task completion time. Firstly, a mixed-integer linear programming (MILP) model is constructed for the problem using a series of decision variables and auxiliary variables. The model can be solved by GUROBI for small-scale instances. Secondly, a two-stage heuristic algorithm is proposed to solve large-scale instances. Experimental results show that this algorithm improves the solution quality by an average of 20.71% and reduces the running time by an average of 60.31% compared to existing algorithms. Finally, a sensitivity analysis is conducted on relevant parameters, revealing that changes in the number and speed of drones significantly affect the solution quality. © 2025 The Author(s). IET intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Sensitivity analysis

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Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model

arXiv

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arXiv 2024年

作者： Zhou, Yonghang Zhu, Hongyi Chai, Yidong Jiang, Yuanchun Liu, Yezheng School of Management Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Department of Information Systems and Cyber Security University of Texas at San Antonio 1 UTSA Circle San AntonioTX78249 United States

Web attacks are one of the major and most persistent forms of cyber threats, which bring huge costs and losses to web application-based businesses. Various detection methods, such as signature-based, machine learning-based, and deep learning-based, have been proposed to identify web attacks. However, these methods either (1) heavily rely on accurate and complete rule design and feature engineering, which may not adapt to fast-evolving attacks, or (2) fail to estimate model uncertainty, which is essential to the trustworthiness of the prediction made by the model. In this study, we proposed an Uncertainty-aware Ensemble Deep Kernel Learning (UEDKL) model to detect web attacks from HTTP request payload data with the model uncertainty captured from the perspective of both data distribution and model parameters. The proposed UEDKL utilizes a deep kernel learning model to distinguish normal HTTP requests from different types of web attacks with model uncertainty estimated from data distribution perspective. Multiple deep kernel learning models were trained as base learners to capture the model uncertainty from model parameters perspective. An attention-based ensemble learning approach was designed to effectively integrate base learners' predictions and model uncertainty. We also proposed a new metric named High Uncertainty Ratio-F Score Curve to evaluate model uncertainty estimation. Experiments on BDCI and SRBH datasets demonstrated that the proposed UEDKL framework yields significant improvement in both web attack detection performance and uncertainty estimation quality compared to benchmark models. © 2024, CC BY.

关键词： Adversarial machine learning

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Exploring the impact of COVID-19 on travel behavior based on EV charging big data

Exploring the impact of COVID-19 on travel behavior based on...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Dingding Hu Fangyi Li Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

The COVID-19 pandemic has forced many governments around the world to implement strict lockdown measures and order citizens to stay at home, which has caused a major change in travel patterns. This study leveraged electric vehicle charging big data in Hefei, Anhui Province, China to estimate electric vehicle charging demand in the absence of the COVID-19 pandemic using multi-layer perceptron model, which quantified the impact of the COVID-19 pandemic. In addition, we employed the vector autoregressive model to investigate the dynamic relationships between the changes in charging demand and various explanatory factors. The results suggest that the daily average charging demand in Hefei decreased by 78.3 % compared to the predicted value during the pandemic. Furthermore, according to the variance decomposition and impulse response function analysis, national confirmed COVID-19 cases play a dominant role in reducing charging demand. The number of daily hospitalizations and Migration Scale Index also have significant and robust effect on the decrease in charging demand. The Air Quality Index and Baidu Index are susceptible to external factors and do not have a direct impact on the change in charging demand. Findings support a better understanding of changes in travel behavior during the pandemic and provide policy makers with references to deal with similar events.

关键词： COVID-19 Reactive power Pandemics Atmospheric modeling Big Data Air quality Electric vehicle charging

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Effectiveness Evaluation Method of Constellation Satellite Communication System with Acceptable Consistency and Consensus Under Probability Hesitant Intuitionistic Fuzzy Preference Relationship

Research Square

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Research Square 2022年

作者： Wang, Yanjun Hu, Xiaoxuan Wang, Lidong School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision Making Ministry of Education Hefei230009 China Department of Mathematics Dalian Maritime University Dalian116026 China

System effectiveness evaluation is an important part of constellation satellite communication system research, with applications in project verification and optimization as well as tactical and technical measurement argumentation. This paper presents a systematic and comprehensive effectiveness evaluation method for a constellation satellite communication system under a probabilistic hesitant intuitionistic fuzzy preference relationship (PHIFPR), aiming to better address the fuzziness and uncertainty in effectiveness evaluation. First, a proposed definition of PHIFPR describes the hesitancy of evaluators, provides hesitancy distribution information, and depicts the worst negative information and risk preferences in effectiveness evaluation. Then, we deduce the approximate consistency index of PHIFPR and establish a mathematical programming model to increase individual consistency when the approximate consistency index does not reach a predetermined level. In the sequel, a proposed group consensus index uses the PHIFPR-based Hausdorff distance to measure the closeness between evaluators’ judgements. Afterwards, a consistency and consensus improvement model is designed to retain the original opinions of evaluators to make the consistency and consensus of PHIFPRs acceptable. Moreover, a goal programming model is established to gain the reliable scheme priority weights by regarding the approximate consistency condition of a PHIFPR as a fuzzy constraint. Finally, an experimental example is offered to highlight the practicability and feasibility of the proposed method, and some comparative analyses with other methods offer insights into the designed method. © 2022, CC BY.

关键词： Satellites

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Application of blockchain in the field of intelligent manufacturing:Theoretical basis,realistic plights,and development suggestions

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Frontiers of Engineering Management 2020年第4期7卷 578-591页

作者： Qiang ZHANG Baoyu LIAO Shanlin YANG School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Intelligent Decision-making(Ministry of Education) Hefei University of TechnologyHefei 230009China

Blockchain technology is considered one of the promising technologies of the information technology *** core features of blockchain,such as decentralization,transparency,high security,and tamper-proof nature,bring great convenience for large-scale social cooperation and data *** has a broad application prospect in the field of intelligent *** key issues of this field,such as distributed collaborative production,industrial big data sharing and security,transparent logistics,and supply chain,are naturally consistent with the core characteristics of the blockchain *** study aims to analyze the application of blockchain in the field of intelligent ***,we introduce the basic connotation and applications of ***,we propose the theoretical basis for the application of blockchain in the field of intelligent ***,we point out the realistic plights and provide some suggestions to promote the application of blockchain in the field of intelligent manufacturing.

关键词： blockchain technology intelligent manufacturing networked collaborative manufacturing full lifecycle management manufacturing model innovation

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Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC 2

Photovoltaic Power Forecasting Based on SVM Optimized by Imp...

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2nd International Conference on Environmental Prevention and Pollution Control Technologies, EPPCT 2020

作者： Liu, Lin Lu, Yanyun Li, Shuqin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China

In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, through calculating the Euclidean distance of history day and measured day meteorological factors to determine similar days. Secondly, select historical data of photovoltaic power output, temperature, humidity and daily radiation on the slope of similar days and temperature, humidity and daily radiation on the slope of test date as input variables of support vector machine. And we adopt the improved artificial bees colony to optimize kernel function parameters and the penalty factor of support vector machine. Finally get the output in each period of photovoltaic power prediction. The experimental results showed that the proposed method can effectively improve the prediction accuracy of photovoltaic power. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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