D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 31 Citations 4,916 201 World Ranking 5904 National Ranking 705

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Electrical engineering
  • Artificial intelligence

His primary areas of investigation include Electric power system, Reliability engineering, Mathematical optimization, Electricity and Reliability. The Electric power system study combines topics in areas such as Wind power, Operations management, Market clearing, Renewable energy and Operations research. His work on Failure rate as part of general Reliability engineering study is frequently connected to Evaluation algorithm, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.

His Mathematical optimization research is multidisciplinary, incorporating elements of Reliability theory, System model, Fuzzy control system and Fuzzy set. The concepts of his Electricity study are interwoven with issues in Distributed generation, Stand-alone power system, Microeconomics and Smart grid. His biological study spans a wide range of topics, including Electrical network, Market structure, State and Fuzzy logic.

His most cited work include:

  • Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response (211 citations)
  • Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers (208 citations)
  • Fuzzy universal generating functions for multi-state system reliability assessment (118 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Electric power system, Reliability engineering, Electricity, Mathematical optimization and Reliability. His Electric power system research incorporates elements of Wind power, Demand response, Renewable energy and Air conditioning. The study incorporates disciplines such as Electricity generation, Monte Carlo method, Markov process and Reliability in addition to Reliability engineering.

His Electricity research incorporates themes from Production, Cogeneration and Reliability. His research in Mathematical optimization intersects with topics in Fuzzy logic, Convergence, Reliability theory and Electric power. His Reliability research is multidisciplinary, relying on both Probabilistic logic, State and Process.

He most often published in these fields:

  • Electric power system (43.27%)
  • Reliability engineering (30.91%)
  • Electricity (20.73%)

What were the highlights of his more recent work (between 2019-2021)?

  • Electric power system (43.27%)
  • Reliability engineering (30.91%)
  • Mathematical optimization (18.55%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Electric power system, Reliability engineering, Mathematical optimization, Electricity and Demand response. His work deals with themes such as Automatic frequency control, Renewable energy and Air conditioning, which intersect with Electric power system. His Reliability engineering research is multidisciplinary, incorporating perspectives in Operational reliability, Electricity generation, Reliability, Monte Carlo method and Reliability.

As part of one scientific family, he deals mainly with the area of Mathematical optimization, narrowing it down to issues related to the Electric power, and often Reliability theory. Yi Ding combines subjects such as Power usage, Cogeneration, Convolutional neural network and Data mining with his study of Electricity. His studies in Demand response integrate themes in fields like Distributed computing, Energy storage, Smart grid, Voltage and Scheduling.

Between 2019 and 2021, his most popular works were:

  • 5G network-based Internet of Things for demand response in smart grid: A survey on application potential (44 citations)
  • Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources (24 citations)
  • Practical Method for Mitigating Three-Phase Unbalance Based on Data-Driven User Phase Identification (18 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Electrical engineering

Yi Ding mostly deals with Electric power system, Reliability engineering, Demand response, Smart grid and Distributed computing. His research on Electric power system often connects related topics like Air conditioning. His research integrates issues of Environmental economics, Electricity, Reliability and Renewable energy in his study of Air conditioning.

The various areas that Yi Ding examines in his Reliability engineering study include Electricity generation and Natural gas. His Demand response study integrates concerns from other disciplines, such as Photovoltaics, Telecommunications, Energy engineering and Tap changer. Yi Ding has included themes like Digitization, Key and Data analysis in his Smart grid study.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers

Anatoly Lisnianski;Ilia Frenkel;Yi Ding.
(2010)

456 Citations

Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response

Qi Wang;Chunyu Zhang;Yi Ding;George Xydis.
Applied Energy (2015)

309 Citations

Fuzzy universal generating functions for multi-state system reliability assessment

Yi Ding;Anatoly Lisnianski.
Fuzzy Sets and Systems (2008)

191 Citations

Long-Term Reserve Expansion of Power Systems With High Wind Power Penetration Using Universal Generating Function Methods

Yi Ding;Peng Wang;L Goel;Poh Chiang Loh.
IEEE Transactions on Power Systems (2011)

134 Citations

Nodal market power assessment in electricity markets

Peng Wang;Yu Xiao;Yi Ding.
IEEE Transactions on Power Systems (2004)

127 Citations

Modeling and Integration of Flexible Demand in Heat and Electricity Integrated Energy System

Changzheng Shao;Yi Ding;Jianhui Wang;Yonghua Song.
IEEE Transactions on Sustainable Energy (2018)

124 Citations

Day-ahead tariffs for the alleviation of distribution grid congestion from electric vehicles

Niamh O’Connell;Qiuwei Wu;Jacob Østergaard;Arne Hejde Nielsen.
Electric Power Systems Research (2012)

124 Citations

Redundancy analysis for repairable multi-state system by using combined stochastic processes methods and universal generating function technique

Anatoly Lisnianski;Yi Ding.
Reliability Engineering & System Safety (2009)

118 Citations

A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty

Ning Zhang;Chongqing Kang;Qing Xia;Yi Ding.
IEEE Transactions on Power Systems (2015)

109 Citations

5G network-based Internet of Things for demand response in smart grid: A survey on application potential

Hongxun Hui;Yi Ding;Qingxin Shi;Fangxing Li.
Applied Energy (2020)

108 Citations

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Best Scientists Citing Yi Ding

Qiuwei Wu

Qiuwei Wu

Technical University of Denmark

Publications: 42

Enrico Zio

Enrico Zio

Politecnico di Milano

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Gregory Levitin

Gregory Levitin

Southwest Jiaotong University

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Yongxin Li

Yongxin Li

Nanyang Technological University

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Chongqing Kang

Chongqing Kang

Tsinghua University

Publications: 25

Yu Liu

Yu Liu

Chinese Academy of Sciences

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Mohammad Shahidehpour

Mohammad Shahidehpour

Illinois Institute of Technology

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Joao P. S. Catalao

Joao P. S. Catalao

University of Porto

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Maxim Finkelstein

Maxim Finkelstein

University of the Free State

Publications: 20

Pierre Pinson

Pierre Pinson

Technical University of Denmark

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Qing Xia

Qing Xia

Tsinghua University

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Liudong Xing

Liudong Xing

University of Massachusetts Dartmouth

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Fangxing Li

Fangxing Li

University of Tennessee at Knoxville

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Zhao Yang Dong

Zhao Yang Dong

UNSW Sydney

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Peng Wang

Peng Wang

Nanyang Technological University

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Ning Zhang

Ning Zhang

Tsinghua University

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