D-Index & Metrics Best Publications
Engineering and Technology
Greece
2022

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 53 Citations 11,443 158 World Ranking 1217 National Ranking 5

Research.com Recognitions

Awards & Achievements

2022 - Research.com Engineering and Technology in Greece Leader Award

2015 - IEEE Fellow For contributions to optimization of power systems operation and scheduling

Overview

What is he best known for?

The fields of study he is best known for:

  • Microeconomics
  • Artificial intelligence
  • Mathematical optimization

His primary scientific interests are in Electric power system, Mathematical optimization, Genetic algorithm, Electricity and Energy storage. His Power flow study in the realm of Electric power system interacts with subjects such as Term. His Mathematical optimization study combines topics from a wide range of disciplines, such as Electricity generation, Dynamic priority scheduling and Profit maximization.

His Genetic algorithm study also includes

  • Power system simulation that connect with fields like Lagrangian relaxation, Job shop scheduling and Robustness,
  • Dynamic programming that connect with fields like Crossover. His study in Electricity is interdisciplinary in nature, drawing from both Bidding and Operations management. His Energy storage study incorporates themes from Distributed generation, Renewable energy, Simulation, Smart grid and Demand response.

His most cited work include:

  • A genetic algorithm solution to the unit commitment problem (1008 citations)
  • Optimal power flow by enhanced genetic algorithm (511 citations)
  • A solution to the unit-commitment problem using integer-coded genetic algorithm (351 citations)

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

His primary areas of study are Electric power system, Mathematical optimization, Electricity market, Electricity and Renewable energy. Anastasios G. Bakirtzis is interested in Power system simulation, which is a branch of Electric power system. His Mathematical optimization study typically links adjacent topics like Profit maximization.

His Electricity market study integrates concerns from other disciplines, such as Bidding, Microeconomics, Market clearing, Transmission and Energy market. His work deals with themes such as Control engineering, Stochastic programming, Environmental economics and Electricity generation, which intersect with Renewable energy. The study incorporates disciplines such as Artificial neural network, Stochastic process and Demand response in addition to Simulation.

He most often published in these fields:

  • Electric power system (42.44%)
  • Mathematical optimization (36.10%)
  • Electricity market (20.98%)

What were the highlights of his more recent work (between 2013-2021)?

  • Electric power system (42.44%)
  • Mathematical optimization (36.10%)
  • Demand response (11.22%)

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

Anastasios G. Bakirtzis mainly focuses on Electric power system, Mathematical optimization, Demand response, Renewable energy and Power system simulation. His research integrates issues of Wind power, Stochastic programming, Reliability engineering, Stochastic process and Simulation in his study of Electric power system. His work carried out in the field of Mathematical optimization brings together such families of science as Equilibrium point, AC power and Market clearing.

He interconnects Distributed generation, Energy management system, Smart grid, Energy storage and Operations research in the investigation of issues within Demand response. His study focuses on the intersection of Renewable energy and fields such as Electricity with connections in the field of Environmental economics. His work in Power system simulation covers topics such as Economic dispatch which are related to areas like Real-time computing.

Between 2013 and 2021, his most popular works were:

  • Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR (244 citations)
  • Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies (206 citations)
  • Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach (175 citations)

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

  • Microeconomics
  • Electrical engineering
  • Mathematical optimization

Anastasios G. Bakirtzis mostly deals with Electric power system, Mathematical optimization, Demand response, Renewable energy and Operations research. He studied Electric power system and Wind power that intersect with Base load power plant. His research investigates the link between Mathematical optimization and topics such as Electricity market that cross with problems in Bidding.

His research in Demand response intersects with topics in Simulation and Smart grid. The concepts of his Renewable energy study are interwoven with issues in Stochastic programming, Reliability engineering, Power system simulation and Industrial engineering. As part of the same scientific family, Anastasios G. Bakirtzis usually focuses on Power system simulation, concentrating on Economic dispatch and intersecting with Information and Computer Science.

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

A genetic algorithm solution to the unit commitment problem

S.A. Kazarlis;A.G. Bakirtzis;V. Petridis.
IEEE Transactions on Power Systems (1996)

1486 Citations

Optimal power flow by enhanced genetic algorithm

A. G. Bakirtzis;P. N. Biskas;C. E. Zoumas;V. Petridis.
IEEE Transactions on Power Systems (2002)

812 Citations

A solution to the unit-commitment problem using integer-coded genetic algorithm

I.G. Damousis;A.G. Bakirtzis;P.S. Dokopoulos.
IEEE Transactions on Power Systems (2004)

489 Citations

A neural network short term load forecasting model for the Greek power system

A.G. Bakirtzis;V. Petridis;S.J. Kiartzis;M.C. Alexiadis.
power engineering society summer meeting (1996)

483 Citations

Genetic algorithm solution to the economic dispatch problem

A. Bakirtzis;V. Petridis;S. Kazarlis.
IEE Proceedings - Generation, Transmission and Distribution (1994)

414 Citations

Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR

Ozan Erdinc;NG Nikolaos Paterakis;Tiago D P Mendes;Anastasios G Bakirtzis.
IEEE Transactions on Smart Grid (2015)

358 Citations

Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets

Stylianos I. Vagropoulos;Anastasios G. Bakirtzis.
IEEE Transactions on Power Systems (2013)

347 Citations

Network-constrained economic dispatch using real-coded genetic algorithm

I. G. Damousis;A. G. Bakirtzis;P. S. Dokopoulos.
IEEE Transactions on Power Systems (2002)

314 Citations

Short term load forecasting using fuzzy neural networks

A.G. Bakirtzis;J.B. Theocharis;S.J. Kiartzis;K.J. Satsios.
IEEE Transactions on Power Systems (1995)

290 Citations

Bidding strategies for electricity producers in a competitive electricity marketplace

V.P. Gountis;A.G. Bakirtzis.
IEEE Transactions on Power Systems (2004)

284 Citations

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