Optimal Power Flow by Enhanced Genetic Algorithm

By Anastasios G. Bakirtzis, Pandelis N. Biskas, C. Zoumas, Vasilios Petridis. IEEE Power Engineering Review, 2002

733 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/52c6497f6ebc2d8e295ce9849e3e67bdca2084c5

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In Control and Systems Engineering

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Citations in the corpus

Reactive Power and Voltage Control in Distribution Systems With Limited Switching Operations (2009, methodology): “The effectiveness of the proposed methodology is demonstrated through the results obtained from applying it to a widely studied distribution system and a real distribution network in China, where the method has been implemented and is in operation since 2003, comparing it with three other known tech”
Multiobjective Optimization for DG Planning With Load Models (2009, methodology): “In this paper, a GA optimization technique developed in a previous work of [17]–[21] has been used for finding the best solutions of the IMO optimization algorithm.”
Optimal power flow using gravitational search algorithm (2012, methodology): “The authors tested their approach on IEEE 30-bus system and the three area IEEE RTS-96 which is a 73 bus, 120-branch system [21].”
Sensitivity-Based Approaches for Handling Discrete Variables in Optimal Power Flow Computations (2010, methodology): “round-off techniques [4] , [5], [7], methods handling discrete variables in NLP and LP OPF formulations by means of penalty functions [8]–[10], ordin al optimization [11], recursive mixed-integer linear progra mming [12], interior point cutting plane [13], [14], global optimzation methods [15]–[18],”
Optimal power flow: a bibliographic survey II (2012, methodology): “Bakirtzis et al. (2002)”
Penalty-Based Nonlinear Solver for Optimal Reactive Power Dispatch With Discrete Controls (2013, methodology): “An application of meta-heuristics can be found in [2] where a genetic algorithm is employed.”
Evolutionary programming techniques for economic load dispatch (2003, background): “In this respect, stochastic search algorithms such as genetic algorithms (GAs) [2]‐[ 13 ], evolution strategies (ESs) [14], [15], evolutionary programming (EP) [16]‐[28], and simulated annealing (SA) [11], [29] may prove to be very effective in solving nonlinear ELD problems without any restrictions”
Optimal Power Flow Using Graph Neural Networks (2019, background): “However, none of these approaches has been shown to work on networks larger than the 30 node IEEE test case [7–9].”
Modified differential evolution algorithm for optimal power flow with non-smooth cost functions (2008, background): “In recent years, many heuristic algorithms such as genetic algorithms (GA) [3,4], evolutionary programming (EP) [5,6], tabu”
Bidding strategies for electricity producers in a competitive electricity marketplace (2004, unknown): “The genetic parameters in the results of Table VII are selected based on experimentation, following the procedure of [29].”