Open AccessOptimal power flow based congestion management using enhanced genetic algorithms
Author(s) -
Seong-Cheol Kim,
Surender Reddy Salkuti
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i2.pp875-883
Subject(s) - computer science , minification , mathematical optimization , congestion management , generator (circuit theory) , database transaction , genetic algorithm , electricity market , compensation (psychology) , power market , power flow , market clearing , power (physics) , electric power system , algorithm , operations research , economics , electricity , mathematics , microeconomics , psychology , physics , quantum mechanics , psychoanalysis , electrical engineering , programming language , engineering
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.