Proceedings ArticleDOI
Multi objective genetic algorithm for congestion management in deregulated power system
S. Sivakumar,R. Narmatha Banu,D. Devaraj +2 more
- pp 1-6
TLDR
Minimization of fuel cost and minimization of congestion cost are taken as two objectives and solved using multi objective genetic algorithm (MOGA) and simulation results based on MOGA is presented for IEEE 30 bus system.Abstract:
Deregulation aims at bringing innovative service practices, reduced cost of electricity, private participation in investments etc. and also brings new issues which are unheard of or not critical in the regulated environment of power system. Congestion management becomes the major issue for the Independent System Operator to handle as the transmission corridor will be exploited by both the buyers and sellers for their own interests. Generation from different power utility will be contracted to flow in the transmission network as per different buyers' requirements, which may create overloading of some of the transmission resources. Congestion thus created or created while planning for any contingency is mitigated by generation rescheduling or load curtailment or both. In this work congestion is mitigated by both generation rescheduling and selected load shedding. Minimization of fuel cost and minimization of congestion cost are taken as two objectives and solved using multi objective genetic algorithm (MOGA). Simulation results based on MOGA is presented for IEEE 30 bus system.read more
Citations
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Proceedings ArticleDOI
Optimal generation rescheduling for congestion management, LMP enhancement and social welfare maximization
TL;DR: Genetic algorithm is employed as an optimization technique to determine the optimal rescheduling of generators outputs in order to alleviate transmission lines congestion and satisfy the objective function which are social welfare maximization by reducing the locational marginal price (LMP) deference between buses.
Journal ArticleDOI
Congestion management in deregulated power system by series facts device using heuristic optimization algorithms
TL;DR: In this article , three heuristic algorithms namely Particle Swarm Optimization (PSO), Symbiotic Organism Search (SOS), and hybrid Quantum based PSO-Bio-geography based krill herd optimization (Q-PSOBBKH) algorithms were applied in finding solution to this complex congestion problem.
Dissertation
Special protection and control scheme based on generation rescheduling using differential evolution and electromagnetism-like algorithm
TL;DR: The results showed that the DEAM based scheme gives better performance than DE and GA in terms of less generation fuel cost and faster fitness convergence, and the proposed algorithm is evaluated on the IEEE 30-bus test system.
References
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Book
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Journal ArticleDOI
Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization
S. Dutta,Shiv P. Singh +1 more
TL;DR: An algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels and handles the binding constraints by a technique different from the traditional penalty function method is proposed.