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LR- EP Approach for solving Profit Based Unit Commitment Problem with Losses in Deregulated Markets

Selvi S., Moses M. B. S., Rajan C. C. A.

Przegląd Elektrotechniczny

2013

R. 89, nr 11

210-213

In this paper, hybrid models between Lagrange Relaxation (LR) with Evolutionary Programming (EP) are used to solve the profit based unit commitment problem in a deregulated electricity market. In this study losses are included and it can be added to the revenue so that profit can be increased compare to other research work. A modest attempt has been made in this paper presents a simulated case study for the profit based unit commitment problem and demonstrates the effectiveness of the proposed approaches.

W artykule analizuje się procesy decyzyjne kiedy i jaką jednostkę generatora można dołączyć i odłączyć od sieci. Uwzględnia się straty w nieregulowanym rynku energii.

Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method

Balci H. H., Valenzuela J. F.

International Journal of Applied Mathematics and Computer Science

2004

Vol. 14, no 3

411-421

This paper describes a procedure that uses particle swarm optimization (PSO) combined with the Lagrangian Relaxation (LR) framework to solve a power-generator scheduling problem known as the unit commitment problem (UCP). The UCP consists of determining the schedule and production amount of generating units within a power system subject to operating constraints. The LR framework is applied to relax coupling constraints of the optimization problem. Thus, the UCP is separated into independent optimization functions for each generating unit. Each of these sub-problems is solved using Dynamic Programming (DP). PSO is used to evolve the Lagrangian multipliers. PSO is a population based search technique, which belongs to the swarm intelligence paradigm that is motivated by the simulation of social behavior to manipulate individuals towards better solution areas. The performance of the PSO-LR procedure is compared with results of other algorithms in the literature used to solve the UCP. The comparison shows that the PSO-LR approach is efficient in terms of computational time while providing good solutions.