A genetic algorithm to solve the thermal unit commitment problem

Labbi, Yacine; Benattous, Djilani

doi:10.1504/ijpec.2014.065511

Cited by 2 publications

(3 citation statements)

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“…Repair X(t) to meet constraints in eq. ( 4) to (7) [48][49] 5. Perform fitness evaluation for the X(t).…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

“…All the constraints of the system are satisfied with achieving minimized cost of generation. The results of the suggested algorithms provide improved convergence and enhanced solution quality [7].…”

Section: Introductionmentioning

confidence: 97%

“…The unit commitment model may be formed by considering different cost functions, constraints, objectives, and other researchers' choice-based parameters. The work suggested in [6][7][8][9][10][11][12][13][14][15][16][17][18][19] solved the deterministic unit commitment problem (DUCP) model. The work suggested in [20][21] solves the UCP in a deregulated environment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

Singh

Mani

Singh

et al. 2023

IJRITCC

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The unit commitment problem is a complex and essential problem in the power generation field, which is solved to obtain the schedule of a large number of generating units to minimize the operating cost and the fulfillment of consumer load demand. The present work solves the unit commitment problem using quantum-inspired evolutionary algorithms with a novel elitist local search method (QIEA-ELS). The proposed algorithm solves the unit commitment problem efficiently and its applicability is verified on various unit test systems. The constraints are satisfied efficiently to find a feasible solution, the novel elitist search method is used to locally explore the search area around the fittest individual to find a better solution in its vicinity in genotype space represent by qubits. The solution of the unit commitment is carried out considering two small population sizes as suggested in earlier work by other authors using QIEA, though it can be extended using larger population size also. The computational time is also reduced by using the suggested method with a novel elitist local search (ELS) method. The results obtained after applying the proposed algorithm are found to better as compared to other well-known solution techniques.

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“…Repair X(t) to meet constraints in eq. ( 4) to (7) [48][49] 5. Perform fitness evaluation for the X(t).…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%