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Profit based unit commitment using memetic binary differential evolution algorithm

Profit-based unit-commitment problem (PBUCP) is a notable combinatorial optimizing problem faced in the deregulated power industry. The PBUCP finds the best profitable solution by committing and scheduling the thermal generating units efficiently. To solve the PBUCP, a new memetic binary differentia...

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Published in:Applied soft computing 2019-08, Vol.81, p.105502, Article 105502
Main Authors: Dhaliwal, Jatinder Singh, Dhillon, J.S.
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description Profit-based unit-commitment problem (PBUCP) is a notable combinatorial optimizing problem faced in the deregulated power industry. The PBUCP finds the best profitable solution by committing and scheduling the thermal generating units efficiently. To solve the PBUCP, a new memetic binary differential evolution algorithm is proposed which considers binary differential evolution (BDE) algorithm as global search operator to improve the exploration aspect and binary hill-climbing (BHC) algorithm as local search operator to improve the exploitation aspect. A binary differential evolution algorithm is introduced whereby a new mutation strategy is implemented. A novel BHC algorithm makes priority-based perturbations on unit’s status to improve the global best solution searched by the BDE algorithm alone. A new excessive unit de-commitment strategy based on priority and total profit is also proposed. The power to committed units is allocated based on priority of units. The efficacy of algorithms has been researched on the PBUCP test systems comprising of 10-, 40- and 100-units over a time horizon. The outcomes of the proposed algorithms are compared with previously known best solutions. Simulated outcomes achieved by the proposed algorithms compete with the already reported algorithms to solve the PBUCP. Wilcoxon signed-rank test proves the predominance of the proposed algorithms statistically. •A new MBDE algorithm is proposed to solve profit-based unit-commitment problem.•The MBDE algorithm uses a BDE algorithm, and a novel binary hill climbing (BHC).•The BDE uses a new mutation strategy to create mutant vectors to raise exploration.•The BHC perturbs unit’s ON/OFF status based on priority to improve exploitation.•A new unit de-commitment strategy based on unit’s priority and profit is proposed.
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subjects Binary differential evolution
Binary hill-climbing
Memetic algorithm
Memetic binary differential evolution
Priority methods
Profit-based unit commitment
title Profit based unit commitment using memetic binary differential evolution algorithm
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