Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Seeded Memetic Algorithm for Large Unit Commitment Problems
Journal of Heuristics
Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization
Journal of Heuristics
Evolving ant colony optimization based unit commitment
Applied Soft Computing
Unit commitment problem using enhanced particle swarm optimization algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
A deterministic annular crossover genetic algorithm optimisation for the unit commitment problem
Expert Systems with Applications: An International Journal
A real-integer-discrete-coded particle swarm optimization for design problems
Applied Soft Computing
A biased random key genetic algorithm approach for unit commitment problem
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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The unit commitment problem (UCP) is a nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems. The problem becomes even more complicated when dynamic power limit based ramp rate constraint is taken into account. Due to the inadequacy of deterministic methods in handling large-size instances of the UCP, various metaheuristics are being considered as alternative algorithms to realistic power systems, among which genetic algorithm (GA) has been investigated widely since long back. Such proposals have been made for solving only the integer part of the UCP, along with some other approaches for the real part of the problem. Moreover, the ramp rate constraint is usually discussed only in the formulation part, without addressing how it could be implemented in an algorithm. In this paper, the GA is revisited with an attempt to solve both the integer and real parts of the UCP using a single algorithm, as well as to incorporate the ramp rate constraint in the proposed algorithm also. In the computational experiment carried out with power systems up to 100 units over 24-h time horizon, available in the literature, the performance of the proposed GA is found quite satisfactory in comparison with the previously reported results.