Thermal unit commitment using hybrid binary particle swarm optimization and genetic algorithm
SMH Hosseini, H Siahkali… - 2012 Asia-Pacific Power …, 2012 - ieeexplore.ieee.org
SMH Hosseini, H Siahkali, Y Ghalandaran
2012 Asia-Pacific Power and Energy Engineering Conference, 2012•ieeexplore.ieee.orgThis paper presents a hybrid algorithm which integrates Particle Swarm Optimization (PSO)
and Genetic Algorithm (GA) for solving thermal unit commitment. The UC problem consists of
two sub-problems: Unit Scheduled problem which is solved by PSO for minimization of
transition cost and Economic Dispatch that can be solved by GA by the means of minimizing
the production cost. The proposed algorithm is demonstrated for a system including ten
thermal units. Running PSO and GA simultaneously justifies the production cost reduction in …
and Genetic Algorithm (GA) for solving thermal unit commitment. The UC problem consists of
two sub-problems: Unit Scheduled problem which is solved by PSO for minimization of
transition cost and Economic Dispatch that can be solved by GA by the means of minimizing
the production cost. The proposed algorithm is demonstrated for a system including ten
thermal units. Running PSO and GA simultaneously justifies the production cost reduction in …
This paper presents a hybrid algorithm which integrates Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for solving thermal unit commitment. The UC problem consists of two sub-problems: Unit Scheduled problem which is solved by PSO for minimization of transition cost and Economic Dispatch that can be solved by GA by the means of minimizing the production cost. The proposed algorithm is demonstrated for a system including ten thermal units. Running PSO and GA simultaneously justifies the production cost reduction in 24 hour period. Choosing varying PSO acceleration coefficients and inertia weight make the system convergence faster and skip the local optimums.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果