@article{2200,
  author = {Liping Xi},
  title = {Optimal Scheduling of the Cascade Hydropower Station and the Solution Based on an Improved PSO Algorithm},
  journal = {Signals and Telecommunication Journal},
  year = {2017},
  volume = {6},
  number = {1},
  doi = {},
  url = {http://www.dline.info/stj/fulltext/v6n1/stjv6n1_2.pdf},
  abstract = {In order to overcome the defect of particle swarm optimization(PSO) that it is easy to be trapped in local optimum, this paper presented a hybrid-advanced strategy, which combined the dual fitness method, dynamic neighborhood operator and randomly dynamically adjusting inertia weight convergence of particles. This calculation example showed
that this advanced strategy could increase the local convergence ability and accelerate the convergence of particles. Thus it was a simple and effective approach to solve nonlinear programming problems with complex and constraint conditions.
This paper discussed the correlative issues in optimal scheduling of the cascade hydropower station and established a longterm optimal scheduling mathematics model of the cascade hydropower station based on the consideration of the electricity
price in wet and dry season. Besides, it sought the solution by the application of improved PSO algorithm. The actual calculation results from the cascade hydropower station show that this model can help coordinate power generation and water consumption, and decrease profitless spill water of cascade hydropower station. This model can not only keep up a balanced power output in the dry season, but meet the need of flood mitigation and water storage in wet season, which is beneficial to a stable operation of the power system. The advanced PSO algorithm is a simple and effective approach to longterm
optimal scheduling of cascade hydropower station based on the strength of quick calculation and precise convergence. },
}