Optimal Dispatching of Wind Storage Combined Power System Based on Particle Swarm Optimization in Low Carbon Economy

Author(s):  
Kangning Sun
2012 ◽  
Vol 608-609 ◽  
pp. 683-686
Author(s):  
Zhi Bin Liu ◽  
Rui Peng Yang

Electricity is the basic industry in China, which has the important strategic significance to maintain the social stability, ensure the national security and promote the economic development. With the rapid development of power market reform and the establishment of bidding for access mechanism, the competition among the power generation enterprises becomes much drastic. To evaluate the development ability of wind power enterprises in the power new energy, the authors proposed a novel particle swarm optimization (PSO) algorithm, which used the randomness, the rapidity and the global characteristics to obtain the pheromone distribution, and had the faster convergence velocity. The development ability evaluation of 12 wind power enterprises showed that the results given by this model were reliable, and it is feasible to evaluate the development ability using this method.


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.


2012 ◽  
Vol 512-515 ◽  
pp. 719-722
Author(s):  
Yan Ren ◽  
Yuan Zheng ◽  
Chong Li ◽  
Bing Zhou ◽  
Zhi Hao Mao

The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. Particle swarm optimization algorithm was a stochastic global optimization algorithm with good convergence and high accuracy, so it was used to optimize the hybrid system in this paper. First, the system reliability model was established. Second, the particle swarm optimization algorithm was used to optimize the system model in Nanjing. Finally, The results were analyzed and discussed. The optimization results showed that the optimal design method of wind/PV/pumped-storage system based on particle swarm optimization could take into account both the local optimization and the global optimization, which has good convergence high precision. The optimal system was that LPSP (loss of power supply probability) was zero.


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