The increasing amount of wind power integrated to power systems presents a number of challenges to the system operation. One issue related to wind power integration concerns the location and capacities of the wind turbines (WTs) in the network. Although the location of wind turbines is mainly determined by the wind resource and geographic conditions, the location of wind turbines in a power system network may significantly affect the distribution of power flow, power losses, etc. Furthermore, modern WTs with power-electronic interface have the capability of controlling reactive power output, which can enhance the power system security and improve the system steady-state performance by reducing network losses. This chapter presents a hybrid optimization method that minimizes the annual system power losses. The optimization considers a 95%-probability of fulfilling the voltage and current limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities of WTs. The gradient-based optimization finds the optimal power factor setting of WTs. The sequential MCS takes into account the stochastic behaviour of wind power generation and load. The proposed hybrid optimization method is demonstrated on an 11 kV 69-bus distribution system.
|Titel||Wind Power Systems: Applications of Computational Intelligence (Green Energy and Technology)|
|Redaktører||Lingfeng Wang, Shanan Singh, Andrew Kusiak|
|Forlag||Springer Publishing Company|
|Publikationsdato||9 jun. 2010|
|Status||Udgivet - 9 jun. 2010|