Optimal Allocation of Power-Electronic Interfaced Wind Turbines Using a Genetic Algorithm - Monte Carlo Hybrid Optimization Method

Peiyuan Chen, Pierluigi Siano, Zhe Chen, Birgitte Bak-Jensen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingBidrag til bog/antologiForskningpeer review

Abstrakt

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.
OriginalsprogEngelsk
TitelWind Power Systems: Applications of Computational Intelligence (Green Energy and Technology)
RedaktørerLingfeng Wang, Shanan Singh, Andrew Kusiak
Antal sider24
ForlagSpringer Publishing Company
Publikationsdato9 jun. 2010
Sider1-23
ISBN (Trykt)978-3-642-13249-0
DOI
StatusUdgivet - 9 jun. 2010

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