Design of optimal wind farm configuration using a binary particle swarm optimization at Huasai district, Southern Thailand
This paper proposes the design of optimal wind farm configuration using a new wind probability distribution
map at Huasai district, the east coast of Southern Thailand. The new wind probability distribution
map integrates both frequency of wind speed and direction data at a monitoring site. The linear wake
effect model is used to determine the wind speed at downstream turbines for the total power extraction
from a wind farm array. The component cost model and learning curve is used to express the initial
investment cost, levelized cost and the annual energy production cost of a wind farm, depending on
the number of wind turbines, the installed size, hub height and wake loss within a wind farm. Based
on Thailand wind energy selling price consisting of the fixed wind premium on top of base tariff, the
profit depends on revenue of selling electricity and cost of energy. In this paper, Binary Particle Swarm
Optimization with Time-Varying Acceleration Coefficients (BPSO-TVAC) is proposed to maximize profit
subject to turbine position, turbine size, hub height, annual energy production, investment budget, land
lease cost, operation and maintenance cost and levelized replacement cost constraints. Test results indicate
that BPSO-TVAC optimally locate wind turbines directly facing the high frequent wind speed and
direction, leading to a higher profit than the conventional wind farm layout.