Evolutionary algorithms for optimal power flow incorporating wind farms

Abstract

In the recent years, there has been an extremely rapid development of wind energy technology. Additionally, the cost for generating wind energy has become less expensive. In many countries, wind energy has emerged as a potential alternative in order to address the rising cost of fossil fuels, concerns over the environment, and an increase in the need for energy. In the future, wind energy will eventually overtake the other forms of energy to meet the energy demand. This is due to the rigorous pollution rules and the decline in wind power cost. However, wind power is characterized as a highly fluctuating source. The presence of fluctuation of wind power in the power system has a significant impact on the frequency control, tap changing, power control, voltage control, and protection of the entire power system. Moreover, very few studies have been done thus far on the usage of real-time wind power statistics in a wind farm incorporated power system. The operators of the power system need to update their operation strategy so that they can respond appropriately to these real-time wind power fluctuations. The optimal power flow plays a significant role in generating the operation strategy. Therefore, it is necessary to develop evolutionary algorithms to attain optimal power flow for wind farm integrated power grids. newline