Dynamic economic dispatch for wind-thermal power system using a novel bi-population chaotic differential evolution algorithm

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Based on in-depth analysis of the stochastic nature of wind power output, the Weibull distribution parameters of regional wind speed for different time intervals are obtained respectively, and then the probability density functions of wind power output for different time intervals are achieved. These functions can be used to calculate output-overestimate and output-underestimate probabilities in each interval, so possible extra costs for maintaining the power system stability caused by incorporating unstable wind power can be calculated. Taking into account the possible costs, a stochastic optimization model for dynamic economic dispatch of wind-thermal power system is established to minimize the comprehensive operation expected cost. Moreover, a new algorithm, bi-population chaotic differential evolution (BPCDE) algorithm is proposed to solve this complicated model. The algorithm introduces bi-population evolution strategy, chaotic map update mechanism and Metropolis rule to improve the standard differential evolution algorithm. These improvements can overcome the premature problem caused by lacking of the individual diversity in the later stage of differential evolution and strengthen the global search ability of the algorithm. The validity and superiority are demonstrated by simulation results on a power system integrated with large scale wind farms.