智能电网实时定价的自适应光学优化算法

摘要

为了解决传统优化算法在求解智能电网的实时电价模型过程中易陷入局部最优的不足,引入光学优化算法对实时电价模型进行求解.采用可变适应度的方法对光学优化算法求解模型过程中的不变适应度进行改进,提出自适应光学优化(Self-adaptive Optics Inspired Optimization,SAOIO)算法.根据迭代次数的变动,自适应地变动适应度从而提升收敛速度,提高求解精度.针对智能电网实时电价模型,对拉格朗日对偶算法和自适应光学优化算法进行同步仿真.结果表明自适应光学优化算法能够更好地寻得全局最优电价,提高结果精度,具有更好的实用性.%In order to solve the shortage of local optimum in the process of solving the real time price model of smart grid by using traditional optimization algorithm,the Optics Inspired Optimization (OIO) is introduced to solve it.The variable fitness method is used to improve the constant fitness of the optics inspired optimization in the process of solving the model.The new algorithm is named Self-adaptive Optics Inspired Optimization (SAOIO) algorithm.According to the changes of the number of iterations,the adaptive degree can be changed by itself to improve the convergence speed and improve the accuracy of the solution.In the real-time pricing model of smart grid,the Lagrange dual algorithm and SAOIO are synchronously simulated.The results show that the SAOIO can better find the global optimal price,improve the accuracy,and have better practicability.