Multi-objective charging and discharging optimization scheduling method for electric vehicles based on cloud adaptive particle swarm optimization

Abstract

The invention belongs to the technical field of prediction or optimization, and discloses a multi-objective charging and discharging optimization scheduling method for electric vehicles based on cloud-adaptive particle swarms, which guides the group to move in the direction of a possible solution through information transmission among individual birds. In the process of iterative solution, a better solution is found. Each bird in the group is abstracted as a particle without mass and volume. The particles cooperate with each other and share information. The influence of the historical optimal position of itself and the group on the current direction and speed of the particle can better coordinate the relationship between the particle itself and the whole, which is conducive to the optimal operation of the group in complex spaces. In many cases, the adaptive particle swarm optimization algorithm cannot reflect the actual search optimization process. The cloud theory introduces the adaptive particle swarm optimization algorithm to use the randomness and stability tendency of cloud droplets, and maintains the diversity of the population to improve the convergence speed of the algorithm.

Description

technical field [0001] The invention belongs to the technical field of prediction or optimization, and in particular relates to a cloud-adaptive particle swarm-based multi-objective optimization scheduling method for electric vehicle charging and discharging. Background technique [0002] At present, the existing technology commonly used in the industry is as follows: the particle swarm optimization algorithm is a new type of intelligent optimization algorithm, which is a further supplement to the traditional optimization algorithm. In 1986, Craig Reynols proposed the Bird model to simulate the behavior of birds gathering and flying through the observation of bird groups in the real world. Frank Heppner redefines the new flock model by adding objective conditions of habitat attractiveness to flocks. Based on the analysis and research of the behavior of birds looking for food, Dr. James Kennedy and Dr. Russell Eberhart proposed the particle swarm optimization algorithm (Part...

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Problems solved by technology

The PSO algorithm also has the problem of being prone to fall into local optimum and premature convergence

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