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Cloud-adaptive particle swarm optimization-based multi-objective electric vehicle char and discharging schedule method

An electric vehicle, self-adaptive technology, applied in the field of prediction or optimization, can solve problems such as premature convergence, achieve fast convergence speed, save charging costs, and improve the effect of convergence speed

Active Publication Date: 2019-01-08
YUNNAN MINZU UNIV
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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

Method used

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  • Cloud-adaptive particle swarm optimization-based multi-objective electric vehicle char and discharging schedule method
  • Cloud-adaptive particle swarm optimization-based multi-objective electric vehicle char and discharging schedule method
  • Cloud-adaptive particle swarm optimization-based multi-objective electric vehicle char and discharging schedule method

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Embodiment Construction

[0051] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0052] As an emerging load, electric vehicles connected to the power grid will have a series of impacts on the power system, such as further increasing the peak-to-valley difference of the load, local overloading of the distribution network load, low local line voltage of the power grid, and increased line loss. Large, distribution network transformer capacity exceeds the limit and other issues. With the large-scale popularization of electric vehicles, the uncertainty of time and space of electric vehicle network access will be highlighted. In view of the development status of domestic electric vehicles, the use of control...

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Abstract

A method for optimize dispatch of charge and discharge of multi-objective electric vehicle based on cloud self-adaptive particle swarm feature that information transmission among birds is used to guide swarm to move in that direction of possible solution, and better solution is found in iterative solution. Each bird in the swarm is abstracted as a particle without mass and volume. The particles cooperate with each other and share information with each other. The velocity of the particles is affected by their own and the historical state of the swarm. The influence of the historical optimal position of the particle and the swarm on the direction and velocity of the current particle movement can better coordinate the relationship between the particle and the whole, which is conducive to theswarm optimization operation in complex space. Adaptive particle swarm optimization algorithm can not reflect the actual search optimization process in many cases. Cloud theory introduces adaptive particle swarm optimization algorithm using cloud droplet randomness and stability tendencies to maintain the diversity of the population to improve the convergence rate 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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Application Information

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IPC IPC(8): H02J3/00B60L53/64B60L53/63
CPCH02J3/00H02J3/008H02J2203/20Y02T90/16Y02T10/40Y02T10/70Y02T90/12Y02T10/7072
Inventor 徐天奇冯培磊李琰
Owner YUNNAN MINZU UNIV
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