Two-stage particle swarm optimization algorithm including independent global search

A particle swarm optimization and global search technology, applied in computing, computing models, data processing applications, etc., can solve problems such as poor global search ability, Lagrangian relaxation method oscillation, singular phenomena, etc.

A particle swarm optimization and global search technology, applied in computing, computing models, data processing applications, etc., can solve problems such as poor global search ability, Lagrangian relaxation method oscillation, singular phenomena, etc.

CN104200264AActive Publication Date: 2014-12-10STATE GRID CORP OF CHINA +3
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  • Two-stage particle swarm optimization algorithm including independent global search
  • Two-stage particle swarm optimization algorithm including independent global search
  • Two-stage particle swarm optimization algorithm including independent global search

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Embodiment Construction

[0039]The present invention will be further described below in conjunction with the accompanying drawings and the control embodiments of the electric vehicle group.

[0040] Such as figure 1 As shown, this algorithm includes the following steps.

[0041] (1) Population initialization, including N=50 for the number of particles, the number of global search iterations M1=5, and the number of local search iterations M2=50. Let there be a total of D=10 electric vehicle groups, X j is the difference between the total power consumption of the jth electric vehicle group and the power consumption quota allocated by the upper-level dispatching system. x j The value range is [-10,10], and the unit is MW. Negative values ​​represent that electric vehicles participate in V2G. The speed range of particle movement is [-2,2], c1max=c2max=2.5, c1min=c2min=1. The fitness function f represents the sum of the squares of the differences between the electric power consumption of all electric ...

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Abstract

The invention discloses a two-stage particle swarm optimization algorithm including independent global search. The two-stage particle swarm optimization algorithm comprises the following steps: species initializing; adopting the chaotization method for initializing the positions X and the speeds V of particles; adopting the fitness function (fitness) to calculate the adaptive values of all current particles, and initializing the record optimal position (pbesti) of each particle and the global optimal position (gbest) of all the particles; carrying out the first stage iterative-global search; carrying out the second stage iterative-local search. The two-stage particle swarm optimization algorithm has the benefits that during each iteration of the first stage iterative-global search, one non-self particle is randomly selected from all the particles for learning, and the random selection guarantees that the species is prevented from tracking the specific particle and that the aggregate phenomenon is avoided; the second stage iterative-local search can quickly converge and can obtain solutions high in accuracy, the accuracy of the optimal solution is increased, and the prematurity defect is remarkably improved.

Description

technical field [0001] The invention relates to a particle swarm optimization algorithm, in particular to a two-stage particle swarm optimization algorithm including independent global search, which can be applied to problems such as power system load scheduling and electric vehicle group control. Background technique [0002] The real-time balance between power generation and load is the basic requirement for maintaining safe and stable operation of electric power. The randomness and volatility of renewable energy power generation output will become a huge challenge to the operation of power systems in the future. The traditional operation strategies and control methods, which use power generation to track load fluctuations to achieve system balance, and use power generation control to adjust the system's operating status, will be difficult to provide. continue. Load scheduling—using load to track changes in the output of renewable energy and controlling the load to adjust...

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Application Information

Patent Timeline
10 Dec 2014
Publication
CN104200264A
IPC
G06N3/00; G06F17/30; G06Q10/04; G06Q50/06
Inventors
侯梅毅; 刘世岭