A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms

A technology for optimal scheduling and microgrids, applied in electrical components, circuit devices, AC network circuits, etc., and can solve problems such as slow convergence, stagnation, and premature maturity.

Inactive Publication Date: 2017-02-22
武汉弘文通信工程有限公司
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Problems solved by technology

The standard PSO algorithm in the existing intelligent optimization algorithm often encounters premature and convergence problems when solving such high-dimensional, non-smooth complex problems. local optimum
On the other hand, the convergence speed of the standard PSO algori...

Method used

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  • A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms
  • A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms
  • A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms

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Embodiment

[0106] The microgrid in this simulation example includes 20kW photovoltaic, 100kW wind power, 100kW micro gas turbine, and 20kW lithium iron phosphate battery energy storage system. The load is normally operated in grid-connected mode, and the microgrid interacts with the upper-level large grid. The proposed algorithm is implemented using Matlab, and the simulation experiment is carried out on a computer with a hardware configuration of i7-4790@3.6GHZ.

[0107] In the calculation example of this system, the micro gas turbine, the interaction of the large power grid, and the hourly power of the battery are all dispatchable decision variables, and the particle dimension is D=24×3=72. The population size in the standard PSO algorithm and the improved PSO algorithm based on the subgradient is both N=100, and the maximum number of iterations is set to T max =500. The two learning factors c in the standard PSO algorithm 1 =c 2 =2, inertia weight ω=0.8. In the improved PSO algori...

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Abstract

A micro-grid grid-connected optimal scheduling method based on improved subgradient particle swarms comprises the steps of establishing a micro-grid grid-connected model including an energy storage device; according to the actual situation, establishing an optimal scheduling objective function at the smallest total micro-gird power generation costs and environmental pollution control costs; establishing operating constraints in a micro-grid system, and separately establishing system power balance constraints, storage battery charge and discharge power constraints, micro-power output power limits and electricity purchasing and selling constraints for interaction between the micro-grid and large grids; improving the standard particle swarm optimization; separately improving the inertia weight and acceleration factors; and proposing to use the sub-gradient optimization method to update the velocity of the particles in the particle swarm optimization. According to the micro-grid grid-connected optimal scheduling method based on the improved subgradient particle swarms, while the micro-grid grid-connected optimization involving the energy-storage device is solved, advantages such as a good optimization searching effect and a fast convergence speed are realized.

Description

technical field [0001] The invention provides a micro-grid grid-connected optimization scheduling method based on improved subgradient particle swarms, and relates to the field of micro-grid grid-connected optimal scheduling. Background technique [0002] With the introduction and popularization of the smart grid concept, the microgrid will become an important part of the smart grid. The real-time scheduling of the microgrid will have a strong negative impact. The grid-connected optimal scheduling problem of microgrid is actually a high-dimensional, nonlinear, non-convex and non-differentiable mathematical optimization problem. Due to the limitation of distribution network transmission capacity and the constraints of system power balance conditions, the corresponding optimization problems often have the characteristics of discontinuity and non-differentiability. The standard PSO algorithm in the existing intelligent optimization algorithm often encounters premature and con...

Claims

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Application Information

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IPC IPC(8): H02J3/38
CPCH02J3/382H02J2203/20Y02P80/14
Inventor 王凌云徐嘉阳丁梦王泉汪德夫黄爽马奇伟
Owner 武汉弘文通信工程有限公司
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