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

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...

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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 algorithm is relatively slow when approaching or entering the optimal point area, especially the optimization results in the later period are not ideal, mainly because the particles converge to the local minimum, and there is no effective way to keep the particles away from local minimum

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