Improved quantum genetic algorithm-based micro-grid energy storage locating and sizing optimization method

Abstract

The invention discloses an improved quantum genetic algorithm-based micro-grid energy storage locating and sizing optimization method. The method comprises the following steps: establishing an energy storage locating and sizing optimization model, wherein the energy storage locating and sizing optimization model comprises a target function formula and a constraint formula; improving a quantum genetic algorithm; and solving the energy storage locating and sizing optimization model by using the improved quantum genetic algorithm. According to the method, the energy storage locating and sizing optimization model is established, energy storage whole life cycle period cost, peak clipping-valley filling earning and grid loss earning are taken as targets, and the trend, energy storage charge-discharge and energy storage charge-discharge energy balance are constrained and considered; the quantum genetic algorithm is corrected, the dynamic adjustment strategy of a quantum revolving door revolving angle is used for improving the search efficiency, and a selection operation implemented by a simulated annealing method and a good point set cross operation can avoid local optimum; a 34-node micro grid is adopted to carry out verification so as to indicate that the disclosed algorithm is feasible, and the convergence efficiency of the quantum genetic algorithm and the ability of jumping out of local optimum are effectively improved.

Description

technical field [0001] The invention relates to an optimization method for site selection and capacity determination of microgrid energy storage. In particular, it relates to a microgrid energy storage site selection and capacity optimization method based on an improved quantum genetic algorithm that can find the most economical energy storage location and capacity in the microgrid. Background technique [0002] Connecting distributed power to large power grids in the form of microgrids is the most effective way to maximize the performance of distributed energy supply systems, and it is also one of the trends in the development of power systems in the future. However, due to the intermittent and fluctuating characteristics of distributed power, the microgrid also has problems such as voltage, power quality, and impact on the main grid. [2] . Energy storage can well solve the adverse effects of distributed power. The difference in energy storage access location and capacit...

AI Technical Summary

Problems solved by technology

At present, the commonly used artificial intelligence modernization optimization methods include particle swarm algorithm, differential algorithm, genetic algorithm, etc., but the above algorithms all have problems such as slow convergence speed and easy to fall into local optimum.

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