A Power Unit Combination Method Based on Grey Predictive Evolutionary Algorithm

Abstract

The invention relates to a method for combining power units based on a grey prediction evolution algorithm, which includes: setting constraints of the power units; establishing a binary matrix representing the switching states of individual populations and a real number matrix of output power; using a binary genetic algorithm to generate the switching states The gray prediction evolution algorithm is used to generate the real number matrix of the output power within the power range of the unit to ensure that the binary matrix and the real number matrix meet the constraints; select the optimal population individuals to enter the next generation until the maximum number of iterations is reached, and then output The binary matrix and the real number matrix are the switch state and output power size of the power unit. The two meta-heuristic algorithms are operated in parallel to solve the two sub-problems of unit switching state and power scheduling, which effectively reflects the respective advantages of the two algorithms, can achieve good results on large-scale units, and significantly save power system costs. , and the input parameters are few, which is convenient for the operator to operate.

Description

technical field [0001] The invention relates to the field of electric power systems, in particular to a combination method of electric power units based on a grey prediction evolution algorithm. Background technique [0002] The power unit combination problem refers to the reasonable arrangement of the start-stop status and output of each unit within a dispatch period under the condition of satisfying the user's load requirements and the constraints of various units, so as to minimize the system operating cost. The methods for dealing with power unit combination problems can be roughly divided into three categories: classical numerical optimization techniques, meta-heuristics and hybrid techniques. [0003] Decision techniques have the advantages of simple expression, strong robustness, non-iterativeness, and fast convergence speed, but they consume a lot of computing time while obtaining good solutions, and are not suitable for large-scale unit combination problems. In ord...

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Problems solved by technology

[0006] Aiming at the technical problems existing in the prior art, the present invention provides a combination method of electric power units based on the gray prediction evolution algorithm, which solves the problem of the two aspects of power combination that cannot be solved at the same time in the prior art. The quality of the balanced solution and the calculation time The problem

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