Multi-region dynamic economy scheduling method and system

Abstract

The invention discloses a multi-region dynamic economy scheduling method and system. The method comprises that a target function of a multi-region economic scheduling problem is established; an initial population is generated by initialization, the fitness of the initial population is calculated, and the initial population serves as a parent population; an NW small world network model is used to obtain an adjacent matrix; the parent population is updated according to the adjacent matrix to obtain a filial population, and the fitness of particles in the filial population is calculated by utilizing a fitness function; the particle fitness in neighborhoods divided by corresponding adjacent matrixes in parent and filial populations is compared by utilizing a competition operator, and particles of high fitness are reserves and serve as a parent population in next iteration; and when a preset maximal iteration frequency is reached, a result of the multi-region economic scheduling problem is output. The NW small world network improved differential crisscross algorithm is used to overcome the defect of population diversity loss in the optimization searching process of basic differential evolution algorithm and crisscross algorithm.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a multi-area dynamic economic scheduling method and system based on NW small-world network differential crisscross algorithm. Background technique [0002] Power system economic dispatch is the main content of energy management system (EMS). In some specific environments, it is conceptually equivalent to power generation plan. Power generation plan includes unit combination, hydrothermal power plan, exchange plan, maintenance plan and fuel plan, etc.; According to the cycle, there are: ultra-short-term plan, that is, automatic generation control (AGC), short-term power generation plan, that is, the plan of the day or week; medium-term power generation plan, that is, the plan and correction of months to years; long-term plan, that is, several years to decades plans, including power development planning and network development planning, etc. [0003] In the operation of powe...

AI Technical Summary

Problems solved by technology

[0004] At present, the differential evolution algorithm (DE) and the crisscross optimization algorithm (CSO) have been widely used in the field of power system economic dispatch as very effective optimization methods. As a classic optimization algorithm, the differential evolution algorithm has a good Convergence speed, but in the later stage of convergence, the problem of population unity will inevitably appear

As a recent optimization algorithm, the crossover algorithm has good global search ability and often has a good performance in solving traditional optimization problems. However, the convergence method of the algorithm determines that the algorithm has a long convergence time and a diverse population in the later stage of convergence. lack of sex

For this reason, it is proposed to fuse the differential evolution algorithm and the crossover algorithm, so that the two algorithms can complement each other. The fused algorithm, the differential crossover algorithm, has a faster convergence speed and a very good global search ability, but The defect of insufficient population diversity in the later stage of convergence has not been well resolved

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