Power system dynamic scheduling method based on improved differential evolution algorithm

Abstract

The invention relates to a power system dynamic scheduling method based on an improved differential evolution algorithm, and the method comprises the steps: 1, coding the differential evolution algorithm based on an elite island population, and initializing the population; 2, dividing the population into a plurality of island populations, and classifying the island populations; 3, acquiring a variant island, and calculating individual fitness of the variant island; 4, performing constraint judgment on each individual in the variant island; 5, judging an execution interval, and executing immigration and individual migration operations; 6, classifying islands again according to fitness values; 7, judging whether iteration stopping conditions are met or not, if so, executing the step 8, and otherwise, returning to the step 3; 8, outputting an optimal solution; and 9, dynamically regulating and controlling a power system. Compared with the prior art, the invention has the advantages of effective reduction of fuel cost waste, good adaptability, stability, reliability, high reaction speed and the like.

Description

technical field [0001] The invention relates to the technical field of power system dynamic dispatching, in particular to a power system dynamic dispatching method based on an improved differential evolution algorithm of an elite island population. Background technique [0002] The differential evolution algorithm iteratively searches for the optimal solution in the solution space by imitating the process of biological evolution. It is widely used to solve various optimization problems in the real world, and the effect is obvious. never stopped. At present, the main improvement methods focus on four aspects: one is the improvement of algorithm control parameters, such as the jDE algorithm, by assigning different mutation and crossover probabilities to each individual, and dynamically adjusting them according to two specified thresholds , so that the algorithm can adaptively balance the global search and local search capabilities. The second is the improvement of the algori...

AI Technical Summary

Problems solved by technology

[0003] At present, the differential evolution algorithm has been used to optimize the dynamic scheduling of power systems, but because the differential evolution algorithm cannot guarantee that the optimization result is the optimal solution, the optimal solution is often replaced by an approximate optimal solution, and due to the accuracy of the algorithm Insufficient, so that there is still a waste of fuel costs after optimization

However, using the improved differential evolution algorithm to optimize the power system for dynamic scheduling also has problems: simply improving the control parameters, mutation strategy or population structure, the improvement effect is limited

However, the comprehensive improved algorithm is difficult to combine the advantages of several improvements. Taking the island model differential evolution algorithm as an example, the control parameters of different islands are manually determined, which may not be consistent with the actual situation of the island, and the adaptability to different problems is insufficient.

The gene exchange process of the algorithm is random and one-way. In the later stage of the iteration, the genes on each island may converge, and the lack of population diversity leads to convergence stagnation.

At the same time, although the individual migration mechanism of the algorithm can enhance the search ability of the current best island, it does not take into account the enhanced local search position, and moving in individuals with poor fitness will interfere with the evolution direction of the best island

When the above algorithm is used for dynamic economic dispatch of the power system, the control accuracy of the power system is not high, and there will still be waste of fuel costs

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