Wind power plant-containing power system optimal scheduling method using improved wolf pack algorithm

Abstract

The invention discloses a wind power plant-containing electric power system optimal scheduling method utilizing an improved wolf pack algorithm. The method comprises the steps: 1, establishing a windpower generation cost model and a thermal power generation cost model; 2, establishing a wind power plant-containing power system optimal scheduling model comprising an objective function and constraint conditions; and 3, solving the wind power plant-containing power system optimal scheduling model by utilizing an improved wolf pack algorithm. When the method is used for optimal scheduling of a power system containing a wind power plant, the total power generation cost can be reduced, and the economy of the power system can be improved.

Description

technical field [0001] The invention relates to the technical field of electrical dispatching of a power system containing a wind farm, in particular to an optimal dispatching method of a power system containing a wind farm using an improved wolf pack algorithm. Background technique [0002] Power system scheduling refers to adjusting the output and operating status of generators in the entire system according to some specific constraints in the process of power load distribution, generally referring to the start or stop of generators and some specific constraints of the system , in order to achieve the purpose of reasonable distribution of system load. Traditional dispatching methods usually aim at the economy such as the lowest system power generation cost. However, with the rise of the new energy industry, more and more clean energy sources such as wind energy and solar energy are connected to the grid, which makes random and fluctuating components appear in the tradition...

AI Technical Summary

Problems solved by technology

When the dynamic programming method involves the power change rate of thermal power generating units, it is difficult to complete the staged conversion, and with the increase of the number of thermal power generating units, it is easy to cause "dimension disaster"; the priority method is difficult to deal with generating units in different time periods Combination problem, resulting in inaccurate calculation of generator start-stop costs; Lagrange relaxation method in solving non-convex objective function, the duality is difficult to guarantee, and there is a certain probability of oscillation when the algorithm iterates; genetic algorithm in the evolution In the process of optimization, it is easy to fall into the local optimal solution, and sometimes it will converge prematurely; the ant colony algorithm uses indirect communication between individuals, and a large number of calculations lead to a long iteration time and slow convergence speed; the particle swarm algorithm in the calculation process In , it is difficult to grasp the global information, which leads to slow convergence of the algorithm and easy to fall into the local optimal solution

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