Hydrothermal economical scheduling method based on improved quantum particle swarm algorithm

Abstract

The invention discloses a hydrothermal economical scheduling method based on an improved quantum particle swarm algorithm, and the method comprises the steps: building a hydrothermal economical scheduling mathematic model comprising a cascade reservoir; setting system parameters, and generating an initial population; carrying out the constraint handling of the initial population through employing a constraint handling method, and enabling each particle in the initial population to meet the system constraint; calculating the adaptability value of each particle, and updating the individual optimal value of each particle and the global optimal value of all particles; calculating the positions of particles according to a position calculation formula of the improved quantum particle swarm algorithm; judging whether an end condition is met or not: stopping the iteration and outputting the optimal value if the end condition is met, or else, carrying out returning. The method can find a solution which is high in robustness, is high in convergence speed, and is better in adaptability value.

Description

technical field [0001] The invention belongs to the field of economic scheduling of water and thermal power in electric power systems, relates to the fields of hydropower and thermal power generation, and specifically relates to a constraint processing and optimization method for economic scheduling of water and thermal power based on an improved quantum particle swarm algorithm. Background technique [0002] Hydropower dispatching is a complex nonlinear optimization problem with multiple constraints and multiple variables in the power system. In recent years, with the soaring demand for electricity and the depletion of fossil energy, it is particularly important to use non-renewable energy more efficiently. Hydro-thermal power dispatching means that within a certain operation period, through the corresponding decision-making criteria, under a series of constraint conditions, the output share of hydropower plants can be fully utilized to achieve the goal of minimizing the fu...

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Problems solved by technology

[0004] The quantum particle swarm optimization algorithm has achieved good results in optimizing various optimization problems, but the traditional quantum particle swarm optimization algorithm is easy to fall into the local optimal solution when facing high-dimensional, large-scale, and multi-constrained hydropower systems. The global convergence cannot be guaranteed, the main reason is that the constraints of the hydrothermal power system are relatively complex

For the processing of equality constraints, the existing processing methods mainly increase the penalty coefficient to suppress the possibility of violating the constraints, but this method cannot completely guarantee that the particles in the iterative process do not violate the constraints, which will produce many infeasible solutions and consume simulation time, reducing the efficiency of the algorithm

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