An Optimal Method for Cascade Reservoir Group Scheduling Based on Firefly Algorithm

Abstract

A method for scheduling optimization of cascade reservoir groups based on the firefly algorithm disclosed in the present invention includes the following steps. Step 1: Taking the maximum power generation of the cascade reservoirs as the optimization objective during the dispatching period, and taking the hydraulic constraints and power constraints of the cascade reservoirs as the constraint conditions, Establish the cascade reservoir optimization model; Step 2: Use the improved firefly algorithm to optimize the cascade reservoir optimization model obtained in Step 1, obtain the maximum power generation and its corresponding optimal reservoir water level, and output the optimal reservoir water level. The invention is a cascade reservoir group dispatching optimization method based on the firefly algorithm, which has important practical significance for enriching the cascade reservoir group optimization dispatching method, improving the joint operation level of the cascade reservoir group, improving the comprehensive utilization benefit of water resources and the economic benefit of power generation enterprises. Value.

Description

technical field [0001] The invention belongs to the technical field of optimization methods for water conservancy engineering and hydropower stations, and in particular relates to an optimization method for cascade reservoir group scheduling based on a firefly algorithm. Background technique [0002] Cascade reservoir group scheduling is a nonlinear, high-dimensional, dynamic complex optimization problem, which is more complex and difficult to solve than general nonlinear constrained optimization. Currently, there are two main methods for solving reservoir scheduling problems: traditional optimal scheduling methods and swarm intelligence optimization algorithms. Traditional methods such as linear programming, dynamic programming, and step-by-step optimization algorithms have defects such as long calculation time, dimensionality disaster, and slow convergence speed, while swarm intelligence algorithms are widely used in reservoir optimization and scheduling due to their simpl...

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Problems solved by technology

[0004] As a novel and efficient search algorithm, foreign countries have begun to apply it to reservoir optimization scheduling problems in recent years, and there are few applications in China.

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