Research on optimal operation of cascade pumping stations based on an improved sparrow search algorithm

Area of Science:

• Engineering
• Computer Science
• Environmental Science

Background:

• Cascade pumping stations often suffer from low efficiency and high energy losses.
• Optimizing their operational scheduling is crucial for maximizing system efficiency and minimizing costs.

Purpose of the Study:

• To develop an optimized scheduling model for cascade pumping stations.
• To propose an improved sparrow search algorithm (BSSA) for solving this complex optimization problem.

Main Methods:

• Developed an optimized scheduling model incorporating multiple constraints.
• Introduced the Bernoulli chaotic map for BSSA initialization to enhance diversity.
• Implemented a random boundary strategy to prevent local optima.
• Validated BSSA performance using benchmark functions and a practical three-stage pumping station case.

Main Results:

• BSSA demonstrated superior convergence accuracy and speed compared to the standard sparrow search algorithm (SSA).
• Compared to existing methods, SSA improved efficiency by 0.72-0.96% and reduced costs by ¥263,000/a-¥363,300/a.
• BSSA further enhanced efficiency by 0.04-0.30% and reduced costs by ¥14,800/a-¥109,900/a.

Conclusions:

• BSSA is a feasible and effective method for optimizing cascade pumping station scheduling.
• The proposed approach offers significant improvements in operational efficiency and cost reduction.
• Findings provide valuable insights for optimizing pumping station systems.