Double-suction centrifugal pumps form an integral part of power plant systems in maintaining operational stability. However, there has been a common problem of achieving a better cavitation performance over a wider operating range because the traditional approach for impeller design often leads to the design effect not meeting the operational needs at off-design conditions. In addressing the problem, an optimization scheme was designed with the hub and shroud inlet angles of the double-suction impeller to minimize the suction performance at non-design flow conditions. A practical approach that speeds up the cavitation simulation process was applied to solve the experimental design, and a multi-layer feed forward artificial neural network (ANN) was combined with the non-dominated sorting genetic algorithm II to solve the multi-objective problem into three-dimensional (3D) Pareto optimal solutions that meet the optimization objective. At the design point, the suction performance was improved by 6.9%. At non-design flow conditions, the cavitation performance was improved by 3.5% at 1.2Q_d overload condition, 4% at 0.8Q_d, and 5% at 0.6Q_d. Additionally, there was significant reduction in the attached cavity distribution in the impeller and suction domains when the optimized model was compared to the original model at off-design points. Finally, the optimization established a faster method for a three-objective optimization of cavitation performance using ANN and 3D Pareto solutions.

Under the background of “peak carbon dioxide emissions by 2030 and carbon neutrality by 2060 strategies” and grid-connected large-scale renewables, the grid usually adopts a method of optimal scheduling to improve its ability to cope with the stochastic and volatile nature of renewable energy and to increase economic efficiency. This article proposes a short-term optimal scheduling model for wind–solar storage combined-power generation systems in high-penetration renewable energy areas. After the comprehensive consideration of battery life, energy storage units, and load characteristics, a hybrid energy storage operation strategy was developed. The model uses the remaining energy in the system after deducting wind PV and energy storage output as the “generalized load”. An improved particle swarm optimization (PSO) is used to solve the scheduling schemes of different running strategies under different objectives. The optimization strategy optimizes the battery life-loss coefficient from 0.073% to 0.055% under the target of minimizing the mean squared deviation of “generalized load”, which was optimized from 0.088% to 0.053% under the minimized fluctuation of combined system output and optimized from 0.092% to 0.081% under the minimized generation costs of the combined system. The results show that the model can ensure a stable operation of the combined system, and the operation strategy proposed in this article effectively reduces battery life loss while reducing the total power generation cost of the system. Finally, the superiority of the improved PSO algorithm was verified.