Research on short-term joint optimization scheduling strategy for hydro-wind-solar hybrid systems considering uncertainty in renewable energy generation

Abstract

Due to its randomness, intermittence, and volatility, the high-proportional integration of wind and solar power poses challenges to the safe and stable operation of power systems. Cascade hydropower stations have a high response speed, high adjustability, and stable output. This study proposed a hydro-wind-solar hybrid system and investigated its short-term optimal coordinated operation based on deep learning and a double-layer nesting algorithm. A stochastic complementary scheduling model was constructed to maximize the cascade energy storage. The particle swarm optimization algorithm-dynamic programming (PSO-DP) coupled with the inner and outer nesting optimization algorithm reduces the problem-solving complexity. The hybrid system was applied to a national comprehensive development base of renewable energy with integrated wind, solar, and hydropower in China. Studies have shown the following: The hydro-wind-solar hybrid system has a certain degree of scalability. The utilization of deep learning methods can fully consider the uncertainty of wind and solar. The internal and external nested optimization algorithm, which realizes the reasonable and efficient distribution of water and electricity, and improves the future power generation capacity of cascade hydropower stations, was used to solve the problem. This study provided a valuable reference for the large-scale utilization of other renewable energy sources worldwide.

Keywords

• Hydro-wind-solar hybrid system
• Deep learning
• Cascaded reservoirs
• Coordinated operation
• Multi-scenario analysis