Multi Scenario Modeling and Aggregation Scheduling of Distributed Resources Based on Stochastic Optimization Algorithm

With the development of renewable energy and the transformation of the global energy structure, the widespread access of distributed resources has brought enormous challenges to the stable operation of the power system. Traditional power systems mainly rely on centralized power generation models, while distributed energy sources such as wind power, photovoltaics, etc. have strong randomness and volatility, bringing many uncertainties to the scheduling and management of power systems. To address this challenge, this study proposes a distributed resource multi scenario modeling and aggregation scheduling method based on stochastic optimization algorithm. This method first analyzes the random characteristics of distributed resources, constructs resource models suitable for different scenarios, and combines actual power grid needs to dynamically aggregate multiple types of resources using random optimization algorithms, thereby improving the scheduling flexibility and stability of the system. In the process of aggregation scheduling, the complementary characteristics between different resources are considered to minimize scheduling errors caused by resource randomness, ensuring the safety and economic operation of the power system. This article reveals that the demand and supply of electricity within 24 hours vary significantly with weather conditions. On clear days, the supply and demand are balanced, while on cloudy and rainy days, the supply and demand are tight, reflecting fluctuations in renewable energy. This provides theoretical support and technical means for efficient scheduling of large-scale distributed resources in future power systems, and has high application value and promotion prospects.