Study on Distributed Energy Uncertainty Analysis and Stochastic Scheduling Method Based on Probabilistic Modeling

With the development of smart grids, distributed generation and other new technologies, the complexity and dynamics of the power system have further intensified. To cope with these challenges, more and more researches are focusing on how to utilize advanced mathematical models and algorithms, especially stochastic optimization techniques. In this paper, a stochastic optimization-based scheduling framework is proposed for the optimization problem of uncertain adjustable resources in grid scheduling under high percentage of new energy access. First, the uncertainty model of distributed adjustable resources (e.g., photovoltaic, energy storage, electric vehicle cluster) and new energy is constructed by analyzing historical data and climate factors; second, the feasible domains of the response of adjustable resources are drawn based on the theory of confidence space, an aggregated characterization method of the uncertainty boundaries is proposed, and an efficient solving strategy is designed. Theoretical support is provided for high uncertainty grid scheduling.