Stochastic Optimal Scheduling for Wind-Solar-Hydrogen Coupled System Under Uncertain Fluctuations

This study proposes a stochastic optimization scheduling strategy for Wind-Solar-Hydrogen Coupled System under generation uncertainties. Firstly, an improved k-means clustering algorithm is applied to generate typical wind power and photovoltaic operation scenarios. A weighted distance function taking the ramp rate variations of renewable generations into account is used to evaluate the similarity of sample power curves, thereby enhancing the accuracy of scenario classification. Finally, a stochastic optimization scheduling model is constructed to minimize the operating costs. Numerical results have indicated that the clustered typical operational curves with the proposed method can better reflect the actual generation characteristics of renewables. Furthermore, by optimizing the day-ahead scheduling of the system, the proposed model ensures that the power supply for hydrogen production meets hydrogen demand while achieving a minimization of the system's daily operating costs. The hydrogen production revenue can be guaranteed with the economic and safe operation manner.