The invention provides a light-preserved system tracking day-ahead plan scheduling method based on chance constrained programming. The method comprises the following steps that (1) the relevant data of a photovoltaic power station and an energy storage system are read; (2) day-ahead actual photovoltaic power generation power is simulated via a monte carlo technique and range of photovoltaic planed output upper and lower limit is set; (3) a chance constrained programming mathematical model including a control coefficient is established based on a short-term photovoltaic prediction power value and a random prediction deviation value; and (4) charge and discharge power of the energy storage system is determined by adopting an improved self-adaptive particle swarm algorithm. Polling of each forecast point is performed one day in advance, and an effect that light-preserved tracking planed output is in the range of the planed upper and lower limit is realized by adopting the improved self-adaptive particle swarm algorithm; besides, tracking control is enabled to be more flexible by adjustment of the control coefficient in a target function, and charge and discharge power and the state of charge of the energy storage system are basically maintained within an appropriate range so that charge and discharge capability is enhanced and the requirement for energy storage is reduced.