Abstract: To maximize the photovoltaic （PV） system tracking scheduleed output, based on the short-term prediction of PV power generation and the randomness of prediction deviation, this paper proposes an energy storage control method that adopts chance-constrained programming. This method takes the PV/energy storage combined output in the upper and lower of scheduled range as the objective, considers the constraints of charge and discharge power and the state of charge （SOC）, and adopts improved adaptive particle swarm optimization algorithm （PSO） based on Monte Carlo simulation to obtain day-ahead each time charge and discharge power. Finally, taking a typical PV output for simulation, we compare the PV/energy storage tracking scheduled output effect and energy storage condition in fixed coefficients situation and variation coefficients situation. The results verify the feasibility and flexibility of the proposed strategy, which can provide effective reference scheme for day-ahead energy storage control.

Key words: photovoltaic/energy storage combined power generation, tracking scheduled output, chance-constrained, Monte Carlo simulation, particle swarm optimization algorithm（PSO）