The invention provides a
virtual power plant day-ahead scheduling optimization model. A
model aggregation unit comprises a gas
turbine, a wind
turbine generator set, a photovoltaic set, a water drawing
energy storage power station and loads. For the characteristics that the
electricity price probability distribution description is relatively accurate and the prediction is relatively high, random
programming is adopted to process the uncertainty of the
electricity price; and for the characteristics that the
wind power and photovoltaic output probability distribution is difficult to precise describe and the prediction precision is relative low, an
information gap decision theory (IGDT) is adopted to process the uncertainty of
wind power and photovoltaic output, different weights are provided to
wind power and photovoltaic output deviation coefficients, and the IGDT is enabled to simultaneously process the uncertainty of wind power and photovoltaic output. In addition, for the
blindness of uncertainty decisions and the different risk degrees of different strategies, the risk cost is introduced, and the risks corresponding to different decision schemes are quantified. According to the invention, a larger
decision making space is provided for a
decision maker, and the VPP is enabled to make the
optimal decision under more conditions, so that the benefit of the
virtual power plant (VPP) is increased.