With the increasing penetration of renewable energy

ensuring reliability and security of power supply has become a significant challenge. In this paper

a novel photovoltaic (PV) intraday power supply guarantee capability forecasting method is proposed. Different from the conventional PV power forecasting methods

it can provide diverse forecasting information

including guarantee power

low output power period

and power supply guarantee probability. PV guarantee power which represents a conservative prediction of PV power is forecasted based on convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) model with a compound loss function. PV low output power period represents the period when the power gap between the theoretical maximum PV power and the actual PV power is higher than a specific threshold. It is forecasted using hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model based on an improved spatiotemporal feature encoding method. The power supply guarantee probability which represents the risk of PV power shortage is obtained using quantile regression model based on gradient boosted regression tree (GBRT) considering different PV power supply demands. Furthermore

new indexes

including guarantee rate

guarantee energy ratio

success index

forecasting gain index

and probability forecasting accuracy are proposed to evaluate the forecasting performance. The effectiveness of the proposed method is verified based on actual operation data in a province in Northwest China.