Economic Optimal Dispatch of Distribution Networks Considering the Stochastic Correlation of Wind and Solar Energy

The traditional optimization scheduling of distribution networks has often only considered the volatility and randomness of wind and solar output. When estimating the prediction errors of wind and solar output, wind turbines and photovoltaics are typically considered separately, overlooking the correlation between them. Accurate modeling of wind and solar output prediction errors is crucial for enhancing the reliability and economy of distribution network scheduling. To address this, this paper proposes a new modeling method. First, based on the volatility and randomness of wind and solar output, it considers the characteristic that wind and solar outputs in the same region at the same time are correlated. A multivariate nonparametric kernel density estimation is introduced to fit the joint prediction error distribution of wind and solar output using historical data. Next, the impact of joint prediction errors on system scheduling costs is considered by introducing a penalty cost in the economic objective function for the errors caused by wind and solar predictions. Additionally, energy storage devices are integrated into the system to smooth power fluctuations, thereby constructing an economically optimized scheduling model for wind–solar–storage distribution networks based on stochastic correlations. Finally, testing is conducted using an improved IEEE-33 node system. The results indicate that the model considering the correlation between wind and solar output significantly improves the fitting accuracy of prediction errors compared to traditional models that only consider randomness. It also enhances the utilization rate of wind and solar energy and improves the economic performance of the distribution network.