Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models

Abstract

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.

A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models

“…Non-parametric forecasting-based methods are completely data-driven and independent of any form of distribution assumption. Most methods are based on DGMs, which have gained increasing popularity in recent years for SPS generation [16,20,26,[78][79][80][81][82][83][84][85][86][87][88][89][90][91][92]. DGMs are completely assumption-free and generate new synthetic data that highly resemble the training samples.…”

“…Scenario forecasting usually refers to short-term forecasted scenarios for day-ahead operations, while scenario generation usually refers to representative (typical) scenarios for long-term resource and allocation planning. Scenario forecasting usually relies on point or probabilistic forecasts, while scenario generation exploits large datasets of historical observations [26]. For the remainder of this paper, both scenario generation and scenario forecasting will be referred to as scenario generation, while specific distinctions will be made when necessary.…”

“…Deep learning methods have also been used to determine weather classifications based on historical data by specifying different weather types in the labels of DGMs. In [26], a data-driven method was proposed to divide weather types into normal and abnormal days. The classification was based on an advanced time-series distance metric called dynamic time warping (DTW).…”

“…Other deep learning structures have also been proposed for SPS generation with GANs. In [26], a cross-correlated conditional recurrent GAN (CCRGAN) was proposed for seasonal solar and load scenario generation. Both the generator and the discriminator comprised three LSTM layers to capture long-term temporal dependencies.…”

Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation

“…Previous approaches for scenario generation modelling rely on statistical and artificial intelligence methods, which are often applied to historical data. Reference [69] proposed a cross-correlated scenario generation approach using implicit generative models to capture the joint probability distribution of renewable energy sources and electricity demand. They emphasised the importance of generating correlated scenarios to capture the interdependence of renewable energy sources and their impact on the operation of the microgrid.…”