Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

By Yize Chen, Yishen Wang, Daniel S. Kirschen, Baosen Zhang. IEEE Transactions on Power Systems, 2017

592 citations recorded by the source corpus.

Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from, our method is data-driven, and captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources. For validation, we use wind and solar times-series data from NREL integration data sets. We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors. We also illustrate how to generate scenarios based on different conditions of interest by using labeled data during training. For example, scenarios can be conditioned on weather events (e.g., high wind day, intense ramp events, or large forecasts errors) or time of the year (e.g., solar generation for a day in July). Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently without sophisticated sampling techniques.

Subfields: Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8f0cd01564255822a547e1483e2e50eba9ff6060

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Citations in the corpus

Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting (2019, background): “As for the WGAN applied in literature [45], its structures of the neural networks in the generator and the discriminator are very complex.”
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “Recently, an emerging deep generative model named generative adversarial networks (GANs) was proposed and has become increasingly popular in various areas, such as image processing [206], renewable scenario generation [207], and molecular designs [208].”
Energy Forecasting: A Review and Outlook (2020, unknown): “using generative adversarial networks [64].”
Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement Learning (2020, background): “for prediction of uncertain factors [12]–[14], smart meter data identification [15], modeling of renewable energy [16], and energy storage dispatch [17].”
Time-series Generative Adversarial Networks (2019, background): “A multitude of applied studies have since utilized these frameworks to generate synthetic sequences in such diverse domains as text [15], finance [16], biosignals [17], sensor [18] and smart grid data [19], as well as renewable scenarios [20].”