Abstract-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 datadriven, 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 timesseries 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.

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 datadriven, 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 timesseries 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.

Recent advances in Machine Learning (ML) have led to its broad adoption in a series of power system applications, ranging from meter data analytics, renewable/load/price forecasting to grid security assessment. Although these datadriven methods yield state-of-the-art performances in many tasks, the robustness and security of applying such algorithms in modern power grids have not been discussed. In this paper, we attempt to address the issues regarding the security of ML applications in power systems. We first show that most of the current ML algorithms proposed in power systems are vulnerable to adversarial examples, which are maliciously crafted input data. We then adopt and extend a simple yet efficient algorithm for finding subtle perturbations, which could be used for generating adversaries for both categorical (e.g., user load profile classification) and sequential applications (e.g., renewables generation forecasting). Case studies on classification of power quality disturbances and forecast of building loads demonstrate the vulnerabilities of current ML algorithms in power networks under our adversarial designs. These vulnerabilities call for design of robust and secure ML algorithms for real world applications.