Steady-State Scenario Development for Synthetic Transmission Systems

Li, Hanyue; Yeo, Ju Hee; Wert, Jessica L.; Overbye, Thomas J.

doi:10.1109/tpec48276.2020.9042493

Cited by 12 publications

(1 citation statement)

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“…This environment was then used to carry out deep reinforcement learning (DRL) experiments in which the algorithm attempts to learn how to best control grid voltages under a diverse set of grid conditions (Thayer, 2020b). • In (Li, Yeo, Wert, & Overbye, 2020), ESA was leveraged to create and simulate different electric grid scenarios where load, renewable generation levels, generation capacities, scheduled outages, and unit commitment were all varied. The resulting scenarios were used in the Grid Optimization (GO) competition hosted by the U.S. Department of Energy (DOE).…”

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Easy SimAuto (ESA): A Python Package that Simplifies Interacting with PowerWorld Simulator

Thayer¹,

Mao²,

Liu³

et al. 2020

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The electric power system is an essential cornerstone of modern society, enabling everything from the internet to refrigeration. Due to a variety of forces including climate change, changing economics, and the digital computer revolution, the electric grid is undergoing a period of major change. In order to overcome current and upcoming challenges in the electric power system, such as integrating renewable resources into a system that was not designed for intermittent power sources, researchers and industry practitioners must simulate the electric grid, its component devices, and its operation. PowerWorld Simulator is a commercial power systems simulation tool that contains a suite of modeling and simulation features including power flow simulation, contingency analysis, transient stability simulation, and more (PowerWorld Corporation, 2020). The Simulator Automation Server (SimAuto) add-on for PowerWorld provides an application programming interface (API) that operates in-memory, allowing users to rapidly configure, run, and obtain results for simulations. PowerWorld and SimAuto are commonly used throughout the research community as well as in industry.

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confidence: 99%