Improving Primary Frequency Response in Networked Microgrid Operations using Multilayer Perceptron-Driven Reinforcement Learning

Journal Article · Sat Aug 01 00:00:00 EDT 2020 · IET Smart Grid

DOI:https://doi.org/10.1049/iet-stg.2019.0261· OSTI ID:1706690

Radhakrishnan, Nikitha  ^[1]; Chakraborty, Indrasis  ^[1]; Xie, Jing ^[1]; Thekkumparambath Mana, Priya ^[1]; Tuffner, Francis K.  ^[1]; Bhattarai, Bishnu P. ^[1]; Schneider, Kevin P.  ^[1]

---

1. BATTELLE (PACIFIC NW LAB)

Individual microgrids can improve the reliability of power grids during extreme events, and networked microgrids can further improve efficiency through resource sharing and increase the resilience of critical end-use loads. However, networked microgrid operations are subject to large switching transients, which can cause dynamic instability and lead to system collapse. These transients are especially prevalent in microgrids with high penetrations of inverter-connected renewable energy resources, which do not provide the system inertia needed to mitigate the transients. Existing generator controls can be modified to invoke a drop in terminal voltage in response to a frequency deviation, thereby reducing load and improving frequency response. This paper investigates the use of a reinforcement-learning--based controller trained over several switching transient scenarios to modify generator controls during large frequency deviations. Compared to previously used proportional integral controllers, the proposed controller can improve primary frequency response while adapting to changes in system topologies and events.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1706690
• Report Number(s): PNNL-SA-147672
• Journal Information: IET Smart Grid, Vol. 3, Issue 4
• Country of Publication: United States
• Language: English