Learning‐based load control to support resilient networked microgrid operations. Issue 5 (16th June 2020)

• Record Type: Journal Article
• Authors: Radhakrishnan, Nikitha; Schneider, Kevin P.; Tuffner, Francis K.; Du, Wei; Bhattarai, Bishnu P.
• Is Part Of: IET smart grid. Volume 3:Issue 5(2020)
• Page Start: 697
• Page End: 704
• Publication Date: 2020-06-16
• Subjects: distributed power generation -- power generation control -- invertors -- power grids -- load regulation -- domestic appliances; improved dynamic stability -- switching operations -- load control -- resilient networked microgrid operations -- effective option -- critical end‐use -- operational experience -- microgrid operators -- isolated microgrids -- transient events -- bulk transmission systems -- larger frequency deviations -- inverter‐connected assets -- Grid Friendly Appliance -- TM controllers -- GFA controllers -- end‐use customers -- device‐level algorithm -- individual GFA controller frequency setpoints -- operational characteristics -- changing grid dynamic characteristics -- switching transients -- adaptive gradient descent‐based algorithm -- end‐use devices -- frequency set‐points -- IEEE 123‐node test system; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0265
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : Networked and interconnected microgrids can improve resilience of critical end‐use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter‐connected assets, resulting in a loss of power to critical end‐use loads. Grid Friendly Appliance TM (GFA) controllers can mitigate the transient event effects by engaging end‐use loads. This paper presents a method to select set‐points for end‐use loads equipped with GFA controllers, while minimizing the interruptions to end‐use customers. An online (i.e. real‐time), device‐level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end‐use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient‐descent‐based algorithm does not require control or coordination amongst end‐use devices for adapting frequency set‐points. The method is validated using dynamic simulations on a modified version of the IEEE 123‐node test system with three microgrids using the GridLAB‐D TM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.