Improving primary frequency response in networked microgrid operations using multilayer perceptron‐driven reinforcement learning. Issue 4 (16th July 2020)

• Record Type: Journal Article
• Authors: Radhakrishnan, Nikitha; Chakraborty, Indrasis; Xie, Jing; Thekkumparambath Mana, Priya; Tuffner, Francis K.; Bhattarai, Bishnu P.; Schneider, Kevin P.
• Is Part Of: IET smart grid. Volume 3:Issue 4(2020)
• Page Start: 500
• Page End: 507
• Publication Date: 2020-07-16
• Subjects: multilayer perceptrons -- invertors -- learning (artificial intelligence) -- frequency response -- power distribution control -- renewable energy sources -- power generation control -- distributed power generation -- frequency control; existing generator controls -- system voltage -- frequency deviation -- improving primary frequency response -- reinforcement‐learning‐based controller -- switching transient scenarios -- system topologies -- networked microgrid operations -- multilayer perceptron‐driven reinforcement learning -- individual microgrids -- power systems -- networked microgrids -- resource sharing -- critical end‐use loads -- system collapse -- grid‐following inverter‐connected renewable energy resources -- system inertia -- fast frequency response; B8110C Power system control -- B8120J Distribution networks -- C3110G Frequency control -- C3340H Control of electric power systems -- C5290 Neural computing techniques -- C6170K Knowledge engineering techniques -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0261
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : Individual microgrids can improve the reliability of power systems 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 can be subject to large transients due to switching and end‐use loads, which can cause dynamic instability and lead to system collapse. These transients are especially prevalent in microgrids with high penetrations of grid‐following inverter‐connected renewable energy resources, which do not provide the system inertia or fast frequency response needed to mitigate the transients. One potential mitigation is to engage the existing generator controls to reduce system voltage in response to a frequency deviation, thereby reducing load and improving primary frequency response. This study 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 conditions.