Deep reinforcement learning-based secondary control for microgrids in islanded mode

Author(s)

Barbalho, P. I. N. ; Lacerda, V. A. ; Fernandes, R. A. S. ; Coury, D., V

• Document type: Journal article
• Source: Electric Power Systems Research; v. 212, p. 7-pg., 2022-07-20.

Abstract

Microgrids are generally low-inertia systems with a high penetration of renewable energy sources. The design of advanced control structures is required to keep these grids' electrical variables within an acceptable range. In this context, the present article proposes an intelligent secondary controller for islanded microgrids using the Deep Deterministic Policy Gradient (DDPG). The DDPG controller changes the output power of the storage elements to secure the voltage and frequency stability. This work tested the designed controller for a microgrid that comprises a synchronous generator, two battery energy storage systems and one photovoltaic generator. The controller performance was compared to droop controllers, considering a short-circuit event, feeder and load disconnections. Results showed a consistent reduction of the microgrid's voltage and frequency deviations with the DDPG algorithm. (AU)

• FAPESP's process: 17/16742-7 - Microgrids:Identification and solution of problems in hybrid distributed generation systems
• Grantee: Denis Vinicius Coury
• Support Opportunities: Regular Research Grants
• FAPESP's process: 21/04872-9 - Evaluating technical impacts on the expansion of distributed generation for smart grids
• Grantee: Denis Vinicius Coury
• Support Opportunities: Regular Research Grants