Plug-and-Play MARL for SoC and Power Balance Regulation of Heterogeneous BESSs

• Published in: 2023 IEEE Third International Conference on Signal, Control and Communication (SCC) pp. 1 - 6
• Main Authors: Al-Saadi, Mudhafar, Short, Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2023
• Subjects: Batteries; Battery Energy Storage; Decentralization; Economics; Microgrids; Multiagent Reinforcement Learning; Plug-and-Play; Real-time systems; Renewable Energy; Renewable energy sources; Switches; Vehicle-to-grid
• DOI: 10.1109/SCC59637.2023.10527486

Intelligent management of power flow and storage balance has proven its worth in supporting economic, sustainable operation of microgrids powered mainly by intermittent renewable energy resources. In particular, the introduction of Multi-Agent Reinforcement Learning (MARL) to solve power management and storage balance problems has been very successful. MARL primary-secondary control was the subject of a recent application in solving power storage flow problems in battery-based micro- and smart-grids, focusing upon vehicle-to-grid applications under realistic environmental considerations such as infrastructural influences. Such influences can worsen the accuracy of plug-and-play batteries' charge-discharge synchronization and hence control stabilization, power flow balance, batteries' health/life, and energy efficiency. This paper proposes a solution to this issue in a DC autonomous microgrid with multiple, heterogeneous batteries. Multiagent-neighbor-to-neighbor information is exploited to enhance the real-time balance of the load participation, and a real-time decentralized infrastructure compensation and power flow organization consumption/loss is established to compensate for infrastructural/environmental influence on the control. Moreover, implementation in a real-time economic sustainable participation policy on each BESS in a test microgrid is explored. The results verify improved synchronization of the batteries' power flow with reduced plug-and-play time by (4.16%), enhanced output voltage balance by (2.76-8%), reduced power consumption by (1.908-2.94%), improved power flow balance by (2.765-6.486%), and better power flow efficiency by (0.9196-2.626%) when compared to a baseline MARL implementation.