Hybrid multi‐agent‐based adaptive control scheme for AC microgrids with increased fault‐tolerance needs

Abstract

This paper presents a fault-tolerant secondary and adaptive primary microgrid control scheme using a hybrid multiagent system (MAS), capable of operating either in a semi-centralised or distributed manner. The proposed scheme includes a droop-based primary level that considers the microgrid energy reserves in production and storage. The secondary level is responsible for: a) the microgrid units' coordination, b) voltage and frequency restoration and c) calculation of the droop/ reversed-droop coefficients. The suggested architecture is arranged upon a group of dedicated asset agents that collect local measurements, take decisions independently and, collaborate in order to achieve more complex control objectives. Additionally, a supervising agent is added to fulfill secondary level objectives. The hybrid MAS can operate either with or without the supervising agent operational, manifesting fast redistribution of the supervising agent tasks. The proposed hybrid scheme is tested in simulation upon two separate physical microgrids using three scenarios. Additionally, a comparison with conventional control methodologies is performed in order to illustrate further the operation of a hybrid approach. Overall, results show that the proposed control framework exhibits unique characteristics regarding reconfigurability and fault-tolerance, while power quality and improved load sharing are ensured even in case of critical component failure. PF min minimum power factor (-) m f-P droop coefficient (Hz/W) n V-Q droop coefficient (Hz/VAr) v ocd compensating d-axis voltage by virtual impedance (V) v ocq compensating q-axis voltage by virtual impedance (V) i od inverter output inductor current on the d-axis (A) i oq inverter output inductor current on the q-axis (A) R v resistive part of the virtual impedance (Ω) L v inductive part of the virtual impedance (H) M on exchanged agent messages during one operation cycle-MGCC operational (-) M off exchanged agent messages during one operation cycle-MGCC inactive (-) N ESS total number of ESSs in a microgrid (-) N PV total number of PVs in a microgrid (-) N Load total number of load buses in a microgrid (-) E nom Nominal Battery Capacity (Wh)

A brief review on microgrids: Operation, applications, modeling, and control

“…190,191 6.2 | Basic control architecture Control system based on architecture is: centralized, decentralized, 192 and multiagent. 193,194 Some contributions of the control strategy based on supervisory control applications in microgrid system are tabulated in Table 6.…”

Agents for Smart Power Grids

“…Multiagent systems (MAS) may be designed to inherit SCADA (supervisory control and data acquisition) architecture while accommodating autonomous and intelligence attributes. MAS technologies have been used in diverse power system applications like disturbance diagnosis, restoration, secondary voltage control, and visualization [5] [6] [7]. Their wider implementation has been demonstrated in solar power generation batteries, controllable loads, converters and inverters, demand-side management, data acquisition and grid planning [5] [8].…”

“…MAS technologies have been used in diverse power system applications like disturbance diagnosis, restoration, secondary voltage control, and visualization [5] [6] [7]. Their wider implementation has been demonstrated in solar power generation batteries, controllable loads, converters and inverters, demand-side management, data acquisition and grid planning [5] [8]. Agents are implemented as software units which accept signals and interactively prioritize loads, specify time and control status of loads [9].…”

Fault Tolerant Control of Distributed System Based on Neural Network

“…Early ensemble approaches ignored the effects of system failures, although they had a significant impact on large distributed resources [5]. Khaldi M proposed a new fault-tolerant method for task clustering, called FT-HCC, which aims to improve performance by including task creation time (makepan) and task constraints.…”