Multi-Agent-Based Controller for Microgrids: An Overview and Case Study

Abstract

A microgrid can be defined as a grid of interconnected distributed energy resources, loads and energy storage systems. In microgrid systems containing renewable energy resources, the coordinated operation of distributed generation units is important to ensure the stability of the microgrid. A microgrid needs a successful control scheme to achieve its design goals. Undesirable situations such as distorted voltage profile and frequency fluctuations are significantly reduced by installing the appropriate hardware such as energy storage systems, and control strategies. The multi-agent system is one of the approaches used to control microgrids. The application of multi-agent systems in electric power systems is becoming popular because of their inherent benefits such as autonomy, responsiveness, and social ability. This study provides an overview of the agent concept and multi-agent systems, as well as reviews of recent research studies on multi-agent systems’ application in microgrid control systems. In addition, a multi-agent-based controller and energy management system design is proposed for the DC microgrid in the study. The designed microgrid is composed of a photovoltaic system consisting of 30 series-connected PV modules, a wind turbine, a synchronous generator, a battery-based energy storage system, critical and non-critical DC loads, the grid and the control system. The microgrid is controlled by the designed multi-agent-based controller. The proposed multi-agent-based controller has a distributed generation agent, battery agent, load agent and grid agent. The roles of each agent and communication among the agents are designed properly and coordinated to achieve control goals, which basically are the DC bus voltage quality and system stability. The designed microgrid and proposed multi-agent-based controller are tested for two different scenarios, and the performance of the controller has been verified with MATLAB/Simulink simulations. The simulation results show that the proposed controller provides constant DC voltage for any operation condition. Additionally, the system stability is ensured with the proposed controller for variable renewable generation and variable load conditions.

Review of Energy Management Systems in Microgrids

“…For DC microgrids, energy management systems using artificial intelligence-based algorithms and multi-agent systems to ensure supply-demand balance and power quality in the system can be used [78]. Additionally, a fully decentralized control approach based on multi-agent systems can also be applied.…”

“…In Ref. [78], a microgrid design including photovoltaic modules, a wind turbine, a lithium-ion battery energy storage system, critical and non-critical DC loads, and a grid is proposed, and energy management of this microgrid system is obtained by using a multi-agent-based control structure. Distributed generation agents, battery agents, load agents, and grid agents are further components of the multi-agent system.…”

Smart Python Agents for Microgrids

“…They possess limited autonomous capabilities and interact with each other via the XMPP server to produce renewable power that meets the end-user power demand while maintaining a reliable microgrid power network [33]. Agent-based microgrid offers various advantages such as flexibility, resilience, and effective management in the increasing complexity of distributed renewable energy resources [34]. In addressing the complex tasks and challenges of distributed energy generation and control, varieties of agent design models are explored.…”

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

“…Agent-based virtual organizations are particularly well suited as support for the development of BEMSs [70][71][72][73][74]. They enable the description of structural compositions and functional behavior and the inclusion of normative regulations for controlling agent behavior, for the dynamic entry/exit of components, and for the dynamic formation of agent groups [75].…”