Abstract
This paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the application of distributed multi-agent system (MAS) theory in multi-objective (MO) power management of MGs to find the Pareto-front of the MO power management problem. In addition, the paper presents the application of Nash bargaining solution (NBS) and the MAS theory to directly obtain the NBS on the Pareto-front. The paper also discusses the progress reported on the above issues from the literature. We also present a MG-based power system architecture for enhancing the resilience and self-healing of the system.
Highlights
The term “resilience” is defined as the ability to prepare for and adapt to changing conditions, as well as withstand and recover rapidly from disruptions [1,2]
Example of the system they are responsible for agents act in a primarily selfCooperative behavior amongst the agents arises from a desire to raise the overall performance interested way, when considering the overall MG objectives agents will need to work of the system they are responsible for agents act in a primarily selftogether, and this often requires one or more assets to sacrifice operation at their locally “best” point interested way, when considering the overall MG objectives agents will need to work to settle at a trade-off solution
This paper presents an extensive review of the related literature on distributed multi-agent system (MAS) and the application of Nash bargaining solution (NBS) to directly find a unique solution on the Pareto-front of the MO power management problem of MGs
Summary
The term “resilience” is defined as the ability to prepare for and adapt to changing conditions, as well as withstand and recover rapidly from disruptions [1,2]. An agent-based distributed dynamic programming algorithm is proposed and implemented in [37] to provide solution for online economic dispatching problem Another class of MAS technique, closely related to the field of distributed optimization, is known as Distributed Constraint Optimization Problems (DCOP) [38,39]. The proposed MO energy management problem of the MG is solved by a central control agent at the highest level of the decision structure Another market-based distributed control mechanism for MG resource allocation is proposed in [56] based on a replicator dynamics strategy. Contract net protocol and multifactor evaluation are employed for coordination of different agents at different levels Another hierarchical MAS-based control architecture within a market context is proposed in [59] for MG power management.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
