Management of Autonomous Microgrids Using Multi-Agent Based Online Optimized NF-PID Controller

Document Type : Original Article


1 Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran.

2 Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran

3 System Engineering Research Group. Department of Engineering and Technology, University of Huddersfield, UK


This paper presents an adaptive multi-agent based online-tuned PID controller using Neuro-Fuzzy (NF) for dynamic management of Distributed Generations (DGs) in an autonomous microgrid. Increasing system stability and decreasing generation costs are the main aims of the proposed management strategy. Instead of one centralized management systems, the management and control function is allocated to several autonomous units which are known as agents. The proposed management system is composed of fixed and variable units. The fixed variables is the three parameters ( and ) of the conventional PID controller which are adjusted based on load variation pattern in offline mode. The parameters of variable unit is generated by NF system. The load pattern is applied to system in offline mode and agent’s optimizing units optimize the system performance. Distributed multi-agent model is considered for tuning the neuro-fuzzy parameters whereas agents establish with neighboring agents. In autonomous mode of the microgrid, the variable units, after tuning, control the system frequency and manage energy generation of DGs, beside fixed units, in an online manner. In the study system, various kinds of DGs including wind turbine, photovoltaic, synchronous generator, and fuel cell are considered. Linear transfer function models are obtained for each DG unit. To achieve a better performance of the proposed management strategy the modified Particle Swarm Optimization algorithm is applied for tuning of the NF based PID controller parameters. Simulation results in various conditions of microgrid confirm the good performance of the proposed management strategy in comparison to the other methods.


Main Subjects

Volume 1, Issue 1
June 2017
Pages 79-87
  • Receive Date: 23 July 2017
  • Revise Date: 11 August 2017
  • Accept Date: 12 August 2017
  • First Publish Date: 12 August 2017