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

In this paper, an adaptive multi-agent based online-tuned PID controller using Neuro-Fuzzy (NF) is proposed 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 system, 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 are the three parameters (Kp, Ki and Kd) of the conventional PID controller which are adjusted based on load variation pattern in offline mode. The parameters (∆Kp, ∆Ki) of variable unit is generated by neuro-fuzzy 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. In order to achieve a better performance of the proposed management strategy the modified Particle Swarm Optimization (MPSO) algorithm is applied for tuning of the NF based PID (NF-PID) controller parameters. Simulation results in various conditions of microgrid confirm the good performance of the proposed multi-agent management strategy in comparison to the other existing methods.