The present research investigations in power management fraternity are focused towards the realization of smart microgrid (MG) technologies. Due to intrinsic advantages of Direct Current (DC) system in terms of compatibility with power generation sources, modern loads and storage devices, DC MG has becoming popular over Alternate Current (AC) system. A secondary voltage and current control schemes of DC MG system are mainly based on the distributed consensus control of Multi-agent system (MAS) to balance generation and the demand. The basic concern of the cooperative control of MAS is consensus, which is to design a suitable control law such that the output of all agents can achieve synchronization. The distributed consensus algorithm requires much less computational power and information exchange in between the neighbor’s agent. Meanwhile the other controllers such as model predictive control (MPC) requires accurate dynamic models with high computational cost and it suffers from lack of flexibility. The hierarchical consensus control technique is classified into three levels according to their features, namely primary, secondary, and tertiary. MAS is a popular distributed platform to efficiently manage the secondary control level for synchronization and communication among the power converters in autonomous MGs. In this article, various primary control techniques for local voltage control, voltage restoration in the secondary control level and tertiary control for energy management techniques are discussed. With this, the key emphasis to reduce the voltage deviation and disturbances in a heterogeneous DC MG network solutions are discussed. Furthermore, to analyze the system response and the charging and discharging characteristics of the battery unit, the developed second order heterogeneous consensus controller is compared with the traditional homogeneous consensus control and droop control methods. Finally a detailed discussion on simulation case study using heterogeneous consensus control method over the traditional methods are provided using MATLAB/Simulink platform.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)