In recent years, due to the wide utilization of dc power sources, such as solar photovoltaics (PV), fuel cell (FC), and various energy storage systems (ESSs) (e.g., batteries, supercapacitors (SCs), and so on), as well as the high penetration of dc loads, like light-emitting diodes, computation devices, and motor drive systems, DC microgrids are gaining increasing attention. Moreover, DC microgrids do not have the issues of synchronization, reactive power flow, harmonics, etc., as their AC counterparts. DC microgrids have been widely applied in renewable energy systems, remote households, data centers, and electric transports including more electric aircraft, electric vehicles, electric ships, etc.

However, there are many challenges to be addressed in DC microgrids, including the critical demand-supply power balance under the intermittent renewable generations, the economic operation under renewable uncertainties and the stability issue emerged from the high penetration of power electronic converters. This tutorial aims to present advanced control, power management and energy management strategies to address the power balance, economic operation and stability issues in DC microgrids.

First, this tutorial will present control and power management of DC microgrids to achieve real-time power balance and stable operation, including novel decentralized and distributed control strategies.

Second, this tutorial will present advanced control methods for stabilization of DC microgrids. The high penetration of power electronic converters into DC microgrids may cause the constant power load (CPL) stability issues, which could lead to large voltage oscillations or even system collapse. However, most of existing methods utilize linearized small signal models and they can only ensure system stability around the operating points, thus cannot guarantee stability under large signal disturbances. Advanced control technologies will be presented, including sliding mode control, model predictive control, passivity-based control, backstepping, optimal control, etc, to provide advantages of robustness, stability, optimality, flexibility, etc; and thus they can significantly improve the performance and stability margin of DC microgrids.

Third, the tutorial will present advanced energy management strategy of fuel cell vehicles, which are typical DC microgrids. The energy management in fuel cell vehicles (FCVs) is crucial to maintain the economical operation of FCVs and the fuzzy logic control (FLC) is mainly used to manage the energy splitting between the fuel cell and other energy sources. To overcome the limitation of traditional fuzzy logic control (FLC), the dependence on expert knowledge leading to the insufficient energy splitting, this paper proposed strategy optimization based on FLC with driving cycles recognition. Initially, the driving cycles recognition is achieved based on K-means clustering method, and characteristic parameters are extracted and classified. Additionally, with the objective function which is the minimum equivalent hydrogen consumption of four typical driving cycles, the centers and widths of FLC membership function are optimized by genetic algorithm (GA), respectively. Finally, the whole FCV model is established, which includes electrical system, vehicle dynamic system, energy management system. The proposed strategy can effectively smooth the output of fuel cell (FC) and enhance the total fuel economy.