This paper presents an algorithm considering both power control and power management for a full direct current (DC) microgrid, which combines grid-connected and islanded operational modes, with real-time demand-side management optimization. The full microgrid is a hybrid dynamic system model consisting of two interacting parts: continuous-time dynamics and discrete-event dynamics. Such a full microgrid consists of photovoltaic sources, a DC load, battery storage systems, supercapacitor storage, a diesel generator, and a public grid connection, all connected on a DC common bus. This full microgrid is more reliable than a microgrid with only renewable sources or with only traditional energy sources, considering the power constraints imposed by the public grid as well as the sluggish dynamic of the diesel generator, self-discharging characteristic of the supercapacitor, and load shedding optimization. Meanwhile, this algorithm can automatically switch between grid-connected and islanded operational modes to optimize the power of the load shedding, take advantage of renewable energy, and keep the power balance in the full DC microgrid. The results under MATLAB/Simulink verify that the real-time control algorithm can maintain the power balance in real-time for the whole day and satisfy the power management strategy.

This paper presents an online multi-level energy management system for local microgrids of commercial buildings that integrate roof-top photovoltaic sources, battery storage systems, utility grids, diesel generators, supercapacitors, and commercial buildings consisting of active orchestrated loads, to solve the uncertainty problem of sources and loads, while also optimizing the local microgrid operation cost of commercial buildings and the utilization rate of local renewable energy. The energy management system includes a long-term rolling optimization level, rule-based optimization level, and load demand optimization level. At the long-term rolling optimization level, an online rolling method of data restructuring is proposed, where measurement data, short-term prediction data, and day-ahead prediction data are reconstructed to reduce the uncertainty in photovoltaic source prediction and load demand prediction. Four methods are proposed for the energy management system and simulated in MATLAB/Simulink under three typical weather conditions, cloudy, sunny, and rainy. Simulation results show that the performance of Method 3 is closest to that of Method 4, whose data conditions are ideal; Method 3 reduces the operational cost of the commercial building microgrid and improves the utilization rate of photovoltaic sources, at the slight cost of non-critical load shedding.

This paper presents a supervisory system that considers converter efficiency for local microgrids of commercial buildings to solve the uncertainty problem of the sources and loads while also optimizing local microgrid operating costs and maintaining power supply quality for commercial buildings. The supervisory system includes an energy management layer and a power management layer. In the energy management layer, a long-term optimization approach is used to reduce the operating costs by considering the dynamic converter efficiency. In the power management layer, a real-time power optimization method is structured to deal with the uncertainty problem of the sources and loads, and to ensure that the direct current bus power is balanced while also guaranteeing the power quality by considering the dynamic converter efficiency. Four cases are proposed for the supervisory system, and these cases are simulated in MATLAB/Simulink under three typical weather conditions: cloud, sunshine, and rain. The comparison of simulation results for cases 1 and 2 illustrates the impact of converter efficiency on energy coordination in microgrids. The simulation results of cases 3 and 4 verify that the performance—in terms of the power supply quality and the operating costs—of the proposed microgrid supervisory system considering dynamic converter efficiency outperforms that of the microgrid supervisory system considering fixed converter efficiency.