A direct current (DC) microgrid has become a superior power system in recent years due to the development of DC loads and higher efficiency of DC systems. One of the challenging problems on DC microgrids operation is protection, and it is still a particular concern associated with the challenges of developing a proper protection scheme owing to its characteristics and lack of standards in DC protection. Due to the significant increasing interest on DC microgrid; this study investigates protection problems and schemes that need to be addressed in modern power systems involving DC microgrids. This study analyses and presents a comprehensive review of the most recent growth in the DC microgrids protection. Additionally, the fault characteristics of DC microgrids, the impact of constant power loads, the protection devices and several proposed methods to overcome the protection problems are discussed. The differences between the proposed protection methods for the DC microgrids are also discussed.

This paper presents a predictive home energy management system for a residential building with integration of a plug-in electric vehicle, a photovoltaic array and a heat pump. A stochastic model predictive control strategy is applied in the home energy management system in order to minimize the home electricity cost and reduce the plug-in electric vehicle battery degradation cost. Moreover, the model predictive control ensures that home load demand, plug-in electric vehicle battery charging requirements and household thermal comfort conditions are met. The model predictive control operates in real-time and thus minimizes the effects of gap between the forecasted and real data on the home energy management system performance by updating its control decisions and the forecast data as the stochastic parameters are realized in each time step. The obtained simulation results demonstrate that the proposed control strategy reaches 96% to 97% of ideal performance achieved by the off-line optimization counterpart with perfect data.

Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their subsequent charging demands introduce substantial intermittency to the electric load profile which imposes techno-economic challenges on power distribution networks. To address the uncertainty in demand, a novel deep learning-based approach equipped with a hybrid classification task is developed which can take into account the travel characteristics of the PEV owners. The classification structure helps us scrutinize the PEVs demand by allocating a specific forecasting network to each cluster of travel behavior patterns. In our hybrid classification task, first, an unsupervised classifier discerns hidden travel-behavior patterns between the historical PEVs data by clustering them; then, a supervised classifier directs each new PEV data to its appropriate clusterspecific forecasting network. The deep learning-based forecasting and classification networks are constructed based on the Long Short-Term Memory networks to investigate long-and short term features in PEV behaviors. The data-driven structure of our proposed method enables us to observe and preserve the correlation between PEV travel data parameters (departure time, arrival time and traveled distance) and avoid the generation of unrealistic travel samples found in scenario-based approaches. To verify the effectiveness of the proposed method in practical environments, we have studied the impact of the precise forecasting of the PEVs demand in an aggregator’s financial profit in the energy market of the California Independent System Operator market. The numerical results confirm the outstanding performance of our proposed deep learning-based method in forecasting PEVs demand against benchmark approaches in this field such as Monte Carlo, Quasi-Monte Carlo, and Copula with only a 6.77% error in comparison with real data.

This paper concentrates on the control of hybrid fuel-cell (FC)/energy-storage distributed generation (DG) systems under voltage sag in distribution systems. The proposed control strategy makes hybrid DG system work properly when a voltage disturbance occurs in distribution system, and it stays connected to the main grid. To distribute the power between dc power sources and stabilize the dc-link power, a Lyapunov-based neuro-fuzzy control strategy has been developed. This controller determines the supercapacitor power that should be generated according to the amount of available energy in dc-link. Also, current control strategies for the FC converter (boost) and supercapacitor converter (buck-boost converter) are designed by proportional-integral and sliding-mode control consequently. Moreover, a complementary control strategy for voltage source converter based on positive and negative symmetrical components is presented to investigate the voltage sag ride-through and voltage control capability. The hybrid system is studied under unbalance voltage sag condition. Simulation results are given to show the overall system performance including active power control and voltage sag ride-through capability of the hybrid DG system.