This paper presents an optimization procedure that enables the optimal dispatching of distributed generators and storage systems in a medium-voltage islanded microgrid. The network is assumed to be supplied by programmable (dispatchable) and nonprogrammable generators (i.e. nondispatchable, such as renewable energy sources-based units). The optimization goal is to minimize the overall microgrid operating cost and the pollutants emission of the programmable generators, assuming that all of the power made available by the renewable generators (photovoltaic and wind systems) is either directly injected into the network or stored in order to be subsequently delivered according to the proposed storage units' management strategy. The optimization is carried out by a niching evolutionary algorithm (NEA) that is able to find multiple optima and the variation of the objective function in their neighborhood. NEAs allow overcoming the performance of standard algorithms used for optimal power-flow calculations in power systems by avoiding falling into local optima. The optimization procedure is performed on a test microgrid and verified by computer simulations. The numerical results show that the solutions can always improve the microgrid performances irrespective of the network operating conditions in all of the considered cases.

This paper presents an innovative generalized systematic approach and related analytical formulation to evaluate distribution system reliability in smart grids where islanded operation of microgrids helps to improve local and overall reliability. In order to do this, the analytical formulation involves the adequacy calculation of conventional and renewable distributed generators supplying microgrids by using probabilistic models. Adequacy is computed by means of a new general analytical expression which takes into account load shedding (user load disconnection) and curtailment (user load reduction) policies.

This paper describes a method to apply a generalized systematic approach, useful to identify the faults' effect on load points' reliability accounting for intentional islanding, with either random or sequential Monte Carlo simulation (RMCS, SMCS) in order to assess distribution system reliability in smart grids. Moreover, a simple RMCS based on an annual branch fault probability, which correlates with the branch fault frequency, is described as well. A study case is presented to validate the results (in terms of expected reliability indices) of the proposed method in comparison with the analytical one. Further, other useful information, such as the best and worst results as well as the probability distribution of load-point annual outages number and duration is presented too. Finally, the results show that islanding positively affects reliability and then it could became a standard practice provided that the main technical issues are addressed.

Telecontrol of distribution switching devices is recognized as a major means for reducing interruption duration after a fault occurrence in modern distribution systems, since it allows one to isolate the faulted sections and to restore the healthy ones as quickly as possible. In order to reduce interruption frequency as well, it is necessary to increase the number of series automatic circuit breakers. Further benefits in terms of service continuity improvement can be obtained by allowing islanded operation of relatively small portions of distribution network (microgrids) in the presence of distributed energy resources in order to supply the local load autonomously during upstream faults. This paper aims to describe an innovative systematic method and its related analytical formulation to evaluate distribution system reliability indices accounting for advanced operation practices such as those based on telecontrol and islanding.

Condition monitoring (CM) of power semiconductor devices enhances converter reliability and customer service. Many studies have investigated the semiconductor devices failure modes, the sensor technologies, and the signal processing techniques to optimize the CM. Furthermore, the improvement of power devices’ CM thanks to the use of the Internet of Things and artificial intelligence technologies is rising in smart grids, transportation electrification, and so on. These technologies will be widespread in the future, where more and more smart techniques and smart sensors will enable a better estimation of the state of the health (SOH) of the devices. Considering the increasing use of power converters, CM is essential as the analysis of the data obtained from multiple sensors enables the prediction of the SOH, which, in turn, enables to properly schedule the maintenance, i.e., accounting for the trade-off between the maintenance cost and the cost and issues due to the device failure. From this perspective, this review paper summarizes past developments and recent advances of the various methods with the aim of describing the current state-of-the-art in CM research.