In this paper, the notion of reliability assessment for distribution system applications is revisited to include a number of practices emerging in the smart grid context. The information on the variations in time of generation and demand is taken into account to establish a reference network configuration that considers the definition of an intraday reconfiguration strategy based on conventional load profiles for different categories of demand (residential, industrial, and commercial). After a fault, the service restoration process is aided by the formation of autonomous islanded subsystems (microgrids). During the restoration period, each subsystem is able to serve the local demand in a given portion of the network and to reconnect to the main network through proper synchronization. Dedicated solutions for mobile generation and storage are exploited to reach the nodes needing additional supply. A sequential Monte Carlo method is used to carry out reliability assessment. The use of this method incorporates the effects of interfering near-coincident faults and time-varying load and local generation patterns. The application on a real distribution network is presented, showing the probability distributions of the reliability indicators (power and energy not supplied), as well as the breakdown of these indicators for different demand categories.

Due to the rapid growth in the amount of wind energy connected to distribution grids, they are exposed to higher network constraints, which poses additional challenges to system operation. Based on regulation, the system operator has the right to curtail wind energy in order to avoid any violation of system constraints. Energy storage systems (ESS) are considered to be a viable solution to solve this problem. The aim of this paper is to provide the best locations of both ESS and wind power by optimizing distribution system costs taking into account network constraints and the uncertainty associated to the nature of wind, load and price. To do that, we use a mixed integer linear programming (MILP) approach consisting of loss reduction, voltage improvement and minimization of generation costs. An alternative current (AC) linear optimal power flow (OPF), which employs binary variables to define the location of the generation, is implemented. The proposed stochastic MILP approach has been applied to the IEEE 69-bus distribution network and the results show the performance of the model under different values of installed capacities of ESS and wind power.