The power flow in distribution grids is becoming more complicated as reverse power flows and undesired voltage rises might occur under particular circumstances due to integration of renewable energy sources, increasing the occurrence of critical bus voltages. To identify these critical feeders the observability of distribution systems has to be improved. To increase the situational awareness of the power system operator data driven methods can be employed. These methods benefit from newly available data sources such as smart meters. This paper presents a voltage estimation method based on neural networks which is robust under complex load and in-feeder generation situations. A major advantage of the proposed method is that the power system does not have to be explicitly modeled.

The increasing share of volatile and inverter-based energy sources render electric power grids increasingly susceptible to disturbances. Established Load Frequency Control (LFC) schemes are rigid and require careful tuning, making them unsuitable for dynamically changing environments. In this paper, we present a fast and tuningless frequency control approach that tackles these shortcomings by means of modern grid monitoring and communications infrastructures in a two-fold concurrent process. First, direct observation of supply and demand enables fast power balancing decoupled from the total system dynamics. Second, primary resources are actively involved in frequency restoration by systematic adjustment of their frequency reference setpoints. In contrast to the commonly used Automatic Generation Control (AGC), the proposed Direct Load Frequency Control (DLFC) does not require an integrator for frequency control in the closed loop even under partial grid observability. The approach is Lyapunov-stable for a wide range of system parameters, including ramping limits of controlled resources. A performance study against AGC has been conducted on a three-area power system in simulations as well as in a real laboratory grid with an installed generation capacity of 110 kW.

The displacement of conventional generation by converter connected resources reduces the available rotational inertia in the power system, which leads to faster frequency dynamics and consequently a less stable frequency behavior. Virtual inertia, employing energy storage systems, could be used to limit the rate of change of frequency of power systems, thus, improving frequency dynamics. Electric vehicles (EVs) can represent a reliable solution to enhance frequency stability due to their fast response and capability to provide a large amount of aggregated power. On one hand, EVs are capable of adjusting the battery charging process (i.e., power flow) according to pre-defined algorithms. On the other hand, in case of islanded operation (i.e., low inertia), some of the EV's technical constraints might cause oscillations. This study presents two control algorithms which show that the EVs are capable of providing virtual inertia support. The first controller employs a traditional droop control, while the second one is equipped with an innovative control algorithm to eliminate likely oscillations. It is shown that, the proposed innovative control algorithm compared to the traditional droop control, assures same effects in terms of frequency but reducing significantly the number of variation of the EV's current set-point.