This paper emphasizes the importance of battery energy storage systems (BESS) for frequency stability in low inertia power systems. A mixed input signal is considered for the BESS control, consisting of the frequency variation and the rate of change of frequency, as a solution to deploy the BESS for providing both inertia and primary frequency control. A sensitivity analysis on the influence of each input signal and the reaction time of the BESS on the frequency control, as well as on the frequency stabilization was performed. Simulation are conducted on a two-area interconnected power system to study and validate the capability of BESS to provide virtual inertia and contribute to system frequency regulation.

The problem of improving the voltage profile and reducing power losses in electrical networks is a task that must be solved in an optimal manner. At present time, this optimality can be achieved by efficient usage of existing facilities alongside with installing FACTS devices. The static VAr compensator (SVC) was chosen for study as its maturity and acceptable costs make it more usable in practical applications than other FACTS devices This paper proposes a genetic algorithm that tries to identify the optimal location and size of an SVC. A multi-criteria function is developed, comprising of both operational objectives and investment costs. The computer program is run on a 13 nodes test system, assessing improvements in voltage profile and reducing power losses. The purpose of this study is to validate the solution method in order for it to be adapted for systems of higher dimensionality.

The latest technological developments are challenging for finding new solutions to mitigate the massive integration of renewable-based electricity generation in the electrical networks and to support new and dynamic energy and ancillary services markets. Smart meters have become ubiquitous equipment in the low voltage grid, enabled by the decision made in many countries to support massive deployments. The smart meter is the only equipment mandatory to be mounted when supplying a grid connected user, as it primarily has the function to measure delivered and/or produced energy on its common coupling point with the network, as technical and legal support for billing. Active distribution networks need new functionalities, to cope with the bidirectional energy flow behaviour of the grid, and many smart grid requirements need to be implemented in the near future. However there is no real coupling between smart metering systems and smart grids, as there is not yet a synergy using the opportunity of the high deployment level in smart metering. The paper presents a new approach for managing the smart metering and smart grid orchestration by presenting a new general design based on an unbundled smart meter (USM) concept, labelled as next generation open real-time smart meters (NORM), for integrating the smart meter, phasor measurement unit (PMU) and cyber-security through an enhanced smart metering gateway (SMG). NORM is intended to be deployed everywhere at the prosumer’s interface to the grid, as it is usually now done with the standard meter. Furthermore, rich data acquired from NORM is used to demonstrate the potential of assessing grid data inconsistencies at a higher level, as function to be deployed in distribution security monitoring centers, to address the higher level cyber-security threats, such as false data injections and to allow secure grid operations and complex market activities at the same time. The measures are considering only non-sensitive data from a privacy perspective, and is therefore able to be applied everywhere in the grid, down to the end-customer level, where a citizen’s personal data protection is an important aspect.

This paper presents a solution for integrating Electrical Vehicles in the Smart Grid through unbundled Smart Metering and Virtual Power Plant technology dealing with multiple objectives. Within this frame, EV can benefit of cost-effective energy during the charging period but can also provide multiple ancillary services to the network, by wisely using their storage capability and their flexibility in coupling to the grid. Simulation of an EV providing services under a multi-objective VPP is also presented, with analysis and conclusions about the technical feasibility of such applications.

A method for on-line voltage stability monitoring of a power system based on Multilayer Perceptron (MLP) neural network is proposed in this paper. Considering that the power system is operating under quasistatic conditions, by using power flow model and singular value decomposition of the reduced Jacobian matrix, a suitable index to quantify the proximity of power system voltage instability is defined. Then, a neuronal network is trained to learn the correlation between the key factors of the voltage stability phenomena and this index. Once trained, the neural network provides the above mentioned voltage stability index as output for a predefined set of input variables that are known as directly influencing the stability conditions of the power system. Since the input variables for the neural network may be obtained from the steady state estimator, the proposed method can be implemented as a function of the Energy Management System (EMS) for on-line voltage stability monitoring. Tests are carried out using the IEEE 30-bus system, where different operating scenarios are considered.