The electricity sector foresees a significant change in the way energy is generated and distributed in the coming years. With the increasing penetration of renewable energy sources, smart algorithms can determine the difference about how and when energy is produced or consumed by residential districts. However, managing and implementing energy demand response, in particular energy flexibility activations, in real case studies still presents issues to be solved. This study, within the framework of the European project “SABINA H2020”, addresses the development of a multi-level optimization algorithm that has been tested in a semi-virtual real-time configuration. Results from a two-day test show the potential of building’s flexibility and highlight its complexity. Results show how the first level algorithm goal to reduce the energy injected to the grid is accomplished as well as the energy consumption shift from nighttime to daytime hours. As conclusion, the study demonstrates the feasibility of such kind of configurations and puts the basis for real test site implementation.

Nowadays, if a fault happens, the system operator has to manage the fault manually to solve the problem. The aim of this article is to provide a method of self-healing and islanding for the electric grid, when a fault appears, due to an electric problem or not. The presented tool has been developed to provide the best configuration of a grid when a problem occurs, with the least amount of changes to the grid and maintaining the power supply to the maximum number of users possible. The tool relies on the Binary Genetic Algorithm.

The high-penetration of Distributed Energy Resources (DER) in low voltage distribution grids, mainly photovoltaics (PV), might lead to overvoltage in the point of common coupling, thus, limiting the entrance of renewable sources to fulfill the requirements from the network operator. Volt-var is a common control function for DER power converters that is used to enhance the stability and reliability of the voltage in the distribution system. In this study, a centralized algorithm provides local volt-var control parameters to each PV inverter, which are based on the electrical grid characteristics. Because accurate information of grid characteristics is typically not available, the parametrization of the electrical grid is done using a local power meter data and a voltage sensitivity matrix. The algorithm has different optimization modes that take into account the minimization of voltage deviation and line current. To validate the effectiveness of the algorithm and its deployment in a real infrastructure, the solution has been tested in an experimental setup with PV emulators under laboratory conditions. The volt-var control algorithm successfully adapted its parameters based on grid topology and PV inverter characteristics, achieving a voltage reduction of up to 25% of the allowed voltage deviation.

Several algorithms combining qualitative and quantitative components are currently used for splitting a large interconnected power grid into islands as a measure to provide the best reconfiguration option when a fault appears. The aim of this article is to compare the clustering results of a binary genetic algorithm and a deep learning based method in order to identify the differences and to find in which cases it is rather better applicable each of the techniques.

Blockchain technology is currently being adopted in several domains and applications, including the energy domain and specifically in smart grids. One of the principal applications of blockchain technology is peer-to-peer energy trading among stakeholders of smart grids, providing trusted transactions without the need of third parties. Also, e-auctions are rapidly growing as a means of e-commerce that allows direct product bidding through the internet, but where mutual trust may otherwise be undermined by possible malicious sellers, buyers or third parties. This paper introduces a blockchain-based e-auction framework to offer a safe, trusted and privacy preserving energy exchange mechanism between EPES stakeholders of an islanded part of the grid. Furthermore, this framework utilises blockchain solutions for monitoring the security status of smart grid devices in order to confront any transactions involving malicious parties or parties with compromised equipment.