The technological leap of smart technologies and the Internet of Things has advanced the conventional model of the electrical power and energy systems into a new digital era, widely known as the Smart Grid. The advent of Smart Grids provides multiple benefits, such as self-monitoring, self-healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that can lead to devastating consequences, including cascading effects with other critical infrastructures or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart Grid resiliency, taking full advantage of the Software-Defined Networking (SDN) technology. The proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk assessment, (b) intrusion detection and correlation and (c) self-healing. The first tier is responsible for evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect and correlate security events and, finally, the last tier mitigates the potential threats, ensuring in parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE architecture interact with the SDN controller either for detecting or mitigating intrusions.

The rapid development of digital technologies and solutions is disrupting the energy sector. In this regard, digitalization is a facilitator and enabler for integrating renewable energies, management and operation. Among these, advanced monitoring techniques and artificial intelligence may be applied in solar PV plants to improve their operation and efficiency and detect potential malfunctions at an early stage. This paper proposes a Digital Twin DT concept, mainly focused on O&M, to obtain more information about the system by using several artificial intelligence boxes. Furthermore, it includes the development of several machine learning (ML) algorithms capable of reproducing the expected behavior of the solar PV plant and detecting the malfunctioning of different components. In this regard, this allows for reducing downtime and optimizing asset management. In this paper, different ML techniques are used and compared to optimize the selected methods for enhanced response. The paper presents all stages of the developed Digital Twin, including ML model development with an accuracy of 98.3% of the whole DT, and finally, a communication and visualization platform. The different responses and comparisons have been made using a model based on MATLAB/Simulink using different cases and system conditions.

In photovoltaic (PV) systems, the reliability of the system components, especially the power converters, is a major concern in obtaining cost effective solutions. In order to guarantee service continuity in the case of failure of elements of the PV converter, in particular, semiconductor switching devices, a solution is to design power converter with fault-tolerance capability. This can be realized by aggregating hardware redundancy on an existing converter, providing the possibility of replacement of faulty elements. This paper evaluates the reliability of a fault-tolerant power electronics converter for PV multistring application. The considered fault-tolerant design includes a single redundant switching leg, which is used in order to reconfigure the structure in case of a switch failure either on DC-AC or DC-DC stages. This paper details the reliability estimation of the considered PV multistring fault-tolerant converter. Furthermore, a comparison with a conventional structure without fault-tolerant capability is provided. The results show that the introduction of a single redundant leg allows for improving the converter mean time to failure by a factor of almost two and it reduces, by half, the power loss due to system-failure shutdowns in PV applications, while only increasing the converter cost by 2–3%.

Lithium-Sulfur is a promising technology for the next generation of batteries and research efforts for early-stage prototype implementation increased in recent years. For the development of a suitable Battery Management System, a state estimator is required; however, lithium-sulfur behavior presents a large non-observable region that may difficult the convergence of the state estimation algorithm leading to large errors or even instability. A dual Extended Kalman Filter is proposed to circumvent the non-observability region. This objective is achieved by combining a parameter estimation algorithm with a cell model that includes non-linear behavior such as self-discharge and cell degradation. The resulting dual Kalman filter is applied to lithium–sulfur batteries to estimate their State-of-Charge incorporating the effects of degradation, temperature, and self-discharge deviations.

Nowadays, automation is not enough in the power grid fostering the evolution towards digital transformation. Smart grids are an example of that digitalization of the electric sector, including more and more devices connected (RTU, smart meters…) and also offering intelligent control communications. But as networks merge and become smarter, along with the benefits of better connectivity, cybersecurity risks and threats also increase. In order to mitigate those threats and make the power grid more resilient some methods as intentional islanding and energy restoration could be applied. The aim of this paper is to describe configuration and implementation of those methods in a laboratory environment, explaining in detail the preparation procedures and the definition of tests. Various scenarios will be executed in the Catalonia Institute for Energy Research (IREC) microgrid laboratory (Energy SmartLab), emulating a distributed system operator (DSO) substation. In this regard, the methodology to develop a Power Hardware in the Loop (PHIL) test is described.