On the assessment of cyber risks and attack surfaces in a real-time co-simulation cybersecurity testbed for inverter-based microgrids

The integration of variable distributed generations (DGs) and loads in microgrids (MGs) has made the reliance on communication systems inevitable for information exchange in both control and protection architectures to enhance the overall system reliability, resiliency and sustainability. This communication backbone in turn also exposes MGs to potential malicious cyber attacks. To study these vulnerabilities and impacts of various cyber attacks, testbeds play a crucial role in managing their complexity. This research work presents a detailed study of the development of a real-time co-simulation testbed for inverter-based MGs. It consists of a OP5700 real-time simulator, which is used to emulate both the physical and cyber layer of an AC MG in real time through HYPERSIM software; and SEL-3530 Real-Time Automation Controller (RTAC) hardware configured with ACSELERATOR RTAC SEL-5033 software. A human–machine interface (HMI) is used for local/remote monitoring and control. The creation and management of HMI is carried out in ACSELERATOR Diagram Builder SEL-5035 software. Furthermore, communication protocols such as Modbus, sampled measured values (SMVs), generic object-oriented substation event (GOOSE) and distributed network protocol 3 (DNP3) on an Ethernet-based interface were established, which map the interaction among the corresponding nodes of cyber-physical layers and also synchronizes data transmission between the systems. The testbed not only provides a real-time co-simulation environment for the validation of the control and protection algorithms but also extends to the verification of various detection and mitigation algorithms. Moreover, an attack scenario is also presented to demonstrate the ability of the testbed. Finally, challenges and future research directions are recognized and discussed

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision