Microgrids are defined as an interconnection of several renewable energy sources in order to provide the load power demand at any time. Due to the intermittence of renewable energy sources, storage systems are necessary, and they are generally used as a backup system. Indeed, to manage the power flows along the entire microgrid, an energy management strategy (EMS) is necessary. This paper describes a microgrid energy management system, which is composed of solar panels and wind turbines as renewable sources, Li-ion batteries, electrical grids as backup sources, and AC/DC loads. The proposed EMS is based on the maximum extraction of energy from the renewable sources, by making them operate under Maximum Power Point Tracking (MPPT) mode; both of those MPPT algorithms are implemented with a multi-agent system (MAS). In addition, management of the stored energy is performed through the optimal control of battery charging and discharging using artificial neural network controllers (ANNCs). The main objective of this system is to maintain the power balance in the microgrid and to provide a configurable and a flexible control for the different scenarios of all kinds of variations. All the system’s components were modeled in MATLAB/Simulink, the MAS system was developed using Java Agent Development Framework (JADE), and Multi-Agent Control using Simulink with Jade extension (MACSIMJX) was used to insure the communication between Simulink and JADE.

The Internet of Things (IoT) is a technological revolution that enables human-to-human and machine-to-machine communication for virtual data exchange. The IoT allows us to identify, locate, and access the various things and objects around us using low-cost sensors. The Internet of Things offers many benefits but also raises many issues, especially in terms of privacy and security. Appropriate solutions must be found to these challenges, and privacy and security are top priorities in the IoT. This study identifies possible attacks on different types of networks as well as their countermeasures. This study provides valuable insights to vulnerability researchers and IoT network protection specialists because it teaches them how to avoid problems in real networks by simulating them and developing proactive solutions. IoT anomalies were detected by simulating message queuing telemetry transport (MQTT) over a virtual network. Utilizing DDoS attacks and some machine learning algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR), as well as an artificial neural network, multilayer perceptron (MLP), naive Bayes (NB) and decision tree (DT) are used to detect and mitigate the attack. The proposed approach uses a dataset of 4998 records and 34 features with 8 classes of network traffic. The classifier RF showed the best performance with 99.94% accuracy. An intrusion detection system using Snort was implemented. The results provided theoretical proof of applicability and feasibility.

Accurate fault location is challenging due to the distribution network’s various branches, complicated topology, and the increasing penetration of distributed energy resources (DERs). The diagnostics for power system faults are based on fault localization, isolation, and smart power restoration. Adaptive multi-agent systems (MAS) can improve the reliability, speed, selectivity, and robustness of power system protection. This paper proposes a MAS-based adaptive protection mechanism for fault location in smart grid applications. This study developed a novel distributed intelligent-based multi-agent prevention and mitigation technique for power systems against electrical faults and cyber-attacks. Simulation studies are performed on a platform constructed by interconnecting the power distribution system of Kenitra city developed in MATLAB/SIMULINK and the multi-agent system implemented in the JADE platform. The simulation results demonstrate the effectiveness of the proposed technique.

Microgrids are small-scale power networks that include renewable energy sources, load, energy storage systems, and energy management systems (EMS). Lithium-ion batteries are the most used battery for energy storage in microgrids due to their advantages over other types of batteries. However, to protect the battery from the explosion and to manage to charge and discharge based on state-of-charge (SoC) value, this type of battery requires the use of an energy management system. The main objective of this paper is to propose an intelligent control strategy for energy management in the microgrid to control the charge and discharge of Li-ion batteries to stabilize the system and reduce the cost of electricity due to the high cost of grid electricity. The proposed technique is based on a fuzzy logic controller (FLC) for voltage control. The FLC is based on the measured voltage of the direct current (DC) bus and the fixed reference voltage to generate buck/boost converter signal control. The proposed technique has been simulated and tested using MATLAB/Simulink software which illustrates the tracking of desired power and DC bus voltage regulation. The simulation results confirm that the proposed systems can diminish the deviations of the system's voltage.

The aim of this paper is to evaluate the ability of the line-graph based model (LGBM) to assess the vulnerability of power systems as part of complex network theory. The nodes in the line graph are the transmission lines, thus emphasizing their vulnerability. We derive and compute line-graph corresponding to several standard IEEE test cases and well as to the Western Systems Coordinating Council (WSCC) power grid. In each case, we compute various graph-theoretic measures, and we compared to the measures corresponding to standard topologies of complex networks (random, scale-free, and small world). We also compare LGBM topological characteristics with those of the bus-based model (BBM). Moreover, we examine the vulnerability of the LGBM nodes (transmission lines) are removed randomly or targeted by a malicious opponent. In both the IEEE models and the WSCC, simulations show that when the network size is large, and the BBM behaves like a scale-free network, the pattern of LGBM node degree distribution is distinct from that of BBM. The LGBM seems to possess a higher clustering coefficient, almost double, suggesting that transmission lines are more tightly connected and interdependent than buses, at least from a topological point of view. In general, our simulations show that attacking transmission lines seem to be more detrimental to the power grid than attacking buses.

This paper presents the results of a vulnerability analysis in different water distribution system (WDS) benchmarks, performed under a framework based on a graph model that integrates topological features and hydraulic characteristics, allowing the comparison between different attack strategies and centrality measures in terms of their ability to predict the shortage of water supply. This vulnerability framework has been previously applied to electric power systems and it employs a vulnerability prediction measure to quantify the amount of damage caused in terms of the physical damage measure. Different attack strategies and centrality measures were applied to four WDS benchmarks: the New York Tunnel, the Hanoi, the Modena, and the Balerma networks. It was determined that removing the most central element and recalculating the centrality for each stage are the most damaging attack strategies. Degree, eigenvector, and Katz centrality measures presented the best performance to predict the elements that are more relevant to the network and can have a larger impact on the water supply. It was demonstrated that the vulnerability framework can be applied to the WDS in the same way it was previously applied to electric power systems. Future work will be oriented to the design of the WDS using optimization techniques to minimize the vulnerability of the network under faults that can be generated by droughts and other extreme weather conditions.