With the increase in Internet usage over the last half century, technology has also benefited from the growth and development of the Internet. It is no wonder that smart grids are emerging as one of the most popular technologies of today. As smart grids are connected to the internet, they are constantly vulnerable to cyberattacks. It is always possible for attackers to gain access to metering system infrastructure which is a major and critical part of smart grids. When this system is not working properly because of some security threats, irreparable damage is in the place for a country and its industries. This article suggests utilizing dynamic Applications that Participate in Their Own Defense defenses to secure such systems and infrastructures.
The concept of smart micro grids has been shaped to locally provide electric power by distributed energy sources at the level of the low voltage system. Distributed power plants are located at close proximity to consumers and are employed for several reasons such as environmental problems of large power plants and their low efficiency, high costs of production and transmission and distribution, etc. The energy management system plays a vital role in coordinating the various devices in smart distribution micro grids. A precise and targeted work plan must be considered to accurately use the equipment and resources in distribution networks for increasing efficiency. Furthermore, the optimal operation of power systems requires proper planning. In this study, the planning and implementation of location and the determination of the optimal capacity of distributed generation plants are discussed to reduce the losses, costs, and environmental pollution's for exploiting smart micro grids in the network. Considering supply constraints and distributed generation sources constraints, the optimal operation of production is examined based on the genetic algorithm for different objectives such as the lowest pollution, losses and the cost of installation and operation. Based on the proposed model, the cost, lose, and emission functions are simultaneously considered for the target function. Finally, the presented results illustrate the high accuracy of the utilized approach for managing the energy resources.
In this study, a structure is presented to manage the energy of the electric vehicle (EV) charger, which will be able to handle the energy of the charger at the same time, predict the production of solar generators located in the charging stations, and manage the EV charger in the workplace busbar. Uncertainty is often considered in the battery capacity of EVs. The objective function is to increase the penetration coefficient of EVs and minimize the deviation of the voltage from the desired value. The studied network is the IEEE 33-busbar test network. This research uses Digsilent software for simulation, uncertainty analysis, and optimization. The algorithm used for optimization is the Cuckoo search algorithm (CSA). The results indicate that the presented structure can significantly increase the percentage of penetration of EVs into the network, minimize the voltage deviation and reduce the cost of purchasing power from the upstream network.
Recently, smart grids have emerged as a new solution for the next generation of power systems. Cyber-attacks can take place against these. System performance may be reduced as a result of this sabotage. The purpose of this research is to the impact of a vandalism model with possible cyber-attacks on the electricity market, which is to make the generation unit or transmission line unavailable. The operator intends to have the minimum cost before and after the system attack from an economic viewpoint. For this purpose, several scenarios have been implemented in the system. The CPLEX solver is used to solve the proposed Mixed Integer Linear Programming (MILP) model. The obtained results show that the costs increased after the attack due to considering combined attacks on the network. Also, the worst-case scenario that has led to the change of output power for peak hours is identified.
This paper presents a novel detection and clarifying algorithm to enhance the resiliency of islanded microgrids against cyber-attacks. The attack type is false data injection (FDI), attacking the decision-making unit of the controlling system. To control these inverter-based resources (IBRs) we use hybrid control, including a proportional-integral (PI) Controller in the primary and model predictive control (MPC) in the secondary control unit of these IBRs. To detect and clarify the cyber-attack, firstly we calculate the residual value. In order to do that we use the difference between the predicted value of frequency and voltage, which was predicted by the MPC interior model in the previous step, and the measured value in the next step. a non-zero residual value reveals an unpredicted change in microgrids such as cyber-attacks. In the following, two additional parts of the algorithm are proposed to identify the frequency, voltage, and active or reactive false data. In the first part of the algorithm, we compare measured and calculated active and reactive power in receiver units. In the following part of the algorithm, we use the power change synchronization between IBRs, which is dictated by droop characterization.
