smart grid

Modern voltage control strategies in the present distribution networks require efficient equipment as well as appropriate communication channels between these equipment, sensors and control centers, which has led to smart distribution networks with a complex cyber-physical nature. One of the efficient equipment for voltage control in modern distribution networks is the DSTATCOM, which uses using multilevel converters in its structure, provides many advantages, such as direct connection to the grid. A DSTATCOM with the multilevel converter requires a cyber-physical network between the controller and its components due to the presence of many controllable components, which makes it vulnerable to cyber-attacks, when connected to the present smart distribution networks. In this paper, a feedback linearization-based controller is developed for the cascaded multilevel DSTATCOM, and a discrete Kalman filter-based method is proposed to detect and compensate false data injection cyber-attacks on voltage sensors of the multilevel converter. The abilities of the proposed nonlinear controller to control the multilevel DSTATCOM as well as the reliable operation against false data injection attacks are verified through the simulation of a test power network in the MATLAB/Simulink environment.

DC microgrids are attaining more attraction in the smart grids on account of major characteristics such as reactive power removal, low power losses and high reliability, stability, controllability and energy efficiency. A suitable peer-to-peer (P2P) local electricity market is very essential to ensure the prosperity of networked DC microgrids in the sustainment of increasing penetration of renewable energy resources. The P2P energy trading provides a beneficial platform so that the peers including the distributed generators, end-use consumers and prosumers can buy or sell electrical energy directly with each other. This paper presents an optimization model of P2P energy trading for interconnected multiple DC microgrids coordinated via an energy management system (EMS) to maximize the total profit of participants. Each DC microgrid includes several photovoltaic arrays and wind turbines, residential loads, and electric vehicles (EVs). In the proposed framework, the permissible value of power losses is considered that reflects the limitation of power exchange between the peers and significantly influences the objective value.

Nowadays, demand response is recognized as an important element in the reliability of smart grid. Smart home energy management systems, which prioritize the start-up of electrical appliances according to the necessity of use and efficiency, play a vital role in the effectiveness of load response strategies in residential areas. Considering the sensor technologies, clarification on electricity consumption details helps to optimally monitor how the appliances are used. In this research, an unsupervised machine learning model was proposed for the clustering of home appliances to manage the bills of customers based on their inherent characteristics. Due to the small number of clusters, it becomes possible to manage electricity consumption. The hierarchical clustering method was used to classify appliances into three clusters. The first cluster is the appliances that are turned on at the discretion of the consumers immediately, the second cluster is the appliances that can be turned on according to the schedule and their usage can be postponed and the third cluster is appliances that are preferred by a limited number of consumers. The silhouette coefficient was developed as a measure of the hierarchical clustering model performance, where the average silhouette coefficient of 0.56 indicates the satisfaction of the model. Based on the results, it was found that the proposed clustering method can rationally classify different types of home appliances by selecting the appropriate characteristics since the appliances in a cluster are very similar to each other and can help users understand the operating conditions of the appliances.

In the optimization problems of intelligent distribution systems where there are many variables and parameters, providing an efficient algorithm that has the ability to converge and solve in large networks is one of the main challenges of researchers. In this paper, a compound integer quadratic optimization model for improving the performance of large-scale distribution network using demand-side management problem, energy storage system, battery-to-metro system, optimal control of OLTC and SVR tap-trans and distributed generation resources. Fossil and renewable are offered alongside capacitor and shunt reactors. The considered multi-objective function is a scenario-based stochastic model, which accurately models the uncertainties in renewable energy sources. According to the considered problems and also the model of large networks of 118 and 874 buses, most of the algorithms are not able to converge due to the existence of many variables. In this article, the optimal performance of the proposed hybrid evolutionary algorithm is shown on large distribution networks, which is able to reach global optimal solutions compared to similar algorithms.

Despite the expansion of the smart grid, information security in this network has faced many challenges, and we need a strong security framework for it. In this article, a compound and efficient method for authentication in the network of smart meters is presented. In the proposed model, first, the authentication of devices and sensor data is done in the smart meter, and then the value of the cumulative hash chain of the authorized devices' data is calculated in the smart meter and along with the cumulative data is sent to the collector. In the first step of the proposed method, devices are connected to the smart meter using RFID tags, and the device ID is processed in the smart meter. We use the Elliptic Curve method for authentication. In the second step, by calculating the value of the cumulative hash chain and comparing it with the received value, the validation of the received messages is done on the collector. Therefore, sending additional data to the collector, occupying the bandwidth, and the possibility of intrusion of malicious devices are prevented. The proposed method, in addition to adapting to the structure of the smart grid, also provides a lower computational and communication cost than the existing methods.

The phenomenon of Fault-Induced Delayed Voltage Recovery (FIDVR) appears in networks with high penetration of induction motor loads because the increase in requested reactive powers of motor loads after clearing the fault prevents the rapid return of the bus voltage to the pre-fault level. Load shedding is one of the effective ways to deal with the FIDVR phenomenon, which causes the amount of demand to approach the production of reactive power. In this paper, a wide-area load-shedding method is presented, which performs based on network conditions and loads. Since the introduced indicators for determining the locations and amounts of loads to be shed are based on the values of bus voltages, loads currents, and network impedance matrix; therefore, the proposed method can effectively shed the loads and deal effectively with FIDVR. The voltage estimation process is an important tool to predict the voltages at future moments and is defined based on the modified Gauss-Seidel load flow and the three-order model of the induction motor. This tool enables the proposed method to understand the effect of applying load shedding on voltage recovery and prevents the application of unnecessary ones.

False data injection attack (FDIA) is a destructive cyber threat to the economic performance of electricity markets in smart grids. A cyber attacker can make a huge financial profit by implementing an FDIA through penetrating the virtual transactions of the electricity markets and manipulating electricity prices. In this paper, a new approach to planning an absolutely stealthily FDIA is presented with the aim of achieving maximum financial profit from the perspective of a cyber attacker participating in virtual transactions from two markets of day-ahead (DA) and real-time (RT). A common hypothesis in studies of FDIAs against electricity markets is that the attacker has complete information about the smart grid. But the fact is that the attacker has limited resources and can hardly access all the network information. This paper proposes a robust approach in designing an attack strategy under incomplete network information conditions. In particular, it is assumed that the attacker has uncertainties about the network modeling matrices. The validity of the proposed method is evaluated based on the IEEE 14-bus standard system using the Matpower tool. Numerical results confirm the relative success of the proposed attack in cases of varying degrees of incomplete information.

In power smart grids, equipment and electrical infrastructure and information smart grids in order to increase efficiency, improved reliability and reducing environmental pollution merged together. One of the basic challenges in such grids, increase uncertainty due to the use of distribution generation sources contain are renewable resources. In recent years has proposed the concept of the micro grid, it means a medium voltage distribution grid or low voltage grid includes distribution generation units, storage devices and different controlling loads with operational capability in both cases grid-connected and islanded. Because micro grid in the islanded, grid is with small power generation units, the smallest error it may be cause instability. It is therefore necessary so when passing through grid-connected to island make sure of sufficient storage in distribution generation generator according to their production capability curve in order to satisfy loads reactive and active power. The main focus of this research is on distribution generation unit’s reactive and active power scheduling for sustainable and economic operation from a micro grid. The need for a sustainable transition from grid-connected to island in case of an error in grid is one of the most important constraints in the micro grids. Therefore, must to be investigated economic scheduling of distribution generation units a micro grid considering these constraints. It should be noted that consideration these constraints in reactive and active power scheduling of distribution generation units can bring on more economic benefits in the steady pass from grid-connected to island. So, in this research will be presented formulation related to planning reactive and active power of distribution generation units by maintaining the constraints that its objective function will be to minimize the cost of power generation.

Individual and public awareness of global warming and pollution has required the different sectors of industry to pay more attention to this issue and take some action in this regard. The electrical industry as the backbone of most other industries should devote more effort for pollution reduction. Unit commitment is the process of determining the state of the units for the day-ahead market. Therefore, managing unit commitment is the best option for contamination reduction. However, in the smart grid, customers can be part of the market via demand response programs. On the other hand, congestion is one of the main problems of the transmission network. Demand response programs could have a positive effect on congestion alleviation as they reduce the total power transmitted via the transmission system. In this paper, the effect of aggregated demand response programs as virtual power plants on operation cost, congestion and emission of network-constrained unit commitment is investigated. Moreover, the best rate of the incentive paid to the demand response programs participants is determined. In this study, according to the simultaneous implementation of the unit commitment problem and the demand response program, which is a complex nonlinear problem with continuous and discrete variables, the MILP method has been applied. Moreover, the transmission system is also considered to fully analyze the impact of demand response programs in the electricity market and its impact on reducing congestion. The recommended strategy is carried out on the IEEE 24 bus reliability test system to prove the effectiveness of demand response programs integration for cost and emission reduction.

Nowadays, using telecommunication systems and advanced measuring devices underlies cyberattacks on electrical grids. Bad data injection and failure to detect it on time, cause drastic damage to the network. This paper presents a new method for bad data detection (BDD) in state estimating when a cyber attacker manipulates the important measurements. Therefore, the new attack index is defined by simultaneously manipulating the network parameters and injecting incorrect data into the measured values. For this purpose, considering the masking and swamping effect, the diagnostic robust generalized potential (DRGP) algorithm detected and isolated high-leverage measurements installed in important locations from low-leverage measurements. Then, the state estimation process performs using low-leverage measurements. The Generalized Studentized Residual (GSR) algorithm detects bad data. With simultaneous manipulation of network parameters and measurement values, conventional BDD methods are unable to detect an attack. To evaluate the performance of the proposed method, they were implemented on the IEEE standard 14-bus network using MATLAB and Rstudio software. The simulation results show the ability of the proposed algorithm to detect a bad data attack.

The power industry is facing problems such as fossil fuel reduction, greenhouse gas emissions and the lack of automated analysis of the power grid. To meet the existing challenges, creating a new infrastructure is essential. The concept of smart grids aims to update existing power grids by integrating advanced communication infrastructure with the existing grid, making power grids more reliable, efficient, and environmentally friendly. Integration of power networks and communication networks, while it has many benefits, it has disadvantages in system security and protection. In this paper, after familiarizing with the smart grid communication infrastructure, their features and applications, the existing threats against the security of communication networks are introduced, and suitable actions are discussed, including prevention, detection, and counteract against security risks. At the security risks prevention stage, solutions such as encryption and blockchain have been studied and reviewed with the purpose of maintaining access to network information for authorized users. In the field of identifying attacks in the smart grid due to the need for a smart and fast solution, deep learning as one of the smart, dynamic and powerful tools with its applications in improving smart grid security has been investigated.