جستجوی مقالات مرتبط با کلیدواژه "شبکه هوشمند" در نشریات گروه "برق"

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.

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.

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.

In the light of the emergence of smart grids, the functions associated with this type of grids in the blocks of the energy management system require the adoption of robust strategies in order to provide a higher level of control and protection. Under-frequency load shedding (UFLS) sheds load blocks when the frequency drop is below the threshold limit. In adaptive UFLS, in an advanced telecommunications platform, the main quantities of the system are measured at each hour and in real-time; besides, the optimal amount of load is shed according to frequency deviation. In this paper, an hourly framework using Mixed-Integer Linear Programming (MILP) is proposed for an optimal design of an adaptive UFLS in smart networks. The goal is to provide a more systematic adaptive UFLS that is less dependent on trial and error. Here, the under-frequency relay is set for each hour in regard to operating conditions and uncertainties resulting from contingencies, to renewable energy sourses (RES), and to load fluctuations at that hour. The simulations are performed on the IEEE 39-bus test system over a 24-hour time horizon.

Under the smart power systems, determining the amount of Demand Response Resources(DRRs) potential is considered as a crucial issue due to affecting in all energy policy decisions. In this paper, the potential of DRRs in presence of cooling and heating equipment are identified using k-means clustering algorithm as a data mining technique. In this regard, the energy consumption dataset are categorized in different clusters by k-means algorithm based upon variations of energy price and ambient temperature during peak hours of hot (Spring and Summer) and cold (Autumn and Winter) periods. Then, the clusters with the possibility of cooling and heating equipment’s commitment are selected. After that, the confidence interval diagram of energy consumption in elected clusters is provided based upon energy price variations. The nominal potential of DRRs, i.e. flexible load, will be obtained regarding the maximum and minimum differences between the average of energy consumption in upper and middle thresholds of the confidence interval diagram. The energy consumption, ambient temperature and energy price related to BOSTON electricity network over a six-year horizon time is utilized to evaluate the proposed model.

Electricity demand has increased drastically during recent years. Expansion of power generation and transmission systems to address the growing demand for electricity is a lengthy process and requires extensive capital investment. Moreover, conventional methods of power generation can cause severe environmental issues. Discussed concerns have attracted considerable attention to demand-side management (DSM) policies by which system operators can monitor and control consumers in order to better match the electricity demand with power supply conditions. Meanwhile, power systems are going through a transition period due to the high penetration of renewable energy resources (RERs) and widespread use of smart devices. To remain efficient, DSM policies need to adapt to these transformations. Transactive energy (TE) is a newly-introduced DSM policy which is intended for the changing environment of power systems and is able to maintain the dynamic power balance in smart grids. This paper studies the reasons that led to the initiation of DSM programs. Further, the concept of demand response (DR) is elaborated and various types of DR programs and the existing challenges toward these programs are evaluated. Last but not least, TE is introduced as a general form of DR and the benefits and challenges that implementation of TE frameworks may bring about are analyzed.

Traditional systems are faced with problems such as energy loss, growing in energy demand, reliability and security, so they are turning into smart grids (SGs) to solve these problems. SGs provide a bi-directional energy connection between service providers and consumer. On the other hand, SGs have various devices for monitoring, analysis and controlling in different parts of the network. Therefore, the SGs needs a communication infrastructure between these different devices. As a result, this connectivity will be achieved by a new infrastructure such as the internet of things (IoT). IoT helps SGs systems to perform various network functions throughout the generation, transmission, distribution, and consumption of energy by using IoT equipment (such as sensors, actuator, and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, the day-ahead generation scheduling is optimized in the deregulated power market with IoT infrastructure where the goal is to maximize the profit of generation companies (GENCOs). Moreover, this paper has used the demand response (DR) programs to better explore the challenges ahead in the future of SGs with IoT infrastructure. The results help to assess the quality and efficiency of such a system.

Under the smart environment, utilizing demand side resources (DSRs), the so-called demand response resources (DRRs), provide more suitable management for the growing energy consumption as well as considerable reduction of system expenditures. Estimation of DSRs potential due to affecting the power system scheduling ranging from short-term to long-term is contemplated as an essential issue. In this paper, a two-stage structure is proposed to determine the potential of DRRs based upon the variation of energy consumption in comparison with the temperature along one year horizon time. In the first stage, utilizing a sample estimation method, the temperature threshold for commitment of cooling and heating appliances is specified. In the next stage, the energy consumption data are clustered in two categories regarding temperature thresholds. Then, the flexible load level, DRRs potential, is determined via implementing multifarious indices to the maximum and average daily load profile. Lastly, the final potential of DSRs will be determined in warm and cold weather conditions regarding the statistical analysis of computed DRRs’ potential in different years. To evaluate the capability of the proposed structure, the energy consumption as well as ambient temperature of Boston city is utilized. The obtained results show the difference between DRRs’ potential in different climate conditions.

With the growth and integration of distributed generation resources in smart grids, net load forecasting is of significant importance. A hybrid optimization method is proposed in this paper for probabilistic net load forecasting using neighborhood component analysis and solving regression problem with the aid of mini-batch LBFGS method. Net load forecasting is suggested in this paper trough forecast combination via adaptive network-based fuzzy inference system. The structure includes a combination of several long-term forecasts, including forecasts of load, the generation of a solar station, and the generation of a wind farm with wind turbines equipped with doubly-fed induction generator. Also, the net load forecasting and the relationship between errors of load, wind and solar predictions are studied in this paper. The simulation results of the proposed method and its comparison with Tao and quantile regression models show that mean absolute percentage error of load forecasting, and the forecasts of solar and wind generations improved by 0.947%, 0.3079% and 0042%, respectively which result to a decrease in net load forecasting error.

The complexity of power systems increases with the advent of intelligent electrical power systems and the use of renewable resources. In order to successfully carry out control and management tasks, the accuracy of the estimated electrical quantities will be an important issue. State estimation plays an important role in this view and is considered as the final loop of the measurement chain. The effect of the errors of measuring devices is such that it directly affects the accuracy of the results, so providing methods to improve robustnessof the estimation algorithm is necessary in relation to the errors in the inputs of this problem. In this paper, a method is proposed to improve the distribution system state estimation (DSSE) algorithm, which provides real and accurate analyzes of the operating conditions of an active distribution system. Thus, in this paper is presented an algorithm using exponential function for weighting tuning of measurement sets of the power system in the form of measurement errors in the presence of wind power plants and loads by non-Gaussian uncertainties and probability functions. For analysis of proposed algorithm, simulations results are carried on IEEE 33-bus and 50-bus distribution systems and demonstrate the effectiveness of the proposed method.

In this paper, a new day ahead energy management system is proposed for a smart grid in which distribution company and residential customers are involved. The interaction between customers and the distribution company is modeled as a single leader-multiple followers Stackelberg game. The leader of Stackelberg game is the distribution company which calculates the price of energy by maximizing its profit in a stochastic optimization framework. The residential customers, who are the followers of Stackelberg game, consider the common constraint of maximum aggregated load regulating in their optimization problem and thus play a general Nash game with each other. It is proved the customers’ general Nash game is strongly monotone and consequently has a unique equilibrium. To achieve the unique Stackelberg equilibrium, the best response-based energy management algorithm is devised, and its convergence condition is proved. It is also proved that the Stackelberg equilibrium is an optimal social equilibrium, in which the sum of customers’ cost is minimized, and the profit of the distribution company is maximized. The proposed energy management mechanism decreases the maximum aggregated load by 21.89% and increases the load factor by 27.72%.

This paper models and investigates the incorporation of optimal transmission switching (TS) into integrated generation and transmission maintenance scheduling (IMS). The IMS with TS (IMSwTS) problem is formulated as a mixed-integer linear programming model. This problem is hard to solve by off-the-shelf commercial optimization solvers since on the one hand maintenance scheduling variables couple several time intervals together and on the other hand TS variables. A solution approach is proposed in this paper to address the problem’s high computational burden which decomposes the IMSwTS  problem into two subproblems, an IMS subproblem and an optimal TS subproblems, and solves them iteratively. The proposed model and decomposition approach are implemented on IEEE reliability test system. The results demonstrate that adding TS to IMS alters the maintenance schedule and brings cost saving.

The Smart Grid is a large-scale electric power grid by using digital technology that integrates the components of power generations (producers and consumers) in order to enhance the reliability, security, and efficiency by utilizing sensing, processing, measurement, and control. Now, the technologies should lie on the abstract mathematics firmly to model and control the operations. Game theory is a mathematical tool to model systems with Multi-agents (rational decision-makers). Game theory models the behavior of independent and rational agents which in they want to maximize their profits. To this end, we model the behavior of agents and the possible behaviors profit. In the new system, users are able to generate the required energy from renewable energy sources such as solar panels, electric generators, and so forth. Hence, in these complex systems, we would make the use of the mathematical tools to model the interactions and features between agents. In this paper, three basic questions will be answered which I) Why cooperation between agents is valuable. II) Which forms of cooperation are forming and III) Why and how game theory can model the cooperation. In the following, we survey a number of Game theory-based applications especially cooperative game theory to solve relevant problems in Smart Grid. The applications of the Game theory have been presented in three scopes: Micro-grid, Demand Side Management (DSM), and Communications that we will describe them in detail in the following.