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With the continued growth of smart grids in electrical systems around the world, large amounts of data are continuously being generated and new opportunities are emerging to use this data in a wide variety of applications. In particular, the analysis of data from distribution systems (such as electrical substations) can lead to improvements in real-time monitoring and load forecasting. This paper presents a methodology for substation data analysis based on the application of a series of data analysis methods aimed at three main objectives: the characterization of demand by identifying different types of consumption, the statistical analysis of the distribution of consumption, and the identification of anomalous behavior. The methodology is tested on a data set of hourly measurements from substations located in various geographical regions of Colombia. The results of this methodology show that the analysis of substations data can effectively detect several common consumption patterns and also isolate anomalous ones, with approximately 4% of the substations being identified as outliers. Therefore, the proposed methodology could be a useful tool for decision-making processes of electricity distributors. [ABSTRACT FROM AUTHOR]

One of the objectives of electrical distribution networks is to provide customers with access to high‐quality electricity. Because any disruptions in these systems result in voltage disorders, different devices are employed to offset these disruptions on consumers who are more susceptible. One of the most important and contemporary pieces of equipment that is connected in series with the network is dynamic voltage restoration (DVR), which shields delicate loads from network voltage issues by injecting the proper voltage. This article presents a DVR control scheme optimized with improved grey wolf optimization (IGWO) that uses a proportional derivative (PD) controller and adaptive notch filter (ANF). The output LC filter's resistance has been removed, and the control system has actively engaged in oscillation damping in order to accelerate dynamic responsiveness and lower system losses. The major component of the voltage, which comprises its frequency, amplitude, and phase, is extracted using ANF. The capacitor current of the output filter in this structure is fed back to the control system and from the current mode control in the inner loop to boost stability. Owing to the occasionally complex dynamic behavior in distribution networks, particularly during a fault, the system's frequency response has been altered and response speed has been accelerated using the PD controller. This kind of controller is distinguished by its accurate functioning in the presence of frequency deviations and its swifter dynamic reaction in the face of voltage swell and sag. In order to improve the THD and voltage sag indicators of the sensitive load, the PD coefficients were adjusted using the IGWO algorithm. As a consequence, the simulation results demonstrated that the suggested controller performed better than traditional controllers. [ABSTRACT FROM AUTHOR]

Solar energy provides one of the most favorable options regarding the transition to clean energy sources. The parameters of a photovoltaic (PV) system play determine its performance under various scenarios. The PV model parameter estimation is an example of nonlinear planning. This study proposes a novel use of the established Smell Agent Optimizer (SAO) algorithm to anticipate the undefined parameters of the PV model's single-diode and two-diode equivalent circuits. This study aims to create a precise PV model that can accurately characterize its performance under changing operational conditions. The desired objective function is defined as the square of the mean squared error between the model's current-voltage curve and the measured curve. [ABSTRACT FROM AUTHOR]

Reactive power compensation (RPC) is a big problem during power system operation. Parenthetically, capacitor allocation and sizing may be the only convenient solution for RPC of power systems. The loss sensitivity factor (LSF) is applied here for finding the optimum capacitor position. This paper presents quasi-oppositional fast convergence evolutionary programming (QOFCEP), fast convergence evolutionary programming (FCEP), and evolutionary programming (EP) for the optimum location and sizing of shunt capacitors in the isolated microgrid (MG) for minimizing total real power loss throughout the day with and without the demand response program (DRP). The 33-node, 69-node, and 118- node isolated MGs have been studied to authenticate the efficacy of the suggested approach. Each MG includes small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), diesel generators (DGs), and plug-in electric vehicles (PEVs). [ABSTRACT FROM AUTHOR]

Non-intrusive load identification can improve the interaction efficiency between the power supply side and the user side of the grid. Applying this technology can alleviate the problem of energy shortage and is a key technique for achieving efficient management on the user side. In response to the cumbersome process of manually selecting load features and the low accuracy of identification in traditional machine learning algorithms for non-intrusive load identification, this paper proposes a method that transforms the one-dimensional reactive electric signal of the load into a two-dimensional image using Gram coding and utilizes the Residual Attention Network (RAN) for load classification and recognition. By transforming the one-dimensional electrical signal into a two-dimensional image as the input to the RAN network, this approach retains the original load information while providing richer information for the RAN network to extract load features. Furthermore, the RAN network effectively addresses the poor performance and gradient vanishing issues of deep learning networks through bottleneck residual blocks. Finally, experiments were conducted on a public dataset to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A combined algorithm for reconfiguring and placement of energy storage systems, electric vehicles, and distributed generation (DG) in a distribution network is presented in this paper. The impact of this technique on increasing the network's resilience during critical periods is investigated, as well as improvements in the network's technical and economic characteristics. The three objective functions in this regard are the network losses, voltage profile, and running costs, which include the costs of purchasing electrical energy from the upstream network and the devices used, as well as the cost of load shedding. In addition, the impacts of the presence of DG on power flow and the modeling of the problem's objective function are examined. To solve the problem of reconfiguration and placement of a multi-objective distribution feeder, a genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) hybrid algorithm is used. The proposed GA-SFLA algorithm is used to solve the problem with changes in its structure and its combination in three stages. Finally, the proposed method is implemented on the 33-bus distribution network. The simulation results show that the proposed method has an effective performance in improving the considered objective functions and by establishing a suitable fit between the different objective functions based on the three-dimensional Pareto front. Moreover, it introduced a more optimized architecture with lower loss, lower operating costs, and greater reliability compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

In this article, the problem of voltage and frequency stability in a hybrid multi-area power system including renewable energy sources (RES) and electric vehicles has been investigated. Fractional order systems have been used to design innovative controllers for both load frequency control (LFC) and automatic voltage regulator (AVR) based on the combination of fractional order proportional-integral and proportional-integral-derivative plus double derivative (FOPI–PIDD2). Here, the dandelion optimizer (DO) algorithm is used to optimize the proposed FOPI–PIDD2 controller to stabilize the voltage and frequency of the system. Finally, the results of simulations performed on MATLAB/Simulink show fast, stable, and robust performance based on sensitivity analysis, as well as the superiority of the proposed optimal control strategy in damping frequency fluctuations and active power, exchanged between areas when faced with step changes in load, the changes in the generation rate of units, and the uncertainties caused by the wide changes of dynamic values. [ABSTRACT FROM AUTHOR]

In this paper, a new control method is proposed to regulate the critical load's voltage at the nominal value for low-voltage microgrids. So that, without telecommunication communication, the impedance voltage drop of the connection between the distributed generation (DG) output to the point of common coupling (PCC) for the passing of reactive and active power will be compensated. Where the Q-V droop control method will be modified to adjust the voltage of critical load to the normal value. Also, the sharing of reactive and active powers is accurately based on the DG resources capacity between them. For distinct control of the reactive and active powers and accurately sharing them between sources from the virtual impedance used with control loops of current and voltage. Also, in this method, the circulating current between sources will be minimized. The proposed method to test its effectiveness was performed on two networks, one with two DG units and the other with four DG units. The simulation results show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

Optimal use of microgrids for efficient and economic management of energy resources is of special importance. At the residential level, energy costs can be reduced by using the concepts of price-based intelligent control (response to demand). In this paper, an energy management problem that proposed to decrease the cost of operating a residential microgrid with Micro-CHP equipped by a smart meter. The problem objective function formulation in the form of short-term production planning is based on the total cost of Micro-CHP gas consumption and auxiliary burner and the cost of electricity purchased from the network or sell to it in 24 hours. The aim is to present the ideal energy management strategy of the resources available based on smart control, and due to the non-linear problem, GAMS software by Mixed Integer Non-Linear Programming (MINLP) was used to solve the problem. Finally, the proposed method is implemented on the sample microgrid and the results are compared with Heat-Led Control, the effect of battery on microgrid participation in load response programs was demonstrated. Also, the effect of different tariffs of the market price on the cost of microgrid operation and how to exchange with the grid in different cases are evaluated that results showed the proper performance of the proposed method. [ABSTRACT FROM AUTHOR]