The accelerating integration of electric vehicles (EVs) presents considerable operational challenges for distribution networks, particularly through aggravated voltage deviations and compromised protection coordination during periods of simultaneous charging. In response, this study introduces a novel protection-constrained Binary Evolutionary Algorithm (BEA) designed for expedited electric vehicle-oriented Distribution Network Reconfiguration (DNR) to enhance EV hosting capacity without necessitating costly infrastructure upgrades. The proposed framework uniquely embeds the inverse time-current characteristics of protective fuses-termed Protection Curve Consideration (PCC)-within the optimization process. By explicitly accounting for the thermal inertia of protection devices, the algorithm identifies reconfiguration strategies that uphold voltage stability under elevated EV transportation loading, including configurations typically deemed infeasible by conventional voltage-driven approaches. This selective coordination precludes unnecessary fuse operations, thereby preserving the continuity of electric vehicle charging services. Simulation results on a 16-bus radial distribution system, evaluated under four high-demand scenarios reflective of concentrated EV transportation charging, validate the efficacy of the BEA-PCC methodology. The approach achieves a maximum voltage deviation reduction of up to 15.2%, thereby enhancing power quality for all consumers. Moreover, compared to standard metaheuristic techniques, it reduces Energy Not Supplied (ENS) by 8% and switching operations by 20%, contributing to improved grid resilience and operational efficiency. These outcomes underscore the potential of BEA-PCC as an effective real-time control strategy for distribution system operators seeking to accommodate increasing electric vehicle penetration while safeguarding protection coordination and minimizing customer disruptions.

Seismic events pose significant challenges to power distribution systems, often leading to widespread outages and delayed recovery. This paper presents a two-stage framework for intentional islanding of active distribution systems to enhance seismic risk mitigation and ensure post-earthquake power supply. In the first stage, an artificial history of the network is generated by analyzing earthquake intensity and estimating the failure probability of electrical components using fragility curves. Monte Carlo Simulation (MCS) is then employed to model uncertainties in Distributed Energy Resource (DER) generation and component failures. In the second stage, intentional islanding is applied to maintain critical load supply under post-earthquake network conditions, utilizing local DERs such as gas turbines, wind turbines, and photovoltaics. The islanding problem is divided into two sub-problems: generation adequacy in a radial network and security adequacy of the island. The first subproblem is formulated as a Tree Knapsack Problem (TKP) and solved using Particle Swarm Optimization (PSO), while the second is addressed as an Optimal Power Flow (OPF) problem aimed at minimizing load curtailment. The proposed framework is tested on the IEEE 33-bus and IEEE 123-bus systems, with results compared to other methods, indicating an increase in restored loads supplied through local DERs.

ABSTRACT Smart grids open up new opportunities through which a cyber intruder can infiltrate or manipulate data to compromise measurement integrity and state estimation accuracy. Advanced methods for detecting false data injection anomalies will be of great importance to the safety and reliability of power system operations. It therefore presents an Improved Threshold Prediction Anomaly FDIA Detection Approach that should finally address inherent limitations in traditional methods: limited adaptability to system changes, reduced sensitivity to complex anomalies, and incomplete coverage of emerging threats. The outputs of individual anomaly detectors are fused by utilizing an ordered weighted averaging fusion scheme in the method proposed herein. It improves sensitivity in detection and accuracy with significant countermeasures against FDIAs. In addition, Bayesian network‐based hyperparameter optimization is utilized for each detector to refine them in a way that produces the best configuration towards maximum performance. Due to that, complementary strengths of the detectors provide a boost toward detection capability. Extensive experiments have been performed on real‐world power grid data from NYISO using an IEEE 14‐bus power system, and the robustness of the approach has been shown. Notably, at injection rates ranging from −20% to +20%, the proposed method demonstrated a 2.1% improvement in detection accuracy at +8% injection and a 10.7% improvement at −2% injection over the second‐best state‐of‐the‐art method. These results confirm the method's effectiveness in diagnosing and mitigating anomalies under a range of intrusion scenarios.

Recent seismic events have highlighted the need to enhance the resilience of electrical distribution systems to minimize prolonged service interruptions for end users. This paper introduces a risk-based resilience-oriented framework for reconfiguring active distribution systems to prioritize critical load supply in post-earthquake conditions. The risk-based framework comprises: (1) modeling earthquake characteristics, (2) assessing seismic component failures, and (3) implementing a resilience-focused reconfiguration strategy. Earthquake features are modeled using attenuation relationship, and component failure probabilities are evaluated through fragility curves and Monte Carlo Simulation. A two-step reconfiguration is then employed to restore critical loads using local Distributed Energy Resources (DERs). The first step maximizes the Restored Load Value while ensuring generation adequacy and maintaining a radial network structure. This step is formulated as a Tree Knapsack Problem and solved using a heuristic Depth-First Search-Particle Swarm Optimization approach. The second step validates the security of the reconfigured network using Optimal Power Flow, minimizing voltage deviations as the objective. The proposed methodology is applied to the IEEE 69-bus test system, considering the seismic vulnerabilities of substations, lines, and DERs, as defined by HAZUS. Simulation results demonstrate the framework's effectiveness, showing its potential as a decision-support tool for Distribution System Operators to enhance resilience and ensure critical load supply after earthquakes.

Environmental problems arisen from burning fossil fuels results in the development of renewable resources for electric power production in the power networks, and electric and hydrogen vehicles in the transportation systems. Among the various renewable sources, wind and photovoltaic power plants have grown more and are widely used to supply loads, especially in microgrids. In this paper, optimal scheduling of a stand-alone microgrid containing wind turbines, photovoltaic power plants and fuel-based generation units is performed. Due to the variation in the wind speed and sun irradiance, the output power of wind turbines and photovoltaic power plants varies. To reduce uncertainty of output power of renewable resources, energy storage system such as electrolysis device- hydrogen tank- fuel cell device can be used in the stand-alone microgrid. The hydrogen produced by electrolysis system is stored in the hydrogen tank. The stored hydrogen can be used in the fuel cell device for electric power generation or sold to the hydrogen vehicles. When, the generated power of renewable resources is less than the required load, the generated power of the fuel cell can compensate all or some of electric power shortage. Besides, the hydrogen vehicles can purchase the electric power stored in their batteries to the microgrid, and participate in the vehicle to microgrid scheme. In this paper, for optimal scheduling of the stand-alone microgrid, operation cost of fuel-based microgrid, the reliability cost associated to the penalty of load curtailment, the income from the sale of electricity and hydrogen and the cost of purchasing electricity from hydrogen vehicles are considered. To clearly investigate the impact of renewable resources, hydrogen storage system and hydrogen vehicles on the optimal scheduling of the stand-alone microgrid, numerical outcomes of a microgrid simulated in the MATLAB software are given.

ABSTRACT While digitalization promotes grid management efficiency, it also makes power systems more vulnerable to a variety of anomalies, especially false data injection (FDI) anomalies. FDI intrusions pose a serious threat to the security of smart grids. The existing approaches, like machine learning, have certain limitations, which can be addressed by proposing the optimized neuro‐fuzzy meta‐learning (ONF‐ML) model. This model combines several machine learning classifiers serving as a two‐step optimization process including hyperparameter optimization for individual classifiers and simulated annealing for tuning neuro‐fuzzy parameters. Simulation results conducted on the IEEE 14‐bus system using MATPOWER demonstrate the superior performance of ONF‐ML in detecting FDI intrusions compared to baseline models, especially for subtle injections. In every bus, FDI intrusion has occurred and average performance metrics are considered. The results illustrate an average detection rate of 91.7% and 81.9% for intrusion samples and 99.9% and 99.8% for normal instances in cases of −3% and +3% occurrences, respectively. While baseline models illustrated critical performance degradation during robust analyses, this technique was remarkably stable, maintaining a detection rate of over 75%, outperforming the second‐best technique by up to 45% in worst‐case scenarios. By addressing real‐world challenges such as sensitivity to noise, inflexibility and incompetence in detecting subtle intruders, the ONF‐ML approach enables continuous learning from new data, ensuring adaptability to new threats. Taken together, these features make ONF‐ML a practical and scalable solution to overcome the limitations of traditional FDI detection techniques and provide a path to improved smart grid security.

Abstract In order to prevent wastage of generated power of renewable resources, the energy storage systems can be utilized in the stand‐alone micro grids to store the excess produced power of the renewable generation units. When the generated power of the renewable resources is less than the required load, the energy storage systems can help to compensate all or part of the power shortage. In the current study, a stand‐alone micro grid including wind and tidal turbines, PV systems, batteries and fuel‐based generation units is considered to supply the required load of the micro grid. The generated power of each dispatch‐able generation units is determined in such a way as to minimize the operating cost. In the operating cost of the micro grid, the operating cost of the fuel‐based generation units and the reliability cost associated to the penalty of the curtailed loads are considered. To calculate the reliability cost of the micro grid, a comprehensive reliability evaluation of the micro grid considering the resource‐dependent failure rates for all composed components is performed. To study the effectiveness of the proposed reliability‐based scheduling approach, the numerical results associated to a stand‐alone micro grid containing wind, tidal, PV and fuel‐based generation units connected to the batteries are given.

The capability of a neuro-fuzzy control approach for frequency fluctuation damping in an isolated hybrid microgrid (IHMG) system (DEG/WTG /PV/FC/ESSs) is investigated in this paper. Due to the intermittent behavior of renewable energy sources (RESs) like wind turbines and photovoltaic arrays and the time-varying nature of demands, frequency fluctuation is more likely, specifically in the grid-connected mode. Model parametric uncertainties as well as load changes, wind power, and solar irradiation variations are the main uncertainty sources of the IHMG system. In the suggested approach, a neuro-fuzzy output feedback controller with three inputs that are inspired by PID control is designed considering the power balance between demands and generations, by optimizing fuzzy membership functions’ locations. The proposed controller is compared with two popular other methods on the investigated IHMG system in terms of time-domain characteristics. The outcome illustrates remarkable merit compared to the state-of-the-art methods in the presence of simultaneous disturbances and the model parametric uncertainties.

In this article, the control method of the economic predictive model for the use of the efficiency tariff of the photovoltaic backup system, diesel generator and microgrid, connected to the grid using the closed loop control system, the optimal open loop control, and also through the control and strengthening of the primary open loop has been The main goal of this study is to minimize the power grid energy and fuel costs by evaluating the limits related to the level of fuel level in diesel fuel tanks. In addition to complying with the restrictions among the controllable variables, this control method also meets the load requirements. In order to obtain the benefits of feedback and predict the optimal power timing as a back-up energy system control problem, as well as the diesel generator connected to the microgrid, it is modeled based on the linear programming structure. Specifically, analysis is divided into two groups. The first case in the alternative model is when: outage occurs between 7 AM and 6 PM and the other in the grid energy state occurs when the grid is available for more than 24 hours. Energy performance shows, cost savings and income, in the control of daily economic forecasting model has improved. As long as, daily energy saving is up to 52%, while diesel energy is up to 85%. Optimum operation control can be well associated with uncertainty and disturbance in the result.

This study addresses the critical issue of fault diagnosis in photovoltaic (PV) arrays, considering the increasing integration of distributed PV systems into power grids. The research employs a novel approach that combines artificial neural networks, specifically radial basis functions (RBFs), with machine learning techniques. The methodology involves training the RBF neural network using input features like voltage, current, temperature, and irradiance, derived from the PV array, to detect and classify various fault types. Notably, it comprehensively evaluates the accuracy of this approach, with a particular focus on detecting maximum power point tracking (MPPT) and mismatch faults. The findings reveal significant advantages, in which the proposed method outperforms existing techniques, achieving an approximately 20

This study examines the implications of day-ahead energy markets (DA) for flexible power management (FPM) in a smart distribution network (SDN) with multiple microgrids (MMGs). MG sources are coordinated with MG operators (MGOs) in the first layer, and MGOs and SDN sources are coordinated with SDN operators in the second layer. An optimization framework with bilevels is used in the scheme. SDN participation in the wholesale and retail DA energy markets is represented by the upper-level model, equivalent to the second-layer FPM. Based on the network's linear operation and flexibility constraints and active load and source operation models, it minimizes the expected SDN energy cost in the markets mentioned. It has the same formulation as the upper-level problem but concerns MG's participation in the retail DA energy market. This corresponds to the first layer of FPM. After that, a single-level formulation is developed using the Karush-Kuhn-Tucker method, and stochastic programming models uncertainty about load, energy price, renewable generation power, and mobile active load energy demand. In this scheme, two-layer power management between microgrids and the distribution network; simultaneous modelling of economic, operation, and flexibility indices; and simultaneous participation of microgrids and the distribution network in the wholesale and retailer energy markets are the novelties. Ultimately, based on quantitative results, the proposed approach is evaluated in terms of its ability to improve the economic, operational, and flexibility state. The proposed scheme reduces the energy loss in the microgrids and the distribution network by around 43%-67% compared to power flow studies. Voltage drop in the proposed scheme is enhanced by about 40%-47% in the suggested scheme compared to power flow analysis. The economic status of the network is improved by roughly 22% in comparison with power flow studies. Also, the presented scheme can provide almost 100% flexibility conditions; however, it is corresponding to increased energy cost.

AbstractThe speed of using renewable resources is expanding day by day. Renewable energy systems have many benefits for energy supply that do not include diesel, natural gas, or coal. Despite the many advantages, the use of renewable resources also includes basic challenges. With the presence of these sources, many technical issues must be considered in the network, the most important of which are voltage quality and network losses. The presence of these power plants can reduce fossil fuel costs and help reduce emissions. However, the high‐capacity connection of these types of power plants in the transmission networks despite the uncertainty may cause the congestion of transmission lines, increase losses and decrease voltage quality. Therefore, to reduce the need to build transmission lines, energy storage devices can be installed and energy can be stored and returned to the network in certain hours. The purpose of this paper is to build the maximum capacity of wind power plants in the transmission network in such a way that its profitability is guaranteed. For this purpose, in addition to considering the costs related to the power plant, the costs of storage devices and the construction of possible new lines have been considered. Also, improving the technical conditions of the network and reducing the maximum emission after installing these units is considered as a multiobjective function. The problem tested on the standard IEEE test transmission network and the results show that it is possible to determine the maximum profitable capacity of wind power plants.

The applicability of a robust linear parameter varying (LPV) control for frequency fluctuation damping in an islanded hybrid microgrid (IHMG) system (DEG/WTG /PV/FC/ESSs) is investigated in this paper. Due to the erratic behavior of the majority of the renewable energy sources (RESs) and the varying time nature of loads, frequency deviation from the nominal value is inevitable, especially in the islanded mode. Load variation, solar irradiation, and wind power disturbance, as well as system parametric uncertainties, can significantly disturb the system frequency operation. In this paper, the wind speed and rotated speed of the nonlinear wind turbine are highlighted as scheduling parameters, and the nonlinearity of the wind turbine is hidden by the LPV approach. In the proposed method, a dynamic output feedback controller is obtained based on the power balance equation, by solving three LMIs to minimize the upper bound of the L2-Norm of the uncertain IHMG in an algorithmic approach. For better evaluation, the ultimate LPV control approach is compared with two other methods on the understudy IHMG model. The result represents a noticeable advantage of the proposed robust method compared to other methods in terms of frequency stability based on time-domain and norms characteristics in presence of the simultaneous disturbances and parametric uncertainties.

Due to the advantages of micro-grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro-grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy-based generation units especially tidal barrages is high, and to generate the cost-effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro-grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro-pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro-grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution.

Due to their clean nature, high capacity and accurate predictability, the basin-based tidal units can be integrated into bulk power systems. The major problem of these units is the high investment cost of barrage construction, and so, among different renewable resources, these units are less developed. For making the tidal barrages cost-effective, new methods should be implemented to increase their produced power that results in the reduction of electricity price. For this purpose, this study suggests new techniques for optimally operating tidal units equipped with hydro pumps. Here, to increase the produced power and energy of the tidal units, at each time, the number of turbines, sluices and hydro-pumps is optimally determined to yield the maximum produced energy of the generation unit using the particle swarm optimization method. For satisfying the effectiveness of the suggested approach, the optimal operation of a tidal unit installed on the estuary of Bacanga is done and the impact of optimal dispatching of the turbines, sluices and hydro-pumps on produced energy of the unit is studied. It is deduced from numerical results that the generated energy of the plant during 24 h is increased by 17% with the implementation of the proposed operation plan.

Among different renewable energy-based power plants, barrage type tidal power plants with high installed capacity can be integrated in the bulk power systems. However, due to the high investment cost associated to the construction of the barrages and related components, barrage type tidal power plants are less developed than other renewable resources such as wind turbines and photovoltaic farms. In order to economize the production of electricity from barrage type tidal power plants, it is necessary to reduce the cost of generated electricity by these power plants with new methods. For this purpose, the plant is considered to generate the electricity in the ebb mode, and using the hydro-pumps the height of the water in the reservoir is increased in the flood state. Based on this fact, in this paper, to maximize the generated energy of the barrage type tidal power plant, an optimal design is proposed and the effective parameters of the plant including the number of turbines, the number and width of the sluices, the turbine diameters and the number of hydro-pumps are determined using of three optimization techniques including particle swarm optimization method, genetic algorithm and imperial competition algorithm.

Due to the growing demand in the electricity sector and the shift to the operation of renewable sources, the use of solar arrays has been at the forefront of the consumers' interests. In the meantime, since the production capacity of each solar cell is limited, in order to increase the production capacity of photovoltaic (PV) arrays, several cells are arranged in parallel or in series to form a panel in order to obtain the expected power. Short-circuit (SC) and open-circuit (OC) faults in the solar PV systems are the main factors that reduce the amount of solar power generation, which has different types. Partial shadow, cable rot, un-achieved maximum power point tracking (MPPT), and ground faults are some of these malfunctions that should be detected and located as soon as possible. Therefore, an effective fault detection strategy is very essential to maintain the proper performance of PV systems in order to minimize the network interruptions. The detection method must also be able to detect, locate, and differentiate between the SC and OC modules in irradiated PV arrays and non-uniform temperature distributions. In this work, based on the artificial intelligence (AI) and neural networks (NN), neutrons can be utilized, as they have been trained in machine learning process, to detect various types of faults in PV networks. The proposed technique is faster than the other artificial neural networks (ANN) methods since it uses an additional hidden layer that can also increase the processing accuracy. The output results prove the superiority of this claim.

The argument of power system planning in home microgrids has become one of the burning topics in the optimization studies today among the researchers. Since the installation and use of high-capacity energy sources in power systems have many limitations and constraints, part of the perspective of power system studies tends to operate the residential micro-grids. For this purpose, in this work, operation planning is based on a residential micro-grid consisting of combined heat and power (CHP), heat storage tank, and boiler, and when possible, surplus electricity is sold to the upstream network to generate revenue. One of the innovations of this work is the use of exergy function to complete the optimization, and in practice, combine energy with economics. Other objective functions of this work are to discuss the reduction in CO2 in the air and the cost of operation. Energy management and planning in this home micro-grid is tested with different capacities and types of CHPs so that the home operator can choose the best mode to use. The multi-stage decision based dynamic programing (MSD-DP) optimization approach is used to minimize the operation costs of the proposed framework. The most important innovation of this work is the use of exergy function for energy management in a residential complex, where CHP can also be used to generate electricity and heat simultaneously. Therefore, determining the capacity of CHP and the possibility of exchanging electricity with the upstream network can be mentioned as other innovations of this research work.

AbstractIn the present study, the economic model predictive control (EMPC) scheme is presented for the time of use tariff application of photovoltaic and diesel generator backup system, which is connected to the grid‐connected microgrid. Through the use of the closed‐loop control system, the prior open‐loop optimal control is enhanced. Minimizing the grid energy and fuel costs by evaluating the fuel level restrictions in the diesel tank is the main goal of this study. To accommodate the restrictions among controllable variables, this control method fulfils the load requirements. To gain the benefits of feedback and predictions, optimal power scheduling is modelled as a control problem. Furthermore, Blockchain technology is applied to secure interchanged information in the grid to avoid manipulating data by attackers. Specifically, the analysis is divided into two scenarios. The first one takes place in the alternative style when a blackout happens between [7 am and 6 pm] h and the other occurs in a grid energy mode when the network is available over 24 h. Energy performance, cost savings, and daily revenue have all been improved by EMPC. As a result, the daily energy savings are up to 52%, whereas diesel energy does not go over 85%.

A suitable mixture of renewable resources including wind, tidal and photovoltaic units can be used in the microgrids installed in coastal areas or islands. However, the variation in the renewable resources such as wind speed, tidal current speed and solar radiation is significant that affects the reliability performance of these microgrids. Thus, to accurately evaluate the reliability of the microgrids, the failure rate of composed components affected by the variation in the renewable resources must be considered. To study the impact of variation in the temperature and renewable resources on the failure rate of components, different equations including Arrhenius law, temperature modification factor, fatigue strength, bending and contact stress, limit state function of turbine, thermal loss of semiconductor devices, the temperature rise of transformer and cable, the temperature coefficient of voltage and power of photovoltaic panels are developed. According to the developed equations, the components failure rate, and consequently the failure rate of the microgrid considering the variation in the temperature and renewable resources are determined. Then, by simulation, the impact of different energy sources on the failure rate of the whole system is evaluated and the reliability in a microgrid consisting of offshore renewable energy sources will be studied.