Search

Power networks, especially distribution networks, have been undergoing substantial changes since the application of new technologies. Technology development in the early part of the 21st century has opened up new horizons for automated, efficient and reliable power grids. New technologies, while enhancing the capability of the electrical networks and providing opportunities and innovative solutions to the challenges of future networks, can also cause drawbacks that should be investigated and taken into account. Distributed generation (DG) is one of the new technologies that improves the operation of power grids. Despite tangible benefits that integration of DG units brings to electrical grids, their notable impacts on protection systems of power networks raise many challenges and concerns on how a fault should be detected and isolated in active distribution networks. Many attempts have been made to investigate the downside of the interconnection of DG units and methods to mitigate their impacts have been proposed. This study reviews the impact of DG integration on protection systems addressed in other research works and recapitulates suggested methods provided by scholars. [ABSTRACT FROM AUTHOR]

A high proportion of distributed generators (DGs) connected to the distribution network causes a significant change in the normal and fault currents of the system as well as in the linearization of the characteristics. It is difficult to adapt to conventional protection. This paper theoretically analyzes the possible impact of fault current characteristics on traditional protection based on single-ended informativeness after connecting to DGs. From the perspective of protection action, the evaluation index system of DG protection is established by considering the maximum short-circuit current output from DG. Combined with the relay protection requirements, the calculation method of evaluation indexes is given concerning the protection characteristics and expert experience. An analytic hierarchy process (AHP) and a CRITIC combination assignment method based on the principle of minimum information identification are proposed. The scores of different types of protection before and after DG access are calculated using the proposed methodology employing a typical distribution network example. The proposed method can quantitatively obtain the distribution network protection adaptability boundary. In the actual calculation example selected in this paper, a DG can reasonably improve the adaptability of the three-stage current protection when it increases the current amplitude at a penetration rate of 50%; the DG needs to adjust the three-stage current protection rectification value when it decreases the current amplitude at a penetration rate of 20%; and adaptive overcurrent protection and inverse time limit current protection need to be adjusted when the penetration rate of DG is 50%. Compared with the traditional protection evaluation method, the method adopted in this paper can intuitively derive the weak link between protection handling faults after DG access as well as the appropriate capacity of DG to improve protection performance. It can provide a powerful reference for the optimization of protection schemes after the high percentage of DG access. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for evaluating distribution voltage regulators using quasi-static hardware-in-the-loop (HIL) simulation. Distribution voltage regulators are used to control the voltage using tap change transformers and are equipped with static control algorithms in order to accomplish different configurations of the distribution electric grid with typical load profiles. The growing penetration of renewable energy in electrical distribution systems is a great challenge for this equipment, and it becomes paramount that its measurement and control algorithms adapt to this new reality. Hardware-in-the-loop testing (HIL) techniques could be used for assessing the performance of the voltage regulator equipment. Most applications of HIL are concerned with accessing the dynamics of electrical system including not only grid modeling, but also the real equipment involved and should be used to test the efficiency of voltage regulation. However, a more simplified approach concerning the HIL tool could be applied to testing distribution voltage regulators in the laboratory prior to installation, evaluating the performance of algorithms and equipment by conducting quasi-static simulations. This paper presents this approach, joining NI LABVIEW laboratory controller software (National Instruments, LabVIEW. Available at https://www.ni.com/pt-br/support/downloads/software-products/download.labview.html#460283, 2023) and an open-source program OpenDSS (The Electric Power Institute, OpenDSS. Available at em https://opendds.org/, 2023). The effectiveness of quasi-static HIL platforms for assessing several aspects of static voltage tap regulators before installing them in the electric grid is demonstrated. The major advantage is the smaller computation complexity of a static simulations compared to dynamic simulators, allowing simpler hardware requirements. In this sense, a large number of medium to long term simulations could be easily performed providing a broader view of the behavior of the electric system including voltage regulators. [ABSTRACT FROM AUTHOR]

The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth's surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model's output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures. [ABSTRACT FROM AUTHOR]

Hybrid microgrid (HMG) control strategies reveal several critical research gaps that must be addressed. Current approaches often fall short in efficiency and reliability due to the inherent complexities of transitioning between grid-connected mode (GCM) and islanded mode (IM), resulting in increased losses and operational difficulties. Additionally, managing overloads, especially in IM, poses significant challenges for maintaining stable voltage and frequency regulation. This paper seeks to address these issues by introducing a novel universal control structure (UCS) for HMGs, which includes a frequency compensation unit (FCU) and a three-stage bidirectional AC/DC droop. It presents a streamlined yet robust solution for handling overloads, maintaining power balance, and ensuring precise voltage and frequency regulation while promising smooth maneuver in both GCM and IM. The proposed UCS provides flexible and reliable operation, allowing for the versatile placement of DC and AC sources and loads. This will reduce power transformation stages thereby lowering cost and increasing efficiency of system. Through experimental validation, this study demonstrates the effectiveness of the proposed control in addressing these critical research gaps and advancing the field of HMG control strategies. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing emphasis on diversifying and decarbonizing the energy matrix, paving the way for the emergence of new energy technologies as promising solutions for sustainable development. Among the emerging energies that have already achieved commercial maturity and deployment in Brazil are photovoltaic panels. This work proposes to analyze the potential impacts of on-load TAP-changers (OLTC) in transformers in industries located in distribution networks with a significant presence of photovoltaic distributed generation (PVDG). Thus, three industries are strategically allocated at different points of the IEEE 8500-node system using the OpenDSS program. The impacts are evaluated in several PVDG penetration scenarios in the network. The results show substantial fluctuations in the flow of energy generated by PVDG within the low-voltage network. This, in turn, induces frequent voltage changes in the medium-voltage network, leading to an increased operational frequency of OLTCs and a subsequent reduction in their lifetime. Moreover, this paper proposes alternative configurations for voltage regulator devices to enhance their lifetime in the face of intermittent voltage variations. The results suggest that adjusting the equipment's actuation delay and bandwidth of the OLTCs can decrease the OLTC switching frequency in a range of 65–85%, resulting in the preservation of the equipment's lifetime. [ABSTRACT FROM AUTHOR]

In the growing world, the utilization of electrical energy is increasing rapidly. Excessive use of fossil fuels will drain them and also invite hazardous pollution. Integrating renewable energy resources as distributed generators (DGs) can fulfill the rapidly increasing electrical energy demand and promote green energy generation to a large extent. The intermittent nature of renewable energy and higher penetration of DG may adversely affect the operation of the distribution network (DN). As a result, power disparity, reverse power flow to the grid, and voltage instability may exist. One key solution is to optimally integrate the renewable energy-based DG and battery energy storage system (BESS) in the coordination of demand response (DR). This paper proposes a multilevel particle swarm optimization technique to synchronize the distributed energy resources (DER) and DR in the DN. The proposed approach is implemented on the IEEE 33 bus system energized by coal power plant (CPP). The first level of optimization finds the sizes and locations of DER (DG and BESS), and the next level determines the optimal power dispatch in the coordination of DR. The outcomes of this framework exhibit effectiveness in the optimal utilization of renewable energy resources and the enhancement of power quality parameters in DN so that CO2 emissions are reduced by 32.71% from CPP. [ABSTRACT FROM AUTHOR]

There are important challenges in modeling large electrical distribution circuits, especially with the presence of distributed renewable generation. Constructing simulations to assess the effect of the penetration of distributed generation on electrical distribution networks has become of great importance for Distribution Network Operators (DNOs). This paper proposes a simulation strategy based on open-source platforms and the integration of scripting tools for the rapid modeling of large-scale electrical distribution circuits with distributed renewable generation. The implementation is based on the adaptation of a tool called QGIS2OpenDSS, which creates OpenDSS distribution network models directly from an open-source geographic information system, QGIS. The plugin's capabilities are demonstrated using a real distribution feeder with more than 60% penetration of renewable generation based on photovoltaic systems. These simulations are carried out using real data from a circuit provided by a DNO in the Dominican Republic, which is used to demonstrate how this approach provides a more accessible and flexible way to simulate and assess the effect of Distributed Energy Resources (DERs) in medium voltage (MV) and low voltage (LV) networks, enabling utilities to evaluate system performance and identify potential issues. The integration of this open-source tool within the DNO software stack enables users to apply it according to specific project needs, enhancing their capability to analyze and manage high DER penetration levels, aiding in better planning, operation, and decision-making processes related to renewable energy projects. [ABSTRACT FROM AUTHOR]

The optimal operation of energy resources in smart distribution system (SDS) is one of the significant concerns that must be properly addressed and is of utmost importance. By optimal operation the operating point of distributed energy resources (DERs) and the strategy of energy exchange with the upstream network can be appropriately determined. Therefore, this work aims to investigate energy loss-constrained optimal operation of SDS. The SDS comprises of dispatchable distributed generations (DDGs), solar generation and electric vehicles (EVs). Participation of DDGs and EVs in both active and reactive power is considered in this paper. The proposed operation of SDS aims at minimizing the total operating cost of SDS. Moreover, to improve the technical efficiency of SDS, energy loss is added as one of the constraints in the optimization problem. The operational problem of SDS with reactive power support from EVs is modeled as a mixed integer second-order cone programming problem and solved using MOSEK solver. Different case studies are framed and compared to investigate the effect of considering energy loss constraint and reactive power from EVs. Simulation results verify the efficacy of proposed model in optimally coordinating different DERs in SDS. [ABSTRACT FROM AUTHOR]

Summary This paper presents the development of a single‐phase voltage source inverter (VSI) of 3.5KW, applied to grid‐connected photovoltaic systems (GCPS). The proposed system has a boost interface converter connected to a full bridge inverter with an LCL filter. Each block that constitutes the proposed GCPS is described in detail, containing the specifications of the solar modules, the design of the power circuit components, the mathematical modeling, and the control design of the converters. The presentation of the Volt‐VAR Control (VVC) highlights the contribution of this work, where it is implemented with the intention of the inverter to supply/consume reactive power, in order to minimize voltage variations at the point of common coupling (PCC). To validate the inverter operation with VVC, three cases are presented, encompassing grid voltage and irradiance variations, and load steps. Through the PCC voltage profiles, currents, and power supplied/consumed, the means to demonstrate the effectiveness of the proposed control is shown. The results obtained through the PSIM simulations and verified by Typhoon Hardware in the Loop (HIL) model, showed the regulation of grid voltage levels for all established conditions. [ABSTRACT FROM AUTHOR]

The major challenges of the distribution system are optimal location and sizing of distributed generation (OLSDG), as these significantly influence the system losses, voltage profile and voltage stability issues. In this paper, the OLSDG problem is solved by a newly developed artificial rabbits optimization (ARO) algorithm. The objective of an OLSDG problem is to minimize real power loss and total voltage deviation (TVD) and enhance the voltage stability index (VSI) in radial distribution systems (RDSs) while satisfying all the constraints. The effectiveness of the ARO has been validated on IEEE 33-bus and large-scale 118-bus RDSs for various power factors of distributed generation (DG), like optimal power factor (OPF), fixed power factor, and unity power factor (UPF) under four different scenarios. It has been found that RDSs' performance in terms of TVD, VSI, and mainly power loss is much improved when DGs have been operated with an OPF. In the case of DGs operating with UPF, the power loss decreases by 65.50% and 60.24% in an IEEE-33 bus and 118-bus RDSs, respectively, whereas DGs operating with OPF, the power loss reduces to 94.44% and 90.28%, respectively. The results obtained by ARO are validated by comparing them with the results of other optimization methods in the recent literature. The results prove that the proposed ARO is more efficient, has less computational time and can provide better solutions for an OLSDG problem. [ABSTRACT FROM AUTHOR]

Before starting stability analysis of the multivirtual synchronous machine (n‐VISMA) power system, it is necessary to obtain the steady‐state operating points (SSOPs) of all dynamic nodes in the network. Modified traditional iterative schemes using the concept of droop bus technique in an islanded microgrid are not feasible for load flow analysis of VISMA microgrid incorporating non‐control dynamics. This paper proposes closed‐form steady‐state, fundamental‐frequency models for islanded VISMA microgrids using the concept of virtual swing buses. In this technique, the virtual internal buses of all VISMAs in the network are governed by the swing equation. The voltage at all buses is variable except the virtual buses in which the pole wheel voltages are prespecified. The algorithm was extended by a droop control localized to each VISMA. The suitability of the proposed algorithm to obtaining SSOPs of VISMA was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines and also on a low‐voltage distribution system. To validate the applicability of the proposed algorithm and prove its accuracy, the case study systems were also modeled in the SIMULINK environment for detailed time domain analysis. The algorithm was found to be computationally effective for a load flow analysis of the VISMA microgrid. The results also reveal that the addition of external droop control improves the frequency stability of the system. [ABSTRACT FROM AUTHOR]

This paper presents the development and practical implementation of a new passive island detection method for grid-connected hybrid distributed generating systems. The method employs positive sequence voltage and current components at the point of common coupling. The product of the difference in positive sequence voltage and the difference in positive sequence current is used to produce the Islanding Detection Index (IDI). Islanding is identified when the IDI magnitude surpasses a predefined threshold over a predefined delay time. Even in the case of a zero power mismatch condition, the proposed method can identify the islanding condition in under 70 msec. Its effectiveness is validated under various operating circumstances. It provides excellent stability against various non-islanding situations, avoiding unwanted tripping. The suggested method also has the advantages of being simple and inexpensive to implement, having no adverse effects on output power quality, not requiring a classifier, not being system-dependent, and working regardless of the quantity and type of distributed generation connected to the utility grid. The obtained detection timings were compared to those previously reported in the literature, confirming the proposed approach's superiority. The suggested work utilizes the OPAL-RT real-time hardware-in-loop environment and MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The suitable placement and power of distributed generation (DG) can bring technical benefits to the distribution network. This paper applies the Coot optimization algorithm to the DG placement problem with the goal of minimizing power loss and feeder balancing load (LBF). The weight method is used to combine the membership objectives. The evaluation results on the network of 70 nodes for different weight values of the objective function show that the optimal power and installation location of DGs significantly reduces power loss and improves LBF index. In this study, eleven cases were considered. As the weight of power loss part W1 increases from 0 to 1, the power loss gradually decreases, the LBF index gradually increases, the maximum current gradually decreases, and the minimum voltage amplitude gradually improves. Comparing the results of Coot with particle swarm optimization (PSO) shows that the indicators are improved. In the three cases where W1 is 0, 0.5, and 1, power loss gained by Coot compared to PSO is less than 47.2663 kW, 73.2725 kW, 30.8708 kW, respectively. LBF index of Coot compared to PSO is less than 98.2%, 88.2%, 81.9%, respectively. The maximum, minimum, average, standard deviation, and CPU time of Coot are smaller than those of PSO. So, Coot is one of the promising methods for this problem. [ABSTRACT FROM AUTHOR]

Existence of nonlinear loads makes load power sharing more challenging in the islanded microgrids. Conventional droop control may cause the poor harmonic power sharing among the distributed generations (DGs) due to the mismatched line impedance. This paper proposes an adaptive regulation method of the virtual harmonic impedance for the accurate harmonic power sharing. Consensus algorithm is used to regulate adaptively the virtual harmonic impedance according to the harmonic power sharing error among the DGs with considering of the mismatched line impedance. This method does not require the prior knowledge of the line impedance. The feasibility of the proposed method is demonstrated by simulation result for achieving the accurate harmonic power sharing to reduce the voltage distortion at the point of common coupling (PCC). [ABSTRACT FROM AUTHOR]

For islanded microgrids, droop-based control concepts have been developed both in single and three-phase variants. The three-phase controllers often assume a balanced network; hence, unbalance sharing and/or mitigation remains a challenging issue. Therefore, in this paper, unbalance is considered in a three-phase islanded microgrid in which the distributed generation (DG) units are operated by the voltage-based droop (VBD) control. For this purpose, the VBD control, which has been developed for single-phase systems, is extended for a three-phase application and an additional control loop is added for unbalance mitigation and sharing. The method is based on an unbalance mitigation scheme by DG units in grid-connected systems, which is altered for usage in grid-forming DG units with droop control. The reaction of the DG units to unbalance is determined by the main parameter of the additional control loop, viz., the distortion damping resistance, Rd. The effect of Rd on the unbalance mitigation is studied in this paper, i.e., dependent on Rd, the DG units can be resistive for unbalance (RU) or they can contribute in the weakest phase (CW). The paper shows that the RU method decreases the line losses in the system and achieves better power equalization between the DG unit's phases. However, it leads to a larger voltage unbalance near the loads. The CW method leads to a more uneven power between the DG unit's phases and larger line losses, but a better voltage quality near the load. However, it can negatively affect the stability of the system. In microgrids with multiple DG units, the distortion damping resistance is set such that the unbalanced load can be shared between multiple DG units in an actively controlled manner rather than being determined by the microgrid configuration solely. The unit with the lowest distortion damping resistance provides relatively more of the unbalanced currents. [ABSTRACT FROM AUTHOR]

Peak shaving techniques have become increasingly important for managing peak demand and improving the reliability, efficiency, and resilience of modern power systems. In this review paper, we examine different peak shaving strategies for smart grids, including battery energy storage systems, nuclear and battery storage power plants, hybrid energy storage systems, photovoltaic system installations, the real-time scheduling of household appliances, repurposed electric vehicle batteries, uni- and bi-directional electric vehicle charging, the demand response, the time-of-use pricing, load shedding systems, distributed generation systems and energy-efficient management. We analyze the potential of each strategy to reduce peak demand and shift energy consumption to off-peak hours, as well as identify the key themes critical to the success of peak shaving for smart grids, including effective coordination and communication, data analytics and predictive modeling and clear policy and regulatory frameworks. Our review highlights the diverse range of innovative technologies and techniques available to utilities and power system operators and it emphasizes the need for continued research and development in emerging areas such as blockchain technology and artificial intelligence. Overall, the implementation of peak shaving strategies represents a significant step toward a more sustainable, reliable and efficient power system. By leveraging the latest technologies and techniques available, utilities and power system operators can better manage peak demand, integrate renewable energy sources, and create a more reliable and secure grid for the future. By discussing cutting-edge technologies and methods to effectively manage peak demand and incorporate renewable energy sources, this review paper emphasizes the significance of peak shaving strategies for smart grids as a crucial pathway towards realizing a more sustainable, dependable and efficient power system. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources is crucial for achieving sustainability and environmental preservation. However, their intermittent nature poses challenges to electrical system stability, requiring robust integration strategies. Microgrids emerge as a flexible solution, but their successful deployment requires meticulous planning and intelligent operation to overcome these challenges. This paper presents the development of a reduced-scale laboratory dedicated to researching both hardware and software solutions for intelligent microgrid management. The laboratory was designed to incorporate key components that are becoming increasingly important in the present microgrid context, including renewable energy generation, storage systems, electrolyzers for hydrogen production, and combined heat and power sources. While some equipment consists of commercial models, the battery bank, converter, and supervisory systems were custom-designed to meet the specific requirements of the laboratory. The laboratory has proven itself as a robust tool for conducting studies on microgrids, effectively incorporating essential components, addressing pertinent system issues, and allowing for several tests on converting control algorithms. [ABSTRACT FROM AUTHOR]

This paper presents the impact of transmission congestion and demand side management on Electricity Producers’ market power using the quantitative investigation of pool-based market clearing mechanism. The mathematical problem formulation for pool-based market clearing consists of maximization of social welfare objective function, which incorporates maximization of demand benefit obtained from flexible demand bids and minimization of Electricity Producers’ real and reactive power costs obtained from actual and strategic supply bids. In this problem formulation, the nonlinear modeling of power system is used for real and reactive power flow equality constraints, transmission line capability inequality constraints (for incorporating the impact of transmission congestion), and generator capability curve equality constraints. The impacts of demand side management have been incorporated in the problem by considering single-period and multi-period demand flexibilities, and distributed generation placement at appropriate locations. The formulated optimization problem has been solved using nonlinear programming, which provides the nodal prices as byproducts of the solution. The electricity producers’ capability to exercise market power has been analyzed from their revenue and surplus indices, which have been obtained from nodal prices. Various case studies have been simulated on IEEE 30-bus system for quantitative determination of electricity producers’ market power. The results obtained are very interesting and demonstrate demand side strategies to tackle the electricity producers’ market power with actual and strategic bidding under normal and congested system conditions. [ABSTRACT FROM AUTHOR]

Microgrids are constantly exposed to a wide range of faults that may affect their performance. The occurrence of a fault may lead to problems such as harming the network's equipment, delaying service to customers, unstabilizing the network, and eventually undermining the network's reliability. Therefore, accurate fault location at the proper time is crucial. In this paper, a method is proposed to determine the fault distance and section of single and multi-phase faults in alternative current microgrids using voltage and current data from the beginning node and nodes at the end of the microgrid. This method obtains the location of the fault based on the calculation of the voltage and current difference on both sides of the fault. Moreover, the data from less than a quarter of the cycle after the occurrence of fault with the presence of renewable resources, and the distributed-parameters line model (DPLM) are used. The proposed method is insensitive to fault resistance, fault inception angles, unbalanced network, different distributed generation operation conditions, and changes in the line parameters, and it is independent of the island and grid-connected modes. To evaluate the proposed scheme, a microgrid with 12 nodes is simulated in MATLAB/SIMULINK. The maximum error obtained from this method is 1%, which demonstrates the acceptable accuracy of the proposed algorithm compared to other similar techniques. [ABSTRACT FROM AUTHOR]

The potential of Electric Vehicles (EVs) to decarbonize the transportation industry has attracted a lot of attention in recent years in response to growing environmental concerns. Electric Vehicle Charging Stations (EVCSs) need to be properly located for widespread EV integration. The distribution system is facing additional challenges due to inclusion of EVCS. The adverse impacts of EVCS on the Radial Distribution Network (RDN) may be minimized using Distributed Generations (DGs) or Distribution Static Compensators (DSTATCOMs) or by reconfiguring the network. This paper uses a novel optimization technique to solve the problem of simultaneous optimal placement of EVCS with network reconfiguration and optimal planning (siting and sizing) of DGs and DSTATCOMs. The multiple objective functions are considered in order to minimize the active power losses, the voltage deviation, the investment costs for DGs and DSTATCOMs, and to increase the voltage stability of the system. A novel meta-heuristic Cheetah Optimization Algorithm (COA) is used to solve the optimization problem. To examine the effectiveness of the suggested strategy on 33-bus and 136-bus networks, several scenarios of simultaneous incorporation of EVCS, DG, and DSTATCOM installations with network reconfiguration are taken into consideration. The COA results are also compared to the results of grey wolf optimization and genetic algorithms. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore,  EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV  load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work. [ABSTRACT FROM AUTHOR]

In recent years, a pronounced transition to the exploitation of renewable energy sources has be observed worldwide, driven by current climate concerns and the scarcity of conventional fuels. However, this paradigm shift is accompanied by new challenges for existing power systems. Therefore, the hosting capacity must be exhaustively assessed in order to maximize the penetration of distributed generation while mitigating any adverse impact on the electrical grid in terms of voltage and the operational boundaries of the equipment. In this regard, multiple aspects must be addressed in order to maintain the proper functioning of the system following the new installations' capacities. This paper introduces a sequential methodology designed to determine the maximum hosting capacity of a power system through the optimal allocation of both active and reactive power. To achieve this goal, an Improved Salp Swarm Algorithm is proposed, aiming to establish the appropriate operational planning of the power grid considering extensive distributed generation integration, while still ensuring a safe operation. The case study validates the relevance of the proposed model, demonstrating a successful enhancement of hosting capacity by 14.5% relative to standard models. [ABSTRACT FROM AUTHOR]

A large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver. [ABSTRACT FROM AUTHOR]

In the context of the energy transition, electrical microgrids have become a key energy solution for isolated zones. For this type of application, low-voltage networks with unbalanced loads are commonly connected. Despite this, many of the hierarchical control strategies presented in the literature have been designed to operate properly only in the presence of balanced loads. For this reason, it is relevant to study how control strategies can be adapted to this scenario, especially those that reduce the dependence on communications to enhance flexibility and reliability. In this sense, this paper presents a secondary layer control strategy that does not require the use of communications to operate in isolated microgrids with unbalanced loads. The strategy guarantees proper performance in terms of power-sharing between the distributed generators of the microgrid. Simulations on Matlab/Simulink are presented to validate the response of proposal. [ABSTRACT FROM AUTHOR]

Due to the growing attention for the environmental impacts and power loss minimization, distributed generators (DGs) have been introduced widely into the electric power system. One of the challenges of integrating with the power system is to determine their optimal placement and sizing, which, when not respected, adversely affects the performance of the electrical network. In this paper, three multi‐objective algorithms of particle swarm optimization (PSO), variable constants (VCPSO) and genetic algorithm (GA) are adopted and implemented. The main objectives are to detect the optimum size and location of multiple DGs aiming to reduce the active power loss and improve bus voltage deviations in the distribution networks. The paper conducts a comprehensive review of the optimal size and location of the DG via systematic procedures, including definition, classifications, technologies of DGs. Then, the performances evaluation of the three optimization methods are presented and compared with other methods. The presented optimization methods are tested on the IEEE‐33 bus, 32‐line radial distribution network. Four different scenario‐based studies including base case and different number of DGs are performed to examine the accuracy of the presented algorithms. The obtained results prove that all the three algorithms are suitable for this multi‐objective optimization and VCPSO offers the best solution in terms of convergence and, and it has lowest average computation time. The performance and accuracy of the presented approaches and their improvements in the power loss and bus voltage profile are discussed and presented in detail. The obtained results show that more than 65% of active power loss reduction has been attained with the proper sizing and placement of DG systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Renewable energy resources (RES) have become multiple, which made the world interested in developing them because of their continuity, relieving pressure on fossil fuels, as well as the environmental impact. Using RERs will lessen reliance on conventional fossil fuel-based electrical power generation and is more ecologically friendly and eternally sustainable. In this paper, modelling of several renewable energy resources (RERs) including photovoltaic solar (PV), wind, hydro power, geothermal, biofuel, and battery power storage is presented. Modeling of these sources is very important because of their impact on the smart grid and because of their variable nature and different from the components of the traditional electrical network. For battery, the battery storage is very important because many RERs are not fixed in the amount of power generation or they are not sufficient for the loads. For hydro power, Water flows at different rates, small hydropower generators operate at different speeds. For geothermal, geothermal thermal energy includes drawing steam or warm water from wells buried kilometers under the surface of the Earth which is constantly changing. Burning a variety of biomass fuels, which after combustion are transformed into various kinds of energy, can provide sustainable biomass energy that is also unstable. This paper discusses the modelling of all of these resources as well as their benefits and drawbacks. [ABSTRACT FROM AUTHOR]

This paper presents a techno-economic analysis of the impact of distributed generation (DG) integration for power loss reduction and reliability of two 33 kV radial outgoing feeders at the Motta distribution substation in Ethiopia. Transient earth faults and permanent short-circuits were identified to be the most common causes of interruptions. The substation's system average interruption frequency index (SAIFI) is 806 and its system average interruption duration index (SAIDI) is 1394.145 for planned outages. This necessitates the pursuit of effective methods to increase the reliability of the distribution system. The results show that the distribution system is more reliable with the SAIFI and customer average interruption frequency index (CAIFI) reduced by 97.8%, SAIDI reduced by 76%, average service availability index (ASAI) increased by 14.38%, and average service unavailability index (ASUI) reduced by 76%, resulting in a total power loss reduction of 0.32 MW. The cost analysis was performed using the Monte Carlo (MC) reliability analysis and the DG net present cost analysis results in a payback period of less than five years indicating that the system is profitable. [ABSTRACT FROM AUTHOR]

The deterministic directional overcurrent relay (DOCR) coordination approaches find limitation in providing fast and reliable protection to today's distribution networks due to growing integration of distributed generations (DGs). In this paper, an adaptive DOCR protection coordination scheme is proposed using an efficient optimization algorithm called the dragonfly algorithm (DA). The main inspiration of the proposed DA optimization technique originates from static and dynamic swarming behaviours of dragonflies. In this approach, the dominant changes in the network topologies are identified by monitoring the status of the circuit breakers connected at the terminals of the DGs and other main power components. When any dominant changes in the network topologies or operating modes are identified, the fault current for that particular condition is calculated and the new optimal DOCRs settings for the prevailing condition are obtained from the substation central computer in online mode. The comparative results with the existing approaches justify the superiority of the proposed scheme in achieving the minimum overall relay operating time and maintaining the coordination between the primary and backup relay pairs. In the proposed adaptive protection scheme, the average percentage reduction in the overall operating times of DOCRs in the 6-bus and the IEEE 14-bus test systems with different levels DG penetration is found to be 49.7% and 15%, respectively, compared to the existing approaches. [ABSTRACT FROM AUTHOR]

In response to the growing demand for economic and social development, there has been a significant increase in the integration of distributed generation (DG) into distribution networks. This paper proposes a dynamic risk assessment method for voltage violations in distribution networks with DG. Firstly, considering the characteristics of random variables such as load and DG, a probability density function estimation method based on boundary kernel density estimation is proposed. This method accurately models the probability of random variables under different time and external environmental conditions, such as wind speed and global horizontal radiation. Secondly, to address the issue of correlated DG in the same region, an independent transformation method based on the Rosenblatt inverse transform is proposed, which enhances the accuracy of probabilistic load flow. Thirdly, a voltage violation severity index based on the utility function is proposed. This index, in combination with probabilistic load flow results, facilitates the quantitative assessment of voltage violation risks. Finally, the accuracy of the proposed method is verified on the IEEE-33 system. [ABSTRACT FROM AUTHOR]

Aiming at the problems of traditional optimization algorithms for reconfiguring distribution networks, which easily fall into a local optimum, have difficulty finding a global optimum, and suffer from low computational efficiency, the proposed algorithm named Chaotic Particle Swarm Chicken Swarm Fusion Optimization (CPSCSFO) is used to optimize the reconfiguration of the distribution network with distributed generation (DG). This article works to solve the problems mentioned above from the following three aspects: Firstly, chaotic formula is used to improve the initialization of the particles and optimize the optimal position. This increases individual randomness while avoiding local optimality for inert particles. Secondly, chicken swarm optimization (CSO) and particle swarm optimization (PSO) are combined. The multi-population nature of the CSO algorithm is used to increase the global search capability, and, at the same time, the information exchange between groups is completed to extend the particle search range, which ensures the independence and excellence of each particle group. Thirdly, the node hierarchy method is introduced to calculate the power flow. The branching loop matrix and the node hierarchy strategy are used to detect the network topology. In this way, improper solutions can be reduced, and the efficiency of the algorithm can be improved. This paper has demonstrated better performance by CPSCSFO based on simulation results. The network loss has been reduced and the voltage level of each node in the optimal reconfiguration with distributed power supply has been improved; the network loss in the optimal reconfiguration with DG is 69.59% lower than that reconfiguration before. The voltage level of each node is increased, the minimum node voltage is increased by 3.44% and a better convergence speed is presented. As a result, the quality of network operation and the distribution network is greatly improved and provides guidance for building a safer, more economical and reliable distribution network. [ABSTRACT FROM AUTHOR]

Advancements in power conversion efficiency and the growing prevalence of DC loads worldwide have underscored the importance of DC microgrids in modern energy systems. Addressing the challenges of power-sharing and voltage stability in these DC microgrids has been a prominent research focus. Sliding mode control (SMC) has demonstrated remarkable performance in various power electronic converter applications. This paper proposes the integration of universal droop control (UDC) with SMC to facilitate distributed energy resource interfacing and power-sharing control in DC microgrids. Compared to traditional Proportional-Integral (PI) control, the proposed control approach exhibits superior dynamic response characteristics. The UDC is strategically incorporated prior to the SMC and establishes limits on voltage variation and maximum power drawn from the DC–DC converters within the microgrid. A dynamic model of the DC–DC converter is developed as the initial stage, focusing on voltage regulation at the DC link through nonlinear control laws tailored for Distributed Generation (DG)-based converters. The UDC ensures voltage stability in the DC microgrid by imposing predetermined power constraints on the DGs. Comparative evaluations, involving different load scenarios, have been conducted to assess the performance of the proposed UDC-based SMC control in comparison to the PI control-based system. The results demonstrate the superior efficiency of the UDC-based SMC control in handling dynamic load changes. Furthermore, a practical test of the proposed controller has been conducted using a hardware prototype of a DC microgrid. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) has been increasingly integrated into distribution systems in the last decade. The selection of the optimal location and capacity of these DGs is an important issue. This selection is based on minimizing losses, reducing emissions, reducing voltage deviation, and much more. This paper proposes a new optimization approach with a multi-objective function for maximizing DG capacities and minimizing both system losses and voltage deviation, which are subject to system constraints. The weight of each attribute of the multi-objective function is estimated using the analytical hierarchical process (AHP). The optimal DG size is calculated for all possible buses, and hence, the optimal bus can be located. Moreover, an algorithm is introduced to decrease the number of iterations when integrating more than one DG. The proposed method is an iterative linear programming method based on the familiar Newton-Raphson sensitivity matrix. The proposed approach has been tested on 69 and IEEE 33 bus systems. The simulation results are compared with previous literature results. [ABSTRACT FROM AUTHOR]

Traditionally, electric power systems are subject to uncertainties related to equipment availability, topological changes, faults, disturbances, behaviour of load, etc. In particular, the dissemination of distributed generation (DG), especially those based on renewable sources, has introduced new challenges to power systems, adding further randomness to the management of this segment. In this context, stochastic analysis could support planners and operators in a more appropriate manner than traditional deterministic analysis, since the former is able to properly model the power system uncertainties. The objective of this work is to present recent achievements of one of the most important techniques for stochastic analysis, the Monte Carlo Method (MCM), to study the technical and operational aspects of electric networks with DG. Besides covering the DG topic itself, this paper also addresses emerging themes related to smart grids and new technologies, such as electric vehicles, storage, demand response, and electrothermal hybrid systems. This review encompasses more than 90 recent articles, arranged according to the MCM application and the type of analysis of power systems. The majority of the papers reviewed apply the MCM within stochastic optimization, indicating a possible trend. [ABSTRACT FROM AUTHOR]

For over a decade, distributed generations (DGs) have sufficiently convinced the researchers that they are the economic and environment-friendly solution that can be integrated with the centralized generations. The optimal planning of distributed generations requires the appropriate location and sizing and their corresponding control with various power network types to obtain the best of the technical, economical, commercial, and regulatory objectives. Most of these objectives are conflicting in nature and require multi-objective solutions. Therefore, this paper brings a comprehensive literature review and a critical analysis of the state of the art of the optimal multi-objective planning of DG installation in the power network with different objective functions and their constraints. The paper considers the adoption of optimization techniques for distributed generation planning in radial distribution systems from different power system performance viewpoints; it considers the use of different DG types, distribution models, DG variables, and mathematical formulations; and it considers the participation of different countries in the stated DG placement and sizing problem. Moreover, the summary of the literature review and critical analysis of this article helps the researchers and engineers to explore the research gap and to find the future recommendations for the robust optimal planning of the DGs working with various objectives and algorithms. The paper considers the adoption of uncertainties on the load and generation side, the introduction of DGs with energy storage backups, and the testing of DG placement and sizing on large and complex distribution networks. [ABSTRACT FROM AUTHOR]

Distributed energy resources have been increasingly integrated into electrical grids. Consequently, electricity markets are expected to undergo changes and become more complex. However, while there are many scientific publications on the topic, a broader discussion is still necessary. Therefore, a systematic literature review on electricity markets in the context of distributed energy resources integration was conducted in this paper to present in-depth discussions on the topic, along with shedding light on current perspectives, the most relevant sources, authors, papers, countries, metrics, and indexes. The software R and its open-source tool Bibliometrix were used to perform the systematic literature review based on the widely recognized databases Web of Science and Scopus, which led to a total of 1685 articles after removing duplicates. The results demonstrate that demand response, renewable energy, uncertainty, optimization, and smart grid are the most-used keywords. By assessing highly impactful articles on the theme, emphasis on energy storage systems becomes clear compared to distributed generation and electric vehicles. However, electric vehicles draw attention in terms of citations. Furthermore, multi-level stochastic programming is the most-applied methodology among highly impactful articles. Due to the relevance of the demand response keyword, this paper also conducts a specific review on the topic aligned with electricity markets and distributed energy resources (296 articles). The results demonstrate that virtually all high-impact publications on the topic address day-ahead or real-time pricing. Based on the literature found, this paper presents a discussion on the main challenges and future perspectives related to the field. The complexity of electrical power systems and electricity markets is increasing substantially according to what this study found. Distributed generation development is already advanced, while energy storage systems and electric vehicles are limited in many countries. Peer-to-peer electricity trading and virtual power plant are newer concepts that are currently incipient, and DR programs showcase an intermediate stage of evolution. A particular lack of research on social issues is verified, and also a lack of all-encompassing studies that address multiple interconnected topics, which should be better addressed in the future. The in-depth assessment carried out in this paper is expected to be of high value to researchers and policy-makers and facilitate future research on the topic. [ABSTRACT FROM AUTHOR]

To cope with the increasing energy demand, power systems, especially distribution networks, face many challenges. Recently, these networks have become complex and large, and their stability and reliability are not easy to be handled. The integration of renewable energy resources and at the same time limiting their accompanied high fault currents is one of the approvable suggestions. Many solutions have appeared to restrict the fault currents, but fault current limiters (FCLs) arise as an efficient and promising solution to whether to interrupt or limit the fault currents to allowable limits. This paper presents a literature review of the integration of renewable energy resources as distributed generation units (DGs) and FCLs in distribution networks. The DGs can be categorized based on their size and ability to deliver active or reactive power in addition to their fuel. All of solar, wind, water, biomass, geothermal, and fuel cell are utilized as the main engine for these units. Additionally, a survey about FCLs is provided, including their diverse types and applications in either medium- or low-voltage networks. FCLs are divided into reactor, pyrotechnic, non-superconducting (solid state), and the last-developed ones, superconducting FCLs. In addition, the implemented optimization techniques are summarized to correctly employ both FCLs and DGs. These techniques vary between classical and modern, whereas more methods are developed to suit the renewable energy intermittence and uncertainty and the power system operators' aspirations. Moreover, in this paper, the optimal allocation of diverse types of DGs correlated with FCLs is presented and applied to the real Egyptian distribution network of the East Delta Network (EDN). The results show the avails obtained where the power losses are significantly reduced, with respect to the total load, from 3.59% in the initial case to 0.296%. In addition, the fault current returns to its initial value, removing the percentage of increase of 20.93%. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to mitigate adverse effects of the fault management of closed/ring grids with high shares of distributed generation (DG). The approach refers to conventional distance protection relays in particular. The rising share of DG, especially in distribution systems, can lead to overloadings and intolerable voltages. Closed-ring structures increase the grid capacity for DG due to more balanced load and voltages profiles. However, malfunctions of protection relays may occur because fault locating and selective fault clearing can be disturbed. This paper presents a distance protection algorithm for feeder relays that complies with the requirements of closed/ring grids with inter/infeeds. The proposed current comparison method (CCM) bases on the comparison of negative/sequence currents. It needs no voltage measurement and remains unaffected from inter/infeeds and fault resistances. Additionally, for relays located at the ring coupler, where the ring can be opened, a coupling relay method (CRM) is proposed. It locates unsymmetrical faults in closed/ring grids with inter/infeeds and enables fast and selective opening of the ring. This helps maintaining reliable power supply for customers in closed rings. Finally, the paper presents a grading strategy reducing the average fault clearing time. Lab tests prove the capability of both proposed algorithms. [ABSTRACT FROM AUTHOR]

In a stand-alone DC microgrid featuring several distributed energy resources (DERs), droop control is adopted to achieve a proportional distribution of current among the DERs within the microgrid. The operation of the droop control mechanism leads to a variation in bus voltage, which is further amplified by the line impedance between the DC bus and DERs. This paper proposes an enhanced distributed secondary control technique aimed at achieving equitable current sharing and voltage regulation simultaneously within a DC microgrid. The proposed distributed secondary control is incorporated into the cyber layer of the microgrid, facilitating the exchange of information among the controllers. In the event of a communication link breakdown, this technique upholds the reliability of the entire system. The control loop utilizes a type-II fuzzy logic control framework for the adaptive selection of the control parameters to improve the control response. Furthermore, the proposed technique can handle both resistive and constant power loads without any particular prerequisites. Utilizing the Lyapunov method, appropriate stability criteria for the proposed controller have been formulated. Various tests were performed across a range of operational scenarios to assess the robustness of the proposed control technique through MATLAB/Simulink ® models, which have been validated with real-time experiments. The outcomes revealed that the proposed control effectively achieves its control objectives within a DC microgrid, showcasing rapid responsiveness and minimal oscillation. [ABSTRACT FROM AUTHOR]

Future power distribution networks will include a significant amount of renewable distributed generation (DG). To increase the system power factor (SPF) and voltage profile (VP) by including DG in the distribution networks, this research proposes a unique approach backed by genetic algorithms (GA) and monte carlo simulation (MCS). Models of constant power (P), constant current (I), and constant impedance (Z) loads (ZIP–LMs). A 75-bus distribution network has been used to test the suggested methodology. From the perspective of minimizing the real power loss of the system, this paper considered the DG consolidated in the distributed networks (DNs) with ZIP–LMs for the enhancement of the SPF and VP by applying a hybrid GA–MCS. The people who work on the subject of integrating renewable energy sources with grids will also benefit from this study. [ABSTRACT FROM AUTHOR]

More and more distributed generation (DG) and energy storage (ES) devices are being connected to the distribution network (DN). They have the potential of maintaining a stable supply load during failure periods when using islanding operations. Therefore, DG and ES have capacity value, i.e., improving the power supply capability of the system. However, there are strong fluctuations in DG outputs, and the operations of ES devices have sequential characteristics. The same capacity of DG has different load-bearing capabilities compared to conventional thermal or hydroelectric units. This paper proposes a method for evaluation of power supply capability improvement in DNs. First, the temporal fluctuation in both power source and load demand during fault periods is considered. A DN island partition model considering the secondary power outage constraint is established. Then, a modified genetic algorithm is designed. The complex island partition model is solved to achieve accurate power supply reliability evaluation. And the incremental power supply capability associated to DG and ES devices is calculated. Finally, a case study is conducted on the PG&E 69-bus system to verify the effectiveness of the proposed method. It is found that with a 20% configuration ratio of ES devices, the power supply capability improvement brought about by 6 MW DG can reach about 773 kW. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed. [ABSTRACT FROM AUTHOR]

The electric power system is undergoing a significant transformation driven by advances in digital technologies. This article provides a comprehensive and detailed analysis of recent advances and the future outlook of electric power systems, with a particular emphasis on the impacts of digital transformation. By conducting a thorough examination of over 150 up-to-date research papers, the study highlights the key developments shaping the future of the field, such as the integration of renewable energy, distributed generation, microgrids, and smart grids. The analysis includes comparison tables and SWOT analysis for each technology, offering insights into their strengths, weaknesses, opportunities, and threats. The study also provides a historical and conceptual overview of digital transformation in electric power systems, exploring recent digital technologies such as cloud computing, the Internet of Things, blockchain, digital twins, and their impact on the electric power system. The article concludes by addressing the barriers and challenges in this transformation. Overall, this research offers a comprehensive and up-to-date analysis of the current state and future outlook of electric power systems, providing valuable insights for researchers, practitioners, and policymakers. [ABSTRACT FROM AUTHOR]

This paper proposes a homotopy-based approach to solve the power flow problem (PFP) in islanded microgrid networks with droop-controlled distributed generation (DG) units. The technique is based on modifying an "easy" problem solution that evolves with the computation of intermediate results to the PFP solution of interest. These intermediate results require the solution of nonlinear equations through Newton–Raphson (NR) method. In favor of convergence, the intermediate solutions are close to each other, strengthening the convergence qualities of the technique for the solution of interest. The DG units are modeled with operational power limits and three types of droop-control strategies, while the loads are both magnitude voltage- and frequency-dependent. To evaluate the method performance, simulations are performed considering the proposed and classical NR methods, both departing from a flat start estimation. Tests are carried out in three test systems. Different load and DG unit scenarios are implemented for a 6-, 38-, and 69-bus test system. A base case is studied for all systems, while for the two larger models, a loading factor is used to simulate the load augmenting up to the maximum value. The results demonstrated that for the largest-size model system, only the homotopy-based approach could solve the PFP for stringent requirements such as the diversification of the load profile and hard loading operation point. [ABSTRACT FROM AUTHOR]

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon. [ABSTRACT FROM AUTHOR]

The goal of this review paper will be to address complex optimization functions in the area of overcurrent relay optimization, to inspect and valorize their objectives and to critically examine their application in real distribution networks. Special emphasis will be put on observing the optimization of discrimination time between primary and backup relay pairs, and methods which prevent the latter becoming penalty functions will be shown. Furthermore, the impact of distributed generation units on the overcurrent relay optimization problem will be elaborated in detail. Protection settings of all the existing and new relays must be thoroughly inspected before the connection of new production units, because contributing fault currents may have a significant effect on the solution of the overcurrent optimization problem. This is because the relays being used are of an inverse-type. Next, meshed network operation will be critically assessed in comparison with the actual practice of radial operation, and some topologies will be highlighted for future research, while others will be discarded. The distribution network in mesh operation implicitly suggests using numerical relays on both sides of protected elements (usually lines), so their total number will be much higher than in radial operation. Finally, a relatively new concept of adaptive distribution network protection will be reviewed, and its potential application to the overcurrent relay optimization problem will be examined. The purpose of this review paper will be to lay a groundwork for future research papers by inspecting the overall development of the overcurrent relay optimization problem for contemporary and future active distribution networks. [ABSTRACT FROM AUTHOR]

The article concerns the energy security of a wastewater treatment process caused by unforeseen situations related to the risk of electrical power outages. In this case, renewable energy sources based on distributed generation power systems can solve this problem in each wastewater treatment plant. The article highlights e related challenges and proposes the direction of solutions in this regard based on Czech conditions. The first part of the paper deals with the consequences of long-term outage of wastewater treatment plants on the population and the environment. There are several solutions presented for blackout conditions, and model calculations are made based on data from a Czech wastewater treatment plant. Diesel engine-generators, biogas as a cogeneration source of heat and electricity, solar panels with storage systems and combined biogas and solar systems were considered as approaches to provide energy autonomy during a blackout in a wastewater treatment plant. Special attention was paid to a combination of CHP units with solar panels and batteries. The results were evaluated for three different locations for this combination. It was concluded that biogas combustion in the CHP unit was the most profitable option, allowing the production of electricity independently of the grid for its own consumption and possibly for other operations. The last part of the paper deals with the transition to island operation, which must occur during a blackout. This transition is more difficult for both solar panels and cogeneration units if they were to supply electricity to the grid before a blackout. The transition to energy island operation could be ensured by frequency relay and processor devices to control the circuit breaker. Then, to maintain island operation, it would be necessary to have an automatic load shedding/application system. [ABSTRACT FROM AUTHOR]

In this paper, a new modulation strategy based on the carrier-based switching strategy for the non-square direct matrix converters (MC) is proposed to control the active power of distributed generation (DG) units. In this strategy, the active power of DGs is controlled by the central input current control of the non-square direct MC independent from the voltage and frequency. Conventionally, each DG has a converter, and for supplying a load with N number of DGs, N number of converters are needed and each converter has its own modulation switching and control strategy to control the power output of each DG. Needless to say, in a microgrid with N number of DGs, the control strategy of each converter has more complex structure than that of a microgrid with one converter, and surely the former strategy entails more volume and price. Using the proposed converter in this paper, it is possible to supply a load with N number of DGs through one converter. Also, the power outputs of all DGs are controlled by a central control strategy. The proposed central control strategy is described and simulated for a typical microgrid. Experimental and simulation results validate the effectiveness of the proposed converter and the proposed strategy. The results demonstrate the applicability and efficiency of the system and verify the theoretical analysis. [ABSTRACT FROM AUTHOR]