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This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems’ sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) Algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm’s superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more efficient and sustainable power systems in the face of evolving energy landscapes.

The emergence of microgrids and the increasing adoption of Distributed Generation Systems (DGS) have created an opportunity to replace traditional fossil fuels with renewable resources. Such a shift poses security and power quality challenges that must be addressed by academics and industrial research paradigms. Unintentional islanding is an important security concern, as it can result in power quality degradation, electrical hazards, and equipment damage. To address this problem and find efficient solutions, many anti-islanding techniques to detect and eliminate the phenomenon can be found in the specialized literature. These solutions can be classified as passive, active, remote, hybrid, or based on machine learning and signal processing techniques. In this context, this paper provides a comprehensive review of existing anti-islanding methods, highlighting their importance in preventing dangerous situations. The review includes a detailed analysis of the advantages and limitations found for each method, as well as its suitability for practical applications. The goal is to provide a valuable resource for researchers and practitioners in the field of distributed power systems, enabling them to choose the most appropriate anti-islanding method for their specific needs. Overall, this paper aims to address the challenges posed by unintentional islanding and promote the adoption of renewable energy resources for a more sustainable future.

Integration of renewable energy sources into the power grid has become a critical research topic in recent years. Microgrid technology has emerged as a promising option to integrate distributed generation and facilitate the widespread use of grid-connected renewable energy. However, ensuring appropriate power quality (PQ) in microgrids is challenging. High PQ is crucial for achieving energy efficiency and proper operation of equipment. This comprehensive review paper offers an overview of PQ issues in microgrids, covering various types of PQ disturbances, their key features, and the most relevant PQ standards. Additionally, it provides an extensive case study review of published research on PQ analysis of microgrid and renewable energy based systems. The key findings of each case study and their investigation method are emphasized. The paper also discusses in detail the different approaches and models employed in these studies to assess PQ issues. A wide range of microgrid systems is presented, featuring various sizes, configurations, topologies, and components, such as photovoltaic and wind systems, energy storage systems, and electric vehicles. Overall, the paper emphasizes the importance of maintaining high PQ in microgrids and provides a valuable resource for researchers and practitioners interested in this topic.

This paper introduces a novel application of the recently developed meta-heuristic algorithm called Geometric Mean Optimization (GMO). The algorithm combines the unique properties of the geometric mean operator in mathematics with the power loss sensitivity index (PLSI) to address various optimization problems in distribution networks. Specifically, the paper focuses on the problems of optimal network reconfiguration (NR), optimal distributed generation (DG) unit allocation with optimal power factor (OPF) and unity power factor (UPF), as well as simultaneous optimal NR and DG unit allocation while considering UPF and OPF. The proposed technique considers operational constraints and three loading levels (0.5 p.u loading - light load level, 1.0 p.u loading - nominal load level, and 1.6 p.u loading - heavy load level) to solve single and multi-objective functions such as maximizing voltage stability index (VSI) and minimizing total active power loss (TAPL) and voltage deviation (VD) in the distribution network (DN). To evaluate the effectiveness of the proposed technique, experiments were conducted on IEEE 33 bus and 69-bus networks. The results of simultaneous optimal NR and DG unit allocation with OPF showed significant improvements in terms of VSI, TAPL, and VD compared to other scenarios, including optimal simultaneous NR and DG unit allocation with UPF, only DG unit allocation with UPF and OPF, and only NR and base case. Moreover, when considering multiple objectives, the simultaneous allocation of NR and DG units with OPF consistently yielded better results for all load conditions. Furthermore, the proposed technique was compared to existing algorithms in the literature, specifically for the objective of TAPL at the nominal load level. The comparison demonstrated that the combined technique outperformed other methods in terms of TAPL for all cases, highlighting its efficacy. The proposed technique exhibited high accuracy and convergence speed, making it a favorable choice for simultaneous optimal NR and DG unit allocation with UPF and OPF across different load conditions.

This research investigates implementing and optimizing microgrid energy management systems (EMS) utilizing artificial intelligence (AI). Inspired by the need for efficient resource utilization and the limitations of traditional control methods, it addresses essential aspects of microgrid design, such as cost-effectiveness, system capacity, power generation mix, and customer satisfaction. The primary goals are to optimize energy management, control techniques, and AI applications in microgrids. The study critically examines the classification of energy management systems, various EMS applications, and their associated challenges. Additionally, it discusses different optimization techniques relevant to EMS, highlighting their applications, benefits, and challenges. The research emphasizes the importance of hybrid systems, demand-side management, and energy storage in addressing the intermittency of renewable energy sources. AI techniques, such as unsupervised learning (USL), supervised learning (SL), and semi-supervised learning (SSL), are extensively analyzed in relation to their specific applications. The study explores AI-based hierarchical controls at primary, secondary, and tertiary levels. Furthermore, AI methods like deep learning for load forecasting and reinforcement learning for optimal control are emphasized for their substantial contributions to enhancing microgrid reliability and efficiency. The research concludes that integrating distributed energy resources (DER) and using advanced optimization algorithms can lead to significant financial benefits and improved sustainability in microgrid operations. Over 200 research papers were referenced in this study.

It is expected worldwide that the increase in renewable penetration will worsen the voltage profile in contrast to the conventional synchronous generators. This is because it is not proper to authorize all independent power producers’ renewable sources to control grid voltages. Although there has been a bunch of research to improve voltage stability, each has the problem of huge computational effort, limited grid reflection, calibration, etc. Thus, the indirect voltage control by droop can be a substantial solution. Then, it is the very one of the critical issues to determine the proper droop ratio, which might be strongly non-linear due to the complexity of the power system. This paper proposes the online nonlinear P-Q droop estimation of distributed generations. It improves voltage stability, which might get worse after the connection of renewable energies or energy storage devices through inverters. First, power sensitivities between multiple P and Q are derived and used to determine the initial state of the linear P-Q droop required for the initial operation that gets data for precise estimation of the P-Q droop. Thus, it enhanced the estimation performance by reducing the required data yet without the extreme P-Q range. Then, the nonlinear P-Q droop estimation is conducted with the Kalman-filter algorithm. The performance is verified by applying it to the real distribution power system of an island in Korea. For the verification, the distribution power system is modeled at the EMT level and simulated using the power system computer-aided design and electromagnetic transient and DC (PSCAD/EMTDC $^{\mathrm {TM}}$ ). The voltage stability was improved by the proposed nonlinear P-Q droop estimation compared to the cases using fixed droop or Q of zero.

Microgrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers’ side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational mode causes protection and control challenges in the microgrids. This research paper suggests an advanced hardware-supported fault detection, phase identification & localization method for AC microgrids. The scheme deploys a Discrete Kalman Filter (DKF) for state estimation of voltage and current signals. Then, a Mathematical Morphology (MM) is engaged for generating a novel fault detection/classification index named segregated energy signature (SES) from estimated voltage and current signals. The system is considered to be faulty if the SES is higher than a predefined threshold setting, while phase identification is achieved by default because of the per-phase implementation of DKF&MM. Moreover, the directional features of the cumulative energy signature (CES) are also computed from MM-based non-fundamental current and voltage to localize the faulty section. The established scheme is tested on the CIGRE microgrid benchmark test bed on Matlab-Simulink software. In addition, the suggested method is also examined on the dSPACE MicroLab testing hardware setup in the Smart grid lab at USPCAS-E, NUST, Pakistan. The result illustrates that the proposed scheme successfully detects, classifies, and localizes the low impedance fault (LIF) as well as high impedance fault (HIF) in both operational modes and topological structures with 96.6% accuracy.

The excessive integration of renewable distributed generation (RDG) and electric vehicles (EVs) could be considered the two most problematic elements representing the greatest threat to the distribution network (DN) technical operation. In order to avoid going beyond technical limitations, the term hosting capacity (HC) was proposed to define the highest permitted amount of distributed generation (DG) or EVs that can be integrated safely into the DN. The connection of RDGs was first brought to the attention of researchers and DN operators since it accounts for the most notable portion of these technical issues. Hence, the phrase ‘DG-HC’ was initially proposed and evolved significantly over the last few years. Currently, EV integration in most DNs worldwide is still low, but given the worldwide support for clean transportation options, expectations are raised for a significant increase. As a result, it is anticipated that over the next years, the effect of EV integration on the DN will be highly noticeable, requiring greater attention from researchers and DN operators to define the accepted limits of EV penetration levels, ‘EV-HC,’ which is expected to pass along the same line of DG-HC. This article provides an in-depth review of both DG-HC and EV-HC. It first analyses how the DG-HC research has grown over the years and then studies the published EV-HC papers, illustrating to what extent there is a similarity between them and, finally, employs these analyses to expect future development in the EV-HC research area. This article includes the different uses of the term HC, the most common performance indices of DG-HC, the various methods for assessing DG-HC, the different techniques for DG-HC enhancement, the effects of integrating EVs on the DG-HC, and finally, calculating and enhancing methods for EV-HC.

In the past decade, there has been unprecedented growth in distributed generation (DG) to cater to more load demand in the power grid. However, as the volume of these resources in the distribution network (DN) continues to increase, new challenges in voltage regulation, system stability, and protection coordination have emerged. The system is substantially changed when multiple types of DGs are incorporated into DN. These include new fault condition sources, different fault stages, a blinding influence on the protection scheme, a decrease in the relays’ range, and a decline in the ability of existent relays to detect low-level fault currents. Due to the bidirectional current flow, this raises the fault current and, if improperly coordinated, causes the relays to trip unintentionally. Since fault current can flow in either direction (i.e., upstream or downstream), it is crucial that grid-mounted relays can detect it. This goal can be reached by including an optimal directional overcurrent relay (DOCR) coordination scheme in the system. In fact, DOCRs in interconnected power grids need to be coordinated effectively. This paper gives a comprehensive overview of the uses of various optimization strategies. The evaluation examines the benefits and drawbacks of the strategies used to address DOCR coordination problems. Additionally, this paper discusses future lines of inquiry for optimum DOCR coordination.

This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system’s PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).

Recently, numerical relays with communication capabilities have been employed to provide adaptive solutions for Distributed Generations (DGs) protection issues. This paper presents an adaptive protection scheme based on centralized and decentralized communication to address DG protection problems. The proposed method uses two directional relays in each feeder, one at the beginning (called forward relay) and one at the end (called reverse relay). These relays can communicate with each other and the central unit. The centralized communication occurs between the central unit and the relays in the system when any relay’s status changes from connected to disconnected or vice versa. It aims to define the operation mode, islanding detection, and activate the relays settings to achieve proper coordination. Protective relays are coordinated using a Genetic Algorithm (GA) to optimize protection time. The optimization problem considers pick up current (Ipu), Time Multiplier Setting (TMS), Plug Setting Multiplier (PSM), and curve type. Decentralized communication, which occurs when any relay detects a fault, increases relays’ accuracy and speed of decision-making. This paper also proposes a novel method for detecting High Impedance Faults (HIF), a significant difficulty for protecting engineers as it draws a very low current that conventional overcurrent relays fail to detect in most cases. HIF is detected using wavelet transform, symlet 8, detail 2 (d2), and relay communication. On the IEC benchmark microgrid, the suggested algorithm is tested. ETAP is used for load flow analysis and short circuit analysis, whereas MATLAB is used for GA optimization, HIF modelling, and wavelet transform routines.

Microgrids are relatively smaller but complete power systems. They incorporate the most innovative technologies in the energy sector, including distributed generation sources and power converters with modern control strategies. In the future smart grids, they will be an essential element in their architecture. Their potential to offer many economic, social and environmental services through advanced electrical techniques has led to a growing interest in the theme. Although the islanding condition is a very important feature of microgrids, only with the implementation of grid connection and seamless transition they will demonstrate their full capacity. However, there are still many questions surrounding these operation modes and this paper tries to answer part of them. To do that, several aspects in the field are approached. The history and late development of microgrids are revisited. The main concepts are presented. The islanded mode is revised, since it is intrinsically linked to the other working states of the microgrid. The requirements for the interconnection of microgrids to an external grid are discussed. The operation elements are also analyzed. A crucial part of the grid-connected microgrids and their seamless transfer conditions, the control methods found in the literature are extensively reviewed. The paper is concentrated in the analysis of control methods for AC microgrids and AC power systems, therefore, it does not enter in detail or investigates profoundly the topologies applied in the power electronics structures nor DC microgrids and DC power systems.

The Grid-Connected Droop-Controlled Distributed Generations (GCDCDGs) are widely used in power systems. However, their power flow is very sensitive to the Upstream Grid (UG) frequency and voltage magnitude fluctuations. This paper focuses on the power and current limiting of inverter-interfaced GCDCDGs under UG frequency and/or voltage magnitude drops. GCDCDG output power and current increase under the UG frequency drop, and if this increase exceeds the maximum of them, current limiters are saturated and according to $P\sim \omega $ droop characteristic the GCDCDG frequency does not track the UG frequency, and this frequency difference leads to power oscillation between DG and UG and the system becomes unstable. In this paper, a new strategy based on the droop-control method is proposed to limit the output power and current of GCDCDGs without using a current limiter that realizes a stable operation under the mentioned conditions. In the proposed method instead of increasing the droop coefficients to limit $P$ and $Q$ at their constraints, the droop curves move down after powers and currents exceed maximum values, using two supplementary control signals. The performance of the proposed method is demonstrated with simulation results using MATLAB/Simulink environment under several case studies.

With a large number of distributed generation connected to the distribution network, the total supply capacity of the distribution network has been improved to a certain extent. However, due to the randomness of its output, it also brings some challenges to the reliable operation of the distribution network and the restoration of power supply after failure. Based on this, this paper proposes an active distribution network reconfiguration strategy with uncertain factors for maximum total supply capacity improvement. Firstly, the evaluation index of the maximum total supply capacity of distribution network is constructed, and the maximum total supply capacity index under the current operation mode is evaluated online through the calculation model of maximum total supply capacity based on N-1 security criterion. Then, the uncertainty of wind power, photovoltaic and electric vehicle DG and the stochastic model of prosumer load are considered, random variables are generated by Latin hypercube sampling (LHS) and their correlation is considered. Then, it introduces that the position of sectionalizing switch and tie switch can be adjusted according to the dispatching demand. Based on the evaluation index of maximum total supply capacity and DG output of uncertain factors, the active reconfiguration model of distribution network after fault is proposed. Finally, a calculation example illustrates the effectiveness and correctness of the model in this paper. It is demonstrated that the model can optimize the selection strategy of Section switch and tie switch after the fault of distribution network, improve the utilization rate of distributed energy, and give full play to the power supply recovery potential of distribution network and reduce the outage time of prosumer groups.

In recent years, power systems have undergone changes in technology and definition of the associated stakeholders. With the increase in distributed renewable generation and small- to medium-sized consumers starting to actively participate on the supply side, a suitable incorporation of decentralized agents into the power system is required. A promising scheme to support this shift is given by local electricity markets. These provide an opportunity to extend the liberal wholesale markets for electrical power found in Europe and the United States to the communal level. Compared to these more established markets, local electricity markets, however, neither have few practical implementations nor standardized frameworks. In order to fill this research gap and classify the types of local electricity markets, the presented paper therefore starts with the challenges that these markets attempt to solve. This is then extended to an analysis of the theoretical and practical background with a focus on these derived challenges. The theoretical background is provided in the form of an introduction to state-of-the-art models and the associated literature, whereas the practical background is provided in form of a summary of ongoing and recent projects on local electricity markets. As a result, this paper presents a foundation for future research and projects attempting to approach the here presented challenges in distribution of generation, integration of demand response, decentralization of markets and legal and social issues via local electricity markets.

One of the general trends in power industry is the penetration of distributed generation (DG) units. The ongoing transformation of electric power systems (EPS) due to this penetration leads to a significant change in the properties of power systems. The problem arises in the ensuring stability of distribution system with DG units and EPS as a whole, especially in the case of large disturbances. The main way of solving this problem is positive-sequence phasor time-domain simulation. However, the known simplifications and limitations are inevitably applied at such simulation. In this regard, the obtained simulation results need to be validated. This paper proposes an alternative approach for validation of processes calculations, based on the use of a benchmark tool instead of field data. The hybrid simulator is proposed to use as a benchmark tool, because such simulator allow to obtain comprehensive information about a single spectrum of wave, electromagnetic, electromechanical processes in power system in cases of different large disturbances. That makes possible to identify the impact of the applied simplifications and limitations in the positive-sequence simulation on the reliability of power system stability assessment in the case of large disturbances. The studies presented in this paper were carried out using the scheme of distribution system with DG units, which is part of a real EPS. The obtained results demonstrate the arising errors in stability calculations, the nature of their changing and causes of occurrence, as well as factors affecting them.

With the increase of distributed generation penetration, the economic operation of microgrid has been fully developed. For islanded microgrid, due to the lack of large grid support and the limited capacity of the distributed generation (DG), the energy supply of microgrid may not meet the need of users in real time. Therefore, it is of great significance to solve the problem of insufficient power supply of islanded microgrid and improve the economy of its operation. Firstly, this paper analyses the frequency characteristics of DG, and establishes power models oriented to the frequency regulation characteristics of microgrid and introduces the mathematical model of DG. Then, an islanded microgrid day-ahead optimization scheduling model considering frequency regulation characteristics of units and demand response (DR) is proposed. The frequency characteristics of controllable units and DR are considered in the model, and different strategies are selected to analyze their impact on microgrid economy. The example shows that the optimization scheduling model of microgrid established in this paper can reduce the power supply pressure during peak load periods, alleviate the problem of insufficient power supply, and effectively improve the overall benefit of microgrid, which verifies the rationality of the model.

This article proposes a new approach to the operation of unbalanced Active Distribution Systems (ADS) using an economic dispatch optimisation model for Active Distribution Systems Management (ADSM). The model proposes a two-level control strategy. The first one poses an optimisation problem with the objective of minimising total active power losses in the ADS and the second one proposes an algorithm that controls the position of the taps of three-phase on-load tap-changer (OLTC) transformers to ensure compliance with the technical constraints imposed by the Distribution System Operator (DSO). The optimisation problem is solved by MATLABR and DIgSILENT PowerFactoryR for power systems static simulations. This paper includes a novel peer to peer communication framework between MATLABR/DIgSILENTR. The control and optimisation strategy is validated on the IEEE 34-Node Distribution Test Feeder. This network incorporates balanced and unbalanced three-phase loads, single-phase loads in the different phases, and two-phase loads. In this scientific paper, photovoltaic (PV) and wind power generation (WT) have been integrated to test feeder operation, with the support of battery energy storage systems (BESS). The correct operation of the proposed ADSM is demonstrated using numerical simulation on five scenarios considering several configurations of the renewable generation units and the batteries. The strategy has also been validated in a more extensive distribution network, proving its good performance.

Microgrids are considered an adequate alternative to overcome the challenges involving integrating distributed energy resources in distribution systems to contribute to the ‘Three D’ trend in the electricity sector, i.e., decentralize, decarbonize, and digitize electricity. This paper reviews the most relevant works to establish a baseline for advancing and developing smart transformer-based meshed hybrid microgrids and energy management systems. First, the structure of the solid-state transformers as smart transformers and their potential application as energy routers in a microgrid is discussed. Then, the principle of conformation of meshed hybrid microgrids based on a smart transformer and the topologies reported in the literature are reviewed. Finally, power management systems integrated into smart transformers-based meshed hybrid microgrids are studied. According to the findings and conclusions, smart transformers-based meshed hybrid microgrids operated by an optimal energy management system under uncertainty are a potentially feasible technological alternative for adequately penetrating distributed energy resources into local distribution systems.

A large number of DGs(distributed generators) in the ADN (active distribution network) bring great uncertainty to the power systems, making it difficult to quantify fault characteristics. Undoubtedly, the setting of traditional protection becomes more complex and difficult. Therefore, the differential protection is an effective solution to cope with it. However, the differential protection requires high time synchronization, which is difficult to be satisfied based on the current communication conditions of ADNs. What’s more, accurate extraction of fundamental frequency components is also very difficult considering the distortion, the frequency offset, and the instability of fault currents. In view of this, a new differential protection scheme that makes full use of fault information is proposed in this paper. First, the pre-fault current waveforms are taken as reference. Then the similarity characteristics of the current waveform sequences at both terminals of the line are calculated separately. Finally, a new differential protection scheme considering weak synchronization conditions are constructed using Hausdorff distance, which is based on wide frequency components. The proposed scheme only transmits calculation results instead of sampling values, which effectively avoids the impact caused by data synchronization errors. Simulation shows that the proposed scheme can reliably identify internal faults in various points of the protected line and external faults. Compared with the conventional differential protections, it also shows great resistance to the transition resistance up to $30\Omega $ , which is common in the ADN. Moreover, it has strong resistance to time synchronization errors, which can withstand time synchronization errors of up to 20ms. The proposed protection scheme has excellent engineering application value, especially suitable for distribution networks with weak communication conditions.

The article describes the new application of a well-known protection criterion - Vector Shift (VS). In the paper, technical problems related to the utilization of this criterion have been described. Additionally, the authors conducted laboratory tests of a digital protection relay. The impact of different disturbances occurring in the power systems on the operation of the Vector Shift criterion has been examined. The obtained results have proved the validity of banning the Vector Shift criterion in some countries. Furthermore, a novel application of the Vector Shift criterion has been presented. This criterion could be utilized to determine the degree of balance of a separated island. This information may be helpful in novel power system protection devices used in Smart Grids or microgrids.

This paper proposes a dynamic programming (DP)-based operational strategy for a biofuel distributed generation system with hybrid energy storage (BDG-HES). The proposed system consists of a biofuel-powered engine and a gen-set, six battery packs, a super-capacitor module, and four power electronic converters. It is primarily designed to meet the electrical demand of a typical UK family while the energy storage system can reduce the operating duration of the engine so as to improve the fuel efficiency. The DP, as an optimization strategy, is used to determine the operational scheme for the BDG-HES. In this paper, four case studies are carried out based on real-world measurement data. The test results have confirmed the effectiveness of the proposed methodology. The overall system efficiency is improved by approximately 19% over the operational range compared to a conventional gen-set based power system.

Batteries are essential for efficiently utilizing the energy from the photovoltaic (PV) modules. However, integration of batteries with PV plants at large scale needs more attention in terms of size, location, and times for the charging and discharging of the batteries. This paper addresses these aspects. It presents a mixed integer optimization using genetic algorithm for determining the optimum size and placement of battery-sourced distributed PV generation (B-SDPVG) in distribution networks. The total energy loss index is formulated as the main objective function and simultaneously, the bus voltage deviations and penetrations of the B-SDPVG are calculated. The yields from the PV plants are estimated using 15 years of weather data modeled with the aid of beta probability density function. Furthermore, a novel charge-discharge control model is developed for determining the choice of the charging and discharging of batteries at each hour. By considering different time varying voltage-dependent load models, the proposed algorithm is applied on the IEEE 33 bus and the IEEE 69 bus test distribution networks. The numerical results of two distribution networks with time-varying loads show the advantages of the proposed methodology. It was revealed that integration of battery storage and intelligent scheduling for charging and discharging of batteries produced much better results and improved the quality of distribution networks. The supply of power from the B-SDPVG during the peak load hours and at night was made possible for each load model. The proposed charge-discharge control model for scheduling the charging and discharging of the batteries was found dynamic. Results of this paper can be of potential importance in planning for the integration of battery storage in distribution networks.

With the increasing use of distributed generations and distribution automation technologies in smart grid, the distribution system operator can alleviate the loss-of-load and improve the reliability of supply by forming intentional islands when emergency situations occur. Considering that the successful operation of the intentional island needs allocation of new circuit breakers (CBs), this paper proposes an integrated optimization method for simultaneous allocation of CBs and distributed energy resources (PV and battery energy storage, DERs) in distribution networks. The proposed approach is established on a multi-objective optimization framework, which determines the positions and sizes of CBs and DERs in order to minimize the total cost and the outage risk of the system, while subject to the voltage stability constraint. As distinct from the previous works, the loss-of-load risk and the effects of the intentional islands on system reliability are especially considered in our paper. To achieve this, an efficient technique to calculate the system risk index that converges with limited computation efforts is developed. The proposed model is solved by using the multi-objective evolutionary optimization algorithm-NSGA-II. Simulations based on RBTS-BUS6 verify the effectiveness of the proposed model and methods. Furthermore, the impacts of CB positions and load characteristics on the planning results are also analyzed.

This paper proposes a practical modelling solution to the problem of sharing distributed renewable electricity generation in the context of renewable energy communities. According to this approach, the economic benefits of community members, derived from their participation in the community, are shared by means of repartition keys, which represent the proportion of total local electricity to be allocated, ex-post, to each community member. These keys are computed through a centralised optimisation framework that optimally allocates the electricity generated within the community (i.e., local electricity) among the community members so as to minimise the sum of the electricity bills of all community members. The electricity bill sent to each community member can be directly extracted from this solution. Building on this concept, we also introduce two additions to the basic algorithm, aiming to enhance the stability of the community, which a global bill minimisation may fail to ensure (e.g., very asymmetrical solutions between members may lead to some of them opting out). The first addition is the computation of the self-sufficiency rates of the community members, defined as the proportion of the electricity demand covered by local electricity, which can be exploited as a revenue sharing mechanism. The second addition is the use of initial repartition keys based on which the optimised ones are computed, so as to ensure a minimum contractual level of revenue for each of them. We have tested our methodology with a broad range of scenarios illustrating its ability to reduce the sum of the electricity bills of the community members and to share the revenues ensuring the stability of the renewable energy community. Our results show that creating a community using this methodology can potentially reduce the electricity costs for all community members, and that self-sufficiency rates and initial keys can be used to stabilise the community by performing revenue sharing among them.

Appropriate installation of renewable energy-based distributed generation units (RDGs) is one of the most important challenges and current topics of interest in the optimal functioning of modern power networks. Due to the intermittent nature of renewable energy sources, optimal allocation and sizing of RDGs, particularly photovoltaic (PV) and wind turbine (WT), remains a critical task. Based on a new metaheuristic known as the Artificial hummingbird algorithm (AHA), this paper provides a novel approach for addressing the problem of RDG planning optimization. Considering various operational constraints, the optimization problem is developed with multiple objectives including power loss reduction, voltage stability margin (VSM) enhancement, voltage deviation minimization, and yearly economic savings. Furthermore, using relevant probability distribution functions, the ambiguities related with the stochastic nature of PV and WT output powers are evaluated. The proposed algorithm was compared to two of the recent metaheuristics applied in this domain known as improved harris hawks and particle swarm optimization algorithm (HHO-PSO) and hybrid of phasor particle swarm and gravitational search algorithm (PPSOGSA). The IEEE 33-bus and 69-bus systems are assessed as the test systems in this study. According to the findings, AHA delivers superior solutions and enhances the techno-economic benefits of distribution systems in all the scenarios evaluated.

This paper aims to present a centralized adaptive protection scheme (APS) for the directional overcurrent relays (DOCRs) considering system dynamics such as load demand variations, topology changes and DGs to mitigate the above-mentioned effects. The scheme is suitable for different types of networks whether its radial or meshed. Afterall, it can determine the actual topology with the micro-PMUs and breaker status, automatically generate relay names accordingly with the topology bus locations, establish protection coordination pairs, coordinate DOCRs for parallel lines, identify transformers to offer backup operation for differential protection, perform load flow and fault studies, execute contingency and sensitivity analysis, and lastly, coordinate the DOCRs using differential evolution algorithm so that they always have the proper settings to operate in appropriate time providing selectivity. The performance of the proposed centralized adaptive overcurrent protection scheme is tested on the highly meshed IEEE 14 bus system. The proposed scheme has shown that it can adequately consider impacts of system dynamics and DG.

The regulation of Distributed Energy Resources (DERs) has cast doubt on the sustainability of utility infrastructure charging models. The development of feed-in tariffs, net-metering, and network charge rebates for distributed generation (DG) has been questioned because of the cross-subsidies that result between passive consumers and DG investors. Besides DG investors, other new entrants, such as owners of energy storage resources and electrical vehicles, are creating challenges for the regulation of distribution service pricing as a whole. This paper proposes a new approach to dealing with the fixed cost element of service pricing, that enhances economic signaling in the distribution network. We isolate and address the issue from a distribution network point of view, by excluding from our model the random variables associated with the complexity of social, environmental and other externalities. The solution developed is particularly relevant at a time when distribution networks, historically dominated by passive users, struggle to adapt to a dramatic increase in the number of users who are active agents. It considers the principles governing tariff design from the perspective of simplicity, economic signaling, and revenue reconciliation. Results are presented of simulations performed with different arrangements of alternative energy generators and energy storage devices, using an actual feeder from a distribution company in Brazil. An analysis of these results is provided that suggests a combination of locational and time-of-use rates can provide effective economic signals to these new types of system user.

This paper describes the novel application of conditional Generative Adversarial Networks (cGANs) for real-time stability region determination (SRD) in power systems. As the network configuration changes during the course of operation, the availability of the stability region would enable the operator to suitably tune the control parameters to maintain stability while maximizing the dynamic performance. Here, the implementation of the cGANs-based SRD is described using transmission and microgrid case studies, where it is demonstrated to adaptively estimate the stability region for different network configurations with high accuracy. It is also shown that the cGANs approach has a significantly shorter execution time as compared to the conventional model-based method, demonstrating its value for real-time use in practical power systems.

The nature and characteristics of distribution systems are changing continuously with the increased penetration of distributed energy resources. With this, the need for examining the transmission and distribution (T&D) interaction while assessing the voltage security has become significant. In much of the analysis and practices reported so far, voltage security assessment studies are performed separately for T&D systems. Thus, a transmission system operator (TSO) is oblivious to the operations and controls taking place at the distribution system (DS) level. Some of the recent studies examine the impact of an active DS on the voltage security assessment of future power systems. However, the effect of various factors like voltage dependency of loads, reactive power capability of renewable energy sources (RES), location of distributed generation units, and network losses on the voltage stability margin (VSM) of transmission system has not been reported comprehensively so far. This paper first theoretically examines the impact of these factors on VSM. Then, to get the more realistic results for VSM, continuation power flow (CPF) is performed in a coordinated fashion including both T&D systems. The simulations show that considering losses, load characteristics dependency on voltage and reactive power limits of RES in a DS for transmission voltage security assessment studies lead to a more rational outcome than when neglected.

This paper proposes a novel approach of voltage stability enhancement and power loss minimization in addition to maintenance of good voltage profile in radial distribution networks through optimally placed distributed generation, network reconfiguration and voltage control of PQV bus through variable reactive power source at P bus. A multi-objective function has been proposed that considers maximum system loadability enhancement and network loss minimization. Optimization of proposed multi-objective function, under distributed generation and network reconfiguration with presence of PQV and P buses in the system have been done using grey wolf optimization technique. Case studies performed on IEEE 33-bus radial distribution system shows that presence of PQV and P buses in the system yields significant enhancement in voltage stability margin under optimal placement of distributed generations and network reconfiguration.

The concept of distributed generators (DG) and their control has been evolved as a key area of research, to ensure the sustainability of a microgrid (MG). Design and implementation of a proactive cascaded control strategy is an effective method to make the MG more sustainable and resilient towards uncertainties. In this paper, a novel cascaded control strategy consisting of two degree of freedom (2DOF) PI and an internal model controller (IMC) is proposed to effectively control the grid interactive converter (GIC). The proposed control strategy exploits the combined benefits of 2DOF-PI and IMC in improving the steady-state and dynamic performance of the GIC. The primary function of the proposed control strategy is to effectively control the GIC to facilitate smooth power flow between MG and utility. In addition to this, the proposed control strategy enables the GIC to offer different ancillary services that include unbalanced load current compensation, reactive power compensation, and harmonic current reduction. To achieve this multi-functional feature, appropriate reference currents are extracted, and a control strategy is implemented in a synchronous reference (dq0) frame. An evaporation rate-based water cycle algorithm (ERWCA) optimization technique is employed to estimate the optimal design parameters of the cascaded controller. The closed-loop stability of the system is verified using frequency and time domain analyses for the estimated optimal design parameters. To show the effectiveness of the proposed control strategy, various case studies are considered, and results are compared with existing methods.

The article presents the method of remote micro-grid synchronization with the utility grid in the presence of voltage disturbances. The proposed algorithm and control devices synchronize different power systems on the basis of the instantaneous voltage phase angle in spite of voltage quality problems and without need of access to point of common coupling. In the presence of grid voltage disturbances, instantaneous grid voltage phase angle is estimated and transmitted remotely with a certain accuracy, which is varies in presence of disturbances of grid voltages or depends on the synchronization algorithm and data transmission rate. The paper focuses on the influence of voltage disturbances on the proposed remote synchronization strategy and the shape of the output current of the synchronized power converter on the micro-grid. The four types of grid voltage disturbances are presented: harmonics, one-phase sag, two-phase sag, and phase voltage jump. The final conclusions present the possibility of using the proposed remote synchronizations algorithm in micro-grid power converter applications in the presence of typical utility grid voltage disturbances.

Nowadays, distributed generation (DG) has made it possible to generate electricity close to the consumption site, resulting in improved efficiency, less environmental pollution, and higher economic profit. These advantages have led to increased penetration of DGs in the distribution system. Protective devices in a distribution system are set by considering the main substation as the only source for feeding short circuit current. However, with the increased influence of DGs as the second main source of short circuit current in distribution systems the short circuit level changes, which leads to false tripping of protective devices, including overcurrent relays. A sympathetic trip, occurring due to a fault in the adjacent feeder, is one of the most serious challenges. This paper analyzes the sympathetic trip in the presence of synchronous based DGs. The equations related to the participation of DGs and upstream network in feeding the short circuit current are obtained. The effect of different parameters on the probability of occurrence of a sympathetic trip is also investigated. Moreover, a novel fast solution is presented for overcoming the sympathetic trip of synchronous based DGs. The proposed method is introduced using the positive-sequence currents of the DGs and main substation. The sympathetic trip is predicted by adopting this prediction index and its occurrence is avoided. The proposed methodology is independent of telecommunication platforms and additional protective devices and can be applied to various short circuits. The method is tested on a network by simulating in DIgSILENT PowerFactory software. Simulation results show the effectiveness of the proposed methodology in predicting and preventing sympathetic trips.

Distribution networks are facing new challenges with the emergence of smart grids, such as capacity limitations, voltage instability, and many others. These challenges can potentially lead to brownouts and blackouts. This paper presents an innovative technique for optimal siting and sizing of distributed generators (DGs) in radial distribution networks (RDNs). The proposed technique uses a novel algorithm that combines improved grey wolf optimization with particle swarm optimization (I-GWOPSO) by incorporating dimension learning-based hunting (DLH). The proposed I-GWOPSO employs a novel aspect of DLH to reduce the gap between local and global searches to maintain a balance. The main optimization objectives aim to optimally site and size the DG with minimization of active power loss, voltage deviation, and improvement of voltage stability in RDNs. Case studies are simulated with IEEE 33-bus and IEEE 69-bus test systems, for the optimal allocation of DG units by considering various power factors. The results validate the efficacy of the proposed algorithm with a significant reduction in real power loss (up to 98.1%), improvement in voltage profile, and optimal reduced cost of DG operation with optimal sizing across all considered cases. A comparative analysis of the proposed approach with existing literature validates the improved performance of the proposed algorithm.

The traditional distributed generation (DG) locating and sizing method is based on a fixed network structure, which is assumed to be unchangeable during the planning period. The flexible distribution network structure is not considered. This paper proposes a novel method for DG locating and sizing in active distribution network (ADN), which mixes DG locating and sizing problem and distribution network reconfiguration problem together. The objective is to minimize the total costs of the distribution network in the planning period. DG locating and sizing considering network reconfiguration is a complex mixed-integer non-linear programming problem. This paper convert the dual-level planning model to the second-order cone programming model, which can be solved with commercial solver GUROBI. The proposed model and method are testified with the modified IEEE 33-bus system.

The optimal integration of electrical units, such as distributed generation units, power electronic devices, and electric vehicles, is a significant development of smart grids. This development has effectively transformed the traditional grid system, promising numerous advantages for economic values and autonomous energy source control. In smart grids development, metaheuristic algorithms are one of the optimization algorithms that have been applied extensively to mitigate the accompanying problems such as voltage instability, power loss, and high installation cost. This paper presents a comprehensive review of metaheuristic techniques for the optimal integration of electrical units in distribution networks, considering different phases of the optimization process. These include the approaches for handling of crucial objective functions and the optimal integration methods for different electrical units. This review shows a need for more research on developing efficient metaheuristic algorithms and the effective handling of multiple objective functions.

This paper proposes a new modified architecture for AC microgrid consisting of multiple grid-supporting master units (MUs) and multiple grid-feeding slave units (SUs). In this study, a coordinated four-layer hierarchal control (HC) approach is applied to the proposed structure for allowing the MUs, SUs and loads to be easily integrated as a microgrid and operated in both grid-integrated and standalone operation mode. The proposed structure of the AC microgrid enhances the system redundancy to prevent the single point of failure of MU and has more stability, efficiency, flexibility and reliability than the conventional structures. Furthermore, optimal design guidelines, based on a new hybrid Harries hawks and particle swarm optimization algorithm (H-HHOPSO) with the cooperation of different types of proposed multi-objective functions, are presented to fulfill the study objectives. The optimization constraints/objectives are employed for optimal parameters selection of HC controllers to improve the power quality, enhance dynamic and steady-state performance and guarantee a seamless transition between operation modes. To accomplish this work, the newly modified structure is modeled, constructed in MATLAB/SIMULINK and tested under the variations of generations and loads. This structure is also examined when the fault occurs at any one of the MUs and during the connecting and disconnecting of utility grid. This testing is to verify its flexibility and reliability, and confirm the effectiveness and robustness of the proposed optimal controllers. Additionally, the experimental work is carried out using the hardware-in-the-loop real-time emulation to prove the optimal controllers' feasibility. Finally, the experimental and simulation results are compared.

Distributed Generation (DG) based on Renewable Energy Sources (RES) are considered as an effective and economical technology for the advancement of an Electric Power System (EPS) to fulfill the load demand. Mostly, studies pertaining to DG planning are performed while considering constant load demand and DG generation. However, these considerations may provide misleading and inconsistent values for loss reduction, voltage profile, power quality, and other operational parameters. Therefore, this paper proposes a novel framework to determine the impact of different Time Varying Voltage Dependent (TVVD) load models on wind DG planning study. Firstly, wind DG optimal allocation is performed using Salp Swarm Algorithm (SSA) for different TVVD load models. Afterwards, impact of different TVVD load models on wind DG planning is investigated. Comparative evaluation of various impact indices, real and reactive power (losses and intakes), penetration level, and apparent power support provided due to integration of wind DG are discussed for various TVVD loads. The analysis of results indicates that TVVD loads have a significant impact on performance of distribution system and DG planning studies.

The on-load tap changer is critical for voltage regulation, however, its voltage regulation philosophy is suitable for one-directional power flow power systems. Since distributed generators introduce bidirectional power flow, the conventional operation of the on-load tap changer will be highly impacted, resulting in high voltage magnitudes that exceed acceptable limits, frequent voltage magnitude fluctuations, and the increase in overall reactive power supply when distributed generators start absorbing/injecting reactive power. Therefore, an adaptive control system is proposed on this paper that alters the existing voltage regulation philosophy of the on-load tap changer and also coordinate the voltage regulation capabilities of distributed generators with those of the on-load tap changer for optimal overall voltage regulation and the reduction of reactive power that flows through the power system, enhancing the power factor thereof. The proposed control system will change the centralized, and single-variable busbar voltage monitoring technique of the conventional philosophy with a decentralized, and multi-variable strategy that controls voltage based on the average voltage of the entire distribution network. The control system will calculate the on-load tap changer and distributed generators setpoints based on the overall average network voltage, average voltage deviation from nominal voltage, generators points of connection voltage and the total reactive power that flows through the power transformer. The control system was tested on a 22kV network modelled in MATLAB SIMULINK and the results attained revealed that the proposed multi-stage control system can successfully control voltage and simultaneously improve power factor.

To enable more wide-scale exploitation of renewable energy sources (RESs), distributed generations (DGs) as in microgrids have gained prominence recently. This study describes the design, modeling, implementation, and operation of a microgrid, in which a standalone hybrid power system has been installed for an education and research laboratory. The unstable nature of RESs results in power compatibility issues on DGs. Therefore, providing a decent power flow from renewables to loads is the scope of this paper. To satisfy defined load profiles and sustain the power with the desired level, the design and operation of power converters are a remarkable part of performing microgrids as well. For a robust microgrid structure, the presented control algorithm includes an energy management system (EMS) between renewables, batteries, and loads. Primarily, modeling of the system has been developed in MATLAB/Simulink environment. Additionally, case studies have been exemplified to further demonstrate the simulated system. Within this scope, certain load profiles not only have been fed but also power flow has been managed and analyzed to ensure effective and flexible operation with two different EMS cases. The real-time operation has been also provided to validate the system under various input and output conditions during a lab course.

Power flow (PF) analysis of microgrids (MGs) has been gaining a lot of attention due to the evolution of islanded MGs. To calculate islanded MGs’ PF solution, a globally convergent technique is proposed using Differential Evolution (DE)- a popular optimization algorithm for global non-convex optimization. This paper formulates the PF problem as a constrained optimization problem (COP) considering all the operating conditions of the Droop Controlled Islanded MGs (DCIMGs). To solve the proposed COP, $\epsilon $ DE-NGM, (Epsilon based Differential Evolution with Newton-Gauss-based mutation) is proposed. The proposed algorithm, $\epsilon $ DE-NGM, is a novel variant of DE since it comprises a novel mutation operator, Newton-Gauss-based mutation (NGM). NGM includes all the important features of DE’s mutation strategies as well as reduces the constraint violation by utilizing the information of constraint-space. Numerical experiments validate that the global convergence ability of proposed algorithms in solving COPs than existing state-of-the-art algorithms. Furthermore, the proposed algorithm as a PF tool has better robustness than existing tools on ill- and well-conditioned systems with heavy loads, different limit violations, and inappropriate final solutions (far from the flat start). The performed comparative analysis confirms good agreement of accuracy and efficacy with the existing method for islanded MG’s PF.

Power grid pattern is expected to evolve from generator-based power systems towards converter-based systems in the forthcoming decades. Therefore, grid-forming converters will be pertinent to interconnected power grids in pursuance of enhancement their resilience against disturbances. This paper introduces a new efficient damping control method for grid-forming converters that provides a smooth power modulation and an efficient damping response against frequency and voltage deviations. First, an averaged state-space representation for a grid forming application in dq synchronization frame is derived. Based on this model, a new hybrid damping controller, including the concept of state feedback control and PI control, is proposed to address the main issues in existing controllers. The state feedback controller is optimally designed using a linear-quadratic regulator (LQR) approach to optimize the system performance. Moreover, the PI controller is optimally designed using the pattern search algorithm. The proposed damping control method integrates optimally between the control loops through a mapping matrix to rapidly synchronize with the grid and efficiently damp the oscillations. Simulations are carried out to prove the proposed method robustness. Finally, a comparative study using controller hardware-in-the-loop (CHiL) is employed against conventional system to validate the proposed damping method.

Permanent magnet generator (PMG)-based wind energy conversion systems (WECSs) with battery units, have become a popular class of distributed generation units. These distributed generation units are typically operated using various types of controllers, including droop controllers. Existing droop controllers are designed to operate grid-side dc-ac power electronic converters (PEC) to ensure stable and reliable power production by a PMG-based WECS. The employment of battery storage units (to mitigate fluctuations in the power produced by a PMG-based WECS) introduces additional considerations for the design of droop controllers. Such considerations are due to the power available from battery units that is dependent on the state-of-charge (SOC). This paper proposes adjustments in the parameters (droop constants) of the droop control (operate the the discharge PEC) based on the SOC of the battery units. These adjustments are made to further support stable and reliable power delivery of the PMG-based WECS into the point-of-common-coupling (PCC). The proposed adjustments of droop constants are evaluated using a 7.5 kW grid-connected PMG-based WECS with 3.52 kW generator-charged battery storage units. Performance tests are carried out for step changes in the active and reactive power demands, changes in the wind speed, and grid-side disturbances. Test results show that the proposed correction of the droop constants is critical for maintaining a stable, effective, and accurate power delivery by the battery units, thus supporting the voltage/frequency stability at the PCC under different operating conditions.

Recently, renewable energy has received considerable attention worldwide due to environmental problems such as climate change. However, there are still many problems to be solved to increase the penetration level of renewable energy sources in a power system. Above all, a power system with a high renewable penetration level requires an entirely novel operational strategy and control approach to achieve reliable operation by considering the intermittency of variable renewable energy sources. This paper proposes power-sensitivities-based indirect voltage control of renewable energy generators with power constraints. The proposed method realizes seamless control of the voltage by compensating with the reactive power if the real power of the distributed generator (DG) is insufficient due to its intermittency. To achieve the proposed method, the real and reactive power references of DGs are initially operated based on the power sensitivities between generation and loads if all the DGs operate within the power constraints. However, if at least one of the DGs reaches its constraint limit, the references can be modified using other sensitivities between real and reactive power generation enabling indirect voltage control of DGs. Therefore, a high level of renewable penetration in power systems can be accomplished with the proposed method. The proposed method is verified with several case studies that are based on a microgrid with practical data from a real island power system. Verification is carried out using the power system computer aided design and electromagnetic transient including DC (PSCAD/EMTDC™) simulation software.

With the increasing penetration of distributed generations, preserving the protection system coordination is of great importance for the distribution network operation. This paper presents a supplementary protection strategy for the multi-agent system (MAS)-based hierarchical protection scheme (HPS). The primary level of MAS-based HPS clears the fault and preserves the protection coordination while the higher levels update the relay settings. The key advantage of the proposed supplementary scheme is that the primary level can autonomously and quickly preserve the protection coordination when the main relay is during the time-consuming process of updating settings in the case of network operating condition change. By adopting converter-based DGs as the only MAS agents, the proposed supplementary scheme divides the distribution network into several zones that communicate together using point-to-point communication. Without communicating with higher protection levels, the supplementary protection limits the influence of DGs on the fault current by controlling the produced power of each zone. The effectiveness of the proposed strategy is validated through several simulation case studies on the Isfahan distribution network.

In power systems modelling, optimization methods based on certain objective function(s) are widely used to provide solutions for decision makers. For complex high-dimensional problems, such as network hosting capacity evaluation of intermittent renewables, simplifications are often used which can lead to the ‘optimal’ solution being suboptimal or nonoptimal. Even where the optimization problem is resolved, it would still be valuable to introduce some diversity to the solution for long-term planning purposes. This paper introduces a general framework for solving optimization for power systems that treats an optimization problem as a history match problem which is resolved via statistical emulation and uncertainty quantification. Emulation constructs fast statistical approximations to the complex computer simulation model in order to identify appropriate choices of candidate solutions for given objective function(s). Uncertainty quantification is adopted to capture multiple sources of uncertainty attached to each candidate solution and is conducted via Bayes linear analysis. It is demonstrated through a hosting capacity case study involving variable wind generation and active network management. The proposed method effectively identified not only the maximum connectable capacities but also a diverse set of near-optimal solutions, and so provided flexible guides for using the existing network to maximize the benefits of renewable generation.

Distribution systems play an important role, delivering the electric power of generation system to individual consumers. Distribution system reconfiguration (DSR) is a large-scale combinatorial optimization problem. For the last 45 years, the DSR problem has been widely studied; nowadays, DSR, combined with new challenges, is being highly investigated, as researchers aim to reach a better solution. This paper presents a complete review and classification of the most significant works to date, providing a literary framework for DSR specialists. A categorization of solution methods, case studies, and novelties of the most relevant works regarding DSR are provided. In order to establish a complete background, not only traditional approaches, but also those involving uncertainty, reliability, electricity market, power quality, distributed generation, capacitor placement, and switching time in DSR are highlighted. This framework can help researches to improve previous formulations and methods and can propose more efficient models to better exploit the existing infrastructure.

The use of distributed generation has received wide attention due to low maintenance costs, reduced transmission line losses and network congestion, as well as minimal impact on climate change and global warming. However, the distributed generation integrated to the distribution network introduces various protection problems that cannot be solved by conventional protection systems. These obstacles include the bi-directional power flow and the variation in the fault current level during the topology change. Thus, appropriate fault detection and protection scheme are strongly recommended to increase safety and reliability of the distributed network. This paper presents a comprehensive overview of the distributed network fault detection and protection strategies incorporated with distributed generation. This review also investigates the various fault detection approaches concerning types, communication methods, operation mode, constraints and benefits. Additionally, numerous island detection techniques are explored, focusing on generation types, parameters, cost and advantages. Moreover, the review outlines the various protection schemes highlighting categories, operation, constraints, strength and shortcomings. The key issues and challenges are discussed along with selective proposals for future research. All the highlighted viewpoints of this research will hopefully be beneficial to power system engineers and researchers for the advancement of distributed network fault and protection strategies for suitable operation and management of future distributed generation systems.

This paper presents an Enhanced Marine Predators Algorithm (EMPA) for simultaneous optimal distribution system reconfigurations (DSRs) and distributed generations (DGs) addition. The proposed EMPA recognizes the changes opportunity in environmental and climatic conditions. The EMPA handles a multi-objective model to minimize the power losses and enhance the voltage stability index (VSI) at different loading levels. The proposed EMPA is performed on IEEE 33-bus and large-scale 137-bus distribution systems (DSs) where three distinct loading conditions are beheld through light, nominal and heavy levels. For the 33-bus DS, the proposed EMPA successfully reduces the cumulative losses by 72.4% compared to 70.36% for MPA for the three-loading levels simultaneously. As a result, significant voltage improvement is achieved for heavy, nominal and light loadings to be 95.05, 97, 98.3%, respectively. For the 137-bus DS, it successfully minimizes the losses of 81.16% under small standard deviation 4.76%. Also, the 83-bus test system is considered for fair comparative between the proposed and previous techniques. The simulation outputs revealed significant improvements in the standard MPA and demonstrated the superiority and effectiveness of the proposed EMPA compared to other reported results by recent algorithms for DSRs associated with DGs integration.