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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.

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.

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.

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.

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.

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 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.