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• Dynamically reconfiguring the structure of multi-microgrid systems to enhance the penetration of distributed energy resources. • Vehicle-to-grid concept is deployed to capture the intermittency of renewable resources and load demand. • To make the model more accurate, the uncertainty of loads and renewables is handled by using stochastic programming. • A mathematical-based convex optimization model is established to reap the benefits of CPLEX and GUROBI as solvers. • Results illustrate that both technical and economic aspects of multi-microgrid system improved significantly. On the one hand, the inherent intermittency in demands and renewable energy sources (RES) frequently bring challenges such as overload or surplus generation within microgrids. On the other hand, electric vehicle aggregations (EVAs) have garnered substantial attention as a pivotal strategy to address climate change and serve as a sustainable substitute for petroleum-based vehicles. However, the uncoordinated deployment of EVAs within microgrids, especially in the face of the intermittent nature of RES, poses a potential threat to the secure operation of microgrid systems. To tackle the mentioned issues, this research concentrates on interconnecting a group of scattered microgrids to create a multi-microgrid system. In more detail, by developing an energy management strategy to reconfigure the interconnections among microgrids, the efficient exchange of power among these multi-microgrid systems is facilitated, addressing the variability in load demands amidst the stochastic generation patterns of RESs. Besides, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) concepts of EVAs are synchronized within the reconfigurable microgrid structure to enhance the flexibility of the model. To evaluate the model under realistic situations, a scenario-based method is also employed to reflect the effects of uncertainties on the model. The proposed approach, characterized by its mathematical convexity, allows for employing efficient solvers like CPLEX, ensuring the attainment of a feasible global solution within a finite timeframe. The effectiveness of the proposed method is demonstrated through its implementation on a modified 33-bus test system operated as multi-microgrid system. The results show the effectiveness of the proposed approach as a promising tool for optimizing the operation of reconfigurable multi-microgrid systems in the presence of EVAs, leading to operational cost reduction and voltage profile enhancement. [ABSTRACT FROM AUTHOR]

Distribution network reconfiguration is one of the well-known and effective strategies in the distribution networks which performs by the status management of the network switches in order to obtain a new optimal configuration for the feeders. This study formulates multi-objective distribution feeder reconfiguration along with optimal sizing of distributed generators and capacitors. The prevalent objective functions in the network reconfiguration studies comprise of power loss and voltage deviations that are considered as the main objectives for traditional distribution systems, however, less attention has been paid to the objective functions of reliability and network voltage security in the previous literatures. Therefore, the main objective of this study is to improve the reliability and network voltage security by solving the distribution network reconfiguration problem. To this end, the energy not supplied and voltage stability index are defined as the objective functions of reliability and voltage security. A modified gravitational search algorithm is suggested to solve the complex and non-convex optimization problem. Ultimately, to demonstrate the efficiency of the proposed method; it has been tested on two 33 and 70-bus test systems, and the results are compared with the results of using other evolutionary algorithms, such as particle swarm optimization and shuffled frog leaping. [ABSTRACT FROM AUTHOR]

Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature. [ABSTRACT FROM AUTHOR]

Large penetration of electrical energy storage (EES) units and renewable energy resources in distribution systems can help to improve network profiles (e.g. bus voltage and branch current profiles), and to reduce operational cost as well as power losses. On the other hand, unsecure system operation as a result of involving these units is another challenge to network operators. Therefore, establishing a trade-off between operational cost and security is very important. This study presents a new approach to determine the optimal charging/discharging schedule of EES units in distribution systems by employing multi-objective optimisation methods, which will effectively reduce operational cost and enhance distribution network security. In this regard, a voltage stability index (VSI) is converted into a security index to improve the radial network security. This VSI index is treated as a separate objective function, and a multi-objective strategy is implemented to obtain a set of non-dominated solutions instead of a single optimal solution, which simultaneously minimise both of the operational cost and security index. In order to assess the effectiveness and applicability of the proposed method, it is applied to IEEE standard 33-bus and 136-bus distribution test systems, and then the obtained results are compared with those of existing methodologies. [ABSTRACT FROM AUTHOR]

Large penetration of electrical energy storage (EES) units and renewable energy resources in distribution systems can help to improve network profiles (e.g. bus voltage and branch current profiles), and to reduce operational cost as well as power losses. On the other hand, unsecure system operation as a result of involving these units is another challenge to network operators. Therefore, establishing a trade‐off between operational cost and security is very important. This study presents a new approach to determine the optimal charging/discharging schedule of EES units in distribution systems by employing multi‐objective optimisation methods, which will effectively reduce operational cost and enhance distribution network security. In this regard, a voltage stability index (VSI) is converted into a security index to improve the radial network security. This VSI index is treated as a separate objective function, and a multi‐objective strategy is implemented to obtain a set of non‐dominated solutions instead of a single optimal solution, which simultaneously minimise both of the operational cost and security index. In order to assess the effectiveness and applicability of the proposed method, it is applied to IEEE standard 33‐bus and 136‐bus distribution test systems, and then the obtained results are compared with those of existing methodologies. [ABSTRACT FROM AUTHOR]

This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard realparameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixedinteger RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results. [ABSTRACT FROM AUTHOR]

Distribution feeder reconfiguration is an important operation problem in distribution system which has been used to improve the efficiency of distribution systems by obtaining the best combination of on/off status of the switches. It is a mixed integer non-linear program problem and hence hard to solve which necessitate employing proper optimization algorithms to converge to global optima or find near global optima. Smart grid implementation has made loads and electricity prices more volatile and as a result makes operational power system problems to be much more time dependent and more complicated rather than before. To cope with these time dependencies, it is crucial to extend the problems on different time intervals. To this end the dynamic distribution feeder reconfiguration, extension of the problem over multiple time intervals, with various objective functions including operation cost, power loss and energy not supplied is developed and investigated in this study. Time varying electricity prices and different load levels juxtapose with the effect of distributed generations are taken into account in order to generalize the proposed approach. Inherent complexities of distribution feeder reconfiguration problem have made proposing solution techniques an ongoing research topic. A new optimization algorithm is proposed to solve the proposed problem. [ABSTRACT FROM AUTHOR]

Distribution Feeder Reconfiguration (DFR) is an important technique to improve the performance of distribution networks. The common objectives considered in the DFR problem are power loss and voltage deviation which are important objectives for traditional distribution systems. Security issues cause by Distributed Generations (DGs) in modern distribution systems which can potentially jeopardize power system security has almost neglected in power system operation problem. Toward this end, this study considers the power loss, Voltage Stability Index (VSI), and number of switching as objective functions which can satisfy both operation and security expectations. The Backward-Forward Sweep (BFS) method known for easy convergence has been employed for power flow calculations. Because of the increase in DG penetration in distributed systems, the impacts of these units are investigated. A powerful optimization algorithm based on hybridization of Shuffled Frog Leaping Algorithm (SFLA) and Particle Swarm Optimization (PSO) is proposed to solve the proposed problem. The proposed algorithm is a combination of strong mutation operator, original SFLA and original PSO algorithms which has high population diversity and search ability. The proposed algorithm has been applied to a complex multimodal benchmark function and also two different distribution networks including 33- and 95-bus test systems. [ABSTRACT FROM AUTHOR]

• An accurate power flow is formulated by utilizing a dynamic approximation. • A robust approach is used to model uncertain parameters, including RESs, loads, and market price. • A mixed-integer linear programming approach is provided for taking the powerful solvers into account. • The proposed linear framework is faster than the previous methods with less error. Decarbonization legislated the risen integration of renewable energy sources (RESs) in the presence of energy storage systems (ESS) in distribution networks. Nonetheless, these resources may impose substantial costs to the utilities for managing frequency. In the present era, the concept of active distribution networks (ADNs) has been taken into consideration in the study of grid penetration of RESs and ESSs. Accordingly, ADN requires a sophisticated AC optimal power flow (OPF) to solve such a complex decision-making process. This paper aims to develop an accurate and effective OPF in ADNs. To this end, this paper contributes to developing a highly efficient tool to operate ADNs under uncertainties in load and renewables. Firstly, a highly accurate and dynamic linear load flow is presented to compensate for the restrictions of nonlinear OPF, such as scalability and robustness. In more detail, the floating tangent surface method is utilized to approximate better the nonlinear and nonconvex equations in the OPF formula. For the iterative process of the dynamic linear approximation, its local convergence can be derived. Then, to make the model much more realistic, the uncertainty of RESs, load, and price is accounted for in a robust approach which alleviates their vulnerability in decisions. The proposed approach is finally formulated as a mixed integer linear problem and implemented in various case studies. After assessing the proposed strategy under different circumstances, the average error of different case studies is 3.30E-06 and 6.24E-02, respectively, for the proposed method and another approach in the literature, which means the proposed method has less error than other methods. From the results, it can be observed that the presented OPF solving method is vastly superior to other prior approaches in terms of more accurate results. [ABSTRACT FROM AUTHOR]

• Coordinating conventional Volt-VAr devices with distributed energy resources. • Incorporating demand response program into the Volt-VAr control problem. • Utilizing ε-constraint approach to generate Pareto optimal solutions. • Using IGDT to develop a robust decision-making approach. • Developing a mixed-integer linear programing framework. Distributed energy resources (DERs), such as renewable energy sources (RESs) and energy storage systems (ESSs), are inevitable parts of future power grids. However, the high penetration of RESs may cause voltage violation issues in distribution levels. To this end, the application of the Volt-VAr management (VVM) scheme is highlighted. This paper aims to maintain voltage levels within standard ranges with optimal coordination of different resources at minimum operational costs and voltage deviation. In more detail, this article develops a new VVM in distribution networks to effectively coordinate novel smart grid technologies such as PV inverters, time of use demand response program (TOU-DRP), energy storage systems (ESSs) with traditional devices, including on-line tap changer transformer (OLTC) and switchable capacitor banks (SCBs). Because the coordinated VVM is a bi-objective model that minimizes both operating cost and voltage deviation, it is proposed to use the ε-constraint technique to identify optimal Pareto solutions. To handle the uncertainty related to RESs and load demands, a robust technique based on information gap decision theory (IGDT) is also utilized. The formulation is configured as a mixed-integer linear program (MILP) and solved by CPLEX. It was implemented on the 118-bus distribution network under various conditions. The simulation results clearly show that the coordination of DERs and traditional devices has a significant impact on maintaining voltage levels within permissible ranges while minimizing operational costs. A higher degree of uncertainty radius can also be obtained if DRP participates in the VVM scheme. [ABSTRACT FROM AUTHOR]

• Mapping second-order cone AC OPF equations via semi-Lorentz transformation. • Transforming both variables and network parameters in Model-A. • Applying the proposed linear transformation on the network parameters in Model-B. • Developing an empirical analysis to obtain a good value for the semi-Lorentz factor. • Improving both optimality gap and relaxation error compared to other methods. Optimal power flow (OPF) has always been one of the most crucial tools for power system operations. OPF problem formulation involves non-linear alternative current (AC) power flow equations, and a wide range of challenges occur as a result. This is because the resulting non-convex optimization problems are not only complex and time-consuming, but also difficult to find a global optimum as many local optimums are present. So far, different relaxations have been provided to address these issues. One of the most effective strategies for convexifying such formulations is second-order cone programming (SOCP). Although SOCP is an efficient instrument for convexifying AC OPF equations, it is unable to reach the global optimal solution compared to other methods. The aim of this paper is therefore to provide a new method to approach the global optimum of AC OPF relaxed by SOCP. This method is obtained with the aid of a new linrear tranfsormation called semi-Lorentz transformation as it similar to the Lorentz transformation in the special relativity theory. In this method second-order cone AC OPF equations are mapped to a new model via semi-Lorentz transformation. In addition, an approximation approach is also presented to reach the best semi-Lorentz factor, the main driver in semi-Lorentz transformation, for each particular problem based on the network parameters. From the comparative analysis in case studies, the proposed OPF solution method has robust precision and higher efficiency while consuming less computing time. [ABSTRACT FROM AUTHOR]

• A bi-objective economic dispatch for a wind-thermal-energy storage system. • Pumped hydroelectric storage system to alleviate the peak-time pressure. • Hybrid optimization algorithm: whale optimization enhanced by wavelet mutation. • Trade-off between operation cost and water consumption as the problem's objectives. This paper proposes a novel approach to discover an optimal solution of multi-area dynamic economic dispatch integrated with wind power and pumped hydroelectric storage. Minimizing of water consumption associated with power production in thermal power plants is yet to be addressed in economic dispatch problem. Thus, it is crucial to modernize the conventional power industry to save the water utilization in the process of generating electric energy. As a result, making a feasible power generation schedule by finding a trade-off between total generation cost and water consumption is the main focus of this paper. Toward this end, this study introduces an effective optimization approach for decreasing the contaminants ‎produced by the combustion-based power plants via reflecting the impacts of wind power and storage systems in the proposed problem. An efficient optimization algorithm considering the merits of whale optimization algorithm and wavelet mutation is also proposed to handle the complications of the trade-off between generation cost and water consumption. The proposed approach is applied to 40-unit 4-area IEEE system by 1) scrutinizing the performance of the hybrid framework from different standpoints, 2) achieving an improvement about 5.4% in the operation cost, and 3) promoting the decarbonized economy by implementing ‎renewable energy. [Display omitted] [ABSTRACT FROM AUTHOR]

While compressed air energy storage (CAES) has many applications in the field of generation and transmission power systems based on the state-of-the-art, this paper proposes the application of small-scale CAESs (SCAESs) in form of a storage aggregator in the daily operation of an active distribution system (ADS), joining the distribution system operator (DSO) for the participation in the day-ahead (DA) wholesale market. An innovative two-agent modeling approach is formulated. The first agent is responsible for aggregating SCAES units and the profit maximization of the aggregator is based on the distribution local marginal price. The DSO as the second agent receives the DA scheduling from the independent SCAES aggregator and is thus responsible for the secure operation of the ADS, utilizing solar and dispatchable distributed generation (DG) as well as purchasing power from the wholesale market. Linear programming is used for the formulation and optimization of the SCAES aggregator, while a bi-objective optimization algorithm (with the objectives of minimum operating cost as well as minimum power loss and emissions in different scenarios) is employed for DSO scheduling. The results show that the CAES aggregator can offer a considerable impact for power loss reduction, specifically, when diesel generators are not committed in the system operation (i.e., where emission has very low values between 10,000 and 12000 kg). Additionally, the CAES aggregator could reduce the operation costs of the grid in a wide range of operations, even though for the scenario in which the CAES units are not under the control of the DSO anymore and also are scheduled to maximize their own profit. Moreover, results demonstrated that CAES units can be a significant voltage control device for a distribution grid with different objectives. Finally, some conclusions are duly drawn. • This paper proposes the application of small-scale compressed air energy storage (SCAESs). • An innovative two-agent modeling approach is formulated. • Linear programming is used for the formulation and optimization of the SCAES aggregator. • Bi-objective optimization algorithm is employed for distribution system operator scheduling. [ABSTRACT FROM AUTHOR]

• A novel concept of mobile energy storage is proposed in this paper. • A detailed modeling of mobile compressed air energy storage with higher dispatchability and storage capacity is presented. • In concern to routing challenges, Google map's Application Programming Interface is used to find the target location. • A new heuristic-based technique that overcomes deficiencies of traditional constraint handling methods is proposed. • The proposed framework is tested through the IEEE 33-bus and 136-bus networks with different scenarios. • Various reliability indices and operating profits are measured to demonstrate the applicability of the proposed technology. Efficient energy storage technology is one of the key elements to enhance the flexibility, economy, and security of the power system. With the continuous development of energy storage technology, containerized mobile energy storage is coming into view, which has offered promising opportunities to improve distribution network (DN) performances and grid operating factors against emergencies. This paper proposes the concept of mobile compressed air energy storage (CAES) for an electric DN. The movable air storage tanks with stored energy are transported by trucks and placed at some distribution nodes/buses to improve DN performance. To overcome routing challenges for trucks, the configuration of the grid is mapped on the urban region of the city of Sydney, Australia, using Google map's Application Programming Interface. This approach can accurately model distances between current and targeted locations, unavailability of tanks during traveling, route congestions, and fuel consumptions. A new heuristic mathematical method is proposed in this paper to convert the constraints of the mobile CAES (MCAES) model into feasible search spaces, which significantly improves the convergence quality and speed. Operating results for both stationary and mobile CAESs are presented and compared. The methodology is applied to IEEE 136-bus DN in addition to IEEE 33-bus DN to demonstrate the competence of MCAES for larger-scale grids in optimizing the total operating profit, active power loss, energy not supplied, and voltage stability index of the grid. The crucial application of the proposed model can deal with natural disasters to avoid large-scale power outages and, eventually, mitigate the power system damages. [ABSTRACT FROM AUTHOR]

Economic dispatch seeks for the most economical combination of generation units in power system while satisfying bunch of physical and operational constraints. Finding the most economical generation strategy in power markets with several generation zones is crucial for power system operation. Conventional economic dispatch models cannot find the most economical generation schedule in power system with different generation zones. Moreover, the most economical schedule of generation cannot satisfy the environmental expectations. Therefore, compromising between generation cost and environmental issues is unavoidable. In this connection, total emission produces by generation units is taken into consideration as an objective function in concert with generation cost function. Furthermore, different operational constraints including valve-point effect, prohibited operating zones and multi-fuel operation are considered to make the proposed approach more realistic. Considering all these restrictions necessitate solving the proposed problem, i.e. multi-objective multi-area economic dispatch problem, by a reliable and strong optimization algorithm. In this regard, a hybrid evolutionary algorithm based on the shuffle frog leaping algorithm and the particle swarm optimization is proposed to solve the proposed problem. Effectiveness of the proposed hybrid algorithm is verified on different test systems. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) are an effective platform for attracting private investment and customer engagement to speed up the integration of green renewable resources. In this paper, a robust bidding strategy to participate in both energy and ancillary service markets in the wholesale electricity market is proposed for a realistic VPP in Western Australia. The strategy is accurate and fast, so the VPP can bid in a very short time period. To engage customers in the demand management schemes of the VPP, the gamified approach is utilized to make the exercise enjoyable while not compromising their comfort levels. The modelling of revenue, expenses, and profit for the load-following ancillary service (LFAS) is provided, and the effective bidding strategy is developed. The simulation results show a significant improvement in the financial indicators of the VPP when participating in both the LFAS and energy markets. The payback period can be improved by 3 years to the payback period of 6 years and the internal rate of return (IRR) by 7.5% to the IRR of 18% by participating in both markets. The accuracy and speed of the proposed bidding strategy method is evident when compared with a mathematical method. [ABSTRACT FROM AUTHOR]

Abstract: Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution Expansion Planning) in the presence of DGs using nonlinear formulations. The objective functions of the MDEP consist of minimization of costs, END (Energy-Not-Distributed), active power losses and voltage stability index based on SCC (Short Circuit Capacity). A MPSO (modified Particle Swarm Optimization) algorithm is developed and used for this multiobjective MDEP optimization. In the proposed MPSO algorithm, a new mutation method is implemented to improve the global searching ability and restrain the premature convergence to local minima. The effectiveness of the proposed method is tested on a typical 33-bus test system and results are presented. [Copyright &y& Elsevier]

The world has been experiencing natural disasters and man-made attacks on power system networks over the past few decades. These occurrences directly affect electricity infrastructures, thereby resulting in immense economic loss. The electric infrastructure is the backbone and one of the most essential components of human life. Thus, a resilient infrastructure must be constructed to cope with events of high-impact, low-possibility. Moreover, achieving resilience in the active distribution system (ADS) has been a vital research field of planning and operation of electric power systems. The incorporation of recent breakthrough technologies, such as micro- and smart grids, can make the distribution system become considerably resilient through planning-operation activities prior, during, and after an extreme event. This study offers the concepts premised on a systematic review of available literature by distinguishing characteristics between reliability and resiliency. Thereafter, the most relevant proceedings in conformity with an overview of the major blackouts, hardening and its guidelines, weather-related scenarios, taxonomies, and remedial actions are discussed. In addition, this research presents the planning, operational, and planning-operational attributes in response to catastrophes. Furthermore, a case study is conducted to support the review work, where the reliability and resilience of the ADS (IEEE 33-bus test system) are evaluated as performance indices with and without the addition of PV units. The performed research is laying out the importance of the distributed generation, such as PV, in the context of resilience, with the inclusion of different faults. • The importance of resilience in the context of distribution systems is presented. • Distinctions between reliability and resiliency are discussed. • The status of resilience research to date with respect to planning and operation,schemes are thoroughly reviewed. • A case study is presented, considering the importance of DG in active distribution systems on account of resiliency. • Extensive research is needed to enhance the resiliency of the distribution system in the face of an extreme event. [ABSTRACT FROM AUTHOR]

Abstract Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods. Highlights • A survey of operational methods of active distribution systems (ADSs) is presented. • A two-stage time-hierarchy methodology is adopted to categorize recent research. • Day-ahead economic management of ADSs is explored in the first stage. • Static, dynamic and transient operations of ADSs are studied in the second stage. • Modeling and control needs of ADS economic/technical operation are reviewed. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective evolutionary algorithm method for Distribution feeder reconfiguration (DFR) with distributed generators (DG) in a practical system. Considering the low inertia constant of DG units in order to take the transient stability of DGs into account is one of the major issues in power systems. Especially when the penetration of DGs is low, the impacts of them on the distribution system transient stability may be neglected. However, when the penetration of DG increases, the transient stability of them must be taken into account (more DGs, more transient issues). To this end, the DFR problem has been solve by an enhanced Gravitational Search Algorithm (EGSA) to improve the transient stability index and decrease losses and operation cost in a distribution test system with multiple micro-turbines. The effectiveness of the proposed approach is studied based on a typical 33-bus test system. For getting close to the practical condition and considering the detailed dynamic models of the generators and other electric devices in power system, simulation and programming of this approach are done by the DIgSILENT® Power Factory software. [ABSTRACT FROM AUTHOR]

• A comprehensive review of recent advancements in the development of ADSs and MGs. • The regulatory requirements for the evolution of passive DNs to ADSs are detailed. • The paper provides some novel factors for transition and formation ADSs to MGs. • The survey assists DSOs to find requirements to participate in day-ahead markets. Restructuring of power systems along with the integration of renewable energy resources in electricity networks have transformed traditional distribution networks (DNs) into new active distribution systems (ADSs). In addition, rapid advancement of technology has enabled the bulk utilization of power generation units and energy storage (ES) systems in distribution networks. The next step in this trend is to decentralize ADSs to microgrids (MGs). This paper aims to present a review on the recent advancements in the development of ADSs and MGs. In this respect, the regulatory requirements and economic concepts, by which the traditional passive DNs are evolved into ADSs, are categorized and illustrated first. Then, the state-of-the-art of ADS formation is detailed based on the novel standpoint of grid operation factors which are involved in deregulated electricity markets at the distribution level. After presenting highlighted projects of MGs across the world, a similar review approach has been adopted to explain the formation of MGs which play a vital role in the decentralization of ADSs. This survey can provide both policy makers and distribution system planners with new perspectives to establish or participate in day-ahead wholesale markets. [ABSTRACT FROM AUTHOR]