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Network reconfiguration and distributed generation (DG) installation are important approaches for loss mitigation and system efficiency improvement. To date, these approaches are generally implemented separately. In this work, simultaneous network reconfigurations and DG installation is solved in order to minimize real power loss and improve system efficiency using popular heuristic algorithms such as Artificial Bee Colony (ABC), Differential Evaluation (DE), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). While decision making is carried out for open switches and DG sizing by given algorithms, optimal locations of DGs are decided using loss sensitivity factor in order to reduce searching space. Different cases of network reconfiguration and DG installation are implemented to compare the performances of given heuristic algorithms. Branch current, bus voltages and DG capacity are considered as constraints and 69 bus system is used for simulation. This study demonstrates that simultaneous network reconfiguration and DG installation presents better solutions than any other cases and ABC has proven minimum losses and maximum voltage improvement. DOI: 10.5755/j01.eie.25.1.22729

Research on network reconfiguration (NR) considering distributed generations (DG) is typically concerns on the issues of power loss, voltage deviation, DG sizing as well as its placement, which are important and required in the planning stage. On the other hand, another important aspect which often neglected in this stage is coordination of protection devices which is essential to prevent the network from damages following system breakdown. Without sufficient attention given to the protection coordination during NR, there is a possibility for the protective devices to miscoordinate and consequently lead to system blackout, due to changes in current flow and fault level. Therefore, this paper proposed an NR method for distribution networks with DG, incorporating protection devices. The proposed method aims to find the optimal configuration and DG size with minimum power loss, and at the same time ensuring protective devices operate correctly during normal and fault condition. Constraints on protection coordination and DG size are explicitly formulated in the proposed method. The validity of the proposed method is analyzed on three commonly used IEEE 33-bus, 69-bus and 118-bus distribution systems, employing the firefly algorithm (FA) and evolutionary programming (EP) algorithm. Comparative studies are done to prove the validity and robustness of the proposed method.

A solution to the power flow problem is imperative for many power system applications and several iterative approaches are employed to achieve this objective. However, the chance of finding a solution is dependent on the choice of the initial point because of the non- convex feasibility region of this problem. In this paper, a non-iterative approach that leverages a convexified relaxed power flow problem is employed to verify the existence of a feasible solution. To ensure the scalability of the proposed convex relaxation, the problem is formulated as a sparse semi-definite programming problem. The variables associated with each maximal clique within the network form several positive semidefinite matrices. Perturbation and network reconfiguration schemes are employed to improve the tightness of the proposed convex relaxation in order to validate the existence of a feasible solution for the original non-convex problem. Multiple case studies including an ill-conditioned power flow problem are examined to show the effectiveness of the proposed approach to find a feasible solution.

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

This article presents a cold-start linear branch flow model named modified DistFlow. In modified DistFlow, the active and reactive power are replaced by their ratios to voltage magnitude as state variables, so that errors introduced by conventional branch flow linearization approaches due to their complete ignoring of network loss terms are reduced. Based on the path-branch incidence matrix, branch power flows and nodal voltage magnitudes can be written in succinct matrix forms. Subsequently, the proposed modified DistFlow model is applied to the problem of reactive power optimization and network reconfiguration, transforming it into a mixed-integer quadratic programming (MIQP). Simulations show that the proposed modified DistFlow has a better accuracy than existing cold-start linear branch flow models, and the resulting MIQP model for reactive power optimization and network reconfiguration is much more computationally efficient than existing benchmarks. [ABSTRACT FROM AUTHOR]

Increasing the integration of distributed energy resources (DERs) inspired by environmental and governmental incentives, beside the introduction of multi-agent structure perspective have led to a paradigm shift of operational conditions in distribution systems. In this context, the traditional concept of fit and forget in the distribution grid management would not be efficient in the modern distribution systems and consequently new mechanisms should be developed in order to enable the distribution system operator (DSO) to efficiently manage the grid congestion caused by peak power output of DERs or load demands requests. In this article, the Stackelberg game concept is employed to develop an incentive-based mechanism that facilitates the contribution of local flexible resources operated by independent agents in the congestion management of distribution grids. Therefore, the proposed bi-level formulation would result in congestion alleviation in the multi-agent system; while the objectives associated with DSO and independent agents are fulfilled. Additionally, the developed framework enables DSO to reconfigure the network to decrease the operational costs associated with the congestion alleviation procedure. Finally, the strong-duality concept is utilized in order to combine the two-level problem into a one-level problem and the obtained model is implemented on IEEE-37 bus test system to investigate its effectiveness in congestion management in the distribution system. [ABSTRACT FROM AUTHOR]

This research deals with the development of a network reconfiguration on a radial distribution system in coordinated planning in the form of a combination of optimization techniques for DG placement, capacitor placement and network reconfiguration simultaneously in order to minimize losses, THDv and voltage deviation considering the spread of harmonics due to nonlinear loads. Particle Swarm Optimization (PSO) Method from previous researches is used in this research. The IEEE 33-bus test standard radial distribution system is tested in several MATLAB-based case studies in order to find optimal results, and this method is compared with the Simulated Annealing and Firefly Algorithm. The results obtained indicate the effectiveness of the approach using the PSO method in finding the objective function with predetermined limits. [ABSTRACT FROM AUTHOR]

Background: When a power system blackout occurs, it affects the economy of the country and every aspect of human life. Cascading failures can easily occur and cause a major blackout in the power grid due to the breakdown or failure of important nodes or links. Recently, transmission network reconfiguration (TNR) becomes a hot topic and has made many concerns after major blackouts of power systems. Aims: TNR is the second‐stage action plan to restore power systems and plays a major role in the process of power system restoration. On the other hand, grid resilience involves a quick dynamic reconfiguration of power systems to minimize the propagation of attack influences on the grid. The motivations to include the works in this survey are based on the quality of the research performed in the transmission network reconfiguration problem for grid resilience. In this article, the state‐of‐the‐art review of recent progress in the network reconfiguration problem of the transmission system for grid resilience is discussed with practical challenges, technical issues, and power industry practices. Materials & Methods: In this paper, complex network theory‐based indices with advantages, disadvantages, and their applications have been discussed to assess the important nodes and lines for network reconfiguration problem during sudden disturbances in power systems. Furthermore, optimization models have been presented with objective functions as well as their constraints. Taken together, optimization methodologies have been discussed to solve network reconfiguration problem with merits and demerits. Results: This survey paper presents current trends in research and future research directions concerning transmission network reconfiguration for academic researchers and practicing engineers. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations. Discussion: Based on a thorough comparison of literature some future perspectives are also discussed for transmission network reconfiguration problem for grid resilience. Conclusion: This review paper provides a comprehensive review of current practices applied to transmission network reconfiguration. The core focus of this paper will remain on complex network theory‐based indices, optimization models, optimization methodologies, challenges, and technical issues, and discusses future direction for transmission network reconfiguration problem for grid resilience. Furthermore, the most current studies in improving transmission network reconfiguration problem are reviewed by highlighting their advantages and limitations. [ABSTRACT FROM AUTHOR]

This paper proposes an effective network reconfiguration (NR) method in the presence of distributed generations (DGs) for energy loss. The proposed method uses average load and average power of DGs instead of the load and DGs' generation curves. For finding the optimal network configuration, pathfinder algorithm (PFA) is used to solve the NR problem. The effectiveness of the proposed method has been validated on two distribution network systems without and with DGs placement. The obtained results show that the proposed method has a good ability to determine the optimal configuration similar to the method based on the graphs of loads and DGs with much shorter calculated time and PFA can reach optimal solution with a much higher success rate and better obtained solution compared with particle swarm optimization and sunflower optimization algorithms. As a result, the proposed method is an effective and reliable method for solving the NR problem for energy loss reduction considering time-varying condition of loads and DGs. [ABSTRACT FROM AUTHOR]

To achieve high reliability, the urban distribution networks are mesh-constructed and radial-operated, in which the outage load can be restored to adjacent feeders via tie-lines after faults. Conventionally, iterative optimization-simulation methods and heuristics are adopted for distribution network planning, which cannot guarantee global optimality. Besides, existing reliability-constrained planning model cannot explicitly assess the reliability indices for mesh distribution networks, so the resulted plan scheme may be overly invested. In this paper, we propose a novel multistage expansion planning model for mesh distribution networks, in which reliability assessment is explicitly implemented as constraints. The different investment/reliability preferences for buses are also customized. Specifically, post-fault load restoration between feeders through tie-lines is modeled as a case of post-fault network reconfiguration. The planning model is then cast as an instance of mixed-integer linear programming and can be effectively solved by off-the-shelf solvers. We use a 54-node system to test the performance of proposed model. Simulation results show the effectiveness and flexibility of this methodology. [ABSTRACT FROM AUTHOR]

This paper presents a new method based on coyote algorithm (COA) which is inspired from the social life of coyotes for the problem of simultaneous network reconfiguration and distributed generation (DG) placement to reduce real power loss. The effectiveness of the proposed COA method has been evaluated on two distribution systems consisting of 69-node and 119-node systems at two scenarios consisting of reconfiguration only and simultaneous reconfiguration and DG placement. The result analysis has indicated that network reconfiguration combination with optimization of location and size of distributed generation (DGs) is more effective for power loss reduction than network reconfiguration only. About the network reconfiguration only, for the 69-node and 119-node systems COA can search out the optimal solution that reduce power loss by 56.16% and 32.86%, respectively. Meanwhile, the optimal solution obtained by the network reconfiguration combination with optimization of location and size of DGs using COA helps to reduce power loss of two aforementioned systems by 84.37% and 55.31%, respectively. The result comparisons with other methods in the literature have also shown that COA has ability to obtain the better network configuration and location and size of DGs than other methods. Therefore, the proposed COA can be a promising method for the problem of simultaneous reconfiguration and DG placement. [ABSTRACT FROM AUTHOR]

This article proposes a two-level network-constrained peer-to-peer (P2P) transactive energy for multi-microgrids (MGs), which guarantees the distribution power network security and allows MGs to trade energy with each other flexibly. At the lower level, a P2P transactive energy is employed for multi-MGs to trade energy with each other. A multi-leader multi-follower (MLMF) Stackelberg game approach is utilized to model the energy trading process among MGs. We prove the existence and the uniqueness of the Stackelberg equilibrium (SE) and provide the closed-form expression for SE. For privacy concerns, we provide several distributed algorithms to obtain SE. At the upper level, the distribution system operator (DSO) reconfigures the distribution network based on the P2P transactive energy trading results by applying the AC optimal power flow considering the distribution network reconfiguration. If there are any network violations, DSO requests trading adjustments at the lower level for network security. We reformulate the DSO operation problem in a mixed-integer second-order cone programming (MISOCP) model and ensure its solution accuracy. Numerical results for a 4-Microgrid system, a modified IEEE 33-bus and 123-bus distribution power system show the effectiveness of the proposed transactive model and its solution technique. [ABSTRACT FROM AUTHOR]

This paper presents simultaneous radial distribution network reconfiguration and switched capacitor bank allocation to enhance the performance of distribution systems. The paper primarily targets minimizing active and reactive power losses and improving the voltage profile of all buses. In the paper, network re-structuring and switched capacitor bank integration are performed to optimally allocate and size switchable capacitor banks and to select the optimal structure of the network. Additionally, a modified particle swarm optimization (MPSO) algorithm is incorporated for both network reconfiguration and optimal capacitor bank allocation, considering different loading condition scenarios such as light, normal and heavy load. The simulation results show that the voltage deviation and active and reactive power losses are substantially reduced for all loading scenarios when the proposed method is applied. This shows that MPSO algorithm is effective in maintaining the bus voltage profile within the allowable threshold value and in significantly minimizing the corresponding power losses. Furthermore, the performance comparison of MPSO and conventional particle swarm optimization in terms of voltage profile and active and reactive power loss has been conducted, and it has been found that the proposed method (MPSO) is more effective to enhance the performance of radial distribution systems.

The topic of power loss reduction in distribution systems has gained significant attention over recent years. Despite the efforts of the European Union towards the minimization of power losses, the decarbonization of the transport sector has raised several concerns, since charging overlaps of Electric Vehicles (EVs) can cause extensive power losses and power quality issues. Considering these, the present paper proposes a two-stage EV charging planning and Network Reconfiguration (NR) methodology, addressing the problem of power loss minimization in both Low-Voltage (LV) and Medium-Voltage (MV) Distribution Networks (DNs), respectively. In the first stage, considering the key role of the aggregator, the EV charging planning is applied to LV DN. In the second stage, the NR technique is applied to the MV DN, by taking into account the hourly power demand of LV DNs as obtained by the aggregators. The proposed methodology has been applied on a benchmarked MV network for which each node is represented by a real LV network. The results indicate that the proposed methodology could yield up to a 63.64% power loss reduction, in respect to the base scenario, i.e., no charging planning and no NR are applied.

Network reconfiguration is an effective way to avoid severe, large-scale power outages and to improve the resilience of active distribution networks (ADNs). Furthermore, the rapid development of distributed energy resources (DERs) provides new perspectives for network reconfiguration. In this paper, the effect of network reconfiguration and DER collaboration on ADN’s resilient restoration are studied. The applications of DERs are fully explored. In order to achieve a better resilient performance, a detailed multiperiod model considering both reconfiguration and multiple DERs is established. Some linearization techniques are used to simplify the proposed model. Then, we build a rolling horizon optimization framework to solve the model. The framework eliminates the adverse effect of prediction errors and speeds up the calculation. By introducing predictions into strategy determination, the framework achieves a better restoration effect than the traditional greedy method. The proposed framework is tested on a 33-bus system. The simulations verify the efficiency of our work.

Smart grid technology has received ample attention in past years to develop the traditional power distribution network and to enable the integration of distributed generation units (DGs) to satisfy increasing demand loads and to improve network performance. In addition to DGs, integration of shunt capacitors (SCs) along with network reconfiguration can also play an important role in improving network performance. Besides, network reconfiguration can help to increase the distributed generation hosting capacity of the network. Some of the research in the literature have presented and discussed the problem of optimal integration of renewable DGs and SCs along with optimal network reconfiguration, while the network load variability and/or the intermittent nature of renewable DGs are neglected. For the work presented in this paper, the SHADE optimization algorithm along with the SOE reconfiguration method have been employed for solving the aforementioned optimization problem with consideration of uncertainty related to both the network load and the output power of the renewable DGs. Maximizing the hosting capacity (HC) of the DGs and reducing network power losses in addition to improving the voltage profile have been considered as optimization objectives. Five different case studies have been conducted considering 33-bus and 59-bus distribution networks. The obtained results validate the effectiveness and the superiority of the employed techniques for maximizing the HC up to 17% and reducing power losses up to 95%. Besides, the results also depict the effect of SC integration and the consideration of uncertainties on achieving the optimization objectives with realistic modeling of the optimization problem.

Estimating the configuration of the distribution network (DN) is essential for coordinated protection, reliable automation, and robust control applications. This article presents a novel approach to detect the configuration of the DN by acquiring and processing real-time measurement from the optimally placed microphasor measurement unit (μPMU). Initially, the μPMU placement problem is formulated subjected to the various working configuration of the distribution network for complete observability. After that, measurements from μPMU nodes are obtained to develop the problem of identifying the network configuration. Conceptual mechanism behind configuration identification approach is to compare time series measurements with the estimated version. The estimated measurement is derived by adopting load-flow analysis pertaining to different network configurations. Comprehensive testing in 33-node DN under different scenarios viz. nominal load, uncertain variation in load, load ratio effect, and evaluation with a reduced number of measuring devices verifies the effectiveness and robustness of the proposed configuration identification approach. [ABSTRACT FROM AUTHOR]

This paper presents simultaneous radial distribution network reconfiguration and switched capacitor bank allocation to enhance the performance of distribution systems. The paper primarily targets minimizing active and reactive power losses and improving the voltage profile of all buses. In the paper, network re-structuring and switched capacitor bank integration are performed to optimally allocate and size switchable capacitor banks and to select the optimal structure of the network. Additionally, a modified particle swarm optimization (MPSO) algorithm is incorporated for both network reconfiguration and optimal capacitor bank allocation, considering different loading condition scenarios such as light, normal and heavy load. The simulation results show that the voltage deviation and active and reactive power losses are substantially reduced for all loading scenarios when the proposed method is applied. This shows that MPSO algorithm is effective in maintaining the bus voltage profile within the allowable threshold value and in significantly minimizing the corresponding power losses. Furthermore, the performance comparison of MPSO and conventional particle swarm optimization in terms of voltage profile and active and reactive power loss has been conducted, and it has been found that the proposed method (MPSO) is more effective to enhance the performance of radial distribution systems. [ABSTRACT FROM AUTHOR]

The concept of applying the game-theoretic approach in wireless dynamic network infrastructure to counteract cyber-attacks is presented. This approach can enable an adaptive reconfiguration of the network structure as various types of cyber-attacks occur and ensure continuous network functioning even in the case of destructive information impacts. [ABSTRACT FROM AUTHOR]

This paper presents an optimal method for optimizing network reconfiguration (NR) problems in a power distribution system (PDS) for the purpose of power loss reduction and voltage profile (VP) improvement. Furthermore, a modified algorithm was presented to address this problem in order to provide a more efficient PDS. Various works which used NR to improve VP and reduce power loss were discussed and summarized in detail. In particular, a modified Selective particle swarm optimization (SPSO) method was used for NR in existing networks considering different loading conditions. The main objective of this study is to minimize real power losses and enhance VP of a distribution system using the proposed SPSO method. The SPSO method was programmed in MATLAB R2016b software and tested using IEEE 33-bus radial distribution system (RDS). The obtained test results show that the real power was enhanced by 99.341%, 97.289%, and 95.389% for the light, normal, and heavy load conditions, respectively. Also, the minimum voltage level in the worst case was significantly enhanced from 0.8841 p.u. to 0.9510 p.u. Towards the end, a comparative analysis of the proposed SPSO with existing methods for distribution network reconfiguration (DNR) is presented. The comparative results show that the proposed SPSO was found to be more efficient in reducing voltage deviation (VD) and power losses in the system.

As the core sub-problem of both network reconfiguration and service restoration of the electrical distribution system (EDS), the load pick-up (LPP) problem in EDS searches the optimal configuration of the EDS, aiming to minimise the power loss or provide as much power as possible to load ends. The piecewise linearisation (PWL) approximation method can be used to tackle the network power flow constraints’ non-linearity in the LPP problem model, and transform the LPP model into a mixed-integer linear programming model (LPP-MILP model). The errors in the PWL approximation of the network power flow constraints may affect the feasibility of the solving results of the LPP-MILP model. The single method to reduce the PWL approximation errors by increasing the number of discretisations in PWL function is not stable. Moreover, the solution efficiency of the LPP-MILP model is sacrificed severely. In this study, a warm-start PWL approximation-based solution for the LPP problem is proposed. The variable upper bounds in the PWL approximation functions are renewed dynamically in the warm-start solution procedure to reduce the PWL approximation errors with higher computational efficiency. Modified IEEE 33-bus radial distribution test system and a real and large distribution system, 1066-bus system, are used to test and verify the effectiveness and robustness of the proposed methodology.

The importance of power in society is indisputable. Virtually all economic activities depend on electricity. The electric power systems are complex, and move studies in different areas are motivated to make them more efficient and solve their operational problems. The smart grids emerged from this approach and aimed to improve the current systems and integrate electric power using alternative and renewable sources. Restoration techniques of these networks are being developed to reduce the impacts caused by the usual power supply interruptions due to failures in the distribution networks. This paper presents the development and evaluation of the performance of a multi-objective version of the Bacterial Foraging Optimization Algorithm for finding the minor handling switches that maximize the number of buses served, keeping the configuration radial system and within the limits of current in the conductors and bus voltage. An electrical system model was created, and routines were implemented for the network verification, which was used as a function of the Multi-Objective Bacterial Foraging Optimization Hybrid Algorithm. The proposed method has been applied in two distribution systems with 70 buses and 201 buses, respectively, and the algorithm’s effectiveness to solve the restoration problem is discussed.

Network virtualization based on software-defined networking (SDN) has become a necessary technology to provide various services in cloud datacenters. Although many network hypervisors have been proposed to support SDN-based network virtualization, their forwarding techniques excessively consume the limited ternary contents addressable memory of OpenFlow-enabled switches. Moreover, they do not consider network reconfiguration after virtual machine migration. In this paper, we propose LiteVisor, that resolves the two problems mentioned above. It develops the Locator, Identifier, and Tenant sEparating (LITE) scheme. The LITE-based forwarding enables flow aggregation that reduces switch memory consumption. In addition, LiteVisor provides seamless network reconfiguration that does not require tenant controllers to be aware of virtual machine migration based on LITE. We evaluate LiteVisor in terms of the number of flow table entries and network reconfiguration time and compare it with OpenVirteX that is an open-source network hypervisor. The results show that the number of flow rules decreases by up to eight times compared with OpenVirteX in a fat-tree topology. We also demonstrate the seamless network reconfiguration of LiteVisor in the experiments and present the results of the reconfiguration time by the topology size and packet sending interval of hosts.

Future distribution systems can be seen with very high penetration of renewable energy sources (RESs) such as solar photovoltaics and wind turbines on account of diverse techno-economic and social concerns. The uncertainty and variability associated with these RESs along with the stochastic nature of load demand imposes real challenges to system operators. More realistic formulations and suitably tailored methodologies can coordinate well-known operational strategies to achieve optimum performance of distribution systems. This article presents a new methodology to optimally coordinate day-ahead scheduling of distributed resources (DRs) with distribution network reconfiguration (DNR). The scheduling problem optimizes economic operation by considering O&M charges of DRs, emission charges of micro turbines, and sale/purchase of electricity to the customers/grid whereas feeder power losses are minimized by solving DNR problem. Proposed methodology coordinates these two key strategies by coarse and fine tuning to optimize several techno-economic and social objectives while duly addressing more realistic scenario of distribution systems. Application results on a modified standard 33-bus distribution system demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

In recent days, the optimal allocation of distributed generators (DGs) problem in the distribution system caught several reader's attention to improve the system efficiency, to meet the future load growth. In this article, a multiobjective approach is proposed to determine the optimal locations for DGs, optimal DGs sizes, optimal power factors of DGs in the presence of optimal network reconfiguration to maximize the maximum loadability (λmax), minimize the active power loss and maximize the loading margin factor (λv) of the system. Butterfly Optimization (BO) algorithm is implemented to optimize the desired objectives. The Ɛ‐constraint method is used for multiobjective optimization, spanning tree technique is used for checking the radiality of the system and modified repetitive power flow using radial load flow algorithm is developed for finding theλmax, λv.The proposed approach is tested on 33 bus and 69 bus radial distribution test systems. Four scenarios are considered to achieve the desired objectives and each scenario consists of two cases: optimal allocation of DGs in the initial configured network, optimal allocation of DGs in the optimal reconfigured network. Results suggest that optimal allocation of DGs in scenario 4 gives a better improvement in maximum system loadability, loading margin factor, and active power loss reduction of the system. Obtained results compared with the suitable methods and algorithms that are available in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of optimizing voltage set points and switching status of components in direct current power networks subject to physical and security constraints. The problem is cast as a mixed-integer nonlinear programming with two sources of computational complexity: i) Non-convex power flow equations, and ii) The presence of binary variables accounting for the on/off status of network components. A strengthened second-order cone programming (SOCP) relaxation is developed to tackle the non-convexity of power flow equations, and a branch-and-bound search is employed for determining optimal network configurations. The efficacy of the proposed method in optimizing the operation while mitigating contingencies is experimentally validated in a real-time hardware-in-the-loop environment using IEEE benchmark data. [ABSTRACT FROM AUTHOR]

This paper proposes a method for solving the distribution network reconfiguration (NR) problem based on runner root algorithm (RRA) for reducing active power loss. The RRA is a recent developed metaheuristic algorithm inspired from runners and roots of plants to search water and minerals. RRA is equipped with four tools for searching the optimal solution. In which, the random jumps and the restart of population are used for exploring and the elite selection and random jumps around the current best solution are used for exploiting. The effectiveness of the RRA is evaluated on the 16 and 69-node system. The obtained results are compared with particle swarm optimization and other methods. The numerical results show that the RRA is the potential method for the NR problem. [ABSTRACT FROM AUTHOR]

Summary: Reconfiguration of the distribution network (DN) is the changing of status of normally open and close switches for a specific purpose. In this paper, a situation is studied in which a fluctuating load (such as a welder) violates the network power quality (PQ) standards. Therefore, in order to mitigate the emission of the generated flicker, an optimization problem is developed for improving the PQ of the network comprising voltage flicker level and voltage profile in addition to the network power loss as a traditional objective of the DN reconfiguration. The optimization procedure is performed by changing the network configuration using tie and sectionalizing switches. Discrete particle swarm optimization is utilized to optimize the network configuration. The proposed method is evaluated in two DNs. IEEE standard 69‐bus DN and 95‐bus practical Iranian DN are considered as case study networks. The results show the effectiveness of the proposed method for improving the network PQ, especially in case of voltage flicker. The presented studies and procedure will guide the utility engineers in making decisions associated with the choice of reasonable mitigation technique to overcome the identified PQ problems. [ABSTRACT FROM AUTHOR]

Network reconfiguration is a process to change the open-switches in distribution system for a minimum power loss. In the past, metaheuristic techniques were applied widely for network reconfiguration with consideration of a fixed loading profile. When the loading changes, the current configuration may not be the optimal one. Thus, the technique needs to be executed to find a new optimal configuration based on the latest loading. The process is time-consuming since metaheuristic techniques commonly require high computational times and produces inconsistent results. Therefore, this paper proposes a network reconfiguration technique based on artificial neural network (ANN) for variable loading conditions. The proposed ANN model is tested on IEEE 33-bus, IEEE 69-bus, and IEEE-118 bus systems. The test results indicate the efficiency of the proposed technique in three aspects: processing time, simple structure, and high accuracy. [ABSTRACT FROM AUTHOR]

This paper presents the static voltage stability analysis of RDS. Initially the performance of RDS is evaluated using backward/forward load flow considering voltage-dependent load modeling. Later, the load flow solution is used for determining the static voltage stability of the system. The analysis is performed for different type of loads such as constant power, constant current, constant impedance, residential, industrial, commercial, agricultural and electric vehicle loads. The simulations are performed for standard and optimal reconfigured topology of standard IEEE 33-bus test system. The comparative study reveals the importance of load type and topology while assessing the static stability analysis of radial distribution systems. [ABSTRACT FROM AUTHOR]

This article presents an economic model predictive control (EMPC) algorithm for reducing losses and increasing the resilience of medium-voltage electricity distribution grids characterized by high penetration of renewable energy sources and possibly subject to natural or malicious adverse events. The proposed control system optimizes grid operations through network reconfiguration, control of distributed energy storage systems (ESSs), and on-load tap changers. The core of the EMPC algorithm is a nonconvex optimization problem integrating the ESSs dynamics, the topological and power technical constraints of the grid, and the modeling of the cascading effects of potential adverse events. An equivalent (i.e., having the same optimal solution) proxy of the nonconvex problem is proposed to make the solution more tractable. Simulations performed on a 16-bus test distribution network validate the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this study, a new resilience-based framework is presented for multi-period simultaneous transmission and substation expansion planning (ST&SEP) considering extreme weather-related events (EWEs). The formation of the proposed planning framework falls into a tri-level optimisation problem with the aim of strengthening power network resilience in response to the EWEs. In the lower-level problem, short-term remedial corrective strategies of the independent system operator (ISO) after the EWEs are determined by applying network reconfiguration and generation redispatch. In the intermediate-level problem, an enhanced scenario-building approach is developed to model and analyse the EWEs, as non-random uncertain events, and their subsequent detrimental effects using the notion of the fragility curves corresponding to power network components. In the upper-level problem, however, long-term remedial preventive strategies of the ISO, as network planner, after the EWEs are obtained through integrated decisions between multi-period ST&SEP and transmission switching equipment planning. The newly proposed planning framework is formulated as a large-scale mixed-integer non-linear tri-level optimisation problem and is solved by a powerful symphony orchestra search algorithm in order to obtain the final optimal solution. The proposed planning framework is examined on the real-world large-scale Iranian 400-kV power transmission network and its profitableness is assured by thorough simulation studies. [ABSTRACT FROM AUTHOR]

Transmission system expansion leads to excessive short-circuit currents that exceed the capacity of circuit breakers. To avoid over short-circuit current; transmission system operators make various alterations in the transmission system by changing the topology of the system. The feeders can be distributed to different buses in substations through disconnecting coupling circuit breaker between buses. Having been interchanged, the transmission system may face up a lot of problems even in single outage. Therefore, the optimal positions of feeders in substations are important in order to maintain system security. However, this optimization problem has non-convex properties due to AC load flow equations and it has a multi-objective structure to provide the limitation of short-circuit current and security of N-1 contingency. The constraints are the short-circuit current, voltage and transmission line limits in the single contingency. In this paper, the Genetic Algorithm and Binary Particle Swarm Optimization methods were utilized to find a near-optimal bus layout. Algorithms coding was made with Python programming language and PSS/E program was used to obtain power flow and short-circuit data. The results of applying the methods to the IEEE 14-bus test system demonstrated the effectiveness of the methods to take overloads away and restrict short-circuit current and hold voltage in its limit. [ABSTRACT FROM AUTHOR]

A new meta-heuristic method, comprehensive teaching learning harmony search optimization algorithm (CTLHSO), is developed in this paper for the simultaneous reconfiguration and optimal allocation of distributed generation resources in radial distribution systems. The proposed method is a hybridization of the teaching–learning-based optimization (TLBO) and the harmony search (HS) algorithms. Primarily, eleven mathematical benchmark functions are used to test the performance of the CTLHSO algorithm. The results are then compared with that of global best artificial bee colony (G-ABC), particle swarm artificial bee colony (PS-ABC), TLBO and improved TLBO (I-TLBO) with identical parameters and initial conditions. The results show that the CTLHSO performance is better than the G-ABC, PS-ABC, TLBO and I-TLBO. Subsequently, CTLHSO is implemented on the IEEE 33-bus and 69-bus radial distribution systems for network reconfiguration and optimal placement of distributed generation resources to minimize the power losses and improve the voltage profiles. Five case studies at three different load levels are carried out. The results obtained are found to be better than those obtained with HS algorithm, genetic algorithm, refined genetic algorithm and fireworks algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a branch exchange based heuristic network reconfiguration method is proposed for obtaining an optimal network in a deregulated power system. A unique bus identification scheme is employed which makes the load flow and loss calculation faster due to its reduced search time under varying network topological environment. The proposed power loss allocation technique eliminates the effect of cross-term analytically from the loss formulation without any assumptions and approximations. The effectiveness of the proposed reconfiguration and loss allocation methods are investigated by comparing the results obtained by the present approach with that of the existing "Quadratic method" using a 33-bus radial distribution system with/without DGs. [ABSTRACT FROM AUTHOR]

The virtual resources of 5G networks are expected to scale and support migration to other locations within the substrate. In this context, a configuration for 5G network slices details the instantaneous mapping of the virtual resources across all slices on the substrate, and a feasible configuration satisfies the Service-Level Objectives (SLOs) without overloading the substrate. Reconfiguring a network from a given source configuration to the desired target configuration involves identifying an ordered sequence of feasible configurations from the source to the target. The proposed solutions for finding such a sequence are optimized for data centers and cannot be used as-is for reconfiguring 5G network slices. We present Matryoshka, our divide-and-conquer approach for finding a sequence of feasible configurations that can be used to reconfigure 5G network slices. Unlike previous approaches, Matryoshka also considers the bandwidth and latency constraints between the network functions of network slices. Evaluating Matryoshka required a dataset of pairs of source and target configurations. Because such a dataset is currently unavailable, we analyze proof of concept roll-outs, trends in standardization bodies, and research sources to compile an input dataset. On using Matryoshka on our dataset, we observe that it yields close-to-optimal reconfiguration sequences 10X faster than existing approaches. [ABSTRACT FROM AUTHOR]

With increase in industrial development, sub urbanization, and population growth, the energy demand is continuously increasing year by year. This excessive power demand has put a strain on the distribution network thus causing increased power losses (I2R) due to flow of electric current. It therefore becomes highly important to minimize power losses at distribution level in order toemaximize the operational efficiencyeof distributionl utilities. Network reconfiguration is one offthe effective way that has been used by the distribution utilities for distribution system loss minimization. In thisipaper, an ImprovediHarmonyiSearch Algorithm (IHSA) inspired from musician's performance is presented to solveSnetwork reconfiguration problem with an objective torminimize distribution system power loss. The IEEE -- 69ibus andiIEEE -- 119ibus radial distribution networks haveibeen used toidemonstrate the efficacyrof therproposed method. The obtained simulation test results demonstrated that proposed IHSA achieved better quality of solutions and can be a promising and efficient optimization technique for solving distribution network reconfiguration problems. [ABSTRACT FROM AUTHOR]

One of the concerns of distribution network operators is the presence of harmonics in the network and their related issues. In most practical distribution networks, there is not enough information available about harmonic contents of customers for network operators. On the other hand, the changing nature of harmonic contents makes their evaluation more complex. Accordingly, in this article, for the first time, a new method for evaluation of harmonic distortion level is presented based on the IEEE‐519 standard. The method focuses on impedance characteristics of the network buses and uses the harmonic current and voltage limits, which are determined by the mentioned standard. The proposed method is utilized as harmonic constraint for network reconfiguration of IEEE 69‐bus and practical Iranian 95‐bus distribution networks. The results indicate the effectiveness of the proposed method for evaluation of harmonic distortion level in the distribution network. The proposed method can also be used as a useful tool in distribution networks where harmonic contents of nonlinear loads are not available. [ABSTRACT FROM AUTHOR]

Distributed environments are widely used for computing complex applications modeled as task graphs. The computer network becomes more complex when the compute nodes are heterogeneous, however by choosing the appropriate network communication links for communication between a pair of compute tasks can enhance the computing efficiency(called network reconfiguration). One of the steps in heterogeneous network reconfiguration problem is mapping the application task graph edges on the network links. High Performance Computing (HPC) systems are usually heterogeneous, therefore mapping task graph edges on the communication links should consider the two factors: communication cost of task graph edges and the communication capability of network links. The system performance enhances if tasks are mapped on the compute nodes based on the computational costs of the tasks and the processing capability of compute nodes in addition to the edge scheduling on network links. In our earlier algorithm, Heterogeneous Edge and Task Scheduling (HETS) both edge and task mapping simultaneously improve the execution performance of task graphs. The proposed Network Reconfiguration Algorithm (NRA) minimizes the communication overhead and optimizes the schedule length with contention-aware model. NRA reduces an attribute Kirchhoff Index (KI) for optimal network reconfiguration providing minimum execution time. Both synthesized and task graphs of real applications are used for evaluation. The simulation results prove the efficiency of NRA in terms of average schedule length, schedule length ratio, speedup and system throughput. Comparisons with the baseline algorithms show that NRA provides 36% improved results specially for communication-intensive applications. [ABSTRACT FROM AUTHOR]

Because of the defects of the nonlinear optimization algorithm and the complexity of the physical constraints of the distribution network reconfiguration, the speed and the convergence of the algorithms are usually poor. A fast algorithm for distribution network reconfiguration based on feasibility analysis unit is presented. Firstly, the network simplification method is presented based on the radial construction of the distribution network, and then the conception of basic loop is proved by using the impedance-branch dissipation power theory. By analyzing the quantitative relationship between load and branch power loss and the characteristics of the nodal impedance matrix, the basic loop that is just the minus feasibility analysis unit of the physical reconfiguration optimization is verified. Based on this theory, the path dissipation factor is presented which formed a novel heuristic rules and an improved branch exchange method in order to solve the distribution network reconfiguration problem. In this proposed method, the processing sequence of all the basic loops is firstly defined by the load dissipation component, and then optimal reconfiguration scheme is simply obtained by repeating the above operation until there is no branch need to exchange. Test results of IEEE-69 buses system verified the efficiency and reliably of the proposed method. [ABSTRACT FROM AUTHOR]

In the present work, Binary Particle Swarm Optimization (BPSO) based optimal re-configuration for balanced and unbalanced radial distribution networks using Affine Arithmetic (AA), with uncertainty in generation and load, is proposed to minimize the system losses. An expression for three phase real affine power loss is derived with partial deviations of real power loss in lines with respect to power injections in other buses and also with respect to power injections in other phases in case of unbalanced distribution systems. The major contribution of the present work is the application of AA based optimal network reconfiguration, to both balanced and unbalanced radial distribution networks with uncertainty. The proposed method is tested on IEEE 16, 33, 85 and 119 bus balanced distribution systems and an unbalanced 123 bus system with Distributed Generation (DG) connected at some buses. The optimal loss intervals obtained by the proposed method are compared with that obtained by Interval Arithmetic (IA) and Monte Carlo (MC) simulations based methods. The simulation results show that proposed AA based analysis gives an optimal reconfiguration, for both balanced and unbalanced radial distribution systems with uncertainty as compared to existing IA based method. [ABSTRACT FROM AUTHOR]

Enormous work has been reported in literature to enhance the performance of metaheuristics by modifying their internal mechanisms via intervening their control equations. Usually, these population based techniques are initiated through random creation of individuals (tentative solutions) to preserve adequate diversity in population and then attempts have been made to maintain a better balance between exploration and exploitation of the problem search space. However, it would be much better if some strategy is employed that could divert tentative solutions toward the promising region. This can be possible if the algorithm has some mechanism to develop certain knowledge (super sense) about the quality of decision variables of the problem. This paper presents super sense genetic algorithm (SSGA) that gradually develops super sense during successive genetic evolutions. The accumulated genetic information so obtained is stored and used to divert individuals near the promising region while preserving adequate diversity. SSGA differs to standard genetic algorithm (GA) only on this aspect. SSGA is applied to solve complex combinatorial network reconfiguration problem of radial distribution systems. The application results highlight the effectiveness of proposed GA. [ABSTRACT FROM AUTHOR]

Analytical methods for evaluating the reliability of simple and radial distribution networks have been well established. Since these analytical methods cannot consider post-fault load transfer between feeders, the reliability indices are significantly underestimated for mesh-constructed distribution networks. To accommodate various application scenarios, Monte-Carlo simulations are widely used for complex distribution networks and heavy computation burden is involved. In this paper, we propose a novel linear programming model which includes precisely assessing reliability and considers post-fault network reconfiguration strategies involving operational constraints. Moreover, this model also can formulate the influences of demand variations, uncertainty of distributed generations and protection failures on the reliability indices. Numerical simulations show that the proposed model yields the same results as the simulation-based algorithm. Specifically, the system average interruption duration indices are reduced when considering post-fault network reconfiguration strategies in all tested systems. Moreover, the proposed model is suitable for inclusion in reliability-constrained operational and planning optimization models for power distribution systems. [ABSTRACT FROM AUTHOR]

The ever-growing requirement for reliability and quality of power supply suggests to enable self-healing features of smart distribution network using intelligent communication and control. In this article, a concept of fully decentralized multi-agent system (FDMAS) automation is proposed to build a unified restoration service framework for distribution network with distribution generators (DGs), where an FDMAS interaction mechanism is designed for establishing a reduced model which can significantly reduce the computational dimensions of service restoration. Furthermore, an FDMAS-based strategy is proposed for service restoration by combining network reconfiguration with intentional islanding; especially a network reconfiguration algorithm based on network flow model is presented, which, along with parameter justification, can mitigate the variations of loads and intermittence of DGs. The simulation studies are carried out on the 84-bus and 22-bus distribution system, respectively, using MATLAB and java agent development framework (JADE) simulation system and dynamic model test platform. The test results show that the proposed strategy can maximize restoration of out-of-service loads with minimum switching times and has an excellent performance on service restoration time. [ABSTRACT FROM AUTHOR]

The distributed generation (DG) planning with the varying pattern of the practical load is difficult as it calls for the frequent changes in DG size and system configuration, which is neither feasible nor permissible. Rather such a DG size and a configuration, which can be utilized over a wider load pattern, are more acceptable. This work presents a coordinated approach for DG planning and system reconfiguration. While to operate a particular DG size and the system configuration over a wide range of loading pattern, the configurations are ranked under different probabilistic loading patterns. Based upon the ranking of the new configuration, the energy performance of the coordinated planning is evaluated. Further, the observations from coordinated planning are imposed on coordinated operation using harmony search algorithm (HSA). The proposed approach is tested for single as well as multi-objectives on a 33-node system. A significant improvement in the computational efforts and energy performance of the resulting configuration have been observed where losses have reduced to 81.11 and 53.77 kW with single DG and multi-DG allocation respectively. [ABSTRACT FROM AUTHOR]

The proliferated penetration of renewable resources along with stochastic consumption pattern of electrical vehicles have arisen the prominence of real-time monitoring of the grid for the sake of obtaining the optimal topology of distribution network. This paper proposes a data-driven method based on the measurements of $\mu $ PMUs to figure out the hourly optimal configuration of distribution grid in a real-time manner. First, the node voltage and injected current phasors measurements captured by $\mu $ PMUs are processed via a linear state estimation to determine the net load at each node. Then, the real-time high resolution data of loads is turned into knowledge through a bi-level unsupervised information granulation technique. In the second stage, based on the uncertainty bounds obtained for each information granule, a stochastic robust optimization (SRO) is developed via second order conic programming method to find out the best network reconfiguration, while minimizing the corresponding objective cost function. The developed method is applied to IEEE 33-node distribution network and Brazilian 135-node test feeder. [ABSTRACT FROM AUTHOR]