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This paper proposes an optimal network reconfiguration (ONR) by integrating the renewable energy (RE) based distributed generation (DG) resources, i.e., wind and solar photovoltaic (PV) modules, and electric vehicle charging stations (EVCS). The uncertainties related to renewable energy sources (RESs) are handled by using probability analysis. In this work, wind uncertainty is handled by using Weibull probability density function (PDF), and solar PV uncertainty is modeled by using Beta PDF. This paper also models the load of EVCSs. The ONR is a tool to operate distribution systems (DSs) at optimum cost/loss. In the literature, most of the ONR problems are solved as single objective type. This neccessiate the development of multi-objective based ONR problem and solved using the multi-objective algorithms by considering multiple objectives. Therefore in this paper, total cost of operation and power losses are considered as two objectives functions. The single objective-based ONR is solved using crow search algorithm (CSA) and multi-objective-based ONR is solved using multi-objective-based CSA. As the DS is unbalanced, the power flow for the unbalanced system will include the three-phase transformer. The ONR problem has been solved by considering 17 bus unbalanced and balanced DSs.

Hosting capacity (HC) is the mathematical interpretation for the maximum aggregated distributed generations (DGs) penetration among the distribution networks. DGs integration has been adopted as a cleaner energy resource, but it has major operational issues like overvoltage, harmonics, and others. In this work, a novel mathematical optimization algorithm is adopted for HC maximization via sequential network reconfiguration followed by soft open points (SOPs) placement. Besides, a novel SOPs allocation index is proposed to reduce the computational burden taken by the optimization techniques while controlling the discrete allocation variables. Thus, to ensure maximum penetration of DG units and optimum operation scheme, HC maximization is formulated as multi-objective optimization approach, while considering system performance indices as a portion of the objective function. The proposed optimization algorithm succeeded in maximizing the HC of the IEEE 33- node distribution network, the IEEE 69- node network, the 59-node Egyptian distribution network and the 135-node Brazilian distribution network efficiently while improving system performance indices. Further analysis is conducted on the studied distribution systems to assess the effect of load growth on system’s HC after allocating both SOPs and DGs.

Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it’s improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values.

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the...

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.

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.

The distribution network is reconfigured by modifying the topology arrangement of the network feeders. Because the voltage stability of the distribution networks can differ within a range following network reconfiguration, the calculation of steady-state voltage stability plays a significant role in real-time feeder reconfiguration. Examining the state of security and estimating it for the next operational configuration is crucial for making real-time decisions. Online security evaluation needs minimal complexity and computing time. Standard methods of assessing the distribution network’s steady-state voltage stability can be insufficient for online and real-time environments due to their high complexity and long computing period. This study proposes a machine learning (ML) approach for classifying configuration states and adopts the decision tree technique to interpret the online applications in the feeder reconfiguration. For the classification, the single line equivalent L-index voltage stability and switching configurations of the feeders are employed as training information for the ML models. A modified IEEE 14-bus and 30-bus test systems verifies the feasibility of the suggested solution. Once trained, the proposed system provides a quick and accurate classification for unknown configurations of the specific security state in 0.3 seconds.

Made available in DSpace on 2022-04-29T08:29:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Distribution network is an essential part of electric power system, which however has higher power losses than transmission system. Distribution losses directly affect the operational cost of the system. Therefore, power loss reduction in distribution network is very important for distribution system users and connected customers. One of the commonly used ways for reducing losses is distribution system reconfiguration (DSR). In this process, configuration of distribution network changes by opening and closing sectional and tie switches in order to achieve the lowest level of power losses, while the network has to maintain its radial configuration and nodal voltage limits, and supply all connected loads. The DSR aiming loss reduction is a complex mixed-integer optimization problem with a quadratic term of power losses in the objective function and a set of linear and non-linear constraints. Accordingly, distribution network researchers have dedicated their efforts to developing efficient models and methodologies in order to find optimal solutions for loss reduction via DSR. In this paper, an efficient mathematical model for loss minimization in distribution network reconfiguration considering the system voltage profile is presented. The model can be solved by commercially available solvers. In the paper, the proposed model is applied to several test systems and real distribution networks showing its high efficiency and effectiveness for distribution systems reconfiguration. Associated Laboratories Institute of Bioenergy Research (IPBEN) São Paulo State University Campus of Ilha Solteira Department of Electrical Power Engineering Tishreen University School of Electrical and Computer Engineering National Technical University of Athens Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Associated Laboratories Institute of Bioenergy Research (IPBEN) São Paulo State University Campus of Ilha Solteira

Unequal reactive power sharing amongst distributed generators (DG) is a significant concern while operating a droop based microgrid. The reasons for this unequal reactive power sharing include the difference in feeder impedances, uneven distribution of loads and DGs in terms of size and locations. In this article, a mixed-integer linear programming (MILP) problem applicable for a droop based microgrid has been proposed to achieve proportional reactive power sharing amongst DGs while maintaining very low line losses. Firstly, the optimal sizing and placement of DGs are investigated. This includes a detailed discussion on the linearisation of various nonlinear terms involved in the formulation. Subsequently, a day–ahead dispatch is generated for these DGs for a given load profile. Additionally, network reconfiguration has been incorporated for further improvement in performance. The effect of considering a practical constant impedance-current-power (ZIP) load model, instead of simply considering constant impedance loads, has also been assessed. The proposed formulation has been tested on a 33 bus network which has been modified to represent an islanded microgrid. The results obtained validate the accuracy of the proposed planning and dispatch method and demonstrate its utility in achieving proportional reactive power sharing amongst the DGs while incurring very low line losses.

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 ...

Requirements of economic and reliability criteria are often stressed simultaneously in operating and planning models for distribution systems. However, conventional reliability evaluation algorithms are hard to be integrated to these optimization models analytically. Recently, several optimization model-based reliability assessment methods have been proposed, which can be embedded into such models. Nevertheless, the detailed placement and actions of circuit breakers and switches are ignored or oversimplified in these methods, leading to an inconsistency with the real world situation. Thus, we propose a new optimization model-based reliability assessment method that fully considers detailed placement and actions of circuit breakers and switches in distribution networks. Based on fictitious fault flows, strategies for tripping circuit breakers to cut fault current, operating switches to isolate the fault and processing post-fault network reconfiguration are linearly modeled as constraints. Therefore, this method can be easily integrated into operating and planning models of distribution networks. Case studies on 54-node, 85-node, 137-node and 417-node distribution networks show the scalability and efficiency of the proposed model.

This research proposes an optimal switching sequence path method in order to minimise power losses during the network switching operation. Apart from this contribution, simultaneous optimal network...

Finding a global optimal solution to the distribution network reconfiguration (DNR) problem in a short time is a challenging task. This study proposes a real-time online data-driven DNR (3DNR) method. Power loss minimisation, lowest bus voltage maximisation and reliability maximisation are taken as objectives. First, in this study, a methodology combining heuristic algorithm and metaheuristic algorithm to solve DNR is proposed. Then a set of data that satisfies the data drive model requirements is obtained. Next, the improved convolution neural network is used to train the data set of DNR. Unlike the state-of-art methods, the proposed 3DNR can realise the real-time online reconfiguration without power flow calculation. The feasibility and effectiveness of the proposed method are demonstrated on IEEE-34, IEEE-123, and a practical distribution system in Taiwan.

The penetration of renewable energy is gradually increasing. Therefore, power system flexibility, which is required to maintain system security and to cope with renewable generation uncertainty, becomes more important. Various technologies, such as flexible generators, demand management, energy storage, network reconfiguration, and even efficient system operations, are capable of improving power system flexibility. Nevertheless, among them, flexible generation resources can provide a direct way to enhance the power system flexibility. This article first applies the fuzzy analytic hierarchy process to calculate the flexibility index of generation resources. Then, the process of unit scheduling is implemented under various operation scenarios to investigate the relationship between the generation flexibility and the cost of unit scheduling. Finally, the required flexibility and the corresponding capacity of flexible generators under a specified penetration of renewable power generation are investigated under different operation scenarios.

This paper presents an algorithm for network reconfiguration which could help in the operation and expansion planning of three-phase distribution systems. Considering the fact that power losses lead to decrease utility benefits, and the three-phase unbalanced voltage can damage electrical machines, the proposed algorithm is presented as a multi-objective optimization problem based on Evolutionary Algorithms where the power losses and three-phase unbalanced voltage are simultaneously minimized. In order to validate the efficiency of the proposed reconfiguration algorithm, a large-scale radial electric distribution system, under maximum load conditions, is considered for operation planning. In addition, different scenarios are compared to test the algorithm as an expansion planning tool. The proposed algorithm maximizes the utility profits and improves system performance reducing power loss and unbalanced voltage.

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...

Several studies of distribution network enhancement focused only on the optimization of either the integration of distributed generations (DGs) or system reconfiguration (SRC). However, very few researches have been exerted for incorporating both where this merge represents further complexities in searching for maximizing their benefits and minimizing the investments. This paper sheds light on the simultaneous allocation of DGs with SRC in distribution systems. It suggests a multi-objective formulation considering multiple objectives: active power loss minimization, total cost reduction, and voltage profile improvement. For solving this problem, an improved spotted hyena optimizer (ISHO) is developed. The SHO demonstrates a good feature in finding the optimal or nearly optimal subset with simple structure to minimize the given fitness function. To support the exploration characteristics of the proposed ISHO, it augments a switch strategy in the updating mechanism of the spotted hyena’s positions and random positions are generated instead of the violated spotted hyenas. The simulations are investigated for the integration of a single and multiple DGs with SRC for two standard systems, which are IEEE 33 and 69-node test systems. The obtained results prove the efficiency of the suggested method for the network manager to find the optimal DGs allocation with SRC considering multiple criteria. These results are compared with previous literatures to demonstrate the effectiveness of the proposed algorithm.

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.

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 feasi...

Natural disasters and cascading events have historically caused severe disruptions of the electric power system. For instance, in 2012, hurricane Sandy left over 8 million people in darkness and caused total damage costing $65 billion. Under emergency conditions, microgrids can help the electric power system recover critical loads, such as hospitals, data centers, and water pumping stations. While the literature has mostly focused on the utilization of existing microgrids, the idea of planning for future microgrids in combination with switching operations to make the grid more resilient against devastating events is investigated in this study. This work aims to simultaneously maximize the resiliency of distribution networks – in terms of service to the critical loads – and minimize the dispatchable generation capacity of microgrids. The considered microgrid model entails dispatchable power generators, renewable energy resources, and electrical energy storage systems (ESS) to serve consumers. Considering the topological and operational limitations, the robust optimization scheme optimizes the objectives by the effective selection of the node for microgrid connection and the minimum change in its generation capacity. While the problem is modeled as a multi-objective, mixed-integer linear programming (MO-MILP) problem, the results show more than 99% accuracy compared to the exact results of an exhaustive search algorithm. Numerical tests are performed on the IEEE 37-node test feeder and the IEEE 123-node test feeder to assess the proposed method’s performance. Given the accuracy, computation time efficacy, and the generic formulation of the problem, the optimization scheme can be easily applied to any real network.

Optimal Network Reconfiguration (NR) is a well-accepted approach to minimize power loss and enhance voltage profile in the Electrical Distribution Networks (EDN). Since the NR problem contains huge combinational search space, most researchers consider the meta-heuristic techniques to attain NR solution. However, these meta-heuristic techniques do not guarantee to obtain the optimal solution besides they require large processing time to converge. This is mainly due to (1) random initialization and updating of population and (2) the continuous verification of population during the search process. With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and enhancing the voltage profile of the EDN, this work proposes a new method based on two-stage optimizations. The proposed method introduces an approach to simplify the network into simplified network graph. Then, this approach is utilized for guided initializations and generations of the population and for the proper population’s codification. The proposed method is implemented using the firefly algorithm and verified on 33-bus and 118-bus test systems. The results show the ability of the proposed method to obtain the optimal solution within fast computational time and with superior consistency compared to the conventional methods.

This paper proposes a method of network reconfiguration based on symbiotic organisms search (SOS) algorithm for reducing power loss of the electric distribution system. The SOS is a recent developed meta-heuristic algorithm inspired from the symbiotic interaction strategies of organisms for surviving and propagating in the ecosystem. Compared to other algorithms, SOS does not need any control parameters during the searching process. The advantages of the proposed SOS method have been validated in two electric distribution systems. Three network cases have been considered for each system, consisting of performing network reconfiguration on the system without distributed generator (DG) placement, the system installed type-P DGs and the system installed type-PQ DGs. The comparison results with particle swarm optimization and other previous methods show that the proposed SOS can be a promising technique for the problem of electric network reconfiguration.

International audience; Optical multilayer optimization periodically reorganizes layer 0-1-2 network elements to handle both existing and dynamic traffic requirements in the most efficient manner. This delays the need for adding new resources in order to cope with the evolution of the traffic, thus saving CAPEX.The focus of this paper is on Layer 2, i.e., on capacity reoptimization at the optical transport network (OTN) layer when routes (e.g., LSPs in MPLS networks) are making unnecessarily long detours to evade congestion. Reconfiguration into optimized routes can be achieved by redefining the routes, one at a time, so that they use the vacant resources generated by the disappearance of services using part of a path that transits the congested section. To maintain the Quality of Service, it is desirable to operate under a Make-Before-Break (MBB) paradigm, with the minimum number of reroutings. The challenge is to determine the best rerouting order while minimizing the bandwidth requirement.We propose an exact and scalable optimization model for computing a minimum bandwidth rerouting scheme subject to MBB in the OTN layer of an optical network. Numerical results show that we can successfully apply it on networks with up to 30 nodes, a very significant improvement with respect to the state of the art. We also provide some reoptimization analysis in terms of the bandwidth requirement vs. the number of reroutings.

International audience; Dynamic traffic leads to bandwidth fragmentation, which drastically reduces network performance, resulting in increased blocking rate and reduced bandwidth usage. When rerouting traffic flows at Layer 3 of an optical network, network operators are interested in minimizing the disturbances in order to satisfy their Service Level Agreements. Therefore, they turn to the Make-Before-Break (MBB) paradigm.In this paper, we revisit MBB rerouting with the objective of identifying the reroute sequence planning that minimizes the number of reroutes in order to minimize the resource usage. We propose a Dantzig-Wolfe decomposition mathematical model to solve this complex rerouting problem. We instigate how multiple or parallel rerouting reduces the overall minimum number of rerouting events (shortest makespan), and achieve the best resource usage. Numerical results bring interesting insights on that question and show a computational time reduction by about one order of magnitude over the state of the art.

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.

The resonant grounding is one of the possible methods of the system neutral grounding for the medium voltage distribution. IEEE standards do define this grounding configuration, but important advantages and drawbacks of the resonant grounding might not be fully known, due to its rather uncommon application in North America. On the other hand, resonant grounding in Europe is imposed by the increased requirements for power quality, especially for medium-voltage (MV) industrial users, imposed to electric utilities by regulatory authorities, with the purpose to protect the interests of users and consumers. Level of the continuity of the service, magnitude and phase of ground-fault currents, magnitude of touch voltages, all depend on how the neutral is connected to ground. In this paper, the authors will discuss electrical safety features of the resonant ground, and analyze issues when a substation, originally operated with different methods of system neutral grounding, is reconfigured with a resonant ground. In particular, a conservative approach for preserving safety during high-voltage (HV)-MV substations' reconfiguration is herein proposed; the cases of the substation reconfiguration from resonant grounding to isolated-from-ground and vice versa shall be analyzed. Finally, the analysis of the impact of the use of Petersen coil on the global grounding systems will be provided.

Distribution system reconfiguration is a complex combinatorial optimisation problem. Finding a globally optimal solution to this problem in a short span of time is a challenging task. Two different network reconfiguration methodologies, namely a look-up table-based algorithm and a Pareto-optimisation-based algorithm have been discussed. Power loss minimisation and reliability maximisation are taken as objectives for both the methodologies when the system is in the normal operating state. The same algorithms also work for maximum service restoration under the post-fault condition. For reliability assessment in the presence of distributed generators (DGs), an algorithm is also proposed using the probabilistic model of components. In the other algorithm, reconfiguration is performed in the presence of DGs using a look-up table. The look-up table is prepared with the help of the proposed Pareto-optimisation-based reconfiguration algorithm to train the system for various loading conditions. The feasibility and effectiveness of the proposed methods are demonstrated using the IEEE 34- and IEEE 123-bus unbalanced radial distribution systems under normal and faulty conditions. The algorithms are also tested on a 13-bus practical distribution system in the field.

This work proposes a data-driven bandwidth allocation (BA) framework for periodically and dynamically reconfiguring an elastic optical network according to predictive bandwidth allocation (PBA) models. The proposed framework is scalable to the number of network connections and also adaptive to the increasing traffic of each network connection separately and to the overall network load as well. This is achieved by formulating the BA problem as a Partially Observable Markov Decision Process (POMDP), which constitutes are reinforcement learning (RL) model. Specifically, RL is performed continuously and independently (locally) for each network connection according to the most recent data that describe the traffic demand behavior of each network connection. A central controller monitors the network performance that is jointly achieved from all the PBA models and is capable of dynamically modifying the reward function of the POMDP, ensuring that the quality-of-service requirements are met. A reward function R(C) is examined with a clear impact on the network performance when C is modified. For evaluating the network performance, for each R(C), the routing and spectrum allocation (RSA) problem is solved according to an Integer Linear Programming (ILP) algorithm and an RSA heuristic alternative, with both the ILP and the heuristic RSA taking as inputs the outputs of the inferred PBA models. Results indicate that with the appropriate settings of C, bandwidth is efficiently allocated, while ensuring that the QoS requirements are met.

One of the main control tasks in a computer network is to organize an effective system of information delivery; this task is of particular relevance in the software defined network. Conventional routing tools do not meet the requirements to service quality and the requirements for equitable distribution of congestion along communication channels. Routing in conventional networks is performed by the shortest path search based on a specified parameter, but these tools do not provide sufficient agility when changing routes in the network. Another drawback is the need to transmit regular updates of routing information by passing the service traffic, thereby dramatically increasing the congestion and reducing the throughput. At present, the most effective way to ensure the assigned quality of service parameters, as well as a promising solution to organize efficient routing under conditions of uncertainty, is a software defined network. This new networking paradigm makes it possible to simplify the process of managing the network, to significantly enhance the use of network resources, and to reduce operating costs. One of the main advantages of such a network is control at the upper levels of the reference model, which makes it possible to simplify both the process of network management and the process to manage traffic in corporate networks and data center networks. A new approach to traffic design in a software defined network has been proposed that employs the making-decision theory oriented towards routing exactly in such networks. If there is a «problematic area» and there is the need to overcome it, the decision-making theory under conditions of uncertainty is used, since the probability of selecting the best way to circumvent it accounts for the patterns in transmitted traffic. Such a method makes it possible to reduce the loss of inelastic traffic that is an important component of the overall amount of transmitted information. From a practical point of view, the algorithm constructed in this work, when compared to known algorithms for traffic engineering, improves the quality of service in software defined networks

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.

This paper presents a new algorithm to solve the problem of reconfiguration of distribution networks using Improved Selective Binary Particle Swarm Optimization (IS-BPSO). The proposed method presents a new sigmoid function capable of promoting a control in the rate of change of the particles and improving the convergence of the results. The proposed algorithm aims to reduce power losses in distribution networks, it was applied in two energy distribution test systems, commonly found in the literature, the 33-Bus and 94-nodes systems. The results of the simulation show that the proposed method is very efficient and guarantees the achievement of the global optimum.

Recent studies show that the majority of the researchers have focused on either Distributed Generation (DG) allocation or network reconfiguration for enhancing distribution network performance. How...

Optimal distribution system reconfiguration (DSR) and distribution generation (DG) allocation are viable solutions for improvement of technical and economic aspects of distribution systems. This paper proposes a stochastic multi-objective DSR (SMO-DSR) model, aims to maximize the DG owner’s profit and minimizes the distribution company’s (DisCo’s) costs. The uncertainties of wind power generation, electricity price, and demand are handled via scenario based approach. The proposed SMO-DSR model is solved via e-constraint method and the best compromise solution is selected by fuzzy satisfying criterion. The model is a mixed integer non-linear programing (MINLP) problem which is implemented on IEEE 33-bus distribution system in General Algebraic Modeling System (GAMS) environment. To show the effectiveness of the proposed SMO-DSR approach, it is studied in different cases. A sensitivity analysis is also performed to show the effect of contract price of wind energy on the objectives of DisCo and DG owner. The obtained results substantiate the interaction between the DSR and DG allocation problems. Also, it is shown that the contract price of wind energy considerably influences both DG owner and DisCo schedules. Besides, when a compromise is made between the DG owner’s profit and DisCo’s cost, the power losses of the network is reduced.

This study proposes a service restoration strategy for an AC/DC hybrid distribution system. A mathematical model for voltage source converter with different control strategies in service restoration is developed. Power storage system scheduling strategy is also designed for restoring more power supply. A mixed-integer second-order cone programming is developed for network reconfiguration, and constraint express can make sure the distribution network operates in a radial manner. With the conditions of power balance in islands as the constraints, the islands are then divided. It adopts minimum loss in the object makes it not only stable but also economically acceptable. Simulation results show that the purposed strategy is feasible and effective to reduce lost load, distribution generation curtailments, and network loss.

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.

This paper presents state of the art reconfiguration techniques used for service restoration in distribution systems under different practical considerations. The different formulations of the problem together with the different solution methods are presented to show the advantages and disadvantages of each formulation and each solution. Other aspects that enhance the solution practicability such as load variations, load priority, cold load pickup, network connectivity representation, and existence of distributed generation are considered with directions for future research to convert current distribution system into an automatic self-healing system, which is the main pillar of the smart grid. The control method and the communication techniques used in the execution of the reconfiguration problem solution are highlighted and the new research directions in each issue are emphasized. The conclusion about the reconfiguration problem the formulation, the solution method, and the guidelines about unexplored research areas in this topic are presented at the end of the paper. Keywords: Distributed generation, IEC 61850, Network reconfiguration, Restoration, Smart grid

Abstract The development and planning of optimal network reconfiguration strategies for electrical networks is greatly improved with proper application of graph theory techniques. This paper investigates the application of Kruskal's maximal spanning tree algorithm in finding the optimal radial networks for different loading scenarios from an interconnected meshed electrical network integrated with distributed generation (DG). The work is done with an objective to assess the prowess of Kruskal's algorithm to compute, obtain or derive an optimal radial network (optimal maximal spanning tree) that gives improved voltage stability and highest loss minimization from among all the possible radial networks obtainable from the DG-integrated mesh network for different time-varying loading scenarios. The proposed technique has been demonstrated on a multiple test systems considering time-varying load levels to investigate the performance and effectiveness of the suggested method. For interconnected electrical networks with the presence of distributed generation, it was found that application of Kruskal's algorithm quickly computes optimal radial configurations that gives the least amount of power losses and better voltage stability even under varying load conditions. Article Highlights Investigated network reconfiguration strategies for electrical networks with the presence of Distributed Generation for time-varying loading conditions. Investigated the application of graph theory techniques in electrical networks for developing and planning reconfiguration strategies. Applied Kruskal’s maximal spanning tree algorithm to obtain the optimal radial electrical networks for different loading scenarios from DG-integrated meshed electrical network.

International audience; Software Defined Networking (SDN) and Network Function Virtualization (NFV) are complementary and core components of modernized networks. In this paper, we consider the problem of reconfiguring Service Function Chains (SFC) with the goal of bringing the network from a sub-optimal to an optimal operational state. We propose optimization models based on the makebefore-break mechanism, in which a new path is set up before the old one is torn down. Our method takes into consideration the chaining requirements of the flows and scales well with the number of nodes in the network. We show that, with our approach, the network operational cost defined in terms of both bandwidth and installed network function costs can be reduced and a higher acceptance rate can be achieved, while not interrupting the flows.

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.

Motivated by applications in evacuation planning, we consider a problem of optimizing flow with arc reversals in which the transit time depends on the orientation of the arc. In the considered problems, the transit time on an arc may change when it is reversed, contrary to the problems considered in the existing literature. Extending the existing idea of auxiliary network construction to allow asymmetric transit time on arcs, we present strongly polynomial time algorithms for solving single-source-single-sink maximum dynamic contraflow problem and quickest contraflow problem. The results are substantiated by a computational experiment in a Kathmandu road network. An algorithm to solve the corresponding earliest arrival contraflow problem with a pseudo-polynomial-time complexity is also presented. The partial contraflow approach for the corresponding problems has also been discussed.

Power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration, including the integration of distributed generation (DG) units in the distribution network. In this regard, the optimal placement and sizing of DGs are crucial. Otherwise, the network performance will be degraded. This study is conducted to optimally locate and sizing of DGs into a radial distribution network before and after reconfiguration. A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network. An optimal network configuration with DG coordination in an active distribution network overcomes power losses, uplifts voltage profiles, and improves the system stability, reliability, and efficiency. For considering the actual power system scenarios, a penalty factor is also considered, this penalty factor plays a crucial role in the minimization of total power loss and voltage profile enhancement. The simulation results showed a significant improvement in the percentage power loss reduction (32% and 68.05% before and after reconfiguration, respectively) with the inclusion of DG units in the test system. Similarly, the minimum bus voltage of the system is improved by 4.9% and 6.53% before and after reconfiguration, respectively. The comparative study is performed, and the results showed the effectiveness of the proposed method in reducing the voltage deviation and power loss of the distribution system. The proposed algorithm is evaluated on the IEEE-33 bus radial distribution system, using MATLAB software.

The uncoordinated integration of electric vehicles (EVs) severely deteriorates the operational performance of a distribution network. To optimize distribution network performance in an EV charging environment, this paper presents a two-stage optimization approach, which integrates coordinated EV charging with network reconfiguration. A formulation to minimize system power loss is presented, and an optimal solution is obtained using a binary particle swarm optimization algorithm. The proposed approach is tested on a modified IEEE 33-bus medium-voltage node test system, coupled with a low voltage distribution network. Results of the coordinated and uncoordinated EV charging are compared with those of the developed integrated approach, and the operational performance of the system is studied. The results show that the integration of network reconfiguration with coordinated EV charging significantly decreases network power losses and fairly improves voltage profile. Thus, the proposed strategy can lead to improved operational performance of the system while dealing with the growing penetration of EVs in the network.

To enhance the reliability of power supply, mesh-designed distribution network configurations now become common in urban area. A mesh-constructed network structure is composed of excessive tie-lines which serve as backups for rerouting power once a failure occurs in the main branches. Compared with radial-constructed networks, it performs better and has higher reliability of power supply. By implementing distribution expansion planning, tie-lines are going to be built for radial networks to improve the connection and coordination between feeders. However, existing planning approaches for tie-lines often use metaheuristics to implement the actions of post-fault network reconfiguration or perform posterior Monte Carlo simulations to assess the reliability of planned networks, which is time consuming and suboptimal. In this paper, a reliability-constrained planning model for tie-lines in distribution networks is proposed based on an analytical reliability assessment model, where post-fault network reconfiguration strategies are implemented to explicitly count the reliability indices. Moreover, the proposed model can also consider different reliability requirements for different load nodes. The planning model is formulated as an MILP problem and can be solved by commercial solvers. A 54-node system is utilized to test the performance of proposed model. Simulation results show that the equipment of tie-lines in distribution networks can evidently reduce the systematic average interruption duration and enhance the reliability of the distribution systems.

This paper presents a new mixed-integer second-order cone programming model for the problem of optimal restoration of distribution networks that, besides network reconfiguration, also considers microgrid formation. General radiality constraints are used to allow the resulting microgrids to operate with single or multiple distributed generators. Furthermore, the proposed radiality formulation allows parts of the network to be de-energized. Thus, the alternative of disconnecting the loads from their nodes, which is used in some works to ensure radiality, may not be practical in some systems and, therefore, it is not considered in this paper. Tests are carried out using a 53-node system, and the results show that the proposed model can solve the problem at low computational times. It can also be verified that by allowing microgrid formation, more load can be reconnected to the system in the service restoration process.

Today’s safety-critical aeronautical networks use two networks concurrently for redundancy. Furthermore, the most critical applications have additional hard-wired end-to-end links for resilience. However, with such networks increasingly being connected to the Internet for added functionality they face risks that cannot be mitigated thus. Consequently, mechanisms permitting network reconfiguration during runtime such as software-defined networking (SDN) receive interest, even though they have not been designed for safety-critical systems. To ensure that safety is maintained at all times, further mechanisms are required. Hence, this paper proposes a novel approachdynamic network reconfiguration(DRN). Using pre-calculated configuration templates obtained using linear optimisation, one of two concurrently served node-and link disjoint paths can safely be reconfigured using SDN processes. To validate the DNR solution, a sample avionics network has been build in Mininet and failures randomly triggered. Experimental results show that using the DRN solution allows to mitigate up to 14 successive failures without loosing safety-critical traffic. Consequently, our solution not only provides more resilience against failures than current solutions, but can also be used to mitigate the effects of network attacks.

Consensus-based distributed average observation has been widely used in decentralized coordination among distributed generators (DGs) in inverter-based autonomous microgrids (MGs). However, its observation accuracy suffers from communication delays. Such observation errors are hard to detect and could deviate the system operation states (e.g., DG operating voltage) from their desired references. This work quantifies the relationship between communication delays and the steady-state observation errors of the delayed dynamic consensus algorithm. It is worth mentioning that system under study operates autonomously in the context of dynamic MGs. Compared to conventional MGs, dynamic MGs operate with reconfigurable cyber-physical networks. The observation errors are minimized by adopting the optimal system topology through cyber-physical network reconfiguration. Detailed control diagrams are designed to observe the average DG operating voltage in the physical networks, while measurements and control references are exchanged with communication delays through the cyber network. The proposed reconfiguration approach along with detailed controller designs are validated on a 12-bus test system in Simulink/MATLAB.

Contemporary distributions are now going to underground their overhead distribution lines due to techno-social reasons. Reliability and loss reduction are the two prime objectives for distribution system operation. Since failure rates of ungrounded cables are the function of Joules heating besides their physical lengths, the reliability evaluation of undergrounded distribution systems needs to be reviewed. This paper suggested a suitable modification in existing reliability indices in order to make them more appropriate for underground distribution systems. A multi-objective network reconfiguration problem is formulated to enhance the reliability and performance of distribution systems while duly addressing the variability and uncertainty in load demand and power generation from renewables. The application results on a standard test bench shift the paradigm of the well-known conflicting nature of reliability and network performance indices defined for overhead distribution systems.

Renewable energy sources (RES) has gained a lot of interest recently. The limited transmission capacity serving RES often leads to network congestion since they are located in remote favorable locations. As a result, when poorly scheduled, the intermittent nature of RES may result in high curtailments of the free resource. Currently, grid operators utilize a static network when performing day-ahead scheduling and ignore transmission flexibility. This paper explores the possibility of utilizing network reconfiguration as a corrective action to reduce the transmission congestion and thereby the reduction of RES curtailments in day-ahead scheduling. To facilitate the RES integration in the grid, a stochastic N-1 security-constrained unit-commitment with corrective network reconfiguration (SSCUC-CNR) is modelled. SSCUC-CNR model is studied on a modified IEEE 24-bus system with RES. The simulation results demonstrate that CNR not only leads to a lower cost solution by reducing network congestion but also facilitates RES integration by reducing congestion-induced curtailments in high penetration cases. Emission studies demonstrate that more green generators are committed resulting in reduced carbon emissions when CNR is implemented., 5 pages, 5 figures, 2020 PESGM

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.

The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime.