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A new chaotic search group algorithm (CSGA) is proposed in this study for simultaneous network reconfiguration and allocation of distributed generation (SNR-DG) problem with the objective of minimum real power loss in the radial distribution network (RDN). The CSGA is an improved metaheuristic algorithm, in which a chaotic local search strategy is incorporated with the original SGA, to enhance its search performance. The proposed CSGA was studied on 33-, 69-, 84- and 118-bus RDNs with three load levels. After SNR-DG application, the voltage profile and real power loss of the system had enhanced significantly. The outcomes yielded by CSGA were compared with those obtained from the original SGA and other techniques depicted in the literature. The comparative findings revealed that CSGA yielded better solution quality than SGA and other techniques. Hence, CSGA is the best technique to address the SNR-DG problem in RDNs.

As the load steadily rises in a distribution system (DS), electrical power generation is improved. Inserting Distributed Generation (DG) unit in a DS has benefits such as enhancement in voltage profile and decreased power losses. In combination with optimal size, these advantages can be obtained to inject DG in the optimal location. Reconfiguration is another framework for optimizing the voltage profile and reduction of energy loss. Furthermore, reconfiguring the distribution network and installing the DG would ultimately improve system efficiency, which is undoubtedly challenging task. In this paper, equilibrium optimizer (EO) is explored to find out how to reconfigure the distribution network and how best DG can be positioned. For the IEEE-33 bus. Reliability Analysis is also done. The suggested method is implemented in MATLAB R2020A platform software. Outcomes of the suggested Technique based on the EO is compared with the existing techniques. There is Substantial improvement in voltage profile in addition to the decline in loss of power and reliability indexes (EENS, SAIFI, and SAIDI) for different cases of System with DG placement and System.

This paper proposes an innovative integrated power and hydrogen distribution system (IPHDS) restoration model in response to multiple outages caused by natural disasters. During the restoration, repair crews (RCs) and mobile battery-carried vehicles (MBCVs) are considered to repair faulted lines and support critical power loads. Also, the network reconfiguration is taken into consideration in the restoration model to pick up loads. Besides, to address the different response time of hydrogen and power systems, the aerodynamic law-based dynamic hydrogen flow model is applied in the hydrogen system. The proposed model is presented as a mixed-integer linear program, which is verified on a 33-bus-48-node IPHDS with multiple outages. The present results verify the effectiveness of the proposed method.

This paper proposed modified sunflower optimization (MSFO) for the combination of network reconfiguration and distributed generation placement problem (NR-DGP) to minimize power loss of the electric distribution system (EDS). Sunflower optimization (SFO) is inspired form the ideal of sunflower plant motion to get the sunlight and its reproduction. To enhance the performance of SFO, it is modified to MSFO wherein, the pollination and mortality techniques have been modified by using Levy distribution and mutation of the best solutions. The results are evaluated on two test systems. The efficiency of MSFO is compared with that of the original SFO and other algorithms in literature. The comparisons show that MSFO outperforms to SFO and other methods in obtained optimal solution. Furthermore, MSFO demonstrates the better statistical results than SFO. So, MSFO can be a powerful approach for the NR-DGP problem.

The optimization of the distribution system is one of the most important operations for running an efficient power system. At the distribution stage, a wide variety of development frameworks are ac...

Power loss reduction is a very important problem in operating the distribution network. This paper presents a method relied on artificial ecosystem-based optimization (AEO) for the network reconfiguration (NR) problem. The considered goal of the NR problem is to reduce power loss. The performance of the proposed AEO method is evaluated on the 14-node, 69-node and 136-node distribution networks. In addition, the NR approach relied on cuckoo search algorithm (CSA) is also implemented to compare with the proposed AEO method. The statistics result comparisons between AEO and CSA demonstrate that AEO outperforms CSA method with the higher success rate and better quality of the obtained solution in finding the optimal network configuration. In addition, AEO is also better than some previous approaches in the literature in term of the obtained network configuration. Consequently, AEO method is a very effective approach for the NR problem.

Promoting home-country industrial innovation via overseas mergers and acquisitions (M&A) integration is crucial for latecomers. However, the transmission mechanism for innovation remains unclear. T...

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Numerous strategies have been put forward to provide remedies to lessen the undesirable effects of these issues. Combining two of these approaches and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies using a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The voltage profile was significantly improved, and the power losses were significantly decreased. When compared to some of the different methods, DOA came out on top.

Epilepsy surgery fails in 30% of patients with focal cortical dysplasia (FCD). The seizure persistence after surgery can be attributed to the inability to precisely localize the tissue with an endogenous potential to generate seizures. In this study, we aimed to identify the critical components of the epileptic network that were actively involved in seizure genesis.The directed transfer function was applied to intracranial EEG recordings and the effective connectivity was determined with a high temporal and frequency resolution. Pre-ictal network properties were compared with ictal epochs to identify regions actively generating ictal activity and discriminate them from the areas of propagation.Analysis of 276 seizures from 30 patients revealed the existence of a seizure-related network reconfiguration in the gamma-band (25-170 Hz; p 0.005) - ictogenic nodes. Unlike seizure onset zone, resecting the majority of ictogenic nodes correlated with favorable outcomes (p 0.012).The prerequisite to successful epilepsy surgery is the accurate identification of brain areas from which seizures arise. We show that in FCD-related epilepsy, gamma-band network markers can reliably identify and distinguish ictogenic areas in macroelectrode recordings, improve intracranial EEG interpretation and better delineate the epileptogenic zone.Ictogenic nodes localize the critical parts of the epileptogenic tissue and increase the diagnostic yield of intracranial evaluation.

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.

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...

In this paper, a long-term Distribution System Planning Model (DSPM) considering network reconfiguration and Distributed Generation (DG) integration, is presented. The DSPM objective functions are ...

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 objective of this study is to analyze feeder loss minimization and load balance under given constrains. Effective methods are required for feeder switching/reconfiguration. Feeder switching is a mixed-integer large-scale combinatorial problem for optimization, not easily solvable with classical optimization techniques, especially involving a great number of switches. This paper proposes a fuzzy indexing algorithm for feeder switching, with membership functions defined for switches such as thermometers or indices. The optimal switches can be determined through fuzzy index operations. With membership functions defined, the developed method used numerical operations for indices instead of the “set” operation or the min-max operations of traditional fuzzy algorithms. The optimization problem becomes a simple numeric calculation instead of a large-scale sorting problem and is much faster than most algorithms. It greatly reduces the computation time and enhances efficiency, which is suitable for either planning or operation purposes. Many algorithms were tested with three typical examples chosen for illustration, including the “optimal” results with an exhausted search. It shows that the proposed algorithm is very effective and can balance the load to reduce the loss and costs in obtaining the solution.

Smart grid systems play a significant role in improving the resilience of distribution systems (DSs). In this paper, two strategies are proposed for implementation of a smart grid application: (a) a network reconfiguration and (b) a network reconfiguration with mobile emergency generator (MEGs) deployment. An improved set of resilience metrics to quantify and enhance the resiliency of distribution systems (DSs) is developed for the proposed optimization. The metrics aim to determine a suitable strategy and the optimal number and capacity of MEGs to restore the disconnected loads through the development of several microgrids. These metrics are then aggregated with the proposed strategy to develop an automated solution provider. The objective is to maximize system resilience considering the priority loads. The proposed resilience metrics are tested on the IEEE 33-Bus radial DSs. The case studies conducted proved the performance of the proposed power outage management strategy and resilience metrics in maximizing system resiliency for smart grids.

Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a non-convex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432.

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.

Smart self-healing is perceived as a new alternative to ensure reliability and quality of power supply with the development of intelligent communication and control technology. On the basis of multistage characteristics of self-healing control, this article proposes an integrated multistage self-healing strategy for smart distribution systems using multiagent system (MAS), in which the complex self-healing problem is decomposed into phased sub-problems and is addressed by a unified control framework composed of different algorithms of stages. In the proposed control framework, decision-making agents vary with fault points and transition between self-healing stages making the technique fully decentralized. Stressing on the coordination between stage algorithms composed of communication self-adaption, fault tolerance, fault location and isolation, service restoration and state regression, the proposed strategy features well real-time control performance and relatively complete self-healing functions. Comprehensive simulation studies are carried out on the 84-bus and 22-bus distribution systems using MATLAB and JADE and the self-healing test platform respectively, and the test results have shown the effectiveness of the proposed strategy.

Increased load demands worsen distribution system problems such as greater line losses, voltage deviation and a plethora of other concerns. This current work presents an approach stressing simultaneous optimal allocation and sizing of capacitor banks and distributed generations, as well as optimal radial distribution system (RDS) reconfiguration, to address these difficulties. The above objectives are accomplished through the maiden application of the proposed quasi-reflection-based slime mould algorithm (QRSMA). The efficacy of QRSMA is established by testing it on different benchmark functions. A new modified backward forward load flow approach is also proposed and validated by comparing its results to those obtained using MATPOWER software for IEEE 69, 85, and 118 bus RDSs. The proposed load flow technique is independent of the sequential bus numbering scheme and may be applied to any RDS network topology. The proposed QRSMA is tested on 118 bus RDS and to prove its effectiveness; its results are compared to those of other studied algorithms. The study takes into account both fixed and variable loading scenarios. A cost-benefit analysis of the strategy is also performed in order to make the methodology more realistic.

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.

The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints. As the exploration of feasible solutions in large and nonconvex search space of DSR is typically hard, it is important to develop efficient algorithms and methods for finding optimal solutions for DSR problem in reasonably short computational times. In traditional DSR, the configuration of distribution network can be changed by opening and closing sectional and tie switches, where active power losses are minimized, while radial network configuration and supply to all connected loads are both preserved. Accordingly, this paper provides a comprehensive review of a number of existing metaheuristic reconfiguration methods and introduces a novel efficient genetic algorithm (efficient GA) for DSR with loss minimization. In order to demonstrate benefits and effectiveness of the proposed efficient GA for DSR, the paper also provides a detailed comparison of results with an improved genetic algorithm (improved GA) for several test systems and real distribution networks. The obtained simulation results clearly show higher accuracy and improved convergence performance of the proposed efficient GA method, compared to the improved GA and other considered reconfiguration methods.

This study proposes a multi-objective optimization for power distribution network reconfiguration by integrating distributed generators using an artificial immune system (AIS) method. The most effective and inexpensive technique in reducing power losses in distribution networks is optimizing the network reconfiguration. On the other hand, small to medium scale renewable energy power plant applications are growing rapidly. These power plants are operated on-grid to a distribution network, known as distributed generation (DG). The presence of DG in this distribution network poses new challenges in distribution network operations. In this study, the distribution network optimization was carried out using the AIS method. In optimization, the goal to be achieved is not only one objective but should be multiple objectives. Multi-objective optimization aims to reduce power losses, improve the voltage profile, and maintain a maintained network load balance. The AIS method has the advantage of fast convergence and avoids local minima. To test the superiority of the AIS method, the distribution network optimization with and without DG integration was carried out for the 33-bus and 71-bus models of the IEEE standard distribution networks. The results show that the AIS method can produce better system operating conditions than before the optimization. The parameters for the success of the optimization are minimal active power losses, suitable voltage profiles, and maintained load balance. This optimization has successfully increased the efficiency of the distribution network by an average of 0.61%.

Contemporary researches offer that most researchers have concentrated on either network reconfiguration or Distributed Generation (DG) units insertion for boosting the performance of the distribution system (DS). However, very few researchers have been studied optimum simultaneous distributed generation units insertion and distribution networks reconfiguration (OSDGIR). In this paper, the stochastic meta-heuristic technique belong to swarm intelligence algorithms is proposed. Salp Swarm Algorithm (SSA) is inspired by the behavior of salps when navigating and foraging in the depth of the ocean. It utilized in solving OSDGIR. The objective function is to reduce power loss and voltage deviation in the Distribution System. The SSA is carried out on two different systems: IEEE 33-bus and local Iraqi radial (AL-Fuhood distribution network). Three cases are implemented; only reconfiguration, only DG units insertion, and OSDGIR. Promising results were obtained, where that power loss reduced by 93.1% and recovery voltage index enhanced by 5.4% for the test system and by 78.77% reduction in power loss and 8.2% improvement in recovery voltage for AL-Fuhood distribution network after applying OSDGIR using SSA. Finally, SSA proved effectiveness after an increase in test system loads by different levels in terms of reduced power loss and voltage deviation comparison with other methods

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 ...

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.

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.

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...

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...

Although R&D networks have been increasingly used by companies to foster innovation through collaboration, there are still challenges regarding how to successfully orchestrate such projects. The goal of this study is to analyse how a hub firm orchestrates an R&D network through collaborative practices. We based our study on the three orchestration subprocesses proposed by Dhanaraj and Parkhe, namely, to foster knowledge mobility, guarantee innovation appropriability, and maintain network stability. To achieve our goal, we analysed the collaborative practices in an R&D network in the semiconductor industry in France. The case consists of an R&D network among 26 companies to create a new transistor. The data include 65 interviews and 192 documents. The study contributes to the literature on network orchestration by showing that specific collaborative practices performed by a hub firm enable a fourth orchestration subprocess—network reconfiguration—which is necessary in long‐lasting and complex R&D networks and also affects the other three subprocesses. Network reconfiguration consists in disassociating and attracting new members to the R&D network to cover knowledge demands, and is performed by the hub‐firm through specific collaborative practices. The results can benefit practitioners by showing which collaborative practices may be useful for a hub firm throughout the development of an R&D network.

An original three-layer planning model of energy storage systems (ESSs) in active distribution networks is proposed in this study, taking demand response (DR) and network reconfiguration (NR) into account. In order to handle the uncertainties of distributed generations and loads, a practical scenario-generation method based on seasonal characteristics is put forward. Based on the time of use price, an ESSs charging and discharging strategy is presented. The mixed-integer nonlinear programming problem is converted to a mixed-integer second-order cone (SOCP) and the SOCP and an improved hybrid intelligent algorithm are applied to solve the ESSs planning model. Finally, the feasibility and effectiveness of the proposed model and solution methods are proven through the improved IEEE 33-bus system. The case study demonstrates the positive effect of DR and NR on optimising system operation and reducing total cost.

Temporal lobe epilepsy (TLE) is nowadays considered a network disorder impacting several cognitive domains. In this work we investigated dynamic network reconfiguration differences in patients with unilateral TLE compared to a healthy control group, focusing on two connectivity indices: flexibility and integration. We apply these indices for the first time to high-density EEG source-based functional connectivity. We observed that patients with TLE exhibited significantly lower flexibility than healthy controls in the Control, Default Mode and Attentive Dorsal networks, expressed in the delta, theta and alpha bands. In addition, patients with TLE displayed greater integration values across the majority of the resting state networks, especially in the delta, theta and gamma bands. Relevantly, a higher integration index in the Control, Attentive Dorsal and Visual networks in the delta band was correlated with lower performance in visual attention and executive functions. Moreover, a greater integration index in the gamma band of the Control, Somatomotor and Temporoparietal networks was related to lower long-term memory performance. These results suggest that patients with TLE display dysregulated network reconfiguration, with lower flexibility in the brain areas related to cognitive control and attention, together with excessive inter-network communication (integration index). Finally, the correlation between network integration and the reduced cognitive performance suggests a potential mechanism underlying specific alterations in neuropsychological profile of patients with TLE.

The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy delivery while minimizing the number of disconnected loads in outage conditions. Power outages are caused by natural hazards, e.g., hurricanes, or system malfunction, e.g., line failure due to aging. In this paper, we first propose a distribution-network optimal power flow formulation (DOPF) and define a new resilience evaluation indicator, the demand satisfaction rate (DSR). DSR is the rate of satisfied load demand in the reconfigured network over the load demand satisfied in the DOPF. Then, we propose a novel model to efficiently find the optimal network reconfiguration by deploying sectionalizing switches during line outages that maximize resilience indicators. Moreover, we analyze a multiobjective scenario to maximize the DSR and minimize the number of utilized sectionalizing switches, which provides an efficient reconfiguration model preventing additional costs associated with closing unutilized sectionalizing switches. We tested our model on a virtually generated 33-bus distribution network and a real 234-bus power distribution network, demonstrating how using the sectionalizing switches can increase power accessibility in outage conditions.

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.