Network Reconfiguration Research Articles

A Novel Solution Method for the Distribution Network Reconfiguration Problem based on an Objective Function and considering the Cost of Electricity Transmission

A Novel Solution Method for the Distribution Network Reconfiguration Problem based on an Objective Function and considering the Cost of Electricity Transmission

Modular brain network in volitional eyes closing: enhanced integration with a marked impact on hubs.

Volitional eyes closing would shift brain's information processing modes from the "exteroceptive" to "interoceptive" state. This transition induced by the eyes closing is underpinned by a large-scale reconfiguration of brain network, which is still not fully comprehended. Here, we investigated the eyes-closing-relevant network reconfiguration by examining the functional integration among intrinsic modules. Our investigation utilized a publicly available dataset with 48 subjects being scanned in both eyes closed and eyes open conditions. It was found that the modular integration was significantly enhanced during the eyes closing, including lower modularity index, higher participation coefficient, less provincial hubs, and more connector hubs. Moreover, the eyes-closing-enhanced integration was particularly noticeable in the hubs of network, mainly located in the default-mode network. Finally, the hub-dominant modular enhancement was positively correlated to the eyes-closing-reduced entropy of BOLD signal, suggesting a close connection to the diminished consciousness of individuals. Collectively, our findings strongly suggested that the enhanced modular integration with substantially reorganized hubs characterized the large-scale cortical underpinning of the volitional eyes closing.

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Objective. General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed. Approach. We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel–Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces. Main results. Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p < 0.001), PFC increased (p < 0.001) and PCMI decreased (p < 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p < 0.001) and nodal efficiency (NE) (p < 0.001) decreased in these two spaces. Global network indicators normalized average path length () (p < 0.01) and modularity (Q) (p < 0.05) only decreased in sensor space, while the normalized average clustering coefficient () and small-world index () did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicators and Q were not significantly different from the preoperative resting state in sensor space. Significance. GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.

Cognitive reserve and resilience of cognitive networks in ageing

AbstractBackgroundIn the ageing process, different cognitive functions decline at different time points and at different rates. Additionally, the inter‐individual variability in cognitive decline could partially depend on cognitive reserve, which represents a resilience factor against age‐related cognitive decline. These complex relationships can be captured with advanced multivariate methods such as graph theory, that can be applied to investigate cognitive networks. The present study aimed to examine the association between cognitive reserve and cognitive networks across age.MethodA total of 334 cognitively healthy participants were stratified into early‐middle‐age (37‐50 years; n = 110), late‐middle‐age (51‐64 years; n = 106), and elderly (65‐78 years; n = 118) groups. Within each age group, individuals were further subdivided into two cognitive reserve levels (high, low) based on a crystallized intelligence proxy. For each subgroup, cognitive networks were constructed based on correlations between 47 z‐scored cognitive variables. Applying graph theory, different global and nodal network measures were compared between subgroups.ResultsGraph‐theoretical analyses revealed that individuals with high cognitive reserve were characterized by a stable cognitive network across age groups. The only consistent difference between the high cognitive reserve groups emerged in modularity with the late‐middle‐age group showing decreased modularity compared to early‐middle‐age and elderly groups. In contrast, individuals with low cognitive reserve showed a marked reconfiguration of cognitive networks across age groups with a pronounced dedifferentiation in late‐middle‐age. This was reflected as a decrease in global efficiency and increase in mean network strength and small‐worldness in the late‐middle‐age group compared to the early‐middle‐age group. The elderly group had a higher global efficiency and lower mean network strength and small‐worldness compared to the late‐middle‐age group.ConclusionA high cognitive reserve was associated with the resilience of the cognitive connectome during the ageing process, while individuals with low cognitive reserve showed prominent changes in their cognitive connectome with increasing age. Gaining further insights into these mechanisms will contribute to our understanding of age‐related cognitive decline and guide the development of strategies to preserve cognitive function in ageing.

Critical analysis of the thermal stability of transesterification vitrimers for 3D‐printing applications based on digital light processing

Abstract3D‐printable vitrimers processed by digital light processing (DLP) have been recently developed, based on the transesterification reaction of β‐hydroxyester structures from mono‐ and diacrylate monomers employed in the formulation. These materials show promising repairability and recyclability, in addition to other interesting features such as shape memory and shape reconfigurability. It has been reported that structural changes taking place due to transesterification promote network rearrangement and an enhancement of the thermomechanical properties. However, the elevated temperatures required for the network reconfiguration cast some doubts on their applicability in real‐life scenarios. In this paper, we analyze the effect of the thermal treatment on the thermomechanical properties and relaxation behavior of these materials and discuss the underlying mechanism explaining the observed changes. We identify some critical issues related to use of a monoacrylate containing β‐hydroxyester moieties, and propose solutions to overcome the most relevant drawbacks. © 2023 The Authors. Polymer International published by John Wiley &amp; Sons Ltd on behalf of Society of Industrial Chemistry.

Cloud-Edge Collaboration-Based Distribution Network Reconfiguration for Voltage Preventive Control

The distribution network reconfiguration can realize the voltage preventive control of the distribution network under alert state, which solves the voltage security issues and maintains the safe operation of distribution network. However, since the great scale and complexity of distribution network would make the traditional centralized distribution network reconfiguration method have a heavy computing burden, a distribution network reconfiguration method based on cloud-edge collaborative architecture for voltage preventive control is proposed, which can reduce the huge computational pressure caused by the excessive concentration of computing tasks. In order to formulate the optimal topology reconfiguration strategy according to the specifics of voltage alerts, a differential hybrid Petri-net model with event-triggered strategy is constructed based on the cloud-edge collaborative architecture to characterize the logical relations of the solution process of the reconfiguration strategy. Considering the short time scale characteristic of preventive control, corresponding to the solution process described by the constructed model, an evaluation network based on graph convolutional neural network is proposed for the reachability discrimination of solutions to significantly reduce the number of candidate solutions, as well as a decision network based on multilayer perceptron is proposed for the selection of the optimal solution among the reachable solutions. Numerical tests are conducted on the modified IEEE 33-bus and IEEE 118-bus distribution systems to validate the effectiveness of the proposed method in dealing with voltage alert problems.

Optimal Scheduling of New Energy and Thermal Power Combined Operation System

Nowadays, the power grid with a high proportion of renewable energy is increasingly popular. Taking a new energy power system including wind power (WP), photovoltaic power (PP), and pumped storage power (PSP) as an example, in order to solve the problem of resource waste such as massive WP-PP abandonment and power losses caused by the randomness and volatility of new energy, this paper establishes a combined power generation model of WP-PP-PSP-thermal power (TP). It achieves the goal of minimum network loss and minimum WP-PP abandonment in the combined power generation system according to the characteristics of PSP and considering various constraints and distribution network reconfiguration. Then, the second-order cone relaxation (SOCR) technology is used to transform the model into a mixed second-order cone programming model. Finally, the design scheme is tested by the improved IEEE 33-bus system, and the effectiveness of the algorithm is analyzed. The results show that peak cutting and valley filling can be realized when PSP energy is added to the WP-PP grid connection system.

A resilient self‐healing approach for active distribution networks considering dynamic microgrid formation

AbstractA large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver.

Distribution network reconfiguration using improved colonial competition optimization algorithm

The implementation of a distribution network is generally aimed at increasing system efficiency, increasing the level of optimal operation, increasing the stability of the power grid, reducing blackouts, and increasing reliability. By providing general definitions of networks and the principles of optimal operation and reliability indicators and loss reduction in power systems, the general principles of network control and optimal operation are presented and finally, practical ideas for the optimal operation of power networks with The approach of increasing system reliability and maximum productivity of distributed production resources is presented. In the presence of a distribution network and by using appropriate control algorithms, it is possible to optimize the power network in different working conditions; After the occurrence of a disturbance in the network, the power system is exposed to instability. The network can restore the stability of the system by communicating critical conditions to the subscribers as well as rearranging the network structure and shut down the least number of subscribers. Implementation of a distribution network in general with the aim of increasing system efficiency, increasing the level of optimal exploitation, increasing the stability of the power grid, reducing outages and increasing reliability.

Enhancing the resilience of zero-carbon energy communities: Leveraging network reconfiguration and effective load carrying capability quantification

The integration of distributed energy resources (DERs) into contemporary energy communities (ECs) has revolutionized power systems, fostering sustainable and clean energy infrastructures. This paper focuses on the effective load-carrying capability (ELCC) to enhance grid resilience in the presence of DERs. We introduce an innovative network topology-based optimization framework that seamlessly integrates economic and resilience metrics within ECs while reducing carbon emissions. The Pareto front of non-dominated solutions for the proposed three-objective optimization problems is extracted, providing a comprehensive visualization of the trade-off between economic, resilience, and emission objectives, enabling informed decision-making. Analytical results, validated on the IEEE 33-bus test system, demonstrate the effectiveness of DER-based ELCC quantification in managing load supply during emergencies. Case studies show how the synergy between economic and resilience-based metrics significantly enhances grid resilience. The proposed framework has diverse applications, including enhancing grid adaptability to climate change, promoting sustainable energy integration, optimizing demand response strategies, and supporting the transition to a decarbonized energy community. This work addresses the challenges and opportunities in the evolving energy landscape, emphasizing the importance of our approach in achieving a cleaner and more resilient energy future.

Designing electricity distribution networks: The impact of demand coincidence

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84% in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e.g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks must consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms for the DNRP-LSDC problem can support operators of distribution networks in strategic and operational network design tasks.

Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder.

Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

A new continuous variables and convex relaxation based formulation for reconfiguration of unbalanced mutually coupled active distribution networks

With the integration of renewable energy-based sources and demand response initiatives, the distribution network has transformed into an active network with bidirectional flows. Network Reconfiguration (NR), i.e., changing network topology, is an alternative to minimize power loss in the distribution network with sectionalizing switches (normally closed) and tie-line switches (normally open). Due to the discrete nature of switches (on or off), the NR formulation is generally a mixed-integer NP-hard nonlinear problem. This paper proposes the first NR formulation for an unbalanced active distribution network with continuous variables to indicate switches’ statuses in NR while considering mutual coupling between line impedances. The obtained solution provides the optimal real power and voltage magnitude settings of dispatchable Distributed Generators (DGs) and network topology for the minimum active power loss in the network. The proposed formulation considers all practical aspects of the distribution network, i.e., mutual coupling, load unbalances, angular unbalance, and the absence of one or two phases in a line section, which, otherwise, have not been considered together in previous formulations. Further, a novel convex relaxation for bus voltage phase angles avoids explicit voltage recovery. Compared to prior formulations, numeric results for unbalanced radial distribution networks showcase the proposed formulation’s effectiveness.

Associations Between Structural Covariance Network and Antipsychotic Treatment Response in Schizophrenia.

Schizophrenia is associated with widespread cortical thinning and abnormality in the structural covariance network, which may reflect connectome alterations due to treatment effect or disease progression. Notably, patients with treatment-resistant schizophrenia (TRS) have stronger and more widespread cortical thinning, but it remains unclear whether structural covariance is associated with treatment response in schizophrenia. We organized a multicenter magnetic resonance imaging study to assess structural covariance in a large population of TRS and non-TRS, who had been resistant and responsive to non-clozapine antipsychotics, respectively. Whole-brain structural covariance for cortical thickness was assessed in 102 patients with TRS, 77 patients with non-TRS, and 79 healthy controls (HC). Network-based statistics were used to examine the difference in structural covariance networks among the 3 groups. Moreover, the relationship between altered individual differentiated structural covariance and clinico-demographics was also explored. Patients with non-TRS exhibited greater structural covariance compared with HC, mainly in the fronto-temporal and fronto-occipital regions, while there were no significant differences in structural covariance between TRS and non-TRS or HC. Higher individual differentiated structural covariance was associated with lower general scores of the Positive and Negative Syndrome Scale in the non-TRS group, but not in the TRS group. These findings suggest that reconfiguration of brain networks via coordinated cortical thinning is related to treatment response in schizophrenia. Further longitudinal studies are warranted to confirm if greater structural covariance could serve as a marker for treatment response in this disease.

Reconfiguration of the real distribution network and providing a new resilience index, considering the uncertainty of renewable energy sources and digital protection devices in order to improve the resilience

Electric energy, as the most widely used form of energy, has always captured human attention to itself; however, with the climate change and the increase of natural phenomena, it is now necessary to provide solutions to improve the resilience of the network. Resilience is defined as the capability of the system to maintain an acceptable level of performance against a severe disturbance and return to its original condition in an appropriate period of time, which demonstrates the degree of endurance, vulnerability, and reversibility of an infrastructure system. The occurrence of adverse weather conditions and natural disasters causes extensive losses and power outages in distribution networks; and the number and severity of such incidents have seen an increase in recent years. Therefore, it is of great significance to evaluate the resilience of the network and its reversibility capability in dealing with adverse weather conditions. In this paper, after defining the concept of resilience in distribution networks, its characteristics are determined; and then, modeling common natural events such as floods and storms, and introducing a new index to evaluate resilience in distribution networks with digital directional overcurrent relays (DOCR) and Digital dual-setting directional overcurrent relays (DS-DOCR) in the presence of renewable energy sources (RES), the proposed scheme is presented.

Mindfulness-based therapy improves brain functional network reconfiguration efficiency

Mindfulness-based interventions are showing increasing promise as a treatment for psychological disorders, with improvements in cognition and emotion regulation after intervention. Understanding the changes in functional brain activity and neural plasticity that underlie these benefits from mindfulness interventions is thus of interest in current neuroimaging research. Previous studies have found functional brain changes during resting and task states to be associated with mindfulness both cross-sectionally and longitudinally, particularly in the executive control, default mode and salience networks. However, limited research has combined information from rest and task to study mindfulness-related functional changes in the brain, particularly in the context of intervention studies with active controls. Recent work has found that the reconfiguration efficiency of brain activity patterns between rest and task states is behaviorally relevant in healthy young adults. Thus, we applied this measure to investigate how mindfulness intervention changed functional reconfiguration between rest and a breath-counting task in elderly participants with self-reported sleep difficulties. Improving on previous longitudinal designs, we compared the intervention effects of a mindfulness-based therapy to an active control (sleep hygiene) intervention. We found that mindfulness intervention improved self-reported mindfulness measures and brain functional reconfiguration efficiency in the executive control, default mode and salience networks, though the brain and behavioral changes were not associated with each other. Our findings suggest that neuroplasticity may be induced through regular mindfulness practice, thus bringing the intrinsic functional configuration in participants’ brains closer to a state required for mindful awareness.

Using fuzzy systems for optimal network reconfiguration of a distribution system with electric vehicle charging stations and renewable generation

Using fuzzy systems for optimal network reconfiguration of a distribution system with electric vehicle charging stations and renewable generation

A KRR-UKF robust state estimation method for distribution networks

State estimation is an integral component of energy management systems. Employing a state estimation methodology that is both accurate and resilient is essential for facilitating informed decision-making processes. However, the complex scenarios (unknown noise, low data redundancy, and reconfiguration) of the distribution network pose new challenges for state estimation. In the context of this study, we introduce a state estimation technique known as the kernel ridge regression and unscented Kalman filter. In normal conditions, the non-linear correlation among data and unknown noise increases the difficulty of modeling the distribution network. Thence, kernel ridge regression is developed to map the data into high-dimensional space that transforms the non-linear problem into linear formulations base on the data rather the complicate grid model, which improves model generalization performance and filters out unknown noises. In addition, with the unique prediction correction mechanism of the Kalman method, the kernel ridge regression-mapped state value can be revised by the measurement, which further enhances model accuracy and robustness. During abnormal operating conditions and taking into account the presence of faulty data within the measurement system, we initiate the use of a long short-term memory network and combined convolutional neural network (CNN) model, referred to as the ATT-CNN-GRU. This model is utilized for the prediction of pseudo-measurements. Subsequently, we use an outlier detection method known as ordering points to identify the clustering structure to effectively identify and substitute erroneous data points. Cases on the IEEE-33 bus system and 109-bus system from a city in China show that the method has superior accuracy and robustness.

Optimal Siting of EV Fleet Charging Station Considering EV Mobility and Microgrid Formation for Enhanced Grid Resilience

Coordinating infrastructure planning for transportation and the power grid is essential for enhanced reliability and resilience during operation and disaster management. This paper presents a two-stage stochastic model to optimize the location of electric vehicle fleet charging stations (FEVCSs) to enhance the resilience of a distribution network. The first stage of this model deals with the decision to place an FEVCS at the most favorable and optimized location, whereas the second stage aims to minimize the weighted sum of the value of lost load in multiple potential scenarios with different faults. Indeed, the second stage is a joint grid restoration scheme with network reconfiguration and microgrid formation using available distributed generators and fleet electric vehicles. The proposed model is tested on a modified IEEE-33 node distribution network and a four-node transportation network. Case studies demonstrate the effectiveness of the proposed model.