Network Reconfiguration Research Articles

Cheetah optimization algorithm for simultaneous optimal network reconfiguration and allocation of DG and DSTATCOM with electric vehicle charging station

The potential of Electric Vehicles (EVs) to decarbonize the transportation industry has attracted a lot of attention in recent years in response to growing environmental concerns. Electric Vehicle Charging Stations (EVCSs) need to be properly located for widespread EV integration. The distribution system is facing additional challenges due to inclusion of EVCS. The adverse impacts of EVCS on the Radial Distribution Network (RDN) may be minimized using Distributed Generations (DGs) or Distribution Static Compensators (DSTATCOMs) or by reconfiguring the network. This paper uses a novel optimization technique to solve the problem of simultaneous optimal placement of EVCS with network reconfiguration and optimal planning (siting and sizing) of DGs and DSTATCOMs. The multiple objective functions are considered in order to minimize the active power losses, the voltage deviation, the investment costs for DGs and DSTATCOMs, and to increase the voltage stability of the system. A novel meta-heuristic Cheetah Optimization Algorithm (COA) is used to solve the optimization problem. To examine the effectiveness of the suggested strategy on 33-bus and 136-bus networks, several scenarios of simultaneous incorporation of EVCS, DG, and DSTATCOM installations with network reconfiguration are taken into consideration. The COA results are also compared to the results of grey wolf optimization and genetic algorithms.

Distribution network reconfiguration optimization method based on undirected-graph isolation group detection and the whale optimization algorithm

<abstract> <p>As distributed generation (DG) becomes increasingly integrated into the distribution grid, the structure of the distribution network is becoming more complex. To enhance the safety and cost-effectiveness of distribution systems, distribution network reconfiguration is gaining significant importance. Achieving optimal distribution network reconfiguration entails two key considerations: A feasible topology and economic efficiency. This paper addresses these challenges by introducing a novel approach that combines the potential island detection in undirected-graphs and the application of a whale optimization algorithm (WOA) for network reconfiguration optimization. To begin, we identified island categories based on the type of switchable-branches connected to these islands, allowing for the construction of potential island groups. Subsequently, unfeasible topologies were eliminated based on the conditions under which islands form within these potential island groups. Feasible topologies were then used to construct a model for network reconfiguration optimization. The optimal distribution network topology is determined using the WOA. In the final phase, the proposed method's effectiveness was demonstrated through a case study on the IEEE-33 node distribution network under scenarios with and without DG integration. The results showed that the proposed method exhibited better performance than traditional approaches in distribution network reconfiguration.</p> </abstract>

Collaborative Reconfiguration of Supply Networks Based on GNN and ALC

With the prevalence of lean and just-in-time principles, traditional supply chains often exhibit inflexibility, leading to challenges in satisfying extensive customized orders and managing risks during disruptions. Thus, there is a need for a more flexible, resilient, and collaborative network and strategies to tackle the aforementioned challenges. In this study, we introduce a new supply network called the industry supply chain, aimed at enabling collaborative decision-making and dynamic reconfiguration. We create a graph neural network model to promptly identify sudden disturbances and devise a distributed multidisciplinary optimization model to facilitate collaborative reconfiguration. The experimental findings from an air-conditioning industry supply chain show that network reconfiguration under real-time disturbance detection reduces losses and improves operational stability.

Resilience-Oriented Coordinated Topology Reconfiguration of Electricity and Drainage Networks With Distributed Mobile Emergency Resources

Resilience-Oriented Coordinated Topology Reconfiguration of Electricity and Drainage Networks With Distributed Mobile Emergency Resources

Coordinated Planning of Integrated Electric and Heating System Considering the Optimal Reconfiguration of District Heating Network

The tight coupling of electric and heating systems would make it necessary to consider the coordinated planning of the energy facilities. The seasonal district heating network (DHN) reconfiguration can notably impact the planning results and offer a potential approach for better wind penetration. To address this issue, a coordinated planning of electric and heating (CPEH) model is proposed in this paper by considering the seasonally DHN reconfiguration, which is formulated as a mixed-integer linear programming problem. Considering the computation difficulties brought in by numerous operation scenarios involved in the CPEH problem, a scenario-oriented generalized Benders decomposition (S-BD) algorithm is proposed to solve the CPEH model with higher efficiency by generating feasibility and optimality cuts with a unified form. The numerical simulations and a practical case in Jilin, China, are simulated to demonstrate the efficiency of the S-BD and illustrate the potential benefits of DHN reconfiguration in enhancing wind energy penetration.

Supply Restoration of Data Centers in Flexible Distribution Networks With Spatial-Temporal Regulation

The reliable operation of Internet data centers (IDCs) is crucial to digitization transformation of society. However, the extreme fault on the IDC-located distribution network is fatal to IDC operation. The evolution of flexible distribution networks (FDNs) can satisfy the deep de-pendence of IDC on high-quality and reliable power supply. In FDNs, AC/DC feeders with multiple voltage levels can be flexibly interconnected and adjusted by flexible stations (FSs). It is promising to flexibly enhance operation per-formance of crucial IDCs after fault isolation in FDNs. In this paper, a coordinated supply restoration method of IDCs in FDNs with spatial-temporal regulation is proposed to minimize IDC data-dropping loss under complex faults. First, a spatial-temporal transfer strategy of IDC work-loads is precisely formulated and an FS-based network reconfiguration strategy is proposed. Then, a mixed-integer second-order conic programming model is established for efficient solving. Spatial-temporal coordination among the source, network, storage, and demand sides can be realized without violating physical feasibility. Finally, the proposed method is validated on the modified IEEE 33-node and modified IEEE 123-node test cases. Case studies show that total outage loss is effectively reduced with sufficient utili-zation of available power. Operational performance of FDN is also enhanced under extreme faults.

A Critical Review of Active Distribution Network Reconfiguration: Concepts, Development, and Perspectives

A Critical Review of Active Distribution Network Reconfiguration: Concepts, Development, and Perspectives

Reliability-Based Planning of Cable Layout for Offshore Wind Farm Electrical Collector System Considering Post-Fault Network Reconfiguration

Reliability-Based Planning of Cable Layout for Offshore Wind Farm Electrical Collector System Considering Post-Fault Network Reconfiguration

Enhancing Resilience and Efficiency in Power System: A Five‐Level Dynamic Planning Model

Researchers consider the planning of power systems to be of utmost importance, and due to its significant budget, further studies on the resilience of energy systems are necessary. Therefore, a five‐level dynamic model is developed to improve resilience to natural disaster events. The initial level focuses on expansion planning and system uncertainties, specifically examining the utilization of wind turbines in the proposed energy hub to incorporate the thermal energy market together with demand response program. Its purposes are to improve resilience, minimize input transmission costs, and minimize operating costs. The second level is related to the corrective actions that the system operator should take after the accident for network reconfiguration. The earthquake‐induced and attack events, considering unknown events, are modeled and assessed in the third level. Generation expansion planning is performed at the fourth level. As with the whole model, this level tries to improve resilience. The fifth level deals with transmission expansion planning to boost resilience, and the capability of transmission switching is regarded as a low‐cost correction factor. The first level optimizes energy hub according to the differential evolutionary algorithm, but the next four levels employ a robust symphony orchestra search algorithm. Finally, the dynamic planning model proposed here is employed on a Garvers 6‐bus system and then on a 400 kV 52‐bus Iranian power grid. According to the findings, the proposed planning model demonstrates a reduction in capital costs of transmission switches by up to 26%. Also, the expected profit of the generation units increases by as much as 3.75%. Accordingly, the results show that the model is both feasible and effective.

Robust Emergency Preparedness Planning for Resilience Enhancement of Energy-Transportation Nexus Against Extreme Rainfalls

This paper proposes a robust emergency preparedness planning (EPP) scheme to optimally pre-position diverse mobile emergency resources (MERs) in staging locations and perform proactive network reconfiguration for resilience enhancement of energy-transportation nexus (ETN) against extreme rainfalls. Based on rainfall-runoff simulation with hydrodynamic partial differential equations, a risk identification approach for flood-prone transportation modeling is developed for vehicle travel time estimation and MER routing optimization. To handle uncertainties in line outages and flooded roads incurred by extreme rainfalls, a tri-level robust EPP model is proposed to determine the optimal emergency preparedness plan immunized against the worst-case realization of uncertainties. Furthermore, a tailored solution method combining nested column-and-constraint generation algorithm with multiple linearization techniques is devised to cope with the proposed nonlinear robust EPP model. Comparative studies have validated the effectiveness of the proposed scheme for resilience enhancement of the ETN confronted with extreme rainfall disasters.

Safe Reinforcement Learning for Active Distribution Networks Reconfiguration Considering Uncertainty

Safe Reinforcement Learning for Active Distribution Networks Reconfiguration Considering Uncertainty

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

Self-Healing Networks AI-Based Approaches for Fault Detection and Recovery

The reliability and robustness of communication networks are critical for ensuring seamless connectivity in today's interconnected world. However, network faults and failures can disrupt services, leading to significant downtime and financial losses. [1]In response to these challenges, self-healing networks empowered by artificial intelligence (AI) have emerged as a promising solution for proactively detecting and autonomously recovering from network faults. This paper presents a comprehensive review and analysis of AI-based approaches for fault detection and recovery in self-healing networks, aiming to enhance network reliability and minimize service disruptions. It provides an overview of the key challenges associated with traditional fault detection and recovery methods in communication networks.[2] It discusses the limitations of reactive approaches that rely on manual intervention or predefined rules to identify and mitigate network faults, highlighting the need for more proactive and adaptive solutions. Subsequently, the paper explores the role of artificial intelligence, particularly machine learning and deep learning techniques, in enabling self-healing capabilities in communication networks. By leveraging vast amounts of network data, AI algorithms can learn complex patterns and anomalies indicative of network faults, enabling timely detection and response. The paper reviews various AI-based fault detection techniques, including anomaly detection, supervised learning, and reinforcement learning approaches. It examines their strengths and limitations in different network environments, considering factors such as data availability, scalability, and real-time processing requirements. Furthermore, the integration of AI-based fault detection with network monitoring systems and telemetry data sources is discussed, highlighting the importance of comprehensive data-driven approaches for accurate fault identification. In addition to fault detection, the paper investigates AI-driven recovery mechanisms for self-healing networks. It explores proactive recovery strategies, such as predictive maintenance and network reconfiguration, enabled by AI-based predictive analytics. By harnessing the power of machine learning and deep learning, communication networks can achieve greater resilience, adaptability, and autonomy in mitigating network faults and ensuring uninterrupted service delivery.

Optimal Reconfiguration of Network with Variant Transmission Times on Arcs

Contraflow means lane reversals on networks. In lane reversal reconfiguration, the capacity of the arc increases by reorienting arcs towards demand nodes, which maximizes the flow value and reduces the travel time. In this work, we survey the existing pieces of literature on single and multi-commodity contraflow problems with symmetric and asymmetric travel times on parallel but oppositely oriented edges. Many illustrations are included to support the main results.

Distribution Network Reconfiguration Using Genetic Algorithm for Loss Reduction: A Case Study of Katunje Feeder, Bhaktapur

The power distribution system has difficulties with regard to power loss and unacceptable voltage drops as a result of the rapidly expanding power system network, rising electrical energy consumption, and longer distances of power distribution. A typical strategy to address the issues with the distribution system is to perform distribution system reconfiguration. The study focuses on distribution feeder reconfiguration of the Katunje Feeder of Bhaktapur, Nepal where optimization problem is formulated to minimize the system active power loss and investment cost of the system. Genetic algorithm is employed in a co-simulation framework to solve the optimization problem where states of 26 different tie switches are to be altered to achieve the desired optimum results. Two cases are formulated: in case I active power loss is assigned more weight than investment cost whereas, in case II equal weights are assigned for active power loss and investment cost. The results showed the reduction in active power loss and investment cost for both the cases. Case I resulted in more active power loss reduction compared to case II, and case II resulted in more investment cost reduction compared to case I. From this, decision makers can obtain insights in adopting one of the cases for distribution feeder reconfiguration based on technical consideration (active power loss reduction) or economic consideration (investment cost reduction).

Toward optimal voltage/VAR control with smart PVs in active distribution networks

Rapidly increasing penetration of distributed PVs brings great challenges to distribution networks. Driven by the revised IEEE 1547 standard, inverter-based smart PVs provide customized control and voltage regulation capabilities. This paper presents a comprehensive study investigating voltage/VAR control considering network reconfiguration, VAR optimization, and smart PVs. To do so, a second-order cone programming-based VAR optimization and network reconfiguration (VONR) model is formulated. The enhanced necessary radiality condition, and the extensions for multiple smart PV operation modes and multi-period operation are provided. A comprehensive case study considering various network and smart PV operation modes, and hourly VONR operation is conducted. The results show that implementing VONR with smart PV reactive power dispatching optimizes the scheduling plans, resulting in a reduction of network loss by 59.97 % and 30.37 % for the 33-bus and 123-bus test systems, respectively, compared to the no-control operation.

Optimal network reconfiguration and distributed generation integration for power loss minimization and voltage profile enhancement in radial distribution system

Network Reconfiguration with Distributed Generation (DG) integration can significantly minimize the power loss and enhance the voltage profile. This paper presents power loss minimization and voltage profile enhancement measures in radial distribution systems through optimal network reconfiguration and DG integration. Initially, the approach was tested in a standard IEEE 33-Bus system in MATLAB using the Backward/Forward Propagation Power Flow method. Voltage Stability Index (VSI) was used to determine the candidate bus to locate for DG integration. Artificial Bee Colony (ABC) algorithm was used to find the optimal solution. Six different scenarios for network reconfiguration and DG integration were analysed by using the real distribution network of the Nepal Electricity Authority (NEA), Kirtipur Distribution Center. The simulated results were compared with the base case scenario and were validated with similar results from the previous studies. Among them, Scenario VI (simultaneous network reconfiguration and DG integration) gave a most favorable result in terms of power loss minimization and voltage profile enhancement. Test results in this study show that the active power loss has reduced by 72.19% and 57.69% for IEEE 33-Bus system and NEA 63-Bus system respectively. It also showed that ABC algorithm has outperformed other popular metaheuristic algorithms.

A novel cost-based optimization model for electric power distribution systems resilience improvement under dust storms

In the recent years, dust storms (DSs) pose a serious threat to critical infrastructure such as power distribution networks (PDNs). During DSs, the contamination of insulators, increases the possibility of damage to the PDNs insulation system and flashover induced power outage may occur. Power outages disrupt the performance of other urban infrastructures and, in addition to heavy financial losses, cause public dissatisfaction. Although this issue is of particular importance in areas with humid climate, a few studies have been reported on PDNs resilience improvement against DSs. This paper proposes a novel cost-based optimization model to make PDNs more resilient to DSs considering uncertainties. The proposed model is based on the two-stage stochastic mixed-integer programming (SMIP). In the first stage, decisions are made to equip repair crews (RCs) with insulator washing machines, hardening distribution lines with silicone-rubber insulators (SIs), and deploy backup distributed generators (DGs). Decisions in the second stage include network reconfiguration, RCs routing, DGs power dispatch, and load shedding as the critical options for PDN outage management during/after DSs. Case studies are evaluated in the IEEE 69-bus test system and a real 209-bus PDN in Khuzestan province, a coastal province in southwestern Iran. The simulation results at different budget levels have confirmed the efficiency of the proposed model for cost-optimal resilience enhancement planning of PDNs against DSs.

A data-driven chance-constrained BESS planning in distribution networks by a decoupling solution method

In this study, a novel approach for the battery energy storage system (BESS) planning considering the integration of photovoltaic (PV) is proposed. A data-driven chance-constrained BESS programming model including the dynamic network reconfiguration (DNR) is developed. Then, a decoupling solution approach for sitting selection and sizing optimization is proposed to obtain optimal solution. According to the approach, comprehensive cost sensitivity factor (CCSF) is formulated for the sitting selection, and the particle swarm optimization (PSO) combined with the Minty algorithm and the switch operation time reduction algorithm is developed to obtain more accurate sizing. In addition, based on the scenarios generated by the spectral clustering approach, a continuous scenario increment approach is proposed to handle chance-constrained constraints. The numerical simulations on a practical 31-bus distribution network show the superiority of the proposed algorithm over existing methods.

Joint optimization of reconfiguration and traffic planning in time‐triggered networks

AbstractThe existing research on time‐triggered network reconfiguration neglects redundant traffic states, posing a challenge in effectively balancing traffic performance and reconfiguration efficiency in practical deployments. In response to these limitations, a novel joint optimization approach is introduced for reconfiguration and traffic planning. Initially, the efficiency of reconfiguration solutions is enhanced by sacrificing the delay performance of redundant flows and prioritizing compressed yet tailored constraints. Subsequently, a tabu‐based optimization algorithm is employed to provide more flexible enhancements to the delay performance of network‐wide traffic. The outcomes demonstrate that the proposed method significantly outperforms incremental reconfiguration, improving the solution efficiency of the reconfiguration recovery phase by over 75.38%. Furthermore, through multiple rounds of reconfiguration, the approach achieves an 8.38% improvement in the average delay performance of all flows, even with redundant flows sacrificed.