The Method of Distribution-Microgrid Collaborative Fault Recovery Considering Network Dynamic Reconfiguration

Increasing penetration of DG brings many uncertain factors to the operation of the distribution network, which will increase difficulties for the recovery of power supply after the distribution network fault happens. Considering the real-time load demand and the random output changes of DG, this paper proposes a dynamic network reconfiguration method for distribution network fault recovery under high penetration of DG. First, a dynamic network reconfiguration model for distribution network fault recovery is established. Then, by introducing intermediate variables, a reasonable second-order cone relaxation is performed on the dynamic reconfiguration model. Third, the principle of dynamic islanding partition is introduced to coordinate with dynamic network reconfiguration. Finally, case studies are carried out on the IEEE 33-bus distribution network to illustrate the effectiveness of the proposed method.

Dynamic reconfiguration of distribution network based on dynamic optimal period division and multi-group flight slime mould algorithm

In order to solve the problem of dynamic reconfiguration of distribution network connected with distributed generation (DG) and electric vehicle (EV), a dynamic reconfiguration method of distribution network based on time series symbolized segmentation is proposed. Firstly, the daily load curve of the distribution network accessed to distributed energy and electric vehicles is predicted. The time series of load is symbolized and the segmentation effect is evaluated by information entropy to determine the time division scheme. Then the model is established with the minimum daily operating loss cost as the objective function, and the distribution network is dynamically reconfigured according to the time division. The particle swarm optimization algorithm (PSO) based on branch group search is proposed to solve the model. Finally, the simulation calculation is carried out by using the IEEE33 node system as an example. The results show that the proposed method can reasonably divide the load time series, so that the daily opHrating loss cost of the distribution network after dynamic reconfiguration is reduced.

A 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.

In order to solve the problem of low efficiency of fault recovery and reconfiguration in traditional distribution networks, a fault recovery method for medium and low voltage distribution networks based on BWO algorithm is proposed. Firstly, the optical storage, wind storage system models and load models are established. The objective function is to minimize the loss of important load after failure, and the preliminary island division of distribution network is carried out. Then, with the goal of minimizing the weighted sum of network loss and switching operation times, the BWO algorithm is used to obtain the fault recovery results of islanding and switching operation. Finally, three scenarios are compared to verify that the proposed method can obtain the optimal results of distribution network fault recovery in different fault periods. Finally, the running results of BWO algorithm, binary particle swarm optimization algorithm and gray wolf optimization algorithm are compared. The experimental results show that the average number of iterations for the BWO algorithm to obtain the optimal solution is only 12.8, which is much less than the GWO and BPSO algorithms. This shows that the BWO algorithm can effectively balance the ability of global search and local development, thus greatly accelerating the convergence speed. Conclusion: The results show that the BWO algorithm can avoid falling into the local optimal solution and has the fastest solution speed, which verifies that the optimization effect of the BWO algorithm is better.

To achieve carbon neutrality goals, large amounts of renewable energy sources (RESs) are being integrated into power systems. In particular, high penetration of distributed photovoltaic (PV) makes distribution networks highly stochastic, calling for dynamic distribution network reconfiguration (DNR). Existing DNR approaches can be broadly categorized into model-driven optimization-based methods and learning-based methods, with deep reinforcement learning (DRL) being a representative paradigm for fast online decision-making. Existing DNR models typically belong to mixed-integer linear programming, which requires solution methods such as deep reinforcement learning (DRL). However, existing methods commonly struggle to account for human factors, i.e., the time-varying preferences of distribution network operators in DRL decisions. To this end, this paper proposes a natural language-driven, human-in-the-loop DNR framework, which combines a DRL base policy for hour-level dynamic reconfiguration with a large language model (LLM)-based instruction supervision layer. Based on this human-in-the-loop framework, commands from operators in natural language are translated into online adjustments of safety-screened DRL switching actions. Therefore, the framework demonstrates the fast, model-free decision capability of DRL while providing an explicit and interpretable interface for incorporating temporary and context-dependent operator requirements without retraining. Case studies on IEEE 16-bus and 33-bus distribution networks show that the proposed framework reduces network losses, improves voltage profiles, and limits switching operations. It also achieves markedly higher compliance with operator instructions than a conventional model-based method and a pure DRL baseline. These results highlight a viable path to embedding natural language guidance into the data-driven operation of active distribution networks.

The potential vulnerability of demand response (DR) to cyber-attacks represents a significant challenge that can have detrimental impacts on the technical and economic performances of the distribution system (DS). This issue is addressed in the proposed grid-aware price-based DR framework under price integrity attack (PIA) as a bi-level defender-attacker problem. The bi-level comprises a distribution system operator (DSO) at the upper-level, and demand response providers (DRPs) and customers at the lower-levels. It uses a grid-aware, distribution locational marginal price as an interaction in DR pricing. The defender-attacker problem is constructed when the attacker devises an adversarial DR attack on the DRP’s price using PIA to manipulate the load consumption of the customers. In response, the DSO uses dynamic Network Reconfiguration (NRE) as a defender action to change the grid-aware price for mitigating adverse DR. The proposed problem is tested on the 33-bus and 119-bus DS and is solved using the nested reformulation & decomposition method. Numerical analyses demonstrate that the proposed methodology effectively shows the substantial change in economic and technical performance of DR under attack and defender action. It shows the significant impact of PIA which reduces DR contribution from 9.60 to 2.15 %, and then improves to 5.30 % with the defender action using dynamic NRE for 33-bus. For the 119 bus, it changes from 9.90 % to 3.73 % then to 7.49 %. Further, the proposed model shows its scalability and tractability on small- to large-scale systems effectively.

This paper investigated an effective water evaporation optimization algorithm based approach to improve the effectiveness of the distribution network. The optimal accommodation of multiple photovoltaic (PVs) generation source and its coordination with network reconfiguration is used to minimize the network energy loss. The coordination of PVs and dynamic network reconfiguration of the distribution network is a complicated optimization problem. The problem of dynamic reconfiguration of the distribution network is mixed-integer, non-differentiable, and of an extremely complex combinatorial nature. Besides, the constraint of radiality usually rises the complexity of the evolutionary meta-heuristic optimization algorithms. The developed methodology is based on the foundation of graph theory, which limits the search space and also prevents repetitive mesh checks. The developed methodology is proficient and ensures that only possible radial topologies are generated. This coordinated operational planning is subjected to the distribution system constraints such as limits of feeder current, limits of node voltage, and power balance, etc. The suggested methodology is tested on the IEEE radial 33-bus distribution system. The results show that optimal accommodation of multiple PVs and its coordination with dynamic network reconfiguration reduces the network loss to 20.24% and also maintains node voltage within the specified limits.KeywordsPhotovoltaicNetwork reconfigurationDistribution networkWater evaporation algorithm

In recent years, the power outages caused by catastrophic weather events have become an imperative issue in power system research. Mutual impacts of pre-and post-event operation, uncertainties during system recovery, as well as binary decision variables are still challenging. To address these issues, this paper proposes an adaptive robust load restoration method for active distribution networks which coordinates network reconfiguration, mobile energy storage systems (MESSs) and repair crews (RCs). In the pre-event stage, pre-positioning of MESSs and proactive network reconfiguration are conducted to enhance system survivability. In the post-event stage, dynamic network reconfiguration is coordinated with scheduling MESSs and RCs, to restore all loads under uncertainties. A mixed integer second-order conic programming model which also contains practical voltage dependent loads is formulated. Further, a two-stage robust optimization method is applied to guarantee solution robustness against multiple uncertainties including fault locations. To optimize the binary recourse variables in the post-event stage, a feasibility pump based solution algorithm is developed. Numerical simulations conducted on a 33-bus system demonstrate effectiveness of the proposed load restoration method, high efficiency of the solution algorithm and high solution robustness.

For the dynamic reconfiguration of the distribution network when distributed generation are connected to the distribution network, a dynamic reconfiguration model of distribution network with on-load tap-changing transformer and static var compensator is proposed. First, Monte Carlo simulation method is used to form curve of wind/photovoltaic power output. Then, a dynamic grid reconfiguration model is established with the goal of minimizing power loss and voltage distribution. And, an improved NSGA-II algorithm integrated with constraint handling technique is proposed to solve the model. Finally, take the IEEE 33-bus system as an example for simulation, and the results show that the model can reduce system operating costs while improving system power quality.

With the increasing penetration of distributed intermittent energy into distribution networks, the self-healing problem of distribution networks faces significant challenges. The load level and demand response must be considered as critical factors affecting fault recovery. This paper proposes a fault recovery strategy that combines islanding division and network reconstruction. First, a distribution network model with a distributed energy storage system is established. To optimize the use of distributed energy resources, controllable loads that can respond to demand are prioritized, and high-priority loads are included in the islanded network after a fault. Based on the islanding division results, the remaining non-faulty power loss areas are restored through main network reconstruction. The improved whale optimization algorithm is employed to solve the problem. Simulation results demonstrate that load demand response is closely linked to the islanding process, and an optimal fault recovery strategy can be achieved by utilizing the distributed energy storage system and the main network.

High penetration of distributed energy resources (DERs) creates various protection challenges, such as protection blinding, false tripping, unsynchronized reclosing, etc. Additionally, adaptation of active network management approaches namely demand response and network reconfiguration also threats existing protection practice. In this study, a combination of a local adaptive and communication assisted central protection is proposed, whereby the relay settings are dynamically updated based on online identification of the network topologies and status of DERs. Particularly, the local adaptive protection updates relay settings based on DERs status (ON/OFF) employing locally acquired information, whereas the centralized protection updates the relay settings during major changes in grid topologies: network reconfiguration and switching between islanded and grid-connected modes. The effectiveness of the proposed algorithm is demonstrated through simulation results performed on a medium voltage (10 kV) distribution network using real time digital simulation (RTDS). Simulation results demonstrates that the proposed algorithm provides good discrimination and selectivity of faults during varied network conditions.

To improve the reliability and reduce power loss of distribution network, the dynamic reconfiguration is widely used. It is employed to find an optimal topology for each time interval while satisfying all the physical constraints. Dynamic reconfiguration is a non-deterministic polynomial problem, which is difficult to find the optimal control strategy in a short time. The conventional methods solved complex model of dynamic reconfiguration in different ways, but only local optimal solutions can be found. In this paper, a data-driven optimization control for dynamic reconfiguration of distribution network is proposed. Through two stages that include rough matching and fine matching, the historical cases which are similar to current case are chosen as candidate cases. The optimal control strategy suitable for the current case is selected according to dynamic time warping (DTW) distances which evaluate the similarity between the candidate cases and the current case. The advantage of the proposed approach is that it does not need to solve complex model of dynamic reconfiguration, and only uses historical data to obtain the optimal control strategy for the current case. The cases study shows that the optimization results and the computation time of the proposed approach are superior to conventional methods.

There is a clear trend in camera networks toward enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper, we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally, we discuss the main open research challenges in camera network reconfiguration.

Increasing remote-controlled switches (RCSs) in the distribution network enhances not only the restoration capability, but also the flexibility to cope with the uncertainties of renewable generation and variable loads through dynamic reconfiguration. However, the switching frequency remains a significant concern for system stability. In this paper, we investigate the potential benefits of reducing switching frequency by coordinating electric vehicles (EVs) with distribution network operation, considering EVs' daily travel patterns and battery degradation cost. The problem is formulated as mixed-integer quadratic programming (MIQP). Testing our model on the IEEE 33-node test feeder, we find that EVs can potentially reduce the frequency of switching while maintaining desirable system performance. In addition, battery degradation cost, incurred by vehicle-to-grid (V2G) services, is only a small portion of the total system operational cost.