Dynamic Distribution Network Reconfiguration Research Articles

Self-healing radial distribution network reconfiguration based on deep reinforcement learning

Distribution network reconfiguration (DNR) has long been used to minimize losses in normal conditions and restore out-of-service areas in fault situations. However, the integration of renewable resources into power systems has made operating strategies more challenging, requiring fast and responsive approaches to handle abrupt interruptions. Traditional DNR methods rely on mathematical programming, heuristic and meta-heuristic methods that cannot generalize unseen generation and load profiles, and may require significant computational time to converge, posing an overrun risk for real-time execution. Recently, deep reinforcement learning (DRL) has been shown to be effective in optimizing DNR in normal conditions with calculation times of only a few milliseconds. However, previously proposed dynamic DNR approaches are not able to self-heal and restore in fault conditions, where timely restoration is critical for customer satisfaction. To address this gap, we propose a restorative dynamic distribution network reconfiguration (RDDNR) framework that enables continuous reconfiguration after faults, enabling the fast restoration of out-of-service areas. Our proposed framework was tested on two balanced test feeders and demonstrated competitiveness regardless of operating states. By leveraging RDDNR, distribution system operators can effectively restore service in a timely manner, improving overall grid resilience and customer satisfaction.

Dynamic reconfiguration of distribution network considering the uncertainty of distributed generation and loads

This study presents a new methodology to perform the distribution network dynamic reconfiguration (DNDR), taking into consideration the stochastic variations of loads and distributed generation (DG) of power. To solve the heavy computational burden that exists in traditional algorithms of the DNDR, this study first establishes the nodal sensitivity models to calculate the nodal variations caused by nodal power variations. Then, the DNDR is executed utilizing a co-evolutionary algorithm with the goal of loss minimization. The stochastic power flow calculations (PFCs) based on the nodal sensitivity are performed in the DNDR to handle the power fluctuations of the DGs and loads. Finally, the modified IEEE 33-bus test system and a practical distribution system are used for simulations. The simulation results validate the quickness and effectiveness of the proposed DNDR method.

Research on Combination of Distributed Generation Placement and Dynamic Distribution Network Reconfiguration Based on MIBWOA

This paper aims to address the combination of distributed generation placement and dynamic distribution network reconfiguration. Herein, a multi-strategy multi-objective improved black widow algorithm is proposed. A model is established, which considers the objectives of minimizing active power loss, voltage deviation, and carbon emission. The proposed algorithm significantly enhances the traversal capability and search speed by employing Cubic–Tent chaotic mapping, involving a novel formula with the fusion of optimal genes, and employing an adaptive mutation of Wald mutation and elite reverse learning mixing. The DeepSCN is employed to forecast the distributed generation (DG) output power and distribution network load. Through various test functions, the capability of the proposed algorithm is demonstrated. Whether single-objective or multi-objective, the algorithm has excellent performance. To showcase the practicality and effectiveness of the model and approach, a simulation experiment was performed on the IEEE-33 node configuration. The solution set provided by MIBWOA can reduce active network loss to improve operating efficiency, increase voltage offset to make operation more stable, and reduce carbon emissions to make operation more environmentally friendly. The proposed algorithm shows excellent performance in distributed generation placement and distribution network reconfiguration compared with the comparison algorithms. The results show that the solution proposed by MIBWOA can enhance the real-time operational parameters of the distribution network with considerable efficiency.

Dynamic DNR and Solar PV Smart Inverter Control Scheme Using Heterogeneous Multi-Agent Deep Reinforcement Learning

The conventional volt-VAR control (VVC) in distribution systems has limitations in solving the overvoltage problem caused by massive solar photovoltaic (PV) deployment. As an alternative method, VVC using solar PV smart inverters (PVSIs) has come into the limelight, which can respond quickly and effectively to solve the overvoltage problem by absorbing reactive power. However, the network power loss, that is, the sum of line losses in the distribution network, increases with reactive power. Dynamic distribution network reconfiguration (DNR), which hourly controls the network topology by controlling sectionalizing and tie switches, can also solve the overvoltage problem and reduce network loss by changing the power flow in the network. In this study, to improve the voltage profile and minimize the network power loss, we propose a control scheme that integrates the dynamic DNR with volt-VAR control of PVSIs. The proposed control scheme is practically usable for three reasons: Primarily, the proposed scheme is based on a deep reinforcement learning (DRL) algorithm, which does not require accurate distribution system parameters. Furthermore, we propose the use of a heterogeneous multiagent DRL algorithm to control the switches centrally and PVSIs locally. Finally, a practical communication network in the distribution system is assumed. PVSIs only send their status to the central control center, and there is no communication between the PVSIs. A modified 33-bus distribution test feeder reflecting the system conditions of South Korea is used for the case study. The results of this case study demonstrates that the proposed control scheme effectively improves the voltage profile of the distribution system. In addition, the proposed scheme reduces the total power loss in the distribution system, which is the sum of the network power loss and curtailed energy, owing to the voltage violation of the solar PV output.

A Dynamic Reconfiguration Method based on a Deterministic Optimization Approach in Active Distribution Systems

This article proposes a method for dynamic distribution network reconfiguration (DDNR) with distributed generation for loss reduction. Advantages and conceptual principles of a deterministic optimization method are exposed, which allows the minimization of technical power losses in distribution systems based on a linear approximation of the loads. In this work is proposed the application of this approach for the modelling of distributed generation sources, obtaining a mathematical formulation that exploits mixed-integer nonlinear programming in solving the optimal reconfiguration problem. The DDNR method is implemented in an automatic tool integrating OpenDSS and GAMS software. IEEE69-Bus test system is considered to demonstrate the effectiveness of the technique. Dynamic results are obtained by implementing the optimal reconfiguration strategy, making the dynamic adjustments of the mathematical modelling in the face of variations in distributed generation and demand profiles. The results reveal schemes that range from one to twenty-four daily control actions.

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

This paper suggests a multi-period dynamic reconfiguration method for the distribution network. The reconfiguration period is preliminarily divided based on the load monotonicity and amplitude change with taking the length of the actual load as the reconfiguration time section. A multi-objective dynamic reconfiguration is modeled based on the time-varying load distribution network considering network active power loss, static voltage stability, and load balance. The optimal period partition function is dynamically adjusted by the time-division method under the constraint of switching actions. The paper also proposes a multi-group flight slime mould algorithm (MFSMA) by applying a multi-group optimization strategy and adaptive Levy flight for the dynamic distribution network reconfiguration problem. Through experiments on IEEE-33 and IEEE-118 bus distribution networks, the experimental results show that the MFSMA for the multi-period dynamic reconfiguration effectively simulates the actual reconfiguration of the distribution network and provides a new scheme for the economic, stable, and safe operation of the distribution network.

Solving dynamic distribution network reconfiguration using deep reinforcement learning

Solving dynamic distribution network reconfiguration using deep reinforcement learning

Multi-objective resilience enhancement program in smart grids during extreme weather conditions

Weather based electrical power outages cover a huge part of consumer interruptions. So, reliable-economical operation of grids during extreme weather conditions is one of challenges for grid operators. In this regard, this paper proposes resilience enhancement programs in order to increase resilience and economic profits in a smart grid. In proposed approach, resilience improvement is done by modeling weather effects on branches outages and then re-scheduling of distributed energy resources and energy storages, load shifting and dynamic reconfiguration of distribution network. In this paper, hourly variation of weather depended failure probabilities are considered. Resilience enhancement programs aim to mitigate effects of events which may cause by extreme weather before fault inception by rescheduling of resources and selecting suitable reconfigurations. Also, reconfiguration isolates damaged parts after fault inception. The objectives in the proposed approach are defined as minimizing operational cost of distribution network and energy not supplied penalty costs from the system operator’s viewpoint, as well as, maximizing benefits of energy resources owners by considering weather conditions. A multi-objective optimization algorithm based on genetic algorithm and epsilon constraint method using fuzzy decision maker is employed to choose the best solution from a provided Pareto optimal set. In order to evaluate performance of proposed resilience enhancement programs and its effect, resilience assessment metrics are studied. Various simulations prove the efficiency of proposed model in compare with traditional grid during extreme weather conditions.

Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The problem is typically solved by physical model-based control algorithms, which not only rely on accurate network parameters but also lack scalability. To address these limitations, this paper develops a data-driven batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR problem. The proposed RL algorithm learns the network reconfiguration control policy from a finite historical operational dataset without interacting with the distribution network. The numerical study results on three distribution networks show that the proposed algorithm not only outperforms state-of-the-art RL algorithms but also improves the behavior control policy, which generated the historical operational data. The proposed algorithm is also very scalable and can find a desirable network reconfiguration solution in real-time.

Dynamic reconfiguration of distribution network with new energy generation considering economic and reliability

The penetration rate of new energy power generation in the distribution network has gradually increased, and the traditional static reconfiguration for a certain time section will be difficult to meet the actual operation requirements of the distribution network. A dynamic reconfiguration model of the distribution network with network loss, voltage offset, and new energy abandonment rate as the optimization goals was established. An adaptive genetic algorithm was used to solve the dynamic reconfiguration optimization of the distribution network. By simplifying the traditional network, a large number of infeasible solutions generated during the encoding process are reduced, and the optimization ability of the algorithm is improved. Based on the IEEE33 node system, four different scenarios were set up for simulation analysis. The results show that the reconfiguration can significantly reduce the network loss and voltage offset of the system, and also significantly improve the utilization efficiency of new energy and effectively improve the operation level of the distribution network. The feasibility and correctness of this method were verified.

Impacts of Integrating Topology Reconfiguration and Vehicle-to-Grid Technologies on Distribution System Operation

Autonomous electric vehicles (AEVs) provide unique opportunities to cope with the uncertainties of distributed energy generation in distribution networks. But the effects are limited by both inherent radial topology and the behaviors of decentralized AEVs. We investigate the potential benefits of dynamic distribution network reconfiguration (DDNR), taking into account AEVs’ spatial-temporal availability and their charging demand. We propose a mixed integer programming model to optimally coordinate the charging/discharging of AEVs with DDNR, while satisfying AEVs’ original travel plan. Numerical studies based on a test system overlaying the IEEE 33-node test feeder and Sioux Falls transportation network show that DDNR and AEV complement each other, which improves the operation of the distribution system. We also conduct sensitivity analyses on inputs including renewable fluctuation and AEVs penetration level.

Energy Management Strategy in Dynamic Distribution Network Reconfiguration Considering Renewable Energy Resources and Storage

Penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in distribution systems is increasing, and it is crucial to investigate their impact on systems’ operation scheme, reliability, and security. In this paper, expected energy not supplied (EENS) and voltage stability index (VSI) of distribution networks are investigated in dynamic balanced and unbalanced distribution network reconfiguration, including RESs and EES systems. Furthermore, due to the high investment cost of the EES systems, the number of charge and discharge is limited, and the state-of-health constraint is included in the underlying problem to prolong the lifetime of these facilities. The optimal charging/discharging scheme for EES systems and optimal distribution network topology are presented in order to optimize the operational costs, and reliability and security indices simultaneously. The proposed strategy is applied to a large-scale 119-bus distribution test network in order to show the economic justification of the proposed approach.

Dynamic reconfiguration of distribution network considering dynamic segmentation of time series load mean values

A novel dynamic reconfiguration model, in which the time interval segmentation is based on the time series load mean values, is proposed in this paper to deal with the access of distributed generation (DG), electric vehicle (EV) and the constraint of switch operation times. Firstly, a dynamic reconfiguration model with the lowest total daily cost is established, with the power outage loss cost considered. Secondly, the segment method of the equivalent daily load curve based on the dynamic segmentation of time series load mean values is proposed. Finally, the IEEE-33 nodes system is adopted to carry out the simulation, and calculation results are used to show the feasibility and effectiveness of the proposed segment method.

Data-Driven Optimization Control for Dynamic Reconfiguration of Distribution Network

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.

An Outage Risk Oriented Dynamic Distribution Network Reconfiguration Methodology Considering the Effects of Weather Conditions on Power Line Failure Rate

A dynamic network reconfiguration methodology considering time-varying weather conditions is proposed. The aim of the proposed methodology is to minimize outage risk. To fully reflect actual weather severity, a classification method for weather states based on weather variables analysis is presented. Considering time-varying nature of weather, an approach to determine the optimal time for reconfiguration by comparing reliability change between adjacent intervals is provided. Then, applying the Quantum Genetic Algorithm to solve reconfiguration optimization problem is introduced. Simulation results on the IEEE 33-node test systems show that the proposed dynamic reconfiguration method can reduce distribution network's outage risk. In addition, weather conditions have impacts on outage risk of distribution network and should be considered in reconfirmation problems.