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

In recent years,the large-scale grid connection of various distributed power sources has made the planning and operation of distribution grids increasingly complex.Consequently,a large number of active distribution network reconfiguration techniques have emerged to reduce system losses,improve system safety,and enhance power quality via switching switches to change the system topology while ensuring the radial structure of the network.While scholars have previously reviewed these methods,they all have obvious shortcomings,such as a lack of systematic integration of methods,vague classification,lack of constructive suggestions for future study,etc.Therefore,this paper attempts to provide a comprehensive and profound review of 52 methods and applications of active distribution network reconfiguration through systematic method classification and enumeration.Specifically,these methods are classified into five categories,i.e.,traditional methods,mathematical methods,meta-heuristic algorithms,machine learning methods,and hybrid methods.A thorough comparison of the various methods is also scored in terms of their practicality,complexity,number of switching actions,performance improvement,advantages,and disadvantages.Finally,four summaries and four future research prospects are presented.In summary,this paper aims to provide an up-to-date and well-rounded manual for subsequent researchers and scholars engaged in related fields.

Optimal Location and Sizing of Distributed Generator via Improved Multi-Objective Particle Swarm Optimization in Active Distribution Network Considering Multi-Resource

In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distributed generation(DG)based on renewable energy is critical for active distribution network operation enhancement.To comprehensively analyze the accessing impact of DG in distribution networks from various parts,this paper establishes an optimal DG location and sizing planning model based on active power losses,voltage profile,pollution emissions,and the economics of DG costs as well as meteorological conditions.Subsequently,multiobjective particle swarm optimization(MOPSO)is applied to obtain the optimal Pareto front.Besides,for the sake of avoiding the influence of the subjective setting of the weight coefficient,the decisionmethod based on amodified ideal point is applied to execute a Pareto front decision.Finally,simulation tests based on IEEE33 and IEEE69 nodes are designed.The experimental results show thatMOPSO can achieve wider and more uniformPareto front distribution.In the IEEE33 node test system,power loss,and voltage deviation decreased by 52.23%,and 38.89%,respectively,while taking the economy into account.In the IEEE69 test system,the three indexes decreased by 19.67%,and 58.96%,respectively.

Blockchain-Based Power Transaction Method for Active Distribution Network

A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading system.Firstly,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in blocks.Secondly,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware requests.Finally,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power suppliers.The experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

A Privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity threats.In this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic cryptosystem.During the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping attacks.The correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically guaranteed.The simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.

Joint Planning of Active Distribution Network and EV Charging Stations Considering Vehicle-to-Grid Functionality and Reactive Power Support

This paper proposes a collaborative planning model for active distribution network(ADN)and electric vehicle(EV)charging stations that fully considers vehicle-to-grid(V2G)function and reactive power support of EVs in different regions.This paper employs a sequential decomposition method based on physical characteristics of the problem,breaking down the holistic problem into two sub-problems for solution.Subproblem I optimizes the charging and discharging behavior of autopilot electric vehicles(AEVs)using a mixed-integer linear programming(MILP)model.Subproblem II uses a mixed-integer secondorder cone programming(MISOCP)model to plan ADN and retrofit or construct V2G charging stations(V2GCS),as well as multiple distributed generation resources(DGRs).The paper also analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning.The presented model is tested in the 47-node ADN in Longgang District,Shenzhen,China,and the IEEE 33-node ADN,demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.

Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks

As numerous distributed energy resources(DERs)are integrated into the distribution networks,the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks(ADNs).Since accurate models are usually unavailable in ADNs,an increasing number of reinforcement learning(RL)based methods have been proposed for the optimal dispatch problem.However,these RL based methods are typically formulated without safety guarantees,which hinders their application in real world.In this paper,we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic(S3AC)for the optimal dispatch of DERs in ADNs,which not only minimizes the operational cost but also satisfies safety constraints during online execution.In the proposed S3AC,the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition(SCADA)system,effectively providing enhanced safety for executed actions.Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.

Optimal Operation Control Strategies for Active Distribution Networks Under Multiple States:A Systematic Review

With the large-scale integration of distributed renewable generation(DRG)and increasing proportion of power electronic equipment,the traditional power distribution network(DN)is evolving into an active distribution network(ADN).The operation state of an ADN,which is equipped with DRGs,could rapidly change among multiple states,which include steady,alert,and fault states.It is essential to manage large-scale DRG and enable the safe and economic operation of ADNs.In this paper,the current operation control strategies of ADNs under multiple states are reviewed with the interpretation of each state and the transition among the three aforementioned states.The multi-state identification indicators and identification methods are summarized in detail.The multi-state regulation capacity quantification methods are analyzed considering controllable resources,quantification indicators,and quantification methods.A detailed survey of optimal operation control strategies,including multi-state operations,is presented,and key problems and outlooks for the expansion of ADN are discussed.

MADDPG-based Active Distribution Network Dynamic Reconfiguration with Renewable Energy

The integration of distributed generations(DG),such as wind turbines and photovoltaics,has a significant impact on the security,stability,and economy of the distribution network due to the randomness and fluctuations of DG output.Dynamic distribution network reconfiguration(DNR)technology has the potential to mitigate this problem effectively.However,due to the non-convex and nonlinear characteristics of the DNR model,traditional mathematical optimization algorithms face speed challenges,and heuristic algorithms struggle with both speed and accuracy.These problems hinder the effective control of existing distribution networks.To address these challenges,an active distribution network dynamic reconfiguration approach based on an improved multi-agent deep deterministic policy gradient(MADDPG)is proposed.Firstly,taking into account the uncertainties of load and DG,a dynamic DNR stochastic mathematical model is constructed.Next,the concept of fundamental loops(FLs)is defined and the coding method based on loop-coding is adopted for MADDPG action space.Then,the agents with actor and critic networks are equipped in each FL to real-time control network topology.Subsequently,a MADDPG framework for dynamic DNR is constructed.Finally,simulations are conducted on an improved IEEE 33-bus power system to validate the superiority of MADDPG.The results demonstrate that MADDPG has a shorter calculation time than the heuristic algorithm and mathematical optimization algorithm,which is useful for real-time control of DNR.

Leader-follower Optimal Selection Method for Distributed Control System in Active Distribution Networks

Aiming at the shortcomings of a traditional centralized control in an active distribution network(AND),this paper proposes a leader-follower distributed group cooperative control strategy to realize multiple operation and control tasks for an ADN.The distributed information exchange protocols of the distributed generation(DG)group devoted to node voltage regulation or exchange power control are developed using a DG power utilization ratio as the consensus variable.On these bases,this study further investigates the leader optimal selection method for a DG group to improve the response speed of the distributed control system.Furthermore,a single or multiple leader selection model is established to minimize the constraints of the one-step convergence factor and the number of leaders to improve the response speed of the distributed control system.The simulation results of the IEEE 33 bus standard test system show the effectiveness of the proposed distributed control strategy.In addition,the response speed of a DG control group can be improved effectively when the single or multiple leaders are selected optimally.

Distributed processing based fault location,isolation,and service restoration method for active distribution network

Active distribution network(ADN)is a solution for power system with interconnection of distributed energy resources(DER),which may change the network operation and power flow of traditional power distribution network.However,in some circumstances the malfunction of protection and feeder automation in distribution network occurs due to the uncertain bidirectional power flow.Therefore,a novel method of fault location,isolation,and service restoration(FLISR)for ADN based on distributed processing is proposed in this paper.The differential-activated algorithm based on synchronous sampling for feeder fault location and isolation is studied,and a framework of fault restoration is established for ADN.Finally,the effectiveness of the proposed algorithm is verified via computer simulation of a case study for active distributed power system.

Situation Awareness of Active Distribution Network:Roadmap,Technologies,and Bottlenecks

With the rapid development of local generation and demand response,the active distribution network(ADN),which aggregates and manages miscellaneous distributed resources,has moved from theory to practice.Secure and optimal operations now require an advanced situation awareness(SA)system so that operators are aware of operation states and potential risks.Current solutions in distribution supervisory control and data acquisition(DSCADA)as well as the distribution automation system(DAS)generally are not able to meet the technology requirements of SA.In this paper,the authors’participation in the project of developing an SA system as the basic component of a practical active distribution management system(ADMS)deployed in Beijing,China,is presented.This paper reviews the ADN’s development roadmap by illustrating the changes that are made in elements,topology,structure,and control scheme.Taking into consideration these hardware changes,a systematic framework is proposed for the main components and the functional hierarchy of an SA system for the ADN.The SA system’s implementation bottlenecks are also presented,including,but not limited to issues in big data platform,distribution forecasting,and security evaluation.Potential technology solutions are also provided.

Robust Optimal Operation of Active Distribution Network Based on Minimum Confidence Interval of Distributed Energy Beta Distribution

With the gradual increase of distributed energy penetration,the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network.In order to deal with the inevitable uncertainty of distributed energy,a new robust optimal operation method is proposed for active distribution network(ADN)based on the minimum confidence interval of distributed energy Beta distribution in this paper.First,an ADN model is established with second-order cone to include the energy storage device,capacitor bank,static var compensator,on-load tap changer,wind turbine and photovoltaic.Then,the historical data of related distributed energy are analyzed and described by the probability density function,and the minimum confidence interval is obtained by interval searching.Furthermore,via taking this minimum confidence interval as the uncertain interval,a less conservative two-stage robust optimization model is established and solved for ADN.The simulation results for the IEEE33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

Economics of energy storage technology in active distribution networks

An economic and environmental evaluation of active distribution networks containing lithium ion batteries(Li-ion),sodium sulfur batteries(NaS)and vanadium redox flow batteries(VRB)was carried out using the EnergyPLAN software.The prioritization schemes of the combination of energy storage systems and intermittent energy systems were studied technically and economically based on some specific situations of the grid integrated with wind power.The results suggest that the technical and economic optimal intermittent energy-storage capacity ratio was 2:1 in predetermined energy system scenarios.Liion batteries storage system performed the best in critical excess electricity production(CEEP)absorption,energy saving and emission reduction while NaS batteries storage system was the most competitive among the three due to its cheaper costs.

Dispatch and bidding strategy of active distribution network in energy and ancillary services market

The active distribution network(ADN)is able to manage distributed generators(DGs),active loads and storage facilities actively.It is also capable of purchasing electricity from main grid and providing ancillary services through a flexible dispatching mode.A competitive market environment is beneficial for the exploration of ADN’s activeness in optimizing dispatch and bidding strategy.In a bilateral electricity market,the decision variables such as bid volume and price can influence the market clearing price(MCP).The MCP can also have impacts on the dispatch strategy of ADN at the same time.This paper proposes a bilevel coordinate dispatch model with fully consideration of the information interaction between main grid and ADN.Simulation results on a typical ADN validate the feasibility of the proposed method.A balanced proportion between energy market and ancillary services market can be achieved.

1-Neighbour knapsack problem and prospective greedy algorithm of intentional islanding in active distribution network

A connected and undirected graph model of active distribution networks with considering the function of interconnecting switches is constructed in this paper.Based on this model,the island partition problem of active distribution networks can be described as a 1-neighbour knapsack problem.An effective heuristic algorithm named prospective greedy algorithm is then proposed to solve this problem.Case studies on PG&E 69-bus network show the validity of the proposed model and algorithm.

Linear Three-Phase Power Flow for Unbalanced Active Distribution Networks with PV Nodes

High penetration of distributed renewable energy promotes the development of an active distribution network(ADN).The power flow calculation is the basis of ADN analysis.This paper proposes an approximate linear three-phase power flow model for an ADN with the consideration of the ZIP model of the loads and PV nodes.The proposed method is not limited to radial topology and can handle high R/X ratio branches.Case studies on the IEEE 37-node distribution network show a high accuracy and the proposed method is applicable to practical uses such as linear or convex optimal power flow of the ADN.

EMTP-type Program Realization of Krylov Subspace Based Model Reduction Methods for Large-Scale Active Distribution Network

Effective model reduction methods are required to deal with new challenges in active distribution network simulations that are on a large scale and have complicated structures.In the development of advanced electromagnetic transient simulation programs,automated model reduction plays an important role.This paper proposes an automated realization algorithm for the Krylov subspace based model reduction methods of an active distribution network with which the reduced model can be automatically established according to a given threshold of reduction error.The combined state-space nodal analysis framework is employed to apply the automated model reduction algorithm in popular EMTP-type simulation programs.Simulations are performed using PSCAD and a self-developed program to show the feasibility and validity of the proposed methods.

Multi-area distributed three-phase state estimation for unbalanced active distribution networks

This paper proposes a new multi-area framework for unbalanced active distribution network(ADN) state estimation. Firstly, an innovative three-phase distributed generator(DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo-measurements for unmonitored DGs is introduced. The states of DGs,together with the states of alternating current(AC) buses in ADNs, were estimated by using the weighted least squares(WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN.Finally, from the simulation results on the modified IEEE123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed.

Total Quadrant Security Region for Active Distribution Network with High Penetration of Distributed Generation

The region-based method has been applied in transmission systems and traditional passive distribution systems without power sources. This paper proposes the model of total quadrant security region(TQSR) for active distribution networks(ADN) with high penetration of distributed generation(DG). Firstly, TQSR is defined as a closed set of all the N-1 secure operation points in the state space of ADN. Then, the TQSR is modeled considering the constraints of state space,normal operation and N-1 security criterion. Then, the characteristics of TQSR are observed and analyzed on the test systems with different DG penetrations. TQSR can be located in any quadrant of the state space. For different DG penetrations,the shape and security features of TQSR are also different. Finally, the region map is discovered, which summarizes the features of different types of distribution networks.