Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Optimization method for evacuation zone with network reconfiguration based on dynamic simulation

Abstract： With a determinate danger zone and evacuation demand caused by an emergency, an optimization method for the evacuation zone with network reconfiguration based on dynamic simulation is proposed. The method contains three modules. First, the network in the evacuation zone is optimized by a model with the integrated strategy of lane reversal and intersection conflict elimination. Secondly, the dynamic evacuation simulation model based on the cell transmission model is applied to simulate the dynamic propagation process of evacuated vehicles in the network in the evacuation zone. The evacuation time for all evacuated vehicles leaving the danger zone is obtained and the setting of the current evacuation zone is fed back. Thirdly, the arrival distributions of evacuated vehicles at critical intersections of the evacuation zone are also obtained to estimate the delay at critical intersection to determine whether the intersection should be taken as the critical intersection in the next iteration. The evacuation zone is expanded gradually through iteration, and the reasonable evacuation zone and the optimal evacuation network is confirmed. Based on the survey of the parking lot and urban street network around Nanjing Olympic Sports Center, the models and the iterative algorithm were applied to obtain the optimal plan of the evacuation zone with network reconfiguration in an evacuation situation to verify the validity of the proposed method.

Multi-agent and ant colony optimization for ship integrated power system network reconfiguration

Electric power is widely used as the main energy source of ship integrated power system(SIPS), which contains power network and electric power network. SIPS network reconfiguration is a non-linear large-scale problem. The reconfiguration solution influences the safety and stable operation of the power system. According to the operational characteristics of SIPS, a simplified model of power network and a mathematical model for network reconfiguration are established. Based on these models, a multi-agent and ant colony optimization(MAACO) is proposed to solve the problem of network reconfiguration. The simulations are carried out to demonstrate that the optimization method can reconstruct the integrated power system network accurately and efficiently.

Network Reconfiguration for Load Balancing in Distribution System with Distributed Generation and Capacitor Placement

This paper presents an efficient algorithm for optimization of radial distribution systems by a network reconfiguration to balance feeder loads and eliminate overload conditions. The system load-balancing index is used to determine the loading conditions of the system and maximum system loading capacity. The index value has to be minimum in the optimal network reconfiguration of load balancing. The tabu search algorithm is employed to search for the optimal network reconfiguration. The basic idea behind the search is a move from a current solution to its neighborhood by effectively utilizing a memory to provide an efficient search for optimality. It presents low computational effort and is able to find good quality configurations. Simulation results for a radial 69-bus system. The study results show that the optimal on/off patterns of the switches can be identified to give the best network reconfiguration involving balancing of feeder loads while respecting all the constraints.

Directed graph-based distribution network reconfiguration for operation mode adjustment and service restoration considering distributed generation

We present a directed graph-based method for distribution network reconfiguration considering distributed generation. Two reconfiguration situations are considered: operation mode adjustment with the objective of minimizing active power loss(situation Ⅰ) and service restoration with the objective of maximizing loads restored(situation Ⅱ). These two situations are modeled as a mixed integer quadratic programming problem and a mixed integer linear programming problem, respectively. The properties of the distribution network with distributed generation considered are reflected as the structure model and the constraints described by directed graph. More specifically, the concepts of "in-degree" and "out-degree"are presented to ensure the radial structure of the distribution network, and the concepts of "virtual node" and"virtual demand" are developed to ensure the connectivity of charged nodes in every independent power supply area.The validity and effectiveness of the proposed method are verified by test results of an IEEE 33-bus system and a 5-feeder system.

A novel genetic algorithm based on all spanning trees of undirected graph for distribution network reconfiguration

Network reconfiguration is of theoretical and practical significance to guarantee safe and economical operation of distribution system.In this paper,based on all spanning trees of undirected graph,a novel genetic algorithm for electric distribution network reconfiguration is proposed.Above all,all spanning trees of simplified graph of distribution network are found.Tie branches are obtained with spanning tree subtracted from simplified graph.There is one and only one switch open on each tie branch.Decimal identity number of open switch on each tie branch is taken as the optimization variable.Therefore,the length of chromosome is very short.Each spanning tree corresponds to one subpopulation.Gene operations of each subpopulation are implemented with parallel computing method.Individuals of offspring after gene operation automatically meet with radial and connected constraints for distribution network operation.Disadvantages of conventional genetic algorithm for network reconfiguration that a large amount of unfeasible solutions are created after crossover and mutation,which result in very low searching efficiency,are completely overcome.High calculation speed and superior capability of the proposed method are validated by two test cases.

Performance Enhancement of Distribution Systems via Distribution Network Reconfiguration and Distributed Generator Allocation Considering Uncertain Environment

The emergence of dispersed generation,smart grids,and deregulated electricity markets has increased the focus on enhancing the performance of distribution systems.This paper proposes a method to reduce the energy loss and improve the reliability of distribution systems by performing distribution network reconfiguration(DNR)and distributed generator(DG)allocation.In this study,the intermittent nature of renewable-based DGs and the load profile are considered using a probabilistic method.The study investigates different annual plans based on the seasonal power profiles of DGs and the load to minimize the combined cost function of annual energy loss and annual energy not served.The proposed method is implemented using the firefly algorithm(FA),which is one of the meta-heuristic optimization algorithms.Several case studies are investigated using the IEEE 33-bus distribution system to highlight the effectiveness of the method.

A compound objective reconfiguration of distribution networks using hierarchical encoded particle swarm optimization

With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.

Fuzzy-GA based algorithm for optimal placement and sizing of distribution static compensator (DSTATCOM) for loss reduction of distribution network considering reconfiguration

This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devices and the cost of system operation, namely, energy loss cost due to both reconfiguration and DSTATCOM placement, are combined to form the objective function to be minimized. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. In the proposed approach, the fuzzy membership function of loss sensitivity is used for the selection of weak nodes in the power system for the placement of DSTATCOM and the optimal parameter settings of the DFACTS device along with optimal selection of tie switches in reconfiguration process are governed by genetic algorithm(GA). Simulation results on IEEE 33-bus and IEEE 69-bus test systems concluded that the combinatorial method using DSTATCOM and reconfiguration is preferable to reduce power losses to 34.44% for 33-bus system and to 45.43% for 69-bus system.

Software defined intelligent satellite-terrestrial integrated networks:Insights and challenges

Satellite-Terrestrial integrated Networks(STNs)have been advocated by both academia and industry as a promising network paradigm to achieve service continuity and ubiquity.However,STNs suffer from problems including poor flexibility of network architecture,low adaptability to dynamic environments,the lack of network intelligence,and low resource utilization.To handle these challenges,a Software defined Intelligent STN(SISTN)architecture is introduced.Specifically,the hierarchical architecture of the proposal is described and a distributed deployment scheme for SISTNs controllers is proposed to realize agile and effective network management and control.Moreover,three use cases in SISTNs are discussed.Meanwhile,key techniques and their corresponding solutions are presented,followed by the identification of several open issues in SISTNs including compatibility with existing networks,the tradeoff between network flexibility and performance,and so on.

Multi-agent System Optimized Reconfiguration of Shipboard Power System

Reconfigurability of the electrical network in a shipboard power system （SPS） after its failure is central to the restoration of power supply and improves survivability of an SPS. The navigational process creates a sequence of different operating conditions. The priority of some loads differs in changing operating conditions. After analyzing characteristics of typical SPS, a model was developed used a grade III switchboard and an environmental prioritizing agent （EPA） algorithm. This algorithm was chosen as it is logically and physically decentralized as well as multi-agent oriented. The EPA algorithm was used to decide on the dynamic load priority, then it selected the means to best meet the maximum power supply load. The simulation results showed that higher priority loads were the first to be restored. The system satisfied all necessary constraints, demonstrating the effectiveness and validity of the proposed method.

Power loss reduction of distribution systems using BFO based optimal reconfiguration along with DG and shunt capacitor placement simultaneously in fuzzy framework

In distribution systems,network reconfiguration and capacitor placement are commonly used to diminish power losses and keep voltage profiles within acceptable limits.Moreover,the problem of DG allocation and sizing is great important.In this work,a combination of a fuzzy multi-objective approach and bacterial foraging optimization(BFO) as a meta-heuristic algorithm is used to solve the simultaneous reconfiguration and optimal sizing of DGs and shunt capacitors in a distribution system.Each objective is transferred into fuzzy domain using its membership function.Then,the overall fuzzy satisfaction function is formed and considered a fitness function inasmuch as the value of this function has to be maximized to gain the optimal solution.The numerical results show that the presented algorithm improves the performance much more than other meta-heuristic algorithms.Simulation results found that simultaneous reconfiguration with DG and shunt capacitors allocation(case 5) has 77.41%,42.15%,and 56.14%improvements in power loss reduction,load balancing,and voltage profile indices,respectively in 33-bus test system.This result found 87.27%,35.82%,and 54.34%improvements of mentioned indices respectively for 69-bus system.

Coordinated Restoration Method for Electric Buses and Network Reconfigurations in Distribution Systems Under Extreme Events

Distribution systems are facing challenges in serving lifeline loads after extreme events.Network reconfiguration is a traditional and practical method for power supply restoration,which has strong but inflexible power transfer capabilities influenced by network topology.Multiple failures of utility power under extreme events will further limit the efficiency of network reconfiguration.Electric buses(EBs)can be utilized to achieve power supply considering their discharging capabilities as mobile storage devices.However,the mobility of EBs and the influences of transport systems must be carefully considered to enhance the resilience of distribution systems.Reconfiguration and EBs are complementary in terms of recovery capabilities and location flexibility,and more important loads can be recovered by the coordination between EBs and network reconfiguration.This paper proposes a coordinated restoration method for EBs and reconfigurations considering the influences of transport systems.The post-disaster restoration problem is formulated as a bi-level model,in which the network topology is optimized in the upperlevel aiming at maximizing restoration loads through the main grid and EBs,while the traffic paths of all EBs are optimized with the goal of maximizing the restoration loads by the EBs in the lower-level considering time consumption and energy consumption during movement.The PSO and a genetic algorithm are used to solve the proposed bi-level optimization problem.Simulation studies are performed to verify the superiority of the proposed method.

Overall optimization of distribution networks

Network reconfiguration and capacitor switching are important measures to reduce power loss and improve security and economy in automation of distribution. A new method based on parallel genetic algorithm is proposed to search the whole problem space for better solution. Multiple populations evolve independently and communicate periodically, which simulates parallel computing process to save computing time. The results show that the method is robust and has better benefit than the alterative iteration method. In addition, the effect of overall optimization is better than optimization alone. Power loss can be reduced and the level of voltage can be greatly improved.

Smart Service System(SSS)：A Novel Architecture Enabling Coordination of Heterogeneous Networking Technologies and Devices for Internet of Things

In Internet of Things(IoT), the devices or terminals are connected with each other, which can be very diverse over the wireless networks. Unfortunately, the current devices are not designed to communicate with the collocated devices which employ different communication technologies. Consequently, the communication between these devices will be realized only by using the gateway nodes. This will cause the inefficient use of wireless resources. Therefore, in this paper, a smart service system(SSS) architecture is proposed, which consists of smart service terminal(SST), and smart service network(SSN), to realize the Io T in a general environment with diverse communication networks, devices, and services. The proposed architecture has the following advantages: i) the devices in this architecture cover multiple types of terminals and sensor-actuator devices; ii) the communications network therein is a converged network, and will coordinate multiple kinds of existing and emerging networks. This converged network offers ubiquitous access for various sensors and terminals; iii) the architecture has services and applications covering all smart service areas. It also provides theadaptability to new services and applications. A SSS architecture-based smart campus system was developed and deployed. Evaluation experiments of the proposed smart campus system demonstrate the SSS's advantages over the existing counterparts, and verify the effectiveness of the proposed architecture.

Sequential Reconfiguration of Unbalanced Distribution Network with Soft Open Points Based on Deep Reinforcement Learning

With the large-scale distributed generations(DGs)being connected to distribution network(DN), the traditional day-ahead reconfiguration methods based on physical models are challenged to maintain the robustness and avoid voltage offlimits. To address these problems, this paper develops a deep reinforcement learning method for the sequential reconfiguration with soft open points(SOPs) based on real-time data. A statebased decision model is first proposed by constructing a Marko decision process-based reconfiguration and SOP joint optimization model so that the decisions can be achieved in milliseconds.Then, a deep reinforcement learning joint framework including branching double deep Q network(BDDQN) and multi-policy soft actor-critic(MPSAC) is proposed, which has significantly improved the learning efficiency of the decision model in multidimensional mixed-integer action space. And the influence of DG and load uncertainty on control results has been minimized by using the real-time status of the DN to make control decisions. The numerical simulations on the IEEE 34-bus and 123-bus systems demonstrate that the proposed method can effectively reduce the operation cost and solve the overvoltage problem caused by high ratio of photovoltaic(PV) integration.

Cognition Based Reconfigurable Wireless Network Technology

The End-to-End Reconfigurability (E2R) project aims at realizing the convergence of the heterogeneous radio networks and the optimal utilization of the radio resources. With the continuous development of E2R technology and cognitive theory, the evolution from existing radio networks to future reconfigurable radio networks with the cognitive ability becomes possible. Nowadays the research aspects of E2R include the system architecture of reconfigurable radio networks and some key technologies for their evolution.

Reconfiguration of Active Distribution Networks Equipped with Soft Open Points Considering Protection Constraints

The purpose of active distribution networks(ADNs)is to provide effective control approaches for enhancing the operation of distribution networks(DNs)and greater accommodation of distributed generation(DG)sources.With the integration of DG sources into DNs,several operational problems have drawn attention such as overvoltage and power flow alteration issues.These problems can be dealt with by utilizing distribution network reconfiguration(DNR)and soft open points(SOPs).An SOP is a power electronic device capable of accurately controlling active and reactive power flows.Another significant aspect often overlooked is the coordination of protection devices needed to keep the network safe from damage.When implementing DNR and SOPs in real DNs,protection constraints must be considered.This paper presents an ADN reconfiguration approach that includes DG sources,SOPs,and protection devices.This approach selects the ideal configuration,DG output,and SOP placement and control by employing particle swarm optimization(PSO)to minimize power loss while ensuring the correct operation of protection devices under normal and fault conditions.The proposed approach explicitly formulates constraints on network operation,protection coordination,DG size,and SOP size.Finally,the proposed approach is evaluated using the standard IEEE 33-bus and IEEE 69-bus networks to demonstrate the validity.

Modified Satin Bowerbird for Distributed Generation in Remotely Controlled Voltage Bus

The distributed generators in the radial distribution network are to improve the Grid performance and its efficiency.These Distributed Generators control the PV bus;it is converted as a remote controlled PVQ bus.This PVQ bus reduces the power loss and reactive power.Initially,the distributed generators were placed in the system using mathematical modelling or the optimization.This approach improves the efficiency but it has no effect in loss minimization.To minimize the loss the reconfigured network with Genetic algorithm based Distributed generator placement proposed as existing work.This approach minimizes the loss effectively;but the genetic algorithm takes more time for DG placement.Hence,in this,the network reconfiguration is performed using a modified Satin bower bird algorithm after DG placement and DG sizing.Initially,the sensitive analysis applied the loadflow analysis to identify the optimal placement for the distributed generator.Then,the modified Satin Bowerbird(SBO)used for the network reconfiguration.This approach minimizes the loss of effectively by combining the network reconfiguration process.The proposed modified SBO-based network reconfiguration implemented on standard bus systems 33 and 69 using MATLAB R2021b version under Windows 10 environment.The proposed approach compared with the existing work in terms of real power loss and loss reduction.

Reliability improvement in distribution systems employing an integrated voltage sag mitigation method using binary gravitational search algorithm

A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.