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.

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.

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.

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.

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.

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.

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.

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.

Optimal Scheduling of Distribution Network Incorporating Topology Reconfiguration,Battery Energy System and Load Response

Distributed generation(DG)is becoming increasingly important due to the serious environmental pollution caused by conventional fossil-energy-based generation and the depletion of non-renewable energy.As the flexible resources in the active distribution network(ADN),battery energy system(BES)and responsive load(RL)are all able to assist renewable DG integration in day-ahead dispatch.In addition,the security and economic level can be significantly improved by adjusting network topology.Therefore,in this paper,a coordinated day-ahead scheduling method incorporating topology reconfiguration,BES optimization and load response is presented to minimize the total day-ahead operational costs in the ADN.Linearized current injection models are presented for renewable DG,RL and BES based on the linear power flow model,and an extensible linear switching operations calculation(ELSOC)method is proposed to address the network reconfiguration.Thus,a mixed integer linear programming(MILP)model is proposed for optimal coordinated operation of an ADN.The correctness and effectiveness of the proposed method are demonstrated by simulations on a modified test system.In addition,the combined scenario and Monte-Carlo method is used to handle the uncertainties of loads and DGs,and the results of different uncertainties can further verify the feasibility of the proposed model.

Multi-Period Resilient Model for VSC-Based AC-DC HDS Considering Public-Safety Power Shutoff to Mitigate Wildfires

This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.

Resilient outage recovery of a distribution system: co-optimizing mobile power sources with network structure

Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system(DS)in extreme weather and with equipment faults.Previous studies have separately considered network reconfigu-ration(NR)and dispatching mobile power sources(MPS)to restore the outage load.However,NR cannot deal with the scenario of an electrical island,while dispatching MPS results in a long power outage.In this paper,a resilient outage recovery method based on co-optimizing MPS and NR is proposed,where the DS and traffic network(TN)are considered simultaneously.In the DS,the switch action cost and power losses are minimized,and the access points of MPSs are changed by carrying out the NR process.In the TN,an MPS dispatching model with the objective of mini-mizing power outage time,routing and power generation cost is developed to optimize the MPSs’schedule.A solu-tion algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy.Finally,numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method.It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS.The results also show that the system operation cost can be reduced by considering power losses in the objective function.

Performance Monitoring in Transparent Reconfigurable WDM Networks

This paper classifies and surveys different approaches proposed for performance monitoring, in particular the optical signal-to-noise ratio (OSNR) monitoring, in transparent reconfigurable WDM networks. Some considerations for future monitoring schemes are discussed.

Magnetic-programmable organohydrogels with reconfigurable network for mechanical homeostasis

Synthetic materials with tunable mechanical properties have great potential in soft robotics and biomedical engineering.However,current materials are limited to the mechanical duality altering their mechanical properties only between soft and hard states and lack of consecutively programmable mechanics.Herein,the magnetic-programmable organohydrogels with heterogeneous dynamic architecture are designed by encasing oleophilic ferrofluid droplets into hydrogel matrix.As magnetic field increases,the mechanical properties of organohydrogels can be consecutively modulated owing to the gradual formation of chain-like assembly structures of nanoparticles.The storage modulus G'increases by 2.5 times when magnetic field goes up to 0.35 T.Small-Angle X-ray Scattering(SAXS)confirms the reconfigurable orientation of nanoparticles and the organohydrogels show reversible modulus switching.Besides,the materials also exhibit high stretchability,magnetic actuation behavior and effective self-healing capability.Furthermore,the organohydrogels are applied into the design of effectors with mechanical adaptivity.When subjected to serious external perturbations,the effector can maintain mechanical homeostasis by regulating modulus of organohydrogel under applied magnetic field.Such materials are applicable to homeostatic systems with mechanically adaptive behaviors and programmed responses to external force stimuli.

Route Guardian: Constructing Secure Routing Paths in Software-Defined Networking

Software-Defined Networking（SDN） decouples the control plane and the data plane in network switches and routers, which enables the rapid innovation and optimization of routing and switching configurations. However,traditional routing mechanisms in SDN, based on the Dijkstra shortest path, do not take the capacity of nodes into account, which may lead to network congestion. Moreover, security resource utilization in SDN is inefficient and is not addressed by existing routing algorithms. In this paper, we propose Route Guardian, a reliable securityoriented SDN routing mechanism, which considers the capabilities of SDN switch nodes combined with a Network Security Virtualization framework. Our scheme employs the distributed network security devices effectively to ensure analysis of abnormal traffic and malicious node isolation. Furthermore, Route Guardian supports dynamic routing reconfiguration according to the latest network status. We prototyped Route Guardian and conducted theoretical analysis and performance evaluation. Our results demonstrate that this approach can effectively use the existing security devices and mechanisms in SDN.

Environmental contact modeling for the earthworm-like robot via the novel elementary mechanical network

The earthworm-like robot is designed for prospective applications such as disaster rescue and pipeline detection in natural environments.However,current studies on the interaction modeling between the earthworm-like robot and the environment only consider rigid contact.This simplification limits the reliability of dynamic analysis and locomotion optimization on soft surfaces,such as sand.Therefore,developing a method for refined contact modeling for the earthworm-like robot and describing the contact effect induced by the soft environmental medium is urgent.To this end,this paper proposes a new modeling architecture called the elementary mechanical network(EMN).EMN is constructed as fractal structures for the convenience of network reconfiguration.First,elementary mechanical elements,such as the damper,spring,and slider,are parallelly connected to constitute a basic module.Second,the modules are serially linked to create a group.Finally,the groups are parallelly assembled to form the network.EMN is also proven to be equivalent to recurrent neural networks in specific forms.Therefore,EMN inherits the advantages of physical interpretability from mechanical elements and universal approximability from conventional networks.In addition,particle swarm optimization and Boolean operation are employed for network weight training and topological minimization.Numerical examples show that using EMNs with identical initial structures can accurately describe diverse empirical models in the minimum realization.EMN is applied for contact modeling for the earthworm-like locomotion robot in the dry sand based on such versatility.The experiment measures the normal and tangential ground reaction forces with different sinkage depths and locomotion speeds.Trained results reveal a common law that the contact effect in the dry sand is similar to Coulomb friction.The proposed EMN does not require prior system knowledge and promises a minimal physical representation,thus encouraging a successful exploration of constitutive modeling in broad scopes.

Optimal distributed generation allocation in radial distribution systems considering customer-wise dedicated feeders and load patterns

Distribution system planners usually provide dedicated feeders to its different class of customers,each of whom has its own characteristic load pattern which varies hourly and seasonally.A more realistic modeling should be devised by considering the daily and seasonal variations in the aggregate load patterns of different class of customers.This paper addresses a new methodology to provide integrated solution for the optimal allocation of distributed generations and network reconfiguration considering load patterns of customers.The objectives considered are to maximize annual energy loss reduction and to maintain a better node voltage profile.Bat algorithm(BA)is a new bio-inspired search algorithm which has shown an advance capability to reach into the promising region,but its exploration is inadequate.The problem is solved by proposing the improved BA(IBA).The proposed method is investigated on the benchmark IEEE 33-bus test distribution system and the results are very promising.

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

Demand response, the reactive power output of distributed generation(DG), and network reconfiguration have significant impacts on a DG allocation strategy. In this context, a novel real-time price-based demand response formulation is integrated into the allocation model of DG. The tariff is regulated by the difference between the load and active power of renewable energy. Meanwhile, network reconfiguration and the capacity curve describing the active and reactive power limits of DG are included in the optimization model for promoting the allocation of DG.With these measures, the optimal allocation model of DG is established with the goal of maximizing the net annual profit while guaranteeing the efficient utilization of renewable energy. In addition, the uncertainties of renewable energy are considered on the basis of a two-stage robust optimization method. Finally, the entire optimization model is solved by the column and constraint generation algorithm in the IEEE 33-bus distribution system and a practical 99-bus distribution system. Numerical simulations show that the proposed model is effective in terms of improving both the usage of renewable energy and net annual profit.