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.

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.

Optimal PV Allocation&Minimal tap-Changing Transformers Achieving Best Distribution Voltage Profile&Minimum Losses in active distribution networks

In distribution systems,voltage levels of the various buses should be maintained within the permissible limits for satisfactory operation of all electrical installations and equipment.The task of voltage control is closely associated with fluctuating load conditions and corresponding requirements of reactive power compensation.The problem of load bus voltage optimization in distribution systems that have distributed generation(DG)has recently become an issue.In Oman,the distribution code limits the load bus voltage variations within±6%of the nominal value.Several voltage control methods are employed in active distribution systems with a high share of photovoltaic systems(PV)to keep the voltage levels within the desirable limits.In addition to the constraint of targeting the best voltage profile,another constraint has to be achieved which is the minimum loss in the distribution network.An optimised solution for voltage of load busses with on-load tap-changing(OLTC)tarnsformers and PV sources is presented in this paper.This study addresses the problem of optimizing the injected power from PV systems associated with the facilities of tap-changing transformers,as it is an important means of controlling voltage throughout the system.To avoid violating tap-changing constraints,a method is depicted for determining the minimal changes in transformer taps to control voltage levels with distributed PV sources.The taps of a range+5 to-15%,can be achieved by tap-changing transformers.The OLTC operation was designed to keep the secondary bus within the voltage standard for MV networks.

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.

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.

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.

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.

Overview of Distribution Network Fault Location

According to the existing research, the fault section location and fault location of passive distribution network and active distribution network are reviewed. Among them, fault location of passive distribution network mainly introduces fault segment location based on transient state and steady state quantity and fault location based on transient quantity. The active distribution network mainly introduces the fault segment location based on the current amount and the switching capacity based on the distribution network topology. On this basis, the difficulties of fault location in the distribution network at present are analyzed, and the future development is prospected.

Active Segment Pre-restoration Mechanism for Distributed Intelligent Optical Network

The development of Internet brings a great challenge to the survivability of the supporting distributed intelligent optical networks. The emergence of Active-Fault-Alarm (AFA) technology makes it possible for the system to be aware of the incoming or possible fault in advance and to provide possibility to develop a more active restoration mechanism. On this base, an Active Segment Pre-Restoration (ASPR) mechanism for distributed optical network is proposed. ASPR allows establishing a Segment Pre-Restoration Path (SPR-Path) as a work path, which is initiated by the local node, in advance of potential fault occuring and keeps the SPR-Path only during the low-quality or fault period. Simulation results show that the ASPR mechanism has better restoration performance compared with that of Active Restoration (AR) scheme.

Analysis on Harmonics of Distribution Network with Distributed Generators

In this paper, the authors analyze the influence that connection of many distributed generators （DGs） has on the harmonics of the distribution network from the viewpoints of harmonic current, harmonic voltage, and voltage total harmonic distortion （THD）, when it is assumed that the harmonic voltages or the harmonic currents in a distribution network increases by connection of many DGs through the inverters. The analysis on the influence of DGs is carried out by using a standard analytical model of distribution network with DGs based on the practical data. The authors evaluate quantitatively the influence which harmonics generated from DGs has on the harmonics of the distribution network, and analyze visually about propagation of harmonics within the distribution network. In addition, the authors also verify about the restraint effect of the harmonics in the network scale by the active filters installation.

IMPLEMENTING SECURITY FOR ACTIVE NETWORKS IN INTERNET

Active networks is primarily a Defense Advanced Research Projects Agency(DARPA)-funded project focusing on the research of mechanisms, applications, and operating systems to develop a reconfigurable network infrastructure. This letter proposes an Secure Active Tracing System (SATS) to implementing security for active networking in Internet. Unlike currently existing schemes, SATS reduces the computational overloads by executing the filtering operation on selected packet streams only when needed.

A Method for Distributed Generator Dispatch Strategy in Distribution Network

Since a load of power system changes continuously,the generation also adjusted for supply-demand balance purpose.If there exist more distributed generators in the distribution network,the dispatch strategy becomes more crucial.The possibility of having numerous controllable microgrids,diesel generator(DG)units and loads for microgrids(MGs)system requires an efficient dispatch strategy in order to balance supply demand for reducing the total cost of the integrated system.In this paper,a method for the dispatch of the distributed generator in distributed power systems has been proposed.The dispatch strategy is such that it keeps a flat voltage profile,reduces the network losses,increases the maximum loading and voltage security margin of the system.The procedure is based on the analysis of continuous power flow.The method is executed on a 34-bus test system.The MATLAB based PSAT packages are used for simulation purpose.

A Distributed Newton Method for Processing Signals Defined on the Large-Scale Networks

In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm.

基于控保协同的有源配网主动注入式保护方法

由于逆变型分布式电源(inverter-interfaced distributed generation,IIDG)的故障特性主要由其控制策略及电网故障情况决定,在不同故障场景和工况下,传统的被动式保护方法存在保护整定和协调困难的问题。为此,提出一种基于控保协同的有源配网主动注入式保护方法,故障期间控制IIDG主动向电网注入特征信号,保护处利用检测到的特征信号来获取IIDG的基波信息,从而消除IIDG对各支路电流及现有电流保护的影响。由于保护利用的故障特征是由IIDG主动注入的,因此该方法在不同场景和工况下都有较好的适用性,且不需要增加额外的通信和检测设备,具有经济性好和实用性高的优点。仿真结果验证了所提方法的有效性。

主动配电网潮流的全纯嵌入计算方法

配电网潮流计算是进行配电网规划和运行的重要基础。目前,配电网潮流求解方法主要有牛拉(Newton Raphson,NR)法和前推回代(forward backward substitution,FBS)法两大类,但NR法和FBS法在潮流收敛性和计算效率等方面均存在一定的不足。全纯嵌入法(holomorphic embedding method,HEM)是一种求解非线性潮流方程的新方法,它可以有效避免NR法和FBS法在配电网潮流求解过程中出现的上述问题,但现有HEM未充分考虑配电网中各类型分布式电源(distributed generation,DG)和智能软开关(soft open point,SOP)控制方式的多样性,且未构建计及负荷电压特性和频率特性的潮流模型,难以适用于不同运行模式下配电网潮流计算。针对上述问题,提出一种可求解含多类型DG和SOP且可运行在并网/孤岛模式的主动配电网统一全纯嵌入潮流计算方法。首先,根据负荷静态电压和频率特性,构建计及配电网频率和节点电压影响的负荷节点全纯嵌入潮流模型;其次,根据DG不同的控制方式以及配电网不同的运行模式,将DG所连节点等效为不同节点类型,构建各类DG的全纯嵌入潮流模型;再次,考虑SOP不同控制方式,构建SOP全纯嵌入潮流模型;从次,依据全纯嵌入潮流模型等式左右两侧同次幂级数系数相等原则,推导配电网各全纯函数幂级数系数的求解递推关系式,进而计算各全纯函数的各项幂级数系数,借助解析延拓理论求取电压和频率逼近值,实现配电网潮流的准确求解;最后,通过修改的IEEE-33节点测试系统和IEEE-123节点测试系统验证所提方法准确性和有效性。

基于分布鲁棒优化的车-站-网日前能量管理与交易

为考虑上级电网电价、光伏出力等多重多层级不确定性对车-站-网互动博弈模型的影响,且充分体现配电网主动管理技术支撑效果,文中提出了一种基于分布鲁棒优化的车-站-网能量管理与交易方法。首先,针对主动配电网内多元主体能量管理与交易问题,建立了配电网运营商、充电站和电动汽车的日前市场互动框架。其次,融合主动网络管理技术和网络约束,在配电网运营商与聚合了电动汽车的多个充电站之间构建了以多主体各自利益最大为目标的双层Wasserstein分布鲁棒互动博弈模型。然后,提出了结合Karush-Kuhn-Tucker条件、对偶原理和大M法的化简方法以解决多层级不确定性造成的求解难题,将双层Wasserstein分布鲁棒模型转化为单层混合整数二阶锥规划模型,并利用商业求解器YALMIP/GUROBI进行了求解。最后,通过算例仿真验证了所提模型和方法的有效性。

高比例新能源接入的主动配电网规划综述

新能源以分布式电源的形式接入配电网,给系统带来了不可控性、随机性和波动性问题。借助现代电力电子、信息通信及自动控制等技术,灵活可控的主动配电网成为发展趋势,其中主动配电网规划是近年来研究的热点之一。综合国内外在这一领域的研究成果,对主动配电网规划相关研究内容及其方法进行总结、分析及展望。对主动配电网基本结构进行了描述,介绍了配电网组成元素的特点;根据其控制变量的不同,对主动配电网规划模型进行了归类,总结了模型中的优化目标;针对常用模型求解算法及其优缺点进行分析、总结;通过对关键性问题的讨论,分析了未来主动配电网的发展趋势。

基于分布式电源主动控制的配电网合环电压波动抑制方法

配电网合环操作可能导致节点电压波动甚至越限,不仅影响负荷的正常运行,还威胁分布式电源(DG)的安全。为此,提出了一种通过DG主动控制,以抑制配电网合环电压波动的新思想。通过分析合环过程中电压波动的产生机理和影响因素,量化了抑制合环过程电压波动的控制需求,进而构建了抑制合环电压波动的DG可行功率集;通过刻画DG的功率可控范围,提出了基于可行功率集与功率可控范围交集判别的DG最优控制点计算方法,并提出了基于DG主动控制的配电网合环电压波动抑制方法。算例表明,该方法能够最大限度上抑制配电网故障恢复过程的合环电压波动,有效提升故障恢复过程的安全性和可靠性。

多园区综合能源系统接入主动配电网双层优化调度研究

随着园区综合能源系统的不断发展与普及,其与上级大电网并网实现交互已成为大势所趋。文中构建了通过联络线相耦合的多园区综合能源系统接入主动配电网的双层优化调度模型。在主动配电网层面,以最小运行成本为目标,充分考虑了联络线功率、分布式电源、储能设备、柔性负荷、电动汽车等可调度资源;在园区层面,以多园区总运行成本最小为目标,考虑了相互联结的多类型园区的并网情况。利用目标级联法对双层模型进行求解。通过算例验证,文中所构建模型具有良好的经济特性与削峰填谷效果,充分证明了文中所提模型的经济性与有效性。