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.

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.

Distribution Network Reconfiguration Based on ESA

An efficient algorithm ESA combining evolution strategies(ES) with simulated annealing(SA) is proposed in this paper. We first use ES to choose an initial temperature, then use a modified SA to find a global optimum for the problem. An efficient load flow method and a heuristic criterion for determining the temperature lowering scheme are employed in order to speed up the computation. The solution algorithm has been tested on a distribution system with very promising results.

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.

Multi-Objective Optimization of the Safe Operation of the Electrical Distribution System by Placing D-FACTS and Network Reconfiguration

The distribution networks of agglomerated areas of the developing countries are generally the seat of overloads, voltage drops, and untimely interruptions of the power supply. This paper consisted of optimizing the grid topology and placement of a DSTATCOM in a SBEE real distribution network in order to improve its technical performance. The modified ant colony algorithms solved this difficult combinatorial problem, which integrated among the criteria, the minimization of the losses and the deviation of the node voltages under operational constraints about distribution networks operation. According to the results obtained, the optimization of the topology of a distribution network and the placement of DSTATCOM contributed qualitatively to improve the losses, voltage and stability plans of the Togba distribution network. Actually, the hybridization of optimization means such as the placement of DSTATCOM and the reconfiguration of the networks applied to the Togba HVA network made the power of DSTATCOM optimization possible by almost 50.71% and reduce losses to 83.57%. The implementations of those algorithms are very efficient and effective, and can be implemented to help distribution system operators, developing countries and in particular, the operators of the Beninese Electric Power Company to perform their electrical network.

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.

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.

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.

考虑电动汽车充电模式的配电网可开放容量提升改造策略

配电网可开放容量代表着现有配电网条件下最大可接入的负荷容量,是评估配电网规划与运行效果的重要指标之一。为应对电动汽车大规模无序充电导致可开放容量大幅度下降的现象,提出一种考虑电动汽车充电模式的配电网网格改造方法,通过优化网格拓扑结构以及有限投资内线路扩容与储能投入的最佳数量、位置和类型,实现可开放容量的最优提升。首先,利用蒙特卡洛法生成电动汽车无序充电场景,并搭建基于充电时序优化的电动汽车有序充电模型;其次,搭建基于网络重构、线路扩容及储能投资协同的配电网可开放容量提升改造模型;然后,通过Benders分解算法降低模型复杂度,并调用商业求解器求解;最后,以某城市实际配电网网格为算例,验证了所提可开放容量提升改造策略的有效性。

混合量子-经典算法的配电网灾后拓扑重构方法

为及时有效地制定配电网拓扑重构策略以提升负荷快速恢复能力,基于量子计算的优越性,提出混合量子-经典(HQC)算法的弹性配电网灾后拓扑重构方法。首先,构建基于HQC算法的灾后配电网拓扑重构模型,以实现实际场景、优化问题、嵌入算法相应模块在量子计算和经典计算环境下的协作交互过程。然后,将配电网拓扑重构问题构造为无约束离散优化子问题和有约束连续优化子问题,提出量子退火嵌入式交替方向乘子(QA-ADMM)算法,将离散子问题等效映射成量子可解释的伊辛模型后,部署在D-Wave量子退火计算机中,并与经典计算机中连续子问题迭代求解,采用自适应惩罚因子调节机制加速算法收敛。最后,通过IEEE 14、33、69、123以及改进的205节点的不同规模配电系统,分析验证了QA-ADMM算法的有效性、稳定性与可扩展性。结果表明,惩罚因子、目标函数惩罚项系数、量子退火中采样读取次数会影响所提算法的精度和收敛速度;优化问题规模扩大后,所提混合量子-经典算法计算优势更加明显,205节点配电系统算例下,计算效率较经典计算可提升约34%。

含储能健康运行域与电压安全约束的配电网重构方法

随着未来各类新能源及用户侧需求互动技术引入配电网,配电网电压安全管理将面临更严峻挑战,带来双向潮流、电压越限等运行问题。在此背景下,该文挖掘网络侧及储能设备的运行灵活性,以限制配电网节点电压波动指数(voltage volatility index,VVI)为目标,提出融合储能能量管理的配电网网络重构方法。同时,为减小储能频繁充放引起的寿命损失,该文基于储能寿命曲面的超空间投影定义储能寿命水平集表征储能健康运行域(health operating area,HOA),并提出水平集凸逼近方法替代非线性储能寿命约束,可在保障储能寿命前提下实现网络重构问题的高效求解。最后,选取IEEE-33节点系统进行分析,验证所提方法的有效性与实用性。

考虑配电网主动重构的分布式光伏消纳能力概率化评估方法

为评估配电网的分布式光伏消纳能力并对其进行有效提升,提出了考虑配电网主动重构的分布式光伏消纳能力概率化评估方法。首先,定义了配电网分布式光伏消纳能力,并构建了以配电网分布式光伏消纳能力最大为目标的优化问题,通过对网络拓扑进行动态优化重构实现分布式光伏消纳能力指标的最大化。其次,为了研究分布式光伏输出的随机特性对配电网光伏消纳能力的影响,以配电网节点电压偏差为机会约束条件,采用机会约束法对配电网最大分布式光伏消纳能力值进行计算。再次,提出了结合遗传算法和线性规划的混合计算方法对优化问题进行高效求解。最后,以IEEE 33节点配电系统为例进行仿真验证。算例结果表明,配电网应用所提方法进行主动重构,可以有效提升分布式光伏消纳能力。

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.

A High-level Architecture for Intrusion Detection on Heterogeneous Wireless Sensor Networks: Hierarchical, Scalable and Dynamic Reconfigurable

Networks protection against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure;i.e. it is designed and applicable, in one, two or three levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Sensor-based Intrusion Detection System (SIDS) on sensor nodes, Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical, distributed and clustering structure along with clusters' overlapping. Finally, this paper has been designed a questionnaire to verify the proposed idea;then it analyzed and evaluated the acquired results from the questionnaires.

计及故障维修与网络重构的灾后配电网综合调度策略

当前,灾后配电网负荷恢复与故障维修多是独立考虑,缺乏有效联动,且没有合理计及包含故障维修时间及新能源出力的不确定性在内的双重因素影响。为此,提出一种灾后配电网维修恢复联合调度策略。首先,提出灾后配电网两阶段求解框架,第一阶段求解故障维修计划及网络重构问题,第二阶段解决移动式储能系统参与下的灾后配电网源荷协同调度问题。其次,构建移动式储能系统与维修人员的调度模型,量化分析移动式储能系统时空转移模型及能量模型,同时基于维修人员的时空转移特性,建立故障维修及网络重构模型。在此基础上,以恢复运行成本最小化为目标,分别采用离散鲁棒与连续鲁棒处理故障维修时间与新能源出力的不确定性,构建灾后配电网综合调度模型。最后,采用改进IEEE 33节点配电网进行仿真测试。结果表明,所提调度策略能够显著降低负荷失电率,减少停电损失。此外,两阶段算法在显著提升求解速度的同时能够保证求解精度,具有可行性和合理性。

非合作博弈背景下基于BSA的配电网优化重构

为缓解分布式电源大规模接入对配电网安全稳定运行的影响,提出一种考虑分布式电源输出功率的不确定性的有源配电网优化重构方法.首先,采用非合作博弈理论研究电网调度人员与“大自然”之间的博弈关系,将配电网系统中光伏单元的不确定性视为“大自然”博弈方;其次,以有功网损、负荷均衡度、电压偏差最小为目标函数,建立有源配电网优化重构模型,通过回溯搜索算法(backtracking search algorithm,BSA)进行迭代求解,得到最优重构方案;最后,在IEEE33节点系统进行仿真分析,验证模型的正确性及求解算法的有效性.研究结果表明,相较传统重构方法,本文方法更充分考虑了分布式电源输出功率的不确定性,并且在最恶劣的情况发生时,得到的重构策略能够使配电网系统的有功网损、负荷均衡度、电压偏差指标分别降低0.31%、0.59%、0.48%.

考虑网络增容与无功支撑的混合型智能软开关优化配置

传统智能软开关(SOP)替换联络开关的工作模式使得支路传输功率完全受到SOP容量约束,限制了含高比例分布式光伏配电网的潮流转移能力。为此设计了结构灵活、控制模式可变的混合型智能软开关(HOP),并提出了考虑网络增容与无功支撑协同的HOP优化配置方法。分析了HOP在不同支路传输容量下的4种控制模式,并建立了考虑联络线和SOP功率流向的HOP功率模型;为获得光伏出力和负荷需求代表性场景,提出了基于堆叠自编码器特征提取的场景生成方法;考虑到HOP联络开关状态对潮流和网络拓扑的影响,推导含HOP的支路潮流方程约束,建立了考虑配电网重构的HOP优化配置模型,并通过线性化处理将优化配置模型转化为混合整数线性规划问题。算例结果表明HOP能够应对支路增容需求,降低配电网规划成本,提升分布式光伏消纳能力。

基于改进FWA的配电网优化重构方法研究

针对配电网重构问题,以降低网络损耗、提高节点电压为目标,建立了配电网重构模型,提出了一种解决配电网重构问题的改进烟花算法(firework algorithm,FWA)。算法使用环路编码压缩解空间,提高求解效率。基于核心烟花给出了火花爆炸幅度的计算方法,采用高斯和均匀分布均值混合的变异方式以及精英选择策略,提高解的多样性、搜索效率和收敛速度。为了验证所提算法的适用性和正确性,在IEEE33和IEEE69节点系统上进行了算法测试,结果表明,重构后的网络损耗明显降低,节点电压得到提高,所提算法具有较好的鲁棒性。将所提算法与其他算法进行比较,结果表明,所提算法在降低网络损耗、提高节点电压、求解效率等方面均优于其他算法。

配电网多目标优化重构模型及多目标烟花求解算法研究

针对配电网优化重构问题,以最小化有功网损、最小化电压偏移度以及最小化负荷平衡度作为目标函数建立配电网多目标优化重构模型。提出一种多策略混合的改进烟花算法(Improved fireworks algorithm,IMFWA)进行求解。算法采用不重复环路编码,压缩解空间,提高搜索效率;采用Sobol序列生成初始种群,增强种群的多样性和遍历性;利用优化烟花和随机烟花进行位移操作,保持种群多样性;采用高斯与柯西的混合变异方式,提高寻优效率;利用Pareto支配关系以及适应度与拥挤度函数组成的综合指标对最优解集进行排列选取,提高收敛速度。对IEEE-33节点系统进行仿真试验,验证了所提出的方法是有效和可行的,试验结果表明优化重构方案能够有效改善配电网的运行指标,并根据不同实际情况为电网人员提供重构方案。