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.

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.

Optimal Location and Sizing ofMulti-Resource Distributed Generator Based onMulti-Objective Artificial Bee Colony Algorithm

Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.

Distribution Network Reconfiguration Based on ESA

本文提出了一种新的用于配电网络重构的优化方法－进化策略退火（ＥＳＡ）法，该方法将模拟进化策略法（ＥＳ）与模拟退火法（ＳＡ）进行了有机结合．首先用ＥＳ法选择ＳＡ法的初始温度，然后用改进ＳＡ法搜索重构的全局最优解．为加快寻优速度，本文提出了一种启发式规则用于降温策略，并采用了有效的潮流算法．

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.

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.

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.

Optimal Resources Dispatching Technology of Distribution Network Rush-Repairing

Confronted with the requirement of higher efficiency and higher quality of distribution network fault rush-repair, the subject addressed in this paper is the optimal resource dispatching issue of the distribution network rush-repair when single resource center cannot meet the emergent resource demands. A multi-resource and multi-center dispatching model is established with the objective of “the shortest repair start-time” and “the least number of the repair centers”. The optimal and worst solutions of each objective are both obtained, and a “proximity degree method” is used to calculate the optimal resource dispatching plan. The feasibility of the proposed algorithm is illustrated by an example of a distribution network fault. The proposed method provides a practical technique for efficiency improvement of fault rush-repair work of distribution network, and thus mostly abbreviates power recovery time and improves the management level of the distribution network.

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.

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.

Multi-objective Dynamic Reconfiguration for Urban Distribution Network Considering Multi-level Switching Modes

The increasing integration of photovoltaic generators(PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution network(UDN). This may lead to undesired consequences, including PVG curtailment, load shedding, and equipment inefficiency, etc. Global dynamic reconfiguration provides a promising method to solve those challenges. However, the power flow transfer capabilities for different kinds of switches are diverse, and the willingness of distribution system operators(DSOs) to select them is also different. In this paper, we formulate a multi-objective dynamic reconfiguration optimization model suitable for multi-level switching modes to minimize the operation cost, load imbalance, and the PVG curtailment. The multi-level switching includes feeder-level switching, transformer-level switching, and substation-level switching. A novel load balancing index is devised to quantify the global load balancing degree at different levels. Then, a stochastic programming model based on selected scenarios is established to address the uncertainties of PVGs and loads. Afterward, the fuzzy c-means(FCMs) clustering is applied to divide the time periods of reconfiguration. Furthermore, the modified binary particle swarm optimization(BPSO)and Cplex solver are combined to solve the proposed mixed-integer second-order cone programming(MISOCP) model. Numerical results based on the 148-node and 297-node systems are obtained to validate the effectiveness of the proposed method.

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.

Multi-objective Day-ahead Polarity Switching Optimization for Voltage Unbalance in Bipolar DC Distribution Networks

Bipolar direct current(DC)distribution networks can effectively improve the connection flexibility for renewable generations and loads.In practice,concerns regarding the potential voltage unbalance issue of the distribution networks and the frequency of switching still remain.This paper proposes a day-ahead polarity switching strategy to reduce voltage unbalance by optimally switching the polarity of renewable generations and loads while minimizing the switching times simultaneously in the range of a full day.First,a multi-objective optimization model is constructed to minimize the weighted sum of voltage unbalance factors and the sum of number of switching actions in the day based on the power flow model.Second,a two-step solution strategy is proposed to solve the optimization model.Finally,the proposed strategy is validated using 11-node and 34-node distribution networks as case studies,and a switching and stabilizing device is designed to enable unified switching of renewable generations and loads.Numerical results demonstrate that the proposed strategy can effectively reduce the switching times without affecting the improvement of voltage balance.

Quick Hosting Capacity Evaluation Based on Distributed Dispatching for Smart Distribution Network Planning with Distributed Generation

The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with DG.DG and ES are usually invested by users or a third party,and they may form friendly microgrids(MGs)and operate independently.Traditional centralized dispatching meth od no longer suits for hosting capacity evaluation of SDN.A quick hosting capacity evaluation method based on distributed optimal dispatching is proposed.Firstly,a multi-objective DG hosting capacity evaluation model is established,and the host ing capacity for DG is determined by the optimal DG planning schemes.The steady-state security region method is applied to speed up the solving process of the DG hosting capacity evalua tion model.Then,the optimal dispatching models are estab lished for MG and SDN respectively to realize the operating simulation.Under the distributed dispatching strategy,the dual-side optimal operation of SDN-MGs can be realized by several iterations of power exchange requirement.Finally,an SDN with four MGs is conducted considering multiple flexible resources.It shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch capacity.Besides,the annual DG electricity oversteps the maximum active power demand value.

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.

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%.

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。

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.