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.

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 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.

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.

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.

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.

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.

A Review of Activity Time Distributions in Risk Analysis

Project Evaluation and Review Technique (PERT) alongside recent modifications is a popular and useful tool in project risk analysis. Over the past seven decades, there have been some modifications in PERT owing to the shift from beta distributed activity times to other activity time distributions. This paper presents a review of activity time distributions in risk analysis as found in literature up to date.

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.

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.

Two-stage Optimal Dispatching of AC/DC Hybrid Active Distribution Systems Considering Network Flexibility

The increasing flexibility of active distribution systems(ADSs)coupled with the high penetration of renewable distributed generators(RDGs)leads to the increase of the complexity.It is of practical significance to achieve the largest amount of RDG penetration in ADSs and maintain the optimal operation.This study establishes an alternating current(AC)/direct current(DC)hybrid ADS model that considers the dynamic thermal rating,soft open point,and distribution network reconfiguration(DNR).Moreover,it transforms the optimal dispatching into a second-order cone programming problem.Considering the different control time scales of dispatchable resources,the following two-stage dispatching framework is proposed.d dispatch uses hourly input data with the goal(1)The day-ahea of minimizing the grid loss and RDG dropout.It obtains the optimal 24-hour schedule to determine the dispatching plans for DNR and the energy storage system.(2)The intraday dispatch uses 15-min input data for 1-hour rolling-plan dispatch but only executes the first 15 min of dispatching.To eliminate error between the actual operation and dispatching plan,the first 15 min is divided into three 5-min step-by-step executions.The goal of each step is to trace the tie-line power of the intraday rolling-plan dispatch to the greatest extent at the minimum cost.The measured data are used as feedback input for the rolling-plan dispatch after each step is executed.A case study shows that the comprehensive cooperative ADS model can release the line capacity,reduce losses,and improve the penetration rate of RDGs.Further,the two-stage dispatching framework can handle source-load fluctuations and enhance system stability.

A Two-stage Stochastic Mixed-integer Programming Model for Resilience Enhancement of Active Distribution Networks

Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks(ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming(SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems(ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm(PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.

Optimal Operation of Flexible Distribution Networks for Security Improvement Considering Active Management

This paper presents a stochastic optimal operation problem of gas turbine integrated distribution networks in the presence of active management schemes,which is formulated as a multi-objective chance-constrained mixed integer nonlinear programming problem.The control variables are the on-load tap-changer tap position,the power provided by the distributed generation(DG),the DG power factor angle,the load participating in demand side management and the switch status.The objectives defined in this paper are to simultaneously minimize the expectation cost and variation coefficient of security distance.Uncertainties related to DG output and load fluctuation and fault power restoration under contingencies are also considered in the optimization problem.The collaboration of normal boundary intersection and the dynamic niche differential evolution algorithm is proposed to handle the optimal operation mode.Simulation results are presented and demonstrate the effectiveness of the proposed model.Compared with the operation result without the consideration of security,the security-constrained operation can reduce the expectation cost.Therefore,the proposed optimization is reasonable and valuable.

A Multi-objective Chance-constrained Information-gap Decision Model for Active Management to Accommodate Multiple Uncertainties in Distribution Networks

The load demand and distributed generation(DG)integration capacity in distribution networks(DNs)increase constantly,and it means that the violation of security constraints may occur in the future.This can be further worsened by short-term power fluctuations.In this paper,a scheduling method based on a multi-objective chance-constrained information-gap decision(IGD)model is proposed to obtain the active management schemes for distribution system operators(DSOs)to address these problems.The maximum robust adaptability of multiple uncertainties,including the deviations of growth prediction and their relevant power fluctuations,can be obtained based on the limited budget of active management.The systematic solution of the proposed model is developed.The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term.Considering the stochastic characteristics and correlations of power fluctuations,the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution.The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network.The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties,which corresponds to an optional active management strategy set for future selection.

Novel Affine Power Flow Method for Improving Accuracy of Interval Power Flow Data in Cyber Physical Systems of Active Distribution Networks

A large number of load power and power output of distributed generation in an active distribution network(ADN)are uncertain,which causes the classical affine power flow method to encounter problems of interval expansion and low efficiency when applied to an AND.This then leads to errors of interval power flow data sources in the cyber physical system(CPS)of an ADN.In order to improve the accuracy of interval power flow data in the CPS of an ADN,an affine power flow method of an ADN for restraining interval expansion is proposed.Aiming at the expansion of interval results caused by the approximation error of non-affine operations in an affine power flow method,the approximation method of the new noise source coefficient is improved,and it is proved that the improved method is superior to the classical method in restraining interval expansion.To overcome the decrease of computational efficiency caused by new noise sources,a novel merging method of new noise sources in an iterative process is designed.Simulation tests are conducted on an IEEE 33-bus,PG&E 69-bus and an actual 1180-bus system,which proves the validity of the proposed affine power flow method and its advantages in terms of computational efficiency and restraining interval expansion.

Deep Reinforcement Learning Based Charging Scheduling for Household Electric Vehicles in Active Distribution Network

With the booming of electric vehicles(EVs) across the world, their increasing charging demands pose challenges to urban distribution networks. Particularly, due to the further implementation of time-of-use prices, the charging behaviors of household EVs are concentrated on low-cost periods, thus generating new load peaks and affecting the secure operation of the medium-and low-voltage grids. This problem is particularly acute in many old communities with relatively poor electricity infrastructure. In this paper, a novel two-stage charging scheduling scheme based on deep reinforcement learning is proposed to improve the power quality and achieve optimal charging scheduling of household EVs simultaneously in active distribution network(ADN) during valley period. In the first stage, the optimal charging profiles of charging stations are determined by solving the optimal power flow with the objective of eliminating peak-valley load differences. In the second stage, an intelligent agent based on proximal policy optimization algorithm is developed to dispatch the household EVs sequentially within the low-cost period considering their discrete nature of arrival. Through powerful approximation of neural network, the challenge of imperfect knowledge is tackled effectively during the charging scheduling process. Finally, numerical results demonstrate that the proposed scheme exhibits great improvement in relieving peak-valley differences as well as improving voltage quality in the ADN.

Multi-stage Coordinated Robust Optimization for Soft Open Point Allocation in Active Distribution Networks with PV

To optimize the placement of soft open points(SOPs)in active distribution networks(ADNs),many aspects should be considered,including the adjustment of transmission power,integration of distributed generations(DGs),coordination with conventional control methods,and maintenance of economic costs.To address this multi-objective planning problem,this study proposes a multi-stage coordinated robust optimization model for the SOP allocation in ADNs with photovoltaic(PV).First,two robust technical indices based on a robustness index are proposed to evaluate the operation conditions and robust optimality of the solutions.Second,the proposed coordinated allocation model aims to optimize the total cost,robust voltage offset index,robust utilization index,and voltage collapse proximity index.Third,the optimization methods of the multiand single-objective models are coordinated to solve the proposed multi-stage problem.Finally,the proposed model is implemented on an IEEE 33-node distribution system to verify its effectiveness.Numerical results show that the proposed index can better reveal voltage offset conditions as well as the SOP utilization,and the proposed model outperforms conventional ones in terms of robustness of placement plans and total cost.

Two-stage Stochastic Programming for Coordinated Operation of Distributed Energy Resources in Unbalanced Active Distribution Networks with Diverse Correlated Uncertainties

This paper proposes a stochastic programming(SP)method for coordinated operation of distributed energy resources(DERs)in the unbalanced active distribution network(ADN)with diverse correlated uncertainties.First,the threephase branch flow is modeled to characterize the unbalanced nature of the ADN,schedule DER for three phases,and derive a realistic DER allocation.Then,both active and reactive power resources are co-optimized for voltage regulation and power loss reduction.Second,the battery degradation is considered to model the aging cost for each charging or discharging event,leading to a more realistic cost estimation.Further,copulabased uncertainty modeling is applied to capture the correlations between renewable generation and power loads,and the twostage SP method is then used to get final solutions.Finally,numerical case studies are conducted on an IEEE 34-bus three-phase ADN,verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.