The Optimization Study about Fault Self-Healing Restoration of Power Distribution Network Based on Multi-Agent Technology

In order to quickly and accurately locate the fault location of the distribution network and increase the stability of the distribution network,a fault recovery method based on multi-objective optimization algorithm is proposed.The optimization of the power distribution network fault system based on multiagent technology realizes fast recovery of multi-objective fault,solve the problem of network learning and parameter adjustment in the later stage of particle swarm optimization algorithm falling into the local extreme value dilemma,and realize the multi-dimensional nonlinear optimization of the main grid and the auxiliary grid.The system proposed in this study takes power distribution network as the goal,applies fuzzy probability algorithm,simplifies the calculation process,avoids local extreme value,and finally realizes the energy balance between each power grid.Simulation results show that the Multi-Agent Technology enjoys priority in restoring important load,shortening the recovery time of power grid balance,and reducing the overall line loss rate of power grid.Therefore,the power grid fault self-healing system can improve the safety and stability of the important power grid,and reduce the economic loss rate of the whole power grid.

Models and Methods for Urban Power Distribution Network Planning

The models, methods and their application experiences of a practical GIS(geographic information system)-based computer decision-making support system of urban power distribution network planning with seven subsystems,termed CNP,are described.In each subsystem there is at least one or one set of practical mathematical methobs.Some new models and mathematical methods have been introduced.In the development of CNP the idea of cognitive system engineering has been insisted on,which claims that human and computer intelligence should be combined together to solve the complex engineering problems cooperatively.Practical applications have shown that not only the optimal plan can be automatically reached with many complicated factors considered, but also the computation,analysis and graphic drawing burden can be released considerably.

Grey series time-delay predicting model in state estimation for power distribution networks

A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.

Problems and Solutions for Medium-Voltage Distribution Network in Nanning City

This paper introduces the problems emerged in the developing process of Nanning Medium-voltage distribution network to adapt the progress of HV network. These problems are: (1) unreasonable structure (large amount of radical type 10 kV lines); (2) one 10 kV line for each customer (causing difficulties for line corridors); (3) circuit breaker are widely used for MV customers (result in complicated substation structure); (4) lots of overhead 10 kV lines in urban area. Ring circuit, insulated cables, load break switches, and fast acting fuses etc. advanced technologies are proposed for the retrofit of urban distribution network.

Global Harmonic Rate Assessment in the Electricity Distribution Network in Niamey City:Case Studies of Domestic,Industrial and Hospital Substations

Like others countries of the world, in Niger also, we are witnessing an increasing use of non-linear electric loads in the domestic, hospital and industrial sectors. However, these loads degrade the shape of the electrical signal and cause disastrous effects to the equipment of the distribution system and the devices which are connected to the network. This article highlights the presence of electric harmonics in the distribution network in Niamey city. In order to do this, measurements were taken at the secondary level of the substations using an energy quality analyze r (FLUKE 1735). By using this measuring instrument, we quantified the voltage and current Total Harmonic Distortion (THD) in the three substations. The results obtained show that, although the statutable rates set by the standards are not exceeded for phase conductors, the neutral contains a very critical percentage of distortion on the residential and hospital substations. Moreover, this assessment made it possible to observe the variation of harmonics in the presence of voltage drops.

Decentralized Bilateral Risk-based Self-healing Strategy for Power Distribution Network with Potentials from Central Energy Stations

Owing to potential regulation capacities from flexible resources in energy coupling,storage,and consumption links,central energy stations(CESs)can provide additional support to power distribution network(PDN)in case of power disruption.However,existing research has not explicitly revealed the emergency response of PDN with leveraging multiple CESs.This paper proposes a decentralized self-healing strategy of PDN to minimize the entire load loss,in which multi-area CESs’potentials including thermal storage and building thermal inertia,as well as the flexible topology of PDN,are reasonably exploited for service recovery.For sake of privacy preservation,the co-optimization of PDN and CESs is realized in a decentralized manner using adaptive alternating direction method of multipliers(ADMM).Furtherly,bilateral risk management with conditional value-at-risk(CVaR)for PDN and risk constraints for CESs is integrated to deal with uncertainties from outage duration.Case studies are conducted on a modified IEEE 33-bus PDN with multiple CESs.Numerical results illustrate that the proposed strategy can fully utilize the potentials of multi-area CESs for coordinated load restoration.The effectiveness of the performance and behaviors’adaptation against random risks is also validated.

Distributed Chunk-Based Optimization for MultiCarrier Ultra-Dense Networks

In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution network operation.Methods:This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs.The solution is designed to ensure the reliable and secure operation of the active power distribution networks,the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand,as the PEVs can be considered as mobile energy storage units.Results:As a result,the charging demands of PEVs are optimally scheduled temporally and spatially,which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing(RTP).Conclusions:The proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.

Data-driven distribution network topology identification considering correlated generation power of distributed energy resource

This paper proposes a data-driven topology identification method for distribution systems with distributed energy resources(DERs).First,a neural network is trained to depict the relationship between nodal power injections and voltage magnitude measurements,and then it is used to generate synthetic measurements under independent nodal power injections,thus eliminating the influence of correlated nodal power injections on topology identification.Second,a maximal information coefficient-based maximum spanning tree algorithm is developed to obtain the network topology by evaluating the dependence among the synthetic measurements.The proposed method is tested on different distribution networks and the simulation results are compared with those of other methods to validate the effectiveness of the proposed method.

Deployment of wireless sensor network in dispersed renewable energy sources for increasing efficiency of power distribution networks

In this work,a novel performance analysis method for evaluating the robustness of emerging power distribution networks(PDNs),which involve deployable renewable energy sources,is proposed.This is realized with the aid of the outage probability(OP)criterion in the context of cooperative communications,which is widely considered in modern wireless communication systems.The main usefulness of this method is that it allows the involved components to communicate to each-other by means of a robust and flexible wireless sensor network architecture.In this context,any conventional medium voltage(MV)bus of the PDN is represented as a wireless relay node where data signals gathered from each MV bus can be forwarded reliably to a control station for the subsequent processing.The received signals at wireless nodes are decoded and then forwarded to ensure minimal errors and maximal robustness at the receiving site.The considered OP analysis denotes the probability that the power of a received information signal drops below a pre-defined threshold which satisfies the acceptable Quality of Service requirements of a reliable signal reception.To this end,simple closed-form expressions are proposed for the OP of a regenerative cooperative-based PDN in the presence of various multipath fading effects,which degrade information signals during wireless transmission.The offered results are rather simple and provide meaningful insights for the design and deployment of smart grid systems.

考虑DG无功支撑和开关重构的主动配电网分布鲁棒无功优化模型

近年来,风光等分布式电源(distributed generation, DG)的大力发展给主动配电网无功优化带来了新的挑战。传统随机优化和鲁棒优化方法在处理清洁能源不确定性方面存在片面性或保守性等问题。基于此,该文构建以系统网损为优化目标,并考虑DG无功支撑和开关重构的主动配电网分布鲁棒无功优化模型。该模型除了包括传统无功优化中的开关、变电站有载调压变压器、离散与连续无功补偿装置等元素,重点对双馈风机和微型燃气轮机的容量曲线进行详细建模,从而融入DG的无功支撑能力,并设计有功无功出力耦合特性约束的线性化方法。结合风电和光伏的典型场景数据,以及决策变量的调节特性,构建基于数据驱动的分布鲁棒两阶段无功优化模型,其中不确定性概率分布置信集合同时受到1-范数和∞-范数约束。模型采用列与约束生成(columns and constraints generation,CCG)算法进行求解,并在IEEE33节点算例上验证模型对系统无功支撑能力的提升和分布鲁棒方法的有效性。

Substation planning method in an active distribution network under low-carbon economy

A substation planning method that accounts for the widespread introduction of distributed generators(DGs)in a low-carbon economy is proposed.With the proliferation of DGs,the capacity that DGs contribute to the distribution network has become increasingly important.The capacity of a DG is expressed as a capacity credit(CC)that can be evaluated according to the principle that the reliability index is unchanged before and after the introduction of the DG.A method that employs a weighted Voronoi diagram is proposed for substation planning considering CC.A low-carbon evaluation objective function is added to the substation planning model to evaluate the contribution of DGs to a low-carbon economy.A case study is analyzed to demonstrate the practicality of the proposed method.

不确定风功率接入下电-气互联系统的协同经济调度

随着天然气发电机组数量的逐渐增长,电力网络和天然气网络之间的耦合加深,它们之间的协同运行变得愈发重要。同时,不确定性新能源的接入给电力和天然气互联系统的经济安全运行带来了挑战。为了应对风电不确定性给互联系统带来的运行风险,采用分布鲁棒机会约束,通过数据驱动的方式,以少量的风电预测误差历史数据得到与矩信息有关的模糊集,并将形成的机会约束问题转化为易于求解的形式。另外,为了保护电、气系统各自的隐私信息,在假设存在第三方可信任的协调者的前提下,利用松弛交替乘子法,实现互联系统的分布式协同运行。仿真结果表明,分布鲁棒优化相比较于传统的随机优化可以实现较低的机会约束违反概率。另外,相比较于传统交替乘子法,松弛交替乘子法能够以更小的迭代次数达到同等的收敛精度。

Distribution Management Systems for Smart Grid:Architecture,Work Flows,and Interoperability

The smart grid integrates advanced sensors,a two-way communication infrastructure,and high-performance computation-based control.The distribution management systems for smart grid include several functions for manipulating legacy voltage control devices and distributed energy resources through closed-loop volt/var control,leading to wide-area regulation of voltages in the presence of fluctuating power.The other primary distribution network analysis application is concerned with automatic fault location and service restoration following fault events,aiming to provide the grid with autonomous intelligence for self-healing.Communication technologies are vital to enable the computing applications of distribution networks,whether they work in centralized or distributed modes.This paper presents the state of the art in distribution management system architectures and modern workflows showing data exchange,practical parallel implementations designed to handle large amounts of data,in addition to communication standards that serve as interoperability enablers.It demystifies the relationship between different functions developed independently by power system researchers and shows their operation as a complete system,thus placing them in a better context for future research and development.

Quick Line Outage Identification in Urban Distribution Grids via Smart Meters

The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution grids,traditional outage detection methods,which rely on customers report and smart meters'“last gasp”signals,will have poor performance,because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages.To address these challenges,we propose a datadriven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee.Specifically,we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages.This makes the theory on optimal change-point detection suitable to identify line outages.However,existing change point detection methods require post-outage voltage distribution,which are unknown in distribution systems.Therefore,we design a maximum likelihood estimator to directly learn distribution parameters from voltage data.We prove the estimated parameters-based detection also achieves optimal performance,making it extremely useful for fast distribution grid outage identifications.Furthermore,since smart meters have been widely installed in distribution grids and advanced infrastructure(e.g,PMU)has not widely been available,our approach only requires voltage magnitude for quick outage identification.Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.

基于数据驱动的主动配电网分布鲁棒有功无功协调优化

为充分发挥分布式电源与可控设备有功无功出力在配电网优化中的作用,减少分布式电源随机出力与负荷波动对系统的影响,首先,从有功无功协调优化角度出发,以有载调压变压器、光伏机组、储能系统、微型燃气轮机及无功补偿设备为控制手段,建立以网损最小为优化目标的主动配电网有功无功协调优化模型;然后,在协调优化模型的基础上,采用场景分析法处理光伏出力及负荷预测的不确定性,并以l-2范数约束构建场景概率分布的不确定集,建立了基于数据驱动的主动配电网分布鲁棒有功无功协调优化模型,采用列与约束生成算法进行求解.经IEEE33节点系统验证,结果证明了协调优化策略能有效地降低网损与分布鲁棒优化策略能有效应对系统中的不确定性.

Optimal dispatch of zero-carbon-emission micro Energy Internet integrated with non-supplementary fired compressed air energy storage system

To utilize heat and electricity in a clean and integrated manner,a zero-carbon-emission micro Energy Internet(ZCE-MEI) architecture is proposed by incorporating non-supplementary fired compressed air energy storage(NSF-CAES) hub.A typical ZCE-MEI combining power distribution network(PDN) and district heating network(DHN) with NSF-CAES is considered in this paper.NSF-CAES hub is formulated to take the thermal dynamic and pressure behavior into account to enhance dispatch flexibility.A modified Dist Flow model is utilized to allow several discrete and continuous reactive power compensators to maintain voltage quality of PDN.Optimal operation of the ZCE-MEI is firstly modeled as a mixed integer nonlinear programming(MINLP).Several transformations and simplifications are taken to convert the problem as a mixed integer linear programming(MILP)which can be effectively solved by CPLEX.A typical test system composed of a NSF-CAES hub,a 33-bus PDN,and an 8-node DHN is adopted to verify the effectiveness of the proposed ZCE-MEI in terms of reducing operation cost and wind curtailment.