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.

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.

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.

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.

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.

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.

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.

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.

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.