Coordinated planning for flexible interconnection and energy storage system in low-voltage distribution networks to improve the accommodation capacity of photovoltaic

The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and voltage violations.To address these problems,we propose a coordinated planning method for flexible interconnections and energy storage systems(ESSs)to improve the accommodation capacity of DPVs.First,the power-transfer characteristics of flexible interconnection and ESSs are analyzed.The equipment costs of the voltage source converters(VSCs)and ESSs are also analyzed comprehensively,considering the differences in installation and maintenance costs for different installation locations.Second,a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity.Within this framework,the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs,whereas the lower-level model optimizes the operating power of the VSCs and ESSs.The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-II).The effectiveness of the proposed planning method is validated through an actual LVDN scenario,which demonstrates its advantages in enhancing PV accommodation capacity.In addition,the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed,demonstrating the adaptability of the proposed coordinated planning method.

Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

Low-Voltage Analysis of Distribution Network Software Design and Application

With the rapid development of urban economy, distribution network construction of city is lagging behind the increment of load, and low-voltage problem has become the main complaints of the power users. Based on Python [1] scripting language, this paper develops a piece of software which can provide a friendly human-machine experience for the user, calculate the voltage of the power distribution area and propose corresponding control measures, then the paper takes the actual power distribution area to verify its effectiveness.

Research of Three-Phase Unbalanced Treatment in Low-Voltage Distribution Network Based on New Commutation Switch

Low-voltage distribution systems in our country are mostly used in agricultural loads and household loads. The value and using time of these kinds of loads are uncontrollable, which lead to the three-phase imbalance in low-voltage distribution system, and seriously affect the quality of power supply. A new type of the commutation system and an improved quantum genetic algorithm (IQGA) are proposed in the paper. At last, the rationality and the efficiency of the method are verified by a practical example.

A Method for Rapidly Determining the Optimal Distribution Locations of GNSS Stations for Orbit and ERP Measurement Based on Map Grid Zooming and Genetic Algorithm

Designing the optimal distribution of Global Navigation Satellite System(GNSS)ground stations is crucial for determining the satellite orbit,satellite clock and Earth Rotation Parameters(ERP)at a desired precision using a limited number of stations.In this work,a new criterion for the optimal GNSS station distribution for orbit and ERP determination is proposed,named the minimum Orbit and ERP Dilution of Precision Factor(OEDOP)criterion.To quickly identify the specific station locations for the optimal station distribution on a map,a method for the rapid determination of the selected station locations is developed,which is based on the map grid zooming and heuristic technique.Using the minimum OEDOP criterion and the proposed method for the rapid determination of optimal station locations,an optimal or near-optimal station distribution scheme for 17 newly built BeiDou Navigation Satellite System(BDS)global tracking stations is suggested.To verify the proposed criterion and method,real GNSS data are processed.The results show that the minimum OEDOP criterion is valid,as the smaller the value of OEDOP,the better the precision of the satellite orbit and ERP determination.Relative to the exhaustive method,the proposed method significantly improves the computational efficiency of the optimal station location determination.In the case of 3 newly built stations,the computational efficiency of the proposed method is 35 times greater than that of the exhaustive method.As the number of stations increases,the improvement in the computational efficiency becomes increasingly obvious.

Effect of uneven noise source and/or station distribution on estimating the azimuth anisotropy of surface waves

With the development of the dense array,the surface wave velocity and azimuthal anisotropy under the array can be directly obtained by beamforming the noise cross-correlation functions(NCFs). However, the retrieval of the Green’s function by cross-correlating the seismic noise requires that the noise source has a uniform distribution. For the case with uneven noise source, the azimuthal dependence on the sources in the expression for the spatial coherence function, which corresponds to the NCF in the time domain,has the same form as the azimuthal dependence of the surface wave velocity in weakly anisotropic media. Therefore, the uneven noise source will affect the surface wave anisotropy extraction. In this study, three passive seismic methods, i.e.,beamforming, SPAC(spatial autocorrelation), and NCF, are compared to demonstrate that an uneven source distribution and uneven station distribution have equivalent effects on the outcome from each method. A beamforming method is proposed to directly extract the velocity and azimuthal anisotropy of surface waves. The effect of uneven noise source and/or station distribution on estimating the azimuth anisotropy of surface waves was investigated using data from the ChinArray Phase Ⅱ. A method for correcting the apparent anisotropy in beamforming results caused by an uneven station distribution is suggested.

MILP formulations for highway petrol station replenishment in initiative distribution mode

To investigate highway petrol station replenishment in initiative distribution mode,this paper develops a mixed-integer linear programming(MILP)model with minimal operational costs that includes loading costs,unloading costs,transport costs and the costs caused by unpunctual distribution.Based on discrete representation,the working day is divided into equal time intervals,and the truck distribution process is decomposed into a pair of tasks including driving,standby,rest,loading and unloading.Each truck must execute one task during a single interval,and the currently executing task is closely related to the preceding and subsequent tasks.By accounting for predictive time-varying sales at petrol stations,real-time road congestion and a series of operational constraints,the proposed model produces the optimal truck dispatch,namely,a detailed task assignment for all trucks during each time interval.The model is tested on a real-world case of a replenishment system comprising eight highway petrol stations,one depot,one garage and eight trucks to demonstrate its applicability and accuracy.

Optimal Planning of Hybrid AC/DC Low-voltage Distribution Networks Considering DC Conversion of Three-phase Four-wire Low-voltage AC Systems

The increasing integration of distributed household photovoltaics(PVs)and electric vehicles(EVs)may further ag gravate voltage violations and unbalance of low-voltage distribu tion networks(LVDNs).DC distribution networks can increase the accommodation of PVs and EVs and mitigate mutilple pow er quality problems by the flexible power regulation capability of voltage source converters.This paper proposes schemes to es tablish hybrid AC/DC LVDNs considering the conversion of the existing three-phase four-wire low-voltage AC systems to DC op eration.The characteristics and DC conversion constraints of typical LVDNs are analyzed.In addition,converter configura tions for typical LVDNs are proposed based on the three-phase four-wire characteristics and quantitative analysis of various DC configurations.Moreover,an optimal planning method of hybrid AC/DC LVDNs is proposed,which is modeled as a bi-level programming model considering the annual investments and three-phase unbalance.Simulations are conducted to verify the effectiveness of the proposed optimal planning method.Sim ulation results show that the proposed optimal planning method can increase the integration of PVs while simultaneously reduc ing issues related to voltage violation and unbalance.

Design scheme for fast charging station for electric vehicles with distributed photovoltaic power generation

The demand for fast charging is increasing owing to the rapid expansion of the market for electric vehicles. In addition, the power generation technology for distributed photovoltaic has matured. This paper presents a design scheme for a fast charging station for electric vehicles equipped with distributed photovoltaic power generation system taking the area with certain conditions in Beijing as an example construction site. The technical indexes and equipment lectotype covering the general framework and subsystems of the charging station are determined by analyzing the charging service demand of fast charging stations. In this study, the layout of the station is developed and the operation benefits of the station is analyzed. The design scheme realizes the design objective of "rationalization, modularization and intelligentization" of the fast charging station and can be used as reference for the construction of a fast charging network in urban area.

INVESTIGATION ON THE DENSITY UNIFORMITY OF PRESSED PARTS BY LOW-VOLTAGE ELECTROMAGNETIC COMPACTION

Powder metallurgy is an efficient approach to fabricate varieties of high performance structure materials, function materials and special materials working under limited conditions. Research and development of new efficient technology to form high-density, high-performance and net shape parts is a key to widen application and development of powder materials. Recently, the low-voltage electromagnetic compaction (EMC) has been used by present authors to compacted copper, tin, aluminum powders and the products with 99% relative density have been acquired. In this work, the research has been extended to investigation on the density uniformity of pressed parts. The analysis results show that the density of the part compacted by low-voltage EMC decreases gradually in press direction as static compaction. But it is higher and more homogeneous. The density of the top part increases gradually from the center to the outer, which is just reversal of the bottom part. In some extent, the higher the discharging voltage is, the higher the density is and the more homogeneous the distribution is. In addition, repetitive compaction can improve the density of powder parts and the distribution uniformity.

Modifying the intensity distribution by assessing the reliability

This article presents an application of a procedure to modify the intensity distribution by assessing the reliability. There are two potential possibilities that may influence the intensity distribution： （1） For the interpolation error, we generate a measured grid across the calculation region. When the point to station spacing is 〈5 km, we consider the results precise; however, some points have less precision because these are farther from the corresponding stations. When the spacing is between 5 and 50 km, we consider the results imprecise and define a reliability factor that correlates with the distance. （2） Some records may have errors that result from local site conditions, equipment problems, or some disturbance such as lightning stroke, which will lead to some grid points having an incorrect intensity. We regress the attenuation relation for sites with abnormal intensities and consider the results to be accurate when the standard deviation （STD） is 〈σ and inaccurate when the STD is 〉 2σ. We then define a reliability factor to correlate with STD between σ and 2σ, such that the intensity distribution is in accord with both wave propagation theory and the investigation intensity.

Distributed power allocation over indoor multi-pico stations

A low-complexity distributed power allocation algorithm is proposed to reduce the interference and improve the transmitting rate of edge users. Different scenarios are considered and user experience of indoor communication is promoted. The simulation results prove the effectiveness of our algorithm. The proposed power control scheme ensures that more users can achieve their required rate and the fairness of different users is improved. Besides, more than 5096 energy can be saved without loss in outage ability, and energy efficiency is also promoted. In addition, the proposed algorithm can be extended to scenarios that the required rates of pico stations can be changed periodically.

Comparative Study on Distributed Waste Pyrolysis Cold Emission Energy Station and Incineration Power Generation

The proper terminal disposal of organic solid waste such as domestic waste is a worldwide issue.Landfill covers a large area,with limited capacity,and a single landfill will be filled one day;incineration is costly to build and operate.These methods all need to transfer and centralized treatment,and secondary pollution is difficult to control,against the purification law of the nature."NIMBY effect"is very serious,and the social cost of treatment is increasing,becoming a heavy financial burden."The Distributed Waste Pyrolysis Cold Emission Energy Station"developed by Hunan Zhongzhou Energy-Saving Technology Co.,Ltd.overcomes these disadvantages and constructs a more appropriate environmental economic industrial chain for the treatment of organic solid waste such as urban and rural household waste.Based on its technical characteristics,this paper compares it with waste incineration power generation project in the aspects of secondary pollution control,treatment effect,energy utilization,investment and operation economy,etc.

Phase Identification of Low-voltage Distribution Network Based on Stepwise Regression Method

Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.

Power and Voltage Control Based on DC Offset Injection for Bipolar Low-voltage DC Distribution System

The bipolar low-voltage DC(LVDC) distribution system has become a prospective solution to better integration of renewables and improvement of system efficiency and reliability. However, it also faces the challenge of power and voltage imbalance between two poles. To solve this problem, an interface converter with bipolar asymmetrical operating capabilities is applied in this paper. The steady-state models of the bipolar LVDC distribution system equipped with this interface converter in the gridconnected mode and off-grid mode are analyzed. A control scheme based on DC offset injection at the secondary side of the interface converter is proposed, enabling the bipolar LVDC distribution system to realize the unbalanced power transfer between two poles in the grid-connected mode and maintain the inherentpole voltage balance in the off-grid mode. Furthermore, this paper also proposes a primary-side DC offset injection control scheme according to the analysis of the magnetic circuit model, which can eliminate the DC bias flux caused by the secondaryside DC offset. Thereby, the potential core magnetic saturation and overcurrent issues can be prevented, ensuring the safety of the interface converter and distribution system. Detailed simulations based on the proposed control scheme are conducted to validate the function of power and voltage balance under the operation conditions of different DC loads.

A Mixed-integer Linear Programming Model for Defining Customer Export Limit in PV-rich Low-voltage Distribution Networks

A photovoltaic(PV)-rich low-voltage(LV)distribution network poses a limit on the export power of PVs due to the voltage magnitude constraints.By defining a customer export limit,switching off the PV inverters can be avoided,and thus reducing power curtailment.Based on this,this paper proposes a mixed-integer nonlinear programming(MINLP)model to define such optimal customer export.The MINLP model aims to minimize the total PV power curtailment while considering the technical operation of the distribution network.First,a nonlinear mathematical formulation is presented.Then,a new set of linearizations approximating the Euclidean norm is introduced to turn the MINLP model into an MILP formulation that can be solved with reasonable computational effort.An extension to consider multiple stochastic scenarios is also presented.The proposed model has been tested in a real LV distribution network using smart meter measurements and irradiance profiles from a case study in the Netherlands.To assess the quality of the solution provided by the proposed MILP model,Monte Carlo simulations are executed in OpenDSS,while an error assessment between the original MINLP and the approximated MILP model has been conducted.

Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm

In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

换热站并联水泵分布式优化控制

针对现有换热站并联水泵优化算法在集中式架构下控制适应性不足的问题,本文提出了一种改进的分布式并联水泵优化算法.首先,建立了并联水泵的分布式控制系统,并对该优化问题的数学模型进行描述,在目标函数中引入自适应非线性因子;然后,设计了改进的分布式果蝇优化算法,在该算法中每台水泵的控制器仅通过与邻居控制器交互信息即可完成并联水泵的优化;并且,在嗅觉搜索阶段,使用正弦余弦策略替代赋予个体距离与方向的随机策略;最后,以两个实际换热站中不同并联水泵系统为例对算法进行仿真验证,并基于仿真结果进行性能分析.结果表明,相较于传统算法,改进的分布式果蝇优化算法能得到更优的控制策略,有着收敛速度快、稳定性好和鲁棒性强的特点;并且该算法适用于不同系统的并联水泵优化问题,具有可扩展性.在实际工程验证中相较于集中式算法,该算法在总功率和计算时间上分别平均降低了5.47%和29.90%,因此,能够满足实际换热站中对并联水泵热负荷优化分配的需求.

基于多特征符号聚合近似和层次聚类的户变关系识别方法

针对低压配电台区拓扑档案中可能存在的户变关系异常问题,文中提出了一种基于多特征符号聚合近似(MF-SAX)和层次聚类的户变关系识别方法。首先,运用符号聚合近似表达方法将用户电压时间序列转化为字符串序列,并引入电压波动系数和电压变化趋势两个附加参数对其特征表达进行强化。然后,基于编辑距离生成用户电压曲线相似性矩阵,并结合层次聚类算法实现户变关系的识别。最后,实际算例结果表明,提出的方法相比于现有方法准确率更高,误报更少,能直接应对数据缺失的情况,且具有更高的效率。