Distribution System Infrastructure Research Articles

Traffic and transportation management data storage terminal based on Internet of Things

Traditional data storage models are inadequate in the face of the growing demand for big data in transportation and transportation management. Its poor horizontal scalability makes it difficult to deal with the growth of massive data; on the other hand, its complex management makes it challenging to achieve unified management and effective resource utilization due to the differences in equipment from different manufacturers. In order to effectively store and manage this huge amount of information, it is urgent to rely on advanced technical tools. In this context, while ensuring data security and simplifying data management, it is also necessary to meet the terminal’s demand for high real-time performance, and these factors jointly promote the continuous attention and improvement of data storage terminal performance. This paper proposes a Hadoop solution based on distributed computing. As a distributed system infrastructure, Hadoop allows users to develop distributed programs without a deep understanding of distributed details, fully using the high-speed computing and storage capabilities of Hadoop clusters, which is especially suitable for big data processing tasks on the Internet of Things (IoT) platform. The experimental results show that for a 10 GB data file, the traditional terminal (Terminal 1) can store 7.8 GB, while the Hadoop-based terminal (Terminal 2) can store 9.9 GB. For 50 GB of data files, Terminal 1 and Terminal 2 can store 40.4 GB and 49.8 GB of data respectively. These results show that Hadoop terminals have significant advantages in processing large-scale data, especially in terms of data storage efficiency, and can use storage resources more effectively to meet the high-performance requirements of traffic and transportation management for data storage terminals.

A data‐driven predictive model for disinfectant residual in drinking water storage tanks

AbstractA data‐driven approach is developed and proven for ranking the risk of low disinfection residual in water distribution storage tanks, 1 month ahead. The forecasting methodology uses water quality data collected from drinking water treatment plants, storage tank outlets, and rainfall data as inputs. This methodology was developed and tested with data from a water utility serving more than 5 million people. Results show high‐risk category prediction accuracy of 75%–80%. Using a final year of unseen validation data, more than 90% of the storage tanks ranked in the top 20 by the forecasting methodology experienced low disinfectant residual in the following month. Storage tanks are critical water distribution system infrastructure that are currently managed reactively. The adoption of such readily transferable machine learning approaches enables direct proactive management strategies and efficient interventions that can help ensure drinking water quality.

Distributed ItemCF Recommendation Algorithm Based on the Combination of MapReduce and Hive

The ItemCF algorithm is currently the most widely used recommendation algorithm in commercial applications. In the early days of recommender systems, most recommendation algorithms were run on a single machine rather than in parallel. This approach, coupled with the rapid growth of massive user behavior data in the current big data era, has led to a bottleneck in improving the execution efficiency of recommender systems. With the vigorous development of distributed technology, distributed ItemCF algorithms have become a research hotspot. Hadoop is a very popular distributed system infrastructure. MapReduce, which provides massive data computing, and Hive, a data warehousing tool, are the two core components of Hadoop, each with its own advantages and applicable scenarios. Scholars have already utilized MapReduce and Hive for the parallelization of the ItemCF algorithm. However, these pieces of literature make use of either MapReduce or Hive alone without fully leveraging the strengths of both. As a result, it has been difficult for parallel ItemCF recommendation algorithms to feature both simple and efficient implementation and high running efficiency. To address this issue, we proposed a distributed ItemCF recommendation algorithm based on the combination of MapReduce and Hive and named it HiMRItemCF. This algorithm divided ItemCF into six steps: deduplication, obtaining the preference matrixes of all users, obtaining the co-occurrence matrixes of all items, multiplying the two matrices to generate a three-dimensional matrix, aggregating the data of the three-dimensional matrix to obtain the recommendation scores of all users for all items, and sorting the scores in descending order, with Hive being used to carry out steps 1 and 6, and MapReduce for the other four steps involving more complex calculations and operations. The Hive jobs and MapReduce jobs are linked through Hive’s external tables. After implementing the proposed algorithm using Java and running the program on three publicly available user shopping behavior datasets, we found that compared to algorithms that only use MapReduce jobs, the program implementing the proposed algorithm has fewer lines of source code, lower cyclomatic complexity and Halstead complexity, and can achieve a higher speedup ratio and parallel computing efficiency when processing all datasets. These experimental results indicate that the parallel and distributed ItemCF algorithm proposed in this paper, which combines MapReduce and Hive, has both the advantages of concise and easy-to-understand code as well as high time efficiency.

Costing of strategies for long-term trunk main discolouration management

Material continually accumulates throughout drinking water distribution systems, as a result episodic maintenance is essential to mitigate uncontrolled mobilisation leading to water quality failings. Focussing on discolouration as the primary issue observed by consumers, this risk is of particular significance in trunk mains that can supply large downstream populations. Long-term total costs are for the first time investigated here by considering future operational and capital interventions to sustain a defined hydraulic capability that limits the discolouration response. To achieve this, accumulation and mobilisation profiles of pipe wall material is simulated using the open source Variable Condition Discolouration Model (VCDM) to develop Pareto trade-off curves between discolouration resilience and maintenance intervention frequency and magnitude. As the rate at which material accumulates is considered a function of water quality, operational savings that could accrue from reduced maintenance following capital investment, such as water treatment upgrades, are also investigated. With the complexity, size and ageing nature of water distribution system infrastructure, the ability to forecast network discolouration behaviour and hence costs is vital for long-term delivery of safe water at least cost to customers.

Risk-Based Active Distribution System Planning for Resilience Against Extreme Weather Events

Enhancing the resilience of power distribution systems to extreme weather events is of critical concern. Upgrading the distribution system infrastructure by system hardening and investing in smart grid technologies effectively enhances grid resilience. Existing distribution system planning methods primarily consider the persistent cost of the expected events (such as faults and outages likely to occur) and aim at improving system reliability. The resilience to extreme weather events requires reducing the impacts of the high impact low probability (HILP) events that are characterized by the tail probability of the event impact distribution. Thus, the resilience-oriented system upgrades solutions need to be driven by the risks imposed by extreme weather events on the power grid infrastructure rather than persistent costs. This paper aims to develop a risk-based approach for the long-term resilience planning of active power distribution systems against extreme weather events. The proposed approach explicitly models (1) the impacts of HILP events using a two-stage risk-averse stochastic optimization framework, thus, explicitly incorporating the risks of HILP events in long-term planning decisions, and (2) the advanced distribution grid operations (in the aftermath of the event) such as intentional islanding into infrastructure planning problem. The inclusion of risk in the planning objective provides additional flexibility to the grid planners to analyze the trade-off between risk-neutral and risk-averse planning strategies.

Distribution Loss Analysis of NEA, Nepal

Nepal Electricity Authority (NEA) is facing the problems in supply chain of electricity that causes the loss of energy which results in loss of economy. NEA has planned to extend the distribution network throughout the country so that every household can have access of Electrical Energy (EE) by 2023. NEA is also working in the sector of maximizing the sales in order to increase its revenue. In order to achieve such goals NEA must enhance its distribution system infrastructure. In this article, distribution losses are to be analyzed for enhancing and upgradation of existing electrical network if any. Some of the electrical components that cause electrical loss in distribution sector are also to be analyzed. This helps on determination of causes of losses. As NEA is importing electrical energy from India, consumer’s sales energy should be analyzed. At present calibration of area specific electricity consumption pattern that too on Time of Day (TOD) basis is not possible. This research paper will determine the process that helps in identifying area specific consumption pattern thus aid operators in laying reliable supply chain infrastructure to meet the demand of electricity. Electrical loss to be minimized by enhancing distribution network as per consumer demand.

A model for evaluating water distribution system capacity as a function of the total pipeline length

Abstract Planners are often faced with the challenge to provide crude estimates of water distribution system (WDS) infrastructure capacity and associated costs in the early phases of greenfield developments. This study investigated the relationship between the physical and hydraulic characteristics of a WDS and the corresponding serviced area. Five physical parameters (a) and two hydraulic parameters (b) describing the serviced area were identified for analysis, namely (a) total pipeline length, land area, area shape factor, terrain index, reservoir distance from area centroid and (b) peak flow rate and average static system pressure. Multiple linear regression was performed on the data. A model was compiled linking the total pipeline length of a WDS to the peak flow rate. The model is applicable to predominantly residential service zones larger than 80 hectares with a peak hour flow rate of <450 L/s. The model enables the prediction of the potable water distribution pipe infrastructure required for future development areas in the absence of basic planning information, such as cadastral layouts. Alternatively, the model can estimate the potential maximum peak flow rate that can be supplied, if the total pipeline length is known.

Factors Related to the Implementation of the Covid-19 Vaccine in the Indonesian Army in District-City, Riau Province

The Indonesian Armed Forces synergizes with the government to accelerate the implementation of the COVID-19 vaccination. Objectives, knowing the factors related to the achievement of the implementation of the COVID-19 vaccine invasion of the Indonesian Army.Method, using a quantitative method with a cross-sectional study approach. The sampling method was accidental sampling. The number of respondents was 96 community respondents and 16 respondents from the vaccination team. Data was collected using questionnaires and observations. Data were analyzed using Chi Square test statistical software. Result,there is a relationship between knowledge and public perception < 0.05, the value of = 0.009, not good with the achievement of vaccine 1 (20%), vaccine 2 (14.5%), vaccine 3 (65.5%). The unrelated factors are the knowledge and skills of the vaccination implementation team > 0.05, the value of = 0.006, the type of vaccine available > 0.05, the value of = 0.585), the COVID-19 vaccine distribution system > 0.05, the value of = 0.359 and facilities and infrastructure > 0.05 the value of = 0.854. The relationship between incentive motivation of the vaccination team and the achievement of vaccine implementation was not good (43.8%) and good (56.3%). Vaccination achievements in the four Military District with good category 70.8%, quite good 17.7% and not good 11.5%. Conclusion, the related factors are knowledge and public perception about the COVID-19 vaccine with the achievement of the implementation of the incursion COVID-19 vaccine. The Army while unrelated factors include the knowledge and skills of the vaccination team, the types of vaccines available, the COVID-19 vaccine distribution system and facilities and infrastructure.

Impact of hydraulic and physicochemical factors on spatiotemporal variations of particle-associated bacteria in a drinking water distribution system

Drinking water distribution systems are critical infrastructure that protect public health by ensuring safe water is transported from centralized treatment facilities to consumers. While growth of bacteria, such as opportunistic pathogens, in distribution system infrastructure is well established as a detriment to water quality, little is known about the role of sediment in conveying bacteria via biofilms throughout these systems. The objective of this study was to quantify the abundance of particle-associated bacteria in a rural drinking water distribution system with a chlorine disinfectant residual. The role of hydraulic and physicochemical factors in influencing the spatiotemporal loading of particle-associated bacteria in the system was also examined. The concentration of particle-associated bacteria averaged 1.28 log10 gene copies per mL, while total bacteria averaged 2.16 log10 gene copies per mL, demonstrating that biofilms formed on the surface of sediment represent a substantial portion of overall loading in the studied distribution system. Total suspended solids concentrations were correlated with particle-associated bacteria, but not total bacteria. Pipe diameter was found to be an important factor associated with the abundance of both total and particle-associated bacteria, as well as total chlorine concentration. Velocity, Reynold's number and the flow regime were also found to be important, as they were associated with both sediment and total bacteria, but not particle-associated bacteria. The results of this study indicate that particle-associated bacteria and total bacteria concentrations often followed disparate trends, demonstrating that their abundance is differentially influenced by a complex combination of physicochemical and hydraulic factors. These findings help to establish sediment as an important conduit for microbial loading in a chlorinated drinking water distribution system.

Analysis of Electrical Faults Caused by Natural Phenomena in the Distribution System of Lagos State, Nigeria

This paper analysed empirical system data of nature-induced electrical faults, its variation, and ratings in the distribution networks of Lagos State, Nigeria, which affect the power distribution system infrastructure, end-users of electricity, and the economic development of the distribution companies, consumers, and the nation at large. Fault-based outage data (at installed 11 kV and 33 kV nominal network voltages) were obtained and analysed by natural phenomena (NPs) using relative frequency, seasonal variation, and probabilistic statistics. From obtained results, the following were established: NP-induced electrical faults are prevalent in the distribution systems of Lagos State, and it is more towards or closer to the mainland; vegetation (67%) and birds/snakes (24%) are the most predominant fault-causing NPs in the distribution networks; floods (0.1%) rarely cause electrical faults in these distribution networks despite being a coastal region; and NP-induced electrical faults vary periodically throughout the year. It was recommended that existing maintenance policy must be enhanced to control vegetation, birds, and reptile incursions into Lagos DNs. Modern software-based sensor technologies for monitoring vegetation growth and repelling bird/snake incursions in the network should be explored. Existing protection scheme should be evaluated for effectiveness in view of ensuing short circuit events from incidents of these NPs at various hotspots.

A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation

Power outages caused by severe storms produce enormous economic losses and societal disruptions. Infrastructure hardening for a more resilient power grid can reduce weather-induced outages but necessitates accurate simulations of intervention efficacy. Data-driven models have been developed to forecast outages but struggle with extreme events where data might be limited. Meanwhile, physics-based models can predict failures under strong winds but have limited scope in lower wind ranges. Despite the two models’ complementary benefits, studies investigating their integration have been limited. In the present study, the physical attributes of the infrastructure system are incorporated into data-driven models by coupling structural fragilities of the pole-wire overhead power distribution system with machine learning (ML) techniques applied in an outage prediction model for the northeastern United States. The ML model is used to calibrate the physics-based fragility curves, which are subsequently used to predict outages for high-impact events where empirical data may be limited. The results of this hybrid physics-based and data-driven (HPD) model indicate modeling improvements using hybrid over strictly data-driven approaches for extreme events. Root mean square error improvements of 48% are exhibited for high-impact event outage prediction. The hybrid model was then utilized to counterfactually assess the impacts of grid hardening activities such as pole replacement, pole class upgrade, improved pole chemical treatment, and undergrounding in reducing pole failures over the last fifteen years. The results indicate selected strategies targeted to the oldest 5% of infrastructure could have reduced over 100 (33% of all) pole failures annually across the state of Connecticut.

A Cloud Infrastructure for Large Scale Health Monitoring in Older Adult Care Facilities

Technology development in the sub-field of older adult care has always been on the back-burner compared to other healthcare areas. But with increasing life expectancy, this is poised to change. With the increasing older adult population, the current older adult care facilities and personnel are struggling to keep up with demand. To address this matter, we proposed a distributed system infrastructure that will enable large-scale monitoring of vital signals and early detection of emergency situations in nursing homes and assisted living communities in this study. The system is mainly comprised of node devices and cloud infrastructure. The node device collects data from multiple users and forwards this data to the cloud service. Communication between the vital-sensing devices on the users and the node device is accomplished using Bluetooth Low-Energy (BLE). The Node device is implemented on a Raspberry Pi Model 3b+ which has built-in BLE capability. A cloud server consists of back-end and front-end components. The back-end handles all the data processing and logical decisions that drive the front-end. The front-end is the interface provided to the end-users, which can monitor all patients in nursing homes at the same time. We examined our system in terms of scalability, real-time operation, cost, and usability and then found that our system provides not only a smart remote monitoring solution that can provide a better aging experience for older adults and their families, but also increase automation in nursing homes leading to a reduction in running cost and an increase in capacity.

OPTIMAL PLACEMENT AND SIZING OF DISTRIBUTED GENERATION IN A RADIAL DISTRIBUTION SYSTEM FOR RESILIENCE ENHANCEMENT AGAINST VOLCANIC ERUPTIONS

High impact low probability (HILP) events significantly impact the power distribution system (PDS) infrastructure. In this context, resilience is becoming of growing concern worldwide. In this paper, a methodological framework to optimal distributed generation (DG) planning for resilience enhancement in PDS against volcanic eruptions focusing on the lahar occurrence is proposed. The proposed methodology includes the concept of vulnerability curves to determine the unavailability of the PDS elements, the Monte-Carlo simulation method (MCS) to characterize the volcanic lahars and a stochastic optimization problem (SOP). The methodological framework is implemented on the IEEE 37-node test feeder. The results reveal that DG is critical for the resilience enhancement of a PDS against lahars formed by a volcanic eruption. In addition, we present preliminary results of the methodology implemented on a feeder of the San Rafael and Salcedo substations in the concession area of the electricity distribution company ELEPCO S.A. in Cotopaxi-Ecuador, which is vulnerable to lahars that could form in a Cotopaxi volcano eruption located at the northeast of Latacunga city.

A New Model for a Resilient Distribution System After Natural Disasters Using Microgrid Formation and Considering ICE Cars

The severe impact of natural disasters on distribution system (DS) infrastructure highlights the importance of enhancing the resiliency of such systems. The loss of critical loads, one of the worst results of these events, increases the significance of resilient restoration approaches. In addition to distributed generators (DGs), which are common resources in restoration schemes, internal combustion engines (ICE) cars can considerably affect DS resiliency. This article first introduces an idea for using ICE vehicle as energy resources in a DS to enhance the system’s flexibility during restoration efforts after natural disasters. The amount of electrical consumption of modern cars and the availability of home inverters makes this idea practical. In addition, a novel comprehensive method is proposed based on the optimal formation of dynamic microgrids (MGs) that satisfies operational constraints and considers the ICE vehicles and is not based on path-based or children-parent nodes concepts. Moreover, the proposed method considers the integration of DGs in one microgrid. The proposed method is based on two-stage stochastic mixed-integer linear programming (MILP), and considers uncertain load consumption and demand response (DR). Finally, the effectiveness of the proposed method is evaluated using a modified 33-bus IEEE test system. The results show that integration of DGs leads to restoring more loads. Moreover, distribution and selection of the buses corresponding to ICE cars affect directly on the amount of restored load. Budget limitation is also considered in this article. Evaluation of the proposed method shows the considerable effectiveness in increasing the resiliency of DS.

Assessment of PV Hosting Capacity in a Small Distribution System by an Improved Stochastic Analysis Method

PV hosting capacity (PVHC) analysis on a distribution system is an attractive technique that emerged in recent years for dealing with the planning tasks on high-penetration PV integration. PVHC uses various system performance indices as judgements to find an available amount of PV installation capacity that can be accommodated on existing distribution system infrastructure without causing any violation. Generally, approaches for PVHC assessments are implemented by iterative power flow calculations with stochastic PV deployments so as to observe the operation impacts for PV installation on distribution systems. Determination of the stochastic PV deployments in most of traditional PVHC analysis methods is automatically carried out by the program that is using random selection. However, a repetitive problem that exists in these traditional methods on the selection of the same PV deployment for a calculation was not previously investigated or discussed; further, underestimation of PVHC results may occur. To assess PVHC more effectively, this paper proposes an improved stochastic analysis method that introduces an innovative idea of using repetitiveness check mechanism to overcome the shortcomings of the traditional methods. The proposed mechanism firstly obtains all PV deployment combinations for the determination of all possible PV installation locations. A quick-sorting algorithm is then used to remove repetitive PV deployments that are randomly selected during the solution procedure. Finally, MATLAB and OpenDSS co-simulations implemented on a small distribution feeder are used to validate the performance of the proposed method; in addition, PVHC enhancement by PV inverter control is investigated and simulated in this paper as well. Results show that the proposed method is more effective than traditional methods in PVHC assessments.

Consider the Top 10 Best Practices for Control Valve Operation

Control valves play a key role in distribution system stability, customer service, and infrastructure maintenance. Operators should expand their understanding of how the valves work with other equipment in the system.

Service pricing and load dispatch of residential shared energy storage unit

Energy storage is playing an increasingly important role in power system operation as a flexible backup asset. At the demand side, it can postpone the upgrade of distribution system infrastructure, such as feeder and transformer, by shaving the peak demand. However, the investment cost of battery storage is still relatively high compared to their service period. In view of the temporal complementarity of energy consumption among different users, shared use of a single energy storage unit is a promising business model in the near future. This paper studies a representative scene of shared energy storage in a residential area and proposes a new method for service pricing and load dispatching in such a circumstance. The service price is determined by the marginal cost of the residential load aggregator, who controls the shared energy storage unit and energy supply for each consumer. Such a pricing scheme is fair to all users. Demand elasticity is also taken into account. Given the interaction between service price and elastic demand, the optimal dispatch is formulated as a fixed-point problem. The existence of an equilibrium solution is discussed, and a sufficient condition for the equilibrium being stable is disclosed. Numerical results based on real load and electricity prices data validate the proposed method.

Assessment Framework for Natural Groundwater Contamination in Arid Regions: Development of Indices and Wells Ranking System Using Fuzzy VIKOR Method

Limited groundwater resources in arid regions have been found polluted for drinking purpose due to the presence of natural minerals and radioactive substances, in the sub-soils, higher than the drinking water quality standards. Municipalities in these regions are spending extensive resources to transport (from well fields) and treat this raw water to provide safe water to the community. Regular monitoring of various physical, chemical, and radioactive water quality parameters (WQPs) in raw water generates large datasets, which makes it difficult to come up with convenient findings for both the decision-makers and general public. A hierarchical water quality assessment framework develops three sub-indices, an overall water quality index, and a system for ranking of groundwater wells. Fuzzy analytical hierarchy process (fuzzy-AHP) establishes the importance weights of different WQPs and the sub-indices based on their impacts on human health, treatment processes, distribution system infrastructure, and irrigation applications. Fuzzy VIekriterijumsko KOmpromisno Rangiranje (fuzzy-VIKOR) method aggregates the WQPs’ performance for each well and ranks all the wells in a well field based on their overall pollution levels, i.e., remoteness from the applicable standards. For evaluating the pragmatism of the framework, data of 11 WQPs were obtained for 39 wells operating in three different well fields located along the boundaries and the central part of Buraydah, Qassim, Saudi Arabia. Spatial water quality maps showing physical, chemical, radioactive, and overall water quality assessment results revealed that the oldest well field located in the middle of the city outperforms the other two more recently developed well fields with lesser anthropogenic activities in their catchments. These findings testify that the primary source of contamination in deep aquifers is the natural sub-soil condition. The water quality indices will be useful to demonstrate the current situation of groundwater quality in Qassim Region and will facilitate the decision-makers for defining the intended uses of raw water sources (i.e., drinking, unrestricted irrigation, and restricted irrigation) and rehabilitation and renewal planning of the groundwater wells. The framework is applicable in the Kingdom of Saudi Arabia (KSA), Gulf Region, and elsewhere for groundwater quality assessment with desired modifications.

Deteriorating Water Distribution Systems Can Impact Public Health.

Distribution system infrastructure issues can affect water reliability and quality and could contribute to waterborne disease outbreaks. It's important to know potential contamination mechanisms and consider rehab and replacement options.

Optimal design and implementation of solar PV-wind-biogas-VRFB storage integrated smart hybrid microgrid for ensuring zero loss of power supply probability

Abstract Uninterrupted access to electric power has become the basic need of today’s world. Rural parts of many countries still do not have access to electricity or have electric power access to weak distribution grids with inadequate transmission and distribution system infrastructure. However, the countries where there is an abundance of solar radiation, a good potential of bio-degradable waste and average availability of wind source, access to electricity for those remote areas can be managed by distributed power generation. Considering the fact that the renewable energy sources (Solar, Wind etc.) are intermittent in nature, battery energy storage systems (BESS) and other reservoirs like biogas energy sources are the potential candidates to be integrated with the renewable sources to ensure continuous access to electricity and energy security. In this paper, a unique combination of Solar PV, Wind, Biomass and Vanadium Redox Flow Battery (VRFB) storage integrated hybrid Microgrid has been modeled and implemented practically for the first time. The capacity selection of different renewable sources for satisfying daily energy demand and their techno-commercial optimization has been performed through HOMER simulation. Further, the peak load shaving that is a limitation of HOMER model, has been established through PSCAD simulation by providing the real life data of different renewable sources, VRFB storage and the load profile as input to the model. The simulation model performances have been validated by a practical 10 kWP solar PV, 1 kW wind and 15 kVA Biogas generator integrated with 1 kW 6 h VRFB storage based Microgrid installed at India Institute of Engineering Science and Technology campus, India. In addition to these, zero loss of power supply probability (LPSP) has been ensured by implementing smart scheduling and controller considering the intermittency of the renewable sources. As a part of the financial analysis, project Investment on Return (IRR) and pay back has been calculated considering initial investment, operation and maintenance cost and revenue of generation.