Analysis Of Distributed Systems Research Articles

Comparative analysis of hydrogen production, accumulation, distribution, and storage systems

The global energy demand in the world continues to grow and environmental pollution caused by fossil fuels becomes increasingly serious, the development and utilization of new energy sources has become a hot topic of global concern. The development and utilization of new energy sources is one such possible solution, which can provide clean, renewable energy and thus reduce the impact on the environment. Hydrogen, as a clean, productive, carbon-free secondary energy source, has the potential to be used as a fuel and essential substance for different fields such as heating, transportation, industry, and power generation. As the international community strives to achieve a shift toward a more eco-conscious and sustainable future, hydrogen has received extensive research and attention due to its abundant resources and environmentally friendly properties. The main objective of this study was to describe and comparatively analyze the efficient production, accumulation, distribution and storage of hydrogen. Today the production of hydrogen is centered on the creation or extraction of hydrogen from primary energy sources. The accumulation of hydrogen involves the preservation of surplus hydrogen for subsequent utilization. The distribution of hydrogen encompasses the conveyance and delivery of hydrogen. The storage systems for hydrogen pertain to the technologies and infrastructure employed to retain hydrogen for future deployment. In complex, these constituents establish a comprehensive hydrogen value chain that facilitates the generation, preservation, and distribution of hydrogen as a sustainable and environmentally friendly energy solution. Keywords: comparative analysis, hydrogen, production, accumulation, distribution, storage systems.

Spatial analysis of electrical distribution system in obio-akpor local government area, rivers state, Nigeria

The distributions of quality electrical energy to end users and, achieving a reasonable level of customer’s satisfaction from the electricity distribution companies servicing most area are faced with divers’ spatial problems particularly with the use of dilapidated infrastructures and poor services. This study used spatial techniques to analyze the spatial characteristics of the electrical distribution system in Obio-Akpor Local Government Area, Rivers State. It also examined the ward pattern of Port Harcourt Electricity Distribution (PHED) customer’s satisfaction and also predicted the ward level of customer’s satisfaction within the study area. Primary and secondary data were used for the study. The primary data on customer’s satisfaction of PHED services was obtained from questionnaire survey. Purposive sampling method was used for this study and the population for the study is the entire customers of PHED in the study area. The secondary data consist of ward boundary map of the study area, satellite image, PHED network spatial data and attribute data of all the 3200 (three thousand two hundred) transformers (11/0.415KVA and 33/0.415KVA) within the study area as at the time of this study. Geospatial analysis such as clip analysis, buffering were carried. The data attribute were analyzed using average nearest neighbor analysis, kernel density, zonal statistics, inverse distance weighting and geographical weighted regression to obtain the desired aim of the study. The result show that, the location pattern of PHED transformers are clustered. Result further reveal that the ward pattern of customer’s satisfaction are geography dependent with either positive, negative or mixed relationship.

Three-phase model of SCIG-based variable speed wind turbine for unbalanced DSLF analysis

Steady state performances of the electric power distribution system are normally assessed or evaluated based on load flow analysis. To properly carry out the analysis, a valid steady state load flow model of each distribution system component, including the wind power plant (WPP), needs to be developed. The present paper proposes a method for modeling and integrating squirrel cage induction generator (SCIG)-based variable speed WPP into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on a single-phase T-circuit model of fixed speed WPP, which has successfully been applied to balanced electric power systems. In the present work, the single-phase T-circuit model is extended and modified to be used in steady state load flow analysis of three-phase unbalanced distribution systems embedded with SCIG-based variable speed WPP. Results of the case studies confirm the validity of the proposed method.

Short-circuit current analysis of DC distribution system in a ship with non-electric propulsion

Converting conventional AC radial shipboard distribution system to DC system has been recognized as a potential high impact solution to reduce ship’s fuel consumption. As big ships in Indonesia commonly use low voltage AC (LVAC) distribution system an effort to apply low voltage direct current (LVDC) distribution system without replace the propulsion system is a plausible choice. However, technical and economical investigations are required before recommendation to convert the shipboard distribution system to LVDC is officially launched. In this study, technical aspect in the term of short-circuit current is discussed. The goal of this study is to analyze how much the impact on the short-circuit current when LVDC system replaces LVAC system. The impact may affect the feasibility of LVDC system as the short-circuit current in a system dictate the scheme and capacity of the protection devices. Numerical simulations on a sample vessel are performed to obtain the profile of maximum short-circuit currents on all panels. The results show that the utilization of LVDC system decreases the short-circuit current by 10 times. Further investigations on the economic aspect needs to be performed to give clearer view of the feasibility of the LVDC system.

Accurate computing paradigm for decoupled harmonic load flow analysis with CONOPT solvers for non-sinusoidal radial distribution systems

In this article, a novel method for performing Decoupled Harmonic Load Flow (DHLF) analysis in radial distribution systems is presented. The proposed method involves solving load flow equations for both fundamental and higher frequencies as constrained minimization problems utilizing a nonlinear programming (NLP) CONOPT solver integrated into the General Algebraic Modeling Systems (GAMS) environment. The effectiveness of the proposed method is assessed across three IEEE test systems: 18-bus, 33-bus, and 69-bus. Five cases of DHLF analysis are taken into consideration. DHLF was performed on standard test systems with incorporated non-linear loads in the first case. Cases two through five examine the DHLF in the presence of distributed generators (DGs), shunt capacitors (SCs), simultaneous DGs and SCs, and electric vehicles (EVs). Comparing the obtained results to those of DHLF analysis tools ETAP and DigSilent, as well as to those found in the literature, proved that the proposed CONOPT solver provides precise solutions.

Correction to: Performance analysis of smart grid distribution system using global EV charging station as smart load based on automatic variac transformer

Correction to: Performance analysis of smart grid distribution system using global EV charging station as smart load based on automatic variac transformer

Reliability analysis in distribution system by deep belief neural network

Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate.

Sympathetic Trip Analysis in Distribution System of PLTU and PLTA Nagan Raya

At the PT unit, PLN (Persero), there is an issue with the 20 kV electric power distribution system, such as feeder disruption. As a result, many felt sorry for the feeders of PLTA Krueng Isep and PLTU Nagan Raya, whose consumers were without power for extended periods of time. Of course, it can impact how well a business performs, particularly in terms of service and electrical revenue. In order to examine the causes of Sympathetic, study is required. In order to conduct the research, data on disturbances are gathered, tests are run, simulation results are analyzed using the ETAP application, non-directional relays are swapped out for directional relays, protection coordination is reset, and evaluation is done. The findings of this study have the potential to improve performance at PT. PLN (Persero) and decrease or even completely eradicate the incidence of sympathy in the 20 kV distribution network.

Water distribution system modelling of GIS-remote sensing and EPANET for the integrated efficient design

Abstract Urban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyse and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for the management of the Bota town's water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analysing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, RS, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town's water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, RS, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues.

A Solution to Investigate Uncertainties in Reliability Analysis of Distribution System Based on Fuzzy Logic Method

A Solution to Investigate Uncertainties in Reliability Analysis of Distribution System Based on Fuzzy Logic Method

An active multiphase probabilistic power flow based on a clustering approach

Probabilistic methods for analyzing power systems have become more widespread, mainly due to the increase in load and generation uncertainties and the current need for planning problems that are sufficiently accurate to take appropriate actions. However, some characteristics need to be better investigated in the Distribution Systems (DS) analysis, such as the input random variables’ modeling using real databases and, mainly, the probabilistic treatment of unbalance and scalability studies. This paper proposes an active Multiphase Probabilistic Power Flow (MPPF) evaluation, considering real measurement databases to represent customer demand uncertainties. It is called Multiphase Probabilistic Power Flow Based on K-Means (MPPFKM), and it encompasses a mixture of a clustering technique and an analytical method. The K-Means algorithm divides the input database into a set of clusters that models the scenarios to be evaluated in the multiphase power flow problem, which considers the distribution network with its inherent unbalance. Subsequently, the output variables’ higher order statistical moments are calculated using the obtained responses for each cluster. Finally, their PDFs and CDFs are recovered using the Gram-Charlier Expansion analytical method. While the clustering algorithm allows working with real data, giving the proposed work greater proximity to real-world results, the analytical tool provides an accurate recovery of the probability functions, because it detects their distortions caused by the nature of the multiphase problem. Estimating the correct number of clusters is a key factor in obtaining the precision required by power systems engineers and achieving significant computational time reduction, especially for large-scale unbalanced systems. Simulations are carried out using several DS feeders to test the proposed method, which specifically addresses DS issues not yet well explored in the literature, such as probabilistic simulations under different unbalance situations, applications with single-phase DGs, and scalability studies are conducted using the IEEE 8500 test feeder. Comparisons are made with usual MPPF algorithms, including Monte Carlo Simulation (MCS) and Point Estimation Methods (PEM). Simulations using large-scale unbalanced systems proved that the output variables' probability functions might present distortions, becoming more challenging for other methods. Under this perspective, MPPFKM presents better exactness in the calculation of higher order statistical moments and, consequently, can estimate the PDFs of the MPPF output variables with great precision, which is crucial for statistical models that use actual input data.

An Active Learning-Based Approach for Hosting Capacity Analysis in Distribution Systems

An Active Learning-Based Approach for Hosting Capacity Analysis in Distribution Systems

Risk-Based Hosting Capacity Analysis in Distribution Systems

Solar hosting capacity analysis (HCA) assesses the ability of a distribution network to host distributed solar generation without seriously violating distribution network constraints. In this paper, we consider risk-sensitive HCA that limits the risk of network constraint violations with a collection of scenarios of solar irradiance and nodal power demands, where risk is modeled via the conditional value at risk (CVaR) measure. First, we consider the question of maximizing aggregate installed solar capacities, subject to risk constraints and solve it as a second-order cone program (SOCP) with a standard conic relaxation of the feasible set of the power flow equations. Second, we design an incremental algorithm to decide whether a configuration of solar installations has acceptable risk of constraint violations, modeled via CVaR. The algorithm circumvents explicit risk computation by incrementally constructing inner and outer polyhedral approximations of the set of acceptable solar installation configurations from prior such tests conducted. Our numerical examples study the impact of risk parameters, the number of scenarios and the scalability of our framework.

Physics-informed Fully Convolutional Network-based Power Flow Analysis for Multi-terminal MVDC Distribution Systems

Physics-informed Fully Convolutional Network-based Power Flow Analysis for Multi-terminal MVDC Distribution Systems

Constant Voltage Model of SCIG-Based Variable Speed Wind Power Plant for Load Flow Analysis

In recent years, the application of variable-speed wind power plants (WPPs) has become more and more popular and surpassed fixed-speed WPPs. This popularity is mainly because variable-speed WPPs are much more efficient in capturing or extracting wind energy than fixed-speed WPPs. Due to its relatively cheaper cost, the use of SCIG (Squirrel Cage Induction Generator)-based variable-speed WPP is currently increasing. This type of WPP is usually equipped with a power electronics converter to obtain a broader range of variable-speed operations. Modeling the power system components is undoubtedly necessary for performing the system analysis (including load flow analysis). This paper discusses the steady-state model of SCIG-based variable-speed WPP for load flow analysis of electric power distribution systems. The proposed model can be used for SCIG-based variable-speed WPP with voltage control operating mode. The model was developed based on formulas that calculate the turbine mechanical power input and the WPP electrical power output. This paper also presents a case study in which the proposed model is applied to a representative electric power distribution system.

AC-DC multi-phase power flow algorithms for active distribution system analysis

AC-DC multi-phase power flow algorithms for active distribution system analysis

Bioburden Analysis and Microbiological Stability of Municipal Distribution System through Examination of Transformed Total Microbial Count Dataset

Bioburden Analysis and Microbiological Stability of Municipal Distribution System through Examination of Transformed Total Microbial Count Dataset

A linear Distflow model considering line shunts for fast calculation and voltage control of power distribution systems

The line shunts are usually ignored by various linear power flow (PF) models in power distribution system analysis, planning and optimization. However, “charging effects” from line shunts of underground/submarine power cables would cause non-negligible model errors for these commonly used PF models. In this paper, we propose a modified Linear Distflow model (LinDist) with line shunts, i.e., LinDistS. We also further propose its extensions considering the ZIP load, weakly-meshed topology and unbalanced three-phase systems. Moreover, the linearization error of voltage component is theoretically analyzed. Case studies show that compared with other models, the proposed LinDistS achieves the descent calculation accuracy and efficiency. We also show the application scope of LinDistS by a Volt/VAr control (VVC) framework with distributed generators, mainly photovoltaic (PV), and the non-negligible “charging effect”. Simulation exhibits that with LinDistS, the VVC can optimally dispatch the shunt capacitors and also optimize the real-time reactive power output of PV. Moreover, with LinDistS, the VVC shows better solutions’ consistency and higher computing efficiency compared to traditional VVC methods.

A study on pipe failure analysis in water distribution systems using logistic regression

Abstract This study presents a pipe failure analysis that aims to identify key factors affecting pipe failure in a pilot study area (PSA). For this purpose, pipe failures that occurred in the PSA were recorded for 2 years. Recorded pipe failures were integrated into the existing geographical information systems of the PSA and then visualized. Then, the logistic regression model that calculates failure probability with unbalanced and small- and medium-sized data was performed to analyze pipe failures. Pipe material, age, diameter, burst pressure, and average water pressure are the factors used in the study. The developed model achieved a success rate of 70.1%, suggesting that it can predict a considerable portion of pipe failures with a relatively satisfactory performance. Findings show that the failure probability of pipes with an age greater than 20 years is 1.675 times higher than that of pipes with an age less than 20 years. Moreover, polyvinyl chloride pipes are 5.265 times and high-density polyethylene pipes are 9.027 times more likely to fail than ductile pipes. Further research should delve into the impacts of the number of service connections, pipe lengths, and traffic load on pipe failures.

A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors

The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of forecast errors. The scenario generation method consists of a process of sampling random numbers and a process of inverse sampling using the cumulative distribution function. In sampling random numbers, random numbers that mimic the spatiotemporal correlation of power generation were generated using the copula function. Furthermore, the cumulative distribution functions of forecast errors according to power generation bins were used, thereby reflecting the probabilistic characteristics of forecast errors. The wind power generation scenarios in Jeju Island, generated by the proposed method, were analyzed through various indices that can assess accuracy. As a result, it was confirmed that by using the proposed scenario generation method, scenarios similar to actual data can be generated, which in turn allows for preparation of situations with a high probability of occurrence within the distribution system.